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1,709 results for “Reflectivity”
Reflections on knowledge, communication and knowledge organization overtime
<p>Moments after mankind started to make moveable physical records describing the world about them people started to store and arrange the records in logical order. Thereafter, records were copied and translated and simple catalogues of collections were compiled. Others, working from these records and from oral sources compiled lists, dictionaries and encyclopaedias. As the means of communication developed from the first revolutionary invention of writing to other revolutions in communication methods, notably printing with moveable type and the computer, techniques of knowledge organization became more sophisticated and powerful. In the first half of the 21st Century we are faced with an unprecedented communications overload and the full range of knowledge organization techniques need to be deployed, further developed and applied.</p>
Development and Characterization of Electrodes for Surface-Specific Attenuated Total Reflection Two-Dimensional Infrared Spectroelectrochemistry
<p>These files contain the data presented in the research article:</p><p>Development and Characterization of Electrodes for Surface-Specific Attenuated Total Reflection Two-Dimensional Infrared Spectroelectrochemistry</p><p>Melissa Bodine, Vepa Rozyyev, Jeffrey W. Elam, Andrei Tokmakoff and Nicholas H. C. Lewis</p><p>J. Phys. Chem. C, (2023)</p>
Dataset for the paper - "Synchronized LFP rhythmicity in the social brain reflects the context of social encounters"
<p>Dataset for the paper:</p><p>Synchronized LFP rhythmicity in the social brain reflects the context of social encounters</p><p>Alok Nath Mohapatra,*, David Peles, Shai Netser, Shlomo Wagner</p><p>*Corresponding author: Alok Nath Mohapatra, Email: thinkalok@gmail.com </p><p>Affiliation: Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, POB. 3338, Haifa 3103301, Israel.</p><p> </p>
Multichannel seismic reflection data used in a geophysical survey at the northern continental margin of the South China Sea
<p>This archive includes the stacked multichannel seismic reflection waveform data. The CDP interval is 12.5 m. This raw data was collected by the Guangzhou Marine Geological Survey (GMGS) between July and September 2020. </p>
Dataset from the paper "Viruses from geothermal springs have ancient origins reflecting long-term interactions with extremophilic red algal mats"
<p>Dataset of viral operational taxonomic units (vOTUs) and viral metagenome-assembled genomes (vMAGs) from the Lemonade Creek, Yellowstone National Park (YNP), USA, from the paper "Viruses from geothermal springs have ancient origins reflecting long-term interactions with extremophilic red algal mats".</p><p>L. Felipe Benites1*, Timothy G. Stephens1, Julia Van Etten1,2, Timeeka James1, William C. Christian4, Kerrie Barry5, Igor V. Grigoriev5,6, Timothy R. McDermott3 and Debashish Bhattacharya1</p><p>1Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, United States of America</p><p>2Graduate Program in Ecology and Evolution, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 United States of America</p><p>3Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America</p><p>4Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, United States of America</p><p>5U.S. Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States of America</p><p>6Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA 94720, United States of America</p>
Evaluation of Family Medicine Residents' Reflection Skills
<p><strong><span>Dataset Info</span></strong></p> <p><strong><span>1) GRAS Preliminary Application Data</span></strong></p> <p><span><span>§<span> </span></span></span><span>The first row of the dataset identifies the columns.</span></p> <p><span><span>§<span> </span></span></span><span>The first column represents the participant numbers in the preliminary application</span></p> <p><span><span>§<span> </span></span></span><span>The second column represents the participants' specializations.</span></p> <p><span><span>§<span> </span></span></span><span>The following 23 columns reveal the first application responses of the participants to the GRAS using a Likert scale (1: Totally Disagree, 5: Totally Agree).</span></p> <p><span><span>§<span> </span></span></span><span>The following 23 columns reveal the second application responses of the participants to the GRAS using a Likert scale (1: Totally Disagree, 5: Totally Agree).</span></p> <p><strong><span>2) GRAS-TR First Phase Application Data</span></strong></p> <p><span><span>§<span> </span></span></span><span>The first row of the dataset identifies the columns.</span></p> <p><span><span>§<span> </span></span></span><span>The first column represents the participant numbers in the first phase of the study.</span></p> <p><span><span>§<span> </span></span></span><span>The second column represents the participants' genders (1: Female, 2: Male).