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Affirmer understands and acknowledges that Creative Commons is not a 120 | party to this document and has no duty or obligation with respect to 121 | this CC0 or use of the Work. 122 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome TDA [![Awesome](https://awesome.re/badge.svg)](https://github.com/sindresorhus/awesome) 2 | 3 | A curated list of [Topological Data Analysis (TDA)](https://en.wikipedia.org/wiki/Topological_data_analysis) tools and resources. 4 | 5 | If you know of any other tools or resources, read [Contribution Guidelines](https://github.com/FatemehTarashi/awesome-tda/blob/master/contributing.md) and feel free to fork/PR or open a new issue. 6 | 7 | ## Contents 8 | 9 | 10 | 11 | - [Theory](#theory) 12 | - [Algorithms](#algorithms) 13 | - [Books](#books) 14 | - [Articles](#articles) 15 | - [Courses](#courses) 16 | - [Tools](#tools) 17 | - [Frameworks and Libs](#frameworks-and-libs) 18 | - [C++](#c) 19 | - [Go](#go) 20 | - [Haskell](#haskell) 21 | - [Java](#java) 22 | - [Julia](#julia) 23 | - [Matlab](#matlab) 24 | - [Python](#python) 25 | - [R](#r) 26 | - [Spark](#spark) 27 | - [Useful Links](#useful-links) 28 | - [Bioinformatics](#bioinformatics) 29 | - [Brain Network Analysis](#brain-network-analysis) 30 | - [Computing Homology](#computing-homology) 31 | - [Computer Vision](#computer-vision) 32 | - [Data Professionals](#data-professionals) 33 | - [Deep Learning](#deep-learning) 34 | - [Machine Learning](#machine-learning) 35 | - [Persistent Homology](#persistent-homology) 36 | - [Use Python](#use-python) 37 | - [Use R](#use-r) 38 | - [Theory and applications of TDA](#theory-and-applications-of-tda) 39 | - [Event](#event) 40 | 41 | 42 | 43 | ## Theory 44 | 45 | ### Algorithms 46 | 47 | 48 | 49 | 50 | 51 | 52 | - [Chunk](https://www.researchgate.net/publication/235766026_Clear_and_Compress_Computing_Persistent_Homology_in_Chunks) 53 | - [Mapper](http://diglib.eg.org/handle/10.2312/SPBG.SPBG07.091-100) 54 | ([brief summary](https://github.com/ognis1205/spark-tda/wiki/Mapper)) 55 | - [PHrow](https://arxiv.org/pdf/1107.5665.pdf) 56 | - [Twist](https://www.researchgate.net/publication/228605960_Persistent_homology_computation_with_a_twist) 57 | - [Vineyards](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.5942&rep=rep1&type=pdf) 58 | - [Zigzag persistent homology](https://www.mrzv.org/publications/zigzags/socg09/) 59 | - [Zigzag Persistent Cohomology](https://arxiv.org/pdf/1608.06039.pdf) 60 | 61 | ### Books 62 | 63 | - [A Short Course in Computational Geometry and Topology](https://www.springer.com/gp/book/9783319059563) - Edelsbrunner, Herbert. 64 | 65 | - [Computational Homology (Applied Mathematical Sciences)](https://www.amazon.com/dp/1441923543/) - Kaczynski, Mischaikow, Mrozek. 66 | 67 | - :open_book: [Computational Topology: An Introduction](https://www.maths.ed.ac.uk/~v1ranick/papers/edelcomp.pdf) - Herbert Edelsbrunner, John L Harer. 68 | 69 | - :open_book: [Computational Topology for Data Analysis](https://www.cs.purdue.edu/homes/tamaldey/book/CTDAbook/CTDAbook.pdf) - Tamal Krishna Dey, Yusu Wang. 70 | 71 | - :open_book: [Elementary Applied Topology](https://www.math.upenn.edu/~ghrist/notes.html) - Robert Ghrist. 72 | 73 | - [Fundamentals of Brain Network Analysis](https://www.amazon.com/Fundamentals-Brain-Network-Analysis-Fornito-ebook/dp/B01CRIU886) - Fundamentals of Brain Network Analysis. 74 | 75 | - [Geometric and Topological Inference](https://www.researchgate.net/publication/320412992_Geometric_and_Topological_Inference) - Boissonnat, Chazal, Yvinec. 76 | 77 | - :open_book: [Persistence Theory: From Quiver Representations to Data Analysis](https://geometrica.saclay.inria.fr/team/Steve.Oudot/books/o-pt-fqrtda-15/surv-209.pdf) - Steve Y Oudot. 