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1,655 results for “Subset”
Subset Data 3: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 5401 to 8100)
<p><em><strong>Subset Data 3: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 5401 to 8100)</strong></em></p> <ul> <li>2700 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 38MB.</li> <li>An excel file: "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "<a href="../records/8107354/files/how_to_replace_point_coordinates.mp4?download=1">how_to_replace_point_coordinates.mp4</a>". It shows how you can replace point coordinates here to the mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/stl.part01.rar?download=1">stl.part01.rar</a> to <a href="../records/8108354/files/stl.part09.rar?download=1">stl.part09.rar</a>).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
Subset Data 7: 607 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 16201 to 16807)
<p><em><strong>Subset Data 7: 607 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 16201 to 16807)</strong></em></p> <ul> <li>607 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 38MB.</li> <li>An excel file: "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "<a href="../records/8107354/files/how_to_replace_point_coordinates.mp4?download=1">how_to_replace_point_coordinates.mp4</a>". It shows how you can replace point coordinates here to the mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/stl.part01.rar?download=1">stl.part01.rar</a> to <a href="../records/8108354/files/stl.part09.rar?download=1">stl.part09.rar</a>).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
11 April 2019 MRMS Data Subset for Harkema et al. (2024)
<p>Subset of Multi-Radar Multi-Sensor (MRMS) data: 3D Reflectivity, Composite Reflectivity, and Isothermal Reflectivity. More specifically, the subset data is between 00 and 16 UTC 11 April 2019. Note: Some time(s) and file(s) maybe be missing between the subset period.</p>
A subset of the EMARS dataset in MY24 and MY26 converted from the sigma-p hybrid coordinate to the pressure coordinate and a list of local dust storms detected during the MYs in western Arcadia Planitia
<p>This dataset includes a subset of EMARS' background mean data (Greybush et al., 2019) converted from the sigma-p hybrid coordinate to the pressure coordinate. Only MY24 and MY26 were used to generate the figures shown in Ogohara (submitted to JGR Planets). <br>Updates from the original EMARS are:</p> <ul> <li>The vertical coordinate has been converted from the sigma-p hybrid coordinate to the pressure coordinate.</li> <li>The variables expressing the Earth date (e.g., year, month, day, etc.) have been combined into one variable, earth_date.</li> <li>A new variable, emars_date, has been created from emars_sol and mars_hour.</li> </ul> <p>In addition, this dataset provides two lists of local dust storms events during MY24 and MY26 which were detected in western Arcadia Planitia using a deep learning-based method proposed by Ogohara and Gichu (2022). The lists are:</p> <ul> <li>[Data Set S1] List of global image swath files examined. Only file names of MGS/MOC red band images are listed. The list consists of 5 columns indicating image ID, observation date, orbit number, solar longitude, and filter name (RED).</li> <li>[Data Set S2] List of global image swath files containing identified dust storms, as well as some attributes of the detected dust storms. Only file names of red band images are listed. The list consists of 7 columns indicating image ID, observation date, orbit number, solar longitude, center longitude and latitude, and area (km2.)</li> </ul>
Oregon Wolfe Barley (Hordeum vulgare) Informative & Spectacular Subset (ISS) vegetative stage growth data
<p>Oregon Wolfe Barley Informative & Spectacular Subset (ISS) was raised at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University (West Lafayette, Indiana, USA) for 42 days. There were 18 genotypes, with two replicates for each genotype (total plants: 36). AAPF is a controlled environment high-throughput phenotyping facility with automated imaging and irrigation systems. A virtual tour of AAPF can be found at <a href="https://ag.purdue.edu/aapf/virtual-tour.html">https://ag.purdue.edu/aapf/virtual-tour.html</a>.</p> <p>Seeds were sown in a 6 L pot with 2.8 L of Profile Porous Ceramic Greens Grade and Berger BM6 each with 10g of Osmocote. Five hundred ml of Turface was laid on top of each pot to avoid effect of algae for RGB data derivation. The growth temperature in the chamber was 72/68 degrees Fahrenheit day/night. Relative humidity was set at 60%. Lighting was 16 h day/8 h night.</p> <p>Plants were imaged with RGB camera from one top and 12 side views three times a week, ranging between 10 days from planting (equivalent to sowing, Dfp) to 42 Dfp. Ground reference data of plant height and tiller count were measured twice a week. </p> <p> </p> <p>RGB imaging data were stored in “OWB_RGB.xlsx”. Datasheet “Information” describes the variables in datasheets for top view, side average view and every side view.</p> <p> </p> <p>Ground reference data for plant height and tiller count were stored in “OWB_ground_reference.xlsx”. Datasheet “Information” describes the variables in datasheet “Data”.</p>
