Ctrl K

All software

1Filter
Keywords
348
Program languages
86
Licenses
26
RSD Host
3
25-36 of 128
Logo for Ginkgo
@helmholtz.software

Ginkgo

Ginkgo is a high-performance math library for the solution of sparse linear systems on GPUs (AMD, Intel, NVIDIA). Ginkgo is implemented using modern C++ and provides preconditioned Krylov solvers, multigrid, sparse direct solvers, mixed precision, and batched functionality.

  • GPU
  • High performance computing
  • Large linear equation system
  • + 4
  • C++
  • CMake
  • Cuda
  • + 4
11
101
Logo for challengeR
@helmholtz.software

challengeR

challengeR: Methods and open-source toolkit for analyzing and visualizing challenge results

  • Computer science
  • Image processing
  • Machine learning
  • + 2
  • R
0
88
@helmholtz.software

UrbEm - Urban Emission downscaling for air quality modeling

UrbEm enables in a modular manner downscaling of gridded regional emissions with specific spatial proxies based on a variety of open access, robust, sustainable and frequently updated sources. UrbEm can be applied to any urban area in Europe and provides methodological homogeneity between cities.

  • Air quality
  • Chemistry Transport Modeling
  • Earth & Environment
  • + 2
  • Python
  • R
9
75
Logo for cola
@helmholtz.software

cola

Subgroup classification is a basic task in genomic data analysis. The cola package provides a general framework for subgroup classification by consensus partitioning.

  • Data analysis
  • Data Visualization
  • FAIR Data
  • + 3
  • R
  • C++
1
65
Logo for VAMBN
nfdi.software

VAMBN

Variational Autoencoders Modular Bayesian Networks (VAMBN) is a modelling approach to generate high quality, longitudinal & heterogeneous synthetic patient level data.

  • synthetic data
  • Python
  • R
4
59
Logo for Aviator
@helmholtz.software

Aviator

Aviator is a web service facilitating easy surveillance of scientific online tools. It currently checks more than 13,000 websites twice a day for availability and saves numerous features (response time, RAM usage security certificates, analytic tools / trackers, etc.) in a FAIR data repository.

  • Data Visualization
  • FAIR Data
  • FAIR Software
  • + 8
  • Python
  • HTML
  • JavaScript
  • + 5
6
55
@research-software-directory.org

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
  • Dockerfile
  • R
  • Shell
1
48
Logo for BIAS
@research-software-directory.org

BIAS

A toolbox for detecting structural bias in continuous optimization heuristics.

  • Bias
  • Metaheuristics
  • optimization
  • + 1
  • Dockerfile
  • Jupyter Notebook
  • Python
  • + 1
3
37
Logo for HilbertCurve
@helmholtz.software

HilbertCurve

Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve.

  • Data analysis
  • Data Science
  • Data Visualization
  • + 3
  • R
  • C++
  • CSS
  • + 1
1
37
@research-software-directory.org

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
  • R
  • Rez
4
37
Logo for UltraMassExplorer (UME)
@helmholtz.software

UltraMassExplorer (UME)

Natural organic matter is the most complex chemical mixture on our planet. UME is an open access, browser-based tool that allows efficient, interactive, transparent, and reproducible exploration and evaluation of ultrahigh resolution mass spectra of complex organic matter.

  • analytical chemistry
  • Carbon cycle
  • Data analysis
  • + 7
  • R
  • BibTeX
  • CSS
3
32
Logo for DoE2Vec
@research-software-directory.org

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
  • Makefile
  • Python
  • R
1
26