78 | 79 | - [Topological and Statistical Methods for Complex Data: Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces](https://www.springer.com/gp/book/9783662448991) - Bennett, Janine, Vivodtzev, Fabien, Pascucci, Valerio. 80 | 81 | - [Topological Based Machine Learning Methods](https://escholarship.org/uc/item/4vr8963d) - Alex Georges. 82 | 83 | - [Topological Data Analysis for Genomics and Evolution: Topology in Biology](https://www.cambridge.org/core/books/topological-data-analysis-for-genomics-and-evolution/FCC8429FAD2B5D1525AEA47A8366D6EB) - Raul Rabadan, Andrew J Blumberg. 84 | 85 | - [Topological Data Analysis for Scientific Visualization](https://www.springer.com/gp/book/9783319715063) - Tierny, Julien. 86 | 87 | - :open_book: [Topological Methods for 3D Point Cloud Processing](https://www-users.cse.umn.edu/~beksi001/publications/topological_methods_for_3d_point_cloud_processing.pdf) - William Joseph Beksi. 88 | 89 | - :open_book: [Topology for Computing](http://directory.umm.ac.id/Networking%20Manual/Topology%20for%20Computing.pdf) - AFRA J ZOMORODIAN. 90 | 91 | - [Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications](https://www.springer.com/gp/book/9783642150135) 92 | - [Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications II](https://www.springer.com/gp/book/9783642231742) 93 | - [Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications III](https://www.springer.com/gp/book/9783319040981) 94 | - [Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications IV](https://www.springer.com/gp/book/9783319446820) 95 | 96 | - [Topology-based Methods in Visualization](https://www.springer.com/gp/book/9783540708223) - Hauser, Helwig, Hagen, Hans, Theisel, Holger (Eds.) 97 | 98 | ### Articles 99 | 100 | - [A Fuzzy Clustering Algorithm for the Mode Seeking Framework](https://arxiv.org/abs/1406.7130) - Bonis, Oudot. 101 | 102 | - [A topological data analysis based classification method for multiple measurements](https://arxiv.org/abs/1904.02971) - Riihimäki, Chachólski, Theorell, Hillert, Ramanujam. 103 | 104 | - [A User's Guide to Topological Data Analysis](https://learning-analytics.info/journals/index.php/JLA/article/view/5196/6089) - Elizabeth Munch. 105 | 106 | - [An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists](https://arxiv.org/abs/1710.04019) - Chazal, Michel. 107 | 108 | - [Barcodes: The Persistent Topology of Data](https://www.math.upenn.edu/~ghrist/preprints/barcodes.pdf) - Robert Ghrist. 109 | 110 | - [Computing Persistent Homology (Discrete and Computational Geometry)](https://geometry.stanford.edu/papers/zc-cph-05/zc-cph-05.pdf) - Zomorodian, Carlsson. 111 | 112 | - [Deep Learning with Topological Signatures](https://papers.nips.cc/paper/6761-deep-learning-with-topological-signatures.pdf) - Hofer, Kwitt, Niethammer, Uhl. 113 | 114 | - [Designing machine learning workflows with an application to topological data analysis](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0225577) - Cawi, La Rosa, Nehorai. 115 | 116 | - [Introduction to the R package TDA](https://cran.r-project.org/web/packages/TDA/vignettes/article.pdf) - Fasy, Jisu Kim, Lecci, Clément Maria, Millman, Rouvreau. 117 | 118 | - [Homological Algebra and Data](https://www.math.upenn.edu/~ghrist/preprints/HAD.pdf) - Robert Ghrist. 119 | 120 | - [Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport](https://arxiv.org/abs/1805.08331) - Lacombe, Cuturi, OUDOT. 121 | 122 | - [Sampling real algebraic varieties for topological data analysis](https://arxiv.org/abs/1802.07716) - Dufresne, Edwards, Harrington, Hauenstein. 123 | 124 | - [Stratifying Multiparmeter Persistent Homology](https://arxiv.org/pdf/1708.07390.pdf) - Harriington, Otter, Schenck, Tillmann. 