</span></p> <p><span><span>§<span> </span></span></span><span>The third column represents the participants' specializations (1: Internal Medicine, 2: Medical Ethics, 3: Physiology, 4: Medical Pathology, 5: Obstetrics And Gynecology, 6: Pediatrics, 7: Public Health, 8: Emergency Medicine, 9: Microbiology, 10: Pharmacology, 11: Psychiatry, 12: Dermatology, 13: Medical Biochemistry, 14: Child And Adolescent Psychiatry, 15: Anatomy, 16: Family Medicine, 17: Radiology, 18: Histology And Embryology, 19: Medical Education, 20: Neurology).</span></p> <p><span><span>§<span> </span></span></span><span>The fourth column represents the participants' education years.</span></p> <p><span><span>§<span> </span></span></span><span>The following 23 columns reveal the responses of the participants' to the GRAS-TR using a Likert scale (1: Totally Disagree, 5: Totally Agree).</span></p> <p><strong><span>3) GRAS-TR-pg Second Phase Application Data</span></strong></p> <p><span><span>§<span> </span></span></span><span>The first row of the dataset identifies the columns.</span></p> <p><span><span>§<span> </span></span></span><span>The first column represents the participant numbers in the first phase of the study.</span></p> <p><span><span>§<span> </span></span></span><span>The second column represents the participants' genders (1: Female, 2: Male)</span></p> <p><span><span>§<span> </span></span></span><span>The third column represents the participants' instutions types (1: Education and Research Hospital, 2: University Hospital)</span></p> <p><span><span>§<span> </span></span></span><span>The fourth column represents the participants' education years (1: FP 1 year, CFP 1-2 years; FP 2 year, CFP 3-4 years; FP 3 year, CFP 5-6 years).</span></p> <p><span><span>§<span> </span></span></span><span>The following 20 columns reveal the responses of the participants' to the GRAS-TR-pg using a Likert scale (1: Totally Disagree, 5: Totally Agree).</span></p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2015): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2015. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Small Variations in Ice Composition and Layer Thickness Explain Bright Reflections Below Martian Polar Cap Without Liquid Water
<p>These files include the model results used in Lalich et al. 2024 as well as the code necessary to analyze and reprooduce those results. See README for more detail. </p>
STEROPES-WP4 EOM reflectance data
<p>Field reflectance spectra of exogeneous organic matters (EOM) on emerging wheat (Dodin et al., 2023), carried out in the MétaMetha experiment of PAO unit, Nouzilly, France, in the framework of the STEROPES project of EJP-SOIL and used in the article https://doi.org/10.1080/10106049.2023.2245371</p> <p>Each sheet comprises the spectral NDVI measurements made on the four spectral measurement zones of each plot at a given date, in Figure 2 of the article.</p> <p>Plots are A, B, C, D, where A= cattle slurry; B=control with mineral N; C=liquid digestate; E= raw digestate</p> <p>A first EOM spreading was carried out on 8 March 2022 on every plot (control set aside).</p> <p>A second EOM spreading was carried out on 6th April 2022 on every plot (control set aside). <br>NDVI columns numbered from 1 to 6 are the repetitions of measurements for each spectral measurement zone. <br> </p>
Supplementary material: Reflected Encounters at Hunter-Gatherer Rock Art Sites by the Water
<p>This audiovisual material brings to life the soundscape of prehistoric rock art sites in Finland, mainly from around 5,000 years ago. The painted cliffs rising directly from the lakes respond to human-made sounds with echoes. Rock art sites with images of animals, humans, boats, and even drummers are believed to have been ritual sites for prehistoric hunter-fisher-gatherers. </p> <p>The digital recreations were made in collaboration between musicologists, cognitive scientists, archaeologists and artists, using in situ impulse response recordings, dry studio recordings, terrestrial and aerial laser scans, spherical photography and convolution reverb and virtual reality techniques. The work was part of the Academy of Finland funded project ”Acoustics and auditory culture at hunter-gatherer rock art sites in Northern Europe, Siberia and North America” (2018–2023) at the University of Helsinki.