├── .github
└── workflows
│ └── build.yml
├── .gitignore
├── .labelbuddy-annotations-repository
├── CITATION.cff
├── LICENSE
├── Makefile
├── README.md
├── analysis
├── book
│ ├── _config.yml
│ ├── _static
│ │ ├── annotation-set.css
│ │ ├── global.css
│ │ ├── label-set.css
│ │ └── participants.css
│ ├── _toc.yml
│ ├── assets
│ │ └── annotate_participants.png
│ ├── contributing_to_this_repository.md
│ ├── how_to_use_the_data.md
│ ├── overview.md
│ ├── participant_demographics.py
│ ├── references.bib
│ └── requirements.txt
├── book_helpers
│ ├── add_project_pages.py
│ ├── figures
│ │ ├── annotate_participants.svg
│ │ └── participant_tree.dot
│ └── templates
│ │ ├── participant_demographics.md
│ │ └── project_page.md
├── dash_app
│ ├── dash_app.py
│ └── requirements.txt
└── labelrepo
│ ├── pyproject.toml
│ ├── setup.cfg
│ └── src
│ └── labelrepo
│ ├── __init__.py
│ ├── _data
│ ├── __init__.py
│ ├── css
│ │ ├── __init__.py
│ │ ├── annotation-set.css
│ │ └── label-set.css
│ └── initialize_db.sql
│ ├── _utils.py
│ ├── database.py
│ ├── datasets.py
│ ├── dbutils.py
│ ├── displays.py
│ ├── documents.py
│ ├── projects
│ ├── __init__.py
│ ├── nv_task
│ │ ├── __init__.py
│ │ └── _nv_task.py
│ └── participant_demographics
│ │ ├── __init__.py
│ │ ├── _data
│ │ ├── __init__.py
│ │ └── templates
│ │ │ ├── __init__.py
│ │ │ ├── annotation_stack.html
│ │ │ ├── annotation_stack_list.html
│ │ │ ├── hide_show_annotations.js
│ │ │ ├── live_report.html
│ │ │ ├── participant_tree.html
│ │ │ ├── participants.css
│ │ │ └── report.html
│ │ ├── _interpreter.py
│ │ ├── _participant_demographics.py
│ │ └── _watcher.py
│ └── repo.py
├── documents
├── pmcid_10011534.jsonl
├── pmcid_10014826.jsonl
├── pmcid_10025232.jsonl
├── pmcid_10025420.jsonl
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├── pmid_11126640.jsonl
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├── pmid_29074403.jsonl
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└── pmid_38275521.jsonl
├── projects
├── NER_biomedical
│ ├── NER_biomedical.
│ ├── NER_biomedical.jsonl
│ ├── README.md
│ ├── annotations
│ │ ├── Kendra_Oudyk.jsonl
│ │ └── README.md
│ ├── datasets.json
│ └── labels
│ │ ├── README.md
│ │ └── labels.jsonl
├── autism_mri
│ ├── LICENSE
│ ├── README.md
│ ├── annotations
│ │ ├── David_Kennedy.jsonl
│ │ └── Jerome_Dockes.jsonl
│ ├── datasets.json
│ └── labels
│ │ └── Article_Terms.json
├── cluster_inference
│ ├── README.md
│ ├── annotations
│ │ ├── Jerome_Dockes.jsonl
│ │ └── Kendra_Oudyk.jsonl
│ ├── datasets.json
│ ├── kendra.jsonl
│ ├── labels
│ │ └── labels_kendra.json
│ ├── pmcids_for_open_fmri_papers.txt
│ └── re_download_papers_in_nqdc_format.sh
├── cobidas
│ ├── README.md
│ ├── annotations
│ │ └── annotations.json
│ ├── labels
│ │ ├── README.md
│ │ ├── cobidas
│ │ │ ├── 0_sample_labels.jsonl
│ │ │ ├── 1_design_labels.jsonl
│ │ │ ├── 2_behavior_labels.jsonl
│ │ │ ├── 3_common_parameters_labels.jsonl
│ │ │ ├── 4_acquisition_labels.jsonl
│ │ │ ├── 5_preprocessing_labels.jsonl
│ │ │ ├── 6_multivariate_labels.jsonl
│ │ │ ├── 7_results_labels.jsonl
│ │ │ ├── 8_data_sharing_labels.jsonl
│ │ │ ├── 9_reproducibility_labels.jsonl
│ │ │ └── cobidas_labels.jsonl
│ │ ├── eyetracking
│ │ │ ├── 0_introduction_labels.jsonl
│ │ │ ├── 10_preprocessing_labels.jsonl
│ │ │ ├── 11_discussion_labels.jsonl
│ │ │ ├── 1_tracker_labels.jsonl
│ │ │ ├── 2_monitor_labels.jsonl
│ │ │ ├── 3_software_labels.jsonl
│ │ │ ├── 4_aoi_definition_labels.jsonl
│ │ │ ├── 5_setup_labels.jsonl
│ │ │ ├── 6_calibration_labels.jsonl
│ │ │ ├── 7_participants_labels.jsonl
│ │ │ ├── 8_data_quality_labels.jsonl
│ │ │ ├── 9_dependent_measures_labels.jsonl
│ │ │ └── eyetracking_labels.jsonl
│ │ ├── labels.json
│ │ └── pet
│ │ │ ├── 0_info_labels.jsonl
│ │ │ ├── 1_radiotracer_labels.jsonl
│ │ │ ├── 2_radiochem_labels.jsonl
│ │ │ ├── 3_time_labels.jsonl
│ │ │ ├── 4_reconstruction_labels.jsonl
│ │ │ ├── 5_continuous_blood_labels.jsonl
│ │ │ ├── 6_cross-calibration_labels.jsonl
│ │ │ ├── 7_external_motion_correction_labels.jsonl
│ │ │ ├── 8_blood_data_processing_labels.jsonl
│ │ │ ├── 9_multiple_testings_labels.jsonl
│ │ │ └── pet_labels.jsonl
│ └── query.txt
├── dynamic_functional_connectivity
│ ├── README.md
│ ├── annotations
│ │ └── mtorabi59.jsonl
│ ├── datasets.json
│ ├── labels
│ │ └── labels.json
│ └── query.txt
├── fmri_datasets
│ ├── README.md
│ └── datasets.json
├── neuro-meta-analyses
│ ├── README.md
│ ├── annotations
│ │ ├── Kendra_Oudyk.jsonl
│ │ └── README.md
│ ├── datasets.json
│ ├── labels
│ │ ├── README.md
│ │ └── labels.jsonl
│ └── neuro-meta-analyses.jsonl
├── neuro-meta-analysis-tables
│ ├── README.md
│ ├── annotations
│ │ ├── Kendra_Oudyk.jsonl
│ │ └── README.md
│ ├── datasets.json
│ ├── labels
│ │ └── README.md
│ └── neuro-meta-analysis-tables.jsonl
├── neuro-meta-analysis_manually-inspected-topics
│ ├── README.md
│ ├── annotations
│ │ ├── Kendra_Oudyk.jsonl
│ │ └── README.md
│ ├── datasets.json
│ └── labels
│ │ └── README.md
├── neurobridge_fmri
│ ├── README.md
│ ├── annotations
│ │ └── README.md
│ └── labels
│ │ └── README.md
├── neurosynth_use
│ ├── README.md
│ ├── annotations
│ │ ├── Kendra_Oudyk.jsonl
│ │ └── README.md
│ ├── datasets.json
│ ├── labels
│ │ ├── README.md
│ │ └── labels.jsonl
│ └── neurosynth_use.jsonl
├── nv_task
│ ├── README.md
│ ├── analysis
│ │ ├── compare.ipynb
│ │ └── pmcids_task_annotated.txt
│ ├── annotations
│ │ ├── alice_chen.jsonl
│ │ ├── delavega-aliceoverlap.jsonl
│ │ ├── delavega-other.jsonl
│ │ └── delavega_nv.jsonl
│ └── labels
│ │ └── neurovault-cobidas.json
├── old_review-neuro-meta-analyses
│ ├── README.md
│ ├── annotations
│ │ ├── Brent_McPherson.jsonl
│ │ ├── Kendra_Oudyk.jsonl
│ │ ├── Michelle_Wang.jsonl
│ │ ├── Mohammad_Torabi.jsonl
│ │ ├── Niusha_Mirhakimi.jsonl
│ │ └── README.md
│ ├── datasets.json
│ ├── keep-only-docs-w-ma-in-title.py
│ ├── labels
│ │ ├── README.md
│ │ └── labels.jsonl
│ └── parkinsons
│ │ ├── README.md
│ │ ├── annotations
│ │ ├── README.md
│ │ └── pubextract_annotations.jsonl
│ │ ├── datasets.json
│ │ ├── ids.json
│ │ └── labels
│ │ └── README.md
├── parkinsons
│ ├── README.md
│ ├── annotations
│ │ ├── README.md
│ │ └── pubextract_annotations.jsonl
│ ├── datasets.json
│ ├── documents
│ │ ├── README.md
│ │ └── datasets.json
│ └── labels
│ │ └── README.md
├── participant_demographics
│ ├── README.md
│ ├── annotations
│ │ ├── Bangli_Cao.jsonl
│ │ ├── Chen-Yang_Su.jsonl
│ │ ├── Flemming_Kondrup_2.json
│ │ ├── Jerome_Dockes.jsonl
│ │ ├── Sean_Moore.jsonl
│ │ ├── Xiaotian_Hua.jsonl
│ │ ├── calvin_surbey.json
│ │ ├── jennifer_siegel_ramsay.json
│ │ ├── joon_hong.jsonl
│ │ ├── ju-chi_yu.json
│ │ ├── kailu_song.jsonl
│ │ ├── maximiliane_jousse.json
│ │ └── ross_blair.jsonl
│ ├── datasets.json
│ └── labels
│ │ └── demographics_labels.json
├── template_project
│ ├── README.md
│ ├── annotations
│ │ └── README.md
│ ├── datasets.json
│ └── labels
│ │ └── README.md
└── tracking_open_datasets
│ ├── README.md
│ ├── annotations
│ ├── Kendra_Oudyk.jsonl
│ └── README.md
│ ├── datasets.json
│ └── labels
│ ├── README.md
│ └── labels.jsonl
├── scripts
├── checkin_docs.py
├── checkout_docs.py
├── download_datasets.py
├── labelrepo
├── make_book.sh
├── make_database.py
├── make_participants_csv.py
├── make_repo_stats_figure.py
├── participants_report.py
├── start_project.py
├── update_annotaitons.py
└── watch_participants.py
└── shared_labels
└── participants.json
/.github/workflows/build.yml:
--------------------------------------------------------------------------------
1 | name: build
2 |
3 | permissions:
4 | contents: write
5 |
6 |
7 | on:
8 | push:
9 | branches:
10 | - main
11 |
12 | jobs:
13 | deploy-book:
14 | runs-on: ubuntu-latest
15 | steps:
16 | - uses: actions/checkout@v2
17 |
18 | - name: Set up Python 3.10
19 | uses: actions/setup-python@v2
20 | with:
21 | python-version: '3.10'
22 |
23 | - name: Install dependencies
24 | run: |
25 | cd analysis/book/
26 | pip install -r requirements.txt
27 |
28 | - name: Install labelrepo
29 | run: |
30 | pip install -e "analysis/labelrepo/"
31 |
32 | - name: Build the book
33 | run: |
34 | make book-full
35 |
36 | - name: GitHub Pages action
37 | uses: peaceiris/actions-gh-pages@v3.6.1
38 | with:
39 | github_token: ${{ secrets.GITHUB_TOKEN }}
40 | publish_dir: analysis/book/_build/html
41 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | projects/sandbox
2 | projects/**/*_report*.html
3 | projects/*/documents
4 |
5 | pubget_data
6 |
7 | # misc
8 | *.~lock.*
9 | */figures/
10 | .DS_Store
11 | analysis/book/_build
12 | analysis/book/assets/generated/*
13 | analysis/data/*
14 | analysis/book/projects/
15 |
16 |
17 | # labelbuddy database files
18 | *.lb
19 | *.labelbuddy
20 |
21 | # sqlite databases
22 | *.sqlite
23 | *.sqlite3
24 |
25 | # python stuff
26 | venv/*
27 | */.ipynb_checkpoints/*
28 | venv
29 | *.pyc
30 | .env
31 |
32 | doc_build/
33 | profile.svg
34 | flycheck__*
35 |
36 | # Byte-compiled / optimized / DLL files
37 | __pycache__/
38 | *.py[cod]
39 | *$py.class
40 |
41 | # C extensions
42 | *.so
43 |
44 | # Distribution / packaging
45 | .Python
46 | build/
47 | develop-eggs/
48 | dist/
49 | downloads/
50 | eggs/
51 | .eggs/
52 | lib/
53 | lib64/
54 | parts/
55 | sdist/
56 | var/
57 | wheels/
58 | *.egg-info/
59 | .installed.cfg
60 | *.egg
61 | MANIFEST
62 |
63 | # PyInstaller
64 | # Usually these files are written by a python script from a template
65 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
66 | *.manifest
67 | *.spec
68 |
69 | # Installer logs
70 | pip-log.txt
71 | pip-delete-this-directory.txt
72 |
73 | # Unit test / coverage reports
74 | htmlcov/
75 | .tox/
76 | .coverage
77 | .coverage.*
78 | .cache
79 | nosetests.xml
80 | coverage.xml
81 | *.cover
82 | .hypothesis/
83 | .pytest_cache/
84 |
85 | # Translations
86 | *.mo
87 | *.pot
88 |
89 | # Django stuff:
90 | *.log
91 | local_settings.py
92 | db.sqlite3
93 |
94 | # Flask stuff:
95 | instance/
96 | .webassets-cache
97 |
98 | # Scrapy stuff:
99 | .scrapy
100 |
101 | # Sphinx documentation
102 | docs/_build/
103 |
104 | # PyBuilder
105 | target/
106 |
107 | # Jupyter Notebook
108 | .ipynb_checkpoints
109 |
110 | # pyenv
111 | .python-version
112 |
113 | # celery beat schedule file
114 | celerybeat-schedule
115 |
116 | # SageMath parsed files
117 | *.sage.py
118 |
119 | # Environments
120 | .env
121 | .venv
122 | env/
123 | venv/
124 | ENV/
125 | env.bak/
126 | venv.bak/
127 |
128 | # Spyder project settings
129 | .spyderproject
130 | .spyproject
131 |
132 | # Rope project settings
133 | .ropeproject
134 |
135 | # mkdocs documentation
136 | /site
137 |
138 | # mypy
139 | .mypy_cache/
140 |
--------------------------------------------------------------------------------
/.labelbuddy-annotations-repository:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/.labelbuddy-annotations-repository
--------------------------------------------------------------------------------
/CITATION.cff:
--------------------------------------------------------------------------------
1 | # This CITATION.cff file was generated with cffinit.
2 | # Visit https://bit.ly/cffinit to generate yours today!
3 |
4 | cff-version: 1.2.0
5 | title: labelbuddy-annotations
6 | message: 'If you use this software/data, please cite it as below.'
7 | type: dataset
8 | authors:
9 | - {}
10 | - given-names: Jérôme
11 | family-names: Dockès
12 | orcid: 'https://orcid.org/0000-0002-5304-2496'
13 | - given-names: Kendra
14 | family-names: Oudyk
15 | orcid: 'https://orcid.org/0000-0003-4087-5402'
16 | - given-names: Alejandro
17 | family-names: de la Vega
18 | orcid: 'https://orcid.org/0000-0001-9062-3778'
19 | - given-names: Mohammad
20 | family-names: Torabi
21 | orcid: 'https://orcid.org/0000-0002-4429-8481'
22 | - given-names: Alice
23 | family-names: Chen
24 | - given-names: Rémi
25 | family-names: Gau
26 | orcid: 'https://orcid.org/0000-0002-1535-9767'
27 | - given-names: Dave
28 | family-names: Kennedy
29 | orcid: 'https://orcid.org/0000-0002-9377-0797'
30 | - given-names: Brent
31 | family-names: McPherson
32 | orcid: 'https://orcid.org/0000-0001-8378-8136'
33 | - given-names: Michelle
34 | family-names: Wang
35 | orcid: 'https://orcid.org/0000-0001-8378-8136'
36 | - family-names: Mirhakimi
37 | given-names: Niusha
38 | - given-names: Jean-Baptiste
39 | family-names: Poline
40 | orcid: 'https://orcid.org/0000-0002-9794-749X'
41 | identifiers:
42 | - type: doi
43 | value: 10.5281/zenodo.15225228
44 | - type: url
45 | value: >-
46 | https://github.com/litmining/labelbuddy-annotations/tree/main
47 | description: GitHub repository
48 | repository-code: >-
49 | https://github.com/litmining/labelbuddy-annotations/tree/main
50 | url: 'https://litmining.github.io/labelbuddy-annotations/'
51 | abstract: >-
52 | Annotations of academic papers. The papers were gathered
53 | using pubget, and the annotations made with labelbuddy.
54 | license: MIT
55 | commit: ff445c6
56 | version: 0.0.1
57 | date-released: '2025-04-15'
58 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2022 Kendra Oudyk
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/Makefile:
--------------------------------------------------------------------------------
1 | labelbuddy_databases := $(shell find . -type f -name '*.labelbuddy')
2 | annotation_files := $(patsubst %.labelbuddy, %.jsonl, $(labelbuddy_databases))
3 | repo_stats_fig := analysis/book/assets/generated/repo_stats.svg
4 | projects_tag_file := analysis/book/projects/DONT_EDIT_THIS_DIRECTORY_IT_IS_AUTOMATICALLY_GENERATED
5 |
6 | .PHONY: all annotations database csv book book-full clean
7 |
8 | all: annotations
9 |
10 | annotations: $(annotation_files)
11 |
12 | database: analysis/data/database.sqlite3
13 |
14 | csv: analysis/data/detailed_annotation.csv
15 |
16 | analysis/data/database.sqlite3:
17 | python3 ./scripts/make_database.py
18 |
19 | analysis/data/detailed_annotation.csv: analysis/data/database.sqlite3
20 | sqlite3 -header -csv $< "select * from detailed_annotation;" > $@
21 |
22 | $(annotation_files): %.jsonl: %.labelbuddy
23 | labelbuddy $< --export-docs $@ --no-text --labelled-only
24 |
25 | $(repo_stats_fig):
26 | python3 scripts/make_repo_stats_figure.py
27 |
28 | $(projects_tag_file):
29 | python3 analysis/book_helpers/add_project_pages.py
30 |
31 | book: $(repo_stats_fig) $(projects_tag_file)
32 | # the prerequisites make sure the files exist so jupyter-book doesn't crash,
33 | # but they don't enforce that they are up to date -- for that use book-full
34 | ./scripts/make_book.sh
35 |
36 | book-full: database csv
37 | rm -rf analysis/book/_build/
38 | rm -rf analysis/book/projects/
39 | python3 scripts/make_repo_stats_figure.py
40 | python3 scripts/make_participants_csv.py
41 | python3 analysis/book_helpers/add_project_pages.py
42 | ./scripts/make_book.sh
43 |
44 | clean:
45 | rm -rf analysis/data/database.sqlite3 analysis/data/detailed_annotation.csv analysis/book/_build analysis/book/projects
46 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # labelbuddy-annotations
2 |
3 | Annotations of academic papers.
4 | The papers were gathered using [pubget](https://github.com/neuroquery/pubget), and the annotations made with [labelbuddy](https://jeromedockes.github.io/labelbuddy).
5 |
6 | See more details in the repository's [website](https://litmining.github.io/labelbuddy-annotations/overview.html).
7 |
8 | Current repository status:
9 |
10 | 
11 |
12 |
--------------------------------------------------------------------------------
/analysis/book/_config.yml:
--------------------------------------------------------------------------------
1 | title: Biomedical literature annotations
2 | author: "https://github.com/neurodatascience/labelbuddy-annotations/graphs/contributors"
3 |
4 | parse:
5 | myst_substitutions:
6 | labelbuddy_home: "[labelbuddy](https://jeromedockes.github.io/labelbuddy/)"
7 | labelbuddy_doc: "[labelbuddy documentation](https://jeromedockes.github.io/labelbuddy/labelbuddy/current/documentation/)"
8 | lb: "**labelbuddy**"
9 | pmc_home: "[PubMedCentral](https://www.ncbi.nlm.nih.gov/pmc/)"
10 | pubget_home: "[pubget](https://neuroquery.github.io/pubget/)"
11 | git: "**Git**"
12 | pg: "**pubget**"
13 | repo_blob_url: "https://github.com/neurodatascience/labelbuddy-annotations/blob/main/"
14 | repo_tree_url: "https://github.com/neurodatascience/labelbuddy-annotations/tree/main/"
15 | repo_issues_url: "https://github.com/neurodatascience/labelbuddy-annotations/issues"
16 | repo_url: "https://github.com/neurodatascience/labelbuddy-annotations/"
17 |
18 | execute:
19 | execute_notebooks: force
20 |
21 | latex:
22 | latex_documents:
23 | targetname: book.tex
24 |
25 | bibtex_bibfiles:
26 | - references.bib
27 |
28 | repository:
29 | url: "https://github.com/neurodatascience/labelbuddy-annotations"
30 | path_to_book: analysis/book
31 | branch: main
32 |
33 | html:
34 | use_issues_button: false
35 | use_repository_button: true
36 |
37 | sphinx:
38 | config:
39 | language: en
40 | nb_custom_formats:
41 | .py:
42 | - jupytext.reads
43 | - fmt: py
44 | html_extra_path: ["assets"]
45 |
--------------------------------------------------------------------------------
/analysis/book/_static/annotation-set.css:
--------------------------------------------------------------------------------
1 | ../../labelrepo/src/labelrepo/_data/css/annotation-set.css
--------------------------------------------------------------------------------
/analysis/book/_static/global.css:
--------------------------------------------------------------------------------
1 | .cell_output * + img {
2 | margin-block-start: 2em;
3 | }
4 |
5 | .menu-dropdown-launch-buttons {
6 | display: none !important;
7 | }
8 |
9 | span.pre, code {
10 | color: #007020;
11 | }
12 |
--------------------------------------------------------------------------------
/analysis/book/_static/label-set.css:
--------------------------------------------------------------------------------
1 | ../../labelrepo/src/labelrepo/_data/css/label-set.css
--------------------------------------------------------------------------------
/analysis/book/_static/participants.css:
--------------------------------------------------------------------------------
1 | ../../labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/participants.css
--------------------------------------------------------------------------------
/analysis/book/_toc.yml:
--------------------------------------------------------------------------------
1 | format: jb-book
2 | root: overview
3 | parts:
4 | - caption: Introduction
5 | chapters:
6 | - file: how_to_use_the_data
7 | - file: contributing_to_this_repository
8 | - caption: Analyses
9 | chapters:
10 | - file: participant_demographics
11 | - caption: Projects
12 | chapters:
13 | - glob: projects/*
14 |
--------------------------------------------------------------------------------
/analysis/book/assets/annotate_participants.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/analysis/book/assets/annotate_participants.png
--------------------------------------------------------------------------------
/analysis/book/references.bib:
--------------------------------------------------------------------------------
1 | ---
2 | ---
--------------------------------------------------------------------------------
/analysis/book/requirements.txt:
--------------------------------------------------------------------------------
1 | # https://issueantenna.com/repo/ipython/ipython/issues/13845
2 | ipython!=8.7.0
3 |
4 | altair
5 | jupyter-book!=0.14
6 | jupytext
7 | matplotlib
8 | pandas
9 | seaborn
10 | plotly
11 | kaleido
12 |
--------------------------------------------------------------------------------
/analysis/book_helpers/figures/participant_tree.dot:
--------------------------------------------------------------------------------
1 | digraph G {
2 | rankdir = BT;
3 | node[shape=box];
4 |
5 | healthy -> "all participants";
6 | patients -> "all participants";
7 |
8 | healthy_anonym[label=""]
9 | healthy_anonym -> healthy;
10 | Sz -> patients;
11 | ASD -> patients;
12 |
13 | ASD_male[label="male"];
14 | ASD_female[label="female"];
15 | ASD_male -> ASD;
16 | ASD_female -> ASD;
17 |
18 | Sz_male[label="male"];
19 | Sz_female[label="female"];
20 | Sz_male -> Sz;
21 | Sz_female -> Sz;
22 |
23 | healthy_male[label="male"];
24 | healthy_female[label="female"];
25 | healthy_male -> healthy_anonym;
26 | healthy_female -> healthy_anonym;
27 |
28 | }
29 |
--------------------------------------------------------------------------------
/analysis/book_helpers/templates/project_page.md:
--------------------------------------------------------------------------------
1 | ---
2 | jupytext:
3 | text_representation:
4 | extension: .md
5 | format_name: myst
6 | format_version: 0.13
7 | jupytext_version: 1.14.1
8 | kernelspec:
9 | display_name: Python 3
10 | language: python
11 | name: python3
12 | ---
13 | {{ warning_automatically_generated_page | safe }}
14 |
15 | # {{ project_name }}
16 |
17 | You can see the full contents of this project [on GitHub](https://github.com/neurodatascience/labelbuddy-annotations/tree/main/projects/{{ project_name }}/).
