Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,429
datasets available to search
ShareScore release 0.9.0
Dataset results
1,429 results for “detail”
Pāṭan, Kathmandu (काठमाडौँ Nepal). Entrance to the Viśvakarmā temple, detail of Ugracaṇḍā.
<p>Pāṭan, Kathmandu (काठमाडौँ Nepal). Entrance to the Viśvakarmā temple, detail of Ugracaṇḍā.</p>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
Agile detail levels
<p>Following <a href="https://en.wikipedia.org/wiki/INVEST_(mnemonic)">INVEST principles (by Bill Wake)</a> allows you to write user stories at different levels of detail. User stories should be scaleable with a certain scope. However, it may helps to group stories under a bigger meaning (Epic, Theme, or Activity). Scalable content means to be negotiable over time by all parties involved. The most detailed level describes the decisions made, all tasks a user story is considered to be done, and the specific acceptance criteria the realized story have to meet. </p>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
Detailed point cloud data on stem size and shape of Scots pine trees
<p>This data set is comprised of three packed zip files and they include text files of 3D information from terrestrial laser scanning (TLS) and aerial imagery from unmanned aerial vehicle (UAV) from individual Scots pine trees within 27 sample plots from three test sites located in southern Finland.</p> <p>TLS data acquisition was carried out with Trimble TX5 3D laser scanner (Trible Navigation Limited, USA) for all three study sites between September and October 2018. Eight scans were placed to each sample plot and scan resolution of point distance approximately 6.3 mm at 10-m distance was used. Artificial constant sized spheres (i.e. diameter of 198 mm) were placed around sample plots and used as reference objects for registering the eight scans onto a single, aligned coordinate system. The registration was carried out with FARO Scene software (version 2018). Aerial images were obtained by using an UAV with Gryphon Dynamics quadcopter frame. Two Sony A7R II digital cameras were mounted on the UAV in +15° and -15° angles. Images were acquired in every two seconds and image locations were recorded for each image. The flights were carried out on October 2, 2018. For each study site, eight ground control points (GCPs) were placed and measured. Flying height of 140 m and a flying speed of 5 m/s was selected for all the flights, resulting in 1.6 cm ground sampling distance. Total of 639, 614 and 663 images were captured for study site 1, 2, and 3, respectively, resulting in 93% and 75% forward and side overlaps, respectively. Photogrammetric processing of aerial images was carried out following the workflow as presented in Viljanen et al. (2018). The processing produced photogrammetric point clouds for each study site with point density of 804 points/m<sup>2</sup>, 976 points/m<sup>2</sup>, and 1030 points/m<sup>2</sup> for study site 1, 2, and 3, respectively.</p> <p>The sample plots within the three test sites have been managed with different thinning treatments in either 2005 or 2006. The experimental design of the sample plots includes two levels of thinning intensity and three thinning types resulting in six different thinning treatments, namely i) moderate thinning from below, ii) moderate thinning from above, iii) moderate systematic thinning, iv) intensive thinning from below, v) intensive thinning from above, and vi) intensive systematic thinning, as well as a control plot where no thinning has been carried out since the establishment. More information about the study sites and samples plots as well as the thinning treatments can be found in Saarinen et al. (2020a).</p> <p>The data set includes stem points of individual Scot pine trees extracted from the point clouds. More about the method of extraction can be found in Saarinen et al. (2020a, 2020b) and Yrttimaa et al. (2020). The title of the zip file refers to the study sites 1, 2, and 3. The title of the text files includes the information on the test site, the plot within the test site, and the tree within the plot. The text files contain stem points extracted from the TLS point clouds. The columns “x” and “y” contain x- and y-coordinates in a local coordinate system (in meters), in column “h” is the height of each point in meters above ground, and treeID is the tree identification number. The columns are separated by space.