├── .gitignore ├── README.md ├── bfi-dataset ├── README.md ├── analysis │ └── codebook.Rmd ├── data │ ├── processed_data │ │ ├── README.md │ │ └── bfi-codebook_data.tsv │ └── raw_data │ │ └── study-bfi_data.csv ├── dataset_description.json └── results │ └── codebook.html ├── complex-metadata-dataset ├── README.md ├── data │ └── study-template_data.csv └── dataset_description.json ├── example_files └── dataset_description.json ├── face-body ├── README.md ├── data │ ├── gender-female_type-bodies_data.csv │ ├── gender-female_type-faces_data.csv │ ├── gender-female_type-ratings_data.csv │ ├── gender-female_type-stimuli_data.csv │ ├── gender-male_type-bodies_data.csv │ ├── gender-male_type-faces_data.csv │ ├── gender-male_type-ratings_data.csv │ └── gender-male_type-stimuli_data.csv └── dataset_description.json ├── img ├── commit-changes.png ├── create-branch.png ├── pull-request-form.png ├── pull-request-prompt.png ├── pull-request-result.png └── upload-files.png ├── informative-mistakes-dataset ├── data │ ├── non_csv_file.txt │ ├── study-validname_type-pdf_data.csv │ ├── study-yarncolor_data.csv │ ├── study-yarncolor_type-badnames_data.csv │ ├── subdir │ │ └── subdir │ │ │ └── study-yarn_location-subdir_data.csv │ └── wrong-name-structure.csv └── dataset_description.json ├── macrophage-conditioning ├── README.md ├── data │ ├── primary_data │ │ ├── IL-6 ELISA 090603.pzf │ │ ├── IL-6 ELISA 090603.pzfx │ │ ├── figures │ │ │ ├── fig 1 il6_log.eps │ │ │ ├── fig 1 il6_log.jpg │ │ │ ├── fig 2 il6 log.eps │ │ │ └── fig 2 il6 log.jpg │ │ └── makrofag_parings_n_evocation_raw.txt │ └── study-1_data.csv └── dataset_description.json ├── mistakes-corrected-dataset ├── data │ ├── study-yarncolor_data.csv │ ├── study-yarncolor_file-badnames_data.csv │ ├── study-yarncolor_file-noncsvfile_data.csv │ ├── study-yarncolor_file-wrongname_data.csv │ └── subdir │ │ └── subdir │ │ └── study-yarn_location-subdir_data.csv └── dataset_description.json ├── nih-reviews ├── Codebook.xlsx ├── README.md ├── data │ └── study-nih_data.csv ├── dataset_description.json ├── nih_data.csv └── raw_data │ └── nih_data.tsv ├── object-orientation ├── LAB_processing.R ├── OrientationCrossLanguages_2018PSA_PP_1.2.0.osexp ├── OrientationCrossLanguages_2018PSA_SP_1.2.0.osexp ├── README.md ├── STI_LISTS.xls ├── data │ ├── PP │ │ ├── subject-1_data.csv │ │ ├── subject-2_data.csv │ │ ├── subject-3_data.csv │ │ └── subject-4_data.csv │ ├── SP │ │ ├── subject-1_data.csv │ │ ├── subject-2_data.csv │ │ ├── subject-3_data.csv │ │ └── subject-4_data.csv │ ├── num-100_conda-PP_data.csv │ ├── num-100_conda-SP_condb-M_data.csv │ └── num-100_conda-SP_condb-V_data.csv └── dataset_description.json ├── safi-survey ├── README.md ├── data │ └── study-safisurvey_data.csv └── dataset_description.json └── template-dataset ├── README.md ├── data └── study-yarncolor_data.csv └── dataset_description.json /.gitignore: -------------------------------------------------------------------------------- 1 | # History files 2 | .Rhistory 3 | .Rapp.history 4 | 5 | # Session Data files 6 | .RData 7 | .RDataTmp 8 | 9 | # User-specific files 10 | .Ruserdata 11 | 12 | # Example code in package build process 13 | *-Ex.R 14 | 15 | # Output files from R CMD build 16 | /*.tar.gz 17 | 18 | # Output files from R CMD check 19 | /*.Rcheck/ 20 | 21 | # RStudio files 22 | .Rproj.user/ 23 | 24 | # produced vignettes 25 | vignettes/*.html 26 | vignettes/*.pdf 27 | 28 | # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 29 | .httr-oauth 30 | 31 | # knitr and R markdown default cache directories 32 | *_cache/ 33 | /cache/ 34 | 35 | # Temporary files created by R markdown 36 | *.utf8.md 37 | *.knit.md 38 | 39 | # R Environment Variables 40 | .Renviron 41 | 42 | # pkgdown site 43 | docs/ 44 | 45 | # translation temp files 46 | po/*~ 47 | 48 | # RStudio Connect folder 49 | rsconnect/ 50 | 51 | # Mac OS 52 | 53 | # MacOS 54 | .DS_Store 55 | */.DS_Store 56 | */*/.DS_Store 57 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Example Datasets for Psych-DS 2 | 3 | This is a gallery of public datasets which have been formatted* according to the [Psych-DS data standard](https://psychds-docs.readthedocs.io/en/latest/guides/1_getting_started/). They primarily come from various subfields of psychology, and at the moment (early 2025) are primarily either versions of datasets that are freely available elsewhere online, or templates designed to help people create new Psych-DS datasets. 4 | 5 | You can browse these datasets to learn more about how Psych-DS works, download one to try out the [Psych-DS in-browser validator](https://psych-ds.github.io/validator/), or use them as test cases to develop tools that are designed to work with Psych-DS data. 6 | 7 | Anyone can contribute to this project! Please have a look at the [Psych-DS code of conduct](https://github.com/psych-ds/psych-DS/blob/master/CODE_OF_CONDUCT.md) for our community guidelines, and feel free to email Melissa Kline Struhl (mekline@mit.edu) with any questions about these datasets or Psych-DS more generally. 8 | 9 | **Status update**: Updated 2025-04-10 in preparation for wider Psych-DS release. 10 | 11 | **Note that there is one dataset in this repository that will *not* successfully validate: `informative-mistakes-dataset/`. That one is a small example designed to show various kinds of mistakes that will cause a dataset to fail validation. Check out its companion `mistakes-corrected-dataset/` for a version that passes validation!* 12 | 13 | [List of datasets](#anchor-1) 14 | 15 | [How to contribute a dataset](#anchor-2) 16 | 17 | [More about Psych-DS and additional resources](#anchor-3) 18 | 19 | ## List of datasets 20 | 21 | #### Datasets designed for testing the specification 22 | 23 | * Template, Complex-Metadata, Informative-Mistakes and Mistakes-Corrected datasets - Melissa Kline Struhl, updated by Brian Leonard 24 | 25 | #### Real datasets contributed by researchers 26 | 27 | * NIH reviews - Patrick S. Forscher 28 | * Faces and Bodies - Lisa DeBruine 29 | * BFI - Ioanna Iro Eleftheriadou 30 | * Object Orientation - Sau-Chin Chen 31 | * Macrophage Conditioning - Love Ahnström 32 | * Safi Survey - Eduard Klapwijk 33 | 34 | ## How to contribute a dataset 35 | 36 | ### Step 1: Prepare Your Dataset 37 | 38 | First, you'll need to create a properly structured dataset following the Psych-DS format. You can find detailed instructions for how to do this in the [Getting Started guide](https://psychds-docs.readthedocs.io/en/latest/guides/1_getting_started/) from the Psych-DS docs. 39 | 40 | Within the metadata file for your dataset, make sure to include a note in the description field with the date that you'll be uploading this dataset to our repository. It is also best to fill the Author field with information about yourself and your fellow authors. If you are not the author of the dataset, you can include your identity in the metadata by adding yourself in the "sdPublisher" field. 41 | 42 | Here's a simplified example metadata file: 43 | 44 | ``` 45 | { 46 | "@context": "https://schema.org/", 47 | "@type": "Dataset", 48 | "name": "Visual Perception Study 2023", 49 | "description": "This dataset contains results from a visual perception experiment conducted in 2023. 50 | Uploaded to the Psych-DS example repository on date XYZ", 51 | "author": { 52 | "@type": "Person", 53 | "givenName": "John", 54 | "familyName: "Doe", 55 | "id": "0000-0002-1825-0097" // this is an ORCID ID, e.g. 56 | }, 57 | "sdPublisher": { 58 | "@type": "Person", 59 | "givenName": "Jane", 60 | "familyName: "Doe", 61 | "id": "0000-0002-3245-1127" // this is an ORCID ID, e.g. 62 | }, 63 | "variableMeasured": [ 64 | "participant_id", 65 | "trial_number", 66 | "stimulus_type", 67 | "response_time_ms", 68 | "accuracy" 69 | ] 70 | } 71 | ``` 72 | 73 | ### Step 2: Validate Your Dataset 74 | 75 | Before submitting, you must validate your dataset using the official Psych-DS validator: 76 | 77 | 1. Visit the [Psych-DS Validator](https://psych-ds.github.io/validator/) 78 | 2. Select your dataset directory 79 | 3. Ensure that your dataset passes all validation checks 80 | 4. If there are any errors, fix them and re-validate until your dataset passes all checks 81 | 82 | ### Step 3: Submit Your Dataset via Pull Request 83 | 84 | Once your dataset is validated, you can submit it to our repository yourself by following the instructions below. 85 | 86 | If you run into trouble, *please* feel free to open an issue on this repository (see the "Issues" tab at the top of this page) and we can help you out!! 87 | 88 | #### Option A: Using GitHub in the Browser 89 | 90 | 1. Fork the Psych-DS example datasets repository 91 | 92 | sc1 93 | 94 | sc2 95 | 96 | 2. Navigate to your forked repository (it should send you there automatically, but otherwise it can be found under your account's repository list with the name "example-datasets" 97 | 98 | sc3 99 | 100 | 3. Click "Add file" → "Upload files" 101 | 102 | sc4 103 | 104 | 4. Drag and drop your entire dataset directory or use the file selector 105 | 5. Add a commit message explaining what dataset you're adding 106 | 6. Click "Commit changes" 107 | 7. Edit the Readme file to include yourself in the contributors section and click "Commit changes" 108 | 109 | sc5 110 | 111 | sc6 112 | 113 | 8. Navigate to the "Pull requests" tab and click "New pull request" 114 | 115 | sc7 116 | 117 | Then: 118 | 119 | sc8 120 | 121 | 9. Select "base repository: psych-ds/example-datasets" and "head repository: your-username/example-datasets" 122 | 123 | sc10 124 | 125 | 10. Click "Create pull request" 126 | 11. Add a title and description for your PR 127 | 12. Click "Create pull request" 128 | 129 | #### Option B: Using Git on the Command Line 130 | 131 | 1. Fork the repository on GitHub 132 | 2. Clone your forked repository: 133 | ``` 134 | git clone https://github.com/your-username/example-datasets.git 135 | ``` 136 | 3. Copy your dataset directory into the repository 137 | 4. Add your name and the name of your dataset the contributors list of the README file 138 | 5. Add your changes: 139 | ``` 140 | git add . 141 | ``` 142 | 6. Commit your changes: 143 | ``` 144 | git commit -m "Add dataset: Visual Perception Study 2023" 145 | ``` 146 | 7. Push to your fork: 147 | ``` 148 | git push origin main 149 | ``` 150 | 8. Go to GitHub and create a pull request from your new branch 151 | 152 | ### What Happens After Submission? 153 | 154 | After submitting your PR: 155 | 1. Our team will review your dataset 156 | 2. We may request changes if needed 157 | 3. Once approved, your dataset will be merged into the main repository 158 | 4. Your name will appear in the contributors list 159 | 160 | ## More about Psych-DS/additional resources 161 | 162 | Psych-DS is a community data standard for research in psychology and other behavioral sciences, which provides a flexible set of conventions for formatting and documenting scientific datasets. It is heavily inspired by the [Brain Image Data Structure (BIDS)](https://bids.neuroimaging.io/) standard for fMRI data. 163 | 164 | Psych-DS provides a simple and easy-to-adopt standard for organizing data in the psychological and behavioral sciences, which aims to help researchers satisfy [FAIR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/) principles for data sharing. 165 | 166 | * Return to the [Psych-DS homepage](https://psych-ds.github.io/). 167 | 168 | * Browser-based [Psych-DS Validator](https://psych-ds.github.io/validator/)(!) 169 | 170 | * [Psych-DS Documentation](https://psychds-docs.readthedocs.io/en/latest/guides/1_getting_started/) 171 | 172 | * [Article on the Schema.org Dataset structure](https://developers.google.com/search/docs/data-types/dataset). Click 'See Markup' under Examples for a pre-populated set of dataset JSON you can play with and validate against! 173 | 174 | 175 | 176 | 177 | 178 | -------------------------------------------------------------------------------- /bfi-dataset/README.md: -------------------------------------------------------------------------------- 1 | # README - Bfi dataset 2 | 3 | Bfi data in Psych-DS compliant dataset form 4 | 5 | 6 | - A list of what files can be found in this folder and what they are 7 | - raw data 8 | - processed data 9 | - analysis 10 | - results 11 | - json sidecar 12 | 13 | -------------------------------------------------------------------------------- /bfi-dataset/analysis/codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Codebook" 3 | output: 4 | html_document: 5 | toc: true 6 | toc_depth: 4 7 | toc_float: true 8 | code_folding: 'hide' 9 | self_contained: true 10 | pdf_document: 11 | toc: yes 12 | toc_depth: 4 13 | latex_engine: xelatex 14 | --- 15 | 16 | Here, we're just setting a few options. 17 | 18 | ```{r setup} 19 | knitr::opts_chunk$set( 20 | warning = TRUE, # show warnings during codebook generation 21 | message = TRUE, # show messages during codebook generation 22 | error = TRUE, # do not interrupt codebook generation in case of errors, 23 | # usually better for debugging 24 | echo = TRUE # show R code 25 | ) 26 | ggplot2::theme_set(ggplot2::theme_bw()) 27 | pander::panderOptions("table.split.table", Inf) 28 | ``` 29 | 30 | Now, we're preparing our data for the codebook. 31 | 32 | ```{r prepare_codebook} 33 | library(codebook) 34 | library(dplyr) 35 | library(tidyverse) 36 | library(labelled) 37 | library(jsonlite) 38 | codebook_data <- rio::import("https://osf.io/s87kd/download", "csv") 39 | bfi_data <- rio::import("https://osf.io/s87kd/download", "csv") 40 | dict <- rio::import("https://osf.io/cs678/download", "csv") 41 | 42 | var_label(codebook_data) <- dict %>% dplyr::select(variable, label) %>% dict_to_list() 43 | 44 | val_labels(codebook_data$gender) <- c("male" = 1, "female" = 2) 45 | val_labels(codebook_data$education) <- c("in high school" = 1, 46 | "finished high school" = 2, 47 | "some college" = 3, 48 | "college graduate" = 4, 49 | "graduate degree" = 5) 50 | 51 | add_likert_labels <- function(x) { 52 | val_labels(x) <- c("Very Inaccurate" = 1, 53 | "Moderately Inaccurate" = 2, 54 | "Slightly Inaccurate" = 3, 55 | "Slightly Accurate" = 4, 56 | "Moderately Accurate" = 5, 57 | "Very Accurate" = 6) 58 | x 59 | } 60 | 61 | likert_items <- dict %>% filter(Big6 != "") %>% pull(variable) 62 | 63 | codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels) 64 | 65 | codebook_data$extraversion <- codebook_data %>% dplyr::select(E1:E5) %>% aggregate_and_document_scale() 66 | 67 | reversed_items <- dict %>% filter(Keying == -1) %>% pull(variable) 68 | 69 | codebook_data <- codebook_data %>% 70 | rename_at(reversed_items, add_R) 71 | 72 | codebook_data <- codebook_data %>% 73 | mutate_at(vars(matches("\\dR$")), reverse_labelled_values) 74 | 75 | codebook_data$extraversion <- codebook_data %>% dplyr::select(E1R:E5) %>% aggregate_and_document_scale() 76 | 77 | codebook_data$plasticity <- codebook_data %>% dplyr::select(E1R:E5, O1:O5R) %>% aggregate_and_document_scale() 78 | 79 | 80 | 81 | 82 | # omit the following lines, if your missing values are already properly labelled 83 | codebook_data <- detect_missing(codebook_data, 84 | only_labelled = TRUE, # only labelled values are autodetected as 85 | # missing 86 | negative_values_are_missing = FALSE, # negative values are missing values 87 | ninety_nine_problems = TRUE, # 99/999 are missing values, if they 88 | # are more than 5 MAD from the median 89 | ) 90 | 91 | # If you are not using formr, the codebook package needs to guess which items 92 | # form a scale. The following line finds item aggregates with names like this: 93 | # scale = scale_1 + scale_2R + scale_3R 94 | # identifying these aggregates allows the codebook function to 95 | # automatically compute reliabilities. 96 | # However, it will not reverse items automatically. 97 | codebook_data <- detect_scales(codebook_data) 98 | ``` 99 | 100 | 101 | Create codebook 102 | 103 | ```{r codebook} 104 | metadata(codebook_data)$name <- "25 Personality items representing 5 factors" 105 | metadata(codebook_data)$description <- "25 personality self report items taken from the International Personality Item Pool (ipip.ori.org)[...]" 106 | metadata(codebook_data)$identifier <- "https://dx.doi.org/10.17605/OSF.IO/K39BG" 107 | metadata(codebook_data)$creator <- "William Revelle" 108 | metadata(codebook_data)$citation <- "Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer." 109 | metadata(codebook_data)$url <- "https://cran.r-project.org/web/packages/psych/index.html" 110 | metadata(codebook_data)$datePublished <- "2010-01-01" 111 | metadata(codebook_data)$temporalCoverage <- "Spring 2010" 112 | metadata(codebook_data)$spatialCoverage <- "Online" 113 | 114 | codebook(codebook_data) 115 | ``` 116 | 117 | ```{r export} 118 | bfi_meta <- metadata_list(codebook_data) 119 | write_json(bfi_meta, "/home/eleftheriadou/Users/EleftheriadouIoannaIro/bfi-dataset/dataset_description.json", pretty = TRUE) 120 | rio::export(codebook_data, "/home/eleftheriadou/Users/EleftheriadouIoannaIro/bfi-dataset/processed_data/bfi-codebook_data.tsv") 121 | ``` 122 | 123 | -------------------------------------------------------------------------------- /bfi-dataset/data/processed_data/README.md: -------------------------------------------------------------------------------- 1 | # README - Bfi dataset 2 | 3 | used codebook package on data 4 | 5 | - labelled variables 6 | - labelled values 7 | - created 2 new variables: extraversion, plasticity 8 | -------------------------------------------------------------------------------- /bfi-dataset/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context": "https://schema.org/", 3 | "@type": "Dataset", 4 | "schemaVersion": "Psych-DS 0.1.0", 5 | "name": "25 Personality items representing 5 factors", 6 | "identifier": "https://dx.doi.org/10.17605/OSF.IO/K39BG", 7 | "creator": [ 8 | { 9 | "@type": "Person", 10 | "name": "William Revelle" 11 | } 12 | ], 13 | "citation": "Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.", 14 | "url": ["https://cran.r-project.org/web/packages/psych/index.html"], 15 | "datePublished": "2010-01-01", 16 | "temporalCoverage": "Spring 2010", 17 | "spatialCoverage": "Online", 18 | "description": "25 personality self report items taken from the International Personality Item Pool (ipip.ori.org)[...]\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name |label |data_type |value_labels |scale_item_names |missing |complete |n |mean |sd |p0 |p25 |p50 |p75 |p100 |hist |\n|:------------|:-----------------------------------------|:---------|:------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------|:-------|:--------|:----|:-----|:-----|:---|:---|:---|:---|:----|:--------|\n|A1R |Am indifferent to the feelings of others. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |16 |2784 |2800 |4.59 |1.41 |1 |4 |5 |6 |6 |▁▂▁▃▃▁▇▇ |\n|A2 |Inquire about others' well-being. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |27 |2773 |2800 |4.8 |1.17 |1 |4 |5 |6 |6 |▁▁▁▁▅▁▇▇ |\n|A3 |Know how to comfort others. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |26 |2774 |2800 |4.6 |1.3 |1 |4 |5 |6 |6 |▁▂▁▂▅▁▇▆ |\n|A4 |Love children. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |19 |2781 |2800 |4.7 |1.48 |1 |4 |5 |6 |6 |▁▂▁▁▃▁▅▇ |\n|A5 |Make people feel at ease. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |16 |2784 |2800 |4.56 |1.26 |1 |4 |5 |5 |6 |▁▂▁▂▅▁▇▆ |\n|C1 |Am exacting in my work. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |21 |2779 |2800 |4.5 |1.24 |1 |4 |5 |5 |6 |▁▁▁▂▅▁▇▅ |\n|C2 |Continue until everything is perfect. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |24 |2776 |2800 |4.37 |1.32 |1 |4 |5 |5 |6 |▁▂▁▂▆▁▇▅ |\n|C3 |Do things according to a plan. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |20 |2780 |2800 |4.3 |1.29 |1 |4 |5 |5 |6 |▁▂▁▂▆▁▇▅ |\n|C4R |Do things in a half-way manner. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |26 |2774 |2800 |4.45 |1.38 |1 |3 |5 |6 |6 |▁▂▁▅▅▁▇▇ |\n|C5R |Waste my time. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |16 |2784 |2800 |3.7 |1.63 |1 |2 |4 |5 |6 |▃▆▁▇▅▁▇▆ |\n|E1R |Don't talk a lot. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |23 |2777 |2800 |4.03 |1.63 |1 |3 |4 |5 |6 |▃▅▁▆▅▁▇▇ |\n|E2R |Find it difficult to approach others. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |16 |2784 |2800 |3.86 |1.61 |1 |3 |4 |5 |6 |▃▅▁▇▅▁▇▆ |\n|E3 |Know how to captivate people. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |25 |2775 |2800 |4 |1.35 |1 |3 |4 |5 |6 |▂▃▁▃▇▁▇▃ |\n|E4 |Make friends easily. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |9 |2791 |2800 |4.42 |1.46 |1 |4 |5 |6 |6 |▁▂▁▂▃▁▇▆ |\n|E5 |Take charge. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |21 |2779 |2800 |4.42 |1.33 |1 |4 |5 |5 |6 |▁▂▁▂▅▁▇▅ |\n|N1R |Get angry easily. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |22 |2778 |2800 |4.07 |1.57 |1 |3 |4 |5 |6 |▂▅▁▆▅▁▇▇ |\n|N2R |Get irritated easily. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |21 |2779 |2800 |3.49 |1.53 |1 |2 |3 |5 |6 |▃▆▁▇▅▁▆▃ |\n|N3R |Have frequent mood swings. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |11 |2789 |2800 |3.78 |1.6 |1 |3 |4 |5 |6 |▃▆▁▇▅▁▇▆ |\n|N4R |Often feel blue. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |36 |2764 |2800 |3.81 |1.57 |1 |3 |4 |5 |6 |▃▅▁▇▅▁▇▆ |\n|N5R |Panic easily. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |29 |2771 |2800 |4.03 |1.62 |1 |3 |4 |5 |6 |▃▃▁▆▅▁▇▇ |\n|O1 |Am full of ideas. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |22 |2778 |2800 |4.82 |1.13 |1 |4 |5 |6 |6 |▁▁▁▂▅▁▇▇ |\n|O2R |Avoid difficult reading material. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |0 |2800 |2800 |4.29 |1.57 |1 |3 |5 |6 |6 |▂▃▁▅▃▁▇▇ |\n|O3 |Carry the conversation to a higher level. