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.
18,621
datasets available to search
ShareScore release 0.9.0
Dataset results
18,621 results for “development;”
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Replication Package of "Developer-Centric Code Readability Assessment: Are We There Yet?"
<p>This repository contains the datasets and the scripts to replicate our work "Developer-Centric Code Readability Assessment: Are We There Yet?".</p>
Where nature and poverty meet: developing a multidimensional environment-poverty measure
<p>Dataset for the article: where nature and poverty meet: developing a multidimensional environment-poverty measure by Schleicher et al., 2024</p>
Systematic review and meta-analysis of the occurrence of Listeria monocytogenes in fresh produce in selected developing countries
<p>This dataset contain the supplementary data for "<a><span>Sy</span></a><span><span></span></span><span>stematic review and meta-analysis of the occurrence of <em>Listeria monocytogenes</em> in fresh produce in selected developing countries"</span></p>
Processed dataset used for developing tsunami inundation emulators(Part-II Test Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed datasets covering testing datasets covering tsunami inputs and outputs (for parameters of offshore waveforms, local deformation fields and inundation depths) used in the evaluation of machine learning emulator discussed in the preprint article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effect with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <br> <div>The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz) typically stored in data/processed:</div> <strong>-test_dZ.tar.gz</strong> (local deformation files)</div> <div><strong>-test_d.tar.gz </strong>(inundation depth files)</div> <div><strong>-test_t.tar.gz</strong> (offshore waveform files)</div> <p>The filename follow nomenclature as below</p> <p><strong>d_CT_0.dat, dZ_CT_0.dat, dflat_CT_0.dat, dZflat_CT_0.dat, t_CT_0.dat,lat_lon_idx_CT_892.npy</strong> where the file names represent <strong>{parameter}_{site}_{size}</strong></p> <ol> <li>The first var represent parameter of the file. <ul> <li>d - 2 dimensional file for inundation depth (<strong>events </strong>x <strong>m </strong>x <strong>n</strong>)</li> <li>dZ - 2 dimensional file for local deformation( <strong>events </strong>x <strong>m</strong> x <strong>n</strong>)</li> <li>dflat - 1 dimensional flat file for inundation ( <strong>events </strong>x <strong>locations</strong>)</li> <li>dZflat - 1 dimensional flat file for local deformation( <strong>events </strong>x <strong>locations</strong>)</li> <li>t - 3 dimension offshore waveform for (<strong>events </strong>x <strong>gauges </strong>x <strong>timesteps</strong>)</li> <li>lat_lon_idx - index file for matching lat long coordinate with location indices(<strong>locations x lat x lon</strong>)</li> </ul> </li> <li>The second var represents site of the file. <ul> <li>CT - Catania</li> <li>SR - Siracusa</li> </ul> </li> <li>The third var represents number of events in the file or the selection size. <ul> <li>0,1,2,3 - represent the approx 50000 test events divided into 4 splits</li> </ul> </li> </ol> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div>
Processed dataset used for developing tsunami inundation emulators(Part-I Training Dataset and Model Weights)
<p>This dataset contains some of the processed training datasets covering tsunami inputs and outputs parameters (for offshore waveforms, local deformation fields and inundation depths used in developing a machine learning emulator discussed in the preprint article -<strong> Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects</strong> with project repo - <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.">https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</a></p> <p>####Code####</p> <p>Source code from the above github project : <strong>ML4SicilyTsunami-NPJ2024.tar.gz</strong></p> <p>The post-processed numpy dat files for the two test location of <strong>Catania(CT) and Siracusa(SR)</strong> are provided for each variable retrieved from data/processed folder of the project as compressed gzip files(.gz):</p> <p>####Model Checkpoints#### <br>ML Model checkpoints from all training experiments.<br><strong>- outSR.tar.gz (Siracusa)</strong><br><strong>- outCT.tar.gz (Catania)</strong></p> <p>####Training Datasets####<br><strong>- t.tar.gz: </strong>Offshore waveforms for gauge sites in a 3D numpy file<br><strong>- dZ.tar.gz: </strong>Local deformation fields in 2D numpy files or reduced 1D flat files on flooded grids<br><strong>- d.tar.gz: </strong>Maximum inundation depth in 2D numpy files or reduced 1D flat files</p> <p>####Flood Mask####<br>- Identifies floodable and non-floodable areas in the domain.