</p> <p>Based on the study site and plot number, files from different thinning treatments can be identified by using the information in Table 1 in Saarinen et al. (2020b).</p> <p> </p> <p><strong>References</strong></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020a. Assessing the effects of stand dynamics on stem growth allocation of individual Scots pines. bioRxiv 2020.03.02.972521. <a href="https://doi.org/10.1101/2020.03.02.972521">https://doi.org/10.1101/2020.03.02.972521</a></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020b. Detailed point cloud data on stem size and shape of Scots pine trees. bioRxiv 2020.03.09.983973. <a href="https://doi.org/10.1101/2020.03.09.983973">https://doi.org/10.1101/2020.03.09.983973</a></p> <p>Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., Kaivosoja, J. 2018. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 8: 70. <a href="https://doi.org/10.3390/agriculture8050070">https://doi.org/10.3390/agriculture8050070</a></p> <p>Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (<em>Pinus sylvestris</em> L.) stands is dependent on forest structural variation. EarthArXiv. March 5. <a href="https://doi.org/10.31223/osf.io/ybs7c">https://doi.org/10.31223/osf.io/ybs7c</a></p>
Anatomically Detailed Human Atrial FE Meshes
<p>The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. We include 3 patient-specific anatomical FE meshes with rule-based myofiber directions of each of the anatomies included in our study ("The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium", BMMB, 2020, <a href="https://pubmed.ncbi.nlm.nih.gov/31802292/">https://pubmed.ncbi.nlm.nih.gov/31802292/</a>).<br> Additionally we include<br> - a model with Gaussian noise added (mean 0 um , standard deviation 100 um) to the initial geometry of patient case 3 and subsequently smoothed using ParaView; and <br> - a mesh with a constant wall thickness of 0.5 mm generated based on the endocardial surface of patient case 3.</p> <p>The meshes are given in VTK file format (.vtu) and in the binary format used for the Cardiac Arrhythmia Research Package simulator, see <a href="https://carpentry.medunigraz.at/carputils/index.html">https://carpentry.medunigraz.at/carputils/index.html</a> and <a href="https://opencarp.org/">https://opencarp.org</a>. Here, for each of the geometries, we include a list of nodal coordinates (.bpts file), a list of triangular elements (.belem file), fiber fields (.blon file), surface files (*.surf files), and surface points (*.surf.vtx files).<br> Surface files include the endocardium (laendo.surf), the epicardium (laepi.surf), the mitral valve ring (mitralvv.surf), the pulmonary outlet rings (pulvring.surf) and lids (lid*.vtx) to close the five in- and outlets of the LA.</p> <p>Using the open source mesh utiliy "MeshTool" (<a href="https://bitbucket.org/aneic/meshtool/src/master/README.md">https://bitbucket.org/aneic/meshtool/src/master/README.md</a>)<br> meshes can be manipulated or converted to VTK or EnSight file formats.</p>
Aukana අවුකන (near Kekirawa) Sri Lanka. Standing Buddha, detail.
<p>Aukana අවුකන (near Kekirawa) Sri Lanka. Standing Buddha, detail, as documented in 02/2012.</p> <p> </p> <p> </p>
Amarāvatī, Andhra Pradesh. Detail of a railing pillar from Amarāvatī.
<p><a href="https://discover.libraryhub.jisc.ac.uk/search?q=author:%20Tripe,%20Linnaeus">Tripe, Linnaeus, </a>Amarāvatī, Andhra Pradesh. Sculptures from Amarāvatī in Madras; the sculpture is now in the British Museum registered under the number 1880,0709.1.</p>
Ujjain (District Ujjain, Madhya Pradesh). Grant of Vākpatirāja saṃvat 1036, detail showing Garuḍa
<p>Ujjain (District Ujjain, Madhya Pradesh). Grant of Vākpatirāja saṃvat 1036, detail showing Garuḍa © British Library.</p>
Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
<p>This repository contains the data released in the paper "Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology catalogues, both volunteer and automated, for Galaxy Zoo DECaLS.</p> <p>- gz_decals_volunteers_1_and_2 contains volunteer classifications for galaxies classified during the GZD-1 and GZD-2 campaigns.</p> <p>- gz_decals_volunteers_5 similarly contains classifications from the GZD-5 campaign. Note that GZD-5 used a modified schema designed to better detect mergers and weak bars, and includes many galaxies with only approx. five volunteer responses.