125 | 126 | - [Text Mining using Topological Data Analysis. An introduction](https://www.researchgate.net/publication/323705256_Text_Mining_using_Topological_Data_Analysis_An_introduction) - Carrazana, Chong. 127 | 128 | - [Topology and Data](https://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01249-X/S0273-0979-09-01249-X.pdf) - Gunnar Carlsson. 129 | 130 | - [Topological Data Analysis](https://arxiv.org/abs/1609.08227) - Larry Wasserman. 131 | 132 | - [Topological Data Analysis and Its Application to Time-Series Data Analysis](https://www.fujitsu.com/global/documents/about/resources/publications/fstj/archives/vol55-2/paper15.pdf) - Umeda, Kaneko, Kikuchi. 133 | 134 | - [Topological Data Analysis and Machine Learning Theory](https://www.birs.ca/workshops/2012/12w5081/report12w5081.pdf) - Carlsson , Jardine, Feichtner-Kozlov, Morozov. 135 | 136 | - [Topological Data Analysis for Object Data](https://arxiv.org/abs/1804.10255) - Patrangenaru, Bubenik, Paige, Osborne . 137 | 138 | - [Two-Tier Mapper: a user-independent clustering method for global gene expression analysis based on topology](https://arxiv.org/abs/1801.01841) - Jeitziner, Carrière, Rougemont, Oudot, Hess, Brisken. 139 | 140 | - [Why Topology for Machine Learning and Knowledge Extraction?](https://res.mdpi.com/d_attachment/make/make-01-00006/article_deploy/make-01-00006.pdf) - Massimo Ferri. 141 | 142 | ### Courses 143 | - [Applied Algebraic Topology Seminars](https://topology.ima.umn.edu/seminars) 144 | 145 | - [Computational Topology and Data Analysis](http://web.cse.ohio-state.edu/~dey.8/course/CTDA/CTDA.html) - Course is not active, but the course notes are useful. 146 | 147 | - [Topological Data Analysis](http://www.enseignement.polytechnique.fr/informatique/INF556/#Synopsis) - Course is not active, but the course notes are useful. 148 | 149 | - [Topics in topology: Scientific and engineering applications of algebraic topology](http://homepage.math.uiowa.edu/~idarcy/AT/prelectures.html) - 2013 lecture series. 150 | 151 | 152 | ## Tools 153 | - [Ctl](https://github.com/appliedtopology/ctl) - (C++11 library) A set of generic tools for Building Neighborhood Graphs and Cellular Complexes, Computing (persistent) homology over finite fields, Parallel algorithms for homology. an be used with c++, Python, MATLAB and R. 154 | 155 | - [Knotter](https://github.com/rosinality/knotter) - Implementation of Mapper algorithm for TDA. 156 | 157 | - [RIVET](https://github.com/rivetTDA/rivet) - For the visualization and analysis of two-parameter persistent homology with [Python API](https://github.com/rivetTDA/rivet-python/). 158 | 159 | - [TdaToolbox](https://github.com/Coricos/TdaToolbox) - Some tools that may be applied to data science in general. 160 | 161 | - [TTk](https://topology-tool-kit.github.io/index.html) - Topological data analysis in scientific visualization. Can be used with C++, python. 162 | 163 | ## Frameworks and Libs 164 | 165 | ### C++ 166 | - [Dionysus](http://mrzv.org/software/dionysus/) - Computing persistent (co)homology, Implementation of the Persistent (co)homology computation, Vineyards, Zigzag persistent homology algorithms. 167 | 168 | - [PHAT](https://bitbucket.org/phat-code/phat) - Persistent Homology Algorithm Toolbox. 169 | 170 | - [Topology ToolKit (TTK)](https://topology-tool-kit.github.io/) - Efficient, generic and easy and Topological data analysis and visualization 171 | 172 | ### Go 173 | - [TDA](https://github.com/kshedden/tda) - Some methods are provided for gridded data (images). 174 | 175 | ### Haskell 176 | - [Persistence](https://hackage.haskell.org/package/Persistence) 177 | 178 | ### Java 179 | - [JavaPlex](https://github.com/appliedtopology/javaplex) - The JavaPlex library implements persistent homology and related techniques. It designed for ease of use from Matlab and java-based systems. 