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1,140 results for “TOPS”
FIGURE 14 in A new dasyurid marsupial from Kroombit Tops, south-east Queensland, Australia: the Silver-headed Antechinus, Antechinus argentus sp. nov. (Marsupialia: Dasyuridae)
FIGURE 14. Scatterplot of anterior palatal vacuity length (APV) versus posterior palatal vacuity length (PPV) measures for male A. argentus (closed circles) and A. subtropicus (closed triangles).
FIGURE 13 in A new dasyurid marsupial from Kroombit Tops, south-east Queensland, Australia: the Silver-headed Antechinus, Antechinus argentus sp. nov. (Marsupialia: Dasyuridae)
FIGURE 13. Scatterplot of posterior palatal vacuity length (PPV) versus skull width level with the junction of the second and third upper molar (R-LM2) measures for female A. argentus (closed circles) and A. stuartii (open triangles).
FIGURE 12 in A new dasyurid marsupial from Kroombit Tops, south-east Queensland, Australia: the Silver-headed Antechinus, Antechinus argentus sp. nov. (Marsupialia: Dasyuridae)
FIGURE 12. Scatterplot of posterior palatal vacuity length (PPV) versus skull width level with the junction of the second and third upper molar (R-LM2) measures for male A. argentus (closed circles) and A. stuartii (open triangles).
IMDB Top 250 Films
<p>IMDB Top 250 Films</p>
Figure 6. Heatmap showing the top 200 in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 6. Heatmap showing the top 200 most abundant ASVs for each sample. The colour range (0 to 4) represents the log10 transformation of the rarefied counts.
FIGURE 3. Top 4 in The Megachilidae (Hymenoptera, Apoidea, Apiformes) of the Democratic Republic of Congo curated at the Royal Museum for Central Africa (RMCA, Belgium)
FIGURE 3. Top 4 of the Megachilidae species represented by the highest number of specimens in the RMCA collection (Tervuren, Belgium). A. Gronoceras cinctum (Fabricius, 1781) (female at nest entrance) (n=1,270 specimens); B. Euaspis abdominalis (Fabricius, 1793) (female on Duranta erecta (Verbenaceae)) (n=394 specimens); C. Megachile rufipes (Fabricius, 1781) (male on Antigonon leptopus (Polygonaceae)) (n=390 specimens); D. Megachile bituberculata Ritsema, 1880 (female on Pueraria javanica (Fabaceae)) (n=281). All photographs by NJ Vereecken.
Bank Top Kilns flue
Source: Objaverse 1.0 / Sketchfab
Data of top 50 most cited articles about COVID-19 and the complications of COVID-19
<p><strong>Background</strong></p> <p>This bibliometric analysis examines the top 50 most-cited articles on COVID-19 complications, offering insights into the multifaceted impact of the virus. Since its emergence in Wuhan in December 2019, COVID-19 has evolved into a global health crisis, with over 770 million confirmed cases and 6.9 million deaths as of September 2023. Initially recognized as a respiratory illness causing pneumonia and ARDS, its diverse complications extend to cardiovascular, gastrointestinal, renal, hematological, neurological, endocrinological, ophthalmological, hepatobiliary, and dermatological systems.</p> <p><strong>Methods</strong></p> <p>Identifying the top 50 articles from a pool of 5940 in Scopus, the analysis spans November 2019 to July 2021, employing terms related to COVID-19 and complications. Rigorous review criteria excluded non-relevant studies, basic science research, and animal models. The authors independently reviewed articles, considering factors like title, citations, publication year, journal, impact factor, authors, study details, and patient demographics.</p> <p><strong>Results</strong></p> <p>The focus is primarily on 2020 publications (96%), with all articles being open-access. Leading journals include The Lancet, NEJM, and JAMA, with prominent contributions from Internal Medicine (46.9%) and Pulmonary Medicine (14.5%). China played a major role (34.9%), followed by France and Belgium. Clinical features were the primary study topic (68%), often utilizing retrospective designs (24%). Among 22,477 patients analyzed, 54.8% were male, with the most common age group being 26–65 years (63.2%). Complications affected 13.9% of patients, with a recovery rate of 57.8%.</p> <p><strong>Conclusion</strong></p> <p>Analyzing these top-cited articles offers clinicians and researchers a comprehensive, timely understanding of influential COVID-19 literature. This approach uncovers attributes contributing to high citations and provides authors with valuable insights for crafting impactful research. As a strategic tool, this analysis facilitates staying updated and making meaningful contributions to the dynamic field of COVID-19 research.</p>
Top peliculas 2020, 2021, 2022
Open the record for dataset details and reuse information.
Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits, Part 1/3
<p>Associated data and code for Hartung et al. "Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits".</p> <ul> <li>This is repository 1/3 and contains all code generated for and used in the paper.</li> <li>This repository contains all data for figures 1, 2 & 4-6 of the paper, as well as electrophysiological data for figure 3.</li> <li>Histological data for figure 3 can be found in repositories 2/3 (10.5281/zenodo.10938467) and 3/3 (10.5281/zenodo.10938471).</li> <li>The code can alternatively also be accessed via GitHub: https://github.com/janH-21/NDNF-interneurons-cortical-circuits</li> <li>Please consider citing our paper if you use our data or code (see GitHub repository for link).</li> <li>Please refer to the README file for orientation and contact JH or JJL if you have further questions (see paper for contact details).</li> </ul>
Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits, Part 2/3
<p>Associated data for Hartung et al. "Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits".</p> <ul> <li>This is repository 2/3 and contains histological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all data for figures 1, 2 & 4-6 of the paper, as well as electrophysiological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all code generated for and used in the paper. </li> <li>Repository 3/3 (10.5281/zenodo.10938471) contains additional histological data for figure 3.</li> <li>The code can alternatively also be accessed via GitHub: https://github.com/janH-21/NDNF-interneurons-cortical-circuits</li> <li>Please consider citing our paper if you use our data or code (see GitHub repository for link).</li> <li>Please refer to the README file for orientation and contact JH or JJL if you have further questions (see paper for contact details).</li> </ul>
Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits, Part 3/3
<p>Associated data for Hartung et al. "Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits".</p> <ul> <li>This is repository 3/3 and contains histological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all data for figures 1, 2 & 4-6 of the paper, as well as electrophysiological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all code generated for and used in the paper. </li> <li>Repository 3/3 (10.5281/zenodo.10938467) contains additional histological data for figure 3.</li> <li>The code can alternatively also be accessed via GitHub: https://github.com/janH-21/NDNF-interneurons-cortical-circuits</li> <li>Please consider citing our paper if you use our data or code (see GitHub repository for link).</li> <li>Please refer to the README file for orientation and contact JH or JJL if you have further questions (see paper for contact details).</li> </ul>
Steam Top Sellers
<p>Dataset that take the latest 4000 games, considered as 'top sellers' in the Steam page</p>
Micro-CT dataset of Rijksmuseum cornett top half (2/2)
<p><strong>Summary</strong></p> <p>This submission contains a micro-CT reconstruction of a cornett from the Rijksmuseum collection (obj. nr. BK-AM-62-B; https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B). This dataset contains tile 9-15 (out of 15), dataset 2 (out of 2) covering the top half of the cornetto.</p> <p>The data relates to [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban, 2020].</p> <p><em> </em></p> <p><strong>Sample Information</strong></p> <p>The sample is cornett, height 56.0 cm x width 10.0 cm x diameter 3.5 cm , c. 1600 - c. 1650 [Rijksmuseum inventory number BK-NM-62-B, https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B]. It was mounted in a custom made foam stand on the rotation stage. See Figure 10 in [Bossema, 2021] for a picture of the object and the mount and results [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022] for analysis of the reconstructed images. <em> <br><br></em></p> <p><strong><em>Experimental Plan</em></strong></p> <p>The data in this submission was collected to illustrate the scanning process to iteratively include feedback from cultural heritage experts [Bossema, 2021] and was later used for a detailed investigation of the current state and earlier restoration treatments [Dorscheid, 2022] and for illustrating the dual space method [Van Liere, 2022].</p> <p>To image the entire object, thirty tiles were scanned with SOD = 734 and SDD = 1098, in two sessions of 15 tiles (5 vertical, 3 horizontal). For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) images taken before the acquisition. The datasets of each tile consist of 1200 projections, at 70kV, 42W, 300ms exposure time.</p> <p>For the reconstructed CT volume, see 10.5281/zenodo.14265065.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>This dataset contains tile 9-15 (out of 15), dataset 2 (out of 2) covering the top half of the cornetto.</p> <p>Each tile data folder (T*) contains:</p> <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</li> <li>flat-field (or open-shutter) image before acquisition, <em>io000000.tif</em></li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata</li> </ul> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a>GitHub page</a>.