The AutoCorrelation Integral Drill (ACID) Test Set
Authors/Creators
Description
This repository contains the scripts and StepUp workflows to regenerate the "AutoCorrelation Integral Drill" (ACID) test set. The ACID test set comprises a diverse collection of algorithmically generated time series designed to evaluate the performance of algorithms that compute the autocorrelation integral. The set contains in total 15360 test cases, and each case consists of one or more time series. The cases differ in the kernel characterizing the time correlations, the number of time series, and the length of the time series. For each combination of kernel, number of sequences and sequence length, 64 test cases are generated with different random seeds to allow for a systematic validation of uncertainty estimates. The total dataset, once generated, is about 80 GB in size.
In addition to the ACID test set, this repository also contains scripts and workflows to validate STACIE, a software package for the computation of the autocorrelation integral. The results of this analysis are discussed in the following paper:
Gözdenur Toraman, Dieter Fauconnier, and Toon Verstraelen "STable AutoCorrelation Integral Estimator (STACIE): Robust and accurate transport properties from molecular dynamics simulations" Journal of Chemical Information and Modeling 2025, 65 (19), 10445–10464, doi:10.1021/acs.jcim.5c01475, arXiv:2506.20438.
This dataset is distributed under a choice of license:
either the Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0)
or the GNU Lesser General Public License, version 3 or later (LGPL-v3+).
The SPDX License Expression for the documentation is CC-BY-SA-4.0 OR LGPL-3.0-or-later.
You should have received a copy of the CC BY-SA 4.0 and LGPL-v3+ licenses along with the data set. If not, see:
Files
acid-dataset.pdf
Files
(14.2 MB)
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Additional details
Related works
- Cites
- Software documentation: https://molmod.github.io/stacie (URL)
- Compiles
- Dataset: https://github.com/molmod/acid (URL)
- Is described by
- Journal article: 10.1021/acs.jcim.5c01475 (DOI)
- Preprint: arXiv:2506.20438 (arXiv)
- Requires
- Software: https://github.com/molmod/stacie (URL)