Sign inGet started
Dataset · Hugging Face

petrrysavy/krebs

The Krebs cycle dataset Motivation This dataset contains simulated time series that mimic Kreb's cycle. The intent of the datasets is for causal discovery from multivariate time series data, provide ground truth causal relationships as well as allow testing on multiple scenarios, including many short time series, few long time series, as well as relative data instead of absolute values. The dataset was created at the Czech Technical University in Prague as part of… See the full description on the dataset page: https://huggingface.co/datasets/petrrysavy/krebs.

Author / organizationpetrrysavy
License (as declared)cc-by-4.0
Downloads1K (as reported by Hugging Face)
Likes2
Created3 June 2024
Last updated at source8 September 2025
Krebs cycletime-seriescausal learning
Embed this entry
<iframe src="https://silytics.si/embed/item/c9c23b1f-3d96-4096-85ff-83a726dcdd76" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Popularity, last 3 days

1,049 now (+203 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.

Recent changes

  • 17 Oct 2026 Description: The Krebs cycle dataset Motivation This dataset contains simulated time series that mimic Kreb's cycle. The intent of the datasets is for causal discovery from multivariate time series data, provide ground truth causal relationships as well → The Krebs cycle dataset Motivation This dataset contains simulated time series that mimic Kreb's cycle. The intent of the datasets is for causal discovery from multivariate time series data, provide ground truth causal relationships as well

Changes detected by Silytics between refreshes of the source.

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.