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Dataset results
21 results for “5k”
2010_2023_ERA5_Precipitation_Daily_Dekadal_Monthly_Annual_5k_ER
<p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2010 - 2023.</p> <p>Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2010 - 2023 . The original data is at 0.25 degree resolution and was downloaded and scaled by ERA extraction algroithms. The daily data have been aggregated into dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <p>This dataset was windowed for E4warning project to Europe and North Africa. </p>
2001_2022_ERA5_SoilMoisture_ER_5k
<p>Soil moisture for WNV. </p> <p><strong>Abstract: </strong></p> <p>Soil moisture data have been downloaded from the ECWMF ERA5 reanalysis dataset and then windowed to provide 5km raster datasets for the MOOD extent for the years 2001- 2022 (Oct 2022)</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>era5corsoilmoist+ Year+ Month +.tif</p> <p> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,18.0000000000000000 : 69.0000000000000000,82.0000000000000000<br><strong>Spatial resolution:</strong><br>0.25 (5000m)<br><strong>Temporal resolution:</strong><br>Monthly from 2001 to 2022</p> <p><br><strong>Pixel values</strong></p> <p>Soil Moisture Precentage</p> <p><strong>Source: </strong><br> ECWMF ERA5 Soil Moisture</p> <p><br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
2010_2022_ERA5_MonthlyPrecipitation_5k
<p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2010 - 2022. </p> <p><strong>Abstract: </strong></p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium-Range Weather Forecasting, for 2010 - 2022. The original data is at 0.25-degree resolution and was downscaled by ERA extraction algorithms. The daily data have been aggregated into decadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>Monthly Precipitation: 2022 <a href="../api/records/13122971/draft/files/moeraprecmmmonthly2022.zip/content" target="_blank" rel="noopener noreferrer">moeraprecmmmonthly2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122971/draft/files/moeraprecmmmonthly20102021.zip/content" target="_blank" rel="noopener noreferrer">moeraprecmmmonthly20102021.zip</a></p> <p>Daily, decadal, and annual precipitation can be found <a href="https://drive.google.com/drive/folders/1HzVeyfGSTms_IRYW5QntD-QqHk1Vyea8?usp=sharing">here. </a></p> <p> </p> <p><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>0.25 (5000m) <br><strong>Temporal resolution:</strong><br>Monthly from 2010 to 2022</p> <p><strong>Pixel values</strong></p> <p>Precipitation in meter</p> <p><br><strong>Source: </strong></p> <p>ERA5 Precipitation by the European Centre for Medium-Range Weather Forecasting</p> <p><br><strong>Software used:</strong><br> <br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
2006_2022_MODIS_EnhancedVegetationIndex_5k_ER
<p>MODIS 16-day Enhanced Vegetation Index, 5km, 2006-2022. </p> <p><strong>Abstract: </strong></p> <p>This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modeled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>There are two zip files. One includes data from 2006 to 2021, and the second is for 2022. </p> <p>the files name are: moeve5km16day + year + day (out of 365) : moeve5km16day2022049.tif</p> <p> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>5k<br><strong>Temporal resolution:</strong><br>16-day from 2006 to 2022</p> <p><br><strong>Pixel values:</strong></p> <p>Vegetation Index</p> <p><strong>Source: </strong><br>MODIS NASA : MOD13C1</p> <p><br><strong>Software used:</strong><br> <br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0</p> <p><br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p> <p> </p> <p> </p> <p> </p> <p> </p>
2010_2022_MODIS_LandSurfaceTemperature_5k_ER