180 | 181 | ### Julia 182 | - [Eirene.jl](https://github.com/Eetion/Eirene.jl) - For homological persistence. 183 | - [TDA.jl](https://github.com/wildart/TDA.jl) - This package provides Persistence Diagram & Barcode, Nerve, Mapper tools for topological data analysis. 184 | 185 | ### Matlab 186 | - [Clique Top](https://github.com/nebneuron/clique-top) - Doing topological analysis of symmetric matrices. 187 | 188 | ### Python 189 | - [GDA Public](https://geomdata.github.io/gda-public/) - Several fundamental tools by Geometric Data Analytics Inc. [geomdata](http://www.geomdata.com) 190 | 191 | - [Giotto TDA(GTDA)](https://giotto-ai.github.io/gtda-docs/0.5.1/library.html) - A high-performance topological machine learning toolbox 192 | 193 | - [GUDHI](http://gudhi.gforge.inria.fr/) - Geometry Understanding in Higher Dimensional with a Python interface. 194 | 195 | - [KeplerMapper](https://github.com/MLWave/kepler-mapper) - TDA Mapper algorithm for visualization of high-dimensional data. it can make use of Scikit-Learn API compatible cluster and scaling algorithms. 196 | 197 | - [Kohonen](https://github.com/lmjohns3/kohonen) - Kohonen-style vector quantizers: Self-Organizing Map (SOM), Neural Gas, and Growing Neural Gas. 198 | 199 | - [Mapper Implementation](https://github.com/ksanjeevan/mapper-tda) - Topological Data Analysis for high dimensional dataset exploration. 200 | 201 | - [MoguTDA](https://github.com/stephenhky/MoguTDA) - Numerical calculation of algebraic topology in an application to topological data analysis: implicial complex, and the estimation of homology and Betti numbers. 202 | 203 | - [OpenTDA](https://github.com/outlace/OpenTDA) 204 | 205 | - [Persim](https://persim.scikit-tda.org/en/latest/) - package for many tools used in analyzing Persistence Diagrams 206 | 207 | - [Python Mapper](http://danifold.net/mapper/introduction.html) - Mapper algorithm implementation + graphical user interface. 208 | 209 | - [Qsv](https://github.com/RottenFruits/qsv) - Data structure visualizer. 210 | 211 | - [Ripser](https://ripser.scikit-tda.org/en/latest/) - lean persistent homology package. 212 | 213 | - [Scikit-TDA](https://scikit-tda.org/) - For non-topologists. 214 | 215 | - [Giotto-TDA](https://giotto-ai.github.io/gtda-docs/) - A ``scikit-learn`` - compatible library for end-to-end topological machine learning including Mapper, persistent homology, vectorization methods for persistence diagrams, and preprocessing components for time series, graphs, images, and point clouds ([paper](https://openreview.net/forum?id=fjQtZJOCTXf)). 216 | 217 | - [ScTDA](https://github.com/CamaraLab/scTDA) - It includes tools for the preprocessing, analysis, and exploration of single-cell RNA-seq data based on topological representations. 218 | 219 | - [Topology ToolKit (TTK)](https://topology-tool-kit.github.io/) - Efficient, generic and easy and Topological data analysis and visualization 220 | 221 | - [TMAP](https://tmap.readthedocs.org) - Population-scale microbiome data analysis. 222 | 223 | ### R 224 | - [TDA](https://cran.r-project.org/web/packages/TDA/) - Tools for the statistical analysis of persistent homology and for density clustering. 225 | 226 | - [TDAmapper](https://github.com/paultpearson/TDAmapper/) - An R package for using discrete Morse theory to analyze a data set using the Mapper algorithm described in G. Singh, F. Memoli, G. Carlsson (2007). 227 | 228 | - [TDAstats](https://github.com/rrrlw/TDAstats) - Computing persistent homology. 229 | 230 | ### Spark 231 | - [Spark Mapper](https://github.com/log0ymxm/spark-mapper) - Estimating a lower dimensional simplicial complex from a dataset. 232 | 233 | - [Spark TDA](https://github.com/ognis1205/spark-tda) - Scalable topological data analysis package. 