- A. Mahdi, P. Blaszczyk, P. Dlotko, D. Salvi, T-S. Chan, J. Harvey, D. Gurnari, Y. Wu, A. Farhat, N. Hellmer, A. Zarebski, B. Hogan, L. Tarassenko, OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19, accepted to Nature Scientific Reports.
- A.D. Smith, P. Dlotko, V.M. Zavala, Topological data analysis: Concepts, computation, and applications in chemical engineering, Computers and Chemical Engineering, 2021, 146, 107202.
- Q.W., Rudkin, S. Rudkinm P. Dłotko, Refining understanding of corporate failure through a topological data analysis mapping of Altman's Z-score model, Expert Systems with Applications, 2020, 156, 113475.
- B. Zielinski, M. Lipinski, M. Juda, M. Zeppelzauer, P. Dlotko, Persistence Codebooks for Topological Data Analysis, Artificial Intelligence Review, Accepted.
- Ciara Frances Loughrey, Nick Orr, Anna Jurek-Loughrey, Paweł Dłotko, Hotspot identification for Mapper graphs
- R BallMapper: R implementation of the Ball Mapper algorithm described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) arXiv:1901.07410. Please consult the following youtube video the idea of functionality. Ball Mapper provides a topologically accurate summary of a data in a form of an abstract graph.
- PyBallMapper: Python implementation of the Ball Mapper algorithm that can be run in a Jupyter notebook. Allows to plot the resulting graphs using Matplotlib or Bokeh.
- Mapper GUI: Python code to interactively compare two Ball Mapper graphs as described here. A short video example can be found on youtube.
- ECC: Python code to compute the Euler Characteristic Curve of a filtered Vietoris-Rips complex. A pipeline for parallel computations using GNU Parallel is also provided. For a theoretical introduction and a description of the algorithm please watch Davide's talk at the Second Symposium on Machine Learning and Dynamical Systems.
- Prokhorov metric: A fork of gudhi implementing the Prokhorov metric for persistence diagrams. Pease watch Niklas's talks at the Second Symposium on Machine Learning and Dynamical Systems for the theoretical background.