<p>MODIS daily, decadal, and monthly land surface Temperature, 5km, 2010-2022. </p> <p><strong>Abstract:</strong></p> <p>This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>Monthly Day LST : 2022 <a href="../api/records/13122960/draft/files/MOODMonthlyDLST2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyDLST2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMonthlyDLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyDLST20102021.zip</a></p> <p>Monthly Night LST 2022 <a href="../api/records/13122960/draft/files/MOODMonthlyNLST2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyNLST2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMonthlyNLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyNLST20102021.zip</a></p> <p>Decadal Day LST: 2022 <a href="../api/records/13122960/draft/files/MOODMOD11c2Dekadaldlst2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11c2Dekadaldlst2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMOD11C2DEKADALDLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11C2DEKADALDLST20102021.zip</a></p> <p>Decadal Night LST: 2022 <a href="../api/records/13122960/draft/files/MOODMODC11dekadalnlst2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMODC11dekadalnlst2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMOD11C2DEKADALNLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11C2DEKADALNLST20102021.zip</a></p> <p>Daily Day LST: 2022 <a href="../api/records/13122960/draft/files/MOODMOD11C1DAILYDLST2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYDLST2022.zip</a> ; 2010 to 2021 <a href="13122960" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYDLST20102021.zip</a></p> <p>Daily Night LST: 2022 <a href="13122960" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYNLST2022.zip</a> ; 2010 to 2021 <a href="13122960" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYNLST20102021.zip</a></p> <p><br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>5k<br><strong>Temporal resolution:</strong><br>Daily, Decadal, and monthly from 2010 to 2022</p> <p><strong>Pixel values</strong></p> <p>Temperature Degree</p> <p><br><strong>Source: </strong><br>MODIS NASA : MOD11A1</p> <p><br><strong>Software used:</strong><br> <br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p> <p> </p>
[Data augmentation in a TTL] - Fictive dataset (27.5M) with up to 5k reactions per template // (13'953 template extracted from USPTO-FULL IBM version)
<p>Full generated fictive dataset, containing 27.5M reactions with up to 5000 reactions per radius 1 reaction template (13'953 reaction templates from USPTO-full, IBM version).</p> <p>Title of the manuscript:</p> <p>"Data augmentation in a Triple Transformer Loop retrosynthesis model"</p> <p>Abstract: </p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div dir="auto"> <div>Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits its usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM®P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM+R by TTL transformer T3. We generated up to 5,000 reactions per template, resulting in 27.5 million validated fictive reactions covering the chemical space of the original UPSTO dataset. To exemplify the use of this dataset, we show that a single-step retrosynthesis transformer model trained with a template equilibrated subset of 1,097,374 fictive reactions outperforms the corresponding model trained on USPTO reactions only.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div></div>
Anndata object of 10x Mouse Brain 5k data set for scDGD training
<p>This data from 10x (5k Adult Mouse Brain Nuclei Isolated with Chromium Nuclei Isolation Kit, Single Cell Gene Expression Dataset by Cell Ranger 7.0.0, 2022) is comprised of 7377 cells from the adult mouse brain with 32285 features. Cell type annotations were approximated using CellTpyist with the `Developing_Mouse_Brain` reference model and majority voting. This resulted in 7 distinct cell types.</p>
Laziness - "Lenost" - lowpoly 5K
Low poly version of original 3d scan from the statue - redone UV topology, baked diffuse and normal map from high poly original. Retouched broken areas with texture paint tools. Photogrammetry scan of Virtues and Vices series by sculptor Matyáš Bernard Braun. Source: Objaverse 1.0 / Sketchfab
Love - "Láska" - 5k lowpoly remesh
Low poly version of original 3d scan from the statue - redone UV topology, baked diffuse and normal map from high poly original. Retouched broken areas with texture paint tools. Source: Objaverse 1.0 / Sketchfab