18 |
19 | {{ readme_content | safe }}
20 |
21 | {% block main_content %}
22 |
23 | ## Labels in this project
24 |
25 | {% if not labels %}
26 | (No labels have been added to this project yet)
27 | {% endif%}
28 |
29 | ```{code-cell}
30 | :tags: [remove-input]
31 |
32 | from labelrepo import displays
33 | text = """
34 |
35 | {% for label in labels %}
36 |
37 | {{ label.name }} ({{ label.n_annotated_docs }} docs)
38 | {% if label.n_annotated_docs > 0 %}
39 | Example annotations:
40 |
41 | {% for annotation in label.example_annotations %}
42 |
43 |
44 | {{ annotation.prefix }}{{ annotation.selected_text }}{{ annotation.suffix }}
45 |
46 |
55 |
56 | {% endfor %}
57 |
58 | {% else %}
59 | (No annotations with this label in the current project)
60 | {% endif %}
61 |
62 | {% endfor %}
63 |
64 | """
65 | displays.HTMLDisplay(text)
66 | ```
67 | {% endblock %}
68 |
--------------------------------------------------------------------------------
/analysis/dash_app/requirements.txt:
--------------------------------------------------------------------------------
1 | pandas
2 | plotly
3 | dash
4 |
--------------------------------------------------------------------------------
/analysis/labelrepo/pyproject.toml:
--------------------------------------------------------------------------------
1 | [build-system]
2 | requires = ["setuptools", "wheel"]
3 | build-backend = "setuptools.build_meta"
--------------------------------------------------------------------------------
/analysis/labelrepo/setup.cfg:
--------------------------------------------------------------------------------
1 | [options]
2 | packages = find:
3 | package_dir =
4 | = src
5 | install_requires =
6 | pandas
7 | jinja2
8 | websockets
9 |
10 | [options.packages.find]
11 | where = src
12 |
13 | [options.package_data]
14 | labelrepo._data =
15 | *
16 | labelrepo._data.css =
17 | *
18 | labelrepo.projects.participant_demographics._data =
19 | *
20 | labelrepo.projects.participant_demographics._data.templates =
21 | *
22 |
23 | [options.exclude_package_data]
24 | * = __pycache__
25 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/__init__.py:
--------------------------------------------------------------------------------
1 | from labelrepo import database, datasets, displays, repo, projects
2 | from labelrepo._utils import read_json, glob_json
3 |
4 | __all__ = [
5 | "database",
6 | "datasets",
7 | "displays",
8 | "repo",
9 | "projects",
10 | "read_json",
11 | "glob_json",
12 | ]
13 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/_data/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/analysis/labelrepo/src/labelrepo/_data/__init__.py
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/analysis/labelrepo/src/labelrepo/_data/css/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/analysis/labelrepo/src/labelrepo/_data/css/__init__.py
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/analysis/labelrepo/src/labelrepo/_data/css/annotation-set.css:
--------------------------------------------------------------------------------
1 | :root {
2 | --annotation-data-font: var(--pst-font-family-monospace, monospace);
3 | }
4 |
5 | .annotation-set {
6 | margin: 0;
7 | display: flex;
8 | flex-wrap: wrap;
9 | justify-content: center;
10 | align-items: stretch;
11 | align-content: stretch;
12 | gap: 1rem;
13 | padding: 0px;
14 | }
15 |
16 | .annotation {
17 | display: flex;
18 | flex-direction: column;
19 | border-radius: 0.25rem;
20 | box-shadow: 0px 4px 4px 0px rgba(0, 0, 0, .2), 0px 4px 10px 0px rgba(0, 0, 0, .2);
21 | overflow: hidden;
22 | font-family: var(--pst-font-family-base, sans-serif);
23 | max-width: 70ch;
24 | }
25 |
26 | .annotation .annotation-header {
27 | font-family: var(--annotation-data-font);
28 | font-weight: bold;
29 | background: var(--label-color, LightGray);
30 | padding: 0.5rem;
31 | border-block-end: 1px solid rgba(0, 0, 0, 0.2);
32 | }
33 |
34 | .annotation .context {
35 | padding-inline-start: 2rem;
36 | padding-inline-end: 2rem;
37 | padding-block-start: 1rem;
38 | padding-block-end: 1rem;
39 | line-height: 1.5;
40 | flex-grow: 1;
41 | }
42 |
43 | .annotation .annotated-text {
44 | background: var(--label-color, LightGray);
45 | font-weight: bold;
46 | padding-inline-start: 0.5rem;
47 | padding-inline-end: 0.5rem;
48 | padding-block-start: 0.25rem;
49 | padding-block-end: 0.25rem;
50 | }
51 |
52 | .annotation .annotation-footer {
53 | display: flex;
54 | flex-wrap: wrap;
55 | justify-content: space-between;
56 | align-content: space-between;
57 | row-gap: 0.5rem;
58 | font-family: var(--annotation-data-font);
59 | font-size: 0.8rem;
60 | padding: 0.5rem;
61 | background: #e0e0e0;
62 | border-block-start: 1px solid rgba(0, 0, 0, 0.2);
63 | }
64 |
65 | .annotation .annotation-footer > * + * {
66 | margin-inline-start: 0.25rem;
67 | }
68 |
69 | .annotation .extra-data::before {
70 | content: "Extra data: ";
71 | font-weight: bold;
72 | }
73 |
74 | .annotation .extra-data:empty {
75 | display: none;
76 | }
77 |
78 | .annotation a, .annotation a:visited {
79 | color: inherit;
80 | text-decoration: underline;
81 | }
82 |
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/analysis/labelrepo/src/labelrepo/_data/css/label-set.css:
--------------------------------------------------------------------------------
1 | .label-set, label-set-wrap, .label-display {
2 | margin: 0px;
3 | }
4 |
5 | .label-set-wrap {
6 | display: inline-block;
7 | }
8 |
9 | .label-set-wrap > * + * {
10 | margin-block-start: 0.5rem;
11 | }
12 |
13 | .label-display {
14 | padding-block: 0.5rem;
15 | padding-inline: 1rem;
16 | border-radius: 0.25rem;
17 | background-color: var(--label-color, LightGray);
18 | font-family: var(--pst-font-family-monospace, monospace);
19 | font-weight: bold;
20 | line-height: 1.1;
21 | }
22 |
23 | .detailed-label-set details + details {
24 | margin-block-start: 0.5rem;
25 | }
26 |
27 | .detailed-label-set details[open] + details {
28 | margin-block-start: 1rem;
29 | }
30 |
31 | .detailed-label-set > details[open] > summary {
32 | margin-block-end: 1rem;
33 | }
34 |
35 | .detailed-label-set .annotation-set {
36 | justify-content: flex-start;
37 | }
38 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/_data/initialize_db.sql:
--------------------------------------------------------------------------------
1 | create table project (
2 | name text not null primary key
3 | );
4 |
5 | create table document(
6 | id integer primary key,
7 | utf8_text_md5_checksum blob unique not null,
8 | text text not null,
9 | pmcid integer,
10 | pmid integer,
11 | publication_year integer,
12 | journal text,
13 | title text
14 | );
15 |
16 | create table label(
17 | id integer primary key,
18 | name text unique not null,
19 | color text
20 | );
21 |
22 | create table project_label(
23 | project_name text not null references project(name) on delete cascade,
24 | label_id integer not null references label(id) on delete cascade,
25 | constraint unique_project_name_label_id unique (project_name, label_id)
26 | on conflict ignore
27 | );
28 |
29 | create table annotator(
30 | name text not null primary key
31 | );
32 |
33 | create table annotation(
34 | id integer primary key,
35 | doc_id not null references document(id) on delete cascade,
36 | label_id not null references label(id) on delete cascade,
37 | annotator_name not null references annotator(name) on delete cascade,
38 | start_char integer not null,
39 | end_char integer not null,
40 | extra_data text,
41 | project_name text not null references project(name) on delete cascade
42 | );
43 |
44 | create table db_info(
45 | key text unique not null,
46 | value
47 | );
48 |
49 | create view detailed_annotation as
50 | with annot as
51 | (select *,
52 | max(0, start_char - 200) as context_start_char,
53 | min(length(document.text), end_char + 200) as context_end_char,
54 | annotation.id as annotation_id
55 | from annotation inner join document on annotation.doc_id = document.id)
56 | select
57 | pmcid, pmid, publication_year, journal, title,
58 | label.name as label_name,
59 | label.color as label_color,
60 | annotator_name,
61 | start_char, end_char, extra_data, project_name,
62 | substr(
63 | text, start_char + 1, end_char - start_char) as selected_text,
64 | substr(
65 | text,
66 | context_start_char + 1,
67 | context_end_char - context_start_char
68 | ) as context,
69 | context_start_char, context_end_char,
70 | length(text) as doc_length,
71 | lower(hex(utf8_text_md5_checksum)) as doc_md5
72 | from annot
73 | inner join label on annot.label_id = label.id
74 | order by doc_id, start_char, end_char,
75 | label_id, annotator_name, annot.id;
76 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/_utils.py:
--------------------------------------------------------------------------------
1 | import itertools
2 | import json
3 | import pathlib
4 | from typing import List, Union, Any, Tuple
5 | import hashlib
6 | from typing import Mapping, Dict
7 |
8 |
9 | def package_root() -> pathlib.Path:
10 | return pathlib.Path(__file__).resolve().parent
11 |
12 |
13 | def package_data() -> pathlib.Path:
14 | return package_root() / "_data"
15 |
16 |
17 | def read_json(json_file: Union[str, pathlib.Path]) -> Any:
18 | json_file = pathlib.Path(json_file)
19 | if json_file.suffix == ".json":
20 | return json.loads(json_file.read_text("UTF-8"))
21 | elif json_file.suffix == ".jsonl":
22 | with open(json_file, "r", encoding="UTF-8") as stream:
23 | return [json.loads(line) for line in stream]
24 | raise ValueError("File extension must be .json or .jsonl")
25 |
26 |
27 | def glob_json(directory: pathlib.Path) -> List[pathlib.Path]:
28 | return sorted(
29 | itertools.chain(directory.glob("*.json"), directory.glob("*.jsonl"))
30 | )
31 |
32 |
33 | def _extract_metadata_from_text(doc_info: Mapping[str, Any]) -> Dict[str, str]:
34 | metadata = {}
35 | for field, (start, end) in doc_info["metadata"].get("field_positions", {}).items():
36 | metadata[field] = doc_info["text"][start:end]
37 | return metadata
38 |
39 |
40 | def process_doc_info(doc_info: dict) -> Tuple[Dict[str, Any], dict]:
41 | metadata_field_types = {
42 | "pmid": int,
43 | "pmcid": int,
44 | "journal": str,
45 | "publication_year": int,
46 | "title": str,
47 | }
48 |
49 | if isinstance(doc_info["metadata"], str):
50 | doc_info["metadata"] = json.loads(doc_info["metadata"])
51 |
52 | doc_row = {}
53 | doc_row["md5"] = hashlib.md5(doc_info["text"].encode("utf-8")).hexdigest()
54 | doc_row["text"] = doc_info["text"]
55 | text_metadata = _extract_metadata_from_text(doc_info)
56 | for field, field_type in metadata_field_types.items():
57 | raw_value = doc_info["metadata"].get(
58 | field, text_metadata.get(field, None)
59 | )
60 | if raw_value is not None:
61 | try:
62 | doc_row[field] = field_type(raw_value)
63 | except (KeyError, ValueError, TypeError):
64 | doc_row[field] = None
65 | else:
66 | doc_row[field] = None
67 |
68 | return doc_row, doc_info
69 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/dbutils.py:
--------------------------------------------------------------------------------
1 | from typing import Optional, Tuple
2 | import contextlib
3 | import pandas as pd
4 | from labelrepo import database
5 |
6 |
7 | def select_annotations(
8 | labels: Optional[Tuple[str]],
9 | annotator_name: Optional[str] = None,
10 | project_name: Optional[str] = None,
11 | pmcid: Optional[int] = None,
12 | ) -> pd.DataFrame:
13 |
14 | labels = ",".join([f"'{label}'" for label in labels])
15 | annotator_query = (
16 | "" if annotator_name is None else "and annotator_name = :annotator"
17 | )
18 | project_query = (
19 | "" if project_name is None else "and project_name = :project"
20 | )
21 | pmcid_query = "" if pmcid is None else "and pmcid = :pmcid"
22 | query = f"""
23 | select pmcid, title, doc_md5, label_name, extra_data, selected_text,
24 | start_char, end_char, project_name, annotator_name,
25 | coalesce(label_color, '#E0E0E0') as label_color, context,
26 | context_start_char, context_end_char, doc_length
27 | from detailed_annotation where label_name in ({labels})
28 | {annotator_query}
29 | {project_query}
30 | {pmcid_query}
31 | """
32 |
33 | with contextlib.closing(database.get_database_connection()) as connection:
34 | with connection:
35 | all_anno = pd.DataFrame(
36 | map(
37 | dict,
38 | connection.execute(
39 | query,
40 | {
41 | "annotator": annotator_name,
42 | "project": project_name,
43 | "pmcid": pmcid,
44 | },
45 | ).fetchall(),
46 | )
47 | )
48 | if not all_anno.shape[0]:
49 | all_anno = pd.DataFrame(
50 | columns="pmcid title doc_md5 label_name extra_data "
51 | "selected_text start_char end_char project_name "
52 | "annotator_name".split()
53 | )
54 | return all_anno
55 |
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/analysis/labelrepo/src/labelrepo/projects/__init__.py:
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https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/analysis/labelrepo/src/labelrepo/projects/__init__.py
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/analysis/labelrepo/src/labelrepo/projects/nv_task/__init__.py:
--------------------------------------------------------------------------------
1 | from labelrepo.projects.nv_task._nv_task import load_annotations
2 |
3 | all = [
4 | "load_annotations",
5 | ]
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/projects/nv_task/_nv_task.py:
--------------------------------------------------------------------------------
1 | from labelrepo.repo import repo_root
2 | from labelrepo.dbutils import select_annotations
3 | from labelrepo.documents import load_central_documents
4 | import json
5 |
6 | PROJ_NAME = 'nv_task'
7 | PROJ_ROOT = repo_root() / 'projects' / 'nv_task'
8 |
9 |
10 | def _get_labels():
11 | semiauto_labels = json.load(
12 | (PROJ_ROOT / 'labels' / 'neurovault-cobidas.json').open()
13 | )
14 | labels = [l['name'] for l in semiauto_labels]
15 |
16 | return labels
17 |
18 |
19 | def _get_section(docs, pmcid, start, end):
20 | doc = docs[pmcid]
21 | for section, (s, e) in doc['metadata']['field_positions'].items():
22 | if start >= s and end <= e:
23 | return section
24 |
25 | return None
26 |
27 |
28 | def load_annotations(annotator_name=None, add_sections=True):
29 | labels = _get_labels()
30 | annotations = select_annotations(
31 | labels=labels, project_name=PROJ_NAME, annotator_name=annotator_name
32 | )
33 |
34 | none_unsure = annotations[annotations.label_name.isin(['None', 'Unsure'])]
35 | annotations = annotations[~annotations.label_name.isin(['None', 'Unsure'])]
36 | annotations['None'] = False
37 | annotations['Unsure'] = False
38 |
39 | id_keys = ['doc_md5', 'annotator_name', 'start_char', 'end_char']
40 | for i, row in none_unsure.iterrows():
41 | matching_anns = annotations[(annotations[id_keys] == row[id_keys]).all(axis=1)]
42 | if len(matching_anns) > 0:
43 | annotations.loc[matching_anns.index, row['label_name']] = True
44 |
45 | if add_sections:
46 | unique_md5 = {}
47 | for pmcid, df in annotations.groupby('pmcid'):
48 | unique_md5[f"pmcid_{pmcid}"] = df.doc_md5.unique().tolist()
49 |
50 | annotated_docs = load_central_documents(unique_md5)
51 |
52 | # Turn into dict with key as pmcid
53 | annotated_docs = {
54 | doc['metadata']['pmcid']: doc for doc in annotated_docs
55 | }
56 |
57 | # Get section for each annotation from document metadata
58 | annotations['section'] = annotations.apply(
59 | lambda x: _get_section(annotated_docs,
60 | x.pmcid, x.start_char, x.end_char), axis=1)
61 |
62 | return annotations
63 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/projects/participant_demographics/__init__.py:
--------------------------------------------------------------------------------
1 | from labelrepo.projects.participant_demographics._participant_demographics import (
2 | get_report,
3 | get_annotation_stacks_display,
4 | report_command,
5 | select_participants_annotations,
6 | get_participant_demographics,
7 | )
8 | from labelrepo.projects.participant_demographics._watcher import (
9 | watch_participants,
10 | get_live_report_path,
11 | )
12 |
13 | __all__ = [
14 | "get_report",
15 | "get_annotation_stacks_display",
16 | "get_participant_demographics",
17 | "report_command",
18 | "select_participants_annotations",
19 | "watch_participants",
20 | "get_live_report_path",
21 | ]
22 |
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/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/__init__.py:
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https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/__init__.py
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/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/__init__.py:
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https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/__init__.py
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/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/annotation_stack.html:
--------------------------------------------------------------------------------
1 |
4 |
5 | {% for anno in annotation_stack["annotations"] %}
6 |
7 |
8 | {{ anno["label_name"] }}
9 |
10 | {% if anno["extra_data"] %}
11 |
14 | {% endif %}
15 |
16 | {% endfor %}
17 |
18 |
19 | {{ annotation_stack["prefix"] }}
20 | {{ annotation_stack["selected_text"] }}
21 | {{ annotation_stack["suffix"] }}
22 |
23 |
24 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/annotation_stack_list.html:
--------------------------------------------------------------------------------
1 | {% if standalone %}
2 |
3 |
4 |
5 | Annotations
6 |
9 |
10 |
11 | {% endif %}
12 |
13 | {% for annotation_stack in annotation_stacks %}
14 | {% include "annotation_stack.html" %}
15 | {% endfor %}
16 |
17 | {% if standalone %}
18 |
19 |
20 | {% endif %}
21 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/hide_show_annotations.js:
--------------------------------------------------------------------------------
1 | function hideAnnotations(element) {
2 | element.querySelectorAll(
3 | "*[data-annotation-stack-is-shown='true']").forEach(
4 | (elem) => {
5 | elem.setAttribute("data-annotation-stack-is-shown", "false");
6 | document.getElementById(
7 | elem.getAttribute("data-reason-target-id")).innerHTML = "";
8 | const allAnnoIds = elem.getAttribute(
9 | 'data-annotation-stack-ids').split(",");
10 | allAnnoIds.forEach((annoId) => {
11 | document.getElementById(annoId).setAttribute(
12 | "data-is-selected", "false");
13 | });
14 | }
15 | );
16 | }
17 |
18 | function showBuddy(element) {
19 | const wasActive = element.getAttribute('data-annotation-stack-is-shown');
20 | const docElem = document.getElementById(element.getAttribute("data-doc-id"));
21 | hideAnnotations(docElem);
22 | if (wasActive !== "true") {
23 | element.setAttribute('data-annotation-stack-is-shown', "true");
24 | const reason = element.getAttribute("data-reason-as-json");
25 | if (reason) {
26 | document.getElementById(
27 | element.getAttribute("data-reason-target-id"))
28 | .innerHTML = ("Computed value: " +
29 | JSON.parse(reason));
30 | }
31 | const allAnnoIds = element.getAttribute(
32 | 'data-annotation-stack-ids').split(",");
33 | allAnnoIds.forEach((annoId) => {
34 | document.getElementById(annoId).setAttribute(
35 | "data-is-selected", "true");
36 | });
37 | }
38 | }
39 |
40 | function addDetailsEvents() {
41 | const allDetails = document.querySelectorAll(
42 | ".labelrepo-debug-details details");
43 | allDetails.forEach((details) => {
44 | details.addEventListener("toggle", (event) => {
45 | if (event.target.hasAttribute("open")) {
46 | return;
47 | }
48 | hideAnnotations(event.target);
49 | });
50 | });
51 | }
52 |
53 | window.addEventListener("DOMContentLoaded", addDetailsEvents);
54 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/live_report.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 | Live participants report
5 |
8 |
11 |
49 |
50 |
51 |
52 |
53 |
54 | Waiting for connection to the live annotation report server
55 | (please make sure one is running at port
56 | localhost:{{ port }}
and reload this page).
57 |
58 |
59 |
60 |
61 |
62 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/projects/participant_demographics/_data/templates/participant_tree.html:
--------------------------------------------------------------------------------
1 | {% macro participant_node(node, name, path, level=0) %}
2 |
3 | {% if name %}
4 |
{{ name }}
5 | {% endif %}
6 |
7 | {% for attribute in node.attributes -%}
8 |
9 | {{ attribute }} = {{ sourced(node.attributes[attribute], doc, attribute + path) }}
10 | {{- ", " if not loop.last -}}
11 |
12 | {% endfor %}
13 | {% for child in node.children %}
14 | {{ participant_node(node.children[child], child, path + "-" + (child | md5), level + 1) }}
15 | {% endfor %}
16 |
17 |
18 | {% endmacro %}
19 |
20 |
21 |
22 | full extraction details
23 | {% if (not tree.children) and (not tree.attributes)%}
24 |
25 | (Empty participants tree.)
26 |
27 | {% endif %}
28 | {{ participant_node(tree, None, ("participants detailed tree" | md5 )) }}
29 |
30 |
31 |
--------------------------------------------------------------------------------
/analysis/labelrepo/src/labelrepo/repo.py:
--------------------------------------------------------------------------------
1 | import os
2 | import pathlib
3 | import subprocess
4 | import socket
5 | from typing import Optional
6 |
7 | from labelrepo import _utils
8 |
9 |
10 | def is_repo_root(dir_path: pathlib.Path) -> bool:
11 | return (dir_path / ".labelbuddy-annotations-repository").is_file()
12 |
13 |
14 | def repo_root() -> pathlib.Path:
15 | repo_env = os.environ.get("LABELBUDDY_ANNOTATIONS_REPO")
16 | pwd = pathlib.Path(".")
17 | package_parent = _utils.package_root().parents[3]
18 | for candidate in (repo_env, pwd, package_parent):
19 | if candidate is not None and is_repo_root(pathlib.Path(candidate)):
20 | return pathlib.Path(candidate)
21 | try:
22 | git_output = (
23 | subprocess.run(
24 | ["git", "rev-parse", "--show-toplevel"], capture_output=True
25 | )
26 | .stdout.strip()
27 | .decode("utf-8")
28 | )
29 | candidate = pathlib.Path(git_output)
30 | if is_repo_root(candidate):
31 | return candidate
32 | except Exception:
33 | pass
34 | raise FileNotFoundError(
35 | "Could not find labelbuddy-annotations repository."