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |28 |2772 |2800 |4.44 |1.22 |1 |4 |5 |5 |6 |▁▁▁▂▆▁▇▅ |\n|O4 |Spend time reflecting on things. |integer |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA |14 |2786 |2800 |4.89 |1.22 |1 |4 |5 |6 |6 |▁▁▁▁▃▁▆▇ |\n|O5R |Will not probe deeply into a subject. |numeric |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA |20 |2780 |2800 |4.51 |1.33 |1 |4 |5 |6 |6 |▁▂▁▃▅▁▇▇ |\n|gender |gender |integer |1. male, - 2. female |NA |0 |2800 |2800 |1.67 |0.47 |1 |1 |2 |2 |2 |▃▁▁▁▁▁▁▇ |\n|education |education |integer |1. in high school, - 2. finished high school, - 3. some college, - 4. college graduate, - 5. graduate degree |NA |223 |2577 |2800 |3.19 |1.11 |1 |3 |3 |4 |5 |▂▂▁▇▁▂▁▃ |\n|age |age |integer |NA |NA |0 |2800 |2800 |28.78 |11.13 |3 |20 |26 |35 |86 |▁▇▆▃▂▁▁▁ |\n|extraversion |5 E items aggregated by rowMeans |numeric |NA |E1R, E2R, E3, E4, E5 |87 |2713 |2800 |4.14 |1.06 |1 |3.4 |4.2 |5 |6 |▁▁▃▅▇▇▇▆ |\n|plasticity |10 items aggregated by rowMeans |numeric |NA |E1R, E2R, E3, E4, E5, O1, O2R, O3, O4, O5R |149 |2651 |2800 |4.37 |0.73 |1.7 |3.9 |4.4 |4.9 |6 |▁▁▂▃▆▇▅▂ |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/).", 19 | "variableMeasured": [ 20 | { 21 | "name": "A1R", 22 | "description": "Am indifferent to the feelings of others.", 23 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 24 | "maxValue": 6, 25 | "minValue": 1, 26 | "@type": "PropertyValue" 27 | }, 28 | { 29 | "name": "A2", 30 | "description": "Inquire about others' well-being.", 31 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 32 | "maxValue": 6, 33 | "minValue": 1, 34 | "@type": "PropertyValue" 35 | }, 36 | { 37 | "name": "A3", 38 | "description": "Know how to comfort others.", 39 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 40 | "maxValue": 6, 41 | "minValue": 1, 42 | "@type": "PropertyValue" 43 | }, 44 | { 45 | "name": "A4", 46 | "description": "Love children.", 47 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 48 | "maxValue": 6, 49 | "minValue": 1, 50 | "@type": "PropertyValue" 51 | }, 52 | { 53 | "name": "A5", 54 | "description": "Make people feel at ease.", 55 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 56 | "maxValue": 6, 57 | "minValue": 1, 58 | "@type": "PropertyValue" 59 | }, 60 | { 61 | "name": "C1", 62 | "description": "Am exacting in my work.", 63 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 64 | "maxValue": 6, 65 | "minValue": 1, 66 | "@type": "PropertyValue" 67 | }, 68 | { 69 | "name": "C2", 70 | "description": "Continue until everything is perfect.", 71 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 72 | "maxValue": 6, 73 | "minValue": 1, 74 | "@type": "PropertyValue" 75 | }, 76 | { 77 | "name": "C3", 78 | "description": "Do things according to a plan.", 79 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 80 | "maxValue": 6, 81 | "minValue": 1, 82 | "@type": "PropertyValue" 83 | }, 84 | { 85 | "name": "C4R", 86 | "description": "Do things in a half-way manner.", 87 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 88 | "maxValue": 6, 89 | "minValue": 1, 90 | "@type": "PropertyValue" 91 | }, 92 | { 93 | "name": "C5R", 94 | "description": "Waste my time.", 95 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 96 | "maxValue": 6, 97 | "minValue": 1, 98 | "@type": "PropertyValue" 99 | }, 100 | { 101 | "name": "E1R", 102 | "description": "Don't talk a lot.", 103 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 104 | "maxValue": 6, 105 | "minValue": 1, 106 | "@type": "PropertyValue" 107 | }, 108 | { 109 | "name": "E2R", 110 | "description": "Find it difficult to approach others.", 111 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 112 | "maxValue": 6, 113 | "minValue": 1, 114 | "@type": "PropertyValue" 115 | }, 116 | { 117 | "name": "E3", 118 | "description": "Know how to captivate people.", 119 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 120 | "maxValue": 6, 121 | "minValue": 1, 122 | "@type": "PropertyValue" 123 | }, 124 | { 125 | "name": "E4", 126 | "description": "Make friends easily.", 127 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 128 | "maxValue": 6, 129 | "minValue": 1, 130 | "@type": "PropertyValue" 131 | }, 132 | { 133 | "name": "E5", 134 | "description": "Take charge.", 135 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 136 | "maxValue": 6, 137 | "minValue": 1, 138 | "@type": "PropertyValue" 139 | }, 140 | { 141 | "name": "N1R", 142 | "description": "Get angry easily.", 143 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 144 | "maxValue": 6, 145 | "minValue": 1, 146 | "@type": "PropertyValue" 147 | }, 148 | { 149 | "name": "N2R", 150 | "description": "Get irritated easily.", 151 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 152 | "maxValue": 6, 153 | "minValue": 1, 154 | "@type": "PropertyValue" 155 | }, 156 | { 157 | "name": "N3R", 158 | "description": "Have frequent mood swings.", 159 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 160 | "maxValue": 6, 161 | "minValue": 1, 162 | "@type": "PropertyValue" 163 | }, 164 | { 165 | "name": "N4R", 166 | "description": "Often feel blue.", 167 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 168 | "maxValue": 6, 169 | "minValue": 1, 170 | "@type": "PropertyValue" 171 | }, 172 | { 173 | "name": "N5R", 174 | "description": "Panic easily.", 175 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 176 | "maxValue": 6, 177 | "minValue": 1, 178 | "@type": "PropertyValue" 179 | }, 180 | { 181 | "name": "O1", 182 | "description": "Am full of ideas.", 183 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 184 | "maxValue": 6, 185 | "minValue": 1, 186 | "@type": "PropertyValue" 187 | }, 188 | { 189 | "name": "O2R", 190 | "description": "Avoid difficult reading material.", 191 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 192 | "maxValue": 6, 193 | "minValue": 1, 194 | "@type": "PropertyValue" 195 | }, 196 | { 197 | "name": "O3", 198 | "description": "Carry the conversation to a higher level.", 199 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 200 | "maxValue": 6, 201 | "minValue": 1, 202 | "@type": "PropertyValue" 203 | }, 204 | { 205 | "name": "O4", 206 | "description": "Spend time reflecting on things.", 207 | "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate", 208 | "maxValue": 6, 209 | "minValue": 1, 210 | "@type": "PropertyValue" 211 | }, 212 | { 213 | "name": "O5R", 214 | "description": "Will not probe deeply into a subject.", 215 | "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate", 216 | "maxValue": 6, 217 | "minValue": 1, 218 | "@type": "PropertyValue" 219 | }, 220 | { 221 | "name": "gender", 222 | "description": "gender", 223 | "value": "1. male,\n2. female", 224 | "maxValue": 2, 225 | "minValue": 1, 226 | "@type": "PropertyValue" 227 | }, 228 | { 229 | "name": "education", 230 | "description": "education", 231 | "value": "1. in high school,\n2. finished high school,\n3. some college,\n4. college graduate,\n5. graduate degree", 232 | "maxValue": 5, 233 | "minValue": 1, 234 | "@type": "PropertyValue" 235 | }, 236 | { 237 | "name": "age", 238 | "description": "age", 239 | "@type": "PropertyValue" 240 | }, 241 | { 242 | "name": "extraversion", 243 | "description": "5 E items aggregated by rowMeans", 244 | "@type": "PropertyValue" 245 | }, 246 | { 247 | "name": "plasticity", 248 | "description": "10 items aggregated by rowMeans", 249 | "@type": "PropertyValue" 250 | }, 251 | { 252 | "name": "A1", 253 | "description": "n/a", 254 | "@type": "PropertyValue" 255 | }, 256 | { 257 | "name": "C4", 258 | "description": "n/a", 259 | "@type": "PropertyValue" 260 | },{ 261 | "name": "C5", 262 | "description": "n/a", 263 | "@type": "PropertyValue" 264 | },{ 265 | "name": "E1", 266 | "description": "n/a", 267 | "@type": "PropertyValue" 268 | }, 269 | { 270 | "name": "E2", 271 | "description": "n/a", 272 | "@type": "PropertyValue" 273 | }, 274 | { 275 | "name": "N1", 276 | "description": "n/a", 277 | "@type": "PropertyValue" 278 | }, 279 | { 280 | "name": "N2", 281 | "description": "n/a", 282 | "@type": "PropertyValue" 283 | }, 284 | { 285 | "name": "N3", 286 | "description": "n/a", 287 | "@type": "PropertyValue" 288 | }, 289 | { 290 | "name": "N4", 291 | "description": "n/a", 292 | "@type": "PropertyValue" 293 | }, 294 | { 295 | "name": "N5", 296 | "description": "n/a", 297 | "@type": "PropertyValue" 298 | }, 299 | { 300 | "name": "O2", 301 | "description": "n/a", 302 | "@type": "PropertyValue" 303 | }, 304 | { 305 | "name": "O5", 306 | "description": "n/a", 307 | "@type": "PropertyValue" 308 | } 309 | ] 310 | } 311 | -------------------------------------------------------------------------------- /complex-metadata-dataset/README.md: -------------------------------------------------------------------------------- 1 | # README - Template project 2 | 3 | This is a README file. It is a [Markdown file](https://en.wikipedia.org/wiki/Markdown), which means it's a simple text document, but you can use simple syntax like you see here and many programs will display it more nicely - for instance, the words 'Markdown file' will appear as a link that goes to `https://en.wikipedia.org/wiki/Markdown`. 4 | 5 | This is the first document someone will read when they access your project! Write something informative in here, such as: 6 | 7 | * A quick summary of what the project is 8 | 9 | * A list of what files can be found in this folder and what they are 10 | 11 | * Instructions for how to cite this datset/project. You can also put your citation information in `dataset_description.JSON`! 12 | -------------------------------------------------------------------------------- /complex-metadata-dataset/data/study-template_data.csv: -------------------------------------------------------------------------------- 1 | participant_id,length_in_smoots,milliseconds,team 2 | 001-bluebell,12.1,4023,Red 3 | 002-sandra,2.3,12909,Blue 4 | 003-stripey,21,12332,NA 5 | 004-heehaw,2000,3904,Red 6 | -------------------------------------------------------------------------------- /complex-metadata-dataset/dataset_description.json: -------------------------------------------------------------------------------- 1 | {"@context":"http://schema.org/", 2 | "@type":"Dataset", 3 | "name":"Psych-DS Example Dataset", 4 | "description":"This is a 'skeleton' dataset for Psych-DS", 5 | "schemaVersion":"Psych-DS 0.1.0", 6 | "creator":[ 7 | {"@type":"Person", 8 | "name":"Melissa Kline"}, 9 | {"@type":"Person", 10 | "name":"Schmelissa Schmine", 11 | "birthDate":"1950-01-01"}], 12 | "citation":"Kline (2018). Not a real paper, No Journal, p. 1-24.", 13 | "sameAs": "https://doi.org/doi-goes-here", 14 | "temporalCoverage":"1950-01-01/2013-12-18", 15 | "keywords":["foo","bar"], 16 | "variableMeasured":[ 17 | {"type": "PropertyValue", 18 | "unitText": "Participant", 19 | "name": "participant_id", 20 | "description": "Identity of each zebra. Provides a unique rowid in this dataset." 21 | }, 22 | {"type": "PropertyValue", 23 | "unitText": "Smoots", 24 | "name": "length_in_smoots", 25 | "description": "The length of a zebra, in smoots", 26 | "minValue":"0" 27 | }, 28 | {"type": "PropertyValue", 29 | "unitCode": "C26", 30 | "name": "milliseconds", 31 | "description": "Time the zebra started running before/after the starting gun goes off" 32 | }, 33 | {"type": "PropertyValue", 34 | "unitText": null, 35 | "name": "team", 36 | "description": "Which team the zebra is on" 37 | }] 38 | } 39 | -------------------------------------------------------------------------------- /example_files/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@type":"Dataset", 3 | "@context":"https://schema.org/", 4 | "name":"Example Dataset", 5 | "description":"This is an example", 6 | "variableMeasured":["a","b","c"] 7 | } -------------------------------------------------------------------------------- /face-body/README.md: -------------------------------------------------------------------------------- 1 | # README - Faces and Bodies 2 | 3 | This project examines the social perception of bodies, in comparison to faces, focusing on the dimension structure from Oosterhof & Todorov (2008, PNAS). 4 | 5 | Data originally at https://osf.io/ztwce/ 6 | 7 | ## Files 8 | 9 | * female_bodies.csv - Reward value data for female bodies 10 | * female_faces.csv - Reward value data for female faces 11 | * female_ratings.csv - Ratings on the 13 Oosterhof & Todorov social perception traits for female faces and bodies 12 | * female_stimuli.csv - Information about the female stimuli: name, sex, age, height(cm), weight(kg), BMI, chest(cm), waist(cm), hips(cm) 13 | * male_bodies.csv - Reward value data for male bodies 14 | * male_faces.csv - Reward value data for male faces 15 | * male_ratings.csv - Ratings on the 13 Oosterhof & Todorov social perception traits for male faces and bodies 16 | * male_stimuli.csv - Information about the male stimuli: name, sex, age, height(cm), weight(kg), BMI, chest(cm), waist(cm), hips(cm) 17 | 18 | ## Citation 19 | 20 | Morrison D, Wang H, Hahn AC, Jones BC, DeBruine LM (2017) Predicting the reward value of faces and bodies from social perception. PLoS ONE 12(9): e0185093. https://doi.org/10.1371/journal.pone.0185093 -------------------------------------------------------------------------------- /face-body/data/gender-female_type-bodies_data.csv: -------------------------------------------------------------------------------- 1 | user_id,sex,sexpref,age,alexandra,anastazie,anezka,bera,bohdana,brenda,carol,christianne,dagmar,debra,dobromila,dusana,edita,eleanora,elena,eugenia,evzenie,gabriela,gejza,ida,ingrid,irena,jindriska,jitka,karina,kordula,lea,linda,livia,lujza,margita,marika,matylda,miloslava,milota,miriama,peggy,perla,radmila,sarlota,saskie,sidonia,stela,tamara,ursula,viktoria,viola,vladena,zelmira,zlata 2 | 477033,female,,30.9,-18,-14,-17,-18,-18,-16,-16,-4,-15,-10,-18,-16,-18,-18,-14,-17,-10,-18,-17,-18,-14,-17,-16,-17,-17,-16,-18,-18,-17,-18,-17,-16,-18,-17,-17,-10,-16,-16,-6,-18,-16,-18,-18,-16,-18,-18,-18,-18,-18,-18 3 | 478230,male,women,25.8,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,15,9,31,0,9,0,0,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 4 | 478291,female,women,23.5,-8,-5,10,-7,26,0,20,12,42,1,15,20,13,6,66,16,10,0,-11,0,27,66,5,-4,12,6,-18,-7,8,21,23,-1,-2,15,-7,17,10,15,-3,10,6,30,-9,37,17,60,0,15,55,0 5 | 479035,female,men,22.6,17,-9,-7,-9,14,-8,10,-9,38,24,2,-10,-11,32,-10,-13,-12,-10,12,-11,0,-11,-7,-8,-8,32,24,-10,21,19,0,31,25,-6,44,27,-6,67,19,-10,-8,-12,29,11,-12,28,31,48,-11,29 6 | 489018,male,men,26.8,-3,9,0,-12,-12,19,-9,-3,0,18,22,43,-1,7,0,-10,0,-6,-8,10,0,-4,0,-10,-5,-13,0,0,24,-4,11,0,42,-2,2,26,0,0,-6,-4,10,-10,-5,0,-4,-18,-2,35,-1,0 7 | 501705,female,either,21.3,21,-4,-4,39,27,11,21,30,43,28,21,-8,31,8,28,-7,11,13,30,43,21,-1,30,23,-8,-8,18,43,49,0,32,19,32,32,32,0,-5,37,37,26,1,35,14,29,-6,28,28,30,31,33 8 | 514003,male,women,30.1,-9,-11,2,-10,-10,-11,-10,-12,3,-10,-10,-12,-10,-12,3,4,-9,-10,-9,-12,-8,-8,-9,4,-8,-10,-9,-10,-10,2,-8,-11,-10,-9,-9,-4,-11,-9,5,1,-12,0,-11,-12,-10,-10,-12,-10,-10,-12 9 | 518109,female,men,20.1,-3,-9,31,-8,-8,-9,-11,-9,11,-4,0,-13,0,-9,0,52,1,-9,9,-1,-7,-12,15,16,-10,-11,-3,79,-10,30,-6,-7,-11,73,0,-11,-11,57,46,24,-9,-2,-9,3,-5,0,-6,13,-9,21 10 | 531458,male,women,19.8,0,-14,0,0,-14,-3,-9,-18,135,-12,-12,0,-13,-11,19,189,-15,2,-12,-6,51,-11,-15,9,-13,-12,-11,-6,-15,-9,-15,-22,-11,-16,-12,-12,-9,0,-16,-9,-7,8,-12,-14,-11,-13,0,0,-11,49 11 | 531826,male,women,27.2,5,-11,-11,-7,-6,-10,-11,-11,49,-11,-14,-13,-6,-1,44,50,9,-8,-11,17,-10,-13,-10,18,-15,-12,-11,-3,10,-5,-11,-12,-6,-6,-10,-14,-7,-13,29,1,-13,60,-12,-4,-11,-11,-11,0,-12,34 12 | 531966,female,men,20.7,3,-6,-4,-3,-8,-6,0,-10,12,3,-7,-9,0,-5,0,0,-4,-6,0,17,0,-3,-5,3,0,-3,5,0,-4,1,-10,0,0,0,6,6,-6,0,-3,-5,2,-2,2,0,-3,-3,0,3,0,0 13 | 531967,female,men,23.1,-10,6,-11,-12,-7,12,8,-6,7,13,-5,8,-3,-2,-7,-4,-6,-11,-2,9,-2,-10,-8,-5,-3,-11,-7,-12,6,-5,-9,-13,6,5,10,6,-11,11,-5,12,-9,-8,5,-6,-10,2,-8,10,-7,-10 14 | 531968,female,either,22.8,29,-10,12,15,19,0,0,-10,21,21,35,-9,13,6,3,10,0,0,16,13,24,5,22,5,4,-10,22,22,-9,33,9,22,-3,26,-9,12,-10,59,22,0,0,21,19,18,11,12,24,21,-2,37 15 | 531969,male,women,28.1,9,-15,-17,-16,-17,-15,-9,-16,-18,-16,-16,-14,-17,-16,-17,-16,-17,-16,-16,28,-18,-16,-16,-17,-17,-15,-16,-16,-17,-18,-14,-18,-16,-13,-16,-15,-17,-16,-15,-16,-18,-17,-16,-16,-16,-17,-15,6,-15,-16 16 | 531970,female,men,21.5,-3,1,-6,-2,-5,-2,7,8,-11,0,-3,-3,-10,-10,-9,-13,4,-10,-11,-4,8,-13,-7,-7,-9,6,5,2,-2,4,-4,-12,2,6,-3,4,-8,-3,-6,-7,-2,-15,8,-1,7,4,2,6,1,6 17 | 531973,female,men,23.7,-9,-9,-7,-10,-13,10,-5,-11,-13,-12,5,-13,-8,-6,-10,-10,-13,-10,-13,0,-12,-13,-10,-10,-8,-11,-6,0,-6,-11,1,-11,-11,-8,-12,-12,-12,5,0,-12,-11,-7,-8,-6,-12,-12,-6,-13,-12,9 18 | 531974,female,men,24.3,14,-4,-8,62,16,18,7,20,88,32,38,11,-9,19,-2,44,21,6,7,17,-8,13,20,40,11,12,-8,58,43,16,-9,-2,32,20,34,52,18,98,66,29,-5,-4,-6,53,-9,17,14,11,30,87 19 | 531975,female,women,34.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 20 | 531976,female,men,50.8,0,0,0,0,-5,-3,-4,-2,-1,-4,-1,-4,-2,-3,-3,-5,0,-5,0,0,1,0,-1,0,0,0,0,0,-3,2,0,6,0,0,-5,-3,0,-1,-2,0,-2,-6,2,0,-5,-2,0,0,0,0 21 | 531977,male,men,21.7,0,-10,-17,-9,-12,-16,-4,-13,-5,-15,-3,-17,9,-16,0,22,-16,-14,-9,0,0,-8,-11,1,-6,-7,-10,0,0,0,0,-15,-11,-14,0,0,-19,1,0,-7,-10,0,0,0,-10,0,0,-3,0,-14 22 | 531978,male,women,22.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 23 | 531980,female,either,18.9,-7,-11,-10,-2,-6,-10,-13,-6,-12,-12,0,-10,-10,6,5,-9,4,-7,-6,-9,1,-10,-12,10,-10,-9,3,-10,35,-10,10,-4,3,22,-8,9,-6,26,-10,2,-8,-12,-10,15,12,23,8,-11,15,16 24 | 531981,male,women,28.3,-16,-8,85,53,23,-14,-10,-4,27,-4,-11,-5,-12,-5,32,24,-16,-9,4,27,-5,3,45,50,-6,-10,43,15,-12,73,-14,-9,-4,16,25,29,-9,-7,37,39,-12,46,-15,-6,-7,-4,-16,-15,25,26 25 | 531982,male,neither,21.4,-2,-4,-5,-4,-6,0,-5,-6,0,-3,0,-2,0,-1,-5,0,0,-1,-9,-9,-6,-1,-6,-6,-5,-6,-2,-4,0,0,-4,0,-7,0,0,-4,-5,-4,-5,-3,0,-1,-6,-1,-7,-5,-4,-6,-5,-5 26 | 531983,female,men,44.6,-3,-10,0,2,0,0,0,0,10,-5,0,-7,-9,0,0,8,6,-10,-10,16,0,4,7,4,0,-7,-8,0,-8,0,-6,-10,0,0,-7,-5,-8,0,0,-7,0,9,-7,0,0,0,-11,6,0,12 27 | 531984,male,,22.7,164,1,0,19,-12,0,8,0,-8,-1,1,-2,-10,1,-2,-2,-2,0,73,7,99,-2,-1,-4,4,-15,-1,-2,0,-1,0,0,233,0,2,3,6,6,-3,-12,81,-2,59,-2,0,0,0,-8,2,-2 28 | 531985,female,men,20.2,-14,-13,-14,-15,-11,-14,-14,-17,-13,-10,-13,-12,-13,-13,-13,0,-11,-14,-14,-15,-15,-13,-13,-15,-15,-16,-15,-12,-14,-10,-13,-15,-16,-14,-11,-14,-10,-13,-15,-12,-14,-15,-15,-15,-14,-14,-15,-15,-14,-14 29 | 531986,female,men,22.2,-14,-14,-15,-15,-15,-16,-15,-15,-14,-16,-15,-11,-13,-15,-14,-11,-10,-14,-14,-15,-15,-15,-10,-15,-11,-14,-10,-10,-16,-1,-15,-15,-14,-15,-15,-16,-15,-16,-15,-15,-12,-15,-14,-13,-15,-16,-13,-16,-15,-14 30 | 531987,female,men,20.3,2,-14,-14,-14,-13,-12,-14,-14,-14,6,-14,-14,-14,-12,-12,-12,-13,-15,-13,-14,-14,-12,-15,-13,-15,-13,-12,-15,18,-14,-14,-13,12,-14,-11,-14,-14,9,-13,-13,20,-12,-12,-14,-14,-14,-12,-9,-14,-14 31 | 531988,male,women,20.4,-9,20,7,-6,11,16,0,28,31,14,22,7,2,2,18,13,11,12,-5,7,2,4,10,6,14,2,12,-3,-4,4,-8,13,36,-6,-6,-9,-5,6,30,16,-3,24,-10,-7,0,26,-8,8,22,7 32 | 531989,female,men,22.2,0,-13,-12,-11,-11,0,-9,-3,0,-4,-10,-8,0,0,-4,0,-5,-13,-10,-8,-5,0,0,-11,-12,-12,0,-4,-5,-2,-5,-8,-14,0,-11,-5,-11,0,0,-8,0,0,2,0,-4,-9,0,-6,-10,0 33 | 531990,male,women,18.2,33,28,-14,32,-8,34,42,-9,-12,17,-12,-10,-9,30,-13,-11,24,-9,6,40,69,-14,-15,-14,-12,19,32,36,24,29,-10,31,-7,40,42,1,-12,-6,-11,34,-10,-10,-10,21,29,-9,32,39,9,54 34 | 531991,female,men,22.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 35 | 531993,male,men,24.0,10,-11,-2,5,8,70,0,8,0,-19,-18,13,2,12,1,-14,-22,6,-9,-12,10,32,0,-15,-10,-16,-22,22,22,-14,-14,5,18,-2,21,11,-8,-12,-16,-15,-1,-20,30,4,0,-12,8,22,-9,-12 36 | 531995,female,neither,21.7,-13,-14,-15,-16,-15,-14,-11,-12,-14,-14,-14,-14,-15,-15,-14,-14,-14,-13,-14,-12,-14,-14,-13,-14,-9,-14,-14,-14,-14,-15,-15,-12,-14,-14,-15,-12,-14,-15,-14,-14,-15,-15,-14,-15,-15,-13,-15,-14,-13,-15 37 | 531996,female,men,24.9,-12,-12,-12,-13,-12,-10,-12,-12,-12,-13,-14,-12,-13,-10,-12,-12,-13,-12,-12,-12,-11,-13,-13,-11,-12,-13,-12,-11,-12,-12,-12,-12,-12,-12,-12,-11,-12,-12,-12,-13,-12,-13,-12,-12,-12,-12,-12,-13,-13,-14 38 | 531998,female,men,41.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 39 | 531999,male,women,29.5,-5,-9,-10,-12,-11,6,0,-6,-11,-13,-13,-14,-13,-12,-12,-12,-9,-9,-13,-7,-5,-12,-8,-11,-11,-11,2,-10,-9,-4,-10,-14,-13,-1,-8,-12,-10,-5,-7,-3,8,-13,-13,-12,-5,-14,-13,2,-13,6 40 | 532000,female,men,19.5,4,-7,-9,-8,-9,-8,6,-8,19,-10,30,-10,-10,26,-9,-8,16,-9,-11,13,6,-9,6,-4,-10,-11,-8,-9,26,41,-8,-8,-9,25,28,-6,-10,24,17,-10,-7,-11,-5,-6,-8,10,0,-9,-7,30 41 | 532182,female,women,21.1,0,-6,17,24,5,-2,5,0,0,-6,18,0,18,3,0,8,16,0,0,21,0,0,-7,8,-9,18,0,26,28,32,24,40,25,-7,-3,-9,-8,12,5,19,39,-3,12,32,-5,52,0,34,0,36 42 | 532183,female,men,43.4,-6,-9,-4,-7,-5,-8,-6,-7,-4,1,-6,-10,-3,-6,-6,-1,-3,-6,-6,-5,-6,-4,-4,-5,-4,-8,-7,-4,-4,-5,-6,-7,-6,-7,-6,-4,-5,-7,-5,-6,-4,-3,-2,-5,-8,-5,-2,-4,-4,-4 43 | 532184,female,men,52.2,-4,-2,-4,4,-4,-5,-4,-4,-4,-2,-4,-6,4,-4,-4,2,-2,-4,2,-4,-4,-2,-4,2,-4,-6,-4,-4,-6,2,-4,-4,-6,-2,-6,-5,-6,-4,4,-2,-2,-2,-6,-2,-4,-4,-4,-4,-4,4 44 | 532185,female,men,24.4,0,0,-4,0,1,-3,1,0,0,0,0,-1,0,0,0,0,0,7,-1,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,3,2,0,0,2,0,0,0,3,2,0,0,3,0,0,0,0 45 | 532192,female,men,21.0,-9,-4,-8,-6,-10,-8,-13,-9,-8,-3,-14,-10,-12,-10,-10,-11,-12,-8,-8,-10,-6,-10,-7,-11,-12,-14,-7,-10,-4,-6,-12,-8,-7,-11,0,-4,-12,-4,-10,-11,-10,-12,-8,-11,-12,-9,-9,-11,-10,-6 46 | 532213,male,women,19.9,-9,-10,-7,-10,-9,0,-11,-11,0,-1,-8,-12,-10,-8,0,0,-6,-12,0,-1,-11,-10,0,0,0,-8,-5,0,-5,0,-3,0,0,-9,-10,0,-10,0,-10,-8,-11,-9,-11,0,-2,-3,-11,0,0,0 47 | 532214,male,women,18.6,-11,-10,-10,-9,0,-9,-9,-20,-9,-7,-10,-10,-10,-10,0,-9,-9,-11,-11,-11,-11,-11,-11,-11,-12,-12,-12,-21,-17,-10,-11,-4,-12,-12,-8,-8,-8,-7,-8,-8,-10,0,-11,-11,-10,-12,-11,-11,-7,-8 48 | 532215,male,women,20.3,13,-1,-5,-12,-9,0,-7,13,13,34,0,-9,0,-14,-12,36,1,-13,-9,-13,8,-13,-10,-9,-14,-9,-8,-12,-4,-10,3,-10,26,-14,37,-6,15,10,-2,-11,-12,-13,6,-8,-8,-11,-14,12,-11,10 49 | 532216,male,women,21.9,0,0,-8,0,-8,-6,14,0,16,0,4,0,0,-3,0,0,1,0,0,6,15,0,0,0,0,0,0,-8,1,-1,0,-10,-12,-8,8,0,-10,0,3,0,0,0,0,0,0,0,0,0,0,22 50 | 532217,male,women,18.3,32,-13,-8,-11,-12,21,19,27,10,-10,-12,-10,37,-5,21,22,-8,-8,7,27,-10,-9,-9,-7,-7,-9,-10,8,0,11,-12,-10,32,-10,-4,26,-12,-11,22,-12,-12,31,-8,-9,-10,-3,-10,20,21,-9 51 | 532218,female,women,19.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 52 | 532219,female,men,19.7,-10,-10,0,-6,-10,-11,-11,0,0,-8,10,-11,-11,-11,-7,-11,-10,0,-7,-13,4,-12,-11,-7,-7,-10,-10,-11,0,-8,7,-7,-10,0,3,-10,0,-7,31,2,-6,-9,-8,0,-6,2,-11,-9,-12,11 53 | 532220,female,men,36.9,0,-10,-4,-9,-11,0,0,0,-8,0,0,-9,0,-11,1,-9,-10,-10,-10,-10,6,-11,0,-11,-7,-10,0,-3,6,-11,0,-10,-7,0,-7,4,-6,-6,-6,-6,-12,-11,-11,-6,-11,0,0,0,-10,-3 54 | 532221,female,men,25.0,-11,-11,63,-9,-10,-10,-11,-14,57,87,-16,-16,-15,-12,137,32,16,-5,-14,-14,-12,-15,59,-13,-13,-15,71,-12,-15,-16,-15,-15,-16,53,-14,-12,-15,25,-13,-16,-15,40,-16,-15,-15,-16,-17,-14,-17,36 55 | 532222,male,women,21.7,0,-15,-18,-1,-17,21,13,-9,12,0,8,-13,-13,0,-14,-11,30,-15,-2,0,-8,-14,-12,0,-16,62,-16,18,0,0,-15,-11,-8,0,51,0,-15,34,111,0,-15,-11,-14,3,-5,-1,-2,25,23,111 56 | 532243,male,women,19.7,14,2,0,4,-2,14,3,-2,0,13,4,-4,-8,0,-6,0,0,-1,0,-6,4,-3,-4,-1,-2,0,0,0,9,11,3,0,0,2,0,18,-4,3,1,0,10,-5,-4,-4,3,-4,0,0,4,2 57 | 532244,female,men,18.5,6,-11,-11,-11,-10,-1,-11,-10,-11,7,9,-9,18,-11,-7,4,-11,-7,9,-10,14,-10,7,-10,-7,12,11,18,16,15,2,0,-10,-10,5,-10,-11,12,11,-8,-8,-9,-9,-6,-10,14,-12,-6,-8,24 58 | 532245,female,either,18.1,8,7,9,6,-5,-5,5,16,-1,10,4,7,5,-3,4,26,4,-6,14,4,7,-6,0,7,8,16,1,13,14,23,2,8,9,6,17,3,6,0,15,13,1,22,-5,7,-6,5,1,4,4,3 59 | 532246,male,women,19.2,3,13,46,87,25,71,-11,17,-5,47,-6,83,-7,110,-6,-14,8,66,36,59,-9,-8,12,-10,29,45,40,9,124,25,6,32,90,68,-10,121,-11,10,16,6,28,9,40,-10,66,12,69,24,-5,18 60 | 532247,male,women,18.2,14,-13,-11,-11,-13,-13,-12,-14,-14,75,-12,-12,-11,-10,-12,-13,-13,-14,-14,-9,-11,-13,13,-12,-14,-15,-12,-14,-12,13,-13,-11,-15,-13,-11,-14,-14,-14,45,-11,-14,1,-14,-10,-13,35,-14,3,-13,56 61 | 532249,male,women,25.3,-3,-15,-14,-4,-13,0,-12,-15,3,-11,-11,-12,-5,-9,0,-7,-14,-14,-2,12,-8,-15,-14,13,-10,-14,-1,0,-7,-3,-15,-14,-9,-10,-12,0,-15,-7,14,-4,-15,-10,-14,0,-15,0,0,2,-14,1 62 | 532250,female,men,19.0,9,-4,4,2,-3,-5,0,-4,11,1,6,-4,3,5,-2,2,5,-3,-2,5,5,-4,9,2,-6,3,2,8,-3,7,6,1,-3,5,-3,-2,-3,9,4,9,-2,-3,-1,11,-4,8,5,3,-3,8 63 | 532251,female,men,20.9,1,-11,-4,9,-4,-8,-11,-5,12,-4,9,-8,-6,-10,-3,-2,-6,-12,2,-1,-6,-8,-5,13,-9,-6,-7,-2,-9,-4,-4,-9,-11,-5,8,23,-3,32,23,-4,-8,5,-9,-1,-10,15,3,9,0,19 64 | 532252,male,women,23.2,-9,0,-19,0,-6,3,0,-14,-10,-8,2,-2,-2,0,4,0,-7,-10,-1,-1,1,-16,0,0,-10,-1,-18,-13,0,0,-7,-2,-13,-12,-1,-16,-15,1,-1,0,0,0,0,0,0,-6,0,0,0,-8 65 | 532253,female,either,28.8,-10,-8,-11,-12,-11,-10,-8,-8,-9,-10,-14,-13,-14,-7,-12,-11,-9,-11,-14,-8,-11,-11,-11,-15,-16,-9,-11,-15,-11,-4,-13,-15,-14,-15,-11,-9,-15,-12,-12,-6,-10,-10,-10,-12,-13,-11,-10,-11,-15,-7 66 | 532254,female,either,19.6,-6,-5,-3,3,-6,-7,0,-5,5,-6,2,-5,-13,0,2,0,-5,-8,-10,-11,-11,-8,0,0,-11,-8,-10,-6,0,-12,-12,-11,-9,0,-2,-10,-11,-4,6,-8,-7,3,0,0,-12,0,-5,-6,-9,6 67 | 532255,male,women,21.5,20,-15,-12,-10,-14,-12,21,-11,24,7,-13,-9,8,7,16,-8,-8,-11,-10,24,-10,-11,17,22,-15,-11,-7,-12,5,23,-13,-10,-10,-6,-1,14,-13,19,19,26,-15,-9,-12,9,17,27,-12,20,1,17 68 | 532256,female,neither,18.6,15,-6,2,7,3,9,5,7,8,6,14,1,10,0,-3,0,8,-2,-4,10,8,3,11,12,-8,-1,-2,10,12,7,-2,11,-4,6,6,2,1,11,8,14,10,8,15,4,0,8,-2,-2,0,8 69 | 532257,female,men,19.3,0,-10,-12,-2,-14,0,-5,-9,-10,-11,-13,-12,-8,-13,0,-10,-9,-10,-7,-13,-10,-18,-11,-4,-10,-13,-9,-3,-12,-16,-3,-14,-6,-3,-6,-16,-9,-14,0,-11,-10,-12,-9,-7,-10,-7,-9,-15,-4,-6 70 | 532492,male,men,25.1,0,-8,-10,15,-9,-4,24,11,-9,14,11,22,32,25,-6,-4,-8,22,14,10,0,-9,18,-6,-7,0,18,19,-6,25,-5,-9,-6,16,9,28,-8,-6,-4,15,15,-10,21,-4,-7,29,-9,-6,7,21 71 | 532493,male,women,18.5,-4,-11,6,26,4,-9,-16,-14,32,5,-11,-11,1,9,-3,-5,-6,-7,11,-9,-16,-8,13,38,4,-8,-4,6,-4,22,-3,-7,-7,1,-6,-8,-14,10,34,9,-6,-6,-7,62,-6,57,-6,4,-5,41 72 | 532495,male,women,19.4,-6,-14,-9,-5,-14,-15,-9,-12,-9,-11,-6,-14,0,-12,0,-5,-13,-11,-2,0,-8,-2,4,8,-3,-6,1,0,0,0,-12,-10,-11,0,-6,0,-13,-2,54,0,-12,5,-12,0,-14,14,0,0,0,70 73 | 532496,male,,18.9,44,-12,-13,32,54,10,-10,-12,30,24,-9,-7,30,30,-11,4,-5,-10,29,42,-12,-12,7,66,-12,-11,-12,42,2,38,0,-9,-4,43,-12,-10,-13,17,34,37,-11,27,18,45,-13,35,-10,34,34,51 74 | 532497,male,women,21.8,4,14,14,5,11,12,13,-2,17,14,13,18,11,4,17,17,10,8,11,4,13,15,22,13,14,20,18,3,2,9,19,16,23,11,11,14,14,3,8,11,12,4,15,12,14,13,15,7,12,26 75 | 532500,male,women,37.1,0,-10,-13,-10,-10,-12,-2,-11,-12,6,-7,-12,-10,0,0,-12,-6,-12,-11,-12,-13,-13,0,-12,-13,0,-14,0,0,-1,0,-11,-6,-4,0,-11,-13,-13,-12,-9,-11,-14,-9,-12,-13,-11,-10,0,-10,-13 76 | 532501,male,either,19.7,2,-2,-1,-1,-1,-2,-2,-2,4,1,4,0,-2,-1,4,2,-2,-2,-2,-2,2,-2,2,-2,2,2,0,-1,2,2,2,-2,-1,-2,1,-2,-2,2,1,-2,-2,2,-1,-2,-2,2,-2,3,1,4 77 | 532503,male,women,18.8,-5,-4,0,-7,0,1,17,-7,-2,4,-4,16,-7,19,-4,-6,0,0,-6,-6,20,-1,7,-7,-6,-4,-2,0,23,2,18,-8,8,0,-4,-5,-5,0,5,-6,-7,-2,-5,-5,-7,-2,-5,19,9,19 78 | 532925,male,women,29.1,0,0,10,10,8,0,0,5,15,0,0,0,0,0,0,14,0,0,0,0,0,11,0,30,10,0,12,0,0,0,7,10,0,11,0,4,0,0,0,0,0,27,0,8,0,0,0,0,0,1 79 | 532926,male,men,20.2,-6,-8,-8,-6,-5,0,-11,-4,0,0,-9,-6,-8,-4,-6,-9,-2,-2,-2,-6,-6,-10,-8,-11,-12,-6,-4,-4,4,-7,-6,-8,14,-10,21,8,-2,-10,0,-12,8,-6,-5,-10,4,-6,0,21,-2,22 80 | 532928,male,men,21.2,0,0,0,0,1,2,-2,0,0,0,0,2,0,0,-2,0,0,-2,0,0,-2,-2,0,0,0,-4,0,1,1,0,2,0,0,0,0,0,0,0,-2,-3,0,-4,0,0,-1,-3,1,0,0,0 81 | 532930,female,men,18.5,-10,21,0,-7,-9,-9,16,-10,23,31,27,-11,18,-6,-10,-7,-10,-10,17,-9,-10,-11,29,22,13,24,23,25,-8,11,-5,-9,-10,-9,-7,-9,-2,23,23,-7,-8,-8,-6,-6,-6,-7,17,23,-5,20 82 | 532931,male,men,23.8,0,0,0,-2,0,0,-2,0,2,2,-2,-6,-2,0,2,0,-4,0,2,0,0,-6,0,0,0,0,0,-2,0,2,-4,-6,0,3,0,0,0,0,0,2,0,-4,-5,-4,2,0,0,0,0,-4 83 | 533255,male,women,26.5,-1,-5,-8,0,-9,-11,8,-5,-6,25,-2,-9,-2,-3,-12,-12,1,-9,-10,0,35,-10,-2,-10,-6,0,0,9,-5,-1,-8,-8,-2,-5,-7,0,-9,-6,-7,-2,-2,-10,-8,-6,-8,-1,-8,0,-4,0 84 | -------------------------------------------------------------------------------- /face-body/data/gender-female_type-faces_data.csv: -------------------------------------------------------------------------------- 1 | user_id,sex,sexpref,age,alexandra,anastazie,anezka,bera,bohdana,brenda,carol,christianne,dagmar,debra,dobromila,dusana,edita,eleanora,elena,eugenia,evzenie,gabriela,gejza,ida,ingrid,irena,jindriska,jitka,karina,kordula,lea,linda,livia,lujza,margita,marika,matylda,miloslava,milota,miriama,peggy,perla,radmila,sarlota,saskie,sidonia,stela,tamara,ursula,viktoria,viola,vladena,zelmira,zlata 2 | 477033,female,,30.9,-18,-19,-18,-16,-20,-18,-19,-18,-18,-18,-2,0,-18,-14,-18,-18,0,-15,-16,-12,-19,-18,-18,-10,-18,-8,-19,-9,-18,-18,-1,-16,-19,-17,-18,-18,-18,-18,-18,-18,-16,-19,-18,-18,-18,-17,-19,-18,-13,-18 3 | 478230,male,women,25.8,0,-11,0,26,0,-4,0,-10,0,0,-9,-8,-6,-3,-6,0,-8,0,-14,0,0,-10,0,-11,-3,0,-13,-14,0,-16,0,0,-13,-10,0,0,-9,0,0,-10,-3,-16,-12,-12,-11,0,0,0,0,0 4 | 478291,female,women,23.5,-11,-14,0,0,7,-16,0,-15,0,0,0,11,-9,6,-13,-14,21,0,0,0,-11,3,-14,-16,-12,0,-12,0,-11,-12,-12,-12,0,-13,-15,-15,-16,-15,-14,0,0,-14,-12,0,-15,-11,-10,-15,-8,-14 5 | 479035,female,men,22.6,-13,-12,-10,-7,0,0,-9,-14,27,-14,9,-11,-14,-13,-10,-7,-10,17,-2,-13,-11,29,-12,0,3,22,41,-12,-12,-9,30,38,-11,-12,0,19,20,-2,-10,3,-11,-11,-11,-14,-14,21,-13,-13,17,10 6 | 489018,male,men,26.8,-11,-9,-12,-11,0,-10,-11,-12,-3,-10,-6,-12,-6,-12,-9,-13,-8,-11,-5,-10,-7,-12,0,-10,-9,-11,-12,-10,-7,-10,-10,-11,-10,-10,-12,-5,-9,-6,-8,-3,-11,-11,-8,0,-8,-13,-11,-9,-11,11 7 | 501705,female,either,21.3,-12,-8,-5,53,31,-12,-10,-8,-10,-11,-4,-4,-12,0,-10,-10,-12,-12,-11,60,-10,-10,-5,-9,61,0,-10,-3,-10,-6,-9,-6,-10,-9,-7,50,40,-3,-12,-8,-10,-12,32,-11,40,35,30,47,103,-7 8 | 514003,male,women,30.1,-9,4,-10,-10,3,-12,-6,-9,-13,-11,-13,-13,-10,-12,-11,-10,-8,-12,-11,-11,-10,-11,-11,4,-9,-10,-15,-10,-12,-13,4,3,-11,-10,-13,6,-10,-9,-10,-9,-12,-11,-9,-11,-10,-12,-11,-11,-10,-9 9 | 518109,female,men,20.1,7,-5,-2,-10,19,4,2,-6,-3,-7,-3,-13,-5,-2,-2,12,-1,14,-7,16,42,10,13,-7,23,-2,15,16,-10,-8,25,-12,-3,-7,-6,-8,3,6,4,10,-12,0,-2,2,-6,-2,35,1,12,4 10 | 531458,male,women,19.8,-17,0,-13,179,-11,-17,-17,-15,-5,-17,-19,-13,-18,-8,-15,0,-13,-18,0,-16,-5,-22,-13,-19,-18,-15,-19,-9,-15,-18,-17,0,-4,-9,-12,-19,35,-19,0,-14,-9,-13,0,-21,-11,-20,-12,-23,-19,0 11 | 531826,male,women,27.2,-7,-6,4,16,0,-10,-7,-1,12,-4,-6,-4,-8,-7,0,0,-5,-10,-7,0,-3,-4,-7,-8,-3,-10,-4,-7,-8,-6,7,-5,-14,-7,-5,-7,11,0,-12,-9,0,-8,-9,-6,50,-8,-10,-4,21,-7 12 | 531966,female,men,20.7,-9,0,15,9,28,-1,-1,0,1,-8,23,4,0,-6,0,-5,-3,0,-3,-6,0,7,11,-2,-2,1,8,-10,6,-4,18,27,4,-5,6,17,6,-7,10,21,-3,-4,0,7,-2,-3,0,-5,-2,-4 13 | 531967,female,men,23.1,-2,14,6,12,24,-10,-7,-3,-11,11,11,4,11,11,-10,-3,-11,1,12,-11,-6,-1,22,10,4,17,-11,-3,-12,13,15,19,23,-9,10,6,8,14,8,20,17,-9,-11,-6,-9,-9,15,-9,14,-11 14 | 531968,female,either,22.8,-8,-8,12,-3,29,41,0,4,6,0,4,0,-5,34,8,63,2,2,14,43,4,41,75,0,17,32,22,12,13,-9,-8,13,0,6,6,37,26,48,9,0,-1,0,14,24,0,41,43,13,24,-8 15 | 531969,male,women,28.1,-17,-20,-15,-15,-20,-18,-20,-18,-11,-16,-18,-19,-19,-19,-18,-19,-18,-18,-19,-19,-18,-17,-18,-19,48,-17,-20,-19,-20,-18,-18,-18,-19,-19,-17,-19,-19,-20,-19,-20,-19,-16,-18,-20,-20,-16,-17,-18,-20,-18 16 | 531970,female,men,21.5,-5,-7,-12,-5,8,-9,0,-10,-9,-10,4,-4,-7,-7,-7,-5,-10,4,-6,-8,-6,-6,-3,2,2,-10,-3,-9,-9,-2,6,-8,6,-9,-7,4,8,-7,-5,-4,-6,-7,2,5,7,15,2,5,-10,-3 17 | 531973,female,men,23.7,-12,-14,-14,-17,-13,-17,-13,-12,-14,-11,-19,-14,-13,-13,-12,-13,-14,-16,-18,-14,-15,-13,-19,-9,-18,-14,-15,-7,-12,-17,-12,-7,0,-12,-10,-20,-11,-15,-16,-20,-14,-19,-11,-20,-11,-16,-15,-20,-13,-18 18 | 531974,female,men,24.3,-10,47,-10,4,6,-11,0,-4,50,-10,12,36,-7,8,-3,-8,6,49,-6,13,-10,-9,-10,23,-8,22,30,-11,-7,-6,55,-6,42,-10,6,-10,42,15,4,-7,21,-11,-3,18,-6,27,-6,-8,-8,30 19 | 531975,female,women,34.