e-TidalGCs

The e-TidalGCs Project aims at modeling and predicting the extra-tidal features surrounding all Galactic globular clusters for which 6D phase space information, masses and sizes are available.

For info and downloads:

https://etidal-project.obspm.fr/index.html

Reference:

Ferrone, Salvatore et al. The e-TidalGCs project. Modeling the extra-tidal features generated by Galactic globular clusters, Astronomy & Astrophysics, Volume 673, id.A44, 50 pp. (2023)

Code documentation:

https://tstrippy.readthedocs.io/en/latest/installation.html

Source code:
https://github.com/salvatore-ferrone/tstrippy

 

INSIDIA

INSIDIA (INvasion SpheroID ImageJ Analysis) is an open-source macro implemented as a customizable software algorithm running on the FIJI platform, that enables high-throughput high-content quantitative analysis of spheroid images (both bright-field gray and fluorescent images) with the output of a range of parameters defining the spheroid “tumor” core and its invasive characteristics.

For info and downloads:

https://valentinapalmieri.wixsite.com/insidia

References:

Moriconi C., Palmieri V et al. INSIDIA: ImageJ Macro for high-throughput and high-content Spheroid Invasion Analysis Biotechnology Journal, Volume12, Issue10 (2017). https://doi.org/10.1002/biot.201700140

Perini, G., Rosa, E., Friggeri, G., Di Pietro, L., Barba, M., Parolini, O., … & Palmieri, V. (2022). INSIDIA 2.0 high-throughput analysis of 3D cancer models: multiparametric quantification of graphene quantum dots photothermal therapy for glioblastoma and pancreatic cancer. International Journal of Molecular Sciences, 23(6), 3217.

Software for Financial Network Analysis

Fermi

fermi is a modular Python framework for analyzing the main Economic Complexity metrics and features. It provides tools to explore the hidden structure of economies through:

  •  Matrix preprocessing: raw cleaning, sparse conversion, Comparative advantage RCA/ICA, transformation and thresholding.
  •  Fitness & complexity: compute Fitness, Complexity ECI, PCI and other metrics via multiple methods.
  •  Relatedness metrics: product space, taxonomy, assist matrix.
  •  Prediction models: GDP forecasting, density models, XGBoost.
  • ✅ Validation metrics: AUC, confusion matrix, prediction@k.

Source code:

https://github.com/EFC-data/fermi

Sapling Similarity

Sapling Similarity: a performing and interpretable memory-based tool for recommendation

Reference:

Giambattista Albora, Lavinia Rossi-Mori, Andrea Zaccaria Sapling Similarity: a performing and interpretable memory-based tool for recommendation https://arxiv.org/abs/2210.07039

Source code:

https://github.com/giamba95/SaplingSimilarity

FIPS

Reference:

Massimiliano Fessina, Giambattista Albora, Andrea Tacchella and Andrea Zaccaria, Identifying Key Products to Trigger New Exports: An Explainable Machine Learning Approach, J. Phys. Complex. 5 025003 (2024) https://iopscience.iop.org/article/10.1088/2632-072X/ad3604

Source code:

https://github.com/mfessina/FIPS

The Intrinsic Dimension of Neural Networks Ensembles

Source code:

https://github.com/tgfrancesco/NN_intrinsic_dimension

 

 

Software for neuronal activity analysis

SPIKY (Matlab graphical user interface)

SPIKY: a graphical user interface for monitoring spike train synchrony.

Download: 

https://www.thomaskreuz.org/source-codes/spiky

Reference:

Kreuz T, Mulansky M, Bozanic N, SPIKY – A graphical user interface for monitoring spike train synchrony JNeurophysiol 113, 3432 (2015)     [PDF]

PySpike (Python command line library)

PySpike is a complement to SPIKY: an open source Python library written by Mario Mulansky and available on github.

For more information:

https://www.thomaskreuz.org/source-codes/pyspike

Source code:

https://github.com/mariomulansky/PySpike

https://mariomulansky.github.io/PySpike/

Reference:

Mulansky M, Kreuz T: PySpike – A Python library for analyzing spike train synchrony Software X 5, 183 and arXiv [PDF] (2016) [PDF]

cSPIKE (Matlab command line library)

cSPIKE is an easy to use spike train analysis software.

Download: 

https://www.thomaskreuz.org/source-codes/cspike

 

Software for computation of epidemic thresholds

When to Boost: How Dose Timing Determines the Epidemic Threshold

Source code:

https://github.com/alecel/When-to-Boost-How-Dose-Timing-Determines-the-Epidemic-Threshold

Reference:

Alessandro Celestini, Francesca Colaiori, Stefano Guarino, Enrico Mastrostefano, Francesca Pelusi, Lena Rebecca Zastrow, When to Boost: How Dose Timing Determines the Epidemic Threshold Phys. Rev. Research7, 033125 (2025) https://doi.org/10.1103/cykd-2rjn

Data:

https://zenodo.org/records/15657089