Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “event extraction”
Event Registry dataset with multiple extracted features (both sparse and dense)
<p>This is a republication of the Event Registry dataset originaly published by:</p> <p>Rupnik, Jan, Andrej Muhic, Gregor Leban, Primoz Skraba, Blaz Fortuna, et Marko Grobelnik. 2016. « News Across Languages - Cross-Lingual Document Similarity and Event Tracking ». <em>Journal of Artificial Intelligence Research</em> 55 (janvier): 283‑316. <a href="https://doi.org/10.1613/jair.4780">https://doi.org/10.1613/jair.4780</a>.</p> <p>And reorganised for document tracking by:</p> <p>Miranda, Sebastião, Artūrs Znotiņš, Shay B. Cohen, et Guntis Barzdins. 2018. « Multilingual Clustering of Streaming News ». In <em>2018 Conference on Empirical Methods in Natural Language Processing</em>, 4535‑44. Brussels, Belgium: Association for Computational Linguistics. <a href="https://www.aclweb.org/anthology/D18-1483/">https://www.aclweb.org/anthology/D18-1483/</a>.</p> <p>In this dataset, we provide multiple features extracted from the text itself. <strong>Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.</strong></p> <p>Features are extracted using:</p> <p>- A corpus of reference articles in multiple languages languages for TF-IDF weighting. (<em>features_news</em>) [1]</p> <p>- A corpus of tweets reporting news for TF-IDF weighting. (<em>features_tweets)</em> [1]</p> <p>- A S-BERT model [2] that uses <em>distiluse-base-multilingual-cased-v1 </em>(called <em>features_use</em>) [3]</p> <p>- A S-BERT model [2] that uses <em>paraphrase-multilingual-mpnet-base-v2 </em>(called <em>features_mpnet</em>) [4]</p> <p><strong>References:</strong></p> <p>[1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406</p> <p>[2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</em>, 3982‑92. Hong Kong, China: Association for Computational Linguistics. <a href="https://doi.org/10.18653/v1/D19-1410">https://doi.org/10.18653/v1/D19-1410</a>.</p> <p>[3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1</p> <p>[4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2</p>
Event Registry dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Event Registry dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630367</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). Event Registry dataset texts with OCR degradations and synthesised segmentation (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6631305</p>
Event Registry titles dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Event Registry titles only dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630447</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). Event Registry titles dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630828</p>
Text-fig. 1. Schematic maps showing location of the Prague Basin in the Barrandian area of the Czech Republic (a), the Koněprusy area (b), quarries in the Koněprusy area (c) and Jirásek's and Preisler's quarries (d). The exact location of the section described by Hladil et al. (1993) (1) and the new sections where the brachiopods were extracted (2) are figured. Abbreviations: UDI – upper dark interval, VČS-E – Velkolom Čertovy schody – východ quarry. in Rhynchonelliform Brachiopods And Trilobites Of The 'Upper Dark Interval' In The Koněprusy Area Devonian, Eifelian, Kačák Event; The Czech Republic
Text-fig. 1. Schematic maps showing location of the Prague Basin in the Barrandian area of the Czech Republic (a), the Koněprusy area (b), quarries in the Koněprusy area (c) and Jirásek's and Preisler's quarries (d). The exact location of the section described by Hladil et al. (1993) (1) and the new sections where the brachiopods were extracted (2) are figured. Abbreviations: UDI – upper dark interval, VČS-E – Velkolom Čertovy schody – východ quarry.