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 20 | 531976,female,men,50.8,-3,-6,-1,-2,-1,-6,-6,-2,-1,6,11,-6,10,-6,-5,-3,-2,1,-2,5,0,7,10,12,3,0,-5,-5,-2,-1,2,13,-7,-6,10,4,-1,-1,-6,7,8,-4,-1,5,-4,-2,-1,-3,-5,4 21 | 531977,male,men,21.7,-13,0,-14,-16,-5,-13,0,-4,7,-6,0,-4,-4,-2,0,-18,-11,0,-7,-15,-18,6,-17,-5,2,-8,-2,-9,0,0,0,1,0,-8,-3,6,0,21,-18,0,-2,0,-2,0,4,-10,-9,-3,-9,0 22 | 531978,male,women,22.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 23 | 531980,female,either,18.9,-10,4,-13,-1,13,-11,-5,-10,-11,-10,-10,-9,-8,-12,-12,-11,-12,-9,-11,-11,-12,-9,3,-9,-10,-11,-12,-6,-10,-8,10,-7,-7,-10,-8,4,11,-11,-10,3,-11,-6,-10,-12,-9,-4,1,-7,-10,2 24 | 531981,male,women,28.3,39,1,0,67,40,14,-5,30,60,48,24,42,17,16,1,2,-12,50,1,6,-8,18,44,46,13,18,0,14,76,3,0,18,-6,11,1,28,18,52,25,25,82,42,-4,2,18,48,40,14,2,54 25 | 531982,male,neither,21.4,-3,-7,-4,0,-9,-5,0,-7,-9,-6,14,-1,-2,-10,-7,-12,-3,-5,-2,0,-13,0,-7,-5,-6,-4,0,-9,0,-3,-7,0,-3,-4,-10,-10,-6,0,-6,-4,-6,-7,-8,-3,-4,-4,0,-3,-3,0 26 | 531983,female,men,44.6,6,0,-2,0,0,-12,-7,-7,11,-9,-9,-6,-8,0,-10,-8,-5,-7,-9,-8,15,-4,-6,0,8,-4,-5,-5,-8,0,13,-9,0,-10,-4,-6,0,0,-5,7,-9,-8,-2,0,-5,-5,-3,-8,0,3 27 | 531984,male,,22.7,-16,-19,-18,-18,-18,-17,-17,-18,-17,-17,-17,-19,-15,-19,-12,-17,-20,-9,0,-19,-15,-16,8,-18,-18,-14,-18,-15,-19,-16,-18,-11,-16,-16,21,-17,-6,-17,-16,-9,0,-11,-14,-16,-11,-9,-16,-15,-18,49 28 | 531985,female,men,20.2,-12,-15,-12,-13,-14,-11,-16,-12,-13,-13,-16,-11,-14,-14,-14,-16,-14,-12,-16,-13,-13,-15,-15,-13,-14,-13,-14,-10,-13,-14,-11,-10,-11,-15,-16,-13,-13,-12,-15,-10,-15,-15,-16,-15,-15,-13,-13,-10,-12,-15 29 | 531986,female,men,22.2,-15,-14,-12,-8,-17,-15,-15,-15,-15,-15,-15,-14,-15,-16,-17,-15,-14,-14,-14,-16,-13,-16,-9,-15,-16,-11,-17,-16,-17,-15,-16,-14,-16,-15,-16,-16,-16,-12,-16,-16,-3,-15,-17,-14,-16,-12,-16,-16,-15,-14 30 | 531987,female,men,20.3,-14,-12,-14,-16,-14,-14,-11,-13,-16,-16,-14,-18,-14,-15,-15,-15,-14,-15,-16,-15,-15,-15,-14,-14,-15,-15,-16,-15,-14,-15,-14,-14,-13,-11,-9,-14,-16,-17,-14,-15,-16,-15,-15,-16,-17,-13,-14,-15,-14,-16 31 | 531988,male,women,20.4,-8,4,-7,6,-7,0,5,-6,-5,-7,-9,-4,-8,-10,3,-2,1,13,-10,6,-9,0,-8,19,2,4,-7,2,0,2,-7,-7,-7,-9,5,7,6,-4,-8,-4,13,5,-9,-8,2,-5,0,-6,-9,-6 32 | 531989,female,men,22.2,-14,-15,-16,30,-14,-17,-16,-14,-18,-11,-14,-14,-15,-16,-14,-16,-16,-14,-15,-13,-15,-12,-13,-14,-18,16,-15,-17,-14,14,-13,-17,18,-16,-16,-15,18,-15,-17,-14,-16,-16,-12,-17,-13,-14,-16,-13,2,30 33 | 531990,male,women,18.2,-13,-12,-13,-14,-8,-11,13,23,21,-13,-5,11,-11,-13,-12,-11,-13,-9,-13,-10,16,-10,32,-13,-8,-10,-4,27,-14,-11,-6,21,-16,-10,17,42,46,36,-10,13,-13,-7,-11,-12,11,40,23,-13,-14,-9 34 | 531991,female,men,22.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 35 | 531993,male,men,24.0,-15,3,-16,0,-5,-16,-20,-11,0,-13,-16,-12,0,6,-12,-19,-15,-17,0,0,-17,0,10,-18,-17,2,-15,-17,-13,-10,-14,-12,-17,-14,17,5,6,-16,-17,5,6,-13,-16,-17,-15,-15,-10,4,-12,-13 36 | 531995,female,neither,21.7,-15,-16,-14,-16,-17,-16,-15,-16,-14,-14,-16,-15,-15,-14,-15,-15,-16,-15,-16,-16,-16,-16,-15,-16,-16,-13,-16,-16,-15,-15,-14,-12,-14,-18,-16,-15,-15,-16,-14,-14,-15,-15,-15,-17,-15,-14,-15,-16,-16,-13 37 | 531996,female,men,24.9,-13,-13,-13,-11,-12,-13,-12,-13,-12,-10,-10,-13,-13,-12,-12,-12,-12,-13,-12,-12,-11,-11,-10,-12,-12,-12,-13,-12,-12,-12,-4,-12,-13,-11,-12,-10,-13,-10,-13,-12,-13,-11,-11,-12,-12,-12,-8,-13,-12,-12 38 | 531998,female,men,41.0,62,65,70,70,62,52,69,75,60,46,60,66,62,63,60,60,60,40,58,60,36,66,60,44,56,68,74,73,62,45,61,59,59,75,63,53,62,65,33,49,46,73,60,68,46,46,60,74,14,61 39 | 531999,male,women,29.5,-14,-12,-3,5,-2,-10,-12,-13,-10,-6,-13,-13,-9,-12,-9,-16,-13,-13,-16,-8,-3,-10,0,-4,-4,0,-12,-18,-20,-9,-13,-15,-11,-12,-4,-20,6,-5,-15,0,-12,-17,-15,-18,-17,-2,2,-3,-16,-11 40 | 532000,female,men,19.5,-10,47,-6,36,13,-9,-10,-9,0,-5,-8,-9,-7,-6,-6,-8,-5,-9,-9,-10,-7,-4,-8,-9,-9,-6,-6,-9,-10,-7,18,23,-8,-9,28,-5,0,-7,-2,-8,-7,-8,-10,-10,-8,-11,-8,-9,-7,-6 41 | 532182,female,women,21.1,-6,32,0,-1,36,0,-7,0,0,0,1,-4,-4,-10,-10,-8,-12,0,14,-2,-8,30,41,0,40,0,0,20,0,1,0,0,0,0,0,0,42,-7,-6,0,38,0,0,-8,0,0,0,0,32,-10 42 | 532183,female,men,43.4,-3,-4,-7,8,5,-1,-9,-4,-2,-7,-4,-6,-2,5,-8,-4,-5,-4,-4,-5,2,-6,6,6,5,-6,-6,-7,-4,-4,-3,4,-1,-5,3,-2,-4,-4,-8,-4,2,-6,2,-3,-4,2,-6,-4,-6,1 43 | 532184,female,men,52.2,-2,2,-2,2,2,2,-2,-2,-1,-2,-2,-2,-2,-2,-2,2,-2,-3,-2,-2,2,-2,2,2,2,2,-2,-2,-2,-2,2,-2,-2,2,2,-2,2,2,-2,2,2,-2,-2,-2,-2,-2,2,-2,2,2 44 | 532185,female,men,24.4,-2,0,-2,-1,-3,0,-2,-2,-2,-1,-2,-2,-2,-2,-2,-1,-1,-2,-2,-2,-1,-1,-1,-2,-1,-1,0,-2,-2,-2,-2,0,-2,-2,-2,-2,-3,-1,-3,-1,-2,-2,0,0,-2,-1,-1,-1,0,0 45 | 532192,female,men,21.0,-9,-8,0,-11,0,-10,0,-11,0,-11,-5,0,-11,-10,-12,0,-13,0,-11,-9,-11,-9,0,-12,0,-11,-11,0,-13,-10,0,-9,-11,-12,0,0,0,0,0,-11,-12,-9,-10,0,-11,-10,0,-10,-10,-12 46 | 532213,male,women,19.9,-9,-12,-12,0,-10,-11,-12,-10,-10,-11,-14,-13,-11,-14,-13,-13,-12,-12,-12,-14,-12,-2,-8,-12,-14,-13,-14,-12,-13,-13,3,-14,-8,-11,-4,-13,-5,-9,-13,-14,0,-12,-8,-12,0,-12,-12,-12,-10,-13 47 | 532214,male,women,18.6,-14,-10,-12,-6,-12,-10,-10,-12,-5,-11,-9,-7,-10,-14,-11,-8,-10,-11,-4,-6,-13,-10,-12,0,-12,-14,-13,-2,-10,-13,-10,-12,-12,-9,-7,-10,-12,-11,-10,-11,-11,-11,-10,-10,-11,0,0,-11,-10,-11 48 | 532215,male,women,20.3,-11,4,-6,7,0,-11,-1,-14,-4,-12,-2,-8,-6,7,-12,-13,-14,-5,-10,-9,-10,-17,-11,-10,-16,-8,-7,-10,-8,-14,-10,-12,-8,-12,-8,5,3,-10,-12,-6,-12,-12,-12,-7,-6,-12,-3,0,-10,-8 49 | 532216,male,women,21.9,-13,0,-14,-11,-14,-16,0,-14,-14,-12,-3,-12,-14,-16,-13,0,-12,-8,0,-15,-13,-15,-14,0,-13,-10,-16,-17,-14,0,-12,-15,-14,-15,-5,-16,-11,-12,-13,-15,-11,-16,-17,-13,-12,-2,-17,-7,-14,-15 50 | 532217,male,women,18.3,-10,17,-9,-9,-9,12,-10,-10,-1,-8,-8,30,3,-8,-7,-6,-10,-9,2,0,-6,-7,-9,-9,-7,-8,-11,-8,-8,-8,25,-9,-7,-10,-8,23,24,-7,-10,-8,-3,-7,13,-10,16,-10,-9,-10,-10,24 51 | 532218,female,women,19.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 52 | 532219,female,men,19.7,-10,-7,0,17,-7,-10,-12,-2,-2,-9,0,-11,-11,-8,-9,9,-12,0,-10,-10,4,-12,-13,2,-10,-10,7,-9,-8,-8,24,-4,-11,3,-11,12,2,4,6,0,-9,-8,-12,-11,-10,-11,-10,-11,15,5 53 | 532220,female,men,36.9,-10,0,-11,0,0,2,-13,-12,0,-11,-6,-11,-11,-12,0,0,-13,0,-8,-9,0,-9,-7,0,0,-12,-9,-9,0,-11,0,-11,0,0,-2,-9,0,5,-3,0,0,-11,-12,-8,0,-8,-9,0,-12,0 54 | 532221,female,men,25.0,-14,59,-18,-14,-18,-15,-17,-17,-13,-18,-16,-16,-18,-13,-15,-17,-17,-17,-14,-17,-17,-16,24,15,-16,45,-14,-18,-18,-8,-16,-11,-16,-16,81,-15,-18,-18,-16,-15,-3,-18,-16,-16,-17,-17,-15,-13,-14,-16 55 | 532222,male,women,21.7,-14,-9,-13,-9,-9,-10,-12,-12,-17,-15,-12,-19,-10,-14,-17,-8,-10,-10,-12,-14,-10,-14,-8,-6,-14,-11,-8,-17,-10,-18,5,0,-13,-16,0,-9,-2,-10,-11,-4,-13,-11,-17,-15,-12,-6,-12,-13,-15,-15 56 | 532243,male,women,19.7,-4,0,-5,10,0,-3,-3,0,6,-2,-3,-8,0,-5,-8,-2,-6,-2,0,-6,0,-3,-5,16,0,-4,0,-6,-2,-5,4,-2,0,-5,0,0,6,-2,0,0,-2,-6,0,-5,-4,0,0,-5,-2,7 57 | 532244,female,men,18.5,-9,-8,-9,19,16,-7,-10,-8,-8,-11,-9,-8,11,-10,-9,-4,-11,-11,20,-12,-10,-11,32,16,-8,26,-8,-10,-9,-8,0,-11,-11,26,-7,15,-9,1,22,17,-10,-9,-11,-8,-13,-10,-7,-9,-6,21 58 | 532245,female,either,18.1,-10,11,-10,4,21,1,-6,6,15,-2,6,-9,-4,4,5,1,-10,11,5,4,15,0,10,19,0,7,8,9,5,0,21,19,13,-4,15,7,4,25,0,7,6,-3,-6,-2,4,8,9,6,32,9 59 | 532246,male,women,19.2,-3,-4,-14,-11,4,-14,-10,-6,7,-9,-14,-13,8,0,-9,-5,-12,-10,-16,-13,-14,9,10,-13,-8,-13,-12,-9,-12,-11,13,-14,-13,-5,23,8,9,2,1,9,-8,-12,-16,-4,-10,-5,6,1,2,-12 60 | 532247,male,women,18.2,-13,-15,-11,-13,-10,-13,-15,-14,-14,-14,-14,-13,-10,-13,-14,-14,-13,-15,-14,-10,-10,-13,18,-14,-8,4,-6,-10,-10,-11,27,30,-8,-14,-9,-14,-14,-7,-13,-14,-14,-14,-14,-14,-11,-14,-14,-14,-12,-14 61 | 532249,male,women,25.3,-7,-3,-14,-14,-4,-11,-13,-12,-3,-5,-9,-10,-14,-12,-14,-14,-13,-7,-7,-12,-4,-10,-3,-3,-9,-14,-7,-11,-13,-10,-1,-11,-12,-6,-5,-4,-5,-5,-4,-5,-12,-13,-10,-10,-8,-6,-13,-14,-12,-3 62 | 532250,female,men,19.0,-6,-4,-2,16,10,-1,-1,-1,0,-5,1,-6,-1,-5,-2,0,-1,3,4,-2,-4,-5,4,-4,-5,5,5,-2,-1,0,7,3,9,-7,14,3,-5,1,-7,-4,-5,-5,-4,-3,-8,20,2,8,-4,-5 63 | 532251,female,men,20.9,-10,-5,-13,-2,4,-4,-11,7,-1,-7,-3,-14,-11,-12,-4,-7,-12,1,-12,-14,-5,-12,4,4,11,-4,-6,-5,-11,-11,-5,-5,-6,-5,-2,0,0,-3,-13,0,-10,-13,-10,-6,-4,16,-1,-5,-13,-8 64 | 532252,male,women,23.2,-20,-21,-21,-17,-18,-15,-20,-13,-19,-17,-19,-21,-20,-7,-13,-21,-10,-19,-18,-17,-16,-8,-17,-19,-16,-18,-13,-15,-20,-19,-16,-13,-17,-17,-20,-19,-16,-5,-18,-16,-12,-18,-19,-18,-17,-19,-18,-18,-17,-20 65 | 532253,female,either,28.8,-13,-13,-13,-11,0,-14,-9,-13,-12,-15,-12,-7,-12,-15,-14,-13,-10,-14,-15,-13,-15,-13,-13,-14,-13,-15,-12,-9,-12,-14,-10,-10,-14,-10,-12,-15,-8,-13,-11,-16,-12,-16,-14,-13,-7,-11,-11,-12,-11,-13 66 | 532254,female,either,19.6,-5,-3,-9,0,9,-11,0,-12,3,-6,-2,-12,-11,7,-12,-6,-11,-1,-10,-6,-8,9,0,-9,0,-1,-14,-6,0,-12,-8,5,-11,-7,0,0,-7,-9,0,-8,3,-11,-10,10,-11,-2,-5,-6,-9,-5 67 | 532255,male,women,21.5,-10,57,-13,32,35,35,-14,-11,22,-8,26,-14,6,-14,-13,44,-11,23,-14,-15,56,-10,33,40,-2,33,34,-10,-14,-9,25,11,-13,43,-9,45,10,53,-14,53,25,-12,15,9,-11,29,11,-10,13,59 68 | 532256,female,neither,18.6,-2,-2,-4,-10,16,7,10,6,-6,-2,9,5,3,8,-3,-3,-5,-2,-7,0,-7,11,8,11,10,-5,8,1,4,9,10,3,11,-2,-7,12,10,6,0,8,-5,-7,-6,-4,10,8,-6,-2,-3,-9 69 | 532257,female,men,19.3,0,12,16,3,-2,-4,-10,-10,-6,-10,0,-8,-8,-6,-8,-12,-10,0,4,-10,0,-4,-6,0,0,-8,-6,-3,0,9,15,-3,-8,7,-4,13,3,0,-9,-9,-5,0,7,-11,0,-6,-8,6,-10,2 70 | 532492,male,men,25.1,-13,-12,-13,-13,35,-13,13,-14,35,-11,-10,-12,-11,-13,14,19,-11,-12,-11,-9,31,10,14,-12,-11,-10,-4,-9,-9,-11,0,20,-14,16,-12,0,-12,21,-12,-13,-12,-11,-10,-10,17,-9,35,-12,-12,32 71 | 532493,male,women,18.5,-13,38,-13,28,-7,-12,-11,-12,22,-9,-11,-11,-9,-11,-6,-17,-9,-10,-9,-16,12,9,-4,25,-11,9,-2,-11,-9,-15,12,17,-11,-5,9,-9,15,9,40,-12,-10,-11,-6,-11,-11,-14,-15,-12,-15,22 72 | 532495,male,women,19.4,-3,-12,0,-9,0,-12,-8,-13,0,0,-9,-13,0,-7,-13,0,-9,-6,-13,-9,-7,0,-8,-6,-10,-10,-12,-9,-13,-10,0,-8,-13,-11,0,10,0,-11,-9,0,0,-11,-9,-12,-9,-4,0,-10,-12,-3 73 | 532496,male,,18.9,3,-12,0,66,0,-6,-13,-12,-8,-12,0,-13,0,0,-13,-12,-12,30,-11,0,-11,-12,-10,58,0,-10,-12,-12,-11,0,27,-11,-8,-10,38,0,0,65,-9,38,-13,-12,-12,0,-12,-11,0,-14,-12,60 74 | 532497,male,women,21.8,41,30,21,26,18,31,13,25,21,14,19,20,42,48,29,10,27,35,19,13,25,19,16,23,17,0,27,31,32,17,37,24,25,16,18,19,17,22,10,28,24,26,34,37,27,29,22,24,33,32 75 | 532500,male,women,37.1,-8,1,-11,-7,-9,-11,-12,-9,-11,-10,-8,-11,-13,-10,-17,-4,-10,-10,-11,-11,-5,-6,-2,-7,-10,-13,-10,-12,-11,-14,-10,-11,-7,-11,-12,-9,0,3,-3,-4,-5,-13,-14,-11,-8,-13,-2,-13,-2,-5 76 | 532501,male,either,19.7,2,-2,-3,6,5,4,-2,-4,2,-3,4,0,2,-2,-1,-3,4,2,-2,-2,1,0,-2,2,-2,4,1,4,1,-2,6,5,-4,-1,1,2,6,-2,0,-2,5,-2,-2,-1,4,0,2,2,2,2 77 | 532503,male,women,18.8,-10,-11,-8,-6,22,-8,-8,-9,-6,-6,-11,-6,-5,-8,-8,-6,-8,-7,22,-9,-8,-6,-8,-8,20,-6,-5,9,-7,-8,-4,-11,22,-6,-8,36,1,14,-7,-6,16,-8,-8,-7,-6,21,-6,-5,-8,-9 78 | 532925,male,women,29.1,0,0,0,18,12,0,0,0,0,0,0,0,0,0,0,0,-8,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-9,0,0,0,15,0,0,0,0,0,0,0 79 | 532926,male,men,20.2,-8,0,-8,-4,12,-10,-4,-4,-9,-4,-8,16,-3,-4,-6,0,-8,-6,-6,-2,-4,-4,-2,-4,-2,-10,12,-1,-9,-8,12,10,-6,-8,-4,-6,0,-6,-2,-6,8,-2,-10,-7,-4,0,-4,-8,-4,-8 80 | 532928,male,men,21.2,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 81 | 532930,female,men,18.5,-6,15,-8,-11,22,-10,-3,0,-4,-8,-10,-11,-8,-9,-9,19,-7,-5,-11,-9,27,-10,-8,10,-3,-7,18,-10,-8,31,32,-10,-12,-9,-7,18,17,-11,-9,-3,-5,-10,-10,-8,7,0,17,-5,-12,-10 82 | 532931,male,men,23.8,-2,1,-1,4,4,0,-1,0,0,-2,2,0,2,2,-2,5,0,2,-2,0,0,2,2,4,0,0,-2,-6,2,0,2,4,0,-2,3,2,4,4,-1,0,0,-3,0,0,2,-3,-2,-2,2,4 83 | 533255,male,women,26.5,0,-4,-12,-6,-11,-5,-13,0,-10,-7,7,-5,-8,-7,-10,-2,-10,-4,-1,-5,-3,-8,-2,-6,-7,-1,-12,-4,-10,-7,3,-6,-8,-8,-7,-4,0,-12,-11,17,-6,-10,-5,-8,-4,-4,-4,-7,-5,-8 84 | -------------------------------------------------------------------------------- /face-body/data/gender-female_type-stimuli_data.csv: -------------------------------------------------------------------------------- 1 | stimulus name,stimulus sex,age,height(cm),weight(kg),BMI,chest(cm),waist(cm),hips(cm) alexandra,female,22,178,55,17.35891933,86,61,92 anastazie,female,22,184,60,17.7221172,86,60,90 anezka,female,26,166,54,19.59645812,86,71,93 bera,female,22,162,46,17.52781588,83,60,83 bohdana,female,22,158,53,21.23057202,83,65,90 brenda,female,25,170,52,17.99307958,83,68,96 carol,female,24,165,44,16.16161616,82,61,86 christianne,female,29,166,56,20.32225287,83,67,94 dagmar,female,27,166,54,19.59645812,87,63,91 debra,female,30,164,55,20.44913742,83,64,91 dobromila,female,23,173,56,18.71094925,89,64,99 dusana,female,28,175,53,17.30612245,80,63,89 edita,female,19,169,58,20.30741221,92,80,100 eleanora,female,22,170,52,17.99307958,80,65,90 elena,female,23,167,58,20.79672989,87,79,95 eugenia,female,28,178,75,23.67125363,105,88,105 evzenie,female,22,180,60,18.51851852,91,75,99 gabriela,female,24,176,68,21.95247934,101,97,107 gejza,female,25,161,57,21.98989237,93,72,97 ida,female,21,158,49,19.6282647,87,70,87 ingrid,female,24,174,53,17.50561501,92,62,88 irena,female,23,163,63,23.71184463,96,81,106 jindriska,female,20,174,59,19.48738275,85,62,91 jitka,female,30,166,54,19.59645812,91,65,95 karina,female,30,155,48,19.97918835,88,64,89 kordula,female,25,165,48,17.63085399,84,72,85 lea,female,24,176,63,20.33832645,89,63,89 linda,female,25,165,58,21.30394858,90,64,94 livia,female,19,172,48,16.22498648,83,64,89 lujza,female,27,170,55,19.03114187,83,66,92 margita,female,23,172,57,19.26717144,88,64,93 marika,female,20,174,60,19.81767737,90,73,102 matylda,female,20,168,48,17.00680272,80,63,88 miloslava,female,23,175,59,19.26530612,90,70,95 milota,female,25,170,50,17.30103806,86,62,90 miriama,female,24,162,46,17.52781588,83,63,89 peggy,female,21,169,48,16.80613424,84,64,87 perla,female,25,166,49,17.78197126,88,60,88 radmila,female,28,166,55,19.95935549,86,68,86 sarlota,female,21,165,55,20.2020202,86,69,97 saskie,female,20,158,42,16.82422689,86,62,84 sidonia,female,29,170,64,22.14532872,104,69,93 stela,female,23,178,56,17.67453604,85,62,94 tamara,female,25,178,63,19.88385305,87,66,93 ursula,female,24,173,61,20.38156971,96,71,97 viktoria,female,20,170,60,20.76124567,92,70,92 viola,female,28,167,50,17.92821543,87,64,85 vladena,female,24,173,52,17.37445287,87,61,90 zelmira,female,26,157,52,21.09619051,86,63,89 zlata,female,25,172,56,18.92915089,88,67,95 -------------------------------------------------------------------------------- /face-body/data/gender-male_type-bodies_data.csv: -------------------------------------------------------------------------------- 1 | user_id,sex,sexpref,age,andrej,aurel,bernard,blazej,boris,bretislav,bystrik,cenek,cestmir,cornelius,cyril,dalimil,denis,dionyz,dominik,drahomir,elias,ferdinand,gabriel,hanus,henrich,hynek,josef,justin,kamil,kazimir,leonard,libor,lumir,maxim,mike,milos,mojmir,moric,oleg,oliver,patrik,prokop,ramiro,rehor,rudolf,sobeslav,stefan,svatopluk,tichomir,tomasi,valer,vasil,vendelin,vladislav 2 | 477033,female,,30.9,-4,-18,-10,-2,-16,-13,40,-16,-6,-18,-14,-18,-5,-18,-14,-18,-18,-15,-18,-16,-14,-17,-16,-15,-18,-13,-16,-13,-12,-16,-4,-18,-14,-16,-16,-17,-8,-18,-16,-18,-18,-18,-16,-16,-18,-16,-16,-18,-14,-16 3 | 478230,male,women,25.8,16,-10,-12,0,-10,-10,27,-8,0,-11,0,-10,13,-11,-13,0,0,0,-16,-15,-10,-7,-4,0,-10,-12,-10,0,0,0,-10,-10,-9,8,0,-11,-13,0,0,-12,-11,-10,0,-10,0,6,0,-15,0,0 4 | 478291,female,women,23.5,-2,-15,0,-16,-17,0,-14,-8,-8,-15,-17,-15,-18,-14,-16,-13,1,-16,-4,-2,-15,-15,-17,7,-15,-16,-16,-12,-14,-15,1,0,-10,-9,-15,4,-16,-12,-15,-15,-15,-15,-17,-15,-11,-14,-12,-17,-1,-11 5 | 479035,female,men,22.6,36,21,-11,40,-10,41,19,-14,-5,-4,28,16,21,9,-10,-11,20,-11,-12,-12,-8,-10,-8,-13,-8,-13,11,-13,21,-8,-10,-12,21,29,-13,-11,-11,-14,-7,-13,14,-12,-9,7,-14,26,18,14,30,-8 6 | 489018,male,men,26.8,85,21,-6,50,11,52,84,-1,7,28,75,34,92,-7,43,-12,51,-9,19,20,27,39,18,20,-11,29,38,18,20,42,40,32,53,31,-12,37,32,77,19,22,9,-9,22,37,-10,53,66,76,114,35 7 | 501705,female,either,21.3,45,33,0,18,0,-7,1,-9,6,-9,-5,-2,29,44,-7,-13,33,-11,-9,-11,-8,-4,24,1,-5,-11,-7,-1,12,-4,0,-12,31,0,0,-9,-10,1,-7,15,0,-12,-9,1,-11,37,4,33,6,40 8 | 514003,male,women,30.1,-10,-11,-10,-12,-3,-9,-9,-8,-13,-11,-10,-9,-11,-12,-12,-8,-10,-10,-10,-11,-10,-12,-11,-9,-9,-11,-9,-9,-10,-9,-11,-10,-6,-10,-11,-11,-6,-8,-10,-5,-5,-12,-10,-10,-10,-10,-11,-9,-10,-10 9 | 518109,female,men,20.1,22,3,4,1,2,3,17,-11,-6,-2,13,0,17,-7,0,-8,14,-8,25,15,-7,5,-8,-10,-8,-11,12,-2,-3,3,-2,-6,12,-1,0,-9,34,-1,19,-7,10,-5,-10,-6,-11,84,-6,0,19,-4 10 | 531458,male,women,19.8,-1,-9,-11,3,-13,-15,5,-12,6,-13,-13,-12,-16,3,2,-12,-14,-7,-14,-15,-10,-13,-14,-15,-16,-10,-14,-11,-17,-11,-2,-9,-16,-15,-15,-17,-18,-15,-13,-13,-10,-13,-15,-2,-18,-18,-14,6,-2,-13 11 | 531826,male,women,27.2,0,-12,-10,-15,-8,-4,2,-11,-13,-6,-12,-12,-14,-8,-10,-15,-13,-6,-14,-14,-14,-9,-12,-11,-14,-9,-12,-14,-11,-9,-6,-13,-12,-10,-11,-13,-8,-10,-13,-11,-11,-10,-6,-11,-13,-8,-6,-13,-15,-17 12 | 531966,female,men,20.7,10,4,-9,-6,-4,14,12,-5,-8,-8,-6,8,29,-7,-7,-7,-7,-11,-10,-10,-12,-5,-3,-8,-5,-8,0,-11,6,-7,11,-11,12,-2,-8,-7,-6,-4,7,-9,0,-9,-9,23,-9,10,0,-5,-6,-10 13 | 531967,female,men,23.1,10,6,-4,22,13,6,9,-11,7,2,-4,-5,8,-5,10,-10,12,4,9,8,8,14,4,-9,-7,-1,-5,-7,-4,12,14,-10,9,18,3,6,6,-10,8,-2,-10,-8,-7,0,-6,-6,6,12,7,-3 14 | 531968,female,either,22.8,29,15,0,12,-3,10,22,0,10,14,5,8,13,13,-10,0,32,31,24,6,-9,2,5,4,0,0,8,9,10,12,-2,13,8,32,0,5,7,0,10,14,9,0,-6,5,3,8,13,6,10,-6 15 | 531969,male,women,28.1,-20,-21,-13,-8,-22,-20,-13,-17,-19,-19,-20,-20,-20,-20,-16,-16,-20,-19,-15,-13,-19,-19,-20,-20,-20,-19,0,-20,-17,-20,-20,-18,-17,-17,-14,-18,-20,-20,-21,-18,-20,-20,-20,-19,-21,-18,-16,-20,-17,-20 16 | 531970,female,men,21.5,10,1,-6,19,-9,8,25,-12,2,-8,-5,4,0,4,-9,-11,7,-8,6,-1,-10,-7,-10,-11,-13,-12,-7,-9,2,0,3,-6,10,2,-8,-13,-2,-8,4,7,-11,-7,-2,3,-14,37,-9,4,-11,-14 17 | 531973,female,men,23.7,-7,-9,-13,-7,-13,-11,-5,-13,-10,-13,-12,-8,-14,-8,-12,-9,-7,-13,-9,-7,-9,-12,-13,-13,-9,-15,-12,-13,-11,-13,-6,-13,-14,-12,-11,-12,-10,-15,-13,-5,-14,-12,-6,-14,-8,-15,-9,-11,-11,-8 18 | 531974,female,men,24.3,-3,-7,-4,5,-8,1,-8,-2,0,3,8,-1,9,-6,-10,0,-8,6,-10,0,-5,-8,-4,-8,12,9,-11,-4,-5,-10,0,-4,-3,-12,10,-6,-6,-4,0,-3,7,4,0,-8,5,-9,-4,-9,-8,-8 19 | 531975,female,women,34.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 20 | 531976,female,men,50.8,0,0,0,0,-1,0,0,0,0,0,1,0,0,0,0,0,-1,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,1,0,0,0,0,0,0,-1,0,0,1,0,0 21 | 531977,male,men,21.7,0,0,-6,0,18,0,6,0,0,-5,-17,0,0,-11,-14,-11,0,0,-3,0,-7,1,0,-15,-1,-1,0,15,-12,7,0,12,-1,0,0,-4,0,6,3,0,-4,-2,0,45,-5,5,1,-6,0,-2 22 | 531978,male,women,22.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 23 | 531980,female,either,18.9,15,-10,-9,-11,-11,17,29,-13,5,-12,2,4,-8,-3,-10,-12,8,-9,-10,-6,-9,9,-10,-11,-11,-12,4,-12,-4,-7,-9,-9,10,-11,-9,-11,-10,-8,16,-10,-10,-12,-5,-11,-11,-5,-10,-6,-10,-10 24 | 531981,male,women,28.3,0,0,0,0,0,0,0,0,-3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-4,0,0,0,0,0,0,0,0,0,0 25 | 531982,male,neither,21.4,0,2,0,-6,0,0,3,0,7,4,-5,-3,0,4,0,0,0,0,0,-6,0,0,0,-6,0,-8,-4,-5,0,0,0,-3,0,-4,0,0,-7,-4,8,-6,-6,-4,0,-1,0,0,0,0,0,-8 26 | 531983,female,men,44.6,0,-8,-12,0,14,6,0,-9,0,-4,0,0,0,0,0,0,13,9,0,0,0,0,2,-6,-9,0,0,0,4,0,-9,-7,23,5,0,-6,0,0,0,0,0,-9,3,0,0,0,0,0,0,0 27 | 531984,male,,22.7,82,-12,-16,16,-8,98,95,-19,-12,0,-14,-16,158,-9,0,-20,-8,-18,-13,189,-11,-15,-11,-18,-15,-16,-20,-16,-18,180,71,-17,-18,-19,-19,-14,-18,-10,-18,-17,-17,-18,-16,40,-6,136,169,-9,-16,14 28 | 531985,female,men,20.2,-14,-15,-15,-14,-14,-12,-8,-14,-15,-14,-15,-14,-15,-15,-16,-15,-16,-11,-15,-13,-13,-16,-15,-13,-14,-16,-14,-16,-15,-15,8,-15,-15,-15,-12,-16,-15,-15,-15,-15,-14,-16,-14,-16,-13,-14,-14,-9,-14,-15 29 | 531986,female,men,22.2,18,-16,-2,3,26,11,6,15,64,-14,-6,8,-9,-13,14,5,22,-14,-13,-13,16,23,31,6,-11,-15,-7,13,-8,-14,-12,-14,-16,22,-12,-8,5,34,6,13,59,-14,-4,2,-14,10,-11,13,1,9 30 | 531987,female,men,20.3,-11,-14,-15,-14,-17,-9,-5,-17,-11,-12,-14,-14,-13,-16,-13,-14,-12,-15,-17,-15,-15,-12,-14,-15,-17,-16,-10,-8,-17,-16,-5,-16,-11,-14,-11,-14,-14,-15,-15,-12,-16,-15,-14,-12,-13,-17,-11,-14,-13,-13 31 | 531988,male,women,20.4,-17,-16,-14,-14,-16,-13,-4,-14,-15,-13,-14,-9,-9,0,-11,-10,-12,-15,-12,-16,-11,-12,-11,-13,-14,-13,-15,-16,-9,-13,-15,-12,-16,-8,-15,-15,-14,-8,-16,-9,-15,-10,-14,-10,-9,-16,-13,0,-12,-13 32 | 531989,female,men,22.2,-17,-16,-16,-15,-15,-14,-16,-15,-14,-14,-17,-13,-16,-16,-16,-7,-16,-13,-15,-17,-14,-14,-15,-15,-14,-16,-14,-17,-14,-15,-16,-15,-16,-16,-7,-15,-16,-11,-14,-15,-16,-16,-16,-16,-5,7,-14,-13,-15,-17 33 | 531990,male,women,18.2,11,10,-12,11,30,11,35,-9,7,-14,-12,21,19,5,-13,-11,-8,-12,-10,-9,-11,-6,-10,-15,-2,21,0,-12,13,17,0,-11,18,15,9,-14,14,-11,30,14,20,-10,9,15,-7,41,-9,0,10,18 34 | 531991,female,men,22.