</p> <p>Sound design: Perttu Kesäniemi, Paavo Rinkkala, Mikko Ojanen, Jaska Uimonen<br>Video design: Paavo Rinkkala <br>Vocal improvisation & wooden percussion plaque: Taina Saarikivi<br>Vocal improvisation: Emmi Kujanpää<br>Drumming: Mikko Heikinpoika <br>Linguistic reconstruction: Janne Saarikivi<br>Impulse response recording: Riitta Rainio, Julia Shpinitskaya, Lauri Tanskanen, Kai Lassfolk<br>Ambient sound recording: Riitta Rainio, YleArkisto<br>Photography: Julia Shpinitskaya, Riitta Rainio, Kai Lassfolk<br>Studio recording: Perttu Kesäniemi, Petteri Falck, Vilja Haapala, Heta Eronen, Tuulikki Toppari, Mikko Ojanen, Paavo Rinkkala, Riitta Rainio<br>Studio: University of Helsinki Music Research Laboratory & Electronic Music Studio<br>Studio software: Logic Pro X Space Designer<br>Virtual reality reconstruction: Paavo Rinkkala, Jami Pekkanen <br>Rendering software: Unreal Engine 5.1 <br>LiDAR scanning: Jari Okkonen <br>Drum & drumstick model: Paavo Rinkkala<br>Mesolithic canoe model: Carolien Bijvoet <br>Night sky: NASA Deep Star Maps 2020 <br>Foliage: Nature Package by SilverTm <br>Rock painting details: Ismo Luukkonen 2021</p> <p>Supplementary material to the article: <br>Rainio, R., Shpinitskaya, J., Rinkkala, P., Pekkanen, J., Kesäniemi, P. & Ojanen, M. 2024. Reflected Encounters at Hunter-Gatherer Rock Art Sites by the Water. <em>Sound Studies: An Interdisciplinary Journal</em>. <a href="https://doi.org/10.1080/20551940.2024.2419293" target="_blank" rel="noopener">https://doi.org/10.1080/20551940.2024.2419293</a></p> <p>The material is also available for streaming/embedding via: <a href="https://www.youtube.com/playlist?list=PLv5RHkY9FT3J-abz-NxFPPkSttVtAD_cC" target="_blank" rel="noopener">https://www.youtube.com/playlist?list=PLv5RHkY9FT3J-abz-NxFPPkSttVtAD_cC</a></p> <h2>Sound sample 1: Fisherman’s spell</h2> <p>This sound sample simulates what talking or incanting would have sounded like at the Keltavuori rock art site in Lappeenranta, about 2,500 years ago. The fisherman recites an incantation in front of the painted lakeshore cliff, 44 m away from the paintings. The lyrics in early Proto-Sámi are based on linguistic and cultural reconstruction, but are fully fictional. According to linguists, early Proto-Sámi could hypothetically have been spoken in Southeastern Finland around 500 BC, when the rock art sites were still being visited. However, the people who originally made the images of animals, humans and boats, probably spoke a completely different language.</p> <h2>Sound sample 2: Hammer vibrato at Vaskivuori</h2> <p>This sound sample simulates what singing would have sounded like at the Vaskivuori rock art site in Luumäki. The singer produces sounds with hammer vibrato technique in front of the painted lakeshore cliff, 44 m away from the paintings.</p> <h2>Sound sample 3: Nasal sounds at Haukkasaari</h2> <p>This sound sample simulates what singing would have sounded like at the Haukkasaari rock art site in Lemi. The singer produces nasal sounds in front of the painted lakeshore cliff, 29 m away from the paintings.</p> <h2>Sound sample 4: Cattle call at Olhavanvuori</h2> <p>This sound sample simulates what singing would have sounded like at the Olhavanvuori rock art site in Kouvola, Finland. The singer performs a traditional Scandinavian-style cattle call in front of the painted lakeshore cliff, 29 m away from the paintings.</p> <h2>Sound sample 5: Viena Karelian <em>Yoik</em> at Kalamaniemi</h2> <p>This sound sample simulates what chanting would have sounded like at the Kalamaniemi rock art site in Luumäki. The chanter performs a traditional Viena Karelian style <em>yoik</em> in front of the painted lakeshore cliff, 44 m away from the paintings.</p> <h2>Sound sample 6: Throat vibrato at Keltavuori</h2> <p>This sound sample simulates what singing would have sounded like at the Keltavuori rock art site in Lappeenranta. The singer produces sounds with throat vibrato technique in front of the painted lakeshore cliff, 24 m away from the paintings.</p> <h2>Video1 : Drumming in the Evening</h2> <p>This video recreates a drumming performance at the Siliävuori rock art site in Luumäki. The performer beats the drum in front of the painted lakeshore cliff, 26 m away from the paintings.</p> <h2>Video 2: Echoes at Siliävuori in Three Stages</h2> <p>This video recreates a chanting performance at the Siliävuori rock art site in Luumäki. At first, the chant is heard as recorded in the studio, without the acoustics of the place. Next, the painted rock cliff rising directly from the lake responds to the song with an echo. Eventually, the echoes from the whole environment are heard joining in.</p> <h2>Video 3: Singing in the Evening</h2> <p>This video brings to life a chanting performance at the Siliävuori rock art site in Luumäki. The chanter performs a traditional Viena Karelian style <em>yoik</em> in front of the painted lakeshore cliff, 26 m away from the paintings.</p> <h2>Video 4: Rock painting scenery at Siliävuori</h2> <p>This video takes a short tour of the digital recreation of the Siliävuori rock art site in Luumäki. </p> <p> </p>
Seismic refraction, reflection and free-air gravity data of OBS2020-3 in the southwest sub-basin, South China Sea
<p>This dataset (OBS2020-3.Files.zip) contains SEGY files of the OBS2020-3 and the NW section of the MCS2020-3 profiles, as well as the free-air gravity anomaly data along the seismic profiles. The time-axis of the SEGY files for the ocean bottom seismometers are reduced by a reduction velocity of 6.0 km/s. </p>
EJP SensRes bi-directional reflectance of soils
