{"id":34658606,"url":"https://github.com/molmod/acid","last_synced_at":"2026-04-01T21:02:13.358Z","repository":{"id":301145585,"uuid":"1008310064","full_name":"molmod/acid","owner":"molmod","description":"The AutoCorrelation Integral Drill (ACID) -- 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Contributors \u003chttps://doi.org/10.5281/zenodo.15722902\u003e\nSPDX-License-Identifier: CC-BY-SA-4.0\n--\u003e\n\n:rotating_light:\nThis repository is being refactored to prepare for the next major release, ACID 2.\nThe information below describes how ACID 2 will be organized after the refactoring has been completed.\n\nYou can view the latest version of the ACID 1 dataset and validation results at the following URLs:\n\n- https://github.com/molmod/acid/tree/acid1\n- https://zenodo.org/records/18044643\n\n# The AutoCorrelation Integral Drill (ACID) 2 -- Dataset\n\nThis repository contains the scripts and\n[StepUp workflow](https://reproducible-reporting.github.io/stepup-core/stable/)\nto regenerate the \"AutoCorrelation Integral Drill\" (ACID) dataset.\nThe ACID dataset set comprises a diverse collection of synthetic time series\ndesigned to evaluate the performance of algorithms that compute the autocorrelation integral.\nThe set contains in total 15360 test cases, and each case consists of one or more time series.\nThe cases differ in the kernel characterizing the time correlations, the number of time series,\nand the length of the time series.\nFor each combination of kernel, number of sequences and sequence length,\n64 test cases are generated with different random seeds\nto allow for a systematic validation of uncertainty estimates.\nThe total dataset, once generated, is about 43 GB in size.\n\nIn ACID 1 releases, the validation of STACIE with ACID was included in this repository.\nAs of ACID 2, the validation results are released separately in a different repository:\n\u003chttps://github.com/molmod/acid-test\u003e.\n\nA description, the full data and and an archived copy of this repository can be found on Zenodo:\n[10.5281/zenodo.15722902](https://doi.org/10.5281/zenodo.15722902).\n\n## License\n\n## License\n\nThe copyright disclaimer and license conditions are specified at the beginning of each file in the repository,\nor, when the header cannot be edited, in the `RESUSE.toml` file.\n\nIn summary, the majority of the files in this repository are licensed under\nthe Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).\nThe main exceptions are the Python files, which have a choice of license between\nCC BY-SA 4.0 and the GNU Lesser General Public License v3.0 or later (LGPL-3.0-or-later).\n\nLicense deeds and legal code for all licenses used in this repository are available in the `LICENSES/` directory.\nThey can also be consulted online at the following URLs:\n\n- \u003chttps://creativecommons.org/licenses/by-sa/4.0/\u003e\n- \u003chttps://www.gnu.org/licenses/lgpl-3.0.html\u003e\n\n## Citation\n\nIf you use this dataset in your research, please cite the following publication:\n\n\u003e Gözdenur Toraman, Dieter Fauconnier, and Toon Verstraelen\n\u003e \"STable AutoCorrelation Integral Estimator (STACIE):\n\u003e Robust and accurate transport properties from molecular dynamics simulations\"\n\u003e *Journal of Chemical Information and Modeling* 2025, 65 (19), 10445–10464,\n\u003e [doi:10.1021/acs.jcim.5c01475](https://doi.org/10.1021/acs.jcim.5c01475),\n\u003e [arXiv:2506.20438](https://arxiv.org/abs/2506.20438).\n\u003e\n\u003e ```bibtex\n\u003e @article{Toraman2025,\n\u003e  author = {G\\\"{o}zdenur Toraman and Dieter Fauconnier and Toon Verstraelen},\n\u003e  title = {STable AutoCorrelation Integral Estimator (STACIE): Robust and accurate transport properties from molecular dynamics simulations},\n\u003e  journal = {Journal of Chemical Information and Modeling},\n\u003e  volume = {65},\n\u003e  number = {19},\n\u003e  pages = {10445--10464},\n\u003e  year = {2025},\n\u003e  month = {sep},\n\u003e  url = {https://doi.org/10.1021/acs.jcim.5c01475},\n\u003e  doi = {10.1021/acs.jcim.5c01475},\n\u003e }\n\u003e ```\n\n## Overview\n\nThis repository consits of two parts:\n\n1. [`1_dataset/`](1_dataset/):\n   A workflow to generate the ACID 2 dataset.