Find research datasets worth reusing
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433
datasets available to search
ShareScore release 0.9.0
Dataset results
433 results for “readiness”
Figure 1 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 1. The National Early Detection Rapid Response framework developed by the U.S. Department of the Interior Invasive Species Task Force. Open circles represent the components to be enacted for effective early detection and rapid response of invasive species. The associated commentary reflects the primary questions, observations, and directives that the process from one component to the next. At the core of the process, are the informational inputs necessary for management decision-making.
Science ready spectra, their best-fitting models and results of Jeans axisymmetric modelling described in the research paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al.
<p>This package contains data presented in the paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al. (2021 Nature Astronomy in press). The dataset can be used to reproduce Figures 3 and 4 from the main manuscript and Extended Data Figures 1, 2, 4, 5 from the Supplementary Information.</p> <p>(1) Python scripts and data points required to reproduce Figure 4 in the manuscript and Extended Data Figure 5 from the Supplementary Information. The data and scripts are presented in a combined .zip archive for both figures.</p> <p>(2) One-dimensional spectra extracted within 1 half-light radius from long-slit Binospec spectra and multi-wavelength far-UV-to-near-IR broadband spectral energy distributions (SEDs) assembled from the photometric measurements extracted within the same aperture for 11 galaxies from the main sample (9 in the Coma cluster and 2 in the Abell 2147 cluster) and 5 galaxies from the supplementary (auxiliary) list. The spectra and SEDs are accompanied with their best-fitting stellar population models and parameters determined by the NBursts+phot algorithm: radial velocity, velocity dispersion, truncation age, final stellar metallicity. The templates are MILES-based models with self-consistent chemical evolution presented in Grishin et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019arXiv190913460G/abstract). The filenames contain the coefficient for galactic winds and the mass fraction of stars in the final starburst, e.g. _l15_60 means lambda=1.5, SSP_frac=60 per cent. The files are presented as binary FITS tables with the fields annotated using unified content descriptors (UCDs) from the list established by the International Virtual Observatory Alliance and physical units where applicable.</p> <p>(3) Two-dimensional profiles of internal kinematics (radial velocity and velocity dispersion) and stellar population properties (truncation age and final stellar metallicity) derived from the analysis of long-slit Binospec spectra for 12 galaxies after adaptive binning, 11 from the main sample and GMP3016 in the Coma cluster from the supplementary sample; best-fitting Jeans axisymmetric models without adaptive binning, i.e. full profiles along the slit. The data are presented in binary FITS tables in the two FITS extensions, one for the profiles derived from the corresponding spectra and the second one for dynamical models.</p>
Perceived stress and e-learning readiness data set during COVID-19
<p>The COVID-19 pandemic has restricted all educational institutions from the traditional campus-based education system to e-learning worldwide. However, adapting to this new platform, e-learning readiness may cause perceived stress among students. This study aimed to examine the association between e-learning readiness and perceived stress as well as the relationship with socio-demographic and e-learning related factors. This cross-sectional study was employed, where 1145 e-learning enrolled university students were surveyed. </p>
E-Learning Readiness In Higher Education Institutions In Nigeria during the COVID-19 Pandemic
<p>Data set for the paper " E-Learning Readiness In Higher Education Institutions In Nigeria during the COVID-19 Pandemic"</p>
Head-Neck-Radiomics-HN1 data processed with Pyradiomics ready to use for data analysis
<p>We downloaded <a href="https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-Radiomics-HN1">Head-Neck-Radiomics-HN1, </a>align CT and segmentation images and use PyRadiomics to extract all kind of features for data analysis.</p> <p>CSV contains all the features. Some of them are settings information, setting information should be remove before any data analysis. By using feature extraction algorithm or... by checking values, all the settings have the same values in the columns. Each row is a patient.</p> <p> </p> <p> </p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014 DDEC Database
<p>~2,900 structures CoRE MOF 2014 structures with DDEC partial atomic charges. </p> <p> </p>
Data of "A network-ready random-access qubits memory"
<p>Data published in "<em>A network-ready random-access qubits memory</em>"</p> <p><em>npj Quantum Information</em></p>
Game Ready Dragon Armor
This armor is part of my [Hack 'n' Slash Online Combat](http://tiny.cc/dttdgz) plugin on Unreal Engine Marketplace, take a look there you might like it. Anyway... I just made the armor free as a sample, I hope you enjoy Source: Objaverse 1.0 / Sketchfab
Low Poly Bordeaux 1 Game Ready