<div> <p><span><span>Repository of the </span></span><span><span>d</span></span><span><span>atabase of bi-directional soil spectral </span></span><span><span>reflectance </span></span><span><span>measurements and </span></span><span><span>the </span></span><span><span>corresponding </span><span>physico</span></span><span><span>-</span></span><span><span>chemical variables for soil carbon estimation. </span></span></p> </div> <div> <p><span><span>Soil samples from the chemistry laboratory </span></span><span><span>were</span></span><span><span> prepared for the spectral measurements</span></span><span><span> that were carried ou</span></span><span><span>t</span></span><span><span> using a </span></span><span><span>full-range </span></span><span><span>PSR-3500 </span></span><span><span>spectrometer</span></span><span><span> in the range 350-2500 nm</span></span><span><span>.</span></span> <span><span>The spectral sensor was mounted on a robotic arm to perform a regular </span></span><span><span>sampling strategy involving multiple</span></span><span><span> viewing</span></span><span><span> angles and different soil water contents</span></span><span><span>. </span></span><span> </span></p> </div> <div> <p><span><span>Three main datasets were </span></span><span><span>compiled,</span></span><span><span> and they </span></span><span><span>are</span></span><span><span> summarized in Table 1 in the related .doc document</span></span><span><span>. </span></span></p> </div>
Data for: Reflections of Grinnellian and Eltonian niches on the distribution of phyllostomid bats in Atlantic Forest of South America
<p>Abstract</p> <p><b>Aim: </b>Ecological niches are complex and the product of interactions with biotic and abiotic environments across the entire geographic range of species. One recent distinction is between Grinnellian niche characteristics that reflect influences at large spatial scales such as climate and Eltonian niche characteristics that reflect influences at the local level such as distribution of resources and how they are shared among species. Aims of this research were to estimate Grinnellian and Eltonian niche characteristics of phyllostomid bats distributed throughout the Atlantic Forest, examine degree of phylogenetic non-independence of distribution and niche characteristics, and estimate relative contribution of niche characteristics to distribution of bats across this large Neotropical region.\</p> <p><b>Location: </b>The Atlantic Forest</p> <p>T<b>axon:</b> Phyllostomid bats</p> <p><b>Methods:</b> Canonical correlation analysis was used to characterize association between Grinnellian and Eltonian niche characteristics. Phylogenetic non-independence was estimated with phylogenetic eigenvector regression. Variation partitioning was used to distinguish relative contributions of different niche characteristics to distribution of bats.</p> <p><b>Results: </b> Grinnellian and Eltonian niche characteristics were strongly and significantly associated. Phylogenetic signal was weak for Grinnellian and strong for Eltonian niche characteristics. Both suites accounted for significant unique variation in distribution of phyllostomid bats in the Atlantic Forest. Grinnellian niche characteristics accounted for more than five times the variation in distribution than Eltonian characteristics.</p> <p><b>Main Conclusions: </b> Distinct Grinnellian and Eltonian perspectives on the niche provide valuable insights into the distribution of species. The degree to which these two different set of characteristics account for distribution is likely scale dependent with Grinnellian characteristics more important at geographic spatial scales and Eltonian characteristics more important at local spatial scales. Grinnellian and Eltonian niches are important corollaries of α- and β- niches and their associated traits and similarities and differences in the two distinct concepts should be better explored across different taxa and geographic domains.</p>
The effects of surface roughness on the spectral (300-1400 nm) bidirectional reflectance distribution function (BRDF) of sea ice
<p>Please cite the following publication when using the data:</p> <p><br> Lamare, M. L., Hedley, J. D., and King, M. D.: The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice, The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, 2023.</p> <p>"BRF_results" contains BRDF output files from the radiative-transfer model PlanarRad.</p> <p>BDRF was computed for three different types of sea ice with varying roughness parameters and <br> thicknesses. </p> <p>The folder tree is constructed with the following structure:</p> <p>BRF_results<br> - Solar Zenith angles<br> - Roughness parameters<br> - Sea ice thicknesses<br> - Wavelengths<br> - Sea ice types</p>
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known. in Towards Target 1 of the Global Strategy for Plant Conservation: A working list of all known plant species - Progress and prospects
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known.
