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.
644
datasets available to search
ShareScore release 0.9.0
Dataset results
644 results for “genre”
Fig. 14. – Murphyalna mughessensis n. gen., n in Un nouveau genre et une nouvelle espèce de Cigale aphone du Malawi (Rhynchota, Cicadidae, Cicadettinae)
Fig. 14. – Murphyalna mughessensis n. gen., n. sp. – 1, ♂ holotype en vue dorsale. – 2, Vue partielle rapprochée du profil gauche révélant l'extrême petitesse de la cymbale vestigiale (Cv). – 3, Bloc génital en vue rapprochée de gauche et très légèrement postérieure. – 4, Vue très grossie de l'opercule (Op) et de la capsule auditive (Ca) du côté droit.
THE USE OF SOCIO-POLITICAL TERMS IN THE NEWS GENRE OF MEDIA TEXT
Open the record for dataset details and reuse information.
MediaEval AcousticBrainz Genre AllMusic
<p><strong>This dataset contains AllMusic ground-truth genre annotations and is complementary to the rest of the AcousticBrainz Genre datasets distributed at <a href="https://zenodo.org/record/2553414">https://zenodo.org/record/2553414</a>.</strong></p> <p>The MediaEval AcousticBrainz Genre datasets are datasets of genre annotations and music features extracted from audio suited for evaluation of hierarchical multi-label genre classification systems.</p> <p>The datasets are used within the <a href="https://multimediaeval.github.io/2018-AcousticBrainz-Genre-Task/">MediaEval AcousticBrainz Genre Task</a>. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the <a href="https://acousticbrainz.org/">AcousticBrainz</a> database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems.</p> <p>We provide four datasets containing genre and subgenre annotations extracted from four different online metadata sources:</p> <ul> <li> <p><strong>AllMusic</strong> and <strong>Discogs</strong> are based on editorial metadata databases maintained by music experts and enthusiasts. These sources contain explicit genre/subgenre annotations of music releases (albums) following a predefined genre namespace and taxonomy. We propagated release-level annotations to recordings (tracks) in AcousticBrainz to build the datasets.</p> </li> <li> <p><strong>Lastfm</strong> and <strong>Tagtraum</strong> are based on collaborative music tagging platforms with large amounts of genre labels provided by their users for music recordings (tracks). We have automatically inferred a genre/subgenre taxonomy and annotations from these labels.</p> </li> </ul> <p>For details on format and contents, please refer to the <a href="https://multimediaeval.github.io/2018-AcousticBrainz-Genre-Task/data/">data webpage</a>.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use the MediaEval AcousticBrainz Genre dataset or part of it, please cite our <a href="http://mtg.upf.edu/node/3960">ISMIR 2019 overview paper</a>:</p> <pre><code>Bogdanov, D., Porter A., Schreiber H., Urbano J., & Oramas S. (2019). The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale. 20th International Society for Music Information Retrieval Conference (ISMIR 2019).</code></pre> <p><strong>Acknowledgements</strong></p> <p>This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 <a href="https://www.audiocommons.org/">AudioCommons</a>.</p>
Towards Better NLI for Spanish: A Multi-Genre Dataset and causal relationships
<p>This project includes the code, dataset and models for the paper: Towards Better NLI for Spanish: A Multi-Genre Dataset and causal<br>relationships.</p> <p>- The params used to extract de data and train the models can be found in the folder params</p> <p>- The dataset can be found in the folder data/base_dataset</p> <p>- The trained models and their metrics can be found in the folder model</p> <p>- The code can be found in the auto_nli folder</p> <p>For execution details read the README file.</p>
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.