234 | 235 | ## Useful Links 236 | 237 | ### Bioinformatics 238 | - [List of Resources for Topological Data Analysis in Bioinformatics](https://github.com/biobai/BioTDA/blob/52715e03fe8cfe1ee11eced06896f74e1e5b4f1a/README.md) 239 | 240 | ### Brain Network Analysis 241 | - [An algebraic topological method for multimodal brain networks comparisons](https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00904/full) 242 | 243 | - [Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey](https://new.hindawi.com/journals/complexity/2017/8362741/) 244 | 245 | - [Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6463121/) 246 | 247 | - [Graph theory methods: applications in brain networks](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136126/) 248 | 249 | - [Topological Methods in Brain Network Analysis](https://braintopology.wordpress.com/tda/) 250 | 251 | ### Computing Homology 252 | - [Computing Homology](https://jeremykun.com/2013/04/10/computing-homology/) 253 | 254 | ### Computer Vision 255 | - [Topological Data Analysis in Computer Vision](https://www.researchgate.net/publication/338956906_Topological_data_analysis_in_computer_vision) 256 | 257 | ### Data Professionals 258 | - [Topological Data Analysis for Data Professionals](https://www.kdnuggets.com/2018/01/topological-data-analysis.html) 259 | 260 | ### Deep Learning 261 | - [Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification!](https://towardsdatascience.com/applied-topological-data-analysis-to-deep-learning-hands-on-arrhythmia-classification-48993d78f9e6) 262 | 263 | - [From Topological Data Analysis to Deep Learning: No Pain No Gain](https://towardsdatascience.com/from-tda-to-dl-d06f234f51d) 264 | 265 | - [On Characterizing the Capacity of Neural Networks Using Algebraic Topology](https://openreview.net/forum?id=H11lAfbCW) 266 | 267 | - [Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks](https://www.ayasdi.com/blog/artificial-intelligence/using-topological-data-analysis-understand-behavior-convolutional-neural-networks/) 268 | 269 | ### Machine Learning 270 | - [How TDA and Machine Learning Enhance Each Other](https://www.ayasdi.com/blog/bigdata/how-tda-and-machine-learning-enhance-each-other/) 271 | 272 | - [Machine Learning Explanations with Topological Data Analysis](https://sauln.github.io/blog/tda_explanations/) 273 | 274 | - [Topological Methods for Machine Learning](http://topology.cs.wisc.edu/) 275 | 276 | - [Topological Machine Learning](https://www.sthu.org/research/topmachinelearning/) 277 | 278 | ### Persistent Homology 279 | - [A roadmap for the computation of persistent homology](https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0109-5#Sec34) 280 | 281 | - [Practical Guide to Persistent Homology](http://www.mrzv.org/software/dionysus2/tutorial/index.html) 282 | 283 | ### Use Python 284 | - [Python Tutorial on Topological Data Analysis](https://0div0-content.s3.amazonaws.com/9ba44202a3de4f5b886f02bcf4509eea.pdf?AWSAccessKeyId=ASIATVY7X35OJYDSO5M4&Signature=LhLsmMXzgk2PBn150kymF1%2FnZnw%3D&x-amz-security-token=IQoJb3JpZ2luX2VjEDMaCXVzLXdlc3QtMiJGMEQCIEWMRBE3XIoEPPFzA9Q%2BI%2FRXYnLh%2FRL%2FMolzUq9hvP1jAiAdUGTJXzcE%2FF138hyhFC4uWzzigHQNauIH0nK%2B4QkUtyqMBAis%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAAaDDI1MjkzMjc3NTc3MiIMYTejad98V6pprpwGKuADDcQmfxdNXLfruXBy6JMMg4AQef6ze7cfrP%2BRW5sqhR1zUVW2KCS4vUTfM5F7te%2Bq6iFNjsq2xEvu88UV7UNBT0lTTxgS5%2BneCOnZNEIE7lja4Io%2F4%2FWOB0qlymBt5atqMOJIwf9v%2BTGBHLej86UKSF06KQA7R5ERGyKEDQvkjMdIj9Nj9DgJsD0AebM%2B5fQYJ1BnUZXQDWEciKxwVf%2Bams7qYyUsmxEIvk1JZ0ir8pCHOx5eiFRd7otCA79KVEUqc5KlPDABqDxrojiMrJNpViyvOn7ivbsxXGxTN1vLkWVz3q6hMPHRLQ%2FCJ964QcMqNTm6aVaST%2FJ1nmPibQZa5JzBY3RYJE%2BHtZOQTmgl%2Fe0YmLbxLrP5GLvHbP63U%2BLkjptV%2F7ju5Yov7qr26dROZx1Czm%2FXn72E1aV%2Bp2EClEDirPL2S18aJRrD2ZRWV8n%2BNcVqwDsjxD%2FN06pjyrajS1q4rJ%2BdDcNkaIhgPNkuPtRFljbH1Ui0Z0G%2BVLEggYz1Hy2EdP7%2B%2F3hFpAkLOG3DyZSKm293SW4ommtqYS4dTGSZBlBtZiBrbDbcPPzyyP4sc5UxK1BQ7PcMN%2FZ8y4R3S9dJNHLDD6RV8Xjjq2fgd0tLQiWCbBcxWXqv8VrD%2B5RyMLKI%2FasGOqYB5dPgkSD0JOhUnb3r7V7JVwcDfWNynjQLskydTrvm%2FJ4o3u7L7hI%2BWu5MtE%2FFTrLThBJSx%2FFqLpP9e5HbinpjtczMX6Ep82shIZ3GEE0PwXAgkstVFu5KIJUnwY39GU%2FCrp97TIuR1KQKgvKUNOgPK8b%2BvA8MYvJ%2Bb8kc5RRFbQCsgczyJ9hLnqb01t7tEXW56%2B3NRosCXKKEAOyDrC73UYh%2FdfEedw%3D%3D&Expires=1703098850) 