Convolutional-neural-network-based reflection-waveform inversion (major revision)
<p>This dataset is used to plot the synthetic results shown in paper: "Convolutional-neural-network-based reflection-waveform inversion"</p>
Animation of Hohenpeissenberg radar reflectivity cross sections (C-band)
<p>This animation displays radar reflectivity cross sections (dBZ) obtained from range height indicator scans plotted over latitude and for altitudes between 0 km and 10 km in a two-minute interval during a one-hour flight above Southern Germany in the scope of the CIRRUS-HL campaign. Data were provided by the MOHp from the <em>Deutscher Wetterdienst</em> (DWD). The orange vertical line marks the LMU radar station that was overpassed during this flight as well. The gray line marks the HALO-aircraft flight path and the black line HALO’s position one min before the cross section is depicted.</p>
Pseudocongruent phylogeography reflects unique responses to environmental perturbations in a biodiversity hotspot
<p><strong>Aim:</strong> Comparative phylogeographic studies provide important insights into the biogeographical processes shaping regional patterns of diversity. Yet, comparative studies are lacking for southern African herpetofauna, despite their high diversity. We statistically compare phylogeographic structure and divergence-time estimates among five co-distributed forest-living herpetofaunal taxa to assess rivers, climatic refugia, and climatic gradients as congruent drivers of phylogeographic diversity. <strong>Location:</strong> Maputoland-Pondoland-Albany biodiversity hotspot, Southern Africa. <strong>Taxon: </strong>herpetofauna (reptiles and amphibians). <strong>Methods:</strong> Phylogeographic structure and divergence-times within species were estimated from mitochondrial and nuclear DNA sequence data. Phylogeographic concordance factors were used to estimate the degree of phylogeographic congruence among sympatric localities. Full-likelihood Bayesian comparisons were used to estimate synchronous divergence between phylogeographic regions and across a putative river barrier. Paleoclimatic niche models were compared among taxa to identify congruent climatic refugia. Non-parametric statistics were used to identify climatic differences between regions and among populations within each species. Finally, redundancy analyses were used to assess geographic distance, climate, and the putative river barrier as explanatory variables to genetic diversity. <strong>Results: </strong>There is comprehensive phylogeographic structuring within each species, comprising distinct northerly and southerly clades. Phylogeographic concordance factors generally support co-diversification in a north/south axis. Yet, analyses of the divergence-time estimates through the Mio/Plio/Pleistocene indicate asynchronous phylogeographic histories. Climatic niche models identified idiosyncratic responses to paleoclimatic change. Climatic variables are significantly different among populations in all species and correlated with latitude. A combined model of distance, climate, and rivers explained the greatest proportion of genetic diversity in most taxa, of which climate explained the highest variance. <strong>Main Conclusions: </strong>Ancient and recent species-specific responses to climatic and geological processes resulted in pseudo-congruent phylogeographic histories among the five co-distributed species. The presence of a congruent north/south pattern in multiple taxonomic groups occupying different forested microhabitats, from fossorial to arboreal, supports latitudinal gradients as global drivers of phylogeographic diversity along the east coast of South Africa.</p>
Estimation of paleotemperature recorded in carbonaceous fault rocks by rock magnetism and vitrinite reflectance — A study from the Haiyuan fault zone in northeastern Tibetan Plateau
<p>A dataset for the article of "<strong>Estimation of paleotemperature recorded in carbonaceous fault rocks by rock magnetism and vitrinite reflectance — A study from the Haiyuan fault zone in northeastern Tibetan Plateau</strong>"</p>
Coupled lithospheric deformation in the Qinling Orogen, central China: Insights from seismic reflection and surface-wave tomography
<p><strong>Data of geochronology of intrusive plutons and selected zircon Hf values shown in Figure S1 and the references cited</strong></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.