E4warning_2017_RoadDensity_5k
<p>Road density for unclassified, minor, major and autoroutes 2017. </p> <p>Abstract: This dataset was produced by extracting all rood data from national level Open Steet Map archives for 2017. The length of each type of road was calcluated for a series of 5 square km grids covering the E4warning study area.</p>
Howff_Pi-COLMAP-Agisoft test02 (5k images)
description forthcoming Source: Objaverse 1.0 / Sketchfab
Wood Box360 5k
Wood Box360 5k Source: Objaverse 1.0 / Sketchfab
Honesty - "Upřímnost" 5K 2Ktex
Photogrammetry scan of statue Honesty from Virtues and Vices series by Matyas Bernard Braun (UNESCO site) spray painted in VR by Natália Peterková https://www.instagram.com/peterkovaa/ and further optimized by Vojtěch Leischner www.trackmeifyoucan.com. Find out more at www.tricktheear.eu. Made for webVR Source: Objaverse 1.0 / Sketchfab
SuperAnimal-TopViewMouse-5K
<h1><strong>Introduction</strong></h1> <p>This dataset supports Ye et al. 2024 Nature Communications (<a href="https://www.nature.com/articles/s41467-024-48792-2">https://www.nature.com/articles/s41467-024-48792-2</a>). Please cite this dataset and paper if you use this resource. Please also see Ye et al. 2024 for the full DataSheet that accompanies this download, including the meta data for how to use this data is you want to compare model results on benchmark tasks. Below is just a summary. Also see the dataset licensing below.</p> <h2><br><strong>Data</strong></h2> <p>SuperAnimal-TopViewMouse-5K includes the following datasets:</p> <p>- <strong>CSI, BM, EPM, LDB, OFT</strong> See full details at (1) and in (2). </p> <p>- <strong>BlackMice</strong> See full details at (3).</p> <p>- <strong>WhiteMice</strong> Courtesy of Prof. Sam Golden and Nastacia Goodwin. See details in SIMBA (4). TriMouse See full details<br>at (5). </p> <p>- <strong>DLC-Openfield </strong>See full details at (6). </p> <p>- <strong>Kiehn-Lab-Openfield, Swimming, and treadmill</strong> Courtesy of Prof. Ole<br>Kiehn, Dr. Jared Cregg, and Prof. Carmelo Bellardita; see details at (7). </p> <p>- <strong>MausHaus</strong> We collected video data from five<br>single-housed C57BL/6J male and female mice in an extended home cage, carried out in the laboratory of Mackenzie Mathis<br>at Harvard University and also EPFL (temperature of housing was 20-25C, humidity 20-50%). Data were recorded at 30Hz<br>with 640 × 480 pixels resolution acquired with White Matter, LLC eV cameras. Annotators localized 26 keypoints across 322<br>frames sampled from within DeepLabCut using the k-means clustering approach (8). All experimental procedures for mice<br>were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by<br>the Harvard Institutional Animal Care and Use Committee (IACUC) (n=1 mouse), and by the Veterinary Office of the Canton<br>of Geneva (Switzerland; license GE01) (n=4 mice).</p> <p><a title="SuperAnimal-TopViewMouse5K" href="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1690986892069-I1DP3EQU14DSP5WB6FSI/modelcard-TVM.png?format=1500w" target="_blank" rel="noopener">Here is an image with examples from the datasets, the distribution of images per dataset, and the keypoint guide</a>.</p> <h2><strong>Ethical Considerations</strong></h2> <p>• Data was collected with IUCAC or other governmental approval. Each individual dataset used in training reports the ethics approval they obtained.</p> <h2><strong>Caveats and Recommendations</strong></h2> <p>• Please note that each training dataset was labeled by separate labs and different individuals, therefore while we map names to a unified pose vocabulary, there will be annotator bias in keypoint placement (See Ye et al. 2024 for our Supplementary Note on annotator bias).</p> <p>• Note the dataset is primarily using C56Blk6/J mice and only some CD1 examples.</p> <h2><strong>License</strong></h2> <p>Modified MIT.