\n1. [`2_zenodo/`](2_zenodo/):\n   A workflow to package and upload the generated data to Zenodo.\n\n## How to Run the Workflows\n\nThe workflows in this repository can be executed on a compute cluster\nthat supports [SLURM](https://slurm.schedmd.com/) job scheduling,\nor on a local machine with sufficient resources.\n\nA Python virtual environment is defined in the `requirements.in` file.\nTo (re)create the virtual environment for a workflow,\nrun or submit the script `setup-venv-pip.sh`.\nIf you want this script to use a specific Python version,\nset the `PYTHON3` environment variable before running it.\nFor example:\n\n```bash\nexport PYTHON3=/usr/bin/python3.13  # optional\nsbatch setup-venv-pip.sh\n```\n\nAfter the virtual environment has been created,\nyou can run or submit the script `job.sh` in `1_dataset/` or `2_zenodo/`.\nIf you want to work interactively within the virtual environment,\nyou can source the `.loadvenv` script.\n\nNote that the workflows and scripts in this repository require Python 3.11 or higher.\nThey have only been tested on an `x86_64` Linux system (so far).\nAll results on Zenodo were generated using the following module\non the Tier2 VSC compute cluster donphan\n\n```bash\nmodule load Python/3.13.5-GCCcore-14.3.0\n```\n\nWhen the `setup-venv-pip.sh` script detects the presence of the `$VSC_HOME`\nenvironment variable, it will automatically load this Python module\nand include it in the generated `.loadvenv` script.\n\n## How to Work With This Git Repository\n\nPlease, follow these guidelines to make clean commits to this repository:\n\n1. Install [pre-commit](https://pre-commit.com/) on your system.\n   (It is included in the `requirements.in` file,\n   so it will be installed in the virtual environment when you run `setup-venv-pip.sh`.)\n1. Install the pre-commit hook by running `pre-commit install` in the root directory of this repository.\n1. Update the file `CHANGELOG.md` to describe changes.\n1. Use `git commit` as you normally would.\n\nIf you are working in an environment with limited permissions,\nyou can install pre-commit locally by running the following commands:\n\n```bash\nwget https://github.com/pre-commit/pre-commit/releases/download/v4.5.1/pre-commit-4.5.1.pyz\npython pre-commit-4.5.1.pyz install\n```\n\n## How to Make a New Release\n\nAfter having updated actual contents of the dataset, the following steps are needed\nto make a new release on GitHub and Zenodo:\n\n- Update `CHANGELOG.md` with a new version section.\n  Double check the changes since the last release.\n\n- Update the version number in `2_zenodo/zenodo.yaml`.\n\n- Upload a draft release to Zenodo by running\n\n  ```bash\n  (cd 2_zenodo/; sbatch job.sh)\n  ```\n\n- Visit the dataset page on Zenodo and click on \"New version\".\n  The files and metadata will already be present due to the previous step.\n  Request the DOI for the new draft and add this information to `CHANGELOG.md`.\n\n- Commit all changes to Git and run `git tag` with the new version number.\n\n- Recompile the PDF file with the dataset description to include the Git hash in the PDF frontmatter:\n\n  ```bash\n  (cd 1_dataset/; sbatch job.sh)\n  ```\n\n- Sync your local data one last time with Zenodo:\n\n  ```bash\n  (cd 2_zenodo/; sbatch job.sh)\n  ```\n\n- Log in to \u003chttps://zenodo.org/\u003e, go to your draft release,\n  check that all files have been uploaded correctly, and publish the release.\n\n- Push your commits and tags to GitHub.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmolmod%2Facid","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmolmod%2Facid","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmolmod%2Facid/lists"}