scida
scida: scalable analysis for scientific big data - Published in JOSS (2024)
Science Score: 95.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 10 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org -
✓Committers with academic emails
2 of 5 committers (40.0%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords from Contributors
Scientific Fields
Repository
scida is an out-of-the-box analysis tool for large scientific datasets. It primarily supports the astrophysics community, focusing on cosmological and galaxy formation simulations using particles or unstructured meshes, as well as large observational datasets. This tool uses dask, allowing analysis to scale.
Basic Info
- Host: GitHub
- Owner: cbyrohl
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://scida.io
- Size: 35.5 MB
Statistics
- Stars: 38
- Watchers: 4
- Forks: 7
- Open Issues: 14
- Releases: 12
Metadata Files
README.md
scida
scida is an out-of-the-box analysis tool for large scientific datasets. It primarily supports the astrophysics community, focusing on cosmological and galaxy formation simulations using particles or unstructured meshes, as well as large observational datasets. This tool uses dask, allowing analysis to scale up from your personal computer to HPC resources and the cloud.
Features
- Unified, high-level interface to load and analyze large datasets from a variety of sources.
- Parallel, task-based data processing with dask arrays.
- Physical unit support via pint.
- Easily extensible architecture.
Requirements
- Python 3.9, 3.10, 3.11, 3.12
Documentation
The documentation can be found here.
Install
pip install scida
First Steps
After installing scida, follow the tutorial.
Citation
If you use scida in your research, please cite the following paper:
text
`Byrohl et al., (2024). scida: scalable analysis for scientific big data. Journal of Open Source Software, 9(94), 6064, https://doi.org/10.21105/joss.06064`
with the following bibtex entry:
text
@article{scida,
title = {scida: scalable analysis for scientific big data},
author = {Chris Byrohl and Dylan Nelson},
doi = {10.21105/joss.06064},
url = {https://doi.org/10.21105/joss.06064}, year = {2024},
publisher = {The Open Journal}, volume = {9}, number = {94},
pages = {6064},
journal = {Journal of Open Source Software}
}
Issues
If you encounter any problems, please file an issue along with a detailed description.
License
Distributed under the terms of the MIT license, scida is free and open source software.
Owner
- Login: cbyrohl
- Kind: user
- Location: Heidelberg
- Company: Institute for Theoretical Astrophysics
- Website: https://cbyrohl.de
- Repositories: 1
- Profile: https://github.com/cbyrohl
JOSS Publication
scida: scalable analysis for scientific big data
Authors
Heidelberg University, Institute for Theoretical Astronomy, Albert-Ueberle-Str. 2, 69120 Heideberg, Germany
Heidelberg University, Institute for Theoretical Astronomy, Albert-Ueberle-Str. 2, 69120 Heideberg, Germany
Editor
Monica Bobra
Tags
simulations i/o point cloudsGitHub Events
Total
- Create event: 17
- Release event: 2
- Issues event: 18
- Watch event: 9
- Delete event: 3
- Issue comment event: 36
- Push event: 33
- Pull request event: 28
- Fork event: 3
Last Year
- Create event: 17
- Release event: 2
- Issues event: 18
- Watch event: 9
- Delete event: 3
- Issue comment event: 36
- Push event: 33
- Pull request event: 28
- Fork event: 3
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Chris Byrohl | 9****l | 689 |
| Chris Byrohl | c****l@w****E | 24 |
| Dylan Nelson | d****n@m****e | 7 |
| dependabot[bot] | 4****] | 3 |
| arkordt | 2****t | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 45
- Total pull requests: 102
- Average time to close issues: 28 days
- Average time to close pull requests: 10 days
- Total issue authors: 8
- Total pull request authors: 4
- Average comments per issue: 1.44
- Average comments per pull request: 0.21
- Merged pull requests: 93
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 10
- Pull requests: 32
- Average time to close issues: 10 days
- Average time to close pull requests: 5 days
- Issue authors: 3
- Pull request authors: 2
- Average comments per issue: 1.8
- Average comments per pull request: 0.06
- Merged pull requests: 30
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- dnelson86 (21)
- kyleaoman (8)
- EGaraldi (7)
- cbyrohl (4)
- Saqib53GB (2)
- evertrol (1)
- jrspreng (1)
- Eshna0106 (1)
Pull Request Authors
- cbyrohl (98)
- arkordt (2)
- dnelson86 (1)
- sparxastronomy (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 41 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 11
- Total maintainers: 1
pypi.org: scida
Convenience wrapper around large scientific datasets to process with dask.
- Documentation: https://scida.readthedocs.io/
- License: mit
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Latest release: 0.3.5
published about 1 year ago