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 35 | 531993,male,men,24.0,9,-5,10,0,5,3,65,-18,-10,0,-17,-16,-17,-13,-14,-20,12,10,11,-19,-9,6,-3,-22,-17,-18,-17,13,6,10,-17,4,33,0,4,-10,0,-16,17,5,-16,-14,-19,2,10,-14,4,-14,-16,-13 36 | 531995,female,neither,21.7,-14,-11,-15,-10,-12,-14,-15,-16,-12,-2,-13,-12,-14,-14,-15,-11,-13,0,-13,-10,-11,-12,-14,-14,-14,-13,-13,-1,-14,-12,-14,-11,-13,4,-14,-14,-16,-13,-14,-12,-12,-13,-4,0,-11,-4,-12,-12,-12,-7 37 | 531996,female,men,24.9,-13,-13,-12,-12,-13,-12,-12,-3,-12,-12,-13,-12,-14,-12,-12,-13,-11,-12,-13,-12,-13,-13,-13,-11,-14,-13,-12,-13,-12,-12,-12,-13,-12,-12,-12,-13,-11,-12,-12,-13,-12,-14,-13,-12,-12,-13,-11,-13,-13,-11 38 | 531998,female,men,41.0,0,0,-14,0,2,0,36,-14,19,0,0,0,0,-13,0,7,41,0,0,6,0,0,0,0,-12,0,0,0,0,0,23,-11,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,0,0,0 39 | 531999,male,women,29.5,-10,0,-14,-7,0,-6,-12,-2,-12,-12,-12,-13,-14,-10,-13,-12,-15,-12,-11,-11,-9,-10,-12,-13,-13,-13,-14,-13,-13,-13,-12,-14,-13,-13,-11,-14,-10,-2,-12,-11,-13,-12,-14,-11,-8,4,-12,-10,0,-11 40 | 532000,female,men,19.5,5,-12,-11,17,-10,37,28,-12,-6,-12,-10,-11,0,-6,-12,-10,-11,-8,-10,-9,-10,-9,-9,-10,-9,-12,-11,-7,-5,-12,-4,-11,-10,-10,-11,-12,-9,-9,23,-7,-10,-8,-11,0,-10,-10,-9,-10,-11,-8 41 | 532182,female,women,21.1,-7,-8,-6,0,-2,7,32,-8,-9,6,-10,6,-5,-8,1,-9,-4,-10,6,0,-6,-4,-4,-6,-2,-6,-4,-7,-9,-4,0,-7,-5,8,5,-7,0,-7,4,-8,-5,-2,-9,-9,-10,0,-2,-6,-5,7 42 | 532183,female,men,43.4,8,5,-3,13,3,9,11,-4,-4,3,6,7,-2,3,1,-2,7,-2,7,4,0,4,-4,0,-2,3,-2,2,-4,-3,-2,-1,12,11,-2,-5,8,-4,11,4,4,-2,4,17,-4,10,4,2,5,-6 43 | 532184,female,men,52.2,-4,-4,2,4,3,4,4,-4,4,-3,-4,4,-4,4,-4,-4,4,4,2,4,-4,2,-3,-5,-3,-4,2,4,-4,-4,4,-4,-4,-4,4,-6,-4,-4,-4,4,-3,2,-4,-4,4,-4,-4,-4,-4,-4 44 | 532185,female,men,24.4,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0 45 | 532192,female,men,21.0,0,0,-12,0,0,0,-12,-12,0,-11,-8,-4,0,0,0,-12,-12,-11,0,-11,0,0,0,0,-14,-10,-12,0,0,-11,0,-2,0,0,-14,0,0,0,-12,-2,-12,-8,-11,0,-12,0,0,0,-14,-11 46 | 532213,male,women,19.9,-10,-14,-14,-14,-13,-13,-9,-14,-13,-14,-14,-13,-14,-14,-14,-11,-14,-13,-13,-12,-14,-14,-12,-12,-14,-12,-15,-13,-14,-14,-14,-12,-14,-14,-13,-14,-14,-14,-14,-13,-15,-12,-14,-13,-14,-8,-14,-14,-14,-14 47 | 532214,male,women,18.6,-18,-22,-19,-21,-17,-18,-9,-20,-18,-12,-16,-20,-20,-17,-20,-20,-18,-14,-1,-20,-20,-16,-19,-20,-19,-18,-16,-16,-18,-19,-18,-15,-17,-21,-18,-19,-20,-15,-13,-18,0,-18,-19,-14,-20,-14,-18,-12,-16,-16 48 | 532215,male,women,20.3,17,-9,-12,-4,-13,8,2,22,-14,-2,-13,2,10,4,-13,-12,-10,-10,-7,-14,-11,0,-11,-10,-14,-9,-4,-7,-12,-3,-2,-12,4,-15,-10,-1,-7,0,-15,-7,-8,-5,-9,-4,-3,33,0,-10,6,-6 49 | 532216,male,women,21.9,-17,0,0,-16,0,0,0,0,-15,-16,-9,-14,-16,-16,-6,0,-17,-16,0,0,-15,0,0,-15,0,0,-16,0,-16,0,7,0,-15,0,-15,0,0,0,0,0,-14,-15,6,19,0,-16,-17,0,-15,0 50 | 532217,male,women,18.3,16,13,-12,-5,-9,15,38,-9,26,-12,12,1,25,-11,-8,-5,25,-12,21,-9,-7,-9,-10,-11,-9,-11,-6,-9,-10,-10,11,-10,-8,10,-12,-9,-10,13,-5,-10,-8,-12,12,-10,-9,11,-7,-8,-9,15 51 | 532218,female,women,19.8,-4,0,0,-9,0,0,-9,0,0,0,0,-7,0,-8,-7,-7,-5,-3,-2,0,-7,0,-7,0,0,-6,0,0,-8,-6,-8,-8,-7,-3,-6,-7,-4,-8,0,0,0,0,-8,0,-6,0,-7,-1,-3,0 52 | 532219,female,men,19.7,12,-10,-11,11,-1,30,30,-11,-11,-12,-2,-11,30,-7,-11,-11,24,-10,-8,-12,-11,-11,0,-10,-8,-13,-12,0,-10,-11,6,-12,18,-10,-12,-9,-9,-14,13,-12,-13,-10,-13,15,-8,19,-12,-12,16,2 53 | 532220,female,men,36.9,0,-11,-13,6,-5,4,2,-14,-6,-11,-3,0,0,-4,-12,-8,-9,-9,7,0,-11,-1,0,-9,-8,-10,-3,-8,-4,0,-3,-4,-8,0,-12,-11,-2,-3,-4,0,-7,-13,-6,-3,-6,12,0,-3,-4,-9 54 | 532221,female,men,25.0,-16,-16,-15,35,-16,-7,55,-8,-17,-3,65,61,24,-12,-14,-16,-8,-17,-16,-15,-15,37,-13,-16,-14,-13,52,-14,-16,-14,-6,-17,-15,-17,-17,-15,-17,-17,62,56,-15,-13,-16,61,-14,32,-17,-16,-17,-15 55 | 532222,male,women,21.7,-4,-16,-16,0,-17,28,42,-16,-16,-4,-9,8,0,-14,-18,-18,-2,-17,16,-16,-14,-19,-6,-19,-17,-14,-11,-16,0,-6,0,-16,-15,-8,-6,-14,-19,-13,-9,-15,-6,-8,-16,0,-18,-4,-4,-16,0,-12 56 | 532243,male,women,19.7,0,0,-3,0,0,0,3,-5,-6,0,0,0,0,-2,0,-4,0,-4,-1,-3,0,0,1,-2,-2,0,0,-2,0,0,3,0,0,-1,-2,0,0,0,-2,-6,-2,0,0,-2,-3,0,0,-1,0,0 57 | 532244,female,men,18.5,19,-10,-10,-2,6,-10,6,-10,-7,-9,0,3,-8,-8,-6,3,-8,-10,-2,-10,-9,-4,-10,-8,-4,0,-1,9,-4,2,-10,-8,-9,-6,0,0,-9,-10,10,-10,0,4,-10,0,-8,-7,0,8,0,-8 58 | 532245,female,either,18.1,10,-2,2,7,-2,20,12,-5,8,1,9,15,6,2,-4,-6,6,2,6,4,-2,-1,-2,0,0,0,-4,4,11,-1,22,8,8,13,7,4,2,-2,8,0,-2,-1,-3,1,13,7,4,-1,2,-5 59 | 532246,male,women,19.2,0,6,0,-2,-3,-4,0,-3,-2,-11,-6,-9,-5,-10,-15,-16,-2,-5,-7,0,-4,0,-12,-8,-14,-14,-4,0,-6,-6,-2,-4,5,-15,-5,-9,-8,-14,-1,-2,0,-12,-14,-7,-10,0,-9,1,-8,7 60 | 532247,male,women,18.2,-10,-8,-15,-4,-11,-14,46,31,-12,-9,-11,3,21,-12,-12,60,-14,-15,-5,-10,-7,-14,-9,-13,-16,-13,-7,-7,-8,-15,-6,-15,-9,-13,-12,-15,-14,-10,0,-8,-12,-13,-6,1,-8,8,-15,-11,-15,25 61 | 532249,male,women,25.3,-8,-14,-14,-13,-13,-3,-4,-12,-11,-14,-14,-14,-9,-16,-14,-13,-7,-14,-10,-14,-13,-14,-13,-12,-12,-15,-12,-14,-3,-13,-4,-9,-14,-12,-15,-13,-14,-12,-11,-14,-14,-14,-14,-8,-12,-6,-10,-14,-11,-15 62 | 532250,female,men,19.0,5,4,-5,7,-4,19,18,-6,-2,-5,6,3,10,-4,-6,-5,-3,-2,-4,-4,-5,3,8,-6,-6,0,-6,-6,-5,4,7,-5,9,-4,-4,-5,2,-5,-6,-2,-5,-4,-4,11,-4,12,5,-6,-4,1 63 | 532251,female,men,20.9,12,-10,-8,-5,0,-1,57,-12,-8,-6,-3,-5,5,-4,-9,-10,0,-8,5,-6,-3,-1,-2,-7,-12,-1,-13,-6,-4,-2,1,-6,-3,-11,-9,-11,-8,-3,3,-3,-7,-10,-1,0,-6,1,-3,-4,-3,-6 64 | 532252,male,women,23.2,-13,-12,-17,35,5,-18,46,-16,-17,-8,36,-2,57,-15,30,-8,-5,-18,-8,-15,8,37,-13,-11,-18,-9,-16,-17,-6,4,11,6,3,18,-19,-7,0,-6,2,-20,-7,-19,-10,-8,15,18,-12,-12,-13,0 65 | 532253,female,either,28.8,-11,-14,-10,-13,-15,-15,-15,-15,-8,-14,-14,-10,-10,-11,-14,-11,-13,-15,-9,-14,-14,-14,-13,-15,-9,-14,-14,-14,-13,-12,-15,-16,-13,-12,-14,-12,-14,-9,-14,-9,-14,-10,-11,-11,-8,-16,-16,-11,-15,-15 66 | 532254,female,either,19.6,-8,0,-10,-3,-11,0,-9,-11,-10,0,-3,0,-12,0,-8,-12,-1,-10,0,0,-10,0,-11,-5,-11,-10,0,-9,0,4,-11,0,0,0,-1,-9,-9,-10,0,0,0,-9,-8,-3,-10,0,-7,-2,-5,-9 67 | 532255,male,women,21.5,-2,-10,-14,-9,-10,39,20,-3,-2,10,-1,0,29,-2,-11,-8,23,-2,30,14,-12,0,-11,1,-10,-11,-10,15,0,-14,16,-10,0,-7,-3,-12,-3,-10,0,0,-12,-11,-10,0,-3,33,23,0,18,4 68 | 532256,female,neither,18.6,10,0,-1,-6,-1,11,6,-8,-2,4,0,6,8,-7,9,-4,-7,4,-5,8,5,6,0,6,-9,7,12,0,-4,-8,7,-6,0,2,-2,11,2,2,8,-6,4,-6,-3,12,5,4,13,9,-1,6 69 | 532257,female,men,19.3,0,0,-9,0,0,19,11,-12,-7,0,0,0,-3,-3,-11,-4,-3,0,0,-6,-10,-8,-8,-7,-8,-3,-6,0,-4,-4,0,-7,0,0,0,-11,-2,-11,-3,0,1,-6,-9,-1,-7,-3,-8,0,0,-9 70 | 532492,male,men,25.1,47,40,-9,34,54,23,145,-14,-8,-9,27,23,40,37,18,-11,174,-12,-13,-12,0,30,12,-9,-12,-7,-12,-11,40,43,43,-10,35,60,-5,-9,-6,-11,39,-9,24,-9,-10,28,-12,131,58,40,-11,-6 71 | 532493,male,women,18.5,3,7,-7,1,-9,10,11,-8,0,-14,0,-14,2,-11,-13,0,4,0,-8,-13,-8,11,-7,6,-15,-12,-10,4,-13,-1,-15,-9,-4,3,0,-12,-11,13,10,-14,0,-10,0,-12,-10,4,-8,5,3,-9 72 | 532495,male,women,19.4,-4,-13,-12,-12,-13,-1,6,-15,-15,-13,-16,-8,-3,-15,-14,-15,-14,-15,-14,-14,-15,-14,-13,-15,-15,-15,-15,-13,-14,-16,-15,-15,-15,-8,-16,-16,-11,-14,-7,-12,-14,-14,-12,-15,-13,-5,-5,-14,-12,-13 73 | 532496,male,,18.9,0,-10,-12,0,0,-9,0,-14,0,-8,0,0,-11,0,0,-10,0,-13,-8,-8,0,-8,-12,0,-8,-13,0,-12,0,0,-12,0,0,-10,0,0,0,0,-10,-12,0,-13,-13,0,-12,0,-9,0,0,0 74 | 532497,male,women,21.8,13,10,4,14,4,18,3,12,0,-3,10,4,7,7,14,9,5,2,6,11,17,5,5,8,6,2,7,13,9,16,11,9,7,16,7,6,4,5,8,11,12,11,3,0,10,12,12,4,3,8 75 | 532500,male,women,37.1,-10,-12,-12,-8,-12,-11,0,-12,-16,-12,-10,-7,-12,-12,-10,-1,-11,-11,-13,-9,-13,-13,0,-14,-13,-11,-14,-12,-13,-13,-12,-11,0,-11,0,-11,-12,0,-9,-14,-12,-9,-13,-9,-12,-11,-11,-10,-12,-10 76 | 532501,male,either,19.7,4,3,-2,2,0,2,4,-4,-2,-2,2,2,6,2,-2,-6,2,-1,4,-2,2,-2,-2,-2,-2,-2,-4,-2,4,-2,-4,-2,2,-2,-2,-2,-2,-2,4,0,0,0,-2,4,-2,6,2,-2,2,2 77 | 532503,male,women,18.8,0,0,0,0,0,0,0,0,0,0,0,0,10,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,-4,0,0,0,0,0,0,0,0,0,0,-3,0 78 | 532925,male,women,29.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 79 | 532926,male,men,20.2,30,10,-8,22,0,40,-10,-6,9,-4,65,32,48,0,-4,-6,26,18,155,27,0,0,-6,-2,0,-2,-2,-2,57,18,43,-8,48,45,0,-10,0,-4,-4,0,-6,-6,52,125,12,73,22,33,-4,-6 80 | 532928,male,men,21.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,2,0,0,0,0 81 | 532930,female,men,18.5,24,-9,-10,-10,26,18,36,-12,15,-11,15,28,-12,-10,-10,-9,0,-10,31,-9,-11,-10,27,-12,-12,-12,-10,-11,0,21,31,-13,-8,16,-12,-12,-11,-12,-12,-2,-7,-2,-10,-10,19,45,-9,-9,-10,-10 82 | 532931,male,men,23.8,6,4,2,6,0,4,12,4,6,2,8,6,6,4,6,0,6,4,4,6,2,2,2,4,6,0,2,2,4,8,4,4,2,4,2,2,2,0,0,4,2,2,2,6,4,2,4,2,10,2 83 | 533255,male,women,26.5,0,-7,-6,0,-4,-4,9,-4,-2,-4,-4,0,-2,-4,-6,-2,0,0,-3,-6,-11,0,-4,0,-6,-6,-5,0,-8,-6,-5,-2,0,-6,0,-9,-6,-6,-8,-3,0,-9,-3,-3,0,-6,-2,0,0,-6 84 | -------------------------------------------------------------------------------- /face-body/data/gender-male_type-faces_data.csv: -------------------------------------------------------------------------------- 1 | user_id,sex,sexpref,age,andrej,aurel,bernard,blazej,boris,bretislav,bystrik,cenek,cestmir,cornelius,cyril,dalimil,denis,dionyz,dominik,drahomir,elias,ferdinand,gabriel,hanus,henrich,hynek,josef,justin,kamil,kazimir,leonard,libor,lumir,maxim,mike,milos,mojmir,moric,oleg,oliver,patrik,prokop,ramiro,rehor,rudolf,sobeslav,stefan,svatopluk,tichomir,tomasi,valer,vasil,vendelin,vladislav 2 | 477033,female,,30.9,-14,-15,-15,-20,-18,-9,-16,-17,0,-14,0,-14,-2,-15,-13,-18,-13,-14,-16,-18,-19,-15,-13,-16,-14,-14,-17,-15,-3,-18,-8,-12,-19,-14,-17,-14,-14,-16,-17,-12,-15,-12,-16,-11,-17,-17,-17,-12,-9,-16 3 | 478230,male,women,25.8,-6,-16,-13,-7,-5,-10,-12,-12,-7,-12,-7,-12,-15,-6,-11,-11,-2,-13,-11,-14,-15,0,-8,-13,0,-13,-8,-12,-11,-14,-10,-7,-12,-8,0,-8,-13,-17,-13,-11,-9,-2,-12,-6,-13,-12,-14,-8,-12,-14 4 | 478291,female,women,23.5,0,-11,-17,0,-18,0,0,0,0,-13,-17,-14,0,0,-13,0,0,-13,0,-16,-17,0,-16,-18,-14,-15,-17,0,0,0,-15,-14,-12,0,-13,-17,-16,-15,0,-17,0,0,0,-17,0,18,-16,0,-17,-16 5 | 479035,female,men,22.6,38,-12,-11,-7,-13,-12,-13,-14,-16,-15,-14,-9,-10,29,14,-11,-14,-15,3,-11,-16,-14,-16,33,-14,-13,-17,-14,5,-13,45,25,39,-14,-14,-12,-12,-16,-16,-12,-16,0,-15,-11,-17,-14,-12,-15,8,41 6 | 489018,male,men,26.8,30,-12,-12,-11,-11,-12,-13,-12,-9,-12,-13,-10,-12,-11,26,-8,-12,-10,-12,-13,-12,-12,-12,-9,-12,-13,-5,-12,-7,-12,-9,-10,24,29,-13,-12,-10,-11,-11,-10,-13,-12,-7,-14,-12,-11,-13,-10,-11,-13 7 | 501705,female,either,21.3,-12,27,-10,0,-4,49,-9,-10,-14,-13,-11,-13,32,54,-8,78,24,-12,-8,-13,33,-5,-2,-12,-14,-10,44,-2,-13,-2,-11,-6,-6,-6,-3,-14,-13,-3,-13,-12,-13,13,-14,0,6,28,-10,20,62,-6 8 | 514003,male,women,30.1,-10,-10,-8,2,-11,-10,-10,-10,-9,-11,-8,-10,-11,-13,-13,-13,-12,-9,0,-8,-11,-10,0,-13,-11,-14,-11,-11,-3,-10,-4,-5,-13,-11,-12,-12,-12,-10,-12,-10,-11,-12,-12,-9,-9,-11,-12,-12,-5,-10 9 | 518109,female,men,20.1,-6,0,-10,0,-5,8,3,8,-6,-9,1,-9,0,0,15,21,-7,-8,13,-9,9,8,22,0,-3,4,8,-9,-10,-3,3,4,11,10,3,-8,-9,-12,-6,-8,-3,-3,-8,3,-1,0,14,1,6,-5 10 | 531458,male,women,19.8,-6,-6,-5,5,-3,-4,0,0,-17,0,0,0,0,-12,0,0,-7,-3,0,0,-6,0,0,0,0,0,0,0,-11,0,0,0,0,-16,-8,-16,0,0,-14,0,-13,-13,-12,0,-11,0,0,0,-12,0 11 | 531826,male,women,27.2,0,-7,-10,-12,-6,-3,-12,-10,-11,-6,-14,-6,0,-10,-5,-9,-10,-8,-11,-11,-9,-10,-10,-12,-11,-10,0,-11,-6,-4,-10,-12,-4,-8,-8,-10,-11,-9,-7,-5,-11,-8,-10,0,-12,-12,-11,-12,-7,-12 12 | 531966,female,men,20.7,8,-5,-9,6,-8,-6,12,-7,-8,1,-12,-8,3,-6,-10,-11,-10,-6,0,-9,-9,-10,2,7,-7,-8,-11,-8,9,-5,0,4,-7,-7,-8,-8,-10,-8,2,-6,8,3,-5,2,0,13,-4,-3,3,12 13 | 531967,female,men,23.1,-8,-9,10,-6,18,-4,9,-3,-13,-11,-12,-10,16,7,19,12,13,-11,25,-12,14,5,-8,-11,-1,-5,5,1,-7,14,10,14,0,12,7,-12,-12,-10,7,0,-8,10,-9,9,-10,6,-5,24,14,-6 14 | 531968,female,either,22.8,48,0,-8,-11,36,-7,0,0,-7,-10,0,-9,56,9,30,8,0,0,82,0,4,4,10,-8,-1,3,28,33,40,18,19,-7,0,13,18,-8,-2,5,0,20,8,2,-6,8,8,-9,-8,16,2,10 15 | 531969,male,women,28.1,-17,-18,-15,-18,-20,-17,-16,-18,-18,-16,-18,-21,-18,-21,-17,-17,-17,-18,-17,-20,-19,-19,-17,-17,-20,-17,-20,-17,-21,-17,-17,-17,-18,-19,-20,-17,-21,-18,-18,-18,-16,-19,-16,-18,-21,-17,-20,-20,-9,-22 16 | 531970,female,men,21.5,4,-14,-4,-1,6,-3,-11,2,6,-15,-6,-9,10,7,4,8,-5,6,8,-7,-12,-7,-8,-11,-10,-9,-12,9,4,1,7,1,7,9,-1,-9,-13,-11,-10,14,0,-6,-10,1,-7,-5,4,3,-9,2 17 | 531973,female,men,23.7,-13,-13,-12,-11,-9,-12,-13,-14,-14,-14,-13,-13,-12,-13,-14,-12,-14,-13,-8,-13,-11,-10,-12,-11,-13,-11,-14,-15,-13,-7,-12,-11,-16,-12,-11,-12,-10,-13,-14,-12,-13,-11,-13,-12,-12,-12,-10,-11,-11,-7 18 | 531974,female,men,24.3,57,26,-10,80,-9,-8,19,-8,46,-10,-9,-10,52,20,41,-6,36,-7,63,59,-11,-13,57,37,16,-13,60,38,60,-11,77,57,-12,-12,-7,-12,18,22,-9,20,54,25,-12,41,37,-9,0,39,41,-8 19 | 531975,female,women,34.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 20 | 531976,female,men,50.8,-3,0,0,0,-4,2,-4,-1,1,0,-4,-1,11,-1,0,-4,-1,0,11,0,-1,-2,0,-1,0,4,7,12,1,-4,11,9,-1,0,-2,8,2,4,-2,-2,1,-10,0,0,0,-1,8,1,0,-4 21 | 531977,male,men,21.7,0,36,0,0,44,11,0,0,24,23,41,54,0,1,51,14,-8,14,8,17,5,6,2,5,23,0,54,0,0,0,0,31,16,6,0,31,4,60,-9,3,0,10,21,4,45,0,0,56,10,26 22 | 531978,male,women,22.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 23 | 531980,female,either,18.9,18,-9,-8,-11,-3,-12,-6,-6,-6,-8,-10,-10,-9,-9,1,8,-6,-11,-10,-11,2,-9,-10,-10,-8,-11,11,-1,-9,-5,8,20,-10,26,-10,-12,-8,-12,-5,15,13,-5,-11,3,-13,-5,-9,4,9,-9 24 | 531981,male,women,28.3,-1,-6,-14,-6,-11,-14,-7,-15,-7,-7,-12,-10,-7,-11,-7,-16,-10,-7,-3,-16,-10,-6,-9,-14,-2,-11,0,-7,-10,-10,-15,-14,-8,-7,-15,-7,-4,-10,-10,-10,-15,-16,4,-14,-16,-6,-6,-17,-17,-9 25 | 531982,male,neither,21.4,-4,-4,0,-2,-6,-4,-2,-5,-5,-5,-6,-5,-4,-2,-6,-5,-4,-1,-6,-8,-4,-6,-6,-2,-8,-4,-6,-7,-5,-4,-6,-6,-1,-4,-6,0,-11,-1,0,-7,-6,-6,-6,-10,-2,-4,-4,0,-10,-7 26 | 531983,female,men,44.6,0,-14,-10,-4,-3,-6,-5,-14,-11,-12,-11,-14,-9,-1,-6,-4,-9,-11,-3,-8,-14,0,-11,0,-14,-12,-9,-8,-9,0,0,0,-5,-3,0,-14,-9,0,-13,-1,-4,-9,-12,-8,-11,-7,0,-1,0,0 27 | 531984,male,,22.7,-16,-13,-18,0,-15,-18,-16,-16,-15,0,-14,-19,-18,-16,-16,10,-18,-17,98,-16,-14,0,0,-18,-17,-16,-13,-16,-18,78,15,-12,-17,251,-17,-15,-14,-16,-17,6,-15,-18,-16,-12,-14,-18,-11,-18,7,-8 28 | 531985,female,men,20.2,-12,-15,-14,-13,-11,-16,-11,-15,-11,-13,-13,-14,-14,-17,-14,-13,-11,-16,-14,-12,-14,-14,-13,-14,-12,-13,-15,-13,-13,-16,-3,-14,-13,-13,-2,-12,-14,-11,-12,-14,-14,-11,-14,-11,-15,-15,-16,-15,-14,-15 29 | 531986,female,men,22.2,-17,-16,-16,-16,-17,-16,-6,-16,-5,-16,-16,-16,-14,-17,-16,-15,-15,-16,-17,-15,-16,-11,-12,-15,-16,-9,-18,-16,-15,-16,-16,-16,-15,-16,-14,-15,-15,-16,-7,-16,0,-17,-16,-17,-16,-13,-17,-16,-17,-15 30 | 531987,female,men,20.3,-12,-13,-12,-16,-15,-14,-14,-11,-16,-16,-13,-13,-12,-14,-11,-14,-14,-13,-16,-15,-14,-13,-14,-15,-18,-14,-14,-14,-13,-15,-15,-15,-16,-9,-13,-13,-13,-14,-14,-12,-14,-15,-12,-19,-13,-14,-14,-12,-11,-14 31 | 531988,male,women,20.4,-2,-14,-15,-11,-13,-11,0,-11,-13,-11,-9,-1,-13,-3,-8,-10,-8,-9,-13,-2,0,-7,-7,-11,-11,-11,-9,-14,-6,-7,-13,-13,-10,-13,-5,-13,-7,-10,-11,-11,-13,-2,-9,-12,-11,-8,-12,0,-13,-14 32 | 531989,female,men,22.2,-13,-14,-16,-12,-15,13,-16,-12,-15,9,-1,-16,-16,-15,-18,-16,-13,-15,-15,-14,-6,-11,-1,5,-17,-16,-16,-16,-14,-16,-14,-13,-16,-15,-14,-17,-13,-15,-13,-15,-17,-15,-13,4,-18,-16,-15,-14,-15,-16 33 | 531990,male,women,18.2,22,-6,-12,-14,25,-12,-14,25,-11,6,-15,-13,40,13,-12,12,-12,-16,30,-13,-11,-11,-11,30,14,-14,5,34,-11,22,11,-14,-10,29,-14,-15,9,-15,-16,5,-14,-11,-10,18,-10,-9,-9,13,31,-15 34 | 531991,female,men,22.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 35 | 531993,male,men,24.0,-10,0,-13,-16,-13,-15,0,4,-14,-16,-16,-17,0,-5,-16,-2,0,-14,0,-11,8,-15,-1,2,-15,-14,-16,-15,0,2,-14,-13,6,10,-6,0,-14,-10,0,4,-10,8,-3,0,-17,8,0,6,4,-7 36 | 531995,female,neither,21.7,-13,-15,-16,-14,-16,-16,-16,-13,-14,-14,-15,-16,-16,-15,-16,-16,-15,-16,-15,-17,-12,-16,-13,-13,-15,-13,-15,-16,-16,-15,-13,-14,-16,-16,-15,-16,-16,-16,-16,-15,-16,-16,-16,-16,-16,-16,-16,-16,-16,-15 37 | 531996,female,men,24.9,-13,-12,-12,-13,-12,-13,-12,-13,-13,-14,-14,-13,-11,-13,-13,-13,-13,-12,-13,-13,-12,-12,-14,-13,-13,-14,-13,-12,-13,-12,-12,-12,-14,-12,-12,-13,-13,-12,0,-13,-13,-13,-13,-14,-13,-13,-12,-13,-13,-14 38 | 531998,female,men,41.0,57,65,63,69,59,79,70,58,78,68,0,65,53,65,64,49,10,33,72,7,42,73,71,74,66,67,75,22,57,64,53,57,50,70,86,14,55,67,56,61,4,67,80,50,70,70,49,65,63,78 39 | 531999,male,women,29.5,-10,-10,-15,-10,-9,-7,-1,-17,-13,-14,-15,-14,-6,-8,-6,-10,-5,-14,-5,-9,-10,-6,-10,-10,-15,-18,-9,-10,-15,-6,-10,-13,-17,-4,-10,-8,-9,-8,-15,-16,-12,-12,-7,-9,-14,-16,-9,-10,-11,-11 40 | 532000,female,men,19.5,-10,-14,-10,-6,-12,-9,-11,-9,-2,-10,-14,-16,-6,0,-10,-12,-11,-7,-13,-10,-14,-9,-8,-6,-8,-10,-9,-14,-11,-12,0,-10,-9,-10,-9,-7,-14,-9,-13,-7,-11,-9,-16,-13,-11,-11,-14,-9,2,-9 41 | 532182,female,women,21.1,-12,-7,-8,-10,-6,-12,19,-9,-11,-4,-10,-10,-8,-12,-6,-12,-10,-7,-14,-12,-12,-10,-10,0,-5,-10,-10,-11,-9,0,-8,12,-5,-8,-8,-5,-9,-11,-9,-9,0,-12,-9,-6,-11,-10,-8,-8,-11,-11 42 | 532183,female,men,43.4,-7,0,-8,12,7,-10,-9,2,-6,-10,-8,-8,-10,11,-9,-7,-10,-8,-5,-5,-11,8,-8,4,-6,-8,0,3,5,-8,8,-6,-4,8,-7,-9,4,-9,-6,3,-9,-8,-10,-8,-8,-8,-6,-10,10,-2 43 | 532184,female,men,52.2,4,6,-4,2,4,-4,-2,4,-2,-2,-4,-2,-4,2,-2,4,-2,-2,3,-2,-4,4,4,4,-4,-4,-4,3,4,2,4,2,2,-1,4,-3,-4,-2,-2,4,4,2,-2,4,-1,-4,-2,-2,4,-3 44 | 532185,female,men,24.4,-2,-2,-1,-2,-1,-2,-1,-2,-2,-1,-2,-2,-2,-2,-1,-2,-2,-2,-2,-2,-1,-1,-2,-2,-2,-2,-1,-1,-1,-2,-1,-4,-2,-2,-2,-1,-2,-2,-1,-2,-2,-2,-1,-1,-2,-1,-2,-1,-1,-2 45 | 532192,female,men,21.0,-13,-12,-10,-11,-11,-7,0,-10,-12,-13,-12,-6,0,-14,-12,-11,-11,-11,0,-11,-10,-10,-12,0,-12,-11,-11,-10,0,0,0,0,-10,-6,-7,-12,-12,-10,-10,-11,0,-12,-12,-11,-13,-11,-9,0,-12,-12 46 | 532213,male,women,19.9,-5,-10,-12,-7,0,-11,-8,0,-10,-12,-12,-4,-11,-13,-10,0,-9,-7,0,-13,-11,-13,-11,-13,-12,-11,-6,-10,-3,-14,-5,-11,0,0,-12,-13,-13,-13,-9,0,-8,0,-13,-9,-11,-10,-6,-10,-9,-10 47 | 532214,male,women,18.6,-11,-11,-12,0,-11,-12,-10,-7,-8,-11,-11,-12,-11,-9,-11,-10,-11,-11,-11,-12,-11,-11,-11,-10,-11,-8,-10,-10,-12,-12,-6,-9,-10,-9,-10,-10,-11,-7,-11,-12,-9,-10,-6,-11,-12,-10,-11,-11,-11,-10 48 | 532215,male,women,20.3,2,-7,3,-11,-7,-5,-13,-6,-10,-9,-10,-12,-5,9,-5,-6,-11,-11,-11,-13,-14,-10,-12,13,-8,18,-13,0,-13,-14,5,-13,-9,-3,-8,-8,-10,-8,-7,-7,-2,-11,-13,-9,-10,-8,6,-13,-10,-8 49 | 532216,male,women,21.9,-17,-12,-12,-13,-15,-17,-13,-15,-9,-16,0,-16,-17,-17,-16,-16,-14,-17,-15,-16,-16,-14,-9,-13,-13,-12,-8,-14,-10,-5,-4,0,-17,-15,-11,-12,-16,-17,-15,-14,-16,-15,-16,-15,-5,-16,-16,-14,-14,-16 50 | 532217,male,women,18.3,-8,-10,25,-10,-6,-10,-9,-11,-10,-8,-6,-10,10,-5,31,20,-8,-10,-7,-8,-12,-7,-12,17,-12,-12,-6,-10,-12,13,9,2,-4,-3,-11,-11,-8,-5,-8,-12,23,-6,-9,-10,-11,-11,-8,-10,-8,-10 51 | 532218,female,women,19.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 52 | 532219,female,men,19.7,-11,-11,-7,-3,-5,13,16,-10,10,-11,-9,-11,5,9,0,0,-10,-9,-11,9,-10,-10,3,-9,-10,-12,-10,-10,17,-11,-8,-7,-8,-6,-10,-11,-10,-10,-8,-9,0,-11,-10,-11,7,-10,-10,-10,18,0 53 | 532220,female,men,36.9,0,-4,-12,-4,5,-6,-2,0,-11,-11,-13,-11,0,-12,-7,-7,0,-13,8,-5,-8,-11,0,-12,-5,-14,-3,-5,-10,0,6,10,0,0,-12,-12,-6,-11,0,-5,-11,-3,-7,-7,-11,-13,-10,0,0,0 54 | 532221,female,men,25.0,-15,-16,-18,-16,-11,62,75,-16,-16,-14,-15,-17,-18,-16,-15,-15,-13,-17,-16,-16,-17,-14,-15,21,-17,-17,-17,-13,-16,-16,54,-5,-12,-19,-15,-14,-14,-17,-14,-13,57,-13,-18,-16,-16,-17,-13,-13,-16,-10 55 | 532222,male,women,21.7,-7,-10,-17,-15,0,-18,-10,-15,-16,-17,-16,-5,0,-3,-6,-9,-17,-18,-10,-1,-11,-10,-17,-16,-3,-17,-8,-16,-16,-2,-12,-4,-13,-17,-15,-18,-17,-6,-7,-5,-14,0,-18,-7,-17,-16,-9,-8,-9,0 56 | 532243,male,women,19.7,0,5,-7,0,0,0,0,-3,-6,-2,-2,-2,0,0,-1,0,-5,-2,0,1,-2,-4,0,0,-6,-4,0,-4,0,-2,0,0,-6,0,0,-3,-6,-9,-6,2,-2,1,-2,-2,0,-6,0,0,0,-2 57 | 532244,female,men,18.5,11,-10,-12,-10,-9,-14,-11,-10,-11,-10,-12,0,-10,-11,-8,-10,-10,0,17,-10,-9,-9,19,13,-10,-10,-10,-5,26,-9,-10,22,-10,-12,-2,-8,-10,-10,-11,0,-12,-10,-9,0,0,13,0,-12,-10,-11 58 | 532245,female,either,18.1,3,9,7,0,16,16,14,1,-7,11,3,11,-6,2,20,7,20,-7,2,7,9,5,25,18,-4,4,5,10,8,-6,3,25,20,24,4,5,-4,30,10,-6,4,5,1,7,17,19,-2,9,4,6 59 | 532246,male,women,19.2,-15,-3,-7,-8,3,-2,-13,3,-6,-12,-14,-6,3,-6,-2,7,-12,-15,-13,-13,-14,3,-6,-6,-14,-15,-3,-4,-7,-5,12,-7,-16,-9,-8,-14,-9,-14,-1,-1,-13,5,-4,-7,2,-7,-3,0,0,7 60 | 532247,male,women,18.2,-15,-14,-14,-16,-16,-14,-13,-17,-15,-15,-13,-12,-15,-12,-18,-12,-14,-14,-14,-14,-14,-13,-15,-15,-10,-17,-15,-14,-15,-14,-17,-5,-1,-15,-15,-15,-12,-16,-15,-14,-14,-13,-16,-14,-13,-11,-14,-15,-17,-14 61 | 532249,male,women,25.3,-10,-11,-11,-8,-12,-5,-12,-7,-12,-11,-14,-10,-11,-13,-11,-11,-10,-14,-13,-12,-14,-12,-12,-11,-14,-10,-10,-12,-12,-12,-10,-11,-9,-10,-13,-15,-15,-10,-13,-7,-14,-12,-12,-12,-9,-14,-13,-12,-12,-6 62 | 532250,female,men,19.0,10,-7,-3,1,14,-5,-9,-11,-8,-9,-9,-10,-2,-5,7,-8,-3,-9,5,-7,-6,7,2,7,-6,-10,-6,-3,7,-5,10,-7,-8,-8,-9,-4,4,-3,-5,-6,12,-5,-8,-6,-5,11,15,11,-4,-1 63 | 532251,female,men,20.9,-2,-4,-11,20,5,-11,-11,-3,-14,-11,-8,-14,-9,2,-6,0,-8,-11,14,-1,-12,-3,18,-2,-9,-12,-4,-10,-6,-6,0,7,18,45,-11,-11,-5,-9,-12,-4,-10,-6,-12,8,-4,-2,-1,-3,12,-4 64 | 532252,male,women,23.2,-17,-15,13,-19,-18,-17,-19,-4,-12,18,1,-16,-18,-18,-17,-7,-17,-20,-18,-12,-16,-17,-19,-19,-19,-17,3,-12,-18,-16,-15,-19,-22,0,-17,-20,-18,12,-19,-19,-15,-20,-15,-19,-13,-19,-22,-14,-18,18 65 | 532253,female,either,28.8,-10,-10,-16,-13,-14,-15,-14,-17,-10,-15,-16,-6,-6,-16,-15,-13,-12,-16,-15,-14,-11,-16,-15,-16,-16,-17,-12,-14,-14,-15,-10,-14,-17,-14,-7,-14,-10,-6,-17,-13,-11,-16,-15,-9,-14,-15,-15,-13,-16,-15 66 | 532254,female,either,19.6,-11,-10,-11,0,-13,-3,6,-7,0,-12,-10,-12,12,-10,-7,-9,-11,-12,-12,-11,-10,-11,-11,-1,-13,-12,-11,-14,-7,0,-1,0,-11,-12,0,-12,-10,-10,-11,-11,-6,-7,-12,-8,-3,-11,-13,-13,0,-8 67 | 532255,male,women,21.5,2,-2,6,0,-10,-14,4,11,-15,2,-10,-8,-14,4,-13,-10,18,-10,3,-12,18,-1,1,4,-10,-14,7,-8,-12,-7,9,-12,15,12,-2,-8,-12,-13,-4,7,-12,7,-8,-7,0,-6,-15,5,11,-10 68 | 532256,female,neither,18.6,0,2,4,10,-9,-4,3,4,-8,-4,-8,-6,3,8,8,8,-7,12,0,-7,-2,-1,1,-7,12,2,-11,8,0,-7,8,0,-6,0,-7,-8,-4,-12,-6,0,2,7,-3,-1,14,8,4,10,9,-7 69 | 532257,female,men,19.3,0,12,-10,-9,-10,-3,-9,0,-12,-8,-5,-9,6,23,-6,-6,0,-8,-4,-10,-4,5,-5,7,-5,-10,-6,-3,-2,0,0,1,-9,9,-7,-7,-5,0,-6,-6,-5,4,-3,10,-10,-10,0,0,0,20 70 | 532492,male,men,25.1,49,0,-9,23,-6,12,33,42,-11,-9,-12,-8,-11,61,25,41,18,-9,17,-7,-11,-10,34,-8,-10,-9,-9,34,43,39,0,36,23,35,-10,-9,-8,-9,-8,15,47,55,-12,38,39,-11,48,42,198,37 71 | 532493,male,women,18.5,-7,-10,-13,10,-7,23,-7,-7,7,12,-4,-14,11,-12,-5,2,-12,-8,-12,18,-13,16,-8,-10,-16,-11,-13,-8,-13,-8,16,7,7,-14,-10,-7,-11,-9,-14,-13,-8,3,-14,-11,12,-13,-10,-9,-8,19 72 | 532495,male,women,19.4,0,-11,-15,-1,-3,-14,-3,-10,-8,-8,-14,-10,-2,0,-13,-3,-12,-13,-1,-10,-2,-8,-3,-15,-7,-15,-13,0,-5,-5,0,0,-4,-5,-13,-10,-15,-8,-8,0,-15,-2,-11,-9,-12,-16,-10,-9,-15,-2 73 | 532496,male,,18.9,-14,0,-4,-11,-9,0,0,0,-12,0,-12,-13,0,0,0,0,-10,0,0,-12,0,0,-12,0,0,-13,0,0,0,0,0,0,0,0,0,0,-11,-11,-13,0,-12,0,-13,-12,0,-14,0,0,0,0 74 | 532497,male,women,21.8,9,5,1,12,14,15,34,2,8,11,-11,-12,11,0,14,33,19,43,8,11,13,7,2,4,31,34,4,21,-8,13,31,15,9,2,24,10,24,18,11,30,34,-5,9,18,36,6,-17,15,9,29 75 | 532500,male,women,37.1,-12,-11,-10,-14,-4,-5,-10,-9,-10,-6,-9,-10,-7,-6,-14,-12,-7,-8,0,-15,-11,-9,-9,0,-10,-15,-13,-15,-6,-12,-12,-4,-14,-9,-9,-16,-14,-9,-13,-15,0,-8,-11,-14,-9,-11,-7,-11,-13,-13 76 | 532501,male,either,19.7,2,4,-2,-4,1,5,6,-2,-2,-1,-5,-2,2,6,4,0,0,-4,4,-2,3,-2,-4,8,-4,-4,1,-3,0,-2,5,4,4,7,-3,-4,-1,1,-2,-4,-1,-4,-4,6,1,-2,-2,-3,1,3 77 | 532503,male,women,18.8,-8,0,-10,-10,-10,-2,-8,-1,0,-8,1,-9,-10,-11,14,0,-11,-6,-11,-9,-8,-7,-9,-10,-8,-10,1,-5,-7,-4,-6,24,-6,3,-12,-9,-3,0,-8,0,-9,-10,-8,-12,-8,-4,0,-10,4,-5 78 | 532925,male,women,29.1,0,0,0,0,0,0,0,0,0,0,0,-3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 79 | 532926,male,men,20.2,0,-8,-8,51,40,-11,50,-6,-13,-11,-13,-11,75,34,-11,-4,-12,-8,-4,15,17,-10,38,-8,-9,-11,-12,-7,10,10,-4,-7,11,9,-8,-7,-12,-9,0,-8,-8,-6,-11,-8,-8,31,-4,29,34,-9 80 | 532928,male,men,21.2,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 81 | 532930,female,men,18.5,-9,-10,-10,0,-11,17,-11,-11,-11,-11,-11,-10,-10,19,17,14,18,-8,15,18,-13,-12,-11,-11,-10,-9,-10,0,-10,-9,21,24,-9,19,18,-12,-8,-10,-10,-9,-9,-9,-9,-10,-12,-12,24,13,-10,-10 82 | 532931,male,men,23.8,8,-2,0,10,2,2,6,0,1,6,-3,2,6,5,4,3,-6,1,-6,2,-4,0,-2,0,0,-6,7,0,8,0,6,8,-4,6,2,-4,-2,2,1,-2,2,4,-2,4,-2,-4,0,8,4,10 83 | 533255,male,women,26.5,0,-10,-5,0,-1,0,0,0,-12,-7,-4,-4,-2,-1,-10,0,-7,-4,0,-7,0,0,-10,0,0,-13,0,0,0,0,0,-2,-8,0,-4,-6,0,-12,-1,3,-1,0,-8,-8,0,-3,-7,0,0,-9 84 | -------------------------------------------------------------------------------- /face-body/data/gender-male_type-stimuli_data.csv: -------------------------------------------------------------------------------- 1 | stimulus name,stimulus sex,age,height(cm),weight(kg),BMI,chest(cm),waist(cm),hips(cm) andrej,male,21,185,73,21.32943755,97,81,95 aurel,male,23,178,70,22.09317005,87,69,101 bernard,male,27,177,91,29.04657027,95,96,112 blazej,male,22,175,75,24.48979592,96,75,83 boris,male,30,200,88,22,91,91,105 bretislav,male,30,176,70,22.5981405,91,74,90 bystrik,male,25,181,87,26.55596594,105,82,107 cenek,male,28,176,90,29.05475207,109,98,114 cestmir,male,27,187,87,24.8791787,100,86,106 cornelius,male,25,178,75,23.67125363,93,85,104 cyril,male,24,183,78,23.29122996,98,77,103 dalimil,male,26,180,84,25.92592593,103,82,104 denis,male,24,172,70,23.66143862,97,77,97 dionyz,male,24,187,82,23.44934084,100,86,98 dominik,male,23,189,72,20.15621063,81,78,96 drahomir,male,25,179,77,24.03170937,95,85,104 elias,male,21,186,90,26.01456816,99,88,105 ferdinand,male,24,183,83,24.78425752,104,86,99 gabriel,male,24,174,68,22.46003435,87,79,100 hanus,male,28,183,86,25.68007405,96,84,106 henrich,male,25,191,74,20.28453167,88,77,102 hynek,male,25,176,78,25.18078512,91,85,99 josef,male,20,191,80,21.92922343,90,76,104 justin,male,19,174,64,21.13885586,84,74,84 kamil,male,22,183,100,29.86055123,115,90,95 kazimir,male,23,182,71,21.43460935,93,73,98 leonard,male,24,182,76,22.94408888,98,78,92 libor,male,29,181,84,25.64024297,101,91,109 lumir,male,20,183,72,21.49959688,107,77,92 maxim,male,25,183,78,23.29122996,92,84,102 mike,male,24,168,59,20.90419501,98,78,88 milos,male,23,175,59,19.26530612,90,70,95 mojmir,male,20,175,65,21.2244898,89,73,89 moric,male,28,190,75,20.77562327,94,80,98 oleg,male,30,181,85,25.94548396,102,90,106 oliver,male,24,178,60,18.9370029,85,78,91 patrik,male,26,189,75,20.99605274,89,85,95 prokop,male,22,176,73,23.56663223,89,80,104 ramiro,male,22,175,75,24.48979592,92,82,97 rehor,male,20,200,100,25,100,86,112 rudolf,male,22,179,70,21.84700852,95,86,103 sobeslav,male,23,187,100,28.59675713,108,97,111 stefan,male,22,180,73,22.5308642,86,81,88 svatopluk,male,21,173,53,17.70857697,90,71,80 tichomir,male,28,187,83,23.73530842,95,84,104 tomasi,male,19,190,75,20.77562327,98,74,92 valer,male,24,184,77,22.74338374,95,84,102 vasil,male,25,175,68,22.20408163,94,77,100 vendelin,male,30,178,75,23.67125363,95,85,104 vladislav,male,20,176,63,20.33832645,85,72,91 -------------------------------------------------------------------------------- /face-body/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context": "https://schema.org/", 3 | "@type": "Dataset", 4 | "name": "Faces and Bodies", 5 | "description": "This project examines the social perception of bodies, in comparison to faces, focusing on the dimension structure from Oosterhof & Todorov (2008, PNAS).", 6 | "schemaVersion": "Psych-DS 0.1.0", 7 | "creator": [ 8 | { 9 | "@type": "Person", 10 | "name": "Lisa DeBruine" 11 | }, 12 | { 13 | "@type": "Person", 14 | "name": "Danielle Morrison" 15 | } 16 | ], 17 | "citation": "Morrison D, Wang H, Hahn AC, Jones BC, DeBruine LM (2017) Predicting the reward value of faces and bodies from social perception. PLoS ONE 12(9): e0185093.", 18 | "sameAs": "https://doi.org/10.1371/journal.pone.0185093", 19 | "temporalCoverage": "2000-01-01/2018-12-31", 20 | "keywords": [ 21 | "faces", 22 | "bodies" 23 | ], 24 | "variableMeasured": ["stimulus name","stimulus sex","age","height(cm)","weight(kg)","BMI","chest(cm)","waist(cm)","hips(cm)","user_id","sex","sexpref","alexandra","anastazie","anezka","bera","bohdana","brenda","carol","christianne","dagmar","debra","dobromila","dusana","edita","eleanora","elena","eugenia","evzenie","gabriela","gejza","ida","ingrid","irena","jindriska","jitka","karina","kordula","lea","linda","livia","lujza","margita","marika","matylda","miloslava","milota","miriama","peggy","perla","radmila","sarlota","saskie","sidonia","stela","tamara","ursula","viktoria","viola","vladena","zelmira","zlata","andrej","aurel","bernard","blazej","boris","bretislav","bystrik","cenek","cestmir","cornelius","cyril","dalimil","denis","dionyz","dominik","drahomir","elias","ferdinand","gabriel","hanus","henrich","hynek","josef","justin","kamil","kazimir","leonard","libor","lumir","maxim","mike","milos","mojmir","moric","oleg","oliver","patrik","prokop","ramiro","rehor","rudolf","sobeslav","stefan","svatopluk","tichomir","tomasi","valer","vasil","vendelin","vladislav","judgment","stimulus_sex","type","rater_sex","rater_age"] 25 | } 26 | -------------------------------------------------------------------------------- /img/commit-changes.