**Low Poly Bordeaux 1 Game Ready** * 2048x2048 Pixel **PNG** Texture. * **PBR**:BaseColor, Ambient Oclusion, Metalness, Roughness and Normalmap. * **Retopology and UV**:Blender. * **Texturing**:SubstancePainter. * **Source 3D Model Website**:[](http://)threedscans.com/lincoln/bordeaux-alter-cast/ Source: Objaverse 1.0 / Sketchfab
Medieval Fork (Game Ready)
227 poly medieval fork with 1024x1024 pbr textures. Textures created using Substance Painter. CC0 - no credit needed. Source: Objaverse 1.0 / Sketchfab
The Capitoline Wolf - Game Ready
A low poly/game ready rework of [the model originally done by Geoffrey Marchal](https://sketchfab.com/3d-models/the-capitoline-wolf-b195f0e1b7be4ec9b8d2f6d55006133a). According to legend of the founding of Rome, [the brothers Romulus and Remus](https://en.wikipedia.org/wiki/Romulus_and_Remus) were cast out by their grand uncle Amulius after he usurped the throne of their grandfather, King Numitor. The young brothers were found and nursed by a she-wolf until they were discovered by a shepherd named Faustulus, who raised them to adulthood. <br>Over the centuries many replicas of this sculpture have been made. The [origins of the original sculpture](http://en.wikipedia.org/wiki/Capitoline_Wolf) have been subject to much debate, but it is generally believed to be of Etruscan origin, and made from anywhere between the fifth to twelfth century. While the sculpture most commonly depicts the young twins as well, their addition came later, in the late 15th century. <br>Includes 4k diffuse/normal/specular/AO maps Source: Objaverse 1.0 / Sketchfab
Gold medieval bowl (Low-poly, game-ready)
3D game-ready model There is no specific reference in this work, I allowed myself to improvise and it occurred to me to make a drawing of an angel * Modeling: Maya & ZBrush * Baking: Marmoset * Texturing: Substance Painter Faces: 306 Triangles: 612 4K png textures Source: Objaverse 1.0 / Sketchfab
Ruin Building - Low Poly Game Ready
A ruin building with detailed wear and damage. Low poly ready for game use. Source: Objaverse 1.0 / Sketchfab
Viking Utility Knife FREE - 3D print ready
When I first saw a one-piece metal knife I found it very interesting, especially the twisted handle to give a better grip. So I decided to create my own version of the Viking Utility Knife (also called a blacksmith knife) for 3D Printing. I also made a game asset version, if you are interested, check my profile. Measurements (X, Y, Z): 300mm / 69mm / 13mm. 11.8 In / 2.71 In / 0.5 In. Social media. https://linktr.ee/Santiago_RS Cheers. Source: Objaverse 1.0 / Sketchfab
Fire-D: Analysis and ML-Ready NASA-Centric Remote Sensing of Wildfire and Smoke
<p>Earth science remote sensing imagery is rich in structural and spectral information, making such data an ideal platform for benchmarking for a broad range of machine learning (ML) tasks, from pattern retrieval to physics-informed classification to anomaly detection to transfer learning. Nevertheless, the utility of Earth science remote sensing data remains largely unexplored by the broader ML community. Our goal is to bridge this gap and bring a rich variety of multisource multi-resolution Earth image data to a wider range of ML researchers who are non-experts in remote sensing, thereby increasing the utility and societal impact of such data products. In particular, motivated by the emerging wildfire crisis, we present radiometrically and geometrically calibrated radiance data from airborne and orbital instruments from the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the Korean Meteorological Administration (KMA).</p> <p>Given the scarce occurrence of wildfires and complex spatio-temporal dependencies in radiance data, these datasets are especially well suited for benchmarking unsupervised and self-supervised learning tasks both on images and non-Euclidean objects. Our experiments on these datasets indicate that contrastive learning and transfer learning algorithms can capture the structures of views and scenes, map pixel space of multi-sensor imagery to a high-level embedding space for further downstream tasks, and facilitate more cohesive integration of the state-of-the-art ML approaches into wildfire risk analytics.</p> <p>All NASA-based observations are freely usable under the <a href="https://science.data.nasa.gov/license/">Creative Commons Zero License</a>.There are also no restrictions on the use of <a href="https://registry.opendata.aws/noaa-goes/">GOES Data</a>. <a href="https://registry.opendata.aws/noaa-gk2a-pds/">GK2A data</a> are also open data without any restrictions on its use.<br><br>For the Planet data, we cannot not share the Radiances, but all masks within this dataset are freely usable with no restrictions.</p> <p> </p> <p>Use:</p> <p>On the data input, input geometrically and radiometrically calibrated radiance data has been pulled from various NASA, NOAA, Planet, and KMA archives. For instruments that have multiple different spatial resolutions within their spectral bands (GOES and GK2A), all bands have been resampled to the lowest collective spatial resolution.