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725
datasets available to search
ShareScore release 0.9.0
Dataset results
725 results for “recommendation”
Data from: Whole-genome sequencing approaches for conservation biology: advantages, limitations, and practical recommendations
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Data from: Rise of the machines – recommendations for ecologists when using next generation sequencing for microsatellite development.
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Data from: Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations
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Data from: A review of riverine ecosystem service quantification: research gaps and recommendations
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Data from: Can plan recommendations improve the coverage decisions of vulnerable populations in health insurance marketplaces?
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Data from: Making job postings more equitable: evidence-based recommendations from an analysis of data professionals job postings between 2013-2018
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Coping with impostor feelings: evidence-based recommendations from a mixed methods study
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The dataset used in the article "Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes"
<p>This is the dataset used in the study "Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes". The dataset is a subset of the LFM-1b LastFM dataset (http://www.cp.jku.at/datasets/LFM-1b/), which contains country-specific music listening events. </p> <p> </p>
Obtaining a Set of Recommendations for Evolving Executable Languages towards Systems-of-Systems Architecture Design
<p>Obtaining a Set of Recommendations for Evolving Executable Languages towards Systems-of-Systems Architecture Design</p>
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
<p>The details can be referred to: <a href="https://kuairand.com/" target="_blank" rel="noopener"><strong>https://kuairand.com/</strong></a></p> <p>If it helps you, please kindly cite:</p> <blockquote> <pre><code>@inproceedings{gao2022kuairand, title = {KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos}, author = {Gao, Chongming and Li, Shijun and Zhang, Yuan and Chen, Jiawei and Li, Biao and Lei, Wenqiang and Jiang, Peng and He, Xiangnan}, url = {https://doi.org/10.1145/3511808.3557624}, doi = {10.1145/3511808.3557624}, booktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management}, series = {CIKM '22}, location = {Atlanta, GA, USA}, numpages = {5}, year = {2022}, pages = {3953–3957} }</code></pre> </blockquote>
Recommended Centrifuge Method: Specific Grain Size Separation in the <63 μm Fraction of Marine Sediments
<p>Isolated grain size fractions using centrifuge of marine sediment samples for submitted manuscript: 'Recommended Centrifuge Method: Specific Grain Size Separation in the <63 μm Fraction of Marine Sediments'</p>
Figure 6 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 6 Simulating inflation, between 2024 and 2040 – Basic number: 2% inflation per year.
Figure 8 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 8 Simulation of inflation, Model B.
Figure 7 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 7 Simulation of inflation, Model A.
Figure 9 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 9 Simulation of inflation, Model C.
Figure 1 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 1 DiSSCo timeline.
Figure 11 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 11 DiSSCo national contribution models.
Figure 2 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 2 DiSSCo general membership fee calculation model.
Figure 2 from: Runnel V, Wetzel F, Groom Q, Koch W, Pe'er I, Valland N, Panteri E, Kõljalg U (2016) Summary report and strategy recommendations for EU citizen science gateway for biodiversity data. Research Ideas and Outcomes 2: e11563. https://doi.org/10.3897/rio.2.e11563
Figure 2 - Break-out group discussion at the 2nd Stakeholder Roundtable (Credit: Florian Wetzel)
Figure 5 from: Runnel V, Wetzel F, Groom Q, Koch W, Pe'er I, Valland N, Panteri E, Kõljalg U (2016) Summary report and strategy recommendations for EU citizen science gateway for biodiversity data. Research Ideas and Outcomes 2: e11563. https://doi.org/10.3897/rio.2.e11563
Figure 5 - Biodiversity observation - from observation to data usage
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.