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><em> </em></p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p> <p> </p> <p><strong>References</strong></p> <p>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1296207421000558"><strong>F.G.Bossema</strong>, S.B. Coban, A. Kostenko, P. van Duin, J. Dorscheid, I. Garachon, E. Hermens, R. van Liere, K. J. Batenburg, “Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects”, Journal of Cultural Heritage, Vol. 49, p38-47, 2021</a></p> <p><a href="https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-022-00800-8">J. Dorscheid, <strong>F.G. Bossema</strong>, P. van Duin, S.B. Coban, R. van Liere, K.J. Batenburg, G.P. Di Stefano, “Looking under the skin – multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum”, Heritage Science 10, 161 (2022)</a></p> <p>R. van Liere, K.J. Batenburg, I. Garachon, C.-L. Wang, J. Dorscheid (2022). The dual space: Concept and applications in cultural heritage. <em>IEEE BITS the Information Theory Magazine</em>, <em>2</em>(1), 49–57. doi:10.1109/MBITS.2022.3202508</p> <p>Kostenko, A., Palenstijn, W.J., Coban, S.B., Hendriksen, A.A., van Liere, R., Batenburg, K.J.,</p> <p>2020. Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11,</p> <p>100364. https://doi.org/10.1016/j.softx.2019.100364</p> <p> </p>
Micro-CT dataset of Rijksmuseum cornett top half (1/2)
<p><strong>Summary</strong></p> <p>This submission contains a micro-CT reconstruction of a cornett from the Rijksmuseum collection (obj. nr. BK-AM-62-B; https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B). This dataset contains tile 1-8 (out of 15), dataset 1 (out of 2) covering the top half of the cornetto.</p> <p>The data relates to [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban, 2020].</p> <p><em> </em></p> <p><strong>Sample Information</strong></p> <p>The sample is cornett, height 56.0 cm x width 10.0 cm x diameter 3.5 cm , c. 1600 - c. 1650 [Rijksmuseum inventory number BK-NM-62-B, https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B]. It was mounted in a custom made foam stand on the rotation stage. See Figure 10 in [Bossema, 2021] for a picture of the object and the mount and results [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022] for analysis of the reconstructed images. <em> <br><br></em></p> <p><strong><em>Experimental Plan</em></strong></p> <p>The data in this submission was collected to illustrate the scanning process to iteratively include feedback from cultural heritage experts [Bossema, 2021] and was later used for a detailed investigation of the current state and earlier restoration treatments [Dorscheid, 2022] and for illustrating the dual space method [Van Liere, 2022].</p> <p>To image the entire object, thirty tiles were scanned with SOD = 734 and SDD = 1098, in two sessions of 15 tiles (5 vertical, 3 horizontal). For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) images taken before the acquisition. The datasets of each tile consist of 1200 projections, at 70kV, 42W, 300ms exposure time.</p> <p>For the reconstructed CT volume, see 10.5281/zenodo.14265065.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>This dataset contains tile 1-8 (out of 15), dataset 1 (out of 2) covering the top half of the cornetto.</p> <p>Each tile data folder (T*) contains:</p> <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</li> <li>flat-field (or open-shutter) image before acquisition, <em>io000000.tif</em></li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata</li> </ul> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a>GitHub page</a>.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><em> </em></p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p> <p> </p> <p><strong>References</strong></p> <p>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1296207421000558"><strong>F.G.Bossema</strong>, S.B. Coban, A. Kostenko, P. van Duin, J. Dorscheid, I. Garachon, E. Hermens, R. van Liere, K. J. Batenburg, “Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects”, Journal of Cultural Heritage, Vol. 49, p38-47, 2021</a></p> <p><a href="https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-022-00800-8">J. Dorscheid, <strong>F.G. Bossema</strong>, P. van Duin, S.B. Coban, R. van Liere, K.J. Batenburg, G.P. Di Stefano, “Looking under the skin – multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum”, Heritage Science 10, 161 (2022)</a></p> <p>R. van Liere, K.J. Batenburg, I. Garachon, C.-L. Wang, J. Dorscheid (2022). The dual space: Concept and applications in cultural heritage. <em>IEEE BITS the Information Theory Magazine</em>, <em>2</em>(1), 49–57. doi:10.1109/MBITS.2022.3202508</p> <p>Kostenko, A., Palenstijn, W.J., Coban, S.B., Hendriksen, A.A., van Liere, R., Batenburg, K.J.,</p> <p>2020. Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11,</p> <p>100364. https://doi.org/10.1016/j.softx.2019.100364</p> <p> </p>
A top-down method for estimating regional fossil fuel carbon emissions based on satellite XCO2 retrievals
Open the record for dataset details and reuse information.