36 | )
37 |
38 |
39 | def data_dir() -> pathlib.Path:
40 | data_dir = repo_root() / "analysis" / "data"
41 | data_dir.mkdir(exist_ok=True, parents=True)
42 | return data_dir
43 |
44 |
45 | def last_modified_labelbuddy_file():
46 | lb_files = sorted(
47 | (lbf.stat().st_mtime, lbf)
48 | for lbf in (repo_root() / "projects").glob("**/*.labelbuddy")
49 | )
50 | if not lb_files:
51 | return None
52 | return lb_files[-1][1]
53 |
54 |
55 | def annotator_name(suggested_name: Optional[str] = None) -> str:
56 | if suggested_name:
57 | return suggested_name
58 | name = os.environ.get("LABELBUDDY_ANNOTATOR_NAME", "")
59 | if name:
60 | return name
61 | try:
62 | name = (
63 | subprocess.run(["git", "config", "User.Name"], capture_output=True)
64 | .stdout.decode("utf-8")
65 | .strip()
66 | .replace(" ", "_")
67 | )
68 | if name:
69 | return name
70 | except Exception:
71 | pass
72 | user_name = pathlib.Path.home().name
73 | host_name = socket.gethostname()
74 | return f"{user_name}_{host_name}"
75 |
76 |
77 | def git_working_directory_is_clean() -> bool:
78 | result = subprocess.run(
79 | ["git", "status", "--porcelain"],
80 | cwd=str(repo_root()),
81 | capture_output=True,
82 | check=True,
83 | )
84 | return result.stdout.strip() == b""
85 |
86 |
87 | def git_head_checksum() -> str:
88 | result = subprocess.run(
89 | ["git", "rev-parse", "HEAD"],
90 | cwd=str(repo_root()),
91 | capture_output=True,
92 | check=True,
93 | )
94 | return result.stdout.strip().decode("UTF-8")
95 |
--------------------------------------------------------------------------------
/documents/pmcid_10417748.jsonl:
--------------------------------------------------------------------------------
1 | {"text": "Shepherd, J. H. and Baten, C. and Klassen, A. and Zamora, G. and Saravia, S. and Pritchard, E. and Ali, Z. and Kahlon, S. K. and Whitelock, K. and Reyes, F. A. and Hedges, D. W. and Hamilton, J. P. and Sacchet, M. D. and Miller, C. H.\nEur Psychiatry, 2023\n\n# Title\n\nThe Effects of Serotonergic Psychedelics on Neural Activity: A Meta-Analysis of Task-Based Functional Neuroimaging Studies\n\n# Keywords\n\n\n\n# Abstract\n \n## Introduction \n \nCuriosity toward the effects of psychedelic drugs on neural activation has increased due to their potential therapeutic benefits, particularly serotonergic psychedelics that act as 5-HT2A receptor agonists such as LSD, psilocybin, and MDMA. However, the pattern of their effects on neural activity in various brain regions in both clinical and healthy populations is still not well understood, and primary studies addressing this issue have sometimes generated inconsistent results. \n\n\n## Objectives \n \nThe present meta-analysis aims to advance our understanding of the most widely used serotonergic psychedelics \u2013 LSD, psilocybin, and MDMA \u2013 by examining their effects on the functional activation throughout the whole brain among both clinical and healthy participants. \n\n\n## Methods \n \nWe conducted this meta-analysis by applying multilevel kernel density analysis (MKDA) with ensemble thresholding to quantitatively combine existing functional magnetic resonance imaging (fMRI) studies that examined whole-brain functional activation of clinical or healthy participants who were administered a serotonergic psychedelic. \n\n\n## Results \n \nSerotonergic psychedelics, including LSD, psilocybin, and MDMA, exhibited significant effects (\u03b1=0.05) on neural activation in several regions throughout the cerebral cortex and basal ganglia, including effects that may be common across and unique within each drug. \n\n\n## Conclusions \n \nThese observed effects of serotonergic psychedelics on neural activity advance our understanding of the functional neuroanatomy associated with their administration and may inform future studies of both their adverse and therapeutic effects, including emerging clinical applications for the treatment of several psychiatric disorders. \n\n\n## Disclosure of Interest \n \nNone Declared \n\n \n\n# Body\n\n", "metadata": {"id": 10417748, "text_md5": "2d413739f27342e7d91a0e38e9871727", "field_positions": {"authors": [0, 234], "journal": [235, 249], "publication_year": [251, 255], "title": [266, 388], "keywords": [402, 402], "abstract": [415, 2254], "body": [2263, 2263]}, "batch": 1, "doi": "10.1192/j.eurpsy.2023.1948", "pmcid": "10417748", "pmc_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417748", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=10417748"}, "display_title": "pmcid: 10417748", "list_title": "PMC10417748 The Effects of Serotonergic Psychedelics on Neural Activity: A Meta-Analysis of Task-Based Functional Neuroimaging Studies"}
2 |
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/documents/pmcid_2448598.jsonl:
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1 | {"text": "Harkema, Henk and Roberts, Ian and Gaizauskas, Rob and Hepple, Mark\nComp Funct Genomics, 2005\n\n# Title\n\nA Web Service for Biomedical Term Look-Up\n\n# Keywords\n\n\n\n# Abstract\n \nRecent years have seen a huge increase in the amount of biomedical information\nthat is available in electronic format. Consequently, for biomedical researchers\nwishing to relate their experimental results to relevant data lurking somewhere within\nthis expanding universe of on-line information, the ability to access and navigate\nbiomedical information sources in an efficient manner has become increasingly\nimportant. Natural language and text processing techniques can facilitate this task\nby making the information contained in textual resources such as MEDLINE\nmore readily accessible and amenable to computational processing. Names of\nbiological entities such as genes and proteins provide critical links between different\nbiomedical information sources and researchers' experimental data. Therefore,\nautomatic identification and classification of these terms in text is an essential\ncapability of any natural language processing system aimed at managing the wealth\nof biomedical information that is available electronically. To support term recognition\nin the biomedical domain, we have developed Termino, a large-scale terminological\nresource for text processing applications, which has two main components: first, a\ndatabase into which very large numbers of terms can be loaded from resources such\nas UMLS, and stored together with various kinds of relevant information; second,\na finite state recognizer, for fast and efficient identification and mark-up of terms\nwithin text. Since many biomedical applications require this functionality, we have\nmade Termino available to the community as a web service, which allows for its\nintegration into larger applications as a remotely located component, accessed through\na standardized interface over the web. \n \n\n# Body\n\n", "metadata": {"pmcid": 2448598, "text_md5": "ead13a4bcd5fb60b756489c643e76d05", "field_positions": {"authors": [0, 67], "journal": [68, 87], "publication_year": [89, 93], "title": [104, 145], "keywords": [159, 159], "abstract": [172, 1938], "body": [1947, 1947]}, "batch": 1, "pmid": 18629294, "doi": "10.1002/cfg.459", "pmc_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2448598", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=2448598"}, "display_title": "pmcid: 2448598", "list_title": "PMC2448598 A Web Service for Biomedical Term Look-Up"}
2 |
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/documents/pmcid_5006857.jsonl:
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1 | {"display_title": "PMID: 25773646 batch: 4", "list_title": "batch 4 PMID25773646 Immediate memory for \u201cwhen, where and what\u201d: Short\u2010delay retrieval using dynamic naturalistic material ", "metadata": {"batch_nb": 4, "journal": "Human brain mapping", "origin_file": "documents_season_1.jsonl", "pmcid": 5006857, "pmid": 25773646, "publication_year": 2015, "text_md5": "3c7d67f6f4694b7dc4475fa94ad240ee", "title": "Immediate memory for \"when, where and what\": Short-delay retrieval using dynamic naturalistic material."}, "text": "PMID25773646 TITLE Immediate memory for \u201cwhen, where and what\u201d: Short\u2010delay retrieval using dynamic naturalistic material ABSTRACT We investigated the neural correlates supporting three kinds of memory judgments after very short delays using naturalistic material. In two functional magnetic resonance imaging (fMRI) experiments, subjects watched short movie clips, and after a short retention (1.5\u20132.5 s), made mnemonic judgments about specific aspects of the clips. In Experiment 1, subjects were presented with two scenes and required to either choose the scene that happened earlier in the clip (\u201cscene\u2010chronology\u201d), or with a correct spatial arrangement (\u201cscene\u2010layout\u201d), or that had been shown (\u201cscene\u2010recognition\u201d). To segregate activity specific to seen versus unseen stimuli, in Experiment 2 only one probe image was presented (either target or foil). Across the two experiments, we replicated three patterns underlying the three specific forms of memory judgment. The precuneus was activated during temporal\u2010order retrieval, the superior parietal cortex was activated bilaterally for spatial\u2010related configuration judgments, whereas the medial frontal cortex during scene recognition. Conjunction analyses with a previous study that used analogous retrieval tasks, but a much longer delay (>1 day), demonstrated that this dissociation pattern is independent of retention delay. We conclude that analogous brain regions mediate task\u2010specific retrieval across vastly different delays, consistent with the proposal of scale\u2010invariance in episodic memory retrieval.Hum Brain Mapp 36:2495\u20132513, 2015. \u00a92015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. BODY Supporting information", "utf8_text_md5_checksum": "3c7d67f6f4694b7dc4475fa94ad240ee"}
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/documents/pmcid_5033031.jsonl:
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1 | {"display_title": "PMID: 26096737 batch: 9", "list_title": "batch 9 PMID26096737 Impaired planning in Parkinson's disease is reflected by reduced brain activation and connectivity ", "metadata": {"batch_nb": 9, "journal": "Human brain mapping", "origin_file": "documents_season_1.jsonl", "pmcid": 5033031, "pmid": 26096737, "publication_year": 2015, "text_md5": "52beeefe0a72662deab77f4cd2f1d1cf", "title": "Impaired planning in Parkinson's disease is reflected by reduced brain activation and connectivity."}, "text": "PMID26096737 TITLE Impaired planning in Parkinson's disease is reflected by reduced brain activation and connectivity ABSTRACT ObjectiveParkinson's disease (PD) often entails impairments of executive functions, such as planning. Although widely held that these impairments arise from dopaminergic denervation of the striatum, not all executive functions are affected early on, and the underlying neural dynamics are not fully understood. In a combined longitudinal and cross\u2010sectional study, we investigated how planning deficits progress over time in the early stages of PD compared to matched healthy controls. We used functional magnetic resonance imaging (fMRI) to identify accompanying neural dynamics.MethodsSeventeen PD patients and 20 healthy controls performed a parametric Tower of London task at two time points separated by \u223c3 years (baseline and follow\u2010up). We assessed task performance longitudinally in both groups; at follow\u2010up, a subset of participants (14 patients, 19 controls) performed a parallel version of the task during fMRI. We performed meta\u2010analyses to localize regions\u2010of\u2010interest (ROIs), that is, the bilateral dorsolateral prefrontal cortex (DLPFC), inferior parietal cortex, and caudate nucleus, and performed group\u2010by\u2010task analyses and within\u2010group regression analyses of planning\u2010related neural activation. We studied task\u2010related functional connectivity of seeds in the DLPFC and caudate nucleus.ResultsPD patients, compared with controls, showed impaired task performance at both time\u2010points, while both groups showed similar performance reductions from baseline to follow\u2010up. Compared to controls, patients showed lower planning\u2010related brain activation together with decreased functional connectivity.ConclusionThese findings support the notion that planning is affected early in the PD disease course, and that this impairment in planning is accompanied by decreases in both task\u2010related brain activity and connectivity.Hum Brain Mapp 36:3703\u20133715, 2015. \u00a92015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. BODY Supporting information", "utf8_text_md5_checksum": "52beeefe0a72662deab77f4cd2f1d1cf"}
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/documents/pmcid_5888645.jsonl:
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1 | {"text": "Rapp, Alexander and Purr, Franziska and Felsenheimer, Anne\nSchizophr Bull, 2018\n\n# Title\n\nT58. SARCASM COMPREHENSION AS A SOCIAL COGNITION MEASURE IN SCHIZOPHRENIA \u2013 A SYSTEMATIC LITERATURE SEARCH AND META-ANALYSIS ON THE USE OF THE TASIT\n\n# Keywords\n\n\n\n# Abstract\n \n## Background \n \nSocial cognition tasks with higher ecologically validity could be helpful both as an outcome measure for training and for social cognition impairment in schizophrenia. The comprehension of sarcasm and irony is a candidate for a valid, replicable task. \n\n\n## Methods \n \nTests and paradigms as well as studies in schizophrenia are available in English, Dutch, German, Italian, Greek, Japanese and other languages. The Awareness of Social Inference Test (TASIT) (McDonald et al.,J head trauma rehabil 2003,) is currently the by far most applied paradigm. Here, we present a systematic literature research and meta-analysis on application of these paradigms in patients with schizophrenia. \n\n\n## Results \n \n25 studies with data from n=2185 patients with schizophrenia and n=1474 controls used the TASIT. This exceeds the numbers for other irony comprehension paradigms. Separate meta-analyses were calculated for the \u201csarcasm-enriched\u201d and \u201csarcasm-minimal\u201d subtests with data from 5 different English language studies. In both subtests, patients with schizophrenia showed significant impairment. Non-English translations of the TASIT show a comparable picture. Longitudinal data are available from 4 studies. Studies in high risk populations showed mixed results, however the TASIT is included in longitudinal cohort studies such as NAPLS-2. \n\n\n## Discussion \n \nWe discuss differences with other task such as paradigms without prosodic or face information or the available fMRI investigations. \n\n \n\n# Body\n\n", "metadata": {"pmcid": 5888645, "text_md5": "19ff03df93538bfe9c631e589346314f", "field_positions": {"authors": [0, 58], "journal": [59, 73], "publication_year": [75, 79], "title": [90, 238], "keywords": [252, 252], "abstract": [265, 1782], "body": [1791, 1791]}, "batch": 1, "doi": "10.1093/schbul/sby016.334", "pmc_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5888645", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=5888645"}, "display_title": "pmcid: 5888645", "list_title": "PMC5888645 T58. SARCASM COMPREHENSION AS A SOCIAL COGNITION MEASURE IN SCHIZOPHRENIA \u2013 A SYSTEMATIC LITERATURE SEARCH AND META-ANALYSIS ON THE USE OF THE TASIT"}
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/documents/pmcid_9567359.jsonl:
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1 | {"text": "Torres, M. and Manghera, P. and Miller, C.\nEur Psychiatry, 2022\n\n# Title\n\nPrediction of Treatment Response in Patients with Major Depressive Disorder: A Meta-Analysis of Functional Magnetic Resonance Imaging Studies\n\n# Keywords\n\ntreatment response\nmeta-analysis\nFunctional Magnetic Resonance Imaging\nmajor depressive disorder\n\n\n# Abstract\n \n## Introduction \n \nIdentifying the optimal treatment for individuals with major depressive disorder (MDD) is often a long and complicated process. Functional magnetic resonance imaging (fMRI) studies have been used to help predict and explain differences in treatment response among individuals with MDD. \n\n\n## Objectives \n \nWe conducted a comprehensive meta-analysis of treatment prediction studies utilizing fMRI in patients with MDD to provide evidence that neural activity can be used to predict response to antidepressant treatment. \n\n\n## Methods \n \nA multi-level kernel density analysis was applied to these primary fMRI studies, in which we analyzed brain activation patterns of depressed patients (N= 364) before receiving antidepressant treatment. \n\n\n## Results \n \nThe results of this analysis demonstrated that hyperactivity in six brain regions significantly predicted treatment response in patients with MDD: the right anterior cingulate, right cuneus, left fusiform gyrus, left middle frontal gyrus, right cingulate gyrus, and left superior frontal gyrus. \n\n\n## Conclusions \n \nThis study provides evidence that neural activity, as measured by standard fMRI paradigms, can be used to successfully predict response to antidepressant treatment. This may be used in the future clinically to improve decision-making processes and treatment outcomes for patients. \n\n\n## Disclosure \n \nNo significant relationships. \n\n \n\n# Body\n\n", "metadata": {"pmcid": 9567359, "text_md5": "92b375eb781e60b2155c930c6c9321ef", "field_positions": {"authors": [0, 42], "journal": [43, 57], "publication_year": [59, 63], "title": [74, 215], "keywords": [229, 326], "abstract": [339, 1771], "body": [1780, 1780]}, "batch": 1, "doi": "10.1192/j.eurpsy.2022.758", "pmc_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9567359", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=9567359"}, "display_title": "pmcid: 9567359", "list_title": "PMC9567359 Prediction of Treatment Response in Patients with Major Depressive Disorder: A Meta-Analysis of Functional Magnetic Resonance Imaging Studies"}
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/documents/pmid_21998649.jsonl:
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1 | {"text": "Sugranyes, Gisela and Kyriakopoulos, Marinos and Corrigall, Richard and Taylor, Eric and Frangou, Sophia\nPLoS One, 2011\n\n# Title\n\nAutism spectrum disorders and schizophrenia: meta-analysis of the neural correlates of social cognition.\n\n# Keywords\n\n\n\n# Abstract\nImpaired social cognition is a cardinal feature of Autism Spectrum Disorders (ASD) and Schizophrenia (SZ). However, the functional neuroanatomy of social cognition in either disorder remains unclear due to variability in primary literature. Additionally, it is not known whether deficits in ASD and SZ arise from similar or disease-specific disruption of the social cognition network. To identify regions most robustly implicated in social cognition processing in SZ and ASD. Systematic review of English language articles using MEDLINE (1995-2010) and reference lists. Studies were required to use fMRI to compare ASD or SZ subjects to a matched healthy control group, provide coordinates in standard stereotactic space, and employ standardized facial emotion recognition (FER) or theory of mind (TOM) paradigms. Activation foci from studies meeting inclusion criteria (n = 33) were subjected to a quantitative voxel-based meta-analysis using activation likelihood estimation, and encompassed 146 subjects with ASD, 336 SZ patients and 492 healthy controls. Both SZ and ASD showed medial prefrontal hypoactivation, which was more pronounced in ASD, while ventrolateral prefrontal dysfunction was associated mostly with SZ. Amygdala hypoactivation was observed in SZ patients during FER and in ASD during more complex ToM tasks. Both disorders were associated with hypoactivation within the Superior Temporal Sulcus (STS) during ToM tasks, but activation in these regions was increased in ASD during affect processing. Disease-specific differences were noted in somatosensory engagement, which was increased in SZ and decreased in ASD. Reduced thalamic activation was uniquely seen in SZ. Reduced frontolimbic and STS engagement emerged as a shared feature of social cognition deficits in SZ and ASD. However, there were disease- and stimulus-specific differences. These findings may aid future studies on SZ and ASD and facilitate the formulation of new hypotheses regarding their pathophysiology. ", "metadata": {"id": 21998649, "text_md5": "6f16f15afee1bf7f2928633c32f33f3e", "field_positions": {"authors": [0, 104], "journal": [105, 113], "publication_year": [115, 119], "title": [130, 234], "keywords": [248, 248], "abstract": [261, 2260], "body": [2269, 2269]}, "batch": 1, "pmid": 21998649, "doi": "10.1371/journal.pone.0025322", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/21998649/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=21998649"}, "display_title": "pmid: 21998649", "list_title": "PMID21998649 Autism spectrum disorders and schizophrenia: meta-analysis of the neural correlates of social cognition."}
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/documents/pmid_23378834.jsonl:
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1 | {"text": "Von Der Heide, Rebecca J and Skipper, Laura M and Olson, Ingrid R\nFront Hum Neurosci, 2013\n\n# Title\n\nAnterior temporal face patches: a meta-analysis and empirical study.\n\n# Keywords\n\nanterior temporal lobe\nfMRI\nface processing\nperson memory\nsemantic memory\nsocial cognition\nsocial networks\ntemporal pole\n\n# Abstract\nEvidence suggests the anterior temporal lobe (ATL) plays an important role in person identification and memory. In humans, neuroimaging studies of person memory report consistent activations in the ATL to famous and personally familiar faces and studies of patients report resection or damage of the ATL causes an associative prosopagnosia in which face perception is intact but face memory is compromised. In addition, high-resolution fMRI studies of non-human primates and electrophysiological studies of humans also suggest regions of the ventral ATL are sensitive to novel faces. The current study extends previous findings by investigating whether similar subregions in the dorsal, ventral, lateral, or polar aspects of the ATL are sensitive to personally familiar, famous, and novel faces. We present the results of two studies of person memory: a meta-analysis of existing fMRI studies and an empirical fMRI study using optimized imaging parameters. Both studies showed left-lateralized ATL activations to familiar individuals while novel faces activated the right ATL. Activations to famous faces were quite ventral, similar to what has been reported in previous high-resolution fMRI studies of non-human primates. These findings suggest that face memory-sensitive patches in the human ATL are in the ventral/polar ATL. ", "metadata": {"id": 23378834, "text_md5": "287a7c345d7af43ca8fcba4004c4533f", "field_positions": {"authors": [0, 65], "journal": [66, 84], "publication_year": [86, 90], "title": [101, 169], "keywords": [183, 303], "abstract": [316, 1644], "body": [1653, 1653]}, "batch": 1, "pmid": 23378834, "doi": "10.3389/fnhum.2013.00017", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/23378834/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=23378834"}, "display_title": "pmid: 23378834", "list_title": "PMID23378834 Anterior temporal face patches: a meta-analysis and empirical study."}
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/documents/pmid_23825451.jsonl:
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1 | {"text": "Crepaldi, Davide and Berlingeri, Manuela and Cattinelli, Isabella and Borghese, Nunzio A and Luzzatti, Claudio and Paulesu, Eraldo\nFront Hum Neurosci, 2013\n\n# Title\n\nClustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing.\n\n# Keywords\n\nclustering algorithm\nleft inferior frontal gyrus\nmeta-analysis\nneuroimaging\nnoun-verb dissociation\ntask demand\n\n# Abstract\nAlthough it is widely accepted that nouns and verbs are functionally independent linguistic entities, it is less clear whether their processing recruits different brain areas. This issue is particularly relevant for those theories of lexical semantics (and, more in general, of cognition) that suggest the embodiment of abstract concepts, i.e., based strongly on perceptual and motoric representations. This paper presents a formal meta-analysis of the neuroimaging evidence on noun and verb processing in order to address this dichotomy more effectively at the anatomical level. We used a hierarchical clustering algorithm that grouped fMRI/PET activation peaks solely on the basis of spatial proximity. Cluster specificity for grammatical class was then tested on the basis of the noun-verb distribution of the activation peaks included in each cluster. Thirty-two clusters were identified: three were associated with nouns across different tasks (in the right inferior temporal gyrus, the left angular gyrus, and the left inferior parietal gyrus); one with verbs across different tasks (in the posterior part of the right middle temporal gyrus); and three showed verb specificity in some tasks and noun specificity in others (in the left and right inferior frontal gyrus and the left insula). These results do not support the popular tenets that verb processing is predominantly based in the left frontal cortex and noun processing relies specifically on temporal regions; nor do they support the idea that verb lexical-semantic representations are heavily based on embodied motoric information. Our findings suggest instead that the cerebral circuits deputed to noun and verb processing lie in close spatial proximity in a wide network including frontal, parietal, and temporal regions. The data also indicate a predominant-but not exclusive-left lateralization of the network. ", "metadata": {"id": 23825451, "text_md5": "fa99610c2edf21870e9310ccf87a6585", "field_positions": {"authors": [0, 130], "journal": [131, 149], "publication_year": [151, 155], "title": [166, 279], "keywords": [293, 403], "abstract": [416, 2298], "body": [2307, 2307]}, "batch": 1, "pmid": 23825451, "doi": "10.3389/fnhum.2013.00303", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/23825451/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=23825451"}, "display_title": "pmid: 23825451", "list_title": "PMID23825451 Clustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing."}
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/documents/pmid_25717297.jsonl:
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1 | {"text": "Li, Ke and Huang, Xiaoyan and Han, Yingping and Zhang, Jun and Lai, Yuhan and Yuan, Li and Lu, Jiaojiao and Zeng, Dong\nFront Hum Neurosci, 2015\n\n# Title\n\nEnhanced Neuroactivation during Working Memory Task in Postmenopausal Women Receiving Hormone Therapy: A Coordinate-Based Meta-Analysis.\n\n# Keywords\n\nALE meta-analysis\nfunctional magnetic resonance imaging\nhormone therapy\nneural activation\npostmenopause\nworking memory\n\n# Abstract\nHormone therapy (HT) has long been thought beneficial for controlling menopausal symptoms and human cognition. Studies have suggested that HT has a positive association with working memory, but no consistent relationship between HT and neural activity has been shown in any cognitive domain. The purpose of this meta-analysis was to assess the convergence of findings from published randomized control trials studies that examined brain activation changes in postmenopausal women. A systematic search for fMRI studies of neural responses during working memory tasks in postmenopausal women was performed. Studies were excluded if they were not treatment studies and did not contain placebo or blank controls. For the purpose of the meta-analysis, 8 studies were identified, with 103 postmenopausal women taking HT and 109 controls. Compared with controls, postmenopausal women who took HT increased activation in the left frontal lobe, including superior frontal gyrus (BA 8), right middle frontal gyrus (BA 9), anterior lobe, paracentral lobule (BA 7), limbic lobe, and anterior cingulate (BA 32). Additionally, decreased activation is noted in the right limbic lobe, including parahippocampal gyrus (BA 28), left parietal lobe, and superior parietal lobule (BA 7). All regions were significant at p\u2009\u2264\u20090.05 with correction for multiple comparisons. Hormone treatment is associated with BOLD signal activation in key anatomical areas during fMRI working memory tasks in healthy hormone-treated postmenopausal women. A positive correlation between activation and task performance suggests that hormone use may benefit working memory. ", "metadata": {"id": 25717297, "text_md5": "57f1285d097a313844f4b3b009a28596", "field_positions": {"authors": [0, 118], "journal": [119, 137], "publication_year": [139, 143], "title": [154, 290], "keywords": [304, 422], "abstract": [435, 2068], "body": [2077, 2077]}, "batch": 1, "pmid": 25717297, "doi": "10.3389/fnhum.2015.00035", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/25717297/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=25717297"}, "display_title": "pmid: 25717297", "list_title": "PMID25717297 Enhanced Neuroactivation during Working Memory Task in Postmenopausal Women Receiving Hormone Therapy: A Coordinate-Based Meta-Analysis."}
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/documents/pmid_26171391.jsonl:
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1 | {"text": "Zang, Yu-Feng and Zuo, Xi-Nian and Milham, Michael and Hallett, Mark\nBiomed Res Int, 2015\n\n# Title\n\nToward a Meta-Analytic Synthesis of the Resting-State fMRI Literature for Clinical Populations.\n\n# Keywords\n\n\n\n# Abstract\n", "metadata": {"id": 26171391, "text_md5": "5d1c00bd2f0be9c8db4c3c9b410ecad9", "field_positions": {"authors": [0, 68], "journal": [69, 83], "publication_year": [85, 89], "title": [100, 195], "keywords": [209, 209], "abstract": [222, 222], "body": [231, 231]}, "batch": 1, "pmid": 26171391, "doi": "10.1155/2015/435265", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/26171391/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=26171391"}, "display_title": "pmid: 26171391", "list_title": "PMID26171391 Toward a Meta-Analytic Synthesis of the Resting-State fMRI Literature for Clinical Populations."}
2 | {"text": "Zang, Yu-Feng and Zuo, Xi-Nian and Milham, Michael and Hallett, Mark\nBioMed research international, 2016\n\n# Title\n\nToward a Meta-Analytic Synthesis of the Resting-State fMRI Literature for Clinical Populations.\n\n# Keywords\n\n\n\n# Abstract\n\n\n", "metadata": {"pmid": "26171391", "journal": "BioMed research international", "publication_year": "2016", "title": "Toward a Meta-Analytic Synthesis of the Resting-State fMRI Literature for Clinical Populations.", "keywords": "", "abstract": "", "authors": "Zang, Yu-Feng and Zuo, Xi-Nian and Milham, Michael and Hallett, Mark"}, "display_title": "pmid: 26171391", "list_title": "PMID26171391 Toward a Meta-Analytic Synthesis of the Resting-State fMRI Literature for Clinical Populations."}
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/documents/pmid_27995059.jsonl:
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1 | {"text": "Andre, Julia and Picchioni, Marco and Zhang, Ruibin and Toulopoulou, Timothea\nNeuroimage Clin, 2016\n\n# Title\n\nWorking memory circuit as a function of increasing age in healthy adolescence: A systematic review and meta-analyses.\n\n# Keywords\n\nBrain activation\nNeurodevelopment\nNeurodevelopmental disorders\nSchizophrenia\nWorking memory\nfMRI\n\n# Abstract\nWorking memory ability matures through puberty and early adulthood. Deficits in working memory are linked to the risk of onset of neurodevelopmental disorders such as schizophrenia, and there is a significant temporal overlap between the peak of first episode psychosis risk and working memory maturation. In order to characterize the normal working memory functional maturation process through this critical phase of cognitive development we conducted a systematic review and coordinate based meta-analyses of all the available primary functional magnetic resonance imaging studies (n\u00a0=\u00a0382) that mapped WM function in healthy adolescents (10-17 years) and young adults (18-30 years). Activation Likelihood Estimation analyses across all WM tasks revealed increased activation with increasing subject age in the middle frontal gyrus (BA6) bilaterally, the left middle frontal gyrus (BA10), the left precuneus and left inferior parietal gyri (BA7; 40). Decreased activation with increasing age was found in the right superior frontal (BA8), left junction of postcentral and inferior parietal (BA3/40), and left limbic cingulate gyrus (BA31). These results suggest that brain activation during adolescence increased with age principally in higher order cortices, part of the core working memory network, while reductions were detected in more diffuse and potentially more immature neural networks. Understanding the process by which the brain and its cognitive functions mature through healthy adulthood may provide us with new clues to understanding the vulnerability to neurodevelopmental disorders. ", "metadata": {"id": 27995059, "text_md5": "d2dcda897860f37d2347e48c24f086d9", "field_positions": {"authors": [0, 77], "journal": [78, 93], "publication_year": [95, 99], "title": [110, 227], "keywords": [241, 337], "abstract": [350, 1951], "body": [1960, 1960]}, "batch": 1, "pmid": 27995059, "doi": "10.1016/j.nicl.2015.12.002", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/27995059/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=27995059"}, "display_title": "pmid: 27995059", "list_title": "PMID27995059 Working memory circuit as a function of increasing age in healthy adolescence: A systematic review and meta-analyses."}
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/documents/pmid_28337136.jsonl:
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1 | {"text": "Keuken, Max C and M\u00fcller-Axt, Christa and Langner, Robert and Eickhoff, Simon B and Forstmann, Birte U and Neumann, Jane\nFront Hum Neurosci, 2017\n\n# Title\n\nCorrigendum: Brain networks of perceptual decision-making: an fMRI ALE meta-analysis.\n\n# Keywords\n\nGingerALE\ncorrigendum\ndecision-making\nfronto-parietal-basal ganglia\nmeta-analysis\n\n# Abstract\n[This corrects the article on p. 445 in vol. 8, PMID: 24994979.]. ", "metadata": {"id": 28337136, "text_md5": "7ef0271b462e8367c4158232f5fa937a", "field_positions": {"authors": [0, 120], "journal": [121, 139], "publication_year": [141, 145], "title": [156, 241], "keywords": [255, 336], "abstract": [349, 415], "body": [424, 424]}, "batch": 2, "pmid": 28337136, "doi": "10.3389/fnhum.2017.00139", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/28337136/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=28337136"}, "display_title": "pmid: 28337136", "list_title": "PMID28337136 Corrigendum: Brain networks of perceptual decision-making: an fMRI ALE meta-analysis."}
2 | {"text": "Keuken, Max C and M\u00fcller-Axt, Christa and Langner, Robert and Eickhoff, Simon B and Forstmann, Birte U and Neumann, Jane\nFrontiers in human neuroscience, 2020\n\n# Title\n\nCorrigendum: Brain networks of perceptual decision-making: an fMRI ALE meta-analysis.\n\n# Keywords\n\nGingerALE \ncorrigendum \ndecision-making \nfronto-parietal-basal ganglia \nmeta-analysis \n\n\n# Abstract\n\n[This corrects the article on p. 445 in vol. 8, PMID: 24994979.]. \n", "metadata": {"pmid": "28337136", "journal": "Frontiers in human neuroscience", "publication_year": "2020", "title": "Corrigendum: Brain networks of perceptual decision-making: an fMRI ALE meta-analysis.", "keywords": "GingerALE \ncorrigendum \ndecision-making \nfronto-parietal-basal ganglia \nmeta-analysis \n", "abstract": "[This corrects the article on p. 445 in vol. 8, PMID: 24994979.]. ", "authors": "Keuken, Max C and M\u00fcller-Axt, Christa and Langner, Robert and Eickhoff, Simon B and Forstmann, Birte U and Neumann, Jane"}, "display_title": "pmid: 28337136", "list_title": "PMID28337136 Corrigendum: Brain networks of perceptual decision-making: an fMRI ALE meta-analysis."}
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/documents/pmid_29497368.jsonl:
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1 | {"text": "Gu\u00e0rdia-Olmos, Joan and Per\u00f3-Cebollero, Maribel and Gudayol-Ferr\u00e9, Esteve\nFrontiers in behavioral neuroscience, 2020\n\n# Title\n\nMeta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with \n\n# Keywords\n\ncognitive neuroscience \neffective connectivity \nfMRI \nfunctional connectivity \nstructural equation models \n\n\n# Abstract\n\nStructural Equation Models (SEM) is among of the most extensively applied statistical techniques in the study of human behavior in the fields of Neuroscience and Cognitive Neuroscience. This paper reviews the application of SEM to estimate functional and effective connectivity models in work published since 2001. The articles analyzed were compiled from Journal Citation Reports, PsycInfo, Pubmed, and Scopus, after searching with the following keywords: fMRI, SEMs, and Connectivity. \n", "metadata": {"pmid": "29497368", "journal": "Frontiers in behavioral neuroscience", "publication_year": "2020", "title": "Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with ", "keywords": "cognitive neuroscience \neffective connectivity \nfMRI \nfunctional connectivity \nstructural equation models \n", "abstract": "Structural Equation Models (SEM) is among of the most extensively applied statistical techniques in the study of human behavior in the fields of Neuroscience and Cognitive Neuroscience. This paper reviews the application of SEM to estimate functional and effective connectivity models in work published since 2001. The articles analyzed were compiled from Journal Citation Reports, PsycInfo, Pubmed, and Scopus, after searching with the following keywords: fMRI, SEMs, and Connectivity. ", "authors": "Gu\u00e0rdia-Olmos, Joan and Per\u00f3-Cebollero, Maribel and Gudayol-Ferr\u00e9, Esteve"}, "display_title": "pmid: 29497368", "list_title": "PMID29497368 Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with "}
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/documents/pmid_29723244.jsonl:
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1 | {"text": "Tso, Ivy F and Rutherford, Saige and Fang, Yu and Angstadt, Mike and Taylor, Stephan F\nPLoS One, 2018\n\n# Title\n\nThe \"social brain\" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study.\n\n# Keywords\n\n\n\n# Abstract\nHow the human brain processes social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of \"social\" were subject to an automated meta-analysis (http://neurosynth.org). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social-non-social contrast in the independent study showed a strong resemblance to the NeuroSynth map. ROI analyses revealed that a social effect was credible in 9 out of the 11 NeuroSynth regions in the independent dataset. The findings support the conclusion that the \"social brain\" is highly sensitive to the mere presence of social information. ", "metadata": {"id": 29723244, "text_md5": "a05103d8d6a52f693894ad9d8fbb8be0", "field_positions": {"authors": [0, 86], "journal": [87, 95], "publication_year": [97, 101], "title": [112, 247], "keywords": [261, 261], "abstract": [274, 2083], "body": [2092, 2092]}, "batch": 1, "pmid": 29723244, "doi": "10.1371/journal.pone.0196503", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/29723244/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=29723244"}, "display_title": "pmid: 29723244", "list_title": "PMID29723244 The \"social brain\" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study."}
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/documents/pmid_30552551.jsonl:
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1 | {"text": "Wilson, Robin Paul and Colizzi, Marco and Bossong, Matthijs Geert and Allen, Paul and Kempton, Matthew and nan, nan and Bhattacharyya, Sagnik\nNeuropsychol Rev, 2018\n\n# Title\n\nCorrection to: The Neural Substrate of Reward Anticipation in Health: A Meta-Analysis of fMRI Findings in the Monetary Incentive Delay Task.\n\n# Keywords\n\n\n\n# Abstract\nThe members of MTAC were removed from the author group and full list are shown in the Acknowledgements section. Also, members \"Roee, A\" and \"Van Amselvoort, T\" should be \"Admon, R\" and \"Van Amelsvoort, T\", respectively. The original article has been corrected. ", "metadata": {"id": 30552551, "text_md5": "b673278a09803462a333c998d4914707", "field_positions": {"authors": [0, 141], "journal": [142, 158], "publication_year": [160, 164], "title": [175, 315], "keywords": [329, 329], "abstract": [342, 603], "body": [612, 612]}, "batch": 1, "pmid": 30552551, "doi": "10.1007/s11065-018-9390-8", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/30552551/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=30552551"}, "display_title": "pmid: 30552551", "list_title": "PMID30552551 Correction to: The Neural Substrate of Reward Anticipation in Health: A Meta-Analysis of fMRI Findings in the Monetary Incentive Delay Task."}
2 | {"text": "Wilson, Robin Paul and Colizzi, Marco and Bossong, Matthijs Geert and Allen, Paul and Kempton, Matthew and Bhattacharyya, Sagnik\nNeuropsychology review, 2022\n\n# Title\n\nCorrection to: The Neural Substrate of Reward Anticipation in Health: A Meta-Analysis of fMRI Findings in the Monetary Incentive Delay Task.\n\n# Keywords\n\n\n\n# Abstract\n\nThe members of MTAC were removed from the author group and full list are shown in the Acknowledgements section. Also, members \"Roee, A\" and \"Van Amselvoort, T\" should be \"Admon, R\" and \"Van Amelsvoort, T\", respectively. The original article has been corrected. \n", "metadata": {"pmid": "30552551", "journal": "Neuropsychology review", "publication_year": "2022", "title": "Correction to: The Neural Substrate of Reward Anticipation in Health: A Meta-Analysis of fMRI Findings in the Monetary Incentive Delay Task.", "keywords": "", "abstract": "The members of MTAC were removed from the author group and full list are shown in the Acknowledgements section. Also, members \"Roee, A\" and \"Van Amselvoort, T\" should be \"Admon, R\" and \"Van Amelsvoort, T\", respectively. The original article has been corrected. ", "authors": "Wilson, Robin Paul and Colizzi, Marco and Bossong, Matthijs Geert and Allen, Paul and Kempton, Matthew and Bhattacharyya, Sagnik"}, "display_title": "pmid: 30552551", "list_title": "PMID30552551 Correction to: The Neural Substrate of Reward Anticipation in Health: A Meta-Analysis of fMRI Findings in the Monetary Incentive Delay Task."}
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/documents/pmid_30793072.jsonl:
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1 | {"text": "Bottenhorn, Katherine L and Flannery, Jessica S and Boeving, Emily R and Riedel, Michael C and Eickhoff, Simon B and Sutherland, Matthew T and Laird, Angela R\nNetw Neurosci, 2019\n\n# Title\n\nCooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results.\n\n# Keywords\n\nClustering analysis\nNaturalistic paradigms\nNeuroimaging meta-analysis\nNeuroinformatics\n\n# Abstract\nCognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses ", "metadata": {"id": 30793072, "text_md5": "83d629067c2584b25e999a5cacd753fb", "field_positions": {"authors": [0, 158], "journal": [159, 172], "publication_year": [174, 178], "title": [189, 310], "keywords": [324, 410], "abstract": [423, 1130], "body": [1139, 1139]}, "batch": 1, "pmid": 30793072, "doi": "10.1162/netn_a_00050", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/30793072/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=30793072"}, "display_title": "pmid: 30793072", "list_title": "PMID30793072 Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results."}
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/documents/pmid_30872887.jsonl:
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1 | {"text": "Rigo, Paola and Kim, Pilyoung and Esposito, Gianluca and Putnick, Diane L and Venuti, Paola and Bornstein, Marc H\nDev Rev, 2019\n\n# Title\n\nSpecific maternal brain responses to their own child's face: An fMRI meta-analysis.\n\n# Keywords\n\nInfant Face\nLeft Hemisphere\nMaternal Brain\nMeta-Analysis\nOwn Child\nfMRI\n\n# Abstract\n", "metadata": {"id": 30872887, "text_md5": "bd1bb30a5ed370cfc5146412da0a9f83", "field_positions": {"authors": [0, 113], "journal": [114, 121], "publication_year": [123, 127], "title": [138, 221], "keywords": [235, 306], "abstract": [319, 319], "body": [328, 328]}, "batch": 1, "pmid": 30872887, "doi": "10.1016/j.dr.2018.12.001", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/30872887/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=30872887"}, "display_title": "pmid: 30872887", "list_title": "PMID30872887 Specific maternal brain responses to their own child's face: An fMRI meta-analysis."}
2 | {"text": "Rigo, Paola and Kim, Pilyoung and Esposito, Gianluca and Putnick, Diane L and Venuti, Paola and Bornstein, Marc H\nDevelopmental review : DR, 2020\n\n# Title\n\nSpecific maternal brain responses to their own child's face: An fMRI meta-analysis.\n\n# Keywords\n\nInfant Face \nLeft Hemisphere \nMaternal Brain \nMeta-Analysis \nOwn Child \nfMRI \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "30872887", "journal": "Developmental review : DR", "publication_year": "2020", "title": "Specific maternal brain responses to their own child's face: An fMRI meta-analysis.", "keywords": "Infant Face \nLeft Hemisphere \nMaternal Brain \nMeta-Analysis \nOwn Child \nfMRI \n", "abstract": "", "authors": "Rigo, Paola and Kim, Pilyoung and Esposito, Gianluca and Putnick, Diane L and Venuti, Paola and Bornstein, Marc H"}, "display_title": "pmid: 30872887", "list_title": "PMID30872887 Specific maternal brain responses to their own child's face: An fMRI meta-analysis."}
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/documents/pmid_31063424.jsonl:
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1 | {"text": "Kim, Tae-Hyung and Woo, Sungmin and Han, Sangwon and Suh, Chong Hyun and Vargas, Hebert Alberto\nAJR Am J Roentgenol, 2019\n\n# Title\n\nThe Diagnostic Performance of MRI for Detection of Extramural Venous Invasion in Colorectal Cancer: A Systematic Review and Meta-Analysis of the Literature.\n\n# Keywords\n\nMRI\ncolorectal cancer\nextramural venous invasion\nmeta-analysis\nsystematic review\n\n# Abstract\n", "metadata": {"id": 31063424, "text_md5": "f3e847b0d637bc07b3a6e602cb850884", "field_positions": {"authors": [0, 95], "journal": [96, 115], "publication_year": [117, 121], "title": [132, 288], "keywords": [302, 382], "abstract": [395, 395], "body": [404, 404]}, "batch": 1, "pmid": 31063424, "doi": "10.2214/AJR.19.21112", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/31063424/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=31063424"}, "display_title": "pmid: 31063424", "list_title": "PMID31063424 The Diagnostic Performance of MRI for Detection of Extramural Venous Invasion in Colorectal Cancer: A Systematic Review and Meta-Analysis of the Literature."}
2 | {"text": "Kim, Tae-Hyung and Woo, Sungmin and Han, Sangwon and Suh, Chong Hyun and Vargas, Hebert Alberto\nAJR. American journal of roentgenology, 2020\n\n# Title\n\nThe Diagnostic Performance of MRI for Detection of Extramural Venous Invasion in Colorectal Cancer: A Systematic Review and Meta-Analysis of the Literature.\n\n# Keywords\n\nMRI \ncolorectal cancer \nextramural venous invasion \nmeta-analysis \nsystematic review \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "31063424", "journal": "AJR. American journal of roentgenology", "publication_year": "2020", "title": "The Diagnostic Performance of MRI for Detection of Extramural Venous Invasion in Colorectal Cancer: A Systematic Review and Meta-Analysis of the Literature.", "keywords": "MRI \ncolorectal cancer \nextramural venous invasion \nmeta-analysis \nsystematic review \n", "abstract": "", "authors": "Kim, Tae-Hyung and Woo, Sungmin and Han, Sangwon and Suh, Chong Hyun and Vargas, Hebert Alberto"}, "display_title": "pmid: 31063424", "list_title": "PMID31063424 The Diagnostic Performance of MRI for Detection of Extramural Venous Invasion in Colorectal Cancer: A Systematic Review and Meta-Analysis of the Literature."}
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/documents/pmid_31313938.jsonl:
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1 | {"text": "Hammond, Christopher J and Allick, Aliyah and Rahman, Naisa and Nanavati, Julie\nJ Child Adolesc Psychopharmacol, 2019\n\n# Title\n\nStructural and Functional Neural Targets of Addiction Treatment in Adolescents and Young Adults: A Systematic Review and Meta-Analysis.\n\n# Keywords\n\naddictive disorders\nadolescent\nmeta-analysis\nneuroimaging\nsubstance use\ntreatment response\n\n# Abstract\n", "metadata": {"id": 31313938, "text_md5": "af5c88eb3fe84b7c6bcfc06c95838bd6", "field_positions": {"authors": [0, 79], "journal": [80, 111], "publication_year": [113, 117], "title": [128, 263], "keywords": [277, 367], "abstract": [380, 380], "body": [389, 389]}, "batch": 3, "pmid": 31313938, "doi": "10.1089/cap.2019.0007", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/31313938/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=31313938"}, "display_title": "pmid: 31313938", "list_title": "PMID31313938 Structural and Functional Neural Targets of Addiction Treatment in Adolescents and Young Adults: A Systematic Review and Meta-Analysis."}
2 | {"text": "Hammond, Christopher J and Allick, Aliyah and Rahman, Naisa and Nanavati, Julie\nJournal of child and adolescent psychopharmacology, 2020\n\n# Title\n\nStructural and Functional Neural Targets of Addiction Treatment in Adolescents and Young Adults: A Systematic Review and Meta-Analysis.\n\n# Keywords\n\naddictive disorders \nadolescent \nmeta-analysis \nneuroimaging \nsubstance use \ntreatment response \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "31313938", "journal": "Journal of child and adolescent psychopharmacology", "publication_year": "2020", "title": "Structural and Functional Neural Targets of Addiction Treatment in Adolescents and Young Adults: A Systematic Review and Meta-Analysis.", "keywords": "addictive disorders \nadolescent \nmeta-analysis \nneuroimaging \nsubstance use \ntreatment response \n", "abstract": "", "authors": "Hammond, Christopher J and Allick, Aliyah and Rahman, Naisa and Nanavati, Julie"}, "display_title": "pmid: 31313938", "list_title": "PMID31313938 Structural and Functional Neural Targets of Addiction Treatment in Adolescents and Young Adults: A Systematic Review and Meta-Analysis."}
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/documents/pmid_31324553.jsonl:
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1 | {"text": "Wang, HongZhou and Wang, WanHua and Wang, XueYang and Hu, JianBin and Yao, LiZheng\nParkinsonism Relat Disord, 2019\n\n# Title\n\nComment on \"resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\".\n\n# Keywords\n\nFunctional connectivity\nMeta-analysis\nParkinson's disease\nResonance imaging\nResting-state functional magnetic\n\n# Abstract\n", "metadata": {"id": 31324553, "text_md5": "fa02c5fb36a08a65c203c00ea124c6e9", "field_positions": {"authors": [0, 82], "journal": [83, 108], "publication_year": [110, 114], "title": [125, 232], "keywords": [246, 355], "abstract": [368, 368], "body": [377, 377]}, "batch": 3, "pmid": 31324553, "doi": "10.1016/j.parkreldis.2019.07.013", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/31324553/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=31324553"}, "display_title": "pmid: 31324553", "list_title": "PMID31324553 Comment on \"resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\"."}
2 | {"text": "Wang, HongZhou and Wang, WanHua and Wang, XueYang and Hu, JianBin and Yao, LiZheng\nParkinsonism & related disorders, 2020\n\n# Title\n\nComment on \"resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\".\n\n# Keywords\n\nFunctional connectivity \nMeta-analysis \nParkinson's disease \nResonance imaging \nResting-state functional magnetic \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "31324553", "journal": "Parkinsonism & related disorders", "publication_year": "2020", "title": "Comment on \"resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\".", "keywords": "Functional connectivity \nMeta-analysis \nParkinson's disease \nResonance imaging \nResting-state functional magnetic \n", "abstract": "", "authors": "Wang, HongZhou and Wang, WanHua and Wang, XueYang and Hu, JianBin and Yao, LiZheng"}, "display_title": "pmid: 31324553", "list_title": "PMID31324553 Comment on \"resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\"."}
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/documents/pmid_31431324.jsonl:
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1 | {"text": "Wolters, Am\u00e9e F and van de Weijer, Sjors C F and Leentjens, Albert F G and Duits, Annelien A and Jacobs, Heidi I L and Kuijf, Mark L\nParkinsonism Relat Disord, 2019\n\n# Title\n\n\"Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\": Answer to Wang and colleagues.\n\n# Keywords\n\nCognitive impairment\nFunctional MRI\nFunctional connectivity\nParkinson's disease\nResting-state network\n\n# Abstract\n", "metadata": {"id": 31431324, "text_md5": "3807f14774d6c1593938bcc72be4f170", "field_positions": {"authors": [0, 132], "journal": [133, 158], "publication_year": [160, 164], "title": [175, 302], "keywords": [316, 417], "abstract": [430, 430], "body": [439, 439]}, "batch": 1, "pmid": 31431324, "doi": "10.1016/j.parkreldis.2019.07.014", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/31431324/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=31431324"}, "display_title": "pmid: 31431324", "list_title": "PMID31431324 \"Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\": Answer to Wang and colleagues."}
2 | {"text": "Wolters, Am\u00e9e F and van de Weijer, Sjors C F and Leentjens, Albert F G and Duits, Annelien A and Jacobs, Heidi I L and Kuijf, Mark L\nParkinsonism & related disorders, 2020\n\n# Title\n\n\"Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\": Answer to Wang and colleagues.\n\n# Keywords\n\nCognitive impairment \nFunctional MRI \nFunctional connectivity \nParkinson's disease \nResting-state network \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "31431324", "journal": "Parkinsonism & related disorders", "publication_year": "2020", "title": "\"Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\": Answer to Wang and colleagues.", "keywords": "Cognitive impairment \nFunctional MRI \nFunctional connectivity \nParkinson's disease \nResting-state network \n", "abstract": "", "authors": "Wolters, Am\u00e9e F and van de Weijer, Sjors C F and Leentjens, Albert F G and Duits, Annelien A and Jacobs, Heidi I L and Kuijf, Mark L"}, "display_title": "pmid: 31431324", "list_title": "PMID31431324 \"Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis\": Answer to Wang and colleagues."}
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/documents/pmid_31474844.jsonl:
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1 | {"text": "Wang, Haixia and Zhang, Jian and Jia, Huiyuan\nFront Hum Neurosci, 2019\n\n# Title\n\nSeparate Neural Systems Value Prosocial Behaviors and Reward: An ALE Meta-Analysis.\n\n# Keywords\n\nALE\nfMRI\nprosocial behaviors\nreward\nsocial heuristic hypothesis\n\n# Abstract\n", "metadata": {"id": 31474844, "text_md5": "e676fc9f0cfda106800331a71e9b4c55", "field_positions": {"authors": [0, 45], "journal": [46, 64], "publication_year": [66, 70], "title": [81, 164], "keywords": [178, 241], "abstract": [254, 254], "body": [263, 263]}, "batch": 1, "pmid": 31474844, "doi": "10.3389/fnhum.2019.00276", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/31474844/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=31474844"}, "display_title": "pmid: 31474844", "list_title": "PMID31474844 Separate Neural Systems Value Prosocial Behaviors and Reward: An ALE Meta-Analysis."}
2 | {"text": "Wang, Haixia and Zhang, Jian and Jia, Huiyuan\nFrontiers in human neuroscience, 2020\n\n# Title\n\nSeparate Neural Systems Value Prosocial Behaviors and Reward: An ALE Meta-Analysis.\n\n# Keywords\n\nALE \nfMRI \nprosocial behaviors \nreward \nsocial heuristic hypothesis \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "31474844", "journal": "Frontiers in human neuroscience", "publication_year": "2020", "title": "Separate Neural Systems Value Prosocial Behaviors and Reward: An ALE Meta-Analysis.", "keywords": "ALE \nfMRI \nprosocial behaviors \nreward \nsocial heuristic hypothesis \n", "abstract": "", "authors": "Wang, Haixia and Zhang, Jian and Jia, Huiyuan"}, "display_title": "pmid: 31474844", "list_title": "PMID31474844 Separate Neural Systems Value Prosocial Behaviors and Reward: An ALE Meta-Analysis."}
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/documents/pmid_31572296.jsonl:
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1 | {"text": "Potvin, St\u00e9phane and Gamache, Lydia and Lungu, Ovidiu\nFront Neurol, 2019\n\n# Title\n\nA Functional Neuroimaging Meta-Analysis of Self-Related Processing in Schizophrenia.\n\n# Keywords\n\nanterior cingulate cortex\nfMRI\nmeta-analysis\nprefrontal cortex and thalamus\nschizophrenia\nself-processing\n\n# Abstract\n", "metadata": {"id": 31572296, "text_md5": "f480d2d6b07d7cdff29400c2d70edd71", "field_positions": {"authors": [0, 53], "journal": [54, 66], "publication_year": [68, 72], "title": [83, 167], "keywords": [181, 286], "abstract": [299, 299], "body": [308, 308]}, "batch": 2, "pmid": 31572296, "doi": "10.3389/fneur.2019.00990", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/31572296/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=31572296"}, "display_title": "pmid: 31572296", "list_title": "PMID31572296 A Functional Neuroimaging Meta-Analysis of Self-Related Processing in Schizophrenia."}
2 | {"text": "Potvin, St\u00e9phane and Gamache, Lydia and Lungu, Ovidiu\nFrontiers in neurology, 2020\n\n# Title\n\nA Functional Neuroimaging Meta-Analysis of Self-Related Processing in Schizophrenia.\n\n# Keywords\n\nanterior cingulate cortex \nfMRI \nmeta-analysis \nprefrontal cortex and thalamus \nschizophrenia \nself-processing \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "31572296", "journal": "Frontiers in neurology", "publication_year": "2020", "title": "A Functional Neuroimaging Meta-Analysis of Self-Related Processing in Schizophrenia.", "keywords": "anterior cingulate cortex \nfMRI \nmeta-analysis \nprefrontal cortex and thalamus \nschizophrenia \nself-processing \n", "abstract": "", "authors": "Potvin, St\u00e9phane and Gamache, Lydia and Lungu, Ovidiu"}, "display_title": "pmid: 31572296", "list_title": "PMID31572296 A Functional Neuroimaging Meta-Analysis of Self-Related Processing in Schizophrenia."}
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/documents/pmid_32174883.jsonl:
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1 | {"text": "Solstrand Dahlberg, Linda and Lungu, Ovidiu and Doyon, Julien\nFront Neurol, 2020\n\n# Title\n\nCerebellar Contribution to Motor and Non-motor Functions in Parkinson's Disease: A Meta-Analysis of fMRI Findings.\n\n# Keywords\n\nParkinson's disease\ncognition\nfMRI\nmeta-analysis\nmotor\nsymptoms\n\n# Abstract\n", "metadata": {"id": 32174883, "text_md5": "5667f81c95db912328fe8cd401fc025b", "field_positions": {"authors": [0, 61], "journal": [62, 74], "publication_year": [76, 80], "title": [91, 205], "keywords": [219, 282], "abstract": [295, 295], "body": [304, 304]}, "batch": 2, "pmid": 32174883, "doi": "10.3389/fneur.2020.00127", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/32174883/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=32174883"}, "display_title": "pmid: 32174883", "list_title": "PMID32174883 Cerebellar Contribution to Motor and Non-motor Functions in Parkinson's Disease: A Meta-Analysis of fMRI Findings."}
2 | {"text": "Solstrand Dahlberg, Linda and Lungu, Ovidiu and Doyon, Julien\nFrontiers in neurology, 2020\n\n# Title\n\nCerebellar Contribution to Motor and Non-motor Functions in Parkinson's Disease: A Meta-Analysis of fMRI Findings.\n\n# Keywords\n\nParkinson's disease \ncognition \nfMRI \nmeta-analysis \nmotor \nsymptoms \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "32174883", "journal": "Frontiers in neurology", "publication_year": "2020", "title": "Cerebellar Contribution to Motor and Non-motor Functions in Parkinson's Disease: A Meta-Analysis of fMRI Findings.", "keywords": "Parkinson's disease \ncognition \nfMRI \nmeta-analysis \nmotor \nsymptoms \n", "abstract": "", "authors": "Solstrand Dahlberg, Linda and Lungu, Ovidiu and Doyon, Julien"}, "display_title": "pmid: 32174883", "list_title": "PMID32174883 Cerebellar Contribution to Motor and Non-motor Functions in Parkinson's Disease: A Meta-Analysis of fMRI Findings."}
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/documents/pmid_32605439.jsonl:
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1 | {"text": "Rasetti, Roberta and Chen, Qiang and Weinberger, Daniel R\nAm J Psychiatry, 2020\n\n# Title\n\nComment on Limbic Hyperactivity in Response to Emotionally Neutral Stimuli in Schizophrenia: A Neuroimaging Meta-Analysis of the Hypervigilant Mind.\n\n# Keywords\n\nAmygdala\nBiological Markers\nBrain Imaging Techniques\nPsychosis\nSchizophrenia\nSchizophrenia Spectrum and Other Psychotic Disorders\nfMRI\n\n# Abstract\n", "metadata": {"id": 32605439, "text_md5": "ed93a356bfecec1ca23d763d7fc6e6ab", "field_positions": {"authors": [0, 57], "journal": [58, 73], "publication_year": [75, 79], "title": [90, 238], "keywords": [252, 386], "abstract": [399, 399], "body": [408, 408]}, "batch": 2, "pmid": 32605439, "doi": "10.1176/appi.ajp.2020.19090973", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/32605439/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=32605439"}, "display_title": "pmid: 32605439", "list_title": "PMID32605439 Comment on Limbic Hyperactivity in Response to Emotionally Neutral Stimuli in Schizophrenia: A Neuroimaging Meta-Analysis of the Hypervigilant Mind."}
2 | {"text": "Rasetti, Roberta and Chen, Qiang and Weinberger, Daniel R\nThe American journal of psychiatry, 2020\n\n# Title\n\nComment on Limbic Hyperactivity in Response to Emotionally Neutral Stimuli in Schizophrenia: A Neuroimaging Meta-Analysis of the Hypervigilant Mind.\n\n# Keywords\n\nAmygdala \nBiological Markers \nBrain Imaging Techniques \nPsychosis \nSchizophrenia \nSchizophrenia Spectrum and Other Psychotic Disorders \nfMRI \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "32605439", "journal": "The American journal of psychiatry", "publication_year": "2020", "title": "Comment on Limbic Hyperactivity in Response to Emotionally Neutral Stimuli in Schizophrenia: A Neuroimaging Meta-Analysis of the Hypervigilant Mind.", "keywords": "Amygdala \nBiological Markers \nBrain Imaging Techniques \nPsychosis \nSchizophrenia \nSchizophrenia Spectrum and Other Psychotic Disorders \nfMRI \n", "abstract": "", "authors": "Rasetti, Roberta and Chen, Qiang and Weinberger, Daniel R"}, "display_title": "pmid: 32605439", "list_title": "PMID32605439 Comment on Limbic Hyperactivity in Response to Emotionally Neutral Stimuli in Schizophrenia: A Neuroimaging Meta-Analysis of the Hypervigilant Mind."}
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/documents/pmid_32845058.jsonl:
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1 | {"text": "Cogdell-Brooke, Lucy S and Sowden, Paul T and Violante, In\u00eas R and Thompson, Hannah E\nHum Brain Mapp, 2020\n\n# Title\n\nA meta-analysis of functional magnetic resonance imaging studies of divergent thinking using activation likelihood estimation.\n\n# Keywords\n\nALE\ndivergent\nfMRI\nmeta-analysis\nsemantic control network\n\n# Abstract\nThere are conflicting findings regarding brain regions and networks underpinning creativity, with divergent thinking tasks commonly used to study this. A handful of meta-analyses have attempted to synthesise findings on neural mechanisms of divergent thinking. With the rapid proliferation of research and recent developments in fMRI meta-analysis approaches, it is timely to reassess the regions activated during divergent thinking creativity tasks. Of particular interest is examining the evidence regarding large-scale brain networks proposed to be key in divergent thinking and extending this work to consider the role of the semantic control network. Studies utilising fMRI with healthy participants completing divergent thinking tasks were systematically identified, with 20 studies meeting the criteria. Activation Likelihood Estimation was then used to integrate the neuroimaging results across studies. This revealed four clusters: the left inferior parietal lobe; the left inferior frontal and precentral gyrus; the superior and medial frontal gyrus and the right cerebellum. These regions are key in the semantic network, important for flexible retrieval of stored knowledge, highlighting the role of this network in divergent thinking. ", "metadata": {"id": 32845058, "text_md5": "cf5554859385dbab8bd13ad9b4e96e17", "field_positions": {"authors": [0, 85], "journal": [86, 100], "publication_year": [102, 106], "title": [117, 243], "keywords": [257, 314], "abstract": [327, 1575], "body": [1584, 1584]}, "batch": 1, "pmid": 32845058, "doi": "10.1002/hbm.25170", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/32845058/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=32845058"}, "display_title": "pmid: 32845058", "list_title": "PMID32845058 A meta-analysis of functional magnetic resonance imaging studies of divergent thinking using activation likelihood estimation."}
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/documents/pmid_33746895.jsonl:
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1 | {"text": "Umana, Giuseppe Emmanuele and Scalia, Gianluca and Graziano, Francesca and Maugeri, Rosario and Alberio, Nicola and Barone, Fabio and Crea, Antonio and Fagone, Saverio and Giammalva, Giuseppe Roberto and Brunasso, Lara and Costanzo, Roberta and Paolini, Federica and Gerardi, Rosa Maria and Tumbiolo, Silvana and Cicero, Salvatore and Federico Nicoletti, Giovanni and Iacopino, Domenico Gerardo\nFrontiers in neurology, 2021\n\n# Title\n\nNavigated Transcranial Magnetic Stimulation Motor Mapping Usefulness in the Surgical Management of Patients Affected by Brain Tumors in Eloquent Areas: A Systematic Review and Meta-Analysis.\n\n# Keywords\n\nNTMs \ncraniotomy \nglioma \nmotor mapping \nsurgical planning \ntractography \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "33746895", "journal": "Frontiers in neurology", "publication_year": "2021", "title": "Navigated Transcranial Magnetic Stimulation Motor Mapping Usefulness in the Surgical Management of Patients Affected by Brain Tumors in Eloquent Areas: A Systematic Review and Meta-Analysis.", "keywords": "NTMs \ncraniotomy \nglioma \nmotor mapping \nsurgical planning \ntractography \n", "abstract": "", "authors": "Umana, Giuseppe Emmanuele and Scalia, Gianluca and Graziano, Francesca and Maugeri, Rosario and Alberio, Nicola and Barone, Fabio and Crea, Antonio and Fagone, Saverio and Giammalva, Giuseppe Roberto and Brunasso, Lara and Costanzo, Roberta and Paolini, Federica and Gerardi, Rosa Maria and Tumbiolo, Silvana and Cicero, Salvatore and Federico Nicoletti, Giovanni and Iacopino, Domenico Gerardo"}, "display_title": "pmid: 33746895", "list_title": "PMID33746895 Navigated Transcranial Magnetic Stimulation Motor Mapping Usefulness in the Surgical Management of Patients Affected by Brain Tumors in Eloquent Areas: A Systematic Review and Meta-Analysis."}
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/documents/pmid_33757302.jsonl:
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1 | {"text": "Ca\u00f1ete-Mass\u00e9, Cristina and Carb\u00f3-Carret\u00e9, Mar\u00eda and Per\u00f3-Cebollero, Maribel and Gu\u00e0rdia-Olmos, Joan\nBrain Connect, 2021\n\n# Title\n\nTask-Related Brain Connectivity Activation Functional Magnetic Resonance Imaging in Intellectual Disability Population: A Meta-Analytic Study.\n\n# Keywords\n\ncognitive task\nfMRI\nintellectual disability\nmeta-analysis\n\n# Abstract\n", "metadata": {"id": 33757302, "text_md5": "ce309f2a8f22ca494da4ba47875e5736", "field_positions": {"authors": [0, 99], "journal": [100, 113], "publication_year": [115, 119], "title": [130, 272], "keywords": [286, 343], "abstract": [356, 356], "body": [365, 365]}, "batch": 4, "pmid": 33757302, "doi": "10.1089/brain.2020.0911", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/33757302/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=33757302"}, "display_title": "pmid: 33757302", "list_title": "PMID33757302 Task-Related Brain Connectivity Activation Functional Magnetic Resonance Imaging in Intellectual Disability Population: A Meta-Analytic Study."}
2 | {"text": "Ca\u00f1ete-Mass\u00e9, Cristina and Carb\u00f3-Carret\u00e9, Mar\u00eda and Per\u00f3-Cebollero, Maribel and Gu\u00e0rdia-Olmos, Joan\nBrain connectivity, 2021\n\n# Title\n\nTask-Related Brain Connectivity Activation Functional Magnetic Resonance Imaging in Intellectual Disability Population: A Meta-Analytic Study.\n\n# Keywords\n\ncognitive task \nfMRI \nintellectual disability \nmeta-analysis \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "33757302", "journal": "Brain connectivity", "publication_year": "2021", "title": "Task-Related Brain Connectivity Activation Functional Magnetic Resonance Imaging in Intellectual Disability Population: A Meta-Analytic Study.", "keywords": "cognitive task \nfMRI \nintellectual disability \nmeta-analysis \n", "abstract": "", "authors": "Ca\u00f1ete-Mass\u00e9, Cristina and Carb\u00f3-Carret\u00e9, Mar\u00eda and Per\u00f3-Cebollero, Maribel and Gu\u00e0rdia-Olmos, Joan"}, "display_title": "pmid: 33757302", "list_title": "PMID33757302 Task-Related Brain Connectivity Activation Functional Magnetic Resonance Imaging in Intellectual Disability Population: A Meta-Analytic Study."}
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/documents/pmid_33760100.jsonl:
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1 | {"text": "Merritt, Carrington C and MacCormack, Jennifer K and Stein, Andrea G and Lindquist, Kristen A and Muscatell, Keely A\nSoc Cogn Affect Neurosci, 2021\n\n# Title\n\nThe neural underpinnings of intergroup social cognition: an fMRI meta-analysis.\n\n# Keywords\n\nfMRI\nintergroup bias\nmeta-analysis\nrace\nsocial cognition\n\n# Abstract\nRoughly 20 years of functional magnetic resonance imaging (fMRI) studies have investigated the neural correlates underlying engagement in social cognition (e.g. empathy and emotion perception) about targets spanning various social categories (e.g. race and gender). Yet, findings from individual studies remain mixed. In the present quantitative functional neuroimaging meta-analysis, we summarized across 50 fMRI studies of social cognition to identify consistent differences in neural activation as a function of whether the target of social cognition was an in-group or out-group member. We investigated if such differences varied according to a specific social category (i.e. race) and specific social cognitive processes (i.e. empathy and emotion perception). We found that social cognition about in-group members was more reliably related to activity in brain regions associated with mentalizing (e.g. dorsomedial prefrontal cortex), whereas social cognition about out-group members was more reliably related to activity in regions associated with exogenous attention and salience (e.g. anterior insula). These findings replicated for studies specifically focused on the social category of race, and we further found intergroup differences in neural activation during empathy and emotion perception tasks. These results help shed light on the neural mechanisms underlying social cognition across group lines. ", "metadata": {"id": 33760100, "text_md5": "96be8ae8db9914eeec0f5fe0f2873bf6", "field_positions": {"authors": [0, 116], "journal": [117, 141], "publication_year": [143, 147], "title": [158, 237], "keywords": [251, 307], "abstract": [320, 1735], "body": [1744, 1744]}, "batch": 1, "pmid": 33760100, "doi": "10.1093/scan/nsab034", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/33760100/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=33760100"}, "display_title": "pmid: 33760100", "list_title": "PMID33760100 The neural underpinnings of intergroup social cognition: an fMRI meta-analysis."}
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/documents/pmid_34381352.jsonl:
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1 | {"text": "Song, Yu and Xu, Wenwen and Chen, Shanshan and Hu, Guanjie and Ge, Honglin and Xue, Chen and Qi, Wenzhang and Lin, Xingjian and Chen, Jiu\nFront Aging Neurosci, 2021\n\n# Title\n\nFunctional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis.\n\n# Keywords\n\nactivation likelihood estimation\namnestic mild cognitive impairment\namplitude of low-frequency fluctuation\nfunctional connectivity\nmild cognitive impairment\nregional homogeneity\nsalience network\n\n# Abstract\n", "metadata": {"id": 34381352, "text_md5": "b9165404e19413c33e317fba18870f79", "field_positions": {"authors": [0, 137], "journal": [138, 158], "publication_year": [160, 164], "title": [175, 282], "keywords": [296, 490], "abstract": [503, 503], "body": [512, 512]}, "batch": 1, "pmid": 34381352, "doi": "10.3389/fnagi.2021.695210", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/34381352/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=34381352"}, "display_title": "pmid: 34381352", "list_title": "PMID34381352 Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis."}
2 | {"text": "Song, Yu and Xu, Wenwen and Chen, Shanshan and Hu, Guanjie and Ge, Honglin and Xue, Chen and Qi, Wenzhang and Lin, Xingjian and Chen, Jiu\nFrontiers in aging neuroscience, 2021\n\n# Title\n\nFunctional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis.\n\n# Keywords\n\nactivation likelihood estimation \namnestic mild cognitive impairment \namplitude of low-frequency fluctuation \nfunctional connectivity \nmild cognitive impairment \nregional homogeneity \nsalience network \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "34381352", "journal": "Frontiers in aging neuroscience", "publication_year": "2021", "title": "Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis.", "keywords": "activation likelihood estimation \namnestic mild cognitive impairment \namplitude of low-frequency fluctuation \nfunctional connectivity \nmild cognitive impairment \nregional homogeneity \nsalience network \n", "abstract": "", "authors": "Song, Yu and Xu, Wenwen and Chen, Shanshan and Hu, Guanjie and Ge, Honglin and Xue, Chen and Qi, Wenzhang and Lin, Xingjian and Chen, Jiu"}, "display_title": "pmid: 34381352", "list_title": "PMID34381352 Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis."}
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/documents/pmid_34630270.jsonl:
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1 | {"text": "Xu, Wenwen and Song, Yu and Chen, Shanshan and Xue, Chen and Hu, Guanjie and Qi, Wenzhang and Ma, Wenying and Lin, Xingjian and Chen, Jiu\nFront Neurol, 2021\n\n# Title\n\nAn ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment.\n\n# Keywords\n\nALE\namplitude of low-frequency fluctuation\nfunctional connectivity\nregional homogeneity\nresting state\nsubcortical vascular cognitive impairment\n\n# Abstract\n", "metadata": {"id": 34630270, "text_md5": "c04f7ab67add950019023151c4d1074a", "field_positions": {"authors": [0, 137], "journal": [138, 150], "publication_year": [152, 156], "title": [167, 268], "keywords": [282, 425], "abstract": [438, 438], "body": [447, 447]}, "batch": 2, "pmid": 34630270, "doi": "10.3389/fneur.2021.649233", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/34630270/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=34630270"}, "display_title": "pmid: 34630270", "list_title": "PMID34630270 An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment."}
2 | {"text": "Xu, Wenwen and Song, Yu and Chen, Shanshan and Xue, Chen and Hu, Guanjie and Qi, Wenzhang and Ma, Wenying and Lin, Xingjian and Chen, Jiu\nFrontiers in neurology, 2021\n\n# Title\n\nAn ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment.\n\n# Keywords\n\nALE \namplitude of low-frequency fluctuation \nfunctional connectivity \nregional homogeneity \nresting state \nsubcortical vascular cognitive impairment \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "34630270", "journal": "Frontiers in neurology", "publication_year": "2021", "title": "An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment.", "keywords": "ALE \namplitude of low-frequency fluctuation \nfunctional connectivity \nregional homogeneity \nresting state \nsubcortical vascular cognitive impairment \n", "abstract": "", "authors": "Xu, Wenwen and Song, Yu and Chen, Shanshan and Xue, Chen and Hu, Guanjie and Qi, Wenzhang and Ma, Wenying and Lin, Xingjian and Chen, Jiu"}, "display_title": "pmid: 34630270", "list_title": "PMID34630270 An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment."}
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/documents/pmid_34734458.jsonl:
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1 | {"text": "Ferraro, Stefania and Klugah-Brown, Benjamin and Tench, Christopher R and Yao, Shuxia and Nigri, Anna and Demichelis, Greta and Pinardi, Chiara and Bruzzone, Maria Grazia and Becker, Benjamin\nHum Brain Mapp, 2022\n\n# Title\n\nDysregulated anterior insula reactivity as robust functional biomarker for chronic pain-Meta-analytic evidence from neuroimaging studies.\n\n# Keywords\n\nABC\nALE\nchronic pain\nfunctional magnetic resonance imaging\nmeta-analysis\n\n# Abstract\nNeurobiological pain models propose that chronic pain is accompanied by neurofunctional changes that mediate pain processing dysfunctions. In contrast, meta-analyses of neuroimaging studies in chronic pain conditions have not revealed convergent evidence for robust alterations during experimental pain induction. Against this background, the present neuroimaging meta-analysis combined three different meta-analytic approaches with stringent study selection criteria for case-control functional magnetic resonance imaging experiments during acute pain processing with a focus on chronic pain disorders. Convergent neurofunctional dysregulations in chronic pain patients were observed in the left anterior insula cortex. Seed-based resting-state functional connectivity based on a large publicly available dataset combined with a meta-analytic task-based approach identified the anterior insular region as a key node of an extended bilateral insula-fronto-cingular network, resembling the salience network. Moreover, the meta-analytic decoding showed that this region presents a high probability to be specifically activated during pain-related processes, although we cannot exclude an involvement in autonomic processes. Together, the present findings indicate that dysregulated left anterior insular activity represents a robust neurofunctional maladaptation and potential treatment target in chronic pain disorders. ", "metadata": {"id": 34734458, "text_md5": "dd60f12ac66f5ecfe0512cf257dec47f", "field_positions": {"authors": [0, 191], "journal": [192, 206], "publication_year": [208, 212], "title": [223, 360], "keywords": [374, 446], "abstract": [459, 1878], "body": [1887, 1887]}, "batch": 2, "pmid": 34734458, "doi": "10.1002/hbm.25702", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/34734458/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=34734458"}, "display_title": "pmid: 34734458", "list_title": "PMID34734458 Dysregulated anterior insula reactivity as robust functional biomarker for chronic pain-Meta-analytic evidence from neuroimaging studies."}
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/documents/pmid_35101767.jsonl:
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1 | {"text": "Lopez-Gamundi, Paula and Yao, Yuan-Wei and Chong, Trevor T-J and Heekeren, Hauke R and Herrero, Ernest Mas and Pallares, Josep Marco\nNeurosci Biobehav Rev, 2022\n\n# Title\n\nCorrigendum to \"The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies\" [Neurosci. Biobehav. Rev. 131 (2021) 1275-1287].\n\n# Keywords\n\n\n\n# Abstract\n", "metadata": {"id": 35101767, "text_md5": "2515c8b818db2c4b56eca279cef0408b", "field_positions": {"authors": [0, 132], "journal": [133, 154], "publication_year": [156, 160], "title": [171, 339], "keywords": [353, 353], "abstract": [366, 366], "body": [375, 375]}, "batch": 2, "pmid": 35101767, "doi": "10.1016/j.neubiorev.2022.104548", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/35101767/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=35101767"}, "display_title": "pmid: 35101767", "list_title": "PMID35101767 Corrigendum to \"The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies\" [Neurosci. Biobehav. Rev. 131 (2021) 1275-1287]."}
2 | {"text": "Lopez-Gamundi, Paula and Yao, Yuan-Wei and Chong, Trevor T-J and Heekeren, Hauke R and Herrero, Ernest Mas and Pallares, Josep Marco\nNeuroscience and biobehavioral reviews, 2022\n\n# Title\n\nCorrigendum to \"The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies\" [Neurosci. Biobehav. Rev. 131 (2021) 1275-1287].\n\n# Keywords\n\n\n\n# Abstract\n\n\n", "metadata": {"pmid": "35101767", "journal": "Neuroscience and biobehavioral reviews", "publication_year": "2022", "title": "Corrigendum to \"The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies\" [Neurosci. Biobehav. Rev. 131 (2021) 1275-1287].", "keywords": "", "abstract": "", "authors": "Lopez-Gamundi, Paula and Yao, Yuan-Wei and Chong, Trevor T-J and Heekeren, Hauke R and Herrero, Ernest Mas and Pallares, Josep Marco"}, "display_title": "pmid: 35101767", "list_title": "PMID35101767 Corrigendum to \"The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies\" [Neurosci. Biobehav. Rev. 131 (2021) 1275-1287]."}
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/documents/pmid_35734557.jsonl:
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1 | {"text": "Watanuki, Shinya and Akama, Hiroyuki\nHeliyon, 2022\n\n# Title\n\nNeural substrates of brand equity: applying a quantitative meta-analytical method for neuroimage studies.\n\n# Keywords\n\nALE\nBrand management\nConsumer decision making\nConsumer neuroscience\nDMN\nNeuromarketing\nfMRI\n\n# Abstract\nAlthough the concept of brand equity has been investigated using various approaches, a comprehensive neural basis for brand equity remains unclear. The default mode network (DMN) as a mental process might influence brand equity related consumers' decision-making, as reported in the marketing literature. While studies on the overlapping regions between the DMN and value-based decision-making related brain regions have been reported in neuroscience literature, relationships between the DMN and a neural mechanism of brand equity have not been clarified. The aim of our study is to identify neural substrates of brand equity and examine brand equity-related mental processes by comparing them to the DMN. To determine the neural substrates of brand equity, we first carried out the activation likelihood estimation (ALE) meta-analysis. We examined 26 studies using branded objects as experimental stimuli for the ALE. Next, we set the output regions from ALE as the region of interest for meta-analytic connectivity modeling (MACM). Further, we compared the brand equity-related brain network (BE-RBN) revealed by the MACM with the DMN. We confirmed that the BE-RBN brain regions overlap with the medial temporal lobule (MTL) sub-system, a module composed of the DMN but excluding the retrosplenial cortex. Further, we discovered that several brain regions apart from the DMN are also distinctive BE-RBN brain regions (i.e., the insula, the inferior frontal gyrus, amygdala, ventral striatum, parietal region). We decoded the BE-RBN brain regions using the BrandMap module. The decoded results revealed that the brand equity-related mental processes are complex constructs integrated via multiple mental processes such as self-referential, reward, emotional, memory, and sensorimotor processing. Our study demonstrated that the DMN alone is insufficient to engage in brand equity-related mental processes. Therefore, marketers are required to make strategic plans to integrate the five consumer's multiple mental processes while building brand equity. ", "metadata": {"id": 35734557, "text_md5": "f80304a497710d0c68a91da6641a7dac", "field_positions": {"authors": [0, 36], "journal": [37, 44], "publication_year": [46, 50], "title": [61, 166], "keywords": [180, 271], "abstract": [284, 2338], "body": [2347, 2347]}, "batch": 1, "pmid": 35734557, "doi": "10.1016/j.heliyon.2022.e09702", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/35734557/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=35734557"}, "display_title": "pmid: 35734557", "list_title": "PMID35734557 Neural substrates of brand equity: applying a quantitative meta-analytical method for neuroimage studies."}
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/documents/pmid_35924231.jsonl:
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1 | {"text": "Dahl\u00e9n, Amelia D and Schofield, Aphra and Schi\u00f6th, Helgi B and Brooks, Samantha J\nFront Neurosci, 2022\n\n# Title\n\nSubliminal Emotional Faces Elicit Predominantly Right-Lateralized Amygdala Activation: A Systematic Meta-Analysis of fMRI Studies.\n\n# Keywords\n\nActivation Likelihood Estimation\namygdala\nemotional faces\nparahippocampal gyrus\nsubliminal\n\n# Abstract\nPrior research suggests that conscious face processing occurs preferentially in right hemisphere occipito-parietal regions. However, less is known about brain regions associated with non-conscious processing of faces, and whether a right-hemispheric dominance persists in line with specific affective responses. We aim to review the neural responses systematically, quantitatively, and qualitatively underlying subliminal face processing. PubMed was searched for Functional Magnetic Resonance Imaging (fMRI) publications assessing subliminal emotional face stimuli up to March 2022. Activation Likelihood Estimation (ALE) meta-analyses and narrative reviews were conducted on all studies that met ALE requirements. Risk of bias was assessed using the AXIS tool. In a meta-analysis of all 22 eligible studies (merging clinical and non-clinical populations, whole brain and region of interest analyses), bilateral amygdala activation was reported in the left (x = -19.2, y = 1.5, z = -17.1) in 59% of studies, and in the right (x = 24.4, y = -1.7, z = -17.4) in 68% of studies. In a second meta-analysis of non-clinical participants only ( ", "metadata": {"id": 35924231, "text_md5": "72b6fb190280bc0e9a005184baffcb61", "field_positions": {"authors": [0, 81], "journal": [82, 96], "publication_year": [98, 102], "title": [113, 243], "keywords": [257, 347], "abstract": [360, 1498], "body": [1507, 1507]}, "batch": 1, "pmid": 35924231, "doi": "10.3389/fnins.2022.868366", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/35924231/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=35924231"}, "display_title": "pmid: 35924231", "list_title": "PMID35924231 Subliminal Emotional Faces Elicit Predominantly Right-Lateralized Amygdala Activation: A Systematic Meta-Analysis of fMRI Studies."}
2 |
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/documents/pmid_36880845.jsonl:
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1 | {"text": "Pollard, Anna A and Hauson, Alexander O and Lackey, Nicholas S and Zhang, Emily and Khayat, Sarah and Carson, Bryce and Fortea, Lydia and Radua, Joaquim and Grant, Igor\nAm J Drug Alcohol Abuse, 2023\n\n# Title\n\nFunctional neuroanatomy of craving in heroin use disorder: voxel-based meta-analysis of functional magnetic resonance imaging (fMRI) drug cue reactivity studies.\n\n# Keywords\n\nCue-reactivity\ncraving\nfunctional magnetic resonance imaging\nheroin\nheroin use disorder\nmeta-analysis\nopioid use disorder\n\n# Abstract\n", "metadata": {"id": 36880845, "text_md5": "7aaebf19851243db9b2b956540bc8b05", "field_positions": {"authors": [0, 168], "journal": [169, 192], "publication_year": [194, 198], "title": [209, 370], "keywords": [384, 505], "abstract": [518, 518], "body": [527, 527]}, "batch": 1, "pmid": 36880845, "doi": "10.1080/00952990.2023.2172423", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/36880845/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=36880845"}, "display_title": "pmid: 36880845", "list_title": "PMID36880845 Functional neuroanatomy of craving in heroin use disorder: voxel-based meta-analysis of functional magnetic resonance imaging (fMRI) drug cue reactivity studies."}
2 | {"text": "Pollard, Anna A and Hauson, Alexander O and Lackey, Nicholas S and Zhang, Emily and Khayat, Sarah and Carson, Bryce and Fortea, Lydia and Radua, Joaquim and Grant, Igor\nThe American journal of drug and alcohol abuse, 2023\n\n# Title\n\nFunctional neuroanatomy of craving in heroin use disorder: voxel-based meta-analysis of functional magnetic resonance imaging (fMRI) drug cue reactivity studies.\n\n# Keywords\n\nCue-reactivity \ncraving \nfunctional magnetic resonance imaging \nheroin \nheroin use disorder \nmeta-analysis \nopioid use disorder \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "36880845", "journal": "The American journal of drug and alcohol abuse", "publication_year": "2023", "title": "Functional neuroanatomy of craving in heroin use disorder: voxel-based meta-analysis of functional magnetic resonance imaging (fMRI) drug cue reactivity studies.", "keywords": "Cue-reactivity \ncraving \nfunctional magnetic resonance imaging \nheroin \nheroin use disorder \nmeta-analysis \nopioid use disorder \n", "abstract": "", "authors": "Pollard, Anna A and Hauson, Alexander O and Lackey, Nicholas S and Zhang, Emily and Khayat, Sarah and Carson, Bryce and Fortea, Lydia and Radua, Joaquim and Grant, Igor"}, "display_title": "pmid: 36880845", "list_title": "PMID36880845 Functional neuroanatomy of craving in heroin use disorder: voxel-based meta-analysis of functional magnetic resonance imaging (fMRI) drug cue reactivity studies."}
3 |
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/documents/pmid_37179185.jsonl:
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1 | {"text": "Gao, Rong and Wang, Ping and Zhou, Sheng and Yao, Hongyan\nAsian J Surg, 2023\n\n# Title\n\nResting-state fMRI study of vulnerable brain regions in patients with end-stage renal disease: An activation likelihood estimation meta-analysis.\n\n# Keywords\n\nALE meta-Analysis\nEnd-stage renal disease\nImpaired brain regions\nResting-state fMRI\nSpontaneous neural activity\n\n# Abstract\n", "metadata": {"id": 37179185, "text_md5": "29bb4e39c4c8d93265ae1245cda03444", "field_positions": {"authors": [0, 57], "journal": [58, 70], "publication_year": [72, 76], "title": [87, 232], "keywords": [246, 357], "abstract": [370, 370], "body": [379, 379]}, "batch": 2, "pmid": 37179185, "doi": "10.1016/j.asjsur.2023.04.126", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/37179185/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=37179185"}, "display_title": "pmid: 37179185", "list_title": "PMID37179185 Resting-state fMRI study of vulnerable brain regions in patients with end-stage renal disease: An activation likelihood estimation meta-analysis."}
2 | {"text": "Gao, Rong and Wang, Ping and Zhou, Sheng and Yao, Hongyan\nAsian journal of surgery, 2023\n\n# Title\n\nResting-state fMRI study of vulnerable brain regions in patients with end-stage renal disease: An activation likelihood estimation meta-analysis.\n\n# Keywords\n\nALE meta-Analysis \nEnd-stage renal disease \nImpaired brain regions \nResting-state fMRI \nSpontaneous neural activity \n\n\n# Abstract\n\n\n", "metadata": {"pmid": "37179185", "journal": "Asian journal of surgery", "publication_year": "2023", "title": "Resting-state fMRI study of vulnerable brain regions in patients with end-stage renal disease: An activation likelihood estimation meta-analysis.", "keywords": "ALE meta-Analysis \nEnd-stage renal disease \nImpaired brain regions \nResting-state fMRI \nSpontaneous neural activity \n", "abstract": "", "authors": "Gao, Rong and Wang, Ping and Zhou, Sheng and Yao, Hongyan"}, "display_title": "pmid: 37179185", "list_title": "PMID37179185 Resting-state fMRI study of vulnerable brain regions in patients with end-stage renal disease: An activation likelihood estimation meta-analysis."}
3 |
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/documents/pmid_38275521.jsonl:
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1 | {"text": "Gronchi, Giorgio and Gavazzi, Gioele and Viggiano, Maria Pia and Giovannelli, Fabio\nBrain Sci, 2024\n\n# Title\n\nDual-Process Theory of Thought and Inhibitory Control: An ALE Meta-Analysis.\n\n# Keywords\n\nDMN\nPARCS theory\ndual process\ninsula\nleft inferior frontal gyrus\nmeta-analysis\npre-SMA\n\n# Abstract\nThe dual-process theory of thought rests on the co-existence of two different thinking modalities: a quick, automatic, and associative process opposed to a slow, thoughtful, and deliberative process. The increasing interest in determining the neural foundation of the dual-process distinction has yielded mixed results, also given the difficulty of applying the fMRI standard approach to tasks usually employed in the cognitive literature. We report an activation likelihood estimation (ALE) meta-analysis to investigate the neural foundation of the dual-process theory of thought. Eligible studies allowed for the identification of cerebral areas associated with dual-process theory-based tasks without differentiating between fast and slow thinking. The ALE algorithm converged on the medial frontal cortex, superior frontal cortex, anterior cingulate cortex, insula, and left inferior frontal gyrus. These structures partially overlap with the cerebral areas recurrently reported in the literature about the neural basis of the dual-process distinction, where the PARCS theory-based interpretation emphasizes the role of the right inferior gyrus. The results confirm the potential (but still almost unexplored) common ground between the dual-process literature and the cognitive control literature. ", "metadata": {"id": 38275521, "text_md5": "bc8fd1aec2b48ba51562d9dfc6d626ab", "field_positions": {"authors": [0, 83], "journal": [84, 93], "publication_year": [95, 99], "title": [110, 186], "keywords": [200, 286], "abstract": [299, 1601], "body": [1610, 1610]}, "batch": 1, "pmid": 38275521, "doi": "10.3390/brainsci14010101", "pmid_url": "https://pubmed.ncbi.nlm.nih.gov/38275521/", "efetch_url": "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=38275521"}, "display_title": "pmid: 38275521", "list_title": "PMID38275521 Dual-Process Theory of Thought and Inhibitory Control: An ALE Meta-Analysis."}
2 |
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/projects/NER_biomedical/NER_biomedical.:
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https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/projects/NER_biomedical/NER_biomedical.