</p> <p>Geometric and radiometric calibration has been done by the science data processing pipelines of the various missions, and would not need to be done by anyone else looking to curate the same data. Further information for each instrument can be found in each of the publicly available Level-1 algorithm theoretical basis documents (ATBDs)</p> <p>All input and label data have been put in GeoTiff format. Each band is in a separate raster band and each scene is in a separate GeoTiff file. Label files and input files are in separate tar files, labeled respectively, and the file names match for input and labels, with the exception of an additional .fire and .smoke in the respective label filenames and subfolders.<br><br>The <a href="https://www.earthdata.nasa.gov/about/esdis/esco/standards-practices/geotiff">GeoTiff</a> data format natively contains geolocation metadata internally, and can be interfaced with via C/C++/Python <a href="https://gdal.org/en/stable">GDAL</a> packages, or other python packages that wrap GDAL, like <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> and <a href="https://corteva.github.io/rioxarray/stable/">rioxarray</a> . The documentation for <a href="https://nicks-personal-organization-2.gitbook.io/sit-fuse">SIT-FUSE</a> , the package with which the labels were generated, also has examples on how to read and interface with various data formats, including GeoTiffs. Lastly, this data can be interfaced with using Geographic Information Systems (GIS), like the free and open-source <a href="https://qgis.org/">QGIS</a>.</p> <p>An example of programmatic data access and usage can be found in the dataset's associated <a href="https://github.com/Fire-D-Dataset/FIRE-D">GitHub repository</a>. </p> <p>A working example using data from this repository for ML tasks is available <a href="https://drive.google.com/drive/folders/16aJO6LhrxJ3gsWoTU9BNN3hsb8W0refG?usp=sharing">here</a>.</p> <p>Timing information can be found in the file names, which all use the standard formats from the various instruments' L1B datasets.</p> <p>V2 includes additional GOES-18 radiance data and associated smoke and fire labels for the recent LA fires (Palisades and Eaton fires in January of 2025).</p> <p>V3 provides a reorganization of all data, and an inclusion of improved and additional data from airborne and satellite platforms in 2019, associated with this study: https://arxiv.org/pdf/2501.15343 . </p> <p>V4 provides additional AVIRIS-C Radiances and fixes the spatial range of the GOES-17 radiances to match that of the associated labels. The AVIRIS-C radiances are split across 5 tar files, ordered temporally - all associated labels are in a single tar file.</p> <p><br>Current fire coverage includes:</p> <ul> <li>2019: Williams Flats, Sheridan, Horsefly, and Mosquito (US)</li> <li>2022: Uljin Forest Fire (S. Korea; largest fire on record in S. Korea)</li> <li>2025: Palisades and Eaton Fires (US)</li> </ul> <p>Additional data for the 2025 Palisades and Eaton fires from the TEMPO instrument is currently being validated and will be released in a V4 shortly.</p> <p>Croissant file for dataset metadata specification is also included</p> <p>Validation:</p> <p>These labels have been extensively validated and further information can be referenced in associated publications:<br><a href="https://doi.org/10.3390/rs13122364">https://doi.org/10.3390/rs13122364</a><br><a href="https://doi.org/10.3390/rs17071267">https://doi.org/10.3390/rs17071267</a></p> <p> </p> <p> </p>
ANR REaDY-SPOK-Semantic Representations
<p>The study investigated changes in preexisting representations stored in semantic memory after a dialogue. Researchers can find the pictures used in the set of three experiments, the data and R-scripts of the experiments, an example of confederate's script, and a new picture database. This research was supported by the French National Research Agency (ANR-19-CE28–0006).</p>
Xena's Sword. Game Ready Model
Xena's sword from the TV series "Xena the Warrior Queen". PBR 4K Textures Source: Objaverse 1.0 / Sketchfab
Cradle Globe (Game Ready / 4K PBR)
Support the Free 3D Model Library for only $1 per month: https://www.patreon.com/meerdigital Check out more cool 3D art from Meerschaum Digital: https://linktr.ee/meerdigital A low poly model of a cradle style globe. This model weighs in at only 7,234 polygons, making it perfect for any real-time applications. A full set of high quality PBR textures were used to detail the model. Features: * Only 7,234 polygons (7,054 quads, 180 tris) * Clean topology; easily subdivide or decimate geometry * High quality textures for PBR workflows (4k Albedo/Color, 4k Normal, 4K Ambient Occlusion, 2k Roughness & 2k Metalness) * Organized, non-overlapping UV Mapping * World scale set to centimeters Cradle Globe, by Daniel R. Strebe, used under CC BY-SA 3.0. Cradle Globe is licensed under CC BY-SA 3.0 by Meerschaum Digital. Link to license: https://creativecommons.org/licenses/by-sa/3.0/ Link to globe texture: https://upload.wikimedia.org/wikipedia/commons/8/82/American_Polyconic_projection.jpg_ Source: Objaverse 1.0 / Sketchfab
Mannequin hand [Photogrammetry | VR Ready]
VR Ready photogrammetry hands, FBX hand from Oculus rig is used and skinned to match the model. Enjoy! Source: Objaverse 1.0 / Sketchfab
Low Poly Bordeaux 3 Game Ready
**Low Poly Bordeaux 3 Game Ready** * 2048x2048 Pixel **PNG** Texture. * **PBR**:BaseColor, Ambient Oclusion, Metalness, Roughness and Normalmap. * **Retopology and UV**:Blender. * **Texturing**:SubstancePainter. * **Source 3D Model Website**:[](http://)threedscans.com/lincoln/bordeaux-alter-cast/ Source: Objaverse 1.0 / Sketchfab
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.