Data for "A Dataset for Multi-lingual Epidemiological Event Extraction"
<p>This is the data for the LREC 2020 paper "<a href="https://zenodo.org/record/3693647">A Dataset for Multi-lingual Epidemiological Event Extraction</a>". If you use this resource, please cite the paper:</p> <pre><code>@inproceedings{mutuvi2020dataset, title = "A Dataset for Multi-lingual Epidemiological Event Extraction", author = {Mutuvi, Stephen and Doucet, Antoine, and Lejeune, Gaël and Odeo, Moses}, booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'20)", year = "2020", location = "Marseille" }</code></pre> <p> </p> <p>This work has been supported by the European Union Horizon 2020 research and innovation programme under grants 825153 (Embeddia) and 770299 (NewsEye).</p>
Semantic Event Extraction Datasets
<p>Two datasets for Semantic Event Extraction that have been created using a distant labelling approach.</p> <p>They contain sentences from Wikipedia and events identified within them, their relations and attributes regarding the classes, properties and entities in the Wikidata and DBpedia knowledge graphs.</p>
Data set for "Token-Level Multilingual Epidemic Dataset for Event Extraction"
<p>This is the data for the TPDL 2021 paper "<a href="https://zenodo.org/record/5780020">Token-Level Multilingual Epidemic Dataset for Event Extraction</a>". If you use this resource, please cite the paper:</p> <pre><code>@inproceedings{mutuvi2021dataset, title = "Token-level Multilingual Epidemic Dataset for Event Extraction", author = {Mutuvi, Stephen and Boros, Emanuela and Doucet, Antoine, and Lejeune, Gaël and Jatowt, Adam and Odeo, Moses}, booktitle = "Proceedings of the 25th International Conference on Theory and Practice of Digital Libraries, September 13–17, 2021, TPDL 2021", year = "2021", location = "Online" }</code></pre> <p> </p> <p>This work has been supported by the European Union Horizon 2020 research and innovation programme under grants 825153 (Embeddia) and 770299 (NewsEye).</p>
Event Registry titles only dataset with multiple extracted features (both sparse and dense)
<p>This is the same content as:</p> <p>Guillaume Bernard. (2022). Event Registry dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630367</p> <p>But with titles of articles only.</p>
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
<p>The PHEE dataset contains over 5,000 finely annotated pharmacovigilance events from public medical case reports. Two types of events, the adverse events and the potential therapeutic events, are annotated. For each event, we annotate the event trigger and hierarchical arguments. The main arguments (coarse-grained spans) include subject, treatment and effect. Further fine-grained sub-arguments - <em>age</em>, <em>gender</em>, <em>race</em>, <em>number of patients</em> (labelled as population) and <em>preexisting conditions</em> (labelled as subject.disorder) for the subject argument and <em>drug </em>(and their combinations), <em>dosage</em>, <em>frequency</em>, <em>route</em>, <em>time-elapsed</em>, <em>duration</em>,<em> target disorder</em> (labelled as treatment.disorder) for the treatment argument - are then annotated upon main arguments.</p> <p>We provide two formats of data: visualisation-friendly brat-format data and structured json data for the convenience of use.</p> <p> </p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
Resulting videos from the article: A Novel Technique for the Extraction of Dynamic Events in Extreme Ultraviolet Solar Images
<p>Videos called 'MFM PCP DMD ...' correspond to Figure 5 in the mentioned article. They show comparison of MFM, PCP, and DMD algorithms. Upper images in each year correspond to the separated background matrix, the lower images correspond to matrix of dynamic component. The videos cover 1.7 hours of observations starting at 06:00:00 UTC on 2011 June 7, 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2.<br><br>The other videos correspond to Figure 7 in the article. They compare the results from PCP algorithm applied to the 30.4 and 17.1 nm AIA bandpasses. The videos starting at 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2, respectively, and cover 1.7 hours of observations.</p>
Data from: Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis
Objectives: Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. To ensure safe cancer treatment, research efforts are needed to comprehensively detect and understand irAEs. Materials and Methods: We manually extracted and standardized irAEs from FDA drug labels for six FDA-approved ICIs. We compared irAE profile similarities among ICIs and 1,507 FDA-approved non-ICI drugs. We investigated how irAEs have differential effects on human organs by classifying irAEs based on their targeted organ systems. Finally, we identified broad-spectrum (non-target specific) and narrow-spectrum (target-specific) irAEs. Results: A total of 893 irAEs were extracted. 31.4% irAEs were shared among ICIs as compared to the 8.0% between ICIs and non-ICI drugs. irAEs were resulted from both on- and off-target effects: irAE profiles were more similar for ICIs with same target than different targets, demonstrating the on-target effects; irAE profile similarity for ICIs with the same target is less than 50%, demonstrating unknown off-target effects. ICIs significantly target many organ systems, including endocrine system (3.4-fold enrichment), metabolism (3.7-fold enrichment), immune system (3.6-fold enrichment) and autoimmune system (4.8-fold enrichment). We identified 21 broad-spectrum irAEs shared among all ICIs, 20 irAEs specific for PD-L1/PD-1 inhibition, and 28 irAEs specific for CTLA-4 inhibition. Discussion and Conclusion: Our study presents the first effort toward building a standardized database of irAEs. The extracted irAEs can serve as a goldstandard for automatic irAE extractions from other data resources and set a foundation to understand biological mechanisms of irAEs.
Data from: Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis
Open the record for dataset details and reuse information.
Widespread specific intron-retention events in nuclear RNA complexes identified by sedimentation analysis of pluripotent cellular extracts
GEO Series GSE284687. Homo sapiens. 63 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.