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/projects/NER_biomedical/README.md:
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1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
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/projects/NER_biomedical/annotations/README.md:
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1 | # Annotations
--------------------------------------------------------------------------------
/projects/NER_biomedical/datasets.json:
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1 | [
2 |
3 | ]
4 |
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/projects/NER_biomedical/labels/README.md:
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1 | # Labels
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/projects/NER_biomedical/labels/labels.jsonl:
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1 | {"color":"#4e9a06","name":"processing (pre or post)","shortcut_key":"p"}
2 | {"color":"#f57900","name":"features","shortcut_key":"f"}
3 | {"color":"#fcaf3e","name":"NER method","shortcut_key":"r"}
4 | {"color":"#729fcf","name":"entity","shortcut_key":"e"}
5 | {"color":"#ad7fa8","name":"location","shortcut_key":"w"}
6 | {"color":"#8ae234","name":"document type","shortcut_key":"d"}
7 | {"color":"#ef2929","name":"performance","shortcut_key":"a"}
8 | {"color":"#babdb6","name":"note","shortcut_key":"q"}
9 | {"color":"#888a85","name":"exclude","shortcut_key":"x"}
10 | {"color":"#eeeeec","name":"interesting","shortcut_key":"i"}
11 |
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/projects/autism_mri/README.md:
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1 | # autism_mri
2 |
3 | Repo to do LabelBuddy annotations of a corpus of Autism MRI literature
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | We ran the following bash command:
9 |
10 | ```bash
11 | pubget run -q "Brain Volume MRI 2022 Autism" pubget/test --labelbuddy --n_jobs 4
12 | ```
13 | ### Where the full papers are stored
14 | The full pubget output is stored on OSF: [https://osf.io/meya4](https://osf.io/meya4).
15 | ## Annotations
16 | ### File(s) being annotated:
17 | - `/projects/autism_mri/documents/documents_00001.jsonl`
18 | - corresponding file in the pubget output:
19 | - `query_6b2c09de69c29c29626f794757ea4c68/subset_allArticles_labelbuddyData/documents_00001.jsonl`
20 |
21 | ### Annotation labels:
22 | - FieldStrength: ``
23 | - Diagnosis: ``
24 | - N_Total:
25 | - N_Total_Male:
26 | - N_Total_Female
27 | - N_Patients
28 | - N_Controls
29 | - N_Controls_Male
30 | - N_Controls_Female
31 | - N_Patients_Male
32 | - N_Patients_Female
33 | - Age_Mean
34 | - Age_Min
35 | - Age_Max
36 | - Scanner
37 | - AnalysisTool
38 | - MRI_Modality
39 | ### Labels found in other projects as well:
40 | - ``
41 |
42 | ### Instructions for annotators
43 | ``
44 |
45 |
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/projects/autism_mri/annotations/Jerome_Dockes.jsonl:
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https://raw.githubusercontent.com/litmining/labelbuddy-annotations/42b1321c1a1ddd031b8c89bdc6e0e999d6e0aad1/projects/autism_mri/annotations/Jerome_Dockes.jsonl
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/projects/autism_mri/datasets.json:
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1 | [
2 | {
3 | "url": "https://osf.io/download/meya4/"
4 | }
5 | ]
6 |
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/projects/autism_mri/labels/Article_Terms.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "color": "#aec7e8",
4 | "name": "FieldStrength",
5 | "shortcut_key": "f"
6 | },
7 | {
8 | "color": "#ffbb78",
9 | "name": "Diagnosis",
10 | "shortcut_key": "d"
11 | },
12 | {
13 | "color": "#98df8a",
14 | "name": "N_Total",
15 | "shortcut_key": "n"
16 | },
17 | {
18 | "color": "#ff9896",
19 | "name": "N_Total_Male"
20 | },
21 | {
22 | "color": "#c5b0d5",
23 | "name": "N_Total_Female"
24 | },
25 | {
26 | "color": "#dbdb8d",
27 | "name": "N_Patients"
28 | },
29 | {
30 | "color": "#9edae5",
31 | "name": "N_Controls"
32 | },
33 | {
34 | "color": "#aec7e8",
35 | "name": "N_Controls_Male"
36 | },
37 | {
38 | "color": "#ffbb78",
39 | "name": "N_Controls_Female"
40 | },
41 | {
42 | "color": "#98df8a",
43 | "name": "N_Patients_Male"
44 | },
45 | {
46 | "color": "#ff9896",
47 | "name": "N_Patients_Female"
48 | },
49 | {
50 | "color": "#c5b0d5",
51 | "name": "Age_Mean"
52 | },
53 | {
54 | "color": "#c49c94",
55 | "name": "Age_Min"
56 | },
57 | {
58 | "color": "#f7b6d2",
59 | "name": "Age_Max"
60 | },
61 | {
62 | "color": "#dbdb8d",
63 | "name": "Scanner"
64 | },
65 | {
66 | "color": "#9edae5",
67 | "name": "AnalysisTool"
68 | },
69 | {
70 | "color": "#aec7e8",
71 | "name": "MRI_Modality"
72 | }
73 | ]
74 |
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/projects/cluster_inference/README.md:
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1 | # cluster_inference
2 |
3 | Repo to do LabelBuddy annotations of a corpus of fMRI literature.
4 | This corpus consists of the open-access portion of NeuroQuery papers that conatain fMRI in the title or abstract.
5 |
6 | ## Papers
7 | ### How the papers were obtained
8 | We rand a script with this code:
9 | ```bash
10 | #!/bin/bash
11 |
12 | pubget run --pmcids_file pmcids_for_open_fmri_papers.txt \
13 | --fit_neuroquery \
14 | --labelbuddy \
15 | --nimare \
16 | ~/data/pubget_data
17 | ```
18 |
19 | The `pmcids_for_open_fmri_papers.txt` file is found in the current directory.
20 | It contains the pmcids for all the papers in the `neuroquery` dataset that could be found on PubMed Central (i.e., that were open access).
21 |
22 |
23 | ### Where the full papers are stored
24 | The full pubget output is stored on OSF: [https://osf.io/gukw2](https://osf.io/gukw2).
25 |
26 |
27 | ## Annotations
28 | ### File(s) being annotated:
29 | - `/projects/cluster_inference/documents/documents.jsonl`
30 | - corresponding file in the pubget output:
31 | - `cluster_inference/pmcidList_a84871e6b29a46f53d30d0463f66407f/subset_allArticles_labelbuddyData/documents00001.jsonl`
32 |
33 | ### Annotation labels:
34 | - smoothing_snippet
35 | - cluster_thresh_used
36 | - cluster_thresh_in_voxels
37 | - cluster_thresh_in_mm
38 | - nonparametric_cluster_thresh
39 | - info_removed_in_name_extract
40 | - is_annotated
41 | - annotation_in_progress
42 | - discard_this_paper
43 |
44 | ### Labels found in other projects as well:
45 | - ``
46 |
47 | ### Instructions for annotators
48 | ``
49 |
50 |
51 |
--------------------------------------------------------------------------------
/projects/cluster_inference/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "url": "https://osf.io/download/gukw2/"
4 | }
5 | ]
6 |
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/projects/cluster_inference/labels/labels_kendra.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "name": "smoothing_snippet",
4 | "color": "#aec7e8",
5 | "shortcut_key": "s"
6 | },
7 | {
8 | "name": "cluster_thresh_used",
9 | "color": "#ffbb78",
10 | "shortcut_key": "c"
11 | },
12 | {
13 | "name": "cluster_thresh_in_voxels",
14 | "color": "#98df8a",
15 | "shortcut_key": "v"
16 | },
17 | {
18 | "name": "cluster_thresh_in_mm",
19 | "color": "#ff9896",
20 | "shortcut_key": "m"
21 | },
22 | {
23 | "name": "nonparametric_cluster_thresh",
24 | "color": "#c5b0d5",
25 | "shortcut_key": "n"
26 | },
27 | {
28 | "name": "info_removed_in_name_extract",
29 | "color": "#c49c94",
30 | "shortcut_key": "i"
31 | },
32 | {
33 | "name": "is_annotated",
34 | "color": "#f7b6d2",
35 | "shortcut_key": "a"
36 | },
37 | {
38 | "name": "annotation_in_progress",
39 | "color": "#b8b8b8",
40 | "shortcut_key": "p"
41 | },
42 | {
43 | "name": "discard_this_paper",
44 | "color": "#dbdb8d",
45 | "shortcut_key": "d"
46 | }
47 | ]
48 |
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/projects/cluster_inference/re_download_papers_in_nqdc_format.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 |
3 | pubget run --pmcids_file pmcids_for_open_fmri_papers.txt \
4 | --fit_neuroquery \
5 | --labelbuddy \
6 | --nimare \
7 | ~/data/pubget_data
--------------------------------------------------------------------------------
/projects/cobidas/README.md:
--------------------------------------------------------------------------------
1 | # COBIDAS checklist
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 |
9 |
12 |
13 | See the pubget `query.txt` file.
14 |
15 |
30 |
31 |
42 |
43 | ### Annotation labels:
44 |
45 | Generated from the [COBIDAS reproschema](https://github.com/ohbm/cobidas_schema):
46 |
47 | Created with [`/tools/create_labelbuddy_labels.py`](https://github.com/ohbm/cobidas_schema/blob/master/tools/create_labelbuddy_labels.py) .
48 |
49 | The labels contained here could be reused to label MRI, PET or eyetracking studies.
50 |
51 | The labels for each subsection of the schema are stored in a separate file prefixed with a number:
52 | for example `cobidas/5_preprocessing_labels.jsonl`.
53 | But there is also a jsonl file that combines all labels from all sections.
54 |
55 |
62 |
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/projects/cobidas/labels/README.md:
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1 | # LabelBuddy Labels
2 |
3 | Created with `../tools/create_labelbuddy_labels.py`.
4 |
5 |
--------------------------------------------------------------------------------
/projects/cobidas/labels/cobidas/0_sample_labels.jsonl:
--------------------------------------------------------------------------------
1 | {"name": "1.1 - Was this study a group comparison? ", "color": "#a6cee3"}
2 | {"name": "1.2 - Number of groups", "color": "#a6cee3"}
3 | {"name": "1.3 - Number of subjects planned", "color": "#a6cee3"}
4 | {"name": "1.4 - Number of subjects approached", "color": "#a6cee3"}
5 | {"name": "1.5 - Number of subjects consented", "color": "#a6cee3"}
6 | {"name": "1.8 - Number of subjects excluded after consenting but before data acquisition", "color": "#a6cee3"}
7 | {"name": "1.9 - Why were subjects excluded?", "color": "#a6cee3"}
8 | {"name": "1.10 - Age of participants: Mean", "color": "#a6cee3"}
9 | {"name": "1.11 - Age of participants: Standard deviation", "color": "#a6cee3"}
10 | {"name": "1.12 - Age of participants: minimum", "color": "#a6cee3"}
11 | {"name": "1.13 - Age of participants: maximum", "color": "#a6cee3"}
12 | {"name": "1.14 - Number of subjects scanned", "color": "#a6cee3"}
13 | {"name": "1.15 - Number of subjects scanned but rejected from analysis", "color": "#a6cee3"}
14 | {"name": "1.17 - Number of subjects included in analysis", "color": "#a6cee3"}
15 | {"name": "1.19 - Percentage of men in the study", "color": "#a6cee3"}
16 | {"name": "1.21 - Education level", "color": "#a6cee3"}
17 | {"name": "1.23 - Percentage of right handed in the study", "color": "#a6cee3"}
18 | {"name": "1.24 - Were there any screening criteria for handedness?", "color": "#a6cee3"}
19 | {"name": "1.25 - Describe any screening criteria, including those applied to “normal” sample such as MRI exclusion criteria", "color": "#a6cee3"}
20 | {"name": "1.26 - Description of the screening criteria.", "color": "#a6cee3"}
21 | {"name": "1.27 - Detail the area of recruitment as well as whether patients were currently in treatment", "color": "#a6cee3"}
22 | {"name": "1.28 - Describe the instruments used to obtain the diagnosis and provide tests of intra or inter-rater reliability.", "color": "#a6cee3"}
23 | {"name": "1.29 - Describe how the different groups were matched.", "color": "#a6cee3"}
24 | {"name": "1.30 - Population from which subjects were drawn, and how and where recruitment took place.", "color": "#a6cee3"}
25 | {"name": "1.33 - Description of the institutional review board that granted the ethics approval", "color": "#a6cee3"}
26 | {"name": "1.35 - Were participants compensated for their participation?", "color": "#a6cee3"}
27 | {"name": "1.36 - How were participants compensated?", "color": "#a6cee3"}
28 | {"name": "1.37 - Other relevant demographic information", "color": "#a6cee3"}
29 |
--------------------------------------------------------------------------------
/projects/cobidas/labels/cobidas/1_design_labels.jsonl:
--------------------------------------------------------------------------------
1 | {"name": "1.1 - Was it task based MRI or resting state MRI?", "color": "#1f78b4"}
2 | {"name": "1.2 - What was the total scanning time?", "color": "#1f78b4"}
3 | {"name": "1.3 - Was the design efficiency optimized?", "color": "#1f78b4"}
4 | {"name": "1.4 - What method was used for optimization?", "color": "#1f78b4"}
5 | {"name": "1.5 - How was the equipment was synched to the scanner?", "color": "#1f78b4"}
6 | {"name": "1.6 - Was a power analysis performed?", "color": "#1f78b4"}
7 | {"name": "1.7 - Effect size used for the power calculation (magnitude and standard deviation)", "color": "#1f78b4"}
8 | {"name": "1.8 - The type of outcome used as the basis of power computations", "color": "#1f78b4"}
9 | {"name": "1.9 - Source of predicted effect size", "color": "#1f78b4"}
10 | {"name": "1.10 - Significance level", "color": "#1f78b4"}
11 | {"name": "1.11 - Target power", "color": "#1f78b4"}
12 | {"name": "1.12 - Any other parameters set (e.g. for spatial methods a brain volume and smoothness may be needed to be specified)", "color": "#1f78b4"}
13 |
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/projects/cobidas/labels/cobidas/7_results_labels.jsonl:
--------------------------------------------------------------------------------
1 | {"name": "1.1 - Type of analysis performed", "color": "#ff7f00"}
2 | {"name": "1.2 - Provide a complete list of tested and omitted effects", "color": "#ff7f00"}
3 | {"name": "1.3 - Define how voxels/elements were selected", "color": "#ff7f00"}
4 | {"name": "1.4 - Unit used to report results ", "color": "#ff7f00"}
5 | {"name": "1.12 - P-value forming basis of inference", "color": "#ff7f00"}
6 | {"name": "1.19 - Link to unthresholded statistic maps", "color": "#ff7f00"}
7 | {"name": "1.20 - Link to thresholded statistic maps", "color": "#ff7f00"}
8 | {"name": "1.21 - Link to effect size maps", "color": "#ff7f00"}
9 | {"name": "1.22 - Size of the analysis volume in voxels", "color": "#ff7f00"}
10 | {"name": "1.23 - Noise smoothness for statistical inference (FWHM)", "color": "#ff7f00"}
11 | {"name": "1.24 - Number of resels", "color": "#ff7f00"}
12 | {"name": "1.25 - For connectivty ICA based methods, report the total number of components (especially when estimated from the data and not fixed). Report the number of these analyzed and the reason for their selection", "color": "#ff7f00"}
13 | {"name": "1.26 - For connectivty graph-based methods, carefully state what is the null hypothesis of the test and how the statistic distribution under the null is computed", "color": "#ff7f00"}
14 | {"name": "1.27 - For multivariate, report optimised evaluation metrics", "color": "#ff7f00"}
15 |
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/projects/cobidas/labels/cobidas/8_data_sharing_labels.jsonl:
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1 | {"name": "1.1 - List types of images and nonimaging data provided", "color": "#cab2d6"}
2 | {"name": "1.2 - Report on the completeness of the data", "color": "#cab2d6"}
3 | {"name": "1.3 - data URL or DOI", "color": "#cab2d6"}
4 | {"name": "1.4 - Specific instructions on how to gain access to the data.", "color": "#cab2d6"}
5 | {"name": "1.5 - Cost of access", "color": "#cab2d6"}
6 | {"name": "1.6 - Confirm that the ethics board of the host institution generating the data approves the sharing of the data made available", "color": "#cab2d6"}
7 | {"name": "1.7 - Clarify any constraints on uses of shared data.", "color": "#cab2d6"}
8 | {"name": "1.8 - Provide URL to documentation, and specify its scope", "color": "#cab2d6"}
9 | {"name": "1.9 - Report the format of the image data shared.", "color": "#cab2d6"}
10 | {"name": "1.10 - Data organization structures, including Data Dictionaries and Schemas.", "color": "#cab2d6"}
11 | {"name": "1.11 - Is the software using an established ontology?", "color": "#cab2d6"}
12 | {"name": "1.12 - Availability of in-resource visualization of the imaging or nonimaging data", "color": "#cab2d6"}
13 | {"name": "1.13 - How, if at all, data are de-identified ?", "color": "#cab2d6"}
14 | {"name": "1.14 - Availability of detailed provenance of preprocessing and analysis of shared data", "color": "#cab2d6"}
15 | {"name": "1.15 - Ability of a repository to work in a multi-database environment, availability of API’s and ability to connect to analysis pipelines", "color": "#cab2d6"}
16 | {"name": "1.16 - Mechanisms available for constructing queries on the repository (e.g. SQL, SPARQL)", "color": "#cab2d6"}
17 | {"name": "1.17 - How users can check version of downloaded data and compare it to the current version at a later time", "color": "#cab2d6"}
18 |
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/projects/cobidas/labels/cobidas/9_reproducibility_labels.jsonl:
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1 | {"name": "1.1 - Tool names, versions, and URLs", "color": "#6a3d9a"}
2 | {"name": "1.2 - Machine CPU model, any use of parallelization", "color": "#6a3d9a"}
3 | {"name": "1.3 - operating system used for analysis", "color": "#6a3d9a"}
4 | {"name": "1.4 - operating system version", "color": "#6a3d9a"}
5 | {"name": "1.5 - Was a workflow system used?", "color": "#6a3d9a"}
6 | {"name": "1.6 - Name of workflow system used", "color": "#6a3d9a"}
7 | {"name": "1.7 - Version of the workflow system used", "color": "#6a3d9a"}
8 | {"name": "1.8 - Provide a permanent identifie to the workflow if possible", "color": "#6a3d9a"}
9 | {"name": "1.9 - State whether detailed provenance information is available", "color": "#6a3d9a"}
10 | {"name": "1.10 - Provide permanent identifier to the data if possible", "color": "#6a3d9a"}
11 | {"name": "1.11 - Provide a URL linking to the relevant resource.", "color": "#6a3d9a"}
12 | {"name": "1.12 - Is the documentation provided in English?", "color": "#6a3d9a"}
13 | {"name": "1.13 - Are the tools publically available?", "color": "#6a3d9a"}
14 | {"name": "1.14 - Is a virtual environment to facilitate a repeated analysis available?", "color": "#6a3d9a"}
15 |
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/projects/cobidas/labels/eyetracking/0_introduction_labels.jsonl:
--------------------------------------------------------------------------------
1 | {"name": "The importance of auxiliary assumptions linking constructs and operational definitions", "color": "#a6cee3"}
2 |
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/projects/cobidas/labels/eyetracking/10_preprocessing_labels.jsonl:
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1 | {"name": "The artefact detection and removal method used", "color": "#ffff99"}
2 | {"name": "Specification of the algorithm to calculate the pupil size", "color": "#ffff99"}
3 | {"name": "The pupil measures used", "color": "#ffff99"}
4 |
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/projects/cobidas/labels/eyetracking/11_discussion_labels.jsonl:
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1 | {"name": "Limitations mentioned due to the eye-tracking methodology, study specific or general stated?", "color": "#b15928"}
2 | {"name": "Limitations stated due to the eye-tracking methodology in general", "color": "#b15928"}
3 |
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/projects/cobidas/labels/eyetracking/1_tracker_labels.jsonl:
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1 | {"name": "The eye-tracker used", "color": "#1f78b4"}
2 | {"name": "The producer of the eye-tracker", "color": "#1f78b4"}
3 | {"name": "The type of eye-tracking device", "color": "#1f78b4"}
4 | {"name": "The type of eye-tracking device", "color": "#1f78b4"}
5 | {"name": "The lens size of the eye-tracker", "color": "#1f78b4"}
6 | {"name": "The sampling procedure used", "color": "#1f78b4"}
7 | {"name": "The sampling rate used", "color": "#1f78b4"}
8 | {"name": "The accuracy of the eye-tracker", "color": "#1f78b4"}
9 | {"name": "The precision of the eye-tracker", "color": "#1f78b4"}
10 | {"name": "The temporal precision", "color": "#1f78b4"}
11 | {"name": "Specification of stimulus-synchronization latencies", "color": "#1f78b4"}
12 | {"name": "Eye-tracker latency", "color": "#1f78b4"}
13 | {"name": "Tracking range of the head box in which participants can move without losing data", "color": "#1f78b4"}
14 | {"name": "If a chinrest was used", "color": "#1f78b4"}
15 |
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/projects/cobidas/labels/eyetracking/2_monitor_labels.jsonl:
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1 | {"name": "The type of monitor used", "color": "#b2df8a"}
2 | {"name": "The resolution of the used monitor", "color": "#b2df8a"}
3 | {"name": "The screen size of the used monitor", "color": "#b2df8a"}
4 | {"name": "Screen refresh rate of the used monitor", "color": "#b2df8a"}
5 |
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/projects/cobidas/labels/eyetracking/3_software_labels.jsonl:
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1 | {"name": "The software used to pre-process the eye-tracking data", "color": "#33a02c"}
2 | {"name": "The software used to present the stimuli", "color": "#33a02c"}
3 |
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/projects/cobidas/labels/eyetracking/4_aoi_definition_labels.jsonl:
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1 | {"name": "The size of the AOIs in pixel or degrees", "color": "#fb9a99"}
2 | {"name": "Overlap between the AOIs", "color": "#fb9a99"}
3 | {"name": "The minimal distance between AOIs in pixel", "color": "#fb9a99"}
4 | {"name": "The relative size of AOIs and Content within AOIs", "color": "#fb9a99"}
5 | {"name": "Example image presented in the paper", "color": "#fb9a99"}
6 | {"name": "Method for stimulus preparation", "color": "#fb9a99"}
7 | {"name": "Matching of the luminance between the stimuli", "color": "#fb9a99"}
8 | {"name": "The size of the stimulus", "color": "#fb9a99"}
9 |
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/projects/cobidas/labels/eyetracking/5_setup_labels.jsonl:
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1 | {"name": "Duration of inter stimulus interval", "color": "#e31a1c"}
2 | {"name": "Presentation duration of the fixation cross", "color": "#e31a1c"}
3 | {"name": "Position of the fixation cross", "color": "#e31a1c"}
4 | {"name": "Duration of stimulus presentation", "color": "#e31a1c"}
