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COPASI

COPASI is an open source software for simulation and analysis of biochemical networks and their dynamics. It provides simulations using ODEs, SDEs, or Gillespie's stochastic simulation algorithm, with optional discrete events. COPASI provides several analyses, optimization, and parameter estimation.

Dynamics
optimization
Parameter estimation
+ 3

C++
Java
Fortran
+ 17

2697

GGIR

Converts raw data from wearables into insightful reports for researchers investigating human daily physical activity and sleep.

Big data

739

ESMValTool

The Earth System Model eValuation Tool is a community diagnostics and performance metrics tool for the evaluation of Earth System Models that allows for routine comparison of models and observations.

Big data
Optimized data handling
Visualization
+ 1

NCL
Python
R
+ 7

390

ESMValCore

ESMValTool Core: core functionalities and driver for ESMValTool, a community diagnostic and performance metrics tool for routine evaluation of Earth System Models in CMIP.

Big data
Optimized data handling
Visualization
+ 1

Python
Jupyter Notebook
HTML
+ 5

128

BridgeDb Java

Java library for identifier mapping

Data
persistent identifiers

Java
Perl
Shell
+ 5

117

splithalfr

Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires.

Data Analysis
Statistics

DeepRank

Deep learning framework for data mining protein-protein interactions using CNN

Big data
Machine learning
Optimized data handling

Python
R
C
+ 2

fsbrain

The fsbrain package provides visualization of neuroimaging data in R. The plots produced by fsbrain can be integrated into R notebooks or written to high-quality bitmap image files, ready for publication.

Computational Neuroscience
Visualization

R
Dockerfile
Shell

BIAS

A toolbox for detecting structural bias in continuous optimization heuristics.

Bias
Metaheuristics
optimization
+ 1

Jupyter Notebook
Python
R

talkr

{talkr} is an R package that offers a set of convenience functions for quality control, visualisation and analysis of conversational data. Most notably it provides a range of plotting functions that play well with ggplot and the tidyverse.

conversation
talk
turn-taking
+ 1

DoE2Vec

DoE2Vec is a self-supervised approach to learn exploratory landscape analysis features from design of experiments. The model can be used for downstream meta-learning tasks such as learninig which optimizer works best on a given optimization landscape.

Machine learning
optimization
python

Python
R
Makefile

FastMLC

Detect clusters in for example large collections of DNA or protein sequences, and visualize the results in a web browser.

Big data
Machine learning
Visualization

C++
C
Makefile
+ 5