</p> <p>- gz_decals_auto_posteriors contains the predicted posteriors for volunteer responses to all galaxies used in any campaign. The full posteriors are recorded as Dirichlet distribution concentrations. gz_decals_auto_posteriors also summarises these posteriors as the automated equivalent of previous Galaxy Zoo data releases;<strong> the expected vote fractions (mean posteriors)</strong>. Note that not all posteriors/vote fractions are relevant for every galaxy; we suggest assessing relevance using the estimated fraction of volunteers that would have been asked each question.</p> <p>We include a schema document, schema.md, to define the column names in each catalogue.</p> <p>We also release the galaxy images shown to volunteers on www.galaxyzoo.org during GZD-5. The images on which the automated classifier was trained may be derived from these volunteer-facing images. These images are split into four zip files, each of which contains images named by iauname inside a subfolder named by the first four characters in their iauname. Not all images were labelled during GZD-5 - refer to the catalog for training labels. We are working with the Zenodo team to add these large files to this repository - meanwhile, you can download them from The University of Manchester <a href="https://docs.google.com/document/d/1YgpnxiSJ7ffOW6FY8pX0pw93LTu8rLIdPL2PYhxW1fo/edit?usp=sharing">here</a>.</p> <p>The .csv and .parquet files contain identical data. Parquet is a fast column-oriented binary format which can be read with pd.read_parquet(loc, columns=[some columns]).</p> <p>You may also be interested in the <a href="https://github.com/mwalmsley/zoobot">github repository</a> which contains code to reproduce the model and to fine-tune it for new tasks (including pretrained weights).</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI to follow on publication) when using the data in this repository.</p> <p>---</p> <p>History</p> <p>v0.0.1 (submission) provides the catalog files.</p> <p>v0.0.2 (first revision) renames the catalog files, adds flags for poorly sized galaxies, and includes the galaxy images via the University of Manchester</p>
Tharaykhittaya Archaeological Museum, Tharakhittaya, Pyay, Myanmar. Silver bowl, detail, circa seventh century.
<p>Tharaykhittaya Archaeological Museum, Tharakhittaya, Pyay, Myanmar (ancient Śrī Kṣetra သရေခေတ္တရာ ပြည်). Silver bowl, imported from China, circa seventh century, with Pyu inscription on the reverse.</p>
Sondni, Madhya Pradesh, India. Vidyādhara couple, detail, early sixth century.
<p>Sondni, Madhya Pradesh, India. Vidyādhara couple, detail, early sixth century. Now National Museum of India. Photograph 1980; digitisation 2016.</p>
Sondni, Madhya Pradesh, India. Vidyādhara couple, detail, early sixth century.
<p>Sondni, Madhya Pradesh, India. Vidyādhara couple, detail, early sixth century. Now National Museum of India. Photograph 1980; digitisation 2016.</p>
Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome
<p>The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 inter-protein and 26 intra-protein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6 and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino-acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/26S-PIPs or the README file.</p>
Bagan, Manadalay, Myanmar. Seated Buddha (detail), National Museum of Myanmar, Yangon.
<p>Bagan, Manadalay, Myanmar. Seated Buddha, cast in a copper alloy (detail), now in the collection of the National Museum of Myanmar, Yangon. Probably 11th century.</p>
Sri Ksetra, Bago, Myanmar. Detail of terracotta plaque.
<p>Sri Ksetra, Bago, Myanmar. Detail of terracotta plaque, showing bearded ascetics with matted locks and wearing deer-skins. From the Khin Ba Mound, excavated 1926-27. Now in the collection of the Sri Ksetra Museum. Probably 5th century.</p>
Udayagiri, Madhya Pradesh. Cave 19, detail of door jamb.
<p>Udayagiri, Madhya Pradesh. Cave 19, detail of door jamb, probably mid-5th century. As documented in 2/2012.</p>
Udayagiri, Madhya Pradesh. Cave 7, detail of Kārttikeya.
<p>Udayagiri, Madhya Pradesh. Cave 7, detail of Kārttikeya, showing the breast of the peacock and damaged figure of the god above; located at the mouth of Cave 7, western side, probably 5th century. As documented in 11/2007.</p>
Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla
<p>Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla, now in the collection of the Malda Museum, Malda, West Bengal, as documented in 2007.</p>
Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla
<p>Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla, now in the collection of the Malda Museum, Malda, West Bengal, as documented in 2007.</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.