Global canopy top height estimates from GEDI LIDAR waveforms for 2019
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S. The map is based on the first four months of L1B Version 1 data (April-July 2019). The sparse footprint level predictions are averaged at 0.5 degree resolution (approx. 55 km raster cells at the equator) to obtain a dense map. We refer to the original research article below for further information, especially on how the predictions were filtered before the aggregation.</p> <p>The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. The file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>contains more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research article. These citations are as follows:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2019 (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5704852</p> <p> </p>
Top-down effects override climate forcing on reproductive success in a declining sea duck
Population performance is predicted to be more strongly influenced by detrimental species interactions such as predation under benign climatic conditions, and by climate forcing under harsh conditions, reflected in geographical gradients in biotic interaction strength. Less appreciated is the potential for site-specific changes in drivers with the advent of anthropogenic alteration of predator-prey relationships, including apex predator restoration and spread of invasive predators. Particularly interesting is the relative impact of climate and biotic interactions on population performance when these conflict. In this 31-year study (1990-2020), we revisit a common eider (Somateria mollissima) population from SW Finland, Baltic Sea, fifteen years on from an earlier study showing that climate warming positively affected reproductive parameters and performance. However, the population is simultaneously exposed to increasing predation by the rapidly recovering native apex predator and invasive mammals. Based on the current population trend, we predicted (i) a weakening of the previously documented positive effects of a warming climate on vital rates, (ii) intensified predation, and (iii) increasing top-down control of vital rates and accompanying population decline. Five out of seven breeding parameters (annual spread in female body condition, breeding phenology and synchrony, interval between arrival and breeding, fledgling production) were best explained by predation indices, whereas climate signals (winter NAO, Baltic Sea maximum ice cover) on breeding parameters have weakened. Particularly intriguing is that the previous positive association between mild ice winters and subsequent reproductive output has disappeared during the past 15 years, highlighting the non-linear nature of climate change responses. Indirect predation effects (selective disappearance, changed reproductive strategies, nest-site selection and population age distribution) can potentially explain also the remaining breeding parameters (annual mean body condition and clutch size). The observed regime shift in predation risk appears to prevent this now endangered population from reaping the potential benefits of a warming climate.
Warming and top predator loss drive direct and indirect effects on multiple trophic groups within and across ecosystems
<p>1. The interspecific interactions within and between adjacent ecosystems strongly depend on the changes in their abiotic and biotic components. However, little is known about how climate change and biodiversity loss in a specific ecosystem can impact the multiple trophic interactions of different biological groups within and across ecosystems.<br> 2. We used natural micro-ecosystems (tank-bromeliads) as a model system to investigate the main and interactive effects of aquatic warming and aquatic top predator loss (i.e., trophic downgrading) on trophic relationships in three integrated food web compartments: i) aquatic microorganisms, ii) aquatic macroorganisms, and iii) terrestrial predators (i.e., via cross ecosystem effects).<br> 3. The aquatic top predator loss substantially impacted the three food web compartments. In the aquatic macrofauna compartment, trophic downgrading increased the filter-feeder richness and abundance directly and indirectly via an increase of detritivore richness, likely through a facilitative interaction. For the microbiota compartment, aquatic top predator loss had a negative effect on algae richness, probably via decreasing the input of nutrients from predator biological activities. Furthermore, the more active terrestrial predators responded more to aquatic top predator loss, via an increase of some components of aquatic macrofauna, than more stationary terrestrial predators. The aquatic trophic downgrading indirectly altered the richness and abundance of cursorial terrestrial predators, but these effects had different direction according to the aquatic functional group, filter-feeder or other detritivores. The web-building predators were indirectly affected by aquatic trophic downgrading due to increased filter-feeder richness. Aquatic warming did not affect the aquatic micro- or macro-organisms but did positively affect the abundance of web-building terrestrial predators.<br> 4. These results allow us to raise a predictive framework of how different anthropogenic changes predicted for the next decades, such as aquatic warming and top predator loss, could differentially affect multiple biological groups through interactions within and across ecosystems.</p>
Excel TOP estudiantes-nota media
<p>Datos de nombres de estudiantes junto con su nota media.</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.