5 | {"name": "The order of the stimulus presentation", "color": "#e31a1c"}
6 | {"name": "Counter-balancing of the stimulus in the presentation across positions", "color": "#e31a1c"}
7 | {"name": "The number of trials in the experiment", "color": "#e31a1c"}
8 | {"name": "Description of the person running the experiment", "color": "#e31a1c"}
9 | {"name": "Settings and locations where data were collected", "color": "#e31a1c"}
10 | {"name": "The distance between participants and the screen", "color": "#e31a1c"}
11 |
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/projects/cobidas/labels/eyetracking/6_calibration_labels.jsonl:
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1 | {"name": "Number of points that appeared in calibration", "color": "#fdbf6f"}
2 | {"name": "The background color of the calibration", "color": "#fdbf6f"}
3 | {"name": "Time, when the calibration was conducted.", "color": "#fdbf6f"}
4 | {"name": "Specification of the calibration procedure", "color": "#fdbf6f"}
5 |
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/projects/cobidas/labels/eyetracking/7_participants_labels.jsonl:
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1 | {"name": "The vision of the participants", "color": "#ff7f00"}
2 | {"name": "The percentage of women", "color": "#ff7f00"}
3 | {"name": "The mean age of participants", "color": "#ff7f00"}
4 | {"name": "Procedure for testing visual acuity or color vision", "color": "#ff7f00"}
5 | {"name": "Color vision ", "color": "#ff7f00"}
6 |
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/projects/cobidas/labels/eyetracking/8_data_quality_labels.jsonl:
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1 | {"name": "Procedure for handling of participant artefacts", "color": "#cab2d6"}
2 | {"name": "The obtained accuracy of the data", "color": "#cab2d6"}
3 | {"name": "Monitoring of data quality during experiment", "color": "#cab2d6"}
4 | {"name": "Percentage of trials excluded for the analysis", "color": "#cab2d6"}
5 | {"name": "Reasons for exclusion", "color": "#cab2d6"}
6 | {"name": "Number of participants excluded from the analysis", "color": "#cab2d6"}
7 | {"name": "The exact quality threshold for exclusion", "color": "#cab2d6"}
8 | {"name": "Percentage of lost data", "color": "#cab2d6"}
9 | {"name": "Test of assumptions for missing data", "color": "#cab2d6"}
10 | {"name": "Methods for addressing missing data", "color": "#cab2d6"}
11 | {"name": "The pre-processing of raw data through denoising, filtering or smoothing", "color": "#cab2d6"}
12 |
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/projects/cobidas/labels/eyetracking/9_dependent_measures_labels.jsonl:
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1 | {"name": "Other transformations of data", "color": "#6a3d9a"}
2 | {"name": "The algorithm used to identify blinks", "color": "#6a3d9a"}
3 | {"name": "Event detection procedure", "color": "#6a3d9a"}
4 | {"name": "The aggregation method for fixations during data preprocessing used", "color": "#6a3d9a"}
5 |
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/projects/cobidas/labels/pet/0_info_labels.jsonl:
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1 | {"name": "Modality", "color": "#a6cee3"}
2 | {"name": "Scanner manufacturer", "color": "#a6cee3"}
3 | {"name": "PET scanner model name", "color": "#a6cee3"}
4 | {"name": "Details of anaesthesia used, if any.", "color": "#a6cee3"}
5 | {"name": "Name of the organ / body region scanned.", "color": "#a6cee3"}
6 | {"name": "Unit of the image file", "color": "#a6cee3"}
7 | {"name": "Experiment date", "color": "#a6cee3"}
8 | {"name": "Imaging center-site", "color": "#a6cee3"}
9 | {"name": "Imaging department", "color": "#a6cee3"}
10 |
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/projects/cobidas/labels/pet/1_radiotracer_labels.jsonl:
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1 | {"name": "Details of the pharmaceutical dose regimen.", "color": "#1f78b4"}
2 | {"name": "Name of the tracer compound used", "color": "#1f78b4"}
3 | {"name": "ID of the tracer compound from the RadLex Ontology.", "color": "#1f78b4"}
4 | {"name": "ID of the tracer compound from the SNOMED Ontology", "color": "#1f78b4"}
5 | {"name": "Name of pharmaceutical coadministered with tracer.", "color": "#1f78b4"}
6 | {"name": "Radioisotope labelling tracer.", "color": "#1f78b4"}
7 | {"name": "Accurate molecular weight of the tracer used.", "color": "#1f78b4"}
8 | {"name": "Time of administration of pharmaceutical dose, relative to time zero", "color": "#1f78b4"}
9 | {"name": "Dose amount of pharmaceutical coadministered with tracer.", "color": "#1f78b4"}
10 |
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/projects/cobidas/labels/pet/2_radiochem_labels.jsonl:
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1 | {"name": "Mode of administration of the injection.", "color": "#b2df8a"}
2 | {"name": "Infusion speed", "color": "#b2df8a"}
3 | {"name": "Injected radioactivity", "color": "#b2df8a"}
4 | {"name": "Total mass of radiolabeled compound injected into subject.", "color": "#b2df8a"}
5 | {"name": "Moles tracer injected", "color": "#b2df8a"}
6 | {"name": "Bodyweight", "color": "#b2df8a"}
7 | {"name": "Molar activity of compound injected.", "color": "#b2df8a"}
8 | {"name": "Time to which molar radioactivity measurement above applies.", "color": "#b2df8a"}
9 | {"name": "Specific radioactivity", "color": "#b2df8a"}
10 | {"name": "Specific radioactivity measurement time", "color": "#b2df8a"}
11 | {"name": "Purity of the radiolabeled compound.", "color": "#b2df8a"}
12 | {"name": "Injected volume", "color": "#b2df8a"}
13 | {"name": "Formulation", "color": "#b2df8a"}
14 |
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/projects/cobidas/labels/pet/3_time_labels.jsonl:
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1 | {"name": "Date of scan", "color": "#33a02c"}
2 | {"name": "Time zero to which all scan and/or blood measurements have been adjusted to.", "color": "#33a02c"}
3 | {"name": "Time of start of scan wrt TimeZero", "color": "#33a02c"}
4 | {"name": "Time of start of injection wrt TimeZero", "color": "#33a02c"}
5 | {"name": "Time of end of injection wrt TimeZero", "color": "#33a02c"}
6 | {"name": "Start times for all frames relative to TimeZero", "color": "#33a02c"}
7 | {"name": "Time duration of each frame", "color": "#33a02c"}
8 |
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/projects/cobidas/labels/pet/4_reconstruction_labels.jsonl:
--------------------------------------------------------------------------------
1 | {"name": "Acquisition mode", "color": "#fb9a99"}
2 | {"name": "Specify whether the image data have been decay-corrected.", "color": "#fb9a99"}
3 | {"name": "Point in time from which the decay correction was applied wrt TimeZero", "color": "#fb9a99"}
4 | {"name": "Reconstruction method", "color": "#fb9a99"}
5 | {"name": "Names, value, units of reconstruction parameters.", "color": "#fb9a99"}
6 | {"name": "Recon-filter-type", "color": "#fb9a99"}
7 | {"name": "Attenuation correction", "color": "#fb9a99"}
8 | {"name": "Postrecon-filter-size", "color": "#fb9a99"}
9 | {"name": "PET voxel size", "color": "#fb9a99"}
10 | {"name": "Matrix Size", "color": "#fb9a99"}
11 | {"name": "Frame-Sequence", "color": "#fb9a99"}
12 | {"name": "Scale factor for each frame", "color": "#fb9a99"}
13 | {"name": "Scatter fraction for each frame", "color": "#fb9a99"}
14 | {"name": "Decay correction factor for each frame", "color": "#fb9a99"}
15 | {"name": "Prompt rate for each frame", "color": "#fb9a99"}
16 | {"name": "Random rate for each frame", "color": "#fb9a99"}
17 | {"name": "Singles rate for each frame", "color": "#fb9a99"}
18 | {"name": "Reconstruction data labels", "color": "#fb9a99"}
19 | {"name": "Reference paper for the attenuation correction method used.", "color": "#fb9a99"}
20 |
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/projects/cobidas/labels/pet/5_continuous_blood_labels.jsonl:
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1 | {"name": "Boolean that specifies if continuous blood measurements are available.", "color": "#e31a1c"}
2 | {"name": "The rate at which the blood was withdrawn from the subject.", "color": "#e31a1c"}
3 | {"name": "Description of the type of tubing used, ideally including the material and (internal) diameter.", "color": "#e31a1c"}
4 | {"name": "The length of the blood tubing, from the subject to the detector.", "color": "#e31a1c"}
5 | {"name": "External dispersion time constant resulting from tubing.", "color": "#e31a1c"}
6 | {"name": "Dispersion correction", "color": "#e31a1c"}
7 | {"name": "Boolean that specifies if discrete blood measurements are available.", "color": "#e31a1c"}
8 | {"name": "Measured haematocrit, i.e. the volume of erythrocytes divided by the volume of whole blood.", "color": "#e31a1c"}
9 | {"name": "Measured blood density.", "color": "#e31a1c"}
10 | {"name": "Boolean that specifies if metabolite measurements are available.", "color": "#e31a1c"}
11 | {"name": "Method used to measure metabolites", "color": "#e31a1c"}
12 | {"name": "Radiolabeled Parent compound fraction", "color": "#e31a1c"}
13 | {"name": "Metabolite data labels", "color": "#e31a1c"}
14 | {"name": "Parent fraction units", "color": "#e31a1c"}
15 | {"name": "Parent fraction measurement error", "color": "#e31a1c"}
16 | {"name": "Metabolite analysis timepoints", "color": "#e31a1c"}
17 | {"name": "Fraction of radiometabolite needing quantitation", "color": "#e31a1c"}
18 | {"name": "Error on above measurement", "color": "#e31a1c"}
19 | {"name": "Radiolabeled metabolite fractions", "color": "#e31a1c"}
20 | {"name": "Metabolite correction", "color": "#e31a1c"}
21 | {"name": "Boolean that specifies if plasma measurements are available.", "color": "#e31a1c"}
22 | {"name": "Measured free fraction in plasma, i.e. concentration of free compound in plasma divided by total concentration of compound in plasma.", "color": "#e31a1c"}
23 | {"name": "Error of free fraction measurement", "color": "#e31a1c"}
24 | {"name": "Method used to estimate free fraction.", "color": "#e31a1c"}
25 | {"name": "decay corrected", "color": "#e31a1c"}
26 | {"name": "decay correction time", "color": "#e31a1c"}
27 | {"name": "plasma data type", "color": "#e31a1c"}
28 | {"name": "plasma data units", "color": "#e31a1c"}
29 | {"name": "hematocrit value", "color": "#e31a1c"}
30 | {"name": "Whole blood radioactivity", "color": "#e31a1c"}
31 | {"name": "Plasma to whole blood ratio", "color": "#e31a1c"}
32 |
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/projects/cobidas/labels/pet/6_cross-calibration_labels.jsonl:
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1 | {"name": "Blood sampling device read", "color": "#fdbf6f"}
2 | {"name": "Blood sampling device measurement time", "color": "#fdbf6f"}
3 | {"name": "Phantom type", "color": "#fdbf6f"}
4 | {"name": "Phantom volume", "color": "#fdbf6f"}
5 | {"name": "Radiotracer-isotope", "color": "#fdbf6f"}
6 | {"name": "Phantom activity", "color": "#fdbf6f"}
7 | {"name": "Activity time", "color": "#fdbf6f"}
8 | {"name": "Residual activity", "color": "#fdbf6f"}
9 | {"name": "Residual activity time", "color": "#fdbf6f"}
10 | {"name": "Phantom acquisition time", "color": "#fdbf6f"}
11 | {"name": "Well counter sample volumes", "color": "#fdbf6f"}
12 | {"name": "Well counter measurement time", "color": "#fdbf6f"}
13 | {"name": "Well counter read", "color": "#fdbf6f"}
14 | {"name": "Phantom data", "color": "#fdbf6f"}
15 |
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/projects/cobidas/labels/pet/7_external_motion_correction_labels.jsonl:
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1 | {"name": "Hardware", "color": "#ff7f00"}
2 | {"name": "External MoCo software", "color": "#ff7f00"}
3 | {"name": "External MoCo software version", "color": "#ff7f00"}
4 |
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/projects/cobidas/labels/pet/8_blood_data_processing_labels.jsonl:
--------------------------------------------------------------------------------
1 | {"name": "Sampling Method", "color": "#cab2d6"}
2 | {"name": "Delay time correction", "color": "#cab2d6"}
3 | {"name": "Fitting", "color": "#cab2d6"}
4 | {"name": "Fitting description", "color": "#cab2d6"}
5 | {"name": "Blood data processing interpolation", "color": "#cab2d6"}
6 | {"name": "Model", "color": "#cab2d6"}
7 | {"name": "Reference volume", "color": "#cab2d6"}
8 | {"name": "Outcome measure", "color": "#cab2d6"}
9 | {"name": "Weighting", "color": "#cab2d6"}
10 | {"name": "Level of analysis", "color": "#cab2d6"}
11 | {"name": "Kinetic modelling software", "color": "#cab2d6"}
12 | {"name": "Kinetic modelling software version", "color": "#cab2d6"}
13 | {"name": "MoCo reference image", "color": "#cab2d6"}
14 | {"name": "MoCo cost function", "color": "#cab2d6"}
15 | {"name": "MoCo interpolation", "color": "#cab2d6"}
16 | {"name": "MoCo smoothing", "color": "#cab2d6"}
17 | {"name": "MoCo quality control", "color": "#cab2d6"}
18 | {"name": "MoCo software", "color": "#cab2d6"}
19 | {"name": "MoCo software version", "color": "#cab2d6"}
20 | {"name": "Registration reference image", "color": "#cab2d6"}
21 | {"name": "Registration target image", "color": "#cab2d6"}
22 | {"name": "Registration cost function", "color": "#cab2d6"}
23 | {"name": "Registration interpolation", "color": "#cab2d6"}
24 | {"name": "Registration smoothing", "color": "#cab2d6"}
25 | {"name": "Resolution of reference-target", "color": "#cab2d6"}
26 | {"name": "Registration quality control", "color": "#cab2d6"}
27 | {"name": "Registration software", "color": "#cab2d6"}
28 | {"name": "Registration software version", "color": "#cab2d6"}
29 | {"name": "Smoothing target image", "color": "#cab2d6"}
30 | {"name": "Shape of smoothing filter", "color": "#cab2d6"}
31 | {"name": "Size of smoothing filter", "color": "#cab2d6"}
32 | {"name": "Justification for smoothing size", "color": "#cab2d6"}
33 | {"name": "Space of smoothing", "color": "#cab2d6"}
34 | {"name": "Volumes of interest obtained from", "color": "#cab2d6"}
35 | {"name": "Volumes of interest operational criteria", "color": "#cab2d6"}
36 | {"name": "Volumes of interest mask", "color": "#cab2d6"}
37 | {"name": "Volumes of interest procedure", "color": "#cab2d6"}
38 | {"name": "Volumes of interest", "color": "#cab2d6"}
39 | {"name": "Volumes of interest software", "color": "#cab2d6"}
40 | {"name": "Partial volume correction technique", "color": "#cab2d6"}
41 | {"name": "Partial volume correction assumptions", "color": "#cab2d6"}
42 | {"name": "Partial volume correction software", "color": "#cab2d6"}
43 | {"name": "Partial volume correction software version", "color": "#cab2d6"}
44 |
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/projects/cobidas/labels/pet/9_multiple_testings_labels.jsonl:
--------------------------------------------------------------------------------
1 | {"name": "Method", "color": "#6a3d9a"}
2 | {"name": "Threshold", "color": "#6a3d9a"}
3 | {"name": "Number of Tests", "color": "#6a3d9a"}
4 | {"name": "Alpha level", "color": "#6a3d9a"}
5 | {"name": "Power", "color": "#6a3d9a"}
6 | {"name": "Effect Size", "color": "#6a3d9a"}
7 | {"name": "Variance Assumption", "color": "#6a3d9a"}
8 | {"name": "Number of Subjects Needed", "color": "#6a3d9a"}
9 | {"name": "Link", "color": "#6a3d9a"}
10 |
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/projects/cobidas/query.txt:
--------------------------------------------------------------------------------
1 | (("resting state"[Abstract] OR "rsfMRI"[Abstract]) AND ("fMRI"[Abstract] OR "functional MRI"[Abstract] OR "functional magnetic resonance imaging"[Abstract]) AND ("2023/01/01"[PDat] : "2023/12/31"[PDat]))
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/projects/dynamic_functional_connectivity/README.md:
--------------------------------------------------------------------------------
1 | # Dynamic Functional Connectivity methods
2 |
3 | The focus of this project is to explore how methods for estimating Dynamic Functional Connectivity (DFC) are used in the literature.
4 |
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/projects/dynamic_functional_connectivity/datasets.json:
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1 | [{"url":"https://osf.io/download/td3cm/"}]
2 |
--------------------------------------------------------------------------------
/projects/dynamic_functional_connectivity/labels/labels.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "color": "#aec7e8",
4 | "name": "Sliding Window"
5 | },
6 | {
7 | "color": "#ffbb78",
8 | "name": "Clustering"
9 | },
10 | {
11 | "color": "#ff9896",
12 | "name": "Hidden Markov Model"
13 | },
14 | {
15 | "color": "#c5b0d5",
16 | "name": "Co-Activation Patterns"
17 | },
18 | {
19 | "color": "#aec7e8",
20 | "name": "Time-Frequency"
21 | },
22 | {
23 | "color": "#ffbb78",
24 | "name": "Window-less"
25 | },
26 | {
27 | "color": "#98df8a",
28 | "name": "others"
29 | },
30 | {
31 | "color": "#98df8a",
32 | "name": "not applied dFC"
33 | },
34 | {
35 | "color": "#ff9896",
36 | "name": "clinical application"
37 | },
38 | {
39 | "color": "#c5b0d5",
40 | "name": "cognitive application"
41 | },
42 | {
43 | "color": "#c49c94",
44 | "name": "other application"
45 | }
46 | ]
47 |
--------------------------------------------------------------------------------
/projects/dynamic_functional_connectivity/query.txt:
--------------------------------------------------------------------------------
1 | (dynamic functional connectivity[Abstract]) OR time-varying functional connectivity[Abstract]
2 |
--------------------------------------------------------------------------------
/projects/fmri_datasets/README.md:
--------------------------------------------------------------------------------
1 | # fMRI datasets
2 | Annotating information about datasets used
3 |
4 | ## Papers
5 |
6 | ### How the papers were obtained
7 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
8 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
9 |
10 | ``
11 |
12 | ### Where the full papers are stored
13 | The full pubget output can be found here on OSF: [https://osf.io/kfmdp/](https://osf.io/kfmdp/)
14 |
15 | ## Annotations
16 | ### File(s) being annotated:
17 | -
18 |
19 | ### Annotation labels:
20 | - `: `
21 | - `: `
22 |
23 | ### Labels found in other projects as well:
24 | - ``
25 |
26 | ### Instructions for annotators
27 | ``
28 |
29 |
--------------------------------------------------------------------------------
/projects/neuro-meta-analyses/README.md:
--------------------------------------------------------------------------------
1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
--------------------------------------------------------------------------------
/projects/neuro-meta-analyses/annotations/README.md:
--------------------------------------------------------------------------------
1 | # Annotations
--------------------------------------------------------------------------------
/projects/neuro-meta-analyses/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/neuro-meta-analyses/labels/README.md:
--------------------------------------------------------------------------------
1 | # Labels
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis-tables/README.md:
--------------------------------------------------------------------------------
1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis-tables/annotations/README.md:
--------------------------------------------------------------------------------
1 | # Annotations
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis-tables/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis-tables/labels/README.md:
--------------------------------------------------------------------------------
1 | # Labels
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis_manually-inspected-topics/README.md:
--------------------------------------------------------------------------------
1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis_manually-inspected-topics/annotations/README.md:
--------------------------------------------------------------------------------
1 | # Annotations
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis_manually-inspected-topics/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/neuro-meta-analysis_manually-inspected-topics/labels/README.md:
--------------------------------------------------------------------------------
1 | # Labels
--------------------------------------------------------------------------------
/projects/neurobridge_fmri/README.md:
--------------------------------------------------------------------------------
1 | # neurobridge_fmri
2 |
3 | This is a satellite project of the [NeuroBridge annotation project](https://github.com/NeuroBridge/Annotation-Project).
4 | The goal is to annotate the tasks used by task-based fMRI acquisitions described in the **NeuroBridge** papers.
5 |
6 | ## Papers
7 | ### How the papers were obtained
8 | The documents were copied from [this directory](https://github.com/NeuroBridge/Annotation-Project/tree/main/Phase_1/INPUT_Papers_for_Annotation/All_Papers_For_Annotation_From_Phase_2) in the **NeuroBridge** repository, and converted from WebAnno3 format to [labelbuddy](https://jeromedockes.github.io/labelbuddy/labelbuddy/current/)'s format using [labelutils](https://github.com/jeromedockes/labelutils).
9 |
10 | As the documents for this project do not come from a [pubget](https://neuroquery.github.io/pubget/pubget.html) download, they have less metadata and a different formatting (no headings or paragraph breaks) than documents in other projects in this repository.
11 | Also, a **pubget** dataset is not available for download with `/scripts/download_datasets.py`.
12 | But keeping the same version of the documents makes it possible to combine the annotations created here with those of the larger **NeuroBridge** project, down to the character positions, which trumps the other details.
13 |
14 | ## Annotations
15 | ### File(s) being annotated:
16 | - `/projects/neurobridge_fmri/documents/neurobridge_documents.jsonl`
17 |
18 | ### Annotation labels:
19 | - `: `
20 | - `: `
21 |
22 | ### Labels found in other projects as well:
23 | - ``
24 |
25 | ### Instructions for annotators
26 | ``
27 |
--------------------------------------------------------------------------------
/projects/neurobridge_fmri/annotations/README.md:
--------------------------------------------------------------------------------
1 | The annotations for this project come here.
