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94 results for “model collection”
Figs. 1–8 in "Collection Bias" and the Importance of Natural History Collections in Species Habitat Modeling: A Case Study UsingThoracophorus costalisErichson (Coleoptera: Staphylinidae: Osoriinae), with a Critique of GBIF.org
Figs. 1–8. Exemplar distribution models. Red circles = specimen localities (county-level). 1) Reference model created using all specimen records. Insert: Thoracophorus costalis; 2) University of Wisconsin Insect Research Center, 4.4% reference area; 3) Mississippi State University, 11.9% reference area; 4) Florida State Collection of Arthropods, 28.9% reference area; 5) Canadian National Collection of Insects, 75.5% reference area, greatest of any single collection; 6) Downie and Arnett (1996), 8.5% reference area; 7) Bugguide.net, 11.9% reference area; 8) GBIF.org, 14.1% reference area.
Fig. 11 in "Collection Bias" and the Importance of Natural History Collections in Species Habitat Modeling: A Case Study UsingThoracophorus costalisErichson (Coleoptera: Staphylinidae: Osoriinae), with a Critique of GBIF.org
Fig. 11. Model area versus number of localities. Red circles = average and 95% confidence intervals of model areas for each set of random localities. Green circles = individual collections. Blue triangles = alternative distribution data.
Fig. 9 in "Collection Bias" and the Importance of Natural History Collections in Species Habitat Modeling: A Case Study UsingThoracophorus costalisErichson (Coleoptera: Staphylinidae: Osoriinae), with a Critique of GBIF.org
Fig. 9. Percentage of the reference area for each model created from individual collection data (see text for collections designated by codens). Bars for alternative data sources and total have tile fill. ** = collections with individually less than 1% of the reference model area.
Including population and environmental dynamic heterogeneities in continuum models of collective behaviour with applications to locust foraging and group structure Data and Code
<p>This dataset includes all data used for the creation of "Including dynamic population and environmental heterogeneity in continuum models of collective behaviour with applications to locust foraging and group structure" as well as a snapshot of the code used.<br><br>Each zip should be unzippable and the code should operate with only the contents of the zip file.</p>
Supplementary material for "A collection of Constraint Programming models for the three-dimensional stable matching problem with cyclic preferences"
<p>No external dependency is needed to compile the code, but MiniZinc is required to run it. Running the code without any argument will give the instructions.</p>
Data Collection on the Model Schools Pediatric Health Initiative at 5 SBHC Sites: COVID-19 Questionnaire
ClinicalTrials.gov study NCT04534595. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Data from: Can opportunistically-collected Citizen Science data fill a data gap for habitat suitability models of less common species?
Open the record for dataset details and reuse information.
Data from: Can collective memories shape fish distributions? A test, linking space-time occurrence models and population demographics
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A Korean raw text collection for creating a language model
<p><strong>A very large Korean raw text collection for creating a language model </strong></p> <p> </p> <p>We collected a very large monolingual dataset for Korean, which contains <strong>over 9.6M sentences and 130.6M eojeols</strong>, to create a language model: Korean Wikipedida (https://dumps.wikimedia.org/kowiki/20201101/, 5.3M sentences and 71.8M eojeols, respectively), the Sejong morphologically analyzed corpus (3.0M and 40.0M), and articles from <em>The Hankyoreh</em> daily newspaper during 2016 (1.2M and 18.6M). </p> <p> </p> <p>We preprocessed raw text into morpheme-segmented text using the POS tagging system (<a href="https://www.aclweb.org/anthology/W19-4022/">park-tyers:2019:LAW</a>). We also attached the POS label to the morpheme-segmented lexicon, and explicitly include a + symbol for consecutive morphemes. </p> <blockquote> <p>시인/NNG 윤동주/NNP +,/SP 이준익/NNP 감독/NNG 영화/NNG +로/JKB 부활/NNG</p> </blockquote> <p> </p> <p>See https://github.com/jungyeul/sjmorph for the POS tagging system described in <a href="https://www.aclweb.org/anthology/W19-4022/">park-tyers:2019:LAW</a>. </p> <p> </p> <p> </p>
Figure 5 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 5 Option C: GDP/cap and GERD. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to Table (left).
Figure 3 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 3 Option A: GDP and GERD testing. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to table (left).
Figure 4 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 4 Option B with GDP and GERD/cap. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to Table (left).
Figure 10 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 10 Visualisation of annual membership fees distribution according to the two proposals selected.
VSDFullBodyBoneReconstruction: Segmentations and surface models of bones of the entire lower body created from cadaver CT scans from the VSDFullBody collection
<p>The segmentations and models of the bones of the lower extremities were created from anonymized postmortem CT scans of the whole body originally published by Michael Kistler in the Swiss Institute for Computer Assisted Surgery Medical Image Repository (<a href="https://www.smir.ch/">smir.ch</a>) as open access Virtual Skeleton Database (VSD). A mirror of the VSD is available at Zenodo: <a href="https://doi.org/10.5281/zenodo.8270364">10.5281/zenodo.8270364</a>. </p><p><strong>However</strong>, this is a stand-alone upload and the full VSD is not required to use this upload. Further information can be found in the following publication:</p><p>Fischer, M. C. M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. <i>Sci. Data</i> <strong>10</strong>, 763; <a href="https://doi.org/10.1038/s41597-023-02669-z">10.1038/s41597-023-02669-z</a> (2023).</p><p>Post-processed 3D surface models stored as MATLAB MAT files were released as Git repository at <a href="https://github.com/MCM-Fischer/VSDFullBodyBoneModels">https://github.com/MCM-Fischer/VSDFullBodyBoneModels</a> and versioned via Zenodo: <a href="https://doi.org/10.5281/zenodo.8316730">10.5281/zenodo.8316730</a>. The use of the MAT files is explained by examples for MATLAB and Python in the Git repository.</p><p><strong>Usage</strong><br>The CT volume data, segmentations, reconstructions and raw PLY mesh files of each subject are linked by a project file (MRML scene file) that can be opened with the open-source medical imaging software 3D Slicer (<a href="https://www.slicer.org/">slicer.org</a>).</p>
Vascular Positioning System G4 Algorithm ECG Data Collection for Model Training Study
ClinicalTrials.gov study NCT05702515. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of ECG Transmission and AI Models Using Apple Watch ECGs and Symptoms Data Collected Using a Mayo iPhone App
ClinicalTrials.gov study NCT05324566. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: A stochastic vision based model inspired by the collective behaviour of zebrafish in heterogeneous environments
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Code and data of MS "Null models for animal social network analysis and data collected via focal sampling: pre-network or node network permutation?"
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A reference collection of cell line and xenograft models of proneural, classical and mesenchymal GBM [cell line]
GEO Series GSE118790. Homo sapiens. 12 samples. Type: Expression profiling by array.
A reference collection of cell line and xenograft models of proneural, classical and mesenchymal GBM [tumor tissue]
GEO Series GSE118791. Homo sapiens. 12 samples. Type: Expression profiling by array.
ScienceDex guides
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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.