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/img/commit-changes.png -------------------------------------------------------------------------------- /img/create-branch.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/img/create-branch.png -------------------------------------------------------------------------------- /img/pull-request-form.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/img/pull-request-form.png -------------------------------------------------------------------------------- /img/pull-request-prompt.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/img/pull-request-prompt.png -------------------------------------------------------------------------------- /img/pull-request-result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/img/pull-request-result.png -------------------------------------------------------------------------------- /img/upload-files.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/img/upload-files.png -------------------------------------------------------------------------------- /informative-mistakes-dataset/data/non_csv_file.txt: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue 3 | r2d2,1999-08-12,scarf,Eggplant 4 | r2d2,2008-12-12,hat,Royal 5 | r2d2,2019,hat,Grey 6 | r2d2,1999,snood,Gold 7 | r2d2,2014-01-01,sweater,Kelly 8 | c3p0,2021,mittens,Lavender 9 | c3p0,2020,glove,Toast 10 | c3p0,2020-12-12,,Mint Green 11 | bb8,2020,scarf,Maroon 12 | bb8,2021,scarf,Pink Carnation 13 | -------------------------------------------------------------------------------- /informative-mistakes-dataset/data/study-validname_type-pdf_data.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/informative-mistakes-dataset/data/study-validname_type-pdf_data.csv -------------------------------------------------------------------------------- /informative-mistakes-dataset/data/study-yarncolor_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue 3 | r2d2,1999-08-12,scarf,Eggplant 4 | r2d2,2008-12-12,hat,Royal 5 | r2d2,2019,hat,Grey 6 | r2d2,1999,snood,Gold 7 | r2d2,2014-01-01,sweater,Kelly 8 | c3p0,2021,mittens,Lavender 9 | c3p0,2020,glove,Toast 10 | c3p0,2020-12-12,,Mint Green 11 | bb8,2020,scarf,Maroon 12 | bb8,2021,scarf,Pink Carnation 13 | -------------------------------------------------------------------------------- /informative-mistakes-dataset/data/study-yarncolor_type-badnames_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,,garment,yarn_color,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue, 3 | r2d2,1999-08-12,scarf,Eggplant, 4 | r2d2,2008-12-12,hat,Royal, 5 | r2d2,2019,hat,Grey, 6 | r2d2,,,, 7 | r2d2,2014-01-01,sweater,Kelly, 8 | c3p0,2021,mittens,Lavender, 9 | c3p0,2020,glove,Toast, 10 | c3p0,2020-12-12,,Mint Green, 11 | bb8,2020,scarf,Maroon, 12 | bb8,2021,scarf,Pink Carnation, 13 | -------------------------------------------------------------------------------- /informative-mistakes-dataset/data/subdir/subdir/study-yarn_location-subdir_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,yarn_color,rating 2 | r2d2,2021-02-21,Country Blue,199 3 | r2d2,1999-08-12,Eggplant,12 4 | r2d2,2008-12-12,Royal,0 5 | r2d2,2019,Grey,-1 6 | r2d2,1999,Gold,14 7 | r2d2,2014-01-01,Kelly, 8 | c3p0,2021,Lavender, 9 | c3p0,2020,Toast, 10 | c3p0,2020-12-12,Mint Green, 11 | bb8,2020,Maroon, 12 | bb8,2021,Pink Carnation,2000 13 | -------------------------------------------------------------------------------- /informative-mistakes-dataset/data/wrong-name-structure.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue 3 | r2d2,1999-08-12,scarf,Eggplant 4 | r2d2,2008-12-12,hat,Royal 5 | r2d2,2019,hat,Grey 6 | r2d2,1999,snood,Gold 7 | r2d2,2014-01-01,sweater,Kelly 8 | c3p0,2021,mittens,Lavender 9 | c3p0,2020,glove,Toast 10 | c3p0,2020-12-12,,Mint Green 11 | bb8,2020,scarf,Maroon 12 | bb8,2021,scarf,Pink Carnation 13 | -------------------------------------------------------------------------------- /informative-mistakes-dataset/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context" : "http://schema.org/", 3 | "@type" : "Dataset", 4 | "name" : "Psych-DS 'Infomative Mistakes' Dataset", 5 | "description" : "This is a minimal example of a dataset which is several key mistakes away from being a valid Psych-DS dataset.", 6 | "variableMeasured" : ["lab_id", "age_years","responded","trial_id","response", "sub_id", "date", "rating"] 7 | } -------------------------------------------------------------------------------- /macrophage-conditioning/README.md: -------------------------------------------------------------------------------- 1 | ## Learning in a simple biological system: a pilot study of classical conditioning of human macrophages in vitro 2 | 3 | This project represents an attempt at standardization according to the [Psych-DS Specification](https://docs.google.com/document/d/1u8o5jnWk0Iqp_J06PTu5NjBfVsdoPbBhstht6W0fFp0/edit#) 4 | Original study conducted by Gustav Nilsonne et al. Full article available [here](https://behavioralandbrainfunctions.biomedcentral.com/articles/10.1186/1744-9081-7-47). 5 | Conformance work according to Psych-DS specification carried out by Love Ahnström during July 2022. -------------------------------------------------------------------------------- /macrophage-conditioning/data/primary_data/IL-6 ELISA 090603.pzf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/macrophage-conditioning/data/primary_data/IL-6 ELISA 090603.pzf -------------------------------------------------------------------------------- /macrophage-conditioning/data/primary_data/IL-6 ELISA 090603.pzfx: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | Project info 1 12 | 13 | Experiment Date2022-07-23 14 | Experiment ID 15 | Notebook ID 16 | Project 17 | Experimenter 18 | Protocol 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | Absolute values 27 | 28 | 29 | Group 1 30 | Group 2 31 | Group 3 32 | Group 4 33 | Group 5 34 | Group 6 35 | 36 | 37 | 38 | Group 39 | 40 | 1 41 | 2 42 | 3 43 | 4 44 | 5 45 | 6 46 | 47 | 48 | 49 | Group 50 | 51 | 1 52 | 2 53 | 3 54 | 4 55 | 5 56 | 6 57 | 58 | 59 | 60 | D1S1 61 | 62 | 0,292 63 | 0,205 64 | 0,724 65 | 6,353 66 | 0,271 67 | 0,436 68 | 69 | 70 | 0,234 71 | 0,195 72 | 0,942 73 | 6,598 74 | 0,348 75 | 0,436 76 | 77 | 78 | 79 | D1S2 80 | 81 | 0,21 82 | 0,376 83 | 0,163 84 | 4,882 85 | 1,341 86 | 1,245 87 | 88 | 89 | 0,245 90 | 0,382 91 | 0,163 92 | 4,816 93 | 1,263 94 | 1,686 95 | 96 | 97 | 98 | D2S1 99 | 100 | 0,172 101 | 0,2 102 | 0,292 103 | 1,021 104 | 0,958 105 | 0,538 106 | 107 | 108 | 0,163 109 | 0,195 110 | 0,224 111 | 1,062 112 | 1,095 113 | 0,493 114 | 115 | 116 | 117 | D2S2 118 | 119 | 0,625 120 | 0,297 121 | 0,154 122 | 1,211 123 | 0,474 124 | 0,353 125 | 126 | 127 | 0,565 128 | 0,281 129 | 0,132 130 | 1,658 131 | 0,468 132 | 0,4 133 | 134 | 135 | 136 | 5 137 | 138 | 139 | 140 | 141 | 6 142 | 143 | 144 | 145 |
146 | 147 | Relative values 148 | 149 | 150 | Group 1 151 | Group 2 152 | Group 3 153 | Group 4 154 | Group 5 155 | Group 6 156 | 157 | 158 | 159 | D1S1 160 | 161 | 1,110266 162 | 0,7794677 163 | 2,752852 164 | 24,15589 165 | 1,030418 166 | 1,657795 167 | 168 | 169 | 0,8897339 170 | 0,7414449 171 | 3,581749 172 | 25,08745 173 | 1,323194 174 | 1,657795 175 | 176 | 177 | 178 | D1S2 179 | 180 | 0,9230769 181 | 1,652747 182 | 0,7164835 183 | 21,45934 184 | 5,894506 185 | 5,472528 186 | 187 | 188 | 1,076923 189 | 1,679121 190 | 0,7164835 191 | 21,16923 192 | 5,551648 193 | 7,410989 194 | 195 | 196 | 197 | D2S1 198 | 199 | 1,026866 200 | 1,19403 201 | 1,743284 202 | 6,095522 203 | 5,719403 204 | 3,21194 205 | 206 | 207 | 0,9731343 208 | 1,164179 209 | 1,337313 210 | 6,340299 211 | 6,537313 212 | 2,943284 213 | 214 | 215 | 216 | D2S2 217 | 218 | 1,05042 219 | 0,4991597 220 | 0,2588235 221 | 2,035294 222 | 0,7966387 223 | 0,5932773 224 | 225 | 226 | 0,9495798 227 | 0,4722689 228 | 0,2218487 229 | 2,786555 230 | 0,7865546 231 | 0,6722689 232 | 233 | 234 | 235 | 5 236 | 237 | 238 | 239 | 240 | 6 241 | 242 | 243 | 244 |
245 | 246 | 247 | 248 | 361 | -------------------------------------------------------------------------------- /macrophage-conditioning/data/primary_data/figures/fig 1 il6_log.eps: -------------------------------------------------------------------------------- 1 | %!PS-Adobe-2.0 EPSF-2.0 2 | %% This is a Stata generated postscript file 3 | %%BoundingBox: 0 0 226 173 4 | %%HiResBoundingBox: 0.000 0.000 224.640 172.224 5 | %%DocumentNeededResources: font Helvetica 6 | /xratio 0.007020 def 7 | /yratio 0.007020 def 8 | /Sbgfill { 9 | /y1 exch def 10 | /x1 exch def 11 | /y0 exch def 12 | /x0 exch def 13 | x0 y0 moveto 14 | x0 y1 lineto x1 y1 lineto x1 y0 lineto x0 y0 lineto 15 | fill 16 | } def 17 | /Spt { 18 | yratio mul 19 | /yp exch def 20 | xratio mul 21 | /xp exch def 22 | Slrgb setrgbcolor 23 | newpath 24 | xp yp moveto 25 | xp Slw add yp 26 | lineto 27 | currentlinecap 28 | 1 setlinecap 29 | stroke 30 | setlinecap 31 | } def 32 | /Sln { 33 | yratio mul 34 | /y1p exch def 35 | xratio mul 36 | /x1p exch def 37 | yratio mul 38 | /y0p exch def 39 | xratio mul 40 | /x0p exch def 41 | Slw setlinewidth 42 | Slrgb setrgbcolor 43 | x0p y0p M x1p y1p lineto S 44 | } def 45 | /Stxtl { 46 | /sp exch def 47 | yratio mul 48 | /sizep exch def 49 | dup 50 | /anglep exch def 51 | 0 exch sub 52 | /angle2p exch def 53 | yratio mul 54 | /y0p exch def 55 | xratio mul 56 | /x0p exch def 57 | Strgb setrgbcolor 58 | x0p y0p M anglep rotate sizep fntsize sp show stroke angle2p rotate 59 | } def 60 | /Stxtc { 61 | /sp exch def 62 | yratio mul 63 | /sizep exch def 64 | dup 65 | /anglep exch def 66 | 0 exch sub 67 | /angle2p exch def 68 | yratio mul 69 | /y0p exch def 70 | xratio mul 71 | /x0p exch def 72 | Strgb setrgbcolor 73 | x0p y0p M anglep rotate sizep fntsize sp stringwidth exch -2 div exch rm sp show stroke angle2p rotate 74 | } def 75 | /Stxtr { 76 | /sp exch def 77 | yratio mul 78 | /sizep exch def 79 | dup 80 | /anglep exch def 81 | 0 exch sub 82 | /angle2p exch def 83 | yratio mul 84 | /y0p exch def 85 | xratio mul 86 | /x0p exch def 87 | Strgb setrgbcolor 88 | x0p y0p M anglep rotate sizep fntsize sp stringwidth 1 index -1 mul exch rm pop sp show stroke angle2p rotate 89 | } def 90 | /Srect { 91 | /sfill exch def 92 | yratio mul 93 | /y1 exch def 94 | xratio mul 95 | /x1 exch def 96 | yratio mul 97 | /y0 exch def 98 | xratio mul 99 | /x0 exch def 100 | sfill 1 eq { 101 | Ssrgb setrgbcolor 102 | x0 y0 moveto 103 | x0 y1 lineto x1 y1 lineto x1 y0 lineto x0 y0 lineto 104 | fill 105 | } if 106 | Slw setlinewidth 107 | Slrgb setrgbcolor 108 | x0 y0 moveto 109 | x0 y1 lineto x1 y1 lineto x1 y0 lineto x0 y0 lineto 110 | stroke 111 | } def 112 | /Stri { 113 | /sfill exch def 114 | xratio mul 115 | /r exch def 116 | yratio mul 117 | /y0 exch def 118 | xratio mul 119 | /x0 exch def 120 | /xcen x0 def 121 | y0 r add 122 | /ytop exch def 123 | r 2 div 124 | y0 exch sub 125 | /ybot exch def 126 | r 3 sqrt 2 div mul dup 127 | xcen exch sub 128 | /xleft exch def 129 | xcen add 130 | /xright exch def 131 | sfill 1 eq { 132 | Ssrgb setrgbcolor 133 | xcen ytop moveto xright ybot lineto xleft ybot lineto xcen ytop lineto fill 134 | } if 135 | Slw setlinewidth 136 | Slrgb setrgbcolor 137 | xcen ytop moveto xright ybot lineto xleft ybot lineto xcen ytop lineto stroke 138 | } def 139 | /Soldtri { 140 | /sfill exch def 141 | xratio mul 142 | /r exch def 143 | yratio mul 144 | /y0 exch def 145 | xratio mul 146 | /x0 exch def 147 | x0 r sub 148 | /x1 exch def 149 | y0 r sub 150 | /y1 exch def 151 | x0 r add 152 | /x2 exch def 153 | y0 r sub 154 | /y2 exch def 155 | /x3 x0 def 156 | y0 r add 157 | /y3 exch def 158 | sfill 1 eq { 159 | Ssrgb setrgbcolor 160 | x1 y1 moveto x2 y2 lineto x3 y3 lineto x1 y1 lineto fill 161 | } if 162 | Slw setlinewidth 163 | Slrgb setrgbcolor 164 | x1 y1 moveto x2 y2 lineto x3 y3 lineto x1 y1 lineto stroke 165 | } def 166 | /Sdia { 167 | /sfill exch def 168 | xratio mul 169 | /r exch def 170 | yratio mul 171 | /y exch def 172 | xratio mul 173 | /x exch def 174 | x r sub 175 | /x0 exch def 176 | /y0 y def 177 | /x1 x def 178 | y r sub 179 | /y1 exch def 180 | x r add 181 | /x2 exch def 182 | /y2 y def 183 | /x3 x def 184 | y r add 185 | /y3 exch def 186 | sfill 1 eq { 187 | Ssrgb setrgbcolor 188 | x0 y0 moveto x1 y1 lineto x2 y2 lineto x3 y3 lineto x0 y0 lineto fill 189 | } if 190 | Slw setlinewidth 191 | Slrgb setrgbcolor 192 | x0 y0 moveto x1 y1 lineto x2 y2 lineto x3 y3 lineto x0 y0 lineto stroke 193 | } def 194 | /Scc { 195 | /sfill exch def 196 | xratio mul 197 | /r0 exch def 198 | yratio mul 199 | /y0 exch def 200 | xratio mul 201 | /x0 exch def 202 | sfill 1 eq { 203 | Ssrgb setrgbcolor 204 | x0 y0 r0 0 360 arc fill 205 | } if 206 | Slw setlinewidth 207 | Slrgb setrgbcolor 208 | x0 y0 r0 0 360 arc stroke 209 | } def 210 | /Spie { 211 | /sfill exch def 212 | /a1 exch def 213 | /a0 exch def 214 | xratio mul 215 | /r exch def 216 | yratio mul 217 | /y exch def 218 | xratio mul 219 | /x exch def 220 | sfill 1 eq { 221 | Ssrgb setrgbcolor 222 | newpath x y moveto x y r a0 a1 arc closepath 223 | fill 224 | } if 225 | Slw setlinewidth 226 | Slrgb setrgbcolor 227 | newpath x y moveto x y r a0 a1 arc closepath 228 | stroke 229 | } def 230 | /Splu { 231 | xratio mul 232 | /r exch def 233 | yratio mul 234 | /y exch def 235 | xratio mul 236 | /x exch def 237 | x r sub 238 | /x0 exch def 239 | x r add 240 | /x1 exch def 241 | x0 y M x1 y L 242 | y r sub 243 | /y0 exch def 244 | y r add 245 | /y1 exch def 246 | x y0 M x y1 L 247 | } def 248 | /Scro { 249 | xratio mul 250 | /r exch def 251 | yratio mul 252 | /y exch def 253 | xratio mul 254 | /x exch def 255 | x r sub 256 | /x0 exch def 257 | y r sub 258 | /y0 exch def 259 | x r add 260 | /x1 exch def 261 | y r add 262 | /y1 exch def 263 | x0 y0 M x1 y1 L 264 | x r add 265 | /x0 exch def 266 | y r sub 267 | /y0 exch def 268 | x r sub 269 | /x1 exch def 270 | y r add 271 | /y1 exch def 272 | x0 y0 M x1 y1 L 273 | } def 274 | /Sm { 275 | yratio mul 276 | /y exch def 277 | xratio mul 278 | /x exch def 279 | x y M 280 | } def 281 | /Sl { 282 | yratio mul 283 | /y exch def 284 | xratio mul 285 | /x exch def 286 | x y L 287 | } def 288 | /SPl { 289 | yratio mul 290 | /y exch def 291 | xratio mul 292 | /x exch def 293 | x y PL 294 | } def 295 | /Lcs { 296 | currentlinecap 297 | 1 setlinecap 298 | } def 299 | /Lcr { 300 | setlinecap 301 | } def 302 | /Sbp { 303 | newpath 304 | } def 305 | /Sep { 306 | /sfill exch def 307 | closepath 308 | sfill 1 eq { 309 | Ssrgb setrgbcolor 310 | gsave 311 | fill 312 | grestore 313 | } if 314 | Slw setlinewidth 315 | Slrgb setrgbcolor 316 | Lcs 317 | stroke 318 | Lcr 319 | } def 320 | /cp {currentpoint} def 321 | /M {moveto} def 322 | /rm {rmoveto} def 323 | /S {stroke} def 324 | /L {Slw setlinewidth Slrgb setrgbcolor lineto Lcs S Lcr} def 325 | /PL {Slw setlinewidth Slrgb setrgbcolor lineto} def 326 | /MF-Helvetica { 327 | /newfontname exch def 328 | /fontname exch def 329 | /fontdict fontname findfont def 330 | /newfont fontdict maxlength dict def 331 | fontdict { 332 | exch dup /FID eq {pop pop} {exch newfont 3 1 roll put} ifelse 333 | } forall 334 | newfont /FontName newfontname put 335 | newfont /Encoding ISOLatin1Encoding put 336 | newfontname newfont definefont pop 337 | } def 338 | /Helvetica /Helvetica-0 MF-Helvetica 339 | /Slw 0.120 def 340 | 1.000 1.000 1.000 setrgbcolor 341 | 0 0 224.640 172.224 Sbgfill 342 | /Slrgb {1.000 1.000 1.000} def 343 | /Strgb {1.000 1.000 1.000} def 344 | /Ssrgb {1.000 1.000 1.000} def 345 | /Slw 0.344 def 346 | 0 0 31999 24533 1 Srect 347 | /Slrgb {0.000 0.000 0.000} def 348 | 3925 2391 31140 23674 0 Srect 349 | /Strgb {0.000 0.000 0.000} def 350 | /Slw 0.517 def 351 | /Slrgb {0.878 0.878 0.878} def 352 | 3925 -8457 31140 -8457 Sln 353 | /Slw 0.344 def 354 | /Slrgb {0.000 0.000 0.000} def 355 | /Slw 0.517 def 356 | /Slrgb {0.878 0.878 0.878} def 357 | 3925 17206 31140 17206 Sln 358 | /Slw 0.344 def 359 | /Slrgb {0.000 0.000 0.000} def 360 | /Slw 0.517 def 361 | /Slrgb {0.878 0.878 0.878} def 362 | 3925 20745 31140 20745 Sln 363 | /Slw 0.344 def 364 | /Slrgb {0.000 0.000 0.000} def 365 | /Slw 0.517 def 366 | /Slrgb {0.878 0.878 0.878} def 367 | 3925 22815 31140 22815 Sln 368 | /Slw 0.344 def 369 | /Slrgb {0.000 0.000 0.000} def 370 | /Ssrgb {0.376 0.376 0.376} def 371 | /Slw 0.517 def 372 | /Slrgb {0.376 0.376 0.376} def 373 | 5164 16966 7067 18261 1 Srect 374 | /Slw 0.775 def 375 | /Slrgb {1.000 1.000 1.000} def 376 | 5164 17496 7067 17496 Sln 377 | /Slw 0.517 def 378 | /Slrgb {0.376 0.376 0.376} def 379 | 6115 16966 6115 16565 Sln 380 | 6115 18261 6115 19054 Sln 381 | 5478 16565 6753 16565 Sln 382 | 5478 19054 6753 19054 Sln 383 | 8970 14686 10873 18670 1 Srect 384 | /Slw 0.775 def 385 | /Slrgb {1.000 1.000 1.000} def 386 | 8970 17814 10873 17814 Sln 387 | /Slw 0.517 def 388 | /Slrgb {0.376 0.376 0.376} def 389 | 9921 14686 9921 12679 Sln 390 | 9921 18670 9921 20010 Sln 391 | 9284 12679 10559 12679 Sln 392 | 9284 20010 10559 20010 Sln 393 | 12775 4559 14678 5699 1 Srect 394 | /Slw 0.775 def 395 | /Slrgb {1.000 1.000 1.000} def 396 | 12775 4826 14678 4826 Sln 397 | /Slw 0.517 def 398 | /Slrgb {0.376 0.376 0.376} def 399 | 13727 4559 13727 4436 Sln 400 | 13727 5699 13727 6074 Sln 401 | 13089 4436 14364 4436 Sln 402 | 13089 6074 14364 6074 Sln 403 | 16581 3772 18484 5079 1 Srect 404 | /Slw 0.775 def 405 | /Slrgb {1.000 1.000 1.000} def 406 | 16581 4374 18484 4374 Sln 407 | /Slw 0.517 def 408 | /Slrgb {0.376 0.376 0.376} def 409 | 17533 3772 17533 3250 Sln 410 | 17533 5079 17533 5699 Sln 411 | 16895 3250 18170 3250 Sln 412 | 16895 5699 18170 5699 Sln 413 | 20387 14083 22290 20917 1 Srect 414 | /Slw 0.775 def 415 | /Slrgb {1.000 1.000 1.000} def 416 | 20387 18660 22290 18660 Sln 417 | /Slw 0.517 def 418 | /Slrgb {0.376 0.376 0.376} def 419 | 21338 14083 21338 12752 Sln 420 | 21338 20917 21338 22164 Sln 421 | 20701 12752 21976 12752 Sln 422 | 20701 22164 21976 22164 Sln 423 | 24193 13808 26095 20213 1 Srect 424 | /Slw 0.775 def 425 | /Slrgb {1.000 1.000 1.000} def 426 | 24193 16427 26095 16427 Sln 427 | /Slw 0.517 def 428 | /Slrgb {0.376 0.376 0.376} def 429 | 25144 13808 25144 12032 Sln 430 | 25144 20213 25144 21789 Sln 431 | 24507 12032 25781 12032 Sln 432 | 24507 21789 25781 21789 Sln 433 | 27998 4529 29901 7240 1 Srect 434 | /Slw 0.775 def 435 | /Slrgb {1.000 1.000 1.000} def 436 | 27998 6273 29901 6273 Sln 437 | /Slw 0.517 def 438 | /Slrgb {0.376 0.376 0.376} def 439 | 28950 4529 28950 3772 Sln 440 | 28950 7240 28950 7382 Sln 441 | 28312 3772 29587 3772 Sln 442 | 28312 7382 29587 7382 Sln 443 | 13727 8365 269 1 Scc 444 | 28950 10025 269 1 Scc 445 | /Slw 0.344 def 446 | /Slrgb {0.000 0.000 0.000} def 447 | 3925 2391 3925 23674 Sln 448 | 3925 -8457 3584 -8457 Sln 449 | 3107 -8457 0 450 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 451 | (0) stringwidth pop add 452 | 2 div xratio div 453 | dup 4 3 roll exch 90.000 cos mul sub 3 1 roll 90.000 sin mul sub 2 copy 454 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 455 | 90.000 1022 456 | (0) Stxtl 457 | (0) stringwidth pop xratio div 458 | dup 4 3 roll exch 90.000 cos mul add 3 1 roll 90.000 sin mul add 2 copy 459 | pop pop pop pop 460 | 3925 17206 3584 17206 Sln 461 | 3107 17206 0 462 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 463 | (1) stringwidth pop add 464 | 2 div xratio div 465 | dup 4 3 roll exch 90.000 cos mul sub 3 1 roll 90.000 sin mul sub 2 copy 466 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 467 | 90.000 1022 468 | (1) Stxtl 469 | (1) stringwidth pop xratio div 470 | dup 4 3 roll exch 90.000 cos mul add 3 1 roll 90.000 sin mul add 2 copy 471 | pop pop pop pop 472 | 3925 20745 3584 20745 Sln 473 | 3107 20745 0 474 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 475 | (2) stringwidth pop add 476 | 2 div xratio div 477 | dup 4 3 roll exch 90.000 cos mul sub 3 1 roll 90.000 sin mul sub 2 copy 478 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 479 | 90.000 1022 480 | (2) Stxtl 481 | (2) stringwidth pop xratio div 482 | dup 4 3 roll exch 90.000 cos mul add 3 1 roll 90.000 sin mul add 2 copy 483 | pop pop pop pop 484 | 3925 22815 3584 22815 Sln 485 | 3107 22815 0 486 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 487 | (3) stringwidth pop add 488 | 2 div xratio div 489 | dup 4 3 roll exch 90.000 cos mul sub 3 1 roll 90.000 sin mul sub 2 copy 490 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 491 | 90.000 1022 492 | (3) Stxtl 493 | (3) stringwidth pop xratio div 494 | dup 4 3 roll exch 90.000 cos mul add 3 1 roll 90.000 sin mul add 2 copy 495 | pop pop pop pop 496 | 1744 13033 0 497 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 498 | (IL-6 \(pg/ml\)) stringwidth pop add 499 | 2 div xratio div 500 | dup 4 3 roll exch 90.000 cos mul sub 3 1 roll 90.000 sin mul sub 2 copy 501 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 502 | 90.000 1022 503 | (IL-6 \(pg/ml\)) Stxtl 504 | (IL-6 \(pg/ml\)) stringwidth pop xratio div 505 | dup 4 3 roll exch 90.000 cos mul add 3 1 roll 90.000 sin mul add 2 copy 506 | pop pop pop pop 507 | 3925 2391 31140 2391 Sln 508 | 6115 1336 0 509 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 510 | (1) stringwidth pop add 511 | 2 div xratio div 512 | dup 4 3 roll exch 0.000 cos mul sub 3 1 roll 0.000 sin mul sub 2 copy 513 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 514 | 0.000 1022 515 | (1) Stxtl 516 | (1) stringwidth pop xratio div 517 | dup 4 3 roll exch 0.000 cos mul add 3 1 roll 0.000 sin mul add 2 copy 518 | pop pop pop pop 519 | 9921 1336 0 520 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 521 | (2) stringwidth pop add 522 | 2 div xratio div 523 | dup 4 3 roll exch 0.000 cos mul sub 3 1 roll 0.000 sin mul sub 2 copy 524 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 525 | 0.000 1022 526 | (2) Stxtl 527 | (2) stringwidth pop xratio div 528 | dup 4 3 roll exch 0.000 cos mul add 3 1 roll 0.000 sin mul add 2 copy 529 | pop pop pop pop 530 | 13727 1336 0 531 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 532 | (3) stringwidth pop add 533 | 2 div xratio div 534 | dup 4 3 roll exch 0.000 cos mul sub 3 1 roll 0.000 sin mul sub 2 copy 535 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 536 | 0.000 1022 537 | (3) Stxtl 538 | (3) stringwidth pop xratio div 539 | dup 4 3 roll exch 0.000 cos mul add 3 1 roll 0.000 sin mul add 2 copy 540 | pop pop pop pop 541 | 17533 1336 0 542 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 543 | (4) stringwidth pop add 544 | 2 div xratio div 545 | dup 4 3 roll exch 0.000 cos mul sub 3 1 roll 0.000 sin mul sub 2 copy 546 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 547 | 0.000 1022 548 | (4) Stxtl 549 | (4) stringwidth pop xratio div 550 | dup 4 3 roll exch 0.000 cos mul add 3 1 roll 0.000 sin mul add 2 copy 551 | pop pop pop pop 552 | 21338 1336 0 553 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 554 | (5) stringwidth pop add 555 | 2 div xratio div 556 | dup 4 3 roll exch 0.000 cos mul sub 3 1 roll 0.000 sin mul sub 2 copy 557 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 558 | 0.000 1022 559 | (5) Stxtl 560 | (5) stringwidth pop xratio div 561 | dup 4 3 roll exch 0.000 cos mul add 3 1 roll 0.000 sin mul add 2 copy 562 | pop pop pop pop 563 | 25144 1336 0 564 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 565 | (6) stringwidth pop add 566 | 2 div xratio div 567 | dup 4 3 roll exch 0.000 cos mul sub 3 1 roll 0.000 sin mul sub 2 copy 568 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 569 | 0.000 1022 570 | (6) Stxtl 571 | (6) stringwidth pop xratio div 572 | dup 4 3 roll exch 0.000 cos mul add 3 1 roll 0.000 sin mul add 2 copy 573 | pop pop pop pop 574 | 28950 1336 0 575 | /Helvetica-0 findfont 1022 yratio mul scalefont setfont 576 | (7) stringwidth pop add 577 | 2 div xratio div 578 | dup 4 3 roll exch 0.000 cos mul sub 3 1 roll 0.000 sin mul sub 2 copy 579 | /fntsize {/Helvetica-0 findfont exch scalefont setfont } def 580 | 0.000 1022 581 | (7) Stxtl 582 | (7) stringwidth pop xratio div 583 | dup 4 3 roll exch 0.000 cos mul add 3 1 roll 0.000 sin mul add 2 copy 584 | pop pop pop pop 585 | S showpage 586 | %%EOF 587 | -------------------------------------------------------------------------------- /macrophage-conditioning/data/primary_data/figures/fig 1 il6_log.