2 |
3 | If exporting annotations manually from labelbuddy, in "export docs & annotations", select a file in this directory named "Your_Name.jsonl"
4 |
--------------------------------------------------------------------------------
/projects/neurobridge_fmri/labels/README.md:
--------------------------------------------------------------------------------
1 | The labels for this project come here.
2 |
3 | After creating some labels in labelbuddy, go to Import & Export > Export labels, and choose a file in this directory with the ".json" extension.
4 |
5 | You can also directly copy JSON files containing labels into this directory
6 |
--------------------------------------------------------------------------------
/projects/neurosynth_use/README.md:
--------------------------------------------------------------------------------
1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
--------------------------------------------------------------------------------
/projects/neurosynth_use/annotations/README.md:
--------------------------------------------------------------------------------
1 | # Annotations
--------------------------------------------------------------------------------
/projects/neurosynth_use/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/neurosynth_use/labels/README.md:
--------------------------------------------------------------------------------
1 | # Labels
--------------------------------------------------------------------------------
/projects/neurosynth_use/labels/labels.jsonl:
--------------------------------------------------------------------------------
1 | {"color":"#fce94f","name":"citing_study_using_neurosynth","shortcut_key":"c"}
2 | {"color":"#ffbb78","name":"using_neurosynth_figure","shortcut_key":"f"}
3 | {"color":"#98df8a","name":"using_neurosynth_data","shortcut_key":"d"}
4 | {"color":"#ff9896","name":"not_sure_what_they_used","shortcut_key":"n"}
5 | {"color":"#c5b0d5","name":"specific_thing_used","shortcut_key":"s"}
6 |
--------------------------------------------------------------------------------
/projects/nv_task/analysis/pmcids_task_annotated.txt:
--------------------------------------------------------------------------------
1 | 10028637
2 | 10031743
3 | 10129386
4 | 10147761
5 | 10318245
6 | 10458690
7 | 10597625
8 | 10615837
9 | 10634720
10 | 10637045
11 | 10641579
12 | 10656574
13 | 10791126
14 | 10990450
15 | 11063816
16 | 11078806
17 | 2241626
18 | 2686646
19 | 3078751
20 | 3445793
21 | 3555187
22 | 3825257
23 | 4110030
24 | 4115625
25 | 4179768
26 | 4374765
27 | 4386762
28 | 4398371
29 | 4440210
30 | 4488375
31 | 4517759
32 | 4526228
33 | 4530666
34 | 4547715
35 | 4914983
36 | 5090046
37 | 5243799
38 | 5324609
39 | 5404760
40 | 5552726
41 | 5607552
42 | 5662181
43 | 5662713
44 | 5716095
45 | 5776089
46 | 5895040
47 | 5973829
48 | 6024199
49 | 6037859
50 | 6102316
51 | 6137311
52 | 6175904
53 | 6200838
54 | 6219793
55 | 6303343
56 | 6331309
57 | 6344321
58 | 6382839
59 | 6391069
60 | 6397754
61 | 6411911
62 | 6463125
63 | 6699247
64 | 6715348
65 | 6821801
66 | 6831914
67 | 6847532
68 | 6969196
69 | 6969350
70 | 6970153
71 | 6981017
72 | 7018765
73 | 7235961
74 | 7377905
75 | 7426775
76 | 7562935
77 | 7582181
78 | 7649291
79 | 7689031
80 | 7836234
81 | 7859438
82 | 7913329
83 | 8104963
84 | 8107785
85 | 8318202
86 | 8342928
87 | 8421705
88 | 8443248
89 | 8564184
90 | 8597975
91 | 8764488
92 | 8857499
93 | 8927597
94 | 8975992
95 | 9148994
96 | 9189080
97 | 9202476
98 | 9261172
99 | 9308012
100 | 9454014
101 | 9729227
102 | 9837608
103 | 9910278
104 | 9949505
--------------------------------------------------------------------------------
/projects/nv_task/labels/neurovault-cobidas.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "color": "#aec7e8",
4 | "name": "TaskName",
5 | "shortcut_key": "t"
6 | },
7 | {
8 | "color": "#ffbb78",
9 | "name": "TaskDescription",
10 | "shortcut_key": "y"
11 | },
12 | {
13 | "color": "#ff9896",
14 | "name": "DesignType-RestingState",
15 | "shortcut_key": "r"
16 | },
17 | {
18 | "color": "#c5b0d5",
19 | "name": "Exclude-MetaAnalysis",
20 | "shortcut_key": "m"
21 | },
22 | {
23 | "color": "#9a9996",
24 | "name": "Unsure",
25 | "shortcut_key": "u"
26 | },
27 | {
28 | "color": "#c0bfbc",
29 | "name": "None",
30 | "shortcut_key": "n"
31 | },
32 | {
33 | "color": "#c5b0d5",
34 | "name": "Condition",
35 | "shortcut_key": "c"
36 | },
37 | {
38 | "color": "#98df8a",
39 | "name": "ContrastDefinition",
40 | "shortcut_key": "d"
41 | },
42 | {
43 | "color": "#bb83c6",
44 | "name": "Exclude-Review",
45 | "shortcut_key": "v"
46 | },
47 | {
48 | "color": "#f7b6d2",
49 | "name": "Modality-StructuralMRI",
50 | "shortcut_key": "s"
51 | },
52 | {
53 | "color": "#c64600",
54 | "name": "Modality-DiffusionMRI"
55 | },
56 | {
57 | "color": "#98df8a",
58 | "name": "Modality-PET"
59 | },
60 | {
61 | "color": "#98df8a",
62 | "name": "Modality-fMRI-BOLD",
63 | "shortcut_key": "f"
64 | },
65 | {
66 | "color": "#aec7e8",
67 | "name": "Modality-MRS"
68 | },
69 | {
70 | "color": "#ffbb78",
71 | "name": "Modality-EEG"
72 | },
73 | {
74 | "color": "#98df8a",
75 | "name": "Modality-fMRI-CBF"
76 | }
77 | ]
78 |
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/annotations/README.md:
--------------------------------------------------------------------------------
1 | # Annotations
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/keep-only-docs-w-ma-in-title.py:
--------------------------------------------------------------------------------
1 | from pathlib import Path
2 |
3 | import pandas as pd
4 |
5 | folder = Path(__file__).resolve().parent
6 | files = sorted(folder.glob('documents*.jsonl'))
7 |
8 |
9 | for file in files:
10 | df = pd.read_json(file, lines=True)
11 | for ind, row in df.iterrows():
12 | print(ind, end='\r')
13 | title = row["list_title"].lower()
14 | if 'meta-analy' in title:
15 | continue
16 | elif 'meta analy' in title:
17 | continue
18 | else:
19 | df.drop(ind, inplace=True)
20 |
21 | df.to_json(file.parent / f'{file.stem}-ma-in-title.jsonl', orient='records', lines=True)
22 |
23 | d = 1
24 |
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/labels/README.md:
--------------------------------------------------------------------------------
1 | # Labels
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/parkinsons/README.md:
--------------------------------------------------------------------------------
1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/parkinsons/annotations/README.md:
--------------------------------------------------------------------------------
1 | # Annotations
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/parkinsons/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/old_review-neuro-meta-analyses/parkinsons/labels/README.md:
--------------------------------------------------------------------------------
1 | # Labels
--------------------------------------------------------------------------------
/projects/parkinsons/README.md:
--------------------------------------------------------------------------------
1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
--------------------------------------------------------------------------------
/projects/parkinsons/annotations/README.md:
--------------------------------------------------------------------------------
1 | # Annotations
--------------------------------------------------------------------------------
/projects/parkinsons/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/parkinsons/documents/README.md:
--------------------------------------------------------------------------------
1 | # Documents
2 |
--------------------------------------------------------------------------------
/projects/parkinsons/documents/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
--------------------------------------------------------------------------------
/projects/parkinsons/labels/README.md:
--------------------------------------------------------------------------------
1 | # Labels
--------------------------------------------------------------------------------
/projects/participant_demographics/README.md:
--------------------------------------------------------------------------------
1 | # participant_demographics
2 |
3 | Annotating information about participants: count, sex, age, and diagnosis.
4 |
5 |
--------------------------------------------------------------------------------
/projects/participant_demographics/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "url": "https://osf.io/download/kfmdp/"
4 | },
5 | {
6 | "url": "https://osf.io/download/meya4/",
7 | "comment": "autism_mri documents"
8 | },
9 | {
10 | "url": "https://osf.io/download/td3cm/",
11 | "comment": "dynamic_functional_connectivity documents"
12 | }
13 | ]
14 |
--------------------------------------------------------------------------------
/projects/participant_demographics/labels/demographics_labels.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "color": "#00ffff",
4 | "name": "healthy",
5 | "shortcut_key": "h"
6 | },
7 | {
8 | "color": "#fff700",
9 | "name": "patients",
10 | "shortcut_key": "p"
11 | },
12 | {
13 | "color": "#6bf700",
14 | "name": "female",
15 | "shortcut_key": "f"
16 | },
17 | {
18 | "color": "#ff65de",
19 | "name": "male",
20 | "shortcut_key": "m"
21 | },
22 | {
23 | "color": "#e6c68f",
24 | "name": "count",
25 | "shortcut_key": "c"
26 | },
27 | {
28 | "color": "#d1cbff",
29 | "name": "age range",
30 | "shortcut_key": "r"
31 | },
32 | {
33 | "color": "#d1cbff",
34 | "name": "age minimum",
35 | "shortcut_key": "j"
36 | },
37 | {
38 | "color": "#d1cbff",
39 | "name": "age maximum",
40 | "shortcut_key": "k"
41 | },
42 | {
43 | "color": "#d1cbff",
44 | "name": "age mean",
45 | "shortcut_key": "u"
46 | },
47 | {
48 | "color": "#d1cbff",
49 | "name": "age median",
50 | "shortcut_key": "n"
51 | },
52 | {
53 | "color": "#fbc5ff",
54 | "name": "diagnosis",
55 | "shortcut_key": "d"
56 | },
57 | {
58 | "color": "#c4c4c4",
59 | "name": "discard",
60 | "shortcut_key": "x"
61 | }
62 | ]
63 |
--------------------------------------------------------------------------------
/projects/template_project/README.md:
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1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
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/projects/template_project/annotations/README.md:
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1 | # Annotations
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/projects/template_project/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
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/projects/template_project/labels/README.md:
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1 | # Labels
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/projects/tracking_open_datasets/README.md:
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1 | # ``
2 |
3 | `<1-2 sentences describing the project>`
4 |
5 | ## Papers
6 |
7 | ### How the papers were obtained
8 | Typically with [pubget](https://neuroquery.github.io/pubget/pubget.html).
9 | We recommend invoking `pubget` with the `--query_file` option, and storing a copy of the query file in the project's directory, or including a copy in the `README.md`.
10 |
11 | ``
12 |
13 | ### Where the full papers are stored
14 |
15 | Typically on [OSF](https://osf.io/).
16 | Please also add a `documents/datasets.json` file containing the URL where the full `pubget` dataset can be downloaded, that looks like:
17 | ```
18 | [
19 | {
20 | "url": "https://osf.io/download/<...>/"
21 | }
22 | ]
23 | ```
24 |
25 | ``
26 |
27 |
28 | ## Annotations
29 | ### File(s) being annotated:
30 | - `/projects//documents/.jsonl`
31 | - corresponding file in the pubget output:
32 | - `/subset_allArticles_labelbuddyData/.jsonl`
33 | - `/projects//documents/.jsonl`
34 | - corresponding file in the pubget output:
35 | - `/subset_allArticles_labelbuddyData/.jsonl`
36 |
37 | ### Annotation labels:
38 | - `: `
39 | - `: `
40 |
41 | ### Labels found in other projects as well:
42 | - ``
43 |
44 | ### Instructions for annotators
45 | ``
46 |
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/projects/tracking_open_datasets/annotations/README.md:
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1 | # Annotations
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/projects/tracking_open_datasets/datasets.json:
--------------------------------------------------------------------------------
1 | [
2 |
3 | ]
4 |
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/projects/tracking_open_datasets/labels/README.md:
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1 | # Labels
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/projects/tracking_open_datasets/labels/labels.jsonl:
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1 | {"color":"#f66151","name":"did not use large open dataset"}
2 | {"color":"#f9f06b","name":"ds - adolescent brain cognitive development"}
3 | {"color":"#f9f06b","name":"ds - amsterdam open mri collection"}
4 | {"color":"#f9f06b","name":"ds - baby connectome project"}
5 | {"color":"#f9f06b","name":"ds - bold5000"}
6 | {"color":"#f9f06b","name":"ds - camcan"}
7 | {"color":"#f9f06b","name":"ds - genomics superstruct project"}
8 | {"color":"#f9f06b","name":"ds - human connectome project","shortcut_key":"h"}
9 | {"color":"#f9f06b","name":"ds - human brain charting"}
10 | {"color":"#f9f06b","name":"ds - individual brain charting"}
11 | {"color":"#f9f06b","name":"ds - midnight scan club"}
12 | {"color":"#f9f06b","name":"ds - myconnectome"}
13 | {"color":"#f9f06b","name":"ds - narratives data"}
14 | {"color":"#f9f06b","name":"ds - natural scenes dataset"}
15 | {"color":"#f9f06b","name":"ds - neuromod"}
16 | {"color":"#f9f06b","name":"ds - open access series imaging studies"}
17 | {"color":"#f9f06b","name":"ds - southwest university longitudinal imaging multimodal"}
18 | {"color":"#f9f06b","name":"ds - studyforrest"}
19 | {"color":"#f9f06b","name":"ds - ukbb"}
20 | {"color":"#77767b","name":"stopped here"}
21 | {"color":"#aec7e8","name":"not sure"}
22 | {"color":"#aaff7f","name":"new dataset"}
23 | {"color":"#ffaa7f","name":"used hcp pipeline"}
24 | {"color":"#ffaa7f","name":"used hcp seed"}
25 | {"color":"#ffaa7f","name":"used hcp atlas"}
26 | {"color":"#ffaa7f","name":"used hcp stimuli"}
27 | {"color":"#ffaa7f","name":"used hcp acquisition parameters"}
28 | {"color":"#ffaa7f","name":"used hcp protocol"}
29 |
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/scripts/checkin_docs.py:
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1 | #! /usr/bin/env python3
2 | # This script is used to centralize the documents in the projects
3 | # Documents that are stores in {project_name}/documents/ and annotated are
4 | # copies to the central location in documents/
5 |
6 | from labelrepo.documents import checkin_docs
7 | import argparse
8 |
9 | if __name__ == '__main__':
10 | parser = argparse.ArgumentParser()
11 | parser.add_argument('--project', help='Project name to centralize')
12 | args = parser.parse_args()
13 |
14 | checkin_docs(args.project)
15 |
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/scripts/checkout_docs.py:
--------------------------------------------------------------------------------
1 |
2 | #! /usr/bin/env python3
3 |
4 | # This script is used to make a temporary copy the documents from the central repository to the projects directory using the ids.json file
5 |
6 | import argparse
7 | from labelrepo.documents import checkout_docs
8 |
9 | if __name__ == '__main__':
10 | parser = argparse.ArgumentParser(
11 | description="Copy documents for a project from the central repository."
12 | )
13 | parser.add_argument("project_name")
14 | parser.add_argument("--batch_size", type=int, default=200)
15 | parser.add_argument("--pmcids_file", type=str, default=None)
16 | parser.add_argument("--prefix", type=str, default=None)
17 | args = parser.parse_args()
18 |
19 | checkout_docs(args.project_name, args.batch_size, args.pmcids_file, args.prefix)
20 |
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/scripts/download_datasets.py:
--------------------------------------------------------------------------------
1 | #! /usr/bin/env python3
2 |
3 | import argparse
4 | import pathlib
5 | import sys
6 |
7 | from labelrepo import datasets
8 |
9 | parser = argparse.ArgumentParser(
10 | description="Download full pubget output for a project."
11 | )
12 | parser.add_argument("project_name")
13 | args = parser.parse_args()
14 |
15 | # in case someone passes the path to the project instead of just the name
16 | # (convenient with tab completion)
17 | project_name = pathlib.Path(args.project_name).name
18 |
19 | stdout = sys.stdout
20 | try:
21 | sys.stdout = sys.stderr
22 | print(f"Fetching datasets for project: {project_name}")
23 | project_datasets = datasets.get_project_datasets(project_name)
24 | finally:
25 | sys.stdout = stdout
26 | for directory in project_datasets:
27 | print(directory)
28 |
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/scripts/labelrepo:
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1 | ../analysis/labelrepo/src/labelrepo
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/scripts/make_book.sh:
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1 | #! /bin/bash
2 |
3 | export LABELREPO_CSS_AVAILABLE=1
4 | export LABELREPO_PROJECTS_BASE_URL="./projects/"
5 | export LABELREPO_PROJECTS_HTML_EXTENSION=1
6 | export LABELREPO_PROJECTS_URL_ESCAPE_DOT=1
7 |
8 | jupyter-book build -W analysis/book
9 |
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/scripts/make_database.py:
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1 | #! /usr/bin/env python3
2 |
3 | from labelrepo import database
4 |
5 | database.make_database()
6 |
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/scripts/make_participants_csv.py:
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1 | #! /usr/bin/env python3
2 |
3 | import pandas as pd
4 |
5 | from labelrepo import repo, database
6 | from labelrepo.projects.participant_demographics import (
7 | get_participant_demographics,
8 | )
9 |
10 | subgroups = get_participant_demographics()
11 |
12 | docs_info = pd.read_sql(
13 | "select pmcid, publication_year, title from document "
14 | "where title is not null",
15 | database.get_database_connection(),
16 | )
17 | subgroups = subgroups.merge(docs_info, on="pmcid").drop_duplicates(
18 | subset=("pmcid", "project_name", "annotator_name")
19 | )
20 |
21 | out_dir = repo.repo_root() / "analysis" / "book" / "assets" / "generated"
22 | out_dir.mkdir(exist_ok=True, parents=True)
23 | subgroups.to_csv(out_dir / "participant_groups.csv", index=False)
24 |
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/scripts/make_repo_stats_figure.py:
--------------------------------------------------------------------------------
1 | #! /usr/bin/env python3
2 |
3 | import pandas as pd
4 | import plotly.graph_objects as go
5 |
6 | from labelrepo import database, repo
7 |
8 | connection = database.get_database_connection()
9 |
10 | df = pd.read_sql(
11 | """
12 | select * from (select project_name, count(distinct doc_id) as documents,
13 | count(distinct label_id) as labels,
14 | count(distinct annotator_name) as annotators,
15 | count(*) as annotations from
16 | annotation group by project_name order by documents desc)
17 | union all
18 | select name, 0 as documents, 0 as labels, 0 as annotators,
19 | 0 as annotations from project
20 | where name not in (select distinct project_name from annotation)
21 | union all
22 | select 'Total' as project_name, count(distinct doc_id) as documents,
23 | count(distinct label_id) as labels,
24 | count(distinct annotator_name) as annotators,
25 | count(*) as annotations
26 | from annotation;
27 | """,
28 | connection,
29 | )
30 | df.iloc[-1] = list(map("{}".format, df.iloc[-1]))
31 |
32 | fig = go.Figure(
33 | data=[
34 | go.Table(
35 | columnwidth=[1.0, 0.5, 0.5, 0.5, 0.5],
36 | header=dict(
37 | values=[f"{c.capitalize()}" for c in df.columns],
38 | align="left",
39 | font=dict(size=16, color="black"),
40 | fill_color="#eeeeee",
41 | line_color="#dddddd",
42 | ),
43 | cells=dict(
44 | values=df.values.T,
45 | align="left",
46 | height=35,
47 | font=dict(size=16, color="black"),
48 | fill_color="white",
49 | line_color="#dddddd",
50 | ),
51 | )
52 | ]
53 | )
54 |
55 | fig.update_layout(width=800, height=35 * (df.shape[0] - 1) + 50 * 2)
56 | fig.update_layout(margin=dict(l=10, r=10, b=10, t=10))
57 |
58 | fig_dir = repo.repo_root() / "analysis" / "book" / "assets" / "generated"
59 | fig_dir.mkdir(exist_ok=True, parents=True)
60 | fig.write_image(fig_dir / "repo_stats.svg")
61 |
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/scripts/participants_report.py:
--------------------------------------------------------------------------------
1 | #! /usr/bin/env python3
2 |
3 | from labelrepo import database
4 | from labelrepo.projects import participant_demographics
5 |
6 | database.make_database()
7 | participant_demographics.report_command()
8 |
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/scripts/start_project.py:
--------------------------------------------------------------------------------
1 | #! /usr/bin/env python3
2 |
3 | import argparse
4 | import pathlib
5 | import subprocess
6 | import sys
7 |
8 | from labelrepo import repo, glob_json
9 |
10 |
11 | def _start_project(
12 | project_dir: pathlib.Path, annotator_name: str
13 | ) -> pathlib.Path:
14 | db_path = project_dir / "annotations" / f"{annotator_name}.labelbuddy"
15 | if db_path.is_file():
16 | return db_path
17 | db_path.parent.mkdir(exist_ok=True)
18 | args = ["labelbuddy", str(db_path)]
19 | for labels_file in glob_json(project_dir / "labels"):
20 | args.extend(["--import-labels", str(labels_file)])
21 | for docs_file in sorted((project_dir / "documents").glob("*.jsonl")):
22 | args.extend(["--import-docs", str(docs_file)])
23 | # if this annotator has already exported annotations but the db was removed
24 | # for some reason, we import the annotations in the new db.
25 | for extension in ".json", ".jsonl":
26 | exported_annotations = db_path.with_suffix(extension)
27 | if exported_annotations.is_file():
28 | args.extend(["--import-docs", str(exported_annotations)])
29 | subprocess.run(args)
30 | return db_path
31 |
32 |
33 | if __name__ == "__main__":
34 | parser = argparse.ArgumentParser()
35 | parser.add_argument("project_name", type=str)
36 | parser.add_argument("--annotator", type=str, default=None)
37 | args = parser.parse_args()
38 |
39 | # in case someone passes the path to the project instead of just the name
40 | # (convenient with tab completion)
41 | project_name = pathlib.Path(args.project_name).name
42 | project_path = repo.repo_root() / "projects" / project_name
43 | if not project_path.is_dir():
44 | print(f"Project {project_path} not found.")
45 | sys.exit(1)
46 | db_path = _start_project(
47 | project_path,
48 | repo.annotator_name(args.annotator),
49 | )
50 | print(
51 | f"\nYour .labelbuddy file for '{project_path.name}' is:\n\n{db_path}"
52 | )
53 |
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/scripts/watch_participants.py:
--------------------------------------------------------------------------------
1 | #! /usr/bin/env python3
2 |
3 | import asyncio
4 | import argparse
5 | import sys
6 | import threading
7 | import time
8 | import webbrowser
9 |
10 | from labelrepo import repo
11 | from labelrepo.projects.participant_demographics import (
12 | watch_participants,
13 | get_live_report_path,
14 | )
15 |
16 | parser = argparse.ArgumentParser()
17 | parser.add_argument("labelbuddy_database", type=str, nargs="?", default=None)
18 | parser.add_argument("-p", "--port", type=int, default=8765)
19 | parser.add_argument("--no_browser", action="store_true")
20 | args = parser.parse_args()
21 |
22 | if args.labelbuddy_database is not None:
23 | labelbuddy_database = args.labelbuddy_database
24 | else:
25 | labelbuddy_database = repo.last_modified_labelbuddy_file()
26 | if labelbuddy_database is None:
27 | print("Could not find a labelbuddy file to watch.")
28 | sys.exit(1)
29 | print(
30 | "Using the most recently modified labelbuddy file: "
31 | f"{labelbuddy_database}\n"
32 | "To watch a different file, pass its path on the command line.\n"
33 | )
34 |
35 | port = args.port
36 | report_path = get_live_report_path(labelbuddy_database, port)
37 | start_time = time.time()
38 |
39 |
40 | def start_browser():
41 | for _ in range(8):
42 | time.sleep(0.5)
43 | try:
44 | m_time = report_path.stat().st_mtime
45 | except FileNotFoundError:
46 | continue
47 | if m_time > start_time:
48 | try:
49 | webbrowser.open(report_path.as_uri())
50 | except Exception:
51 | pass
52 | return
53 |
54 |
55 | if not args.no_browser:
56 | opener = threading.Thread(target=start_browser)
57 | opener.start()
58 |
59 | try:
60 | asyncio.run(watch_participants(labelbuddy_database, port))
61 | except KeyboardInterrupt:
62 | print()
63 | sys.exit(0)
64 |
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/shared_labels/participants.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "color": "#00ffff",
4 | "name": "healthy",
5 | "shortcut_key": "h"
6 | },
7 | {
8 | "color": "#fff700",
9 | "name": "patients",
10 | "shortcut_key": "p"
11 | },
12 | {
13 | "color": "#6bf700",
14 | "name": "female",
15 | "shortcut_key": "f"
16 | },
17 | {
18 | "color": "#ff65de",
19 | "name": "male",
20 | "shortcut_key": "m"
21 | },
22 | {
23 | "color": "#e6c68f",
24 | "name": "count",
25 | "shortcut_key": "c"
26 | },
27 | {
28 | "color": "#d1cbff",
29 | "name": "age range",
30 | "shortcut_key": "r"
31 | },
32 | {
33 | "color": "#d1cbff",
34 | "name": "age minimum",
35 | "shortcut_key": "j"
36 | },
37 | {
38 | "color": "#d1cbff",
39 | "name": "age maximum",
40 | "shortcut_key": "k"
41 | },
42 | {
43 | "color": "#d1cbff",
44 | "name": "age mean",
45 | "shortcut_key": "u"
46 | },
47 | {
48 | "color": "#d1cbff",
49 | "name": "age median",
50 | "shortcut_key": "n"
51 | },
52 | {
53 | "color": "#fbc5ff",
54 | "name": "diagnosis",
55 | "shortcut_key": "d"
56 | }
57 | ]
58 |
--------------------------------------------------------------------------------