</p> <p>Copyright 2023-present by Mackenzie Mathis, Shaokai Ye, and contributors. </p> <p>Permission is hereby granted to you (hereafter "LICENSEE") a fully-paid, non-exclusive,<br>and non-transferable license for academic, non-commercial purposes only (hereafter “LICENSE”)<br>to use the "DATASET" subject to the following conditions:</p> <p>The above copyright notice and this permission notice shall be included in all copies or substantial<br>portions of the Software:</p> <p>This data or resulting software may not be used to harm any animal deliberately.</p> <p>LICENSEE acknowledges that the DATASET is a research tool. <br>THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING <br>BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.</p> <p>If this license is not appropriate for your application, please contact Prof. Mackenzie W. Mathis <br>(mackenzie@post.harvard.edu) for a commercial use license.</p> <p>Please cite Ye et al if you use this DATASET in your work.</p> <h2><strong>References</strong></h2> <p>1. Oliver Sturman, Lukas von Ziegler, Christa Schläppi, Furkan Akyol, Mattia Privitera, Daria Slominski, Christina Grimm, Laetitia Thieren, Valerio Zerbi, Benjamin Grewe, et al. Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions. Neuropsychopharmacology, 45(11):1942–1952, 2020.<br>2. Lukas von Ziegler, Oliver Sturman, and Johannes Bohacek. Videos for deeplabcut, noldus ethovision X14 and TSE multi conditioning systems comparisons. https://doi.org/10.5281/zenodo.3608658. Zenodo, January 2020.<br>3. Isaac Chang. Trained DeepLabCut model for tracking mouse in open field arena with topdown view. https://doi.org/10.5281/zenodo.3955216. Zenodo, July 2020.<br>4. Simon RO Nilsson, Nastacia L. Goodwin, Jia Jie Choong, Sophia Hwang, Hayden R Wright, Zane C Norville, Xiaoyu Tong, Dayu Lin, Brandon S. Bentzley, Neir Eshel, Ryan J McLaughlin, and Sam A. Golden. Simple behavioral analysis (simba) – an open source toolkit for computer classification of complex social behaviors in experimental animals. bioRxiv, 2020.<br>5. Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, Venkatesh N. Murthy, George Lauder, Catherine Dulac, Mackenzie W. Mathis, and Alexander Mathis. Multi-animal pose estimation, identification and tracking with deeplabcut. Nature Methods, 19:496 – 504, 2022.<br>6. Alexander Mathis, Pranav Mamidanna, Kevin M Cury, Taiga Abe, Venkatesh N Murthy, Mackenzie Weygandt Mathis, and Matthias Bethge. Deeplabcut: markerless pose estimation of user-defined body parts with deep learning. Nature neuroscience, 21:1281–1289, 2018.<br>7. Jared M. Cregg, Roberto Leiras, Alexia Montalant, Paulina Wanken, Ian R. Wickersham, and Ole Kiehn. Brainstem neurons that command mammalian locomotor asymmetries. Nature neuroscience, 23:730 – 740, 2020</p> <p><strong>V2 release note:</strong></p> <p>- Upon export for data packaging, there was an error with some annotation files in the Kiehn-Lab data. This has now been fixed.</p>
Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (10X 2x 5K cells 250K reads)
GEO Series GSE133535. Homo sapiens; Mus musculus; Canis lupus familiaris. 2 samples. Type: Expression profiling by high throughput sequencing.
Small datasets(~5k cells) for UUATAC-AI test evaluation
<p>Small datasets(~5k cells) for UUATAC-AI test evaluation, and corresponding scripts are provided here.</p>
French Colorful Manhole Cover LOD0 ( 5k Tris )
Photoscan of an old rusty metalic cover, in the oldest neighbourhood of Marseille, South of France. We loved the fact that it was naturaly aging in a very coloful way but this aspect was also enhanced by some dripped spray paint form graphitis around this cover, it gives it a lot of personality. So take this one and give a bit of eyectcahing color to your next street scene! Enjoy! Source: Objaverse 1.0 / Sketchfab
Envy - Závist 5K 2 Ktex
Statue Envy by Matyáš Bernard Braun (UNESCO site Kuks) spray painted in VR by Natália Peterková https://www.instagram.com/peterkovaa/ and further optimized by Vojtěch Leischner www.trackmeifyoucan.com. Find out more at www.tricktheear.eu Source: Objaverse 1.0 / Sketchfab
Figure 5K
<p>3D distribution</p>
doi_dedup___::2c5a62f111f45e5e7c0215b225a3d659
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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.