<br><strong> - zero_mask_SR_961.npy.gz</strong><br><strong> - zero_mask_CT_892.npy.gz</strong></p> <p>####Flood Indices####<br>Links between 2D and 1D numpy files:<br>- Indices connecting 2D (events x m x n) numpy files to flattened 1D (events x m) numpy files:<br><strong> - zero_indices_SR_961.npy.gz</strong><br><strong> - zero_indices_CT_892.npy.gz</strong><br>- Indices connecting latitude-longitude coordinates to 1D (events x m) numpy files:<br><strong> - lat_lon_idx_CT_892.npy.gz</strong><br><strong> - lat_lon_idx_SR_961.npy.gz</strong></p> <p>The filenames follow nomenclature representing <strong>{parameter}_{site}_{size}</strong></p> <ol> <li>The first var represents the parameter of the file. <ul> <li>d - 2 dimensional file for inundation depth (<strong>events </strong>x <strong>m </strong>x <strong>n</strong>)</li> <li>dZ - 2 dimensional file for local deformation( <strong>events </strong>x <strong>m</strong> x <strong>n</strong>)</li> <li>dflat - 1 dimensional flat file for inundation ( <strong>events </strong>x <strong>locations</strong>)</li> <li>dZflat - 1 dimensional flat file for local deformation( <strong>events </strong>x <strong>locations</strong>)</li> <li>t - 3 dimension offshore waveform for (<strong>events </strong>x <strong>gauges </strong>x <strong>timesteps</strong>)</li> <li>lat_lon_idx - index file for matching lat long coordinate with location indices(<strong>locations x lat x lon</strong>)</li> </ul> </li> <li>The second var represents site of the file. <ul> <li>CT - Catania</li> <li>SR - Siracusa</li> </ul> </li> <li>The third var represents number of events in the file or the selection size.<br> <ul> <li>892 - represents the training events for the file, of 4 different sizes for Catania and Siracusa each</li> </ul> </li> </ol> <div>More information on the attached readme.</div>
Constellation analysis of automated local public transport shuttles in the north western development area of Berlin
<h2><strong><span>Konstellationsanalyse: Autonome ÖPNV-Shuttles im Entwicklungsband Nordwest </span></strong></h2> <p>Das Dokument beinhalten stellt eine deutschsprachige Ergebnisdokumentation einer Konstellationsanalyse aus dem Projekt „NOWEL4 – Berliner NordWestraum Level 4" dar . Die Konstellationsanalyse ist ein Brückenkonzept für inter- und transdisziplinäre Zusammenarbeit, das u.a. in der Nachhaltigkeits- und Technikforschung eingesetzt werden kann. Die Ergebnisse zeigen eine gegenwärtige Konstellation und eine Zielkonstellation der Einbindung autonomer Shuttles in den ÖPNV im Berliner Entwicklungsband NordWest. </p> <h2> </h2> <p> </p>
DIDACTIC STRATEGY BASED ON EXELEARNING FOR THE DEVELOPMENT OF PEDAGOGICAL SKILLS IN BIOTECHNOLOGY STUDENTS - DIAGNOSIS
<p>Los datos corresponden al diagnóstico realizado a estudiantes a través de un cuestionario, en una investigación para la elaboración de una estrategia didáctica basada en ExeLearning</p>
Figure 8. A in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 8. A maximum likelihood phylogenetic tree based on the alignment of partial sequences of COI* 16S* ITS* CAD* and 28S genes (4654 bp including gaps). Support of less than 50% bootstrap value is not shown.
Figure 7 in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 7. Third instar larva of Ishigakidiplosis karamae. A* spatula. B* abdominal segment VIII and terminal segment Ventrally. C* abdominal segment VIII and terminal segment dorsally. Scale bars: 50 µm.
Figure 3. Ishigakidiplosis karamae. A in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 3. Ishigakidiplosis karamae. A* tarsomere I and acromere. B* wing. C* female terminal abdominal segments. Scale bars: 50 µm.
Figure 2 in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 2. Adult head of Ishigakidiplosis karamae. A* head. B* occiput with dorsal protuberance bearing two setal bases. C* ventral view of female antennal flagellomere V. D* female antennal flagellomeres. E* dorsal view of male flagellomere III. F* male antennal flagellomeres. G* dorsolateral view of mouthparts. H* lateral view of mouthparts. Scale bars: 50 µm.
Figure 1. Ishigakidiplosis karamae and its host plant. A in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 1. Ishigakidiplosis karamae and its host plant. A* Pongamia pinnata (Fabaceae) growing behind mangroves on Ishigaki Island* Japan. B* flower buds of P. pinnata infested with I. karamae (red arrows). C* immature larvae in a flower bud that has been cut open. D* mature larva in a flower bud that has been cut open. E* freshly emerged female. F* freshly emerged male (terminal tarsal segments are lost).
Figure 4 in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 4. Female abdomen of Ishigakidiplosis karamae. A* terminal abdominal segments. B* lateral view of abdominal segment VIII and protrusible part of ovipositor. C* lateral view of cercus. Scale bars: 50 µm.
Figure 5 in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 5. Male abdomen of Ishigakidiplosis karamae. A* tergites VI–VIII. B* sternites VI–VIII. C* ventral view of hypoproct. D* dorsal view of hypoproct. E. ventral view of gonostylus. F* dorsal view of terminalia (hypoproctal setae not shown). G* dorsal view of aedeagus. Scale bar: 50 µm.
Figure 6 in Integrative taxonomy reveals a new gall midge genus and species (Diptera: Cecidomyiidae) developing in the flower buds of Pongamia pinnata (Fabaceae) in Japan
Figure 6. Pupal exuviae of Ishigakidiplosis karamae. A* exuviae of freshly emerged adults in soil. B* ventral view of head. C* prothoracic spiracle. D* dorsal view of abdomen. Scale bars: 50 µm.
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.