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/macrophage-conditioning/data/primary_data/figures/fig 1 il6_log.jpg -------------------------------------------------------------------------------- /macrophage-conditioning/data/primary_data/figures/fig 2 il6 log.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/macrophage-conditioning/data/primary_data/figures/fig 2 il6 log.jpg -------------------------------------------------------------------------------- /macrophage-conditioning/data/primary_data/makrofag_parings_n_evocation_raw.txt: -------------------------------------------------------------------------------- 1 | Well ID id pairing dubblett il6 il6_mean 2 | SPL1 1 1 1 3.115 3.232 3 | 1 1 2 3.348 4 | SPL2 2 1 1 3.262 3.137 5 | 2 1 2 3.012 6 | SPL3 1 2 1 2.679 2.915 7 | 1 2 2 3.151 8 | SPL4 2 2 1 3.284 3.369 9 | 2 2 2 3.453 10 | SPL5 1 3 1 3.243 3.266 11 | 1 3 2 3.289 12 | SPL6 2 3 1 3.207 3.051 13 | 2 3 2 2.895 14 | SPL7 1 4 1 0.82 0.565 15 | 1 4 2 0.309 16 | SPL8 2 4 1 0.766 0.929 17 | 2 4 2 1.093 18 | SPL9 1 5 1 0.54 0.625 19 | 1 5 2 0.711 20 | SPL10 2 5 1 0.558 0.631 21 | 2 5 2 0.705 22 | SPL11 3 1 1 2.912 2.938 23 | 3 1 2 2.964 24 | SPL12 4 1 1 3.037 2.971 25 | 4 1 2 2.906 26 | SPL13 3 2 1 2.057 2.076 27 | 3 2 2 2.095 28 | SPL14 4 2 1 2.377 2.271 29 | 4 2 2 2.164 30 | SPL15 3 3 1 3.493 3.217 31 | 3 3 2 2.942 32 | SPL16 4 3 1 3.462 3.382 33 | 4 3 2 3.301 34 | SPL17 5 1 1 1.279 1.413 35 | 5 1 2 1.547 36 | SPL18 6 1 1 2.044 1.968 37 | 6 1 2 1.891 38 | SPL19 5 2 1 0.907 1.109 39 | 5 2 2 1.311 40 | SPL20 6 2 1 0.782 0.829 41 | 6 2 2 0.875 42 | SPL21 5 3 1 1.422 1.422 43 | 5 3 2 1.423 44 | SPL22 6 3 1 1.672 1.658 45 | 6 3 2 1.644 46 | SPL23 7 1 1 1.255 1.409 47 | 7 1 2 1.563 48 | SPL24 8 1 1 3.001 3.093 49 | 8 1 2 3.184 50 | SPL25 7 2 1 1.238 1.29 51 | 7 2 2 1.342 52 | SPL26 8 2 1 0.83 0.84 53 | 8 2 2 0.85 54 | SPL27 7 3 1 2.757 2.667 55 | 7 3 2 2.576 56 | SPL28 8 3 1 2.672 2.416 57 | 8 3 2 2.16 58 | SPL29 7 1 1.058 1.078 59 | 7 2 1.097 60 | SPL30 8 1 1.536 1.436 61 | 8 2 1.336 62 | SPL31 7 1 0.993 1.017 63 | 7 2 1.04 64 | SPL32 8 1 1.196 1.236 65 | 8 2 1.277 66 | SPL33 7 1 0.083 0.085 67 | 7 2 0.088 68 | SPL34 8 1 0.085 0.082 69 | 8 2 0.079 70 | SPL35 7 1 0.068 0.076 71 | 7 2 0.084 72 | SPL36 8 1 0.081 0.079 73 | 8 2 0.077 74 | SPL37 7 1 2.533 2.641 75 | 7 2 2.748 76 | SPL38 8 1 2.403 2.258 77 | 8 2 2.112 78 | SPL39 7 1 2.053 2.114 79 | 7 2 2.175 80 | SPL40 8 1 2.341 2.454 81 | 8 2 2.566 82 | SPL41 7 1 0.109 0.111 83 | 7 2 0.113 84 | SPL42 8 1 0.087 0.085 85 | 8 2 0.083 86 | 1 6 1 0.657 0.647 87 | 1 6 2 0.638 88 | 2 6 1 0.731 0.75 89 | 2 6 2 0.77 -------------------------------------------------------------------------------- /macrophage-conditioning/data/study-1_data.csv: -------------------------------------------------------------------------------- 1 | group,donor_strand,measurement,il6_secretion_abs,il6_secretion_rel 2 | 1,D1S1,1,0.292,1.110266 3 | 2,D1S1,1,0.205,0.7794677 4 | 3,D1S1,1,0.724,2.752852 5 | 4,D1S1,1,6.353,24.15589 6 | 5,D1S1,1,0.271,1.030418 7 | 6,D1S1,1,0.436,1.657795 8 | 1,D1S1,2,0.234,0.8897339 9 | 2,D1S1,2,0.195,0.7414449 10 | 3,D1S1,2,0.942,3.581749 11 | 4,D1S1,2,6.598,25.08745 12 | 5,D1S1,2,0.348,1.323194 13 | 6,D1S1,2,0.436,1.657795 14 | 1,D1S2,1,0.21,0.9230769 15 | 2,D1S2,1,0.376,1.652747 16 | 3,D1S2,1,0.163,0.7164835 17 | 4,D1S2,1,4.882,21.45934 18 | 5,D1S2,1,1.341,5.894506 19 | 6,D1S2,1,1.245,5.472528 20 | 1,D1S2,2,0.245,1.076923 21 | 2,D1S2,2,0.382,1.679121 22 | 3,D1S2,2,0.163,0.7164835 23 | 4,D1S2,2,4.816,21.16923 24 | 5,D1S2,2,1.263,5.551648 25 | 6,D1S2,2,1.686,7.410989 26 | 1,D2S1,1,0.172,1.026866 27 | 2,D2S1,1,0.2,1.19403 28 | 3,D2S1,1,0.292,1.743284 29 | 4,D2S1,1,1.021,6.095522 30 | 5,D2S1,1,0.958,5.719403 31 | 6,D2S1,1,0.538,3.21194 32 | 1,D2S1,2,0.163,0.9731343 33 | 2,D2S1,2,0.195,1.164179 34 | 3,D2S1,2,0.224,1.337313 35 | 4,D2S1,2,1.062,6.340299 36 | 5,D2S1,2,1.095,6.537313 37 | 6,D2S1,2,0.493,2.943284 38 | 1,D2S2,1,0.625,1.05042 39 | 2,D2S2,1,0.297,0.4991597 40 | 3,D2S2,1,0.154,0.2588235 41 | 4,D2S2,1,1.211,2.035294 42 | 5,D2S2,1,0.474,0.7966387 43 | 6,D2S2,1,0.353,0.5932773 44 | 1,D2S2,2,0.565,0.9495798 45 | 2,D2S2,2,0.281,0.4722689 46 | 3,D2S2,2,0.132,0.2218487 47 | 4,D2S2,2,1.658,2.786555 48 | 5,D2S2,2,0.468,0.7865546 49 | 6,D2S2,2,0.4,0.6722689 -------------------------------------------------------------------------------- /macrophage-conditioning/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context": "http://schema.org/", 3 | "@type": "Dataset", 4 | "name": "Learning in a simple biological system: a pilot study of classical conditioning of human macrophages in vitro", 5 | "description": "Learning in a simple biological system: a pilot study of classical conditioning of human macrophages in vitro", 6 | "schemaVersion": "Psych-DS 0.4.0", 7 | "creator": [ 8 | { 9 | "@type": "Person", 10 | "name": "Gustav Nilsonne" 11 | }, 12 | { 13 | "@type": "Person", 14 | "name": "Alva Appelgren" 15 | }, 16 | { 17 | "@type": "Person", 18 | "name": "John Axelsson" 19 | }, 20 | { 21 | "@type": "Person", 22 | "name": "Mats Fredrikson" 23 | }, 24 | { 25 | "@type": "Person", 26 | "name": "Mats Lekander" 27 | } 28 | ], 29 | "citation": "Nilsonne, G., Appelgren, A., Axelsson, J. et al. Learning in a simple biological system: a pilot study of classical conditioning of human macrophages in vitro. Behav Brain Funct 7, 47 (2011).", 30 | "sameAs": "https://doi.org/10.1186/1744-9081-7-47", 31 | "temporalCoverage": "2008-07-15/2011-12-18", 32 | "keywords": [ 33 | "associative learning", 34 | "conditioning", 35 | "habituation", 36 | "in vitro", 37 | "monocyte", 38 | "macrophage", 39 | "lipopolysaccharide", 40 | "streptomycin", 41 | "interleukin-6" 42 | ], 43 | "variableMeasured": [ 44 | { 45 | "type": "PropertyValue", 46 | "unitText": "Group", 47 | "name": "group", 48 | "description": "The type of stimulus received. Group 1: training=CS+UCS - evocation=CS. Group 2: training=CS+UCS - evocation=none. Group 3: training=CS - evocation=none. Group 4: training=CS - evocation=CS. Group 5: training=UCS - evocation=none. Group 6: training=UCS - evocation=CS. Group 7: training=none - evocation=none." 49 | }, 50 | { 51 | "type": "PropertyValue", 52 | "unitText": "Donor/Strand", 53 | "name": "donor_strand", 54 | "description": "The donor of the macrophages id and strand number combined." 55 | }, 56 | { 57 | "type": "PropertyValue", 58 | "unitText": "Measurement", 59 | "name": "measurement", 60 | "description": "The measurement number for a given combination of donor/strand and group" 61 | }, 62 | { 63 | "type": "PropertyValue", 64 | "unitText": "IL-6 secretion absolute (pg/ml)", 65 | "name": "il6_secretion_abs", 66 | "description": "The measured absolute secretion of IL-6 in pg/ml." 67 | }, 68 | { 69 | "type": "PropertyValue", 70 | "unitText": "IL-6 secretion relative (fold change)", 71 | "name": "il6_secretion_rel", 72 | "description": "The measured relative secretion of IL-6 in fold change." 73 | } 74 | ] 75 | } -------------------------------------------------------------------------------- /mistakes-corrected-dataset/data/study-yarncolor_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue 3 | r2d2,1999-08-12,scarf,Eggplant 4 | r2d2,2008-12-12,hat,Royal 5 | r2d2,2019,hat,Grey 6 | r2d2,1999,snood,Gold 7 | r2d2,2014-01-01,sweater,Kelly 8 | c3p0,2021,mittens,Lavender 9 | c3p0,2020,glove,Toast 10 | c3p0,2020-12-12,,Mint Green 11 | bb8,2020,scarf,Maroon 12 | bb8,2021,scarf,Pink Carnation 13 | -------------------------------------------------------------------------------- /mistakes-corrected-dataset/data/study-yarncolor_file-badnames_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color,yarn_color2 2 | r2d2,2021-02-21,hat,Country Blue, 3 | r2d2,1999-08-12,scarf,Eggplant, 4 | r2d2,2008-12-12,hat,Royal, 5 | r2d2,2019,hat,Grey, 6 | r2d2,,,, 7 | r2d2,2014-01-01,sweater,Kelly, 8 | c3p0,2021,mittens,Lavender, 9 | c3p0,2020,glove,Toast, 10 | c3p0,2020-12-12,,Mint Green, 11 | bb8,2020,scarf,Maroon, 12 | bb8,2021,scarf,Pink Carnation, 13 | -------------------------------------------------------------------------------- /mistakes-corrected-dataset/data/study-yarncolor_file-noncsvfile_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue 3 | r2d2,1999-08-12,scarf,Eggplant 4 | r2d2,2008-12-12,hat,Royal 5 | r2d2,2019,hat,Grey 6 | r2d2,1999,snood,Gold 7 | r2d2,2014-01-01,sweater,Kelly 8 | c3p0,2021,mittens,Lavender 9 | c3p0,2020,glove,Toast 10 | c3p0,2020-12-12,,Mint Green 11 | bb8,2020,scarf,Maroon 12 | bb8,2021,scarf,Pink Carnation 13 | -------------------------------------------------------------------------------- /mistakes-corrected-dataset/data/study-yarncolor_file-wrongname_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue 3 | r2d2,1999-08-12,scarf,Eggplant 4 | r2d2,2008-12-12,hat,Royal 5 | r2d2,2019,hat,Grey 6 | r2d2,1999,snood,Gold 7 | r2d2,2014-01-01,sweater,Kelly 8 | c3p0,2021,mittens,Lavender 9 | c3p0,2020,glove,Toast 10 | c3p0,2020-12-12,,Mint Green 11 | bb8,2020,scarf,Maroon 12 | bb8,2021,scarf,Pink Carnation 13 | -------------------------------------------------------------------------------- /mistakes-corrected-dataset/data/subdir/subdir/study-yarn_location-subdir_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,yarn_color,rating 2 | r2d2,2021-02-21,Country Blue,199 3 | r2d2,1999-08-12,Eggplant,12 4 | r2d2,2008-12-12,Royal,0 5 | r2d2,2019,Grey,-1 6 | r2d2,1999,Gold,14 7 | r2d2,2014-01-01,Kelly, 8 | c3p0,2021,Lavender, 9 | c3p0,2020,Toast, 10 | c3p0,2020-12-12,Mint Green, 11 | bb8,2020,Maroon, 12 | bb8,2021,Pink Carnation,2000 13 | -------------------------------------------------------------------------------- /mistakes-corrected-dataset/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context" : "http://schema.org/", 3 | "@type" : "Dataset", 4 | "name" : "Psych-DS 'Mistakes Corrected' Dataset", 5 | "description" : "This is a minimal example of a dataset which has now been corrected to meet the Psych-DS spec (according to the sips-2022 validator tool)", 6 | "variableMeasured" : ["sub_id", "date", "rating", "yarn_color", "yarn_color2", "garment"] 7 | } -------------------------------------------------------------------------------- /nih-reviews/Codebook.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/nih-reviews/Codebook.xlsx -------------------------------------------------------------------------------- /nih-reviews/README.md: -------------------------------------------------------------------------------- 1 | # NIH reviews 2 | 3 | The nih-reviews project contains data taken from [Forscher, Cox, Brauer, and Devine (2019)](https://psyarxiv.com/r2xvb), in which 446 scientists each reviewed 3 of 48 NIH R01 grant proposals. 412 of these scientists were used for the primary analysis in the manuscript. The nih-reviews folder contains: 4 | 5 | * *nih_data.tsv*. This is a tab-delimited file in which each row represents a review of a single grant proposal by a single reviewer 6 | * *dataset_description.json*. This is a json file containing machine-readable meta-data for this project 7 | * *codebook.xlsx*. This is a codebook containing descriptions of each variable in nih_data.tsv 8 | * *raw_data*. This contains the raw and source datafiles from this project 9 | 10 | The data can be cited as Forscher, P. S., Cox, W. T. L., Brauer, M., & Devine, P. G. (2018, May 25). Little race or gender bias in an experiment of initial review of NIH R01 grant proposals. https://doi.org/10.31234/osf.io/r2xvb 11 | -------------------------------------------------------------------------------- /nih-reviews/data/study-nih_data.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/nih-reviews/data/study-nih_data.csv -------------------------------------------------------------------------------- /nih-reviews/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context": "https://schema.org/", 3 | "@type": "Dataset", 4 | "name": "NIH reviews", 5 | "description": "Reviewer scores of different subsets of 48 NIH grant proposals from this paper: https://psyarxiv.com/r2xvb", 6 | "schemaVersion": "Psych-DS 0.1.0", 7 | "creator": [ 8 | { 9 | "@type": "Person", 10 | "name": "Patrick S. Forscher" 11 | } 12 | ], 13 | "citation": "Forscher, P. S., Cox, W. T. L., Brauer, M., & Devine, P. G. (2018, May 25). Little race or gender bias in an experiment of initial review of NIH R01 grant proposals. https://doi.org/10.31234/osf.io/r2xvb", 14 | "sameAs": "https://doi.org/10.31234/osf.io/r2xvb", 15 | "temporalCoverage": "2015-05-26/2017-04-24", 16 | "keywords": [ 17 | "gender bias", 18 | "race bias", 19 | "science policy" 20 | ], 21 | "variableMeasured": ["participant_id","grant_id","deception_check","list","set","outside_resources_text","participant_race","participant_sex","participant_title","concern_women_r","concern_race","concern_efficient","concern_cut","concern_foreign","concern_transparent","concern_prestige_inst","concern_blind","concern_flaw","concern_new","concern_old","concern_elected","concern_good","study_section","institute","old_score","quality","quality_dich","quality_gmc","quality_cmc","diff_qual","dual_pi","foreign_pi","condition","wf","bm","bf","imp_score","sig_score","inv_score","inn_score","app_score","env_score","imp_text","sig_text_str","sig_text_wkn","inv_text_str","inv_text_wkn","inn_text_str","inn_text_wkn","app_text_str","app_text_wkn","env_text_str","env_text_wkn","imp_text_cln","sig_text_str_cln","sig_text_wkn_cln","inv_text_str_cln","inv_text_wkn_cln","inn_text_str_cln","inn_text_wkn_cln","app_text_str_cln","app_text_wkn_cln","env_text_str_cln","env_text_wkn_cln","imp_text_n","sig_text_str_n","sig_text_wkn_n","inv_text_str_n","inv_text_wkn_n","inn_text_str_n","inn_text_wkn_n","app_text_str_n","app_text_wkn_n","env_text_str_n","env_text_wkn_n","imp_text_achieve","imp_text_ability","imp_text_research","imp_text_standout","imp_text_agentic","imp_text_negeval","imp_text_poseval","imp_text_negate","imp_text_pronouns","sig_text_str_achieve","sig_text_str_ability","sig_text_str_research","sig_text_str_standout","sig_text_str_agentic","sig_text_str_negeval","sig_text_str_poseval","sig_text_str_negate","sig_text_str_pronouns","sig_text_wkn_achieve","sig_text_wkn_ability","sig_text_wkn_research","sig_text_wkn_standout","sig_text_wkn_agentic","sig_text_wkn_negeval","sig_text_wkn_poseval","sig_text_wkn_negate","sig_text_wkn_pronouns","inv_text_str_achieve","inv_text_str_ability","inv_text_str_research","inv_text_str_standout","inv_text_str_agentic","inv_text_str_negeval","inv_text_str_poseval","inv_text_str_negate","inv_text_str_pronouns","inv_text_wkn_achieve","inv_text_wkn_ability","inv_text_wkn_research","inv_text_wkn_standout","inv_text_wkn_agentic","inv_text_wkn_negeval","inv_text_wkn_poseval","inv_text_wkn_negate","inv_text_wkn_pronouns","inn_text_str_achieve","inn_text_str_ability","inn_text_str_research","inn_text_str_standout","inn_text_str_agentic","inn_text_str_negeval","inn_text_str_poseval","inn_text_str_negate","inn_text_str_pronouns","inn_text_wkn_achieve","inn_text_wkn_ability","inn_text_wkn_research","inn_text_wkn_standout","inn_text_wkn_agentic","inn_text_wkn_negeval","inn_text_wkn_poseval","inn_text_wkn_negate","inn_text_wkn_pronouns","app_text_str_achieve","app_text_str_ability","app_text_str_research","app_text_str_standout","app_text_str_agentic","app_text_str_negeval","app_text_str_poseval","app_text_str_negate","app_text_str_pronouns","app_text_wkn_achieve","app_text_wkn_ability","app_text_wkn_research","app_text_wkn_standout","app_text_wkn_agentic","app_text_wkn_negeval","app_text_wkn_poseval","app_text_wkn_negate","app_text_wkn_pronouns","env_text_str_achieve","env_text_str_ability","env_text_str_research","env_text_str_standout","env_text_str_agentic","env_text_str_negeval","env_text_str_poseval","env_text_str_negate","env_text_str_pronouns","env_text_wkn_achieve","env_text_wkn_ability","env_text_wkn_research","env_text_wkn_standout","env_text_wkn_agentic","env_text_wkn_negeval","env_text_wkn_poseval","env_text_wkn_negate","env_text_wkn_pronouns"] 22 | } -------------------------------------------------------------------------------- /nih-reviews/nih_data.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/nih-reviews/nih_data.csv -------------------------------------------------------------------------------- /object-orientation/LAB_processing.R: -------------------------------------------------------------------------------- 1 | # Be sure all the raw data are in "raw_data" before you run this code. 2 | # This script has to be located in the same folder where we stored "raw_data" 3 | 4 | # load required packages 5 | if(!require(tidyverse)) {install.packages("tidyverse");library(tidyverse)} else (library(tidyverse)) 6 | if(!require(readbulk)) {install.packages("readbulk");library(readbulk)} else (library(readbulk)) 7 | 8 | # Import and Merge SP raw data 9 | LAB_SP_raw <- read_opensesame(directory = "raw_data",subdirectories = "SP") 10 | 11 | # Split the data of verification and memory 12 | LAB_SP_memory <- LAB_SP_raw %>% subset( Task == "M") 13 | LAB_SP_verification <- LAB_SP_raw %>% subset(Task == "V") 14 | 15 | # Import and Merge PP raw data 16 | LAB_PP_raw <- read_opensesame(directory = "raw_data",subdirectories = "PP") 17 | 18 | # Check LAB ID 19 | # There will be the code to import the mappings of LAB ID and SEED. 20 | 21 | ## Filter the variables to be analyzed 22 | SP_verification_variables <- c(names(LAB_SP_raw)[c(155,157,5,2,3,4,6,7,120,21,134,223,104,115,116,94)]) 23 | SP_memory_variables <- c(names(LAB_SP_raw)[c(155,157,5,2,19,120,21,134,104,223,115,116,94)]) 24 | PP_variables <- c(names(LAB_PP_raw))[c(126,5,1,3,4,6,7,101,19,110,83,176,93,82,178,95,96,72)] 25 | 26 | ## Export the raw data to be further analyzed. 27 | LAB_SP_verification[,SP_verification_variables] %>% 28 | write.csv(file = paste0("./raw_data/",LAB_SP_raw$LAB_SEED %>% unique,"_SP_V.csv"), row.names = FALSE) 29 | LAB_SP_memory[,SP_memory_variables] %>% 30 | write.csv(file = paste0("./raw_data/",LAB_SP_raw$LAB_SEED %>% unique,"_SP_M.csv"), row.names = FALSE) 31 | LAB_PP_raw[,PP_variables] %>% 32 | write.csv(file = paste0("./raw_data/",LAB_PP_raw$LAB_SEED %>% unique,"_PP.csv"), row.names = FALSE) 33 | 34 | 35 | ## Retrieve the verification response data 36 | ## Erase the participants which an accuracy lower than 70% 37 | ## Reserve correct responses of S-P verification task 38 | ## Data set for mixed-effect model, all observations are reserved 39 | (Complete_LAB_SP = df_LAB_SP %>% 40 | subset( Task == "V") %>% 41 | filter(Match %in% c('Y','N')) %>% 42 | filter(correct == 1)) %>% 43 | write.csv(file = paste0(df_LAB_SP$LID %>% levels(),"_SP_complete.csv")) 44 | 45 | ## Summarize the cleaned data of this Lab 46 | ( Participant_complete_LAB_SP = Complete_LAB_SP %>% 47 | filter( !(Complete_LAB_SP$PID %in% Excluded_ID) ) %>% 48 | group_by(LID, PID, ORDER, Match) %>% 49 | summarise(V_RT = median(response_time, na.rm = TRUE), V_Acc = 100*n()/12) %>% 50 | left_join(LAB_SP_memory, by = "PID") %>% 51 | arrange( as.numeric(as.character(PID) ) ) ) %>% 52 | write.csv(file = paste0(Complete_LAB_SP$LID %>% levels(),"_SP.csv") ) 53 | 54 | ## Transform the cleaned data for packaged software 55 | Participant_complete_LAB_SP %>% filter(Match == "Y") %>% 56 | select(LID, PID, V_RT, V_Acc, M_acc) %>% 57 | left_join( (Participant_complete_LAB_SP %>% 58 | filter(Match == "N") %>% 59 | select(LID, PID, V_RT, V_Acc, M_acc)), by = c("LID","PID") ) %>% 60 | rename(V_RT_Y = V_RT.x, V_RT_N = V_RT.y, V_acc_Y = V_Acc.x, V_acc_N = V_Acc.y, M_acc_Y = M_acc.x, M_acc_N = M_acc.y) %>% 61 | write.csv(file = paste0(Complete_LAB_SP$LID %>% levels(),"_SP_JASP.csv") ) 62 | 63 | 64 | ## Count the number of available participants 65 | Participant_complete_LAB_SP %>% pull(PID) %>% 66 | unique() %>% 67 | length() 68 | 69 | ########### 70 | 71 | # Access PP raw data 72 | LAB_PP_raw <- paste0(csv_dir_path,"\\", list.files(path = csv_dir_path, pattern = "PP", all.files = TRUE,include.dirs = TRUE, recursive = TRUE)) 73 | 74 | # Define the IDs during imported data 75 | LAB_PP <- lapply(LAB_PP_raw,function(i){ 76 | cbind(LID = (i %>% stri_replace_all_fixed(pattern = paste0(csv_dir_path,"\\"), replacement="") %>% 77 | stri_split_fixed(pattern = "_") %>% unlist(use.names=FALSE) %>% 78 | stri_replace_all_fixed(pattern = ".csv", replacement = ""))[1], ## Lab ID 79 | PID = (i %>% stri_replace_all_fixed(pattern = paste0(csv_dir_path,"\\"), replacement="") %>% 80 | stri_split_fixed(pattern = "_") %>% unlist(use.names=FALSE) %>% 81 | stri_replace_all_fixed(pattern = ".csv", replacement = ""))[3], ## Participant ID 82 | read.csv(i, header=TRUE)) 83 | }) 84 | 85 | # Combine all data into one dataframe 86 | suppressMessages( df_LAB_PP <- do.call(rbind.data.frame, LAB_PP) ) 87 | 88 | ## Compute the accuracies of verification responses by participant 89 | LAB_PP_accuracy <- df_LAB_PP %>% 90 | group_by(PID) %>% 91 | summarise(C_acc = mean(correct)*100, trials_N=n()) 92 | 93 | ## Print the number of participants had a lower 70% P-P accuracy 94 | (LAB_PP_accuracy$C_acc < 70) %>% sum() 95 | 96 | ## Reserve correct responses of P-P verification task 97 | ## Data set for mixed-effect model, all observations are reserved 98 | (Complete_LAB_PP = df_LAB_PP %>% 99 | filter(Identical %in% c('Y','N')) %>% 100 | filter(correct == 1)) %>% 101 | write.csv(file = paste0(df_LAB_PP$LID %>% levels(),"_PP_complete.csv")) 102 | 103 | ## Summarize the cleaned data of this Lab 104 | (Participant_complete_LAB_PP = Complete_LAB_PP %>% 105 | filter( !(PID %in% Excluded_ID) ) %>% 106 | group_by(LID, PID, Identical) %>% 107 | summarise(V_RT = median(response_time, na.rm = TRUE), V_Acc = 100*n()/12) %>% 108 | arrange( as.numeric(as.character(PID) ) ) ) %>% 109 | write.csv(file = paste0(Complete_LAB_PP$LID %>% levels(),"_PP.csv") ) 110 | 111 | ## Transform the cleaned data for packaged software 112 | Participant_complete_LAB_PP %>% filter(Identical == "Y") %>% select(LID, PID, V_RT, V_Acc) %>% 113 | left_join( (Participant_complete_LAB_PP %>% filter(Identical == "N") %>% select(LID, PID, V_RT, V_Acc)), by = c("LID","PID") ) %>% 114 | rename(V_RT_Y = V_RT.x, V_RT_N = V_RT.y, V_acc_Y = V_Acc.x, V_acc_N = V_Acc.y) %>% 115 | write.csv(file = paste0(Complete_LAB_PP$LID %>% levels(),"_PP_JASP.csv") ) 116 | -------------------------------------------------------------------------------- /object-orientation/OrientationCrossLanguages_2018PSA_PP_1.2.0.osexp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/object-orientation/OrientationCrossLanguages_2018PSA_PP_1.2.0.osexp -------------------------------------------------------------------------------- /object-orientation/OrientationCrossLanguages_2018PSA_SP_1.2.0.osexp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/object-orientation/OrientationCrossLanguages_2018PSA_SP_1.2.0.osexp -------------------------------------------------------------------------------- /object-orientation/README.md: -------------------------------------------------------------------------------- 1 | # README - Faces and Bodies 2 | 3 | This project evaluate the replicability of object orientation effect across languages. The original paradigm was developed in English (Stanfield and Zwaan, 2001). The available materials were released by Zwaan and Pecher (2012). 