{"url":"/dataset/eigenworms","name":"EigenWorms","full_name":null,"description_markdown":"Caenorhabditis elegans is a roundworm commonly used as a model organism in the study of genetics. The movement of these worms is known to be a useful indicator for understanding behavioural genetics. Brown {\\em et al.}[1] describe a system for recording the motion of worms on an agar plate and measuring a range of human-defined features[2]. It has been shown that the space of shapes Caenorhabditis elegans adopts on an agar plate can be represented by combinations of six base shapes, or eigenworms. Once the worm outline is extracted, each frame of worm motion can be captured by six scalars representing the amplitudes along each dimension when the shape is projected onto the six eigenworms. Using data collected for the work described in[1], we address the problem of classifying individual worms as wild-type or mutant based on the time series. The data were extracted from the C. elegans behavioural database [3]. We have 259 cases, which we split 131 train and 128 test. We have truncated each series to the shortest usable. Each series has 17984 observations. Each worm is classified as either wild-type (the N2 reference strain) or one of four mutant types: goa-1; unc-1; unc-38 and unc-63. [1] A. Brown, E. Yemini, L. Grundy, T. Jucikas, and W. Schafer, A dictionary of behavioral motifs reveals clusters of genes affecting caenorhabditis elegans locomotion, Proceedings of the National Academy of Sciences of the United States of America (PNAS), vol. 10, no. 2, pp. 791 796, 2013. [2] E. Yemini, T. Jucikas, L. Grundy, A. Brown, and W. Schafer,  A database of caenorhabditis elegans behavioral phenotypes, Nature Methods, vol. 10, pp. 877 879, 2013. [3] C. elegans behavioural database","description_withheld":null,"homepage":"http://www.timeseriesclassification.com/description.php?Dataset=EigenWorms","introduced_date":"2018-10-31","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-uea-multivariate-time-series","title":"The UEA multivariate time series classification archive, 2018","first_author":"Anthony Bagnall","url":null},"license":null,"modalities":[],"tasks":[{"name":"Time Series Analysis","url":"/task/time-series","datasets_with_task":"/datasets/task/time-series"},{"name":"Time Series Classification","url":"/task/time-series-classification","datasets_with_task":"/datasets/task/time-series-classification"}],"languages":[],"variants":["EigenWorms"],"data_loaders":[{"repo":"https://github.com/tk-rusch/lem","url":"https://github.com/tk-rusch/lem","frameworks":["pytorch"]}],"num_papers_in_archive":21,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-classification-on-eigenworms","task":"Time Series Classification","dataset_variant":"EigenWorms","rows":8,"metrics":["% Test Accuracy"],"first_row_in_archive_order":{"model":"LEM","paper":"/paper/long-expressive-memory-for-sequence-modeling-1","metrics":{"% Test Accuracy":"92.3"},"code_links":[{"title":"tk-rusch/lem","url":"https://github.com/tk-rusch/lem"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/parallelizing-non-linear-sequential-models","title":"Parallelizing non-linear sequential models over the sequence length","date":"2023-09-21","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tsem-temporally-weighted-spatiotemporal","title":"TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series","date":"2022-05-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/long-expressive-memory-for-sequence-modeling-1","title":"Long Expressive Memory for Sequence Modeling","date":"2021-10-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":1,"samples_unverified":9,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unicornn-a-recurrent-model-for-learning-very","title":"UnICORNN: A recurrent model for learning very long time dependencies","date":"2021-03-09","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neural-cdes-for-long-time-series-via-the-log","title":"Neural Rough Differential Equations for Long Time Series","date":"2020-09-17","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":18,"samples_ran":8,"samples_unverified":10,"pointer_only_for_licence":8,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}