285 | 286 | - [Topological Data Analysis - A Python tutorial](https://www.thekerneltrip.com/statistics/topological-data-analysis-tutorial/) 287 | 288 | ### Use R 289 | - [A Mathematician's Perspective on Topological Data Analysis and R](https://rviews.rstudio.com/2018/11/14/a-mathematician-s-perspective-on-topological-data-analysis-and-r/) 290 | 291 | ### Theory and applications of TDA 292 | - [A concrete application of Topological Data Analysis](https://towardsdatascience.com/a-concrete-application-of-topological-data-analysis-86b89aa27586) 293 | 294 | - [A Guide to Data Science from mathematics](https://github.com/Hulalazz/A-_Guide_-to_Data_Sciecne_from_mathematics/blob/f42b92bb6a34f32d8c4e7afe98c9bdc889413688/TDA.md) 295 | 296 | - [An Algebraic Geometry Perspective on Topological Data Analysis](https://sinews.siam.org/Details-Page/an-algebraic-geometry-perspective-on-topological-data-analysis) 297 | 298 | - [Topological Data Analysis - A Very Short Introduction](https://medium.com/@varad.deshmukh/topological-data-analysis-a-very-short-introduction-611d3238a0bd) 299 | 300 | - [Topological Data Analysis](https://people.clas.ufl.edu/peterbubenik/intro-to-tda/) - UFL. 301 | 302 | - [Topological Data Analysis](https://researcher.watson.ibm.com/researcher/view_group.php?id=6585) - IBM. 303 | 304 | - [Theory and applications of topological data analysis](https://datawarrior.wordpress.com/category/tda/) 305 | 306 | ## Event 307 | - [Conferences and workshops](https://people.clas.ufl.edu/peterbubenik/conferences/) 308 | 309 | - [International Conference on Computational Topology and Topological Data Analysis](https://waset.org/computational-topology-and-topological-data-analysis-conference) 310 | 311 | ### 2024 312 | 313 | - [Jan. 30 - Feb. 2 | The 4th POSTECH MINDS Workshop on Topological Data Analysis and Machine Learning | Korea ](https://sites.google.com/view/2024-tda-ml/home) 314 | 315 | - [Feb. 7 - Apr. 1 | self-study group for textbook: "Computational Topology for Data Analysis" | zoom ](https://www.erdosinstitute.org/programs/spring-2024/topological-data-analysis) 316 | 317 | - [Mar. 21 - Mar. 22 | Workshop on Topological Data Analysis | India ](https://cus.ac.in/images/content/dynamic/noti/2024/March/Workshop%20Poster.pdf) 318 | 319 | ### 2025 320 | 321 | - [Jan. 8 | AMS Special Session on Topological Data Analysis: Theory and Applications](https://jointmathematicsmeetings.org/meetings/national/jmm2025/2314_program_ss121.html) 322 | 323 | - [Jun. 30 - Jul. 4 | EWM-EMS Summer School: Stability in Topological Data Analysis | Sweden](https://www.mittag-leffler.se/activities/ewm-ems-summer-school-stability-in-topological-data-analysis/#program) 324 | -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | # Contribution Guidelines 2 | 3 | Please ensure your pull request adheres to the following guidelines: 4 | 5 | - Search previous suggestions before making a new one, as yours may be a duplicate. 6 | - Make an individual pull request for each suggestion. 7 | - Use the following format: `[Item Name](link) - Author (University)` 8 | - Check your spelling and grammar. 9 | - The pull request and commit should have a useful title. 10 | 11 | Thank you for your suggestions! 12 | --------------------------------------------------------------------------------