4 | 5 | ## Files 6 | 7 | **STI_LIST.xls**: The sheets of all materials and stimuli lists. 8 | - *STI*: Collection of all critical stimuli. `Picture_H`: File names of object pictures presented in horizontal; `Picture_V`: File names of object pictures presented in vertical; `XXX_Sentence_H`: Sentences implied the horizontal orientation of the target object; `XXX_Sentence_V`: Sentences implied the vertical orientation of the target object. XXX stands for the which language the sentences written in. 9 | - *Fillers*: Collection of all filler stimuli. `Picture_Fillers_A` and `Picture_Fillers_B`: File names of object pictures which are irrelavent to the corresponding sentences; `XXX_Sentence_Fillers_A` and `XXX_Sentence_Fillers_B`: Sentences which do not imply the particular orientation of object; XXX stands for the which language the sentences written in. 10 | - *Prac1* and *Prac2*: Stimuli list of practice trials. 11 | - *XXX_LIST1*, *XXX_LIST2*, *XXX_LIST3*, and *XXX_LIST4*: Stimuli lists of experimental trials for sentence-picture verification. XXX stands for the which language the sentences written in. 12 | - *PP_LIST1*, *PP_LIST2*, *PP_LIST3*, and *PP_LIST4*: Stimuli lists of experimental trials for picture-picture verification. 13 | 14 | **LAB_processing.R**: R script which merge raw data and filter the variables to be analyzed. There will be three csv files created in the folder `raw_data`: `ID_SP_V.csv`,`ID_SP_M.csv`, and `ID_PP.csv`. `ID` stands for the specific ID for the study site. This script is for the processing of the data from one study site. 15 | 16 | **OrientationCrossLanguages_2018PSA_SP_1.2.0.osexp**: Opensesame script of sentence-picture verification task for one study site. The instructions and materials will be translated to the site langauge before the beginning. 17 | 18 | **OrientationCrossLanguages_2018PSA_PP_1.2.0.osexp**: Opensesame script of picture-picture verification task for one study site. The instructions and materials will be translated to the site langauge before the beginning. 19 | 20 | ## Codebooks of data files 21 | - **ID_SP_V.csv** 22 | subject_nr: Participant ID (Integers: 1 ~ 160) 23 | task_order: Yes(SP task was the first study); No (SP task was after the other study); none of above(SP task was conducted for the other reason) 24 | PList: List files for practice trials. 25 | List: List files for experimental trails. 26 | Match: Matching of sentence and target object orientation. 27 | Orientation: Defined orientation of the target object. 28 | Probe: Probe sentences in the particular trials. 29 | Target: Target object files. 30 | response: The triggered response key in the particular trials. 31 | correct: Correctness of participant's response in the particular trials. 32 | response_time: Reaction time to trigger the response in the particular trials. 33 | fullscreen: yes(SP task was conducted under fullscreen mode);no(SP task was not conducted under fullscreen mode) 34 | File: Source file names of the participants' raw data. 35 | opensesame_codename: Code name of Opensesame installed in the study site. 36 | opensesame_version: Version of Opensesame installed in the study site. 37 | experiment_file: Filename of Opensesame script this study site applied for their participants. 38 | 39 | - **ID_SP_M.csv** 40 | subject_nr: Participant ID (Integers: 1 ~ 160) 41 | task_order: Yes(SP task was the first study); No (SP task was after the other study); none of above(SP task was conducted for the other reason) 42 | PList: List files for practice trials. 43 | List: List files for experimental trails. 44 | compensation: The probe sentences were selected for the memory trials. 45 | response: The triggered response key in the particular trials. 46 | correct: Correctness of participant's response in the particular trials. 47 | response_time: Reaction time to trigger the response in the particular trials. 48 | fullscreen: yes(SP task was conducted under fullscreen mode);no(SP task was not conducted under fullscreen mode) 49 | File: Source file names of the participants' raw data. 50 | opensesame_codename: Code name of Opensesame installed in the study site. 51 | opensesame_version: Version of Opensesame installed in the study site. 52 | experiment_file: Filename of Opensesame script this study site applied for their participants. 53 | 54 | - **ID_PP.csv** 55 | subject_nr: Participant ID (Integers: 1 ~ 160) 56 | PPList: List files for experimental trials. 57 | Identical: Y(Two object were presented in the same orientation), N(Two object were presented in different orientation). 58 | Orientation1: Orientation of left object. 59 | Orientation2: Orientation of right object 60 | Picture1: Picure file name of left object. 61 | Picture2: Picure file name of right object. 62 | response: The triggered response key in the particular trials. 63 | correct: Correctness of participant's response in the particular trials. 64 | response_time: Reaction time to trigger the response in the particular trials. 65 | gender: Participant's gender (Male, Female, Not to be identified, No will to say). 66 | year: Participant's birth year (4 digits integer) 67 | month: Participant's birth month (1~12) 68 | fullscreen: yes(PP task was conducted under fullscreen mode);no(PP task was not conducted under fullscreen mode) 69 | File: Source file names of the participants' raw data. 70 | opensesame_codename: Code name of Opensesame installed in the study site. 71 | opensesame_version: Version of Opensesame installed in the study site. 72 | experiment_file: Filename of Opensesame script this study site applied for their participants. 73 | 74 | ## Citation 75 | 76 | Stanfield, R. A., & Zwaan, R. A. (2001). The effect of implied orientation derived from verbal context on picture recognition. Psychological Science, 12(2), 153–156. https://doi.org/10.1111/1467-9280.00326 77 | 78 | Zwaan, R. A., & Pecher, D. (2012). Revisiting Mental Simulation in Language Comprehension: Six Replication Attempts. PLoS ONE, 7, e51382. https://doi.org/10.1371/journal.pone.0051382 79 | -------------------------------------------------------------------------------- /object-orientation/STI_LISTS.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psych-ds/example-datasets/b0b7ef053cb7a09693b5e3617afec7e4723dd46f/object-orientation/STI_LISTS.xls -------------------------------------------------------------------------------- /object-orientation/data/num-100_conda-SP_condb-M_data.csv: -------------------------------------------------------------------------------- 1 | "subject_nr","task_order","PList","List","compensation","response","correct","response_time","File","opensesame_codename","opensesame_version","experiment_file" 2 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",0,1091,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 3 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",1,1112,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 4 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",1,636,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 5 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",0,693,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 6 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",0,800,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 7 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",0,863,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 8 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",0,603,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 9 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",0,676,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 10 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",1,761,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 11 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",1,815,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 12 | 1,"none of above","ENG_Prac2.csv","ENG_LIST1.csv",0,"j",1,804,"subject-1.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 13 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",0,1290,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 14 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",1,800,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 15 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",1,1530,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 16 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",0,1162,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 17 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",1,759,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 18 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",1,838,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 19 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",1,692,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 20 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",0,910,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 21 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",0,1254,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 22 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",1,962,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 23 | 2,"none of above","ENG_Prac2.csv","ENG_LIST2.csv",0,"j",1,833,"subject-2.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 24 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,964,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 25 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,1283,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 26 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,1035,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 27 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,635,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 28 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",1,1034,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 29 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",1,792,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 30 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,722,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 31 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,2024,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 32 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",1,825,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 33 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,907,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 34 | 3,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",1,843,"subject-3.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 35 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,740,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 36 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,634,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 37 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,850,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 38 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,774,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 39 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",1,1011,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 40 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",1,625,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 41 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",1,616,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 42 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,908,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 43 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,915,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 44 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,749,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 45 | 4,"none of above","ENG_Prac1.csv","ENG_LIST3.csv",0,"j",0,573,"subject-4.csv","Kafkaesque Koffka","3.2.6","OrientationCrossLanguages_2018PSA_SP_1.1.0.osexp" 46 | -------------------------------------------------------------------------------- /object-orientation/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context":"https://schema.org/", 3 | "@type":"Dataset", 4 | "description":"n/a", 5 | "name": "object-orientation", 6 | "variableMeasured": ["subject_nr","task_order","PList","List","compensation","response","correct","response_time","File","opensesame_codename","opensesame_version","experiment_file","PPList","Identical","Orientation1","Orientation2","Picture1","Picture2","gender","year","month","fullscreen","Match","Orientation","Probe","Target","LAB_SEED","Task","acc","accuracy","alt_task","average_response_time","avg_rt","background","bidi","canvas_backend","clock_backend","color_backend","coordinates","correct_Introduction_E","correct_Probe_Prac_response","correct_Probe_response","correct_Probe_sti_response","correct_Start_E","correct_Target_response","correct_Target_response_Prac","correct_Welcome_E","correct_count","correct_response","count_Check_This","count_Disable_Mouse","count_EXP","count_First_Fixation","count_First_Fixation_Prac","count_Introduction_C","count_Introduction_E","count_LAB_SEED","count_Language_Selection","count_List_Select_E","count_List_Select_TC","count_List_Selection","count_Memory_loop","count_Memory_sequence","count_Prac","count_Prac_Count","count_Prac_Reset","count_Prac_Trial","count_Prac_loop","count_Prac_seq","count_Prac_setup","count_Probe","count_Probe_Collect","count_Probe_Prac","count_Probe_Prac_response","count_Probe_import","count_Probe_response","count_Probe_sti","count_Probe_sti_correct","count_Probe_sti_response","count_Probe_sti_wrong","count_STUDY_order","count_Second_Fixation","count_Second_Fixation_Prac","count_Start_C","count_Start_E","count_Target","count_Target_Prac","count_Target_response","count_Target_response_Prac","count_Target_response_correct","count_Target_response_correct_Prac","count_Target_response_noans","count_Target_response_noans_Prac","count_Target_response_wrong","count_Target_response_wrong_Prac","count_Trial_logger","count_Verification_Break","count_Verification_End","count_Verification_Exp","count_Verification_Prac","count_Verification_Start","count_Verification_Trial","count_Verification_Welcome","count_Verification_loop","count_Welcome_C","count_Welcome_E","count_presetup","datetime","description","disable_garbage_collection","empty_column","experiment_path","font_bold","font_family","font_italic","font_size","font_underline","foreground","form_clicks","form_response","height","keyboard_backend","language1","language2","live_row","live_row_Prac","live_row_Prac_loop","live_row_Verification_loop","logfile","mouse_backend","rectarget","repeat_cycle","reset_loop","response_Introduction_C","response_Introduction_E","response_Probe_Prac_response","response_Probe_response","response_Probe_sti_response","response_Start_C","response_Start_E","response_Target_response","response_Target_response_Prac","response_Verification_Break","response_Verification_End","response_Welcome_C","response_Welcome_E","response_time_Introduction_C","response_time_Introduction_E","response_time_Probe_Prac_response","response_time_Probe_response","response_time_Probe_sti_response","response_time_Start_C","response_time_Start_E","response_time_Target_response","response_time_Target_response_Prac","response_time_Verification_Break","response_time_Verification_End","response_time_Welcome_C","response_time_Welcome_E","round_decimals","sampler_backend","sound_buf_size","sound_channels","sound_freq","sound_sample_size","start","subject_parity","time_Check_This","time_Disable_Mouse","time_EXP","time_First_Fixation","time_First_Fixation_Prac","time_Introduction_C","time_Introduction_E","time_LAB_SEED","time_Language_Selection","time_List_Select_E","time_List_Select_TC","time_List_Selection","time_Memory_loop","time_Memory_sequence","time_Prac","time_Prac_Count","time_Prac_Reset","time_Prac_Trial","time_Prac_loop","time_Prac_seq","time_Prac_setup","time_Probe","time_Probe_Collect","time_Probe_Prac","time_Probe_Prac_response","time_Probe_import","time_Probe_response","time_Probe_sti","time_Probe_sti_correct","time_Probe_sti_response","time_Probe_sti_wrong","time_STUDY_order","time_Second_Fixation","time_Second_Fixation_Prac","time_Start_C","time_Start_E","time_Target","time_Target_Prac","time_Target_response","time_Target_response_Prac","time_Target_response_correct","time_Target_response_correct_Prac","time_Target_response_noans","time_Target_response_noans_Prac","time_Target_response_wrong","time_Target_response_wrong_Prac","time_Trial_logger","time_Verification_Break","time_Verification_End","time_Verification_Exp","time_Verification_Prac","time_Verification_Start","time_Verification_Trial","time_Verification_Welcome","time_Verification_loop","time_Welcome_C","time_Welcome_E","time_presetup","title","total_correct","total_response_time","total_responses","uniform_coordinates","width","correct_Pictures_response","correct_Welcome_PP_E","count_List_Select","count_Pictures_Break","count_Pictures_End","count_Pictures_Exp","count_Pictures_Fixation","count_Pictures_Fixation_Prac","count_Pictures_Prac","count_Pictures_Start","count_Pictures_Target","count_Pictures_Trial","count_Pictures_Welcome","count_Pictures_loop","count_Pictures_response","count_Pictures_response_correct","count_Pictures_response_noans","count_Pictures_response_wrong","count_Prac_Count_1","count_Target_response_noans_Prac_1","count_Target_response_wrong_Prac_1","count_Welcome_PP_C","count_Welcome_PP_E","count_postdata_C","count_postdata_E","live_row_Pictures_loop","osf_always_upload_experiment","osf_id","response_Pictures_Break","response_Pictures_End","response_Pictures_response","response_Welcome_PP_C","response_Welcome_PP_E","response_time_Pictures_Break","response_time_Pictures_End","response_time_Pictures_response","response_time_Welcome_PP_C","response_time_Welcome_PP_E","time_List_Select","time_Pictures_Break","time_Pictures_End","time_Pictures_Exp","time_Pictures_Fixation","time_Pictures_Fixation_Prac","time_Pictures_Prac","time_Pictures_Start","time_Pictures_Target","time_Pictures_Trial","time_Pictures_Welcome","time_Pictures_loop","time_Pictures_response","time_Pictures_response_correct","time_Pictures_response_noans","time_Pictures_response_wrong","time_Prac_Count_1","time_Target_response_noans_Prac_1","time_Target_response_wrong_Prac_1","time_Welcome_PP_C","time_Welcome_PP_E","time_postdata_C","time_postdata_E","count_Post","time_Post"] 7 | } -------------------------------------------------------------------------------- /safi-survey/README.md: -------------------------------------------------------------------------------- 1 | # SAFI Survey example dataset for Psych-DS specification 2 | 3 | It serves as an example for how a Psych-DS dataset could look like. Dataset used in this example is the SAFI (Studying African Farmer-Led Irrigation) Survey, part of a project about farming and irrigation methods. This is survey data relating to households and agriculture in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017. The survey covered such things as; household features (e.g. construction materials used, number of household members), agricultural practices (e.g. water usage), assets (e.g. number and types of livestock) and details about the household members. Original data source: 4 | 5 | *Woodhouse, Philip; Veldwisch, Gert Jan; Brockington, Daniel; Komakech, Hans C.; Manjichi, Angela; Venot, Jean-Philippe (2018). SAFI Survey Results. figshare. Dataset. (CC0 license)* 6 | 7 | This dataset is also used in the [R for Social Scientists](https://datacarpentry.org/r-socialsci/) lesson from [Data Carpentry](https://datacarpentry.org/). The `SAFI_clean.csv` file was used to create a simple example dataset for Psych-DS. 8 | 9 | ### Related resources 10 | 11 | We also used the same dataset as an example for how a publication package could look like (not in Psych-DS format). If you are interested, you can find it on Zenodo: 12 | 13 | *Klapwijk, Eduard, Sunami, Nami, Mania, Joanna, Lushaj, Bora, & Volkova, Anna. (2023). EUR publication package example [Data set]. Zenodo. * 14 | 15 | ### Files 16 | 17 | `data/study-safi-survey_data.csv` - raw data file of the SAFI survey 18 | 19 | ### Validation 20 | 21 | 2023-06-24: dataset successfully validated using the [Psych-DS Checker - SIPS 2022 Prototype](https://github.com/psych-ds/pds-validator-sips2022) and `dataset_description.json` validated with 0 warning and 0 errors using the [Schema.org validator](https://validator.schema.org) 22 | -------------------------------------------------------------------------------- /safi-survey/data/study-safisurvey_data.csv: -------------------------------------------------------------------------------- 1 | key_ID,village,interview_date,no_membrs,years_liv,respondent_wall_type,rooms,memb_assoc,affect_conflicts,liv_count,items_owned,no_meals,months_lack_food,instanceID 2 | 1,God,2016-11-17T00:00:00Z,3,4,muddaub,1,NULL,NULL,1,bicycle;television;solar_panel;table,2,Jan,uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef 3 | 1,God,2016-11-17T00:00:00Z,7,9, muddaub,1,yes,once,3,cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,2,Jan;Sept;Oct;Nov;Dec,uuid:099de9c9-3e5e-427b-8452-26250e840d6e 4 | 3,God,2016-11-17T00:00:00Z,10,15, burntbricks,1,NULL,NULL,1,solar_torch,2,Jan;Feb;Mar;Oct;Nov;Dec,uuid:193d7daf-9582-409b-bf09-027dd36f9007 5 | 4,God,2016-11-17T00:00:00Z,7,6, burntbricks,1,NULL,NULL,2,bicycle;radio;cow_plough;solar_panel;mobile_phone,2,Sept;Oct;Nov;Dec,uuid:148d1105-778a-4755-aa71-281eadd4a973 6 | 5,God,2016-11-17T00:00:00Z,7,40,burntbricks,1,NULL,NULL,4,motorcyle;radio;cow_plough;mobile_phone,2,Aug;Sept;Oct;Nov,uuid:2c867811-9696-4966-9866-f35c3e97d02d 7 | 6,God,2016-11-17T00:00:00Z,3,3,muddaub,1,NULL,NULL,1,NULL,2,Aug;Sept;Oct,uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70 8 | 7,God,2016-11-17T00:00:00Z,6,38,muddaub,1,no,never,1,motorcyle;cow_plough,3,Nov,uuid:ae20a58d-56f4-43d7-bafa-e7963d850844 9 | 8,Chirodzo,2016-11-16T00:00:00Z,12,70,burntbricks,3,yes,never,2,motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;fridge,2,Jan,uuid:d6cee930-7be1-4fd9-88c0-82a08f90fb5a 10 | 9,Chirodzo,2016-11-16T00:00:00Z,8,6,burntbricks,1,no,never,3,television;solar_panel;solar_torch,3,Jan;Dec,uuid:846103d2-b1db-4055-b502-9cd510bb7b37 11 | 10,Chirodzo,2016-12-16T00:00:00Z,12,23,burntbricks,5,no,never,2,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table,3,Jan;Oct;Nov;Dec,uuid:8f4e49bc-da81-4356-ae34-e0d794a23721 12 | 11,God,2016-11-21T00:00:00Z,6,20,sunbricks,1,NULL,NULL,2,radio;cow_plough,2,Oct;Nov,uuid:d29b44e3-3348-4afc-aa4d-9eb34c89d483 13 | 12,God,2016-11-21T00:00:00Z,7,20,burntbricks,3,yes,never,2,cow_cart;bicycle;radio;cow_plough;table,3,Sept;Oct,uuid:e6ee6269-b467-4e37-91fc-5e9eaf934557 14 | 13,God,2016-11-21T00:00:00Z,6,8,burntbricks,1,no,never,3,bicycle;radio;cow_plough;mobile_phone,2,Sept;Oct;Nov,uuid:6c00c145-ee3b-409c-8c02-2c8d743b6918 15 | 14,God,2016-11-21T00:00:00Z,10,20,burntbricks,3,NULL,NULL,3,bicycle;radio;cow_plough;solar_panel;table;mobile_phone,3,June;July;Aug;Sept;Oct;Nov,uuid:9b21467f-1116-4340-a3b1-1ab64f13c87d 16 | 15,God,2016-11-21T00:00:00Z,5,30,sunbricks,2,yes,once,3,bicycle;radio;cow_plough;solar_panel;table,2,Jan;Feb;Mar;Apr;May;June;July;Aug;Sept;Oct;Nov,uuid:a837e545-ff86-4a1c-a1a5-6186804b985f 17 | 16,God,2016-11-24T00:00:00Z,6,47,muddaub,1,NULL,NULL,4,radio;cow_plough;solar_panel;solar_torch,3,Jan;Feb,uuid:d17db52f-4b87-4768-b534-ea8f9704c565 18 | 17,God,2016-11-21T00:00:00Z,8,20,sunbricks,1,NULL,NULL,1,mobile_phone,2,Nov;Dec,uuid:4707f3dc-df18-4348-9c2c-eec651e89b6b 19 | 18,God,2016-11-21T00:00:00Z,4,20,muddaub,1,NULL,NULL,3,bicycle;mobile_phone,2,Oct;Nov,uuid:7ffe7bd1-a15c-420c-a137-e1f006c317a3 20 | 19,God,2016-11-21T00:00:00Z,9,23,burntbricks,2,NULL,NULL,2,bicycle;radio;cow_plough;solar_panel;solar_torch;mobile_phone,3,Oct;Nov;Dec,uuid:e32f2dc0-0d05-42fb-8e21-605757ddf07d 21 | 20,God,2016-11-21T00:00:00Z,6,1,burntbricks,1,NULL,NULL,1,bicycle;cow_plough;solar_torch,2,Oct;Nov,uuid:d1005274-bf52-4e79-8380-3350dd7c2bac 22 | 21,God,2016-11-21T00:00:00Z,8,20,burntbricks,1,no,never,3,NULL,2,Jan;Feb;Mar;Oct;Nov;Dec,uuid:6570a7d0-6a0b-452c-aa2e-922500e35749 23 | 22,God,2016-11-21T00:00:00Z,4,20,muddaub,1,NULL,NULL,1,radio,2,Jan;Feb;Mar;Apr;Aug;Sept;Oct;Nov;Dec,uuid:a51c3006-8847-46ff-9d4e-d29919b8ecf9 