A Rescaled Subset of the Alternative Data Release 1 of the TIFR GMRT Sky Survey
<p>The catalogue in FITS format from this paper: http://adsabs.harvard.edu/abs/2017arXiv170306635H</p> <p>This Rescaled Subset of the Alternative Data Release 1 to the Tata Institute of Fundamental Physics Giant Metrewave Radio Telescope Sky Survey (TGSS-RSADR1) modifies the initial data release of TGSS-ADR1 (Intema et al. 2017) to bring that catalogue to the same flux scale as the extragalactic catalogue from the GaLactic and Extragalactic All-sky Murchison Widefield Array survey (GLEAM: Wayth et al. 2015; Hurley-Walker et al. 2017). In this paper we motivate the derivation of correct and complementary flux density scales, introduce a methodology for correction based on radial basis functions, apply it to TGSS-ADR1, and create a modified catalogue, TGSS-RSADR1. This catalogue comprises 383,589 TGSS-ADR1 sources with updated flux density and flux density uncertainty values, and covers $\mathrm{Declination}\leq+30^\circ$, $|b|\geq10^\circ$, a sky area of 18,800 deg$^2$.</p>
Various Gaia DR2 subsets
<p>This dataset contains various subsets of DR2 data;</p> <p>- within 20 pc<br> - within 25 pc<br> - within 100 pc<br> - within 125 pc<br> - within 200 pc<br> - between 200 and 300 pc<br> - "clean" version of 100 pc (several astrometric quality flag filters applied for cleaner color-mag diagrams)<br> - "clean" version of 200 pc<br> - All white dwarfs within 100 pc<br> - All white dwarfs within 300 pc<br> - All white dwarfs in the Limoges et al. 2015 sample<br> - Rough cut around NGC 6774<br> - Rough cut around M67</p> <p>The file names should be self-explanatory</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: PIDOP subset reanalysis
<p>This is a subset of the full PIDOP dataset. The derived subset contains cross-sectional survey results from the PIDOP questionnaire survey that were collected in 9 European countries (incl. Turkey) during a period of 16-26 year old in 2011. The data set includes 9060 individual cases. The questionnaire used in the survey is published in Barrett, M. & Zani, B. (Eds.) (2015). <em>Political and civic engagement: Multidisciplinary perspectives.</em> Hove: Routledge (p.519-534).</p>
Smart Insurance datasets subset of the AEGIS project
<p>The example dataset was produced within the Smart Insurance demonstrator of the AEGIS project. It contains a sample of the SYNTHETIC data that have been created for the demonstrator purposes. </p>
GeneWiki subset created with WDSub at Biohackathon 2022
<p>Subset of Wikidata obtained using this Shape Expression: https://github.com/kg-subsetting/datasets-biohackathon2022/blob/main/GeneWiki/GeneWiki.shex</p> <p>And the wdsub tool version 0.0.28: https://github.com/weso/wdsub</p> <p>The input dump is: wikidata-20180115-all</p> <p>And the dumpformat is JSON</p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Generated Wikidata Subset for Taxons based on dump: 20201102-all
<p>Source file: GeneTaxon_wikidata-20201102-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</p>
Generated Wikidata Subset for Taxons based on dump: GeneTaxon_wikidata-20190121-all
<p>Source file: GeneTaxon_wikidata-20190121-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</p>
Generated Wikidata Subset for Taxons based on dump: GeneTaxon_wikidata-20180115-all
<p>Source file: GeneTaxon_wikidata-20180115-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</p>
Generated Wikidata Subset for Taxons based on dump: 20170821-all
<p>Source file: GeneTaxon_wikidata-20170821-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</p>
Generated Wikidata Subset for Taxons based on dump: wikidata-20150601-all
<p>Source file: GeneTaxon_wikidata-20150601-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</p>
Generated Wikidata Subset for Taxons based on dump: 20160613-all
<p>Source file: GeneTaxon_wikidata-20160613-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
Generated Wikidata Subset for Taxons based on dump: 20220630-all
<p>Source file: GeneTaxon_wikidata-20220630-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</p>
Generated Wikidata Subset for Taxons based on dump: 20210531-all
<p>Source file: GeneTaxon_wikidata-20210531-all.ttl.gz</p> <p>ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex</p> <p>More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation</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.