24 | 23,Ruaca,2016-11-21T00:00:00Z,10,20,burntbricks,4,NULL,NULL,3,cow_cart;bicycle;television;radio;cow_plough;solar_panel;electricity;mobile_phone,3,none,uuid:58b37b6d-d6cd-4414-8790-b9c68bca98de 25 | 24,Ruaca,2016-11-21T00:00:00Z,6,4,burntbricks,2,no,never,3,radio;table;sofa_set;mobile_phone,2,Nov;Dec,uuid:661457d3-7e61-45e8-a238-7415e7548f82 26 | 25,Ruaca,2016-11-21T00:00:00Z,11,6,burntbricks,3,no,never,2,cow_cart;motorcyle;television;radio;cow_plough;solar_panel;solar_torch;table;sofa_set;mobile_phone,2,Jan;Feb;Oct,uuid:45ed84c4-114e-4df0-9f5d-c800806c2bee 27 | 26,Ruaca,2016-11-21T00:00:00Z,3,20,burntbricks,2,no,never,2,radio;cow_plough;table;mobile_phone,2,none,uuid:1c54ee24-22c4-4ee9-b1ad-42d483c08e2e 28 | 27,Ruaca,2016-11-21T00:00:00Z,7,36,burntbricks,2,NULL,NULL,3,bicycle;radio;cow_plough;solar_panel;solar_torch;mobile_phone,3,none,uuid:3197cded-1fdc-4c0c-9b10-cfcc0bf49c4d 29 | 28,Ruaca,2016-11-21T00:00:00Z,2,2,muddaub,1,no,more_once,1,NULL,3,Aug;Sept;Oct,uuid:1de53318-a8cf-4736-99b1-8239f8822473 30 | 29,Ruaca,2016-11-21T00:00:00Z,7,10,burntbricks,2,yes,frequently,1,motorcyle;bicycle;radio;table;mobile_phone,3,Jan;Feb,uuid:adcd7463-8943-4c67-b25f-f72311409476 31 | 30,Ruaca,2016-11-21T00:00:00Z,7,22,muddaub,2,NULL,NULL,1,bicycle;radio;mobile_phone,2,Jan;Feb,uuid:59341ead-92be-45a9-8545-6edf9f94fdc6 32 | 31,Ruaca,2016-11-21T00:00:00Z,3,2,muddaub,1,NULL,NULL,1,NULL,3,none,uuid:cb06eb49-dd39-4150-8bbe-a599e074afe8 33 | 32,Ruaca,2016-11-21T00:00:00Z,19,69,muddaub,2,yes,more_once,5,cow_cart;motorcyle;radio;cow_plough;solar_panel;mobile_phone,2,none,uuid:25597af3-cd79-449c-a48a-fb9aea6c48bf 34 | 33,Ruaca,2016-11-21T00:00:00Z,8,34,muddaub,1,no,more_once,2,cow_cart;lorry;motorcyle;sterio;cow_plough;solar_panel;mobile_phone,2,none,uuid:0fbd2df1-2640-4550-9fbd-7317feaa4758 35 | 34,Chirodzo,2016-11-17T00:00:00Z,8,18,burntbricks,3,yes,more_once,3,television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,2,Jan;Dec,uuid:14c78c45-a7cc-4b2a-b765-17c82b43feb4 36 | 35,Chirodzo,2016-11-17T00:00:00Z,5,45,muddaub,1,yes,more_once,2,bicycle;cow_plough,3,Jan;Sept;Oct;Nov;Dec,uuid:ff7496e7-984a-47d3-a8a1-13618b5683ce 37 | 36,Chirodzo,2016-11-17T00:00:00Z,6,23,sunbricks,1,yes,once,3,cow_cart;bicycle;radio;cow_plough;solar_panel;mobile_phone,3,none,uuid:c90eade0-1148-4a12-8c0e-6387a36f45b1 38 | 37,Chirodzo,2016-11-17T00:00:00Z,3,8,burntbricks,1,NULL,NULL,2,bicycle;television;radio;cow_plough;solar_panel;solar_torch;mobile_phone,3,Jan;Nov;Dec,uuid:408c6c93-d723-45ef-8dee-1b1bd3fe20cd 39 | 38,God,2016-11-17T00:00:00Z,10,19,muddaub,1,yes,never,3,bicycle;radio;cow_plough;solar_panel;table;mobile_phone,3,Nov,uuid:81309594-ff58-4dc1-83a7-72af5952ee08 40 | 39,God,2016-11-17T00:00:00Z,6,22,muddaub,1,NULL,NULL,1,NULL,3,Nov,uuid:c0fb6310-55af-4831-ae3d-2729556c3285 41 | 40,God,2016-11-17T00:00:00Z,9,23,burntbricks,1,yes,never,1,bicycle;radio;cow_plough;solar_panel;table;mobile_phone,3,Sept;Oct;Nov,uuid:c0b34854-eede-4e81-b183-ef58a45bfc34 42 | 41,God,2016-11-17T00:00:00Z,7,22,muddaub,1,NULL,NULL,2,motorcyle;bicycle;radio;cow_plough;table,3,Oct;Nov,uuid:b3ba34d8-eea1-453d-bc73-c141bcbbc5e5 43 | 42,God,2016-11-17T00:00:00Z,8,8,sunbricks,1,no,never,3,mobile_phone,3,Jan;Nov;Dec,uuid:e3a1dd8a-1bda-428c-a014-2b527f11ae64 44 | 43,Chirodzo,2016-11-17T00:00:00Z,7,29,muddaub,1,no,never,2,cow_plough;mobile_phone,2,Jan;Feb;Oct;Nov;Dec,uuid:b4dff49f-ef27-40e5-a9d1-acf287b47358 45 | 44,Chirodzo,2016-11-17T00:00:00Z,2,6,muddaub,1,NULL,NULL,3,radio;solar_torch,2,Jan;Dec,uuid:f9fadf44-d040-4fca-86c1-2835f79c4952 46 | 45,Chirodzo,2016-11-17T00:00:00Z,9,7,muddaub,1,no,never,4,motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,none,uuid:e3554d22-35b1-4fb9-b386-dd5866ad5792 47 | 46,Chirodzo,2016-11-17T00:00:00Z,10,42,burntbricks,2,no,once,2,motorcyle;computer;television;sterio;solar_panel;solar_torch;table;mobile_phone,2,Sept;Oct;Nov,uuid:35f297e0-aa5d-4149-9b7b-4965004cfc37 48 | 47,Chirodzo,2016-11-17T00:00:00Z,2,2,muddaub,1,yes,once,1,solar_torch;mobile_phone,3,none,uuid:2d0b1936-4f82-4ec3-a3b5-7c3c8cd6cc2b 49 | 48,Chirodzo,2016-11-16T00:00:00Z,7,58,muddaub,1,NULL,NULL,3,radio,3,June;July;Aug;Sept;Oct;Nov,uuid:e180899c-7614-49eb-a97c-40ed013a38a2 50 | 49,Chirodzo,2016-11-16T00:00:00Z,6,26,burntbricks,2,NULL,NULL,2,bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,Jan;Nov;Dec,uuid:2303ebc1-2b3c-475a-8916-b322ebf18440 51 | 50,Chirodzo,2016-11-16T00:00:00Z,6,7,muddaub,1,yes,never,1,solar_torch,2,June;July;Aug;Sept;Oct;Nov;Dec,uuid:4267c33c-53a7-46d9-8bd6-b96f58a4f92c 52 | 51,Chirodzo,2016-11-16T00:00:00Z,5,30,muddaub,1,NULL,NULL,1,radio,3,Oct;Nov,uuid:18ac8e77-bdaf-47ab-85a2-e4c947c9d3ce 53 | 52,Chirodzo,2016-11-16T00:00:00Z,11,15,burntbricks,3,no,never,3,motorcyle;television;radio;cow_plough;solar_panel;mobile_phone,3,Aug;Sept;Oct;Nov,uuid:6db55cb4-a853-4000-9555-757b7fae2bcf 54 | 21,Chirodzo,2016-11-16T00:00:00Z,8,16,burntbricks,3,yes,frequently,2,bicycle;radio;mobile_phone,2,Nov,uuid:cc7f75c5-d13e-43f3-97e5-4f4c03cb4b12 55 | 54,Chirodzo,2016-11-16T00:00:00Z,7,15,muddaub,1,no,never,1,NULL,2,Sept;Oct;Nov,uuid:273ab27f-9be3-4f3b-83c9-d3e1592de919 56 | 55,Chirodzo,2016-11-16T00:00:00Z,9,23,muddaub,2,NULL,NULL,1,television;cow_plough;mobile_phone,2,Oct;Nov,uuid:883c0433-9891-4121-bc63-744f082c1fa0 57 | 56,Chirodzo,2016-11-16T00:00:00Z,12,23,burntbricks,2,yes,never,2,motorcyle;bicycle;mobile_phone,3,none,uuid:973c4ac6-f887-48e7-aeaf-4476f2cfab76 58 | 57,Chirodzo,2016-11-16T00:00:00Z,4,27,burntbricks,1,no,never,1,radio,2,none,uuid:a7184e55-0615-492d-9835-8f44f3b03a71 59 | 58,Chirodzo,2016-11-16T00:00:00Z,11,45,burntbricks,3,no,never,3,motorcyle;bicycle;television;radio;cow_plough;solar_panel;mobile_phone,2,none,uuid:a7a3451f-cd0d-4027-82d9-8dcd1234fcca 60 | 59,Chirodzo,2016-11-16T00:00:00Z,2,60,muddaub,3,NULL,NULL,3,NULL,2,none,uuid:1936db62-5732-45dc-98ff-9b3ac7a22518 61 | 60,Chirodzo,2016-11-16T00:00:00Z,8,15,burntbricks,2,no,never,4,cow_plough,2,none,uuid:85465caf-23e4-4283-bb72-a0ef30e30176 62 | 61,Chirodzo,2016-11-16T00:00:00Z,10,14,muddaub,1,yes,more_once,3,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;table;mobile_phone,3,Jan;Feb;Dec,uuid:2401cf50-8859-44d9-bd14-1bf9128766f2 63 | 62,Chirodzo,2016-11-16T00:00:00Z,5,5,muddaub,1,NULL,NULL,1,bicycle;radio;mobile_phone,3,Aug;Sept;Oct;Nov,uuid:c6597ecc-cc2a-4c35-a6dc-e62c71b345d6 64 | 63,Chirodzo,2016-11-16T00:00:00Z,4,10,muddaub,1,NULL,NULL,1,NULL,3,Jan;Oct;Nov;Dec,uuid:86ed4328-7688-462f-aac7-d6518414526a 65 | 64,Chirodzo,2016-11-16T00:00:00Z,6,1,muddaub,1,NULL,NULL,1,bicycle;solar_torch;table;sofa_set;mobile_phone,3,Jan;Feb;Dec,uuid:28cfd718-bf62-4d90-8100-55fafbe45d06 66 | 65,Chirodzo,2016-11-16T00:00:00Z,8,20,burntbricks,3,no,once,3,motorcyle;radio;cow_plough;table,3,Jan;Feb;Mar,uuid:143f7478-0126-4fbc-86e0-5d324339206b 67 | 66,Chirodzo,2016-11-16T00:00:00Z,10,37,burntbricks,3,yes,frequently,4,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;mobile_phone,3,none,uuid:a457eab8-971b-4417-a971-2e55b8702816 68 | 67,Chirodzo,2016-11-16T00:00:00Z,5,31,burntbricks,2,no,more_once,4,motorcyle;radio;cow_plough;solar_panel;mobile_phone,3,none,uuid:6c15d667-2860-47e3-a5e7-7f679271e419 69 | 68,Chirodzo,2016-11-16T00:00:00Z,8,52,burntbricks,3,no,more_once,3,motorcyle;television;sterio;solar_panel;mobile_phone,3,none,uuid:ef04b3eb-b47d-412e-9b09-4f5e08fc66f9 70 | 69,Chirodzo,2016-11-16T00:00:00Z,4,12,muddaub,1,no,more_once,1,bicycle;radio;solar_torch;mobile_phone,3,none,uuid:f86933a5-12b8-4427-b821-43c5b039401d 71 | 70,Chirodzo,2016-11-16T00:00:00Z,8,25,burntbricks,2,no,more_once,4,cow_cart;bicycle;radio;cow_plough;solar_panel;mobile_phone,2,none,uuid:1feb0108-4599-4bf9-8a07-1f5e66a50a0a 72 | 71,Ruaca,2016-11-18T00:00:00Z,6,14,burntbricks,1,yes,more_once,3,radio;cow_plough;mobile_phone,2,Aug;Sept;Oct;Nov,uuid:761f9c49-ec93-4932-ba4c-cc7b78dfcef1 73 | 127,Chirodzo,2016-11-16T00:00:00Z,4,18,burntbricks,8,NULL,NULL,1,mobile_phone,2,Aug;Sept;Oct,uuid:f6d04b41-b539-4e00-868a-0f62b427587d 74 | 133,Ruaca,2016-11-23T00:00:00Z,5,25,burntbricks,2,no,never,5,cow_cart;car;lorry;motorcyle;bicycle;television;sterio;cow_plough;solar_panel;solar_torch;electricity;table;sofa_set;mobile_phone;fridge,3,Jan;Oct;Nov,uuid:429d279a-a519-4dcc-9f64-4673b0fd5d53 75 | 152,Ruaca,2016-11-24T00:00:00Z,10,16,burntbricks,1,yes,once,3,motorcyle;bicycle;radio;sterio;cow_plough;solar_panel;mobile_phone,3,none,uuid:59738c17-1cda-49ee-a563-acd76f6bc487 76 | 153,Ruaca,2016-11-24T00:00:00Z,5,41,burntbricks,1,NULL,NULL,1,NULL,2,Oct;Nov,uuid:7e7961ca-fa1c-4567-9bfa-a02f876e4e03 77 | 155,God,2016-11-24T00:00:00Z,4,4,burntbricks,1,NULL,NULL,1,electricity,2,Jan;Sept;Oct;Nov;Dec,uuid:77b3021b-a9d6-4276-aaeb-5bfcfd413852 78 | 178,Ruaca,2016-11-25T00:00:00Z,5,79,burntbricks,2,yes,frequently,3,radio;cow_plough;solar_panel;mobile_phone,3,none,uuid:2186e2ec-f65a-47cc-9bc1-a0f36dd9591c 79 | 177,God,2016-11-25T00:00:00Z,10,13,sunbricks,1,no,more_once,2,motorcyle;television;cow_plough;solar_panel;mobile_phone,3,Nov,uuid:87998c33-c8d2-49ec-9dae-c123735957ec 80 | 180,Ruaca,2016-11-25T00:00:00Z,7,50,muddaub,1,no,never,3,cow_plough;solar_panel,3,Oct;Nov,uuid:ece89122-ea99-4378-b67e-a170127ec4e6 81 | 181,God,2016-11-25T00:00:00Z,11,25,sunbricks,2,yes,more_once,3,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;mobile_phone,3,none,uuid:bf373763-dca5-4906-901b-d1bacb4f0286 82 | 182,God,2016-11-25T00:00:00Z,7,21,muddaub,3,no,more_once,2,solar_panel,3,Jan;Feb;Nov;Dec,uuid:394033e8-a6e2-4e39-bfac-458753a1ed78 83 | 186,God,2016-11-28T00:00:00Z,7,24,muddaub,1,no,more_once,2,cow_plough;mobile_phone,3,none,uuid:268bfd97-991c-473f-bd51-bc80676c65c6 84 | 187,God,2016-11-28T00:00:00Z,5,43,muddaub,2,yes,more_once,4,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;mobile_phone,3,none,uuid:0a42c9ee-a840-4dda-8123-15c1bede5dfc 85 | 195,God,2016-11-28T00:00:00Z,5,48,burntbricks,1,no,never,3,cow_cart;bicycle;radio;cow_plough;solar_torch,2,Sept;Oct;Nov,uuid:2c132929-9c8f-450a-81ff-367360ce2c19 86 | 196,God,2016-11-28T00:00:00Z,7,49,burntbricks,2,yes,more_once,3,radio;cow_plough;mobile_phone,3,none,uuid:44e427d1-a448-4bf2-b529-7d67b2266c06 87 | 197,God,2016-11-28T00:00:00Z,5,19,burntbricks,2,no,more_once,3,bicycle;television;radio;cow_plough;solar_torch;table;mobile_phone,2,Nov,uuid:85c99fd2-775f-40c9-8654-68223f59d091 88 | 198,God,2016-11-28T00:00:00Z,3,49,burntbricks,1,no,never,1,NULL,3,Nov,uuid:28c64954-739c-444c-a6e0-355878e471c8 89 | 201,God,2016-11-21T00:00:00Z,4,6,muddaub,2,NULL,NULL,2,bicycle;radio;solar_torch;mobile_phone,2,Oct;Nov;Dec,uuid:9e79a31c-3ea5-44f0-80f9-a32db49422e3 90 | 202,God,2016-11-17T00:00:00Z,12,12,burntbricks,4,yes,more_once,3,cow_cart;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,Jan;Feb;Mar;Oct;Nov;Dec,uuid:06d39051-38ef-4757-b68b-3327b1f16b9d 91 | 72,Ruaca,2017-04-26T00:00:00Z,6,24,muddaub,1,yes,more_once,3,bicycle;radio;cow_plough,2,Jan;Aug;Sept;Oct;Nov;Dec,uuid:c4a2c982-244e-45a5-aa4b-71fa53f99e18 92 | 73,Ruaca,2017-04-26T00:00:00Z,7,9,burntbricks,2,yes,more_once,3,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;table;mobile_phone,3,Jan;Sept;Oct,uuid:ac3da862-9e6c-4962-94b6-f4c31624f207 93 | 76,Ruaca,2017-04-26T00:00:00Z,17,48,burntbricks,2,yes,more_once,4,bicycle;radio;cow_plough;solar_panel;mobile_phone,3,none,uuid:4178a296-903a-4a8e-9cfa-0cd6143476e8 94 | 83,Ruaca,2017-04-27T00:00:00Z,5,22,burntbricks,1,yes,never,2,radio;cow_plough;solar_torch,2,Aug;Sept;Oct,uuid:a1e9df00-c8ae-411c-931c-c7df898c68d0 95 | 85,Ruaca,2017-04-27T00:00:00Z,7,40,sunbricks,1,no,never,2,radio;cow_plough,2,Oct;Nov,uuid:4d0f472b-f8ae-4026-87c9-6b5be14b0a70 96 | 89,God,2017-04-27T00:00:00Z,5,10,burntbricks,2,no,never,3,bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,Oct;Nov,uuid:b3b309c6-f234-4830-8b30-87d26a17ee1d 97 | 101,God,2017-04-27T00:00:00Z,3,4,muddaub,1,no,never,1,bicycle;solar_torch,3,Sept;Oct;Nov,uuid:3c174acd-e431-4523-9ad6-eb14cddca805 98 | 103,Ruaca,2017-04-27T00:00:00Z,6,96,sunbricks,1,no,never,5,cow_cart;cow_plough;solar_panel;sofa_set;mobile_phone,3,Jan;Feb;Dec,uuid:e9d79844-ef14-493b-bbd6-d13691cc660e 99 | 102,Ruaca,2017-04-28T00:00:00Z,12,15,burntbricks,2,yes,frequently,2,cow_plough;table;sofa_set;mobile_phone,3,Jan;Feb,uuid:76206b0b-af74-4344-b24f-81e839f0d7b0 100 | 78,Ruaca,2017-04-28T00:00:00Z,6,48,burntbricks,1,no,more_once,2,cow_plough,2,Aug;Sept;Oct,uuid:da3fa7cc-5ce9-44fd-9a78-b8982b607515 101 | 80,Ruaca,2017-04-28T00:00:00Z,5,12,muddaub,1,no,more_once,1,cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch,3,none,uuid:a85df6df-0336-46fa-a9f4-522bf6f8b438 102 | 104,Ruaca,2017-04-28T00:00:00Z,14,52,sunbricks,1,yes,never,4,cow_cart;bicycle;cow_plough,3,Jan;Feb;Dec,uuid:bb2bb365-7d7d-4fe9-9353-b21269676119 103 | 105,Ruaca,2017-04-28T00:00:00Z,6,40,sunbricks,1,yes,frequently,2,motorcyle;radio;cow_plough;solar_panel;mobile_phone,3,Jan;Feb;Dec,uuid:af0904ee-4fdb-4090-973f-599c81ddf022 104 | 106,God,2017-04-30T00:00:00Z,15,22,sunbricks,5,no,never,2,cow_cart;motorcyle;bicycle;radio;sterio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,Oct;Nov;Dec,uuid:468797c1-4a65-4f35-9c83-e28ce46972a2 105 | 109,God,2017-05-03T00:00:00Z,4,12,sunbricks,1,NULL,NULL,3,cow_cart;bicycle;radio;cow_plough;table,3,July;Aug;Sept;Oct;Nov,uuid:602cd3f6-4a97-49c6-80e3-bcfd5c78dfa4 106 | 110,Ruaca,2017-05-03T00:00:00Z,6,22,sunbricks,3,no,never,3,bicycle;radio;cow_plough;table;mobile_phone,2,none,uuid:e7c51ac4-24e4-475e-88e7-f85e896945e3 107 | 113,Ruaca,2017-05-03T00:00:00Z,11,26,burntbricks,3,no,never,4,cow_cart;motorcyle;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,none,uuid:01210861-aba1-4268-98d0-0260e05f5155 108 | 118,Ruaca,2017-05-04T00:00:00Z,5,25,muddaub,1,NULL,NULL,1,radio;solar_torch;mobile_phone,3,Oct;Nov;Dec,uuid:77335b2e-8812-4a35-b1e5-ca9ab626dfea 109 | 125,Ruaca,2017-05-04T00:00:00Z,5,14,burntbricks,1,no,more_once,2,bicycle;radio;cow_plough;solar_panel;solar_torch;mobile_phone,3,Jan;Sept;Oct;Nov;Dec,uuid:02b05c68-302e-4e7a-b229-81cb1377fd29 110 | 119,Ruaca,2017-05-04T00:00:00Z,3,14,muddaub,1,no,never,4,bicycle;cow_plough;solar_panel;mobile_phone,3,none,uuid:fa201fce-4e94-44b8-b435-c558c2e1ed55 111 | 115,Ruaca,2017-05-11T00:00:00Z,4,16,sunbricks,2,NULL,NULL,3,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,none,uuid:628fe23d-188f-43e4-a203-a4bf3257d461 112 | 108,God,2017-05-11T00:00:00Z,15,22,burntbricks,2,no,never,4,cow_cart;bicycle;radio;cow_plough;solar_panel;table;mobile_phone,3,Aug;Sept;Oct;Nov,uuid:e4f4d6ba-e698-45a5-947f-ba6da88cc22b 113 | 116,Ruaca,2017-05-11T00:00:00Z,5,25,burntbricks,3,NULL,NULL,3,motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,Jan;Nov;Dec,uuid:cfee6297-2c0e-4f8a-94cc-9aaee0bd64cb 114 | 117,Ruaca,2017-05-11T00:00:00Z,10,28,muddaub,4,NULL,NULL,1,motorcyle;television;radio;solar_panel;solar_torch;table;mobile_phone,3,Jan;Feb;Nov;Dec,uuid:3fe626b3-c794-48e1-a80f-5bfe440c507b 115 | 144,Ruaca,2017-05-18T00:00:00Z,7,5,burntbricks,4,no,frequently,4,cow_cart;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,2,none,uuid:0670cef6-d233-4852-89d8-36955261b0a3 116 | 143,Ruaca,2017-05-18T00:00:00Z,10,24,burntbricks,2,no,frequently,3,cow_cart;motorcyle;television;radio;cow_plough;solar_torch;table;mobile_phone,3,Jan;Dec,uuid:9a096a12-b335-468c-b3cc-1191180d62de 117 | 150,Ruaca,2017-05-18T00:00:00Z,7,8,muddaub,1,no,never,1,mobile_phone,3,Sept;Oct;Nov,uuid:92613d0d-e7b1-4d62-8ea4-451d7cd0a982 118 | 159,God,2017-05-18T00:00:00Z,4,24,sunbricks,1,no,never,1,radio;solar_panel;solar_torch,3,Sept;Oct;Nov,uuid:37577f91-d665-443e-8d70-b914954cef4b 119 | 160,God,2017-06-03T00:00:00Z,7,13,burntbricks,2,yes,frequently,2,cow_cart;cow_plough;solar_torch;mobile_phone,2,Nov,uuid:f22831ec-6bc3-4b73-9197-4b01e01abb66 120 | 165,Ruaca,2017-06-03T00:00:00Z,9,14,burntbricks,1,no,never,3,cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_torch;electricity;table;sofa_set;mobile_phone;fridge,3,none,uuid:62f3f7af-f0f3-4f88-b9e0-acf8baa49ae4 121 | 166,Ruaca,2017-06-03T00:00:00Z,11,16,muddaub,1,no,never,1,bicycle;solar_torch;mobile_phone,2,Feb;Mar,uuid:40aac732-94df-496c-97ba-5b67f59bcc7a 122 | 167,Ruaca,2017-06-03T00:00:00Z,8,24,muddaub,1,no,never,3,motorcyle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,2,Jan;Nov;Dec,uuid:a9d1a013-043b-475d-a71b-77ed80abe970 123 | 174,Ruaca,2017-06-03T00:00:00Z,12,25,burntbricks,2,no,never,3,car;lorry;motorcyle;radio;sterio;cow_plough;solar_panel;solar_torch;table;sofa_set;mobile_phone;fridge,3,Jan;Feb;Dec,uuid:43ec6132-478c-4f87-878d-fb3c0c4d0c74 124 | 175,Ruaca,2017-06-03T00:00:00Z,7,36,burntbricks,1,no,never,4,motorcyle;bicycle;radio;sterio;cow_plough;solar_panel;table;mobile_phone,2,Jan;Oct;Nov;Dec,uuid:64fc743e-8176-40f6-8ae4-36ae97fac1d9 125 | 189,Ruaca,2017-06-03T00:00:00Z,15,16,sunbricks,1,no,never,3,motorcyle;radio;sterio;cow_plough;solar_panel;table;mobile_phone,3,Nov,uuid:c17e374c-280b-4e78-bf21-74a7c1c73492 126 | 191,Ruaca,2017-06-03T00:00:00Z,10,5,burntbricks,4,no,never,1,radio;cow_plough;solar_panel;solar_torch;mobile_phone,2,Oct;Nov;Dec,uuid:dad53aff-b520-4015-a9e3-f5fdf9168fe1 127 | 192,Chirodzo,2017-06-03T00:00:00Z,9,20,burntbricks,1,no,once,1,bicycle;television;radio;sterio;solar_panel;solar_torch;table;mobile_phone,3,Jan;Nov;Dec,uuid:f94409a6-e461-4e4c-a6fb-0072d3d58b00 128 | 126,Ruaca,2017-05-18T00:00:00Z,3,7,burntbricks,1,no,more_once,3,motorcyle;radio;solar_panel,3,Oct;Nov;Dec,uuid:69caea81-a4e5-4e8d-83cd-9c18d8e8d965 129 | 193,Ruaca,2017-06-04T00:00:00Z,7,10,cement,3,no,more_once,3,car;lorry;television;radio;sterio;cow_plough;solar_torch;electricity;table;sofa_set;mobile_phone;fridge,3,none,uuid:5ccc2e5a-ea90-48b5-8542-69400d5334df 130 | 194,Ruaca,2017-06-04T00:00:00Z,4,5,muddaub,1,no,more_once,1,radio;solar_panel;solar_torch;mobile_phone,3,Sept;Oct;Nov,uuid:95c11a30-d44f-40c4-8ea8-ec34fca6bbbf 131 | 199,Chirodzo,2017-06-04T00:00:00Z,7,17,burntbricks,2,yes,more_once,2,cow_cart;lorry;motorcyle;computer;television;radio;sterio;cow_plough;solar_panel;solar_torch;electricity;mobile_phone,3,Nov;Dec,uuid:ffc83162-ff24-4a87-8709-eff17abc0b3b 132 | 200,Chirodzo,2017-06-04T00:00:00Z,8,20,burntbricks,2,NULL,NULL,3,radio;cow_plough;solar_panel;solar_torch;table;mobile_phone,3,Oct;Nov,uuid:aa77a0d7-7142-41c8-b494-483a5b68d8a7 -------------------------------------------------------------------------------- /safi-survey/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context": "http://schema.org/", 3 | "@type": "Dataset", 4 | "schemaVersion": "Psych-DS 0.1.0", 5 | "name": "SAFI (Studying African Farmer-Led Irrigation)", 6 | "description": "Project about farming and irrigation methods. This is survey data relating to households and agriculture in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017. The survey covered such things as; household features (e.g. construction materials used, number of household members), agricultural practices (e.g. water usage), assets (e.g. number and types of livestock) and details about the household members. Original data: Woodhouse, Philip; Veldwisch, Gert Jan; Brockington, Daniel; Komakech, Hans C.; Manjichi, Angela; Venot, Jean-Philippe (2018). SAFI Survey Results. figshare. Dataset. https://doi.org/10.6084/m9.figshare.6262019.v4", 7 | "author": [ 8 | { 9 | "@type": "Person", 10 | "name": "Eduard Klapwijk", 11 | "@id": "https://orcid.org/0000-0002-8936-0365" 12 | } 13 | ], 14 | "url": "https://doi.org/10.5281/zenodo.7956600", 15 | "citation": "Klapwijk, Eduard, Sunami, Nami, Mania, Joanna, Lushaj, Bora, & Volkova, Anna. (2023). EUR publication package example [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7956600", 16 | "variableMeasured": [ 17 | { 18 | "type": "PropertyValue", 19 | "name": "key_ID", 20 | "description": "Unique Id for each observation" 21 | }, 22 | { 23 | "type": "PropertyValue", 24 | "name": "village", 25 | "description": "Village name" 26 | }, 27 | { 28 | "type": "PropertyValue", 29 | "name": "interview_date", 30 | "description": "Date of interview" 31 | }, 32 | { 33 | "type": "PropertyValue", 34 | "name": "no_membrs", 35 | "description": "Number of household members" 36 | }, 37 | { 38 | "type": "PropertyValue", 39 | "name": "years_liv", 40 | "description": "Number of years living in a given village" 41 | }, 42 | { 43 | "type": "PropertyValue", 44 | "name": "respondent_wall_type", 45 | "description": "Type of wall in the house" 46 | }, 47 | { 48 | "type": "PropertyValue", 49 | "name": "rooms", 50 | "description": "Number of rooms for sleeping" 51 | }, 52 | { 53 | "type": "PropertyValue", 54 | "name": "memb_assoc", 55 | "description": "Membership irrigation association" 56 | }, 57 | { 58 | "type": "PropertyValue", 59 | "name": "affect_conflicts", 60 | "description": "Frequency affected by a conflict" 61 | }, 62 | { 63 | "type": "PropertyValue", 64 | "name": "liv_count", 65 | "description": "Livestock count" 66 | }, 67 | { 68 | "type": "PropertyValue", 69 | "name": "items_owned", 70 | "description": "Items owned by a household" 71 | }, 72 | { 73 | "type": "PropertyValue", 74 | "name": "no_meals", 75 | "description": "Number of meals per day" 76 | }, 77 | { 78 | "type": "PropertyValue", 79 | "name": "months_lack_food", 80 | "description": "Month with no food available" 81 | }, 82 | { 83 | "type": "PropertyValue", 84 | "name": "instanceID", 85 | "description": "Unique identifier for the form data submission" 86 | } 87 | ] 88 | } -------------------------------------------------------------------------------- /template-dataset/README.md: -------------------------------------------------------------------------------- 1 | # README - Template project 2 | 3 | This is a README file. It is a [Markdown file](https://en.wikipedia.org/wiki/Markdown), which means it's a simple text document, but you can use simple syntax like you see here and many programs will display it more nicely - for instance, the words 'Markdown file' will appear as a link that goes to `https://en.wikipedia.org/wiki/Markdown`. 4 | 5 | This is the first document someone will read when they access your project! Write something informative in here, such as: 6 | 7 | * A quick summary of what the project is 8 | 9 | * A list of what files can be found in this folder and what they are 10 | 11 | * Instructions for how to cite this datset/project. You can also put your citation information in `dataset_description.JSON`! 12 | -------------------------------------------------------------------------------- /template-dataset/data/study-yarncolor_data.csv: -------------------------------------------------------------------------------- 1 | sub_id,date,garment,yarn_color 2 | r2d2,2021-02-21,hat,Country Blue 3 | r2d2,1999-08-12,scarf,Eggplant 4 | r2d2,2008-12-12,hat,Royal 5 | r2d2,2019,hat,Grey 6 | r2d2,1999,snood,Gold 7 | r2d2,2014-01-01,sweater,Kelly 8 | c3p0,2021,mittens,Lavender 9 | c3p0,2020,glove,Toast 10 | c3p0,2020-12-12,,Mint Green 11 | bb8,2020,scarf,Maroon 12 | bb8,2021,scarf,Pink Carnation 13 | -------------------------------------------------------------------------------- /template-dataset/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context": "https://schema.org/", 3 | "@type": "Dataset", 4 | "name": "Psych-DS Example Dataset", 5 | "description": "This is a 'skeleton' dataset for Psych-DS", 6 | "schemaVersion": "Psych-DS 0.1.0", 7 | "creator": [ 8 | { 9 | "@type": "Person", 10 | "name": "Melissa Kline" 11 | }, 12 | { 13 | "@type": "Person", 14 | "name": "Schmelissa Schmine", 15 | "birthDate": "1950-01-01", 16 | "favoriteSandwich": "grilled cheese" 17 | } 18 | ], 19 | "citation": "Kline (2018). Not a real paper, No Journal, p. 1-24.", 20 | "sameAs": "https://doi.org/doi-goes-here", 21 | "temporalCoverage": "1950-01-01/2013-12-18", 22 | "keywords": [ 23 | "foo", 24 | "bar" 25 | ], 26 | "variableMeasured": [ 27 | { 28 | "@type": "PropertyValue", 29 | "unitText": "Participant", 30 | "name": "participant_id", 31 | "description": "Identity of each zebra. Provides a unique rowid in this dataset.", 32 | "naValues": "NA" 33 | }, 34 | { 35 | "@type": "PropertyValue", 36 | "unitText": "Smoots", 37 | "name": "length_in_smoots", 38 | "description": "The length of a zebra, in smoots", 39 | "minValue": "0", 40 | "naValues": "NA" 41 | }, 42 | { 43 | "@type": "PropertyValue", 44 | "unitCode": "C26", 45 | "name": "milliseconds", 46 | "description": "Time the zebra started running before/after the starting gun goes off" 47 | }, 48 | { 49 | "@type": "PropertyValue", 50 | "unitText": null, 51 | "name": "team", 52 | "description": "Which team the zebra is on", 53 | "levels": [ 54 | "Red team", 55 | "Blue team" 56 | ], 57 | "ordered": "False", 58 | "naValues": [ 59 | "NA", 60 | "No team" 61 | ] 62 | }, 63 | { 64 | "@type": "PropertyValue", 65 | "name": "sub_id", 66 | "description": "subject ID" 67 | }, 68 | { 69 | "@type": "PropertyValue", 70 | "name": "date", 71 | "description": "Date when response was made" 72 | }, 73 | { 74 | "@type": "PropertyValue", 75 | "name": "garment" 76 | }, 77 | { 78 | "@type": "PropertyValue", 79 | "name": "yarn_color" 80 | } 81 | ] 82 | } 83 | --------------------------------------------------------------------------------