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.
3,225
datasets available to search
ShareScore release 0.9.0
Dataset results
3,225 results for “Case of study”
"COVID toes": a meta-analysis of case and observational studies on clinical, histopathological and laboratory findings.
<p><strong>Background: </strong>Coronavirus disease (COVID-19) is related to several extrapulmonary disorders; however, little is known about the clinical, laboratory and histopathological characteristics of pernio-like skin lesions associated with COVID-19 infection. <strong>Objective: </strong>To evaluate and summarize the clinical, laboratory and histopathological characteristics of pernio-like lesions reported in the literature. <strong>Methods: </strong>We conducted a search of the PubMed, SciELO and ScienceDirect databases for articles published between 1 January 2020 and 30 November 2020, following the PRISMA recommendations (PROSPERO registration ID: CRD42020225055). The target population was individuals with suspected or laboratory-confirmed COVID-19 with pernio-like lesions. Observational studies, research letters and case/series reports were all eligible for inclusion. Observational studies were evaluated using a random-effects model to calculate the weighted mean prevalence, overall mean and 95% confidence interval. We evaluated case studies using the chi-square test for dichotomous variables and the Mann-Whitney test for continuous variables. <strong>Results:</strong> A total of 187 patients from case reports and 715 patients from 18 observational studies were included. The mean age of patients was 16.6 (14.5–18.8) years. Feet were affected in 91.4% (87.0–94.4%) of patients in observational studies. The proportion of patients with a positive RT-PCR test was less than 15%. Lesion topography and morphology were associated with age. <strong>Conclusion:</strong> Lesions mostly occurred in pediatric patients, and the morphological characteristics tended to differ between pediatric and non-pediatric populations. There is a possible multifactorial component in lesion pathophysiology. The non-positivity of laboratory tests does not exclude an association with COVID-19. Chilblain-like lesions may be a late manifestation of COVID-19.</p>
Dataset to: The efficiency of retention measures in continuous-cover forestry for conserving epiphytic cryptogams: A case study on Abies alba
<p>The file contains data used in the paper mentioned aboved.</p> <p>Abbreviations of variables, species etc. please see Table 1 and Appendix S5 in the publication, respectively.</p> <p>The variables dbh and ele (elevation) are already transformed.</p> <p>The sheets "Ordination_Epiphytes_spec" and "Ordination_Epiphytes_env" were also used for detecting significant associations of species to the tree types (HT vs. AT).</p> <p> </p>
Dataset and Code for "A case study of space-time performance comparison of wind turbines on a wind farm"
<p>This is the computer code and partial dataset for producing the results in the paper, Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” <em>Renewable Energy</em>, Vol. 171, pp. 735-746 .</p> <p>[<strong>Note 1</strong>: In the Reproducibility Report, The table numbers are off by one, namely that Table 2 should be Table 3, and Table 3 should be Table 4.]</p> <p>[<strong>Note 2:</strong> The results in Table 4 included in the paper were produced by the DSWE version 1.3.4. Those results are still reproducible if using the same version of DSWE. Since then, DSWE went through some changes and updates. When using DSWE 1.5.1 version (the latest version as of May 10, 2022) and setting the optimization method to 'L-BFGS-B' (because version 1.3.4’s default optimization setting was 'L-BFGS-B'), the results corresponding to the second row in Table 4 are somewhat different. For the specific results using DSWE 1.5.1, please see the note section at the end of the <a href="https://aml.engr.tamu.edu/wp-content/uploads/sites/164/2022/05/J77_Reproducibility_Report_v2.pdf">updated Reproducibility Report</a>.]</p>
Combined palaeohistological and isotopic inferences of thermometabolism in extinct Neosuchia, using Goniopholis and Dyrosaurus (Pseudosuchia: Crocodylomorpha) as case studies
<p>The evolution of thermometabolism in pseudosuchians (Late Triassic to the present) remains a partly unsolved issue: extant taxa (crocodilians) are ectothermic, but the clade was inferred ancestrally endothermic. Here we inferred the thermometabolic regime of two neosuchian groups, Goniopholididae (Early Jurassic to Late Cretaceous) and Dyrosauridae (Middle Cretaceous to Late Eocene), close relatives of extant crocodilians, in order to elucidate the evolutionary pattern across Metasuchia (Early Jurassic to the present), a clade comprising Neosuchia (Early Jurassic to the present) and Notosuchia (Middle Jurassic until Late Miocene). We propose a new integrative approach combining geochemical analyses to infer body temperature from the stable oxygen isotope composition of tooth phosphate and palaeohistology and phylogenetic comparative methods to infer resting metabolic rates (RMR) and red blood cells dimensions. †Dyrosaurus and †Goniopholis share with extant crocodilians similar lifestyles, body forms, bone tissue organization, body temperatures (Tb), metabolic rates and red blood cells dimensions. Consistently, we infer ectothermy for †Dyrosaurus and †Goniopholis with the parsimonious implication of neosuchians and metasuchians as being primitively ectothermic.</p>
Integrating socio-ecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate
<p>1. Translocations are essential for reestablishing wildlife populations. As they sometimes fail, it is critical to assess factors that influence their success pre-translocation.</p> <p>2. Socio-ecological suitability models (SESMs) integrate social acceptance and ecological suitability to enable identification of areas where wildlife populations will expand, which makes it likely that SESMs will also be useful for predicting translocation success.</p> <p>3. To inform site-selection for potential elk (Cervus canadensis) reintroduction to northeastern Minnesota, USA, we developed broad-scale maps of social acceptance from surveys of local residents and landowners, animal use equivalence (AUE) from forage measured in the field, and empirical conflict risk from geospatial data. Resulting SESMs integrated social acceptance favorability scores, AUE, and conflict risk, and weighted SESMs showed the relative influences of acceptance and conflict.</p> <p>4. Social acceptance was positive for local residents and landowners (mean ≥ 5.4; scale of 1 to 7). AUE (scaled to an elk home range) ranged between 1 and 9 elk/16 km2 during winter, and from 14 to 83 elk/16 km2 during summer. Human-elk conflict risk was low (mean ≤ 0.10; scaled 0 to 1), increasing from north to south. Geographical distributions differed for social acceptance, AUE, and conflict risk, and weighted SESMs revealed unsuitable areas that were otherwise obscured.</p> <p>5. <em>Synthesis and applications</em>. Integrating human-wildlife conflict risk into SESMs shows where social acceptance of translocated species is likely to erode, even where viewed favorably pre-translocation, to inform translocation planning by highlighting interactions between key factors. Such integrated models supplement existing reintroduction biology frameworks by supporting decision-making and knowledge development. In northeastern Minnesota, natural resource managers who are considering elk reintroductions are using SESMs reported here to identify where human-elk conflict is likely to result in an isolated elk population and where addressing concerns for area residents about conflict risk is essential.</p>
CS16: Policy for sustainable development ( development of "Croatian Case Study - Legumes: their potential role in Croatian agricultural production")
<p>(i)The main aim is to develop a document that will gave an overview of current situation regarding legumes consumption and production in Croatia and also to develope a reccomendations for policy makers. <br> (ii) Purpose of this Case Study and the whole project implementation is to raise awareness about consumption and production of legumes both for human consumption and animal feed.<br> (iii) April 2019.- September 2019. <br> (iv) Species - N.A. <br> (v) PIRED facilities<br> (vi) Following different methodological tools was applied like: desk research, analysis of available quantitative historical data, interviews with key regional retailers and company managers, focus groups, benchmarking, expert analysis and expert drafting<br> (vii) detection of number of producers, utilized areas, processing infrastructure and development of recommendations for policy makers and local decision makers. <br> (viii) in progress<br> (ix) This document will have significant impact on policy, mainly on local level ( for local decision makers) because developed recommendations will gave an directions, guideline and examples of good practice.</p>
CS16: Policy for sustainable development ( development of "Croatian Case Study - Legumes: their potential role in Croatian agricultural production")
<p>(i)The main aim is to develop a document that will gave an overview of current situation regarding legumes consumption and production in Croatia and also to develop recommendations for policy makers. <br> (ii) Purpose of this Case Study and the whole project implementation is to raise awareness about consumption and production of legumes both for human consumption and animal feed.<br> (iii) April 2019.- September 2019. <br> (iv) Species - N.A. <br> (v) PIRED facilities</p>
Ancient volcanos as species pumps: A case study of freshwater amphipods in Northeast Asia
<p>Volcano-tectonic processes have been viewed as primary divers in the formation of present-day diversity. Volcanos associated with mountain uplifts drives allopatric speciation through vicariance and may impact the surrounding areas like species pump or species attractor. However, the application of these hypotheses to aquatic fauna has rarely been tested explicitly. We conducted this research in the Changbai Mountains (Mts), which are one of the most typical, active volcanic ranges in Northeast (NE) Asia with a long and turbulent geological history. The <i>Gammarus nekkensis</i> species complex of amphipod crustaceans, widely distributed throughout NE Asia with poor dispersal abilities and a long evolutionary history, is a suitable model for testing these hypotheses. Phylogenetic and ancestral range reconstructions demonstrated that the studied amphipod originated from the Changbai Mts approximately 27 Ma and diverged into eastern (Clade I) and western (Clade II) clades, which corresponds well with the initial volcanic eruption of the Changbai Mts in the Late Oligocene. The subsequent diversifications of subclades CI-3, CII-1a and CII-2a were likely driven by second and third eruptions of the Changbai Mts during the Miocene. In particular, the Changbai lineages had spread to the Russian Far East multiple times since the Early Miocene, and widely colonized the region during the Pleistocene. Our discoveries suggest that the ancient volcanos of the Changbai Mts act as species pumps in NE Asia, resulted in burst of diversification around the Changbai Mts and subsequent dispersals into adjacent regions.</p>
CStone paper: Case study 1 - simulated data
<p><strong>Summary</strong></p> <p>cDNA libraries from fruit fly, leopard, rat and canary were downloaded from Ensembl [1]. For each ≈10 million read pairs, of length 200 nt, insert size 300 nt and containing no read error, were generated from transcripts that ranged in length from 300 to 5000 nt in a manner that reflects uniform coverage. Further details are described in our manuscript: "<a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009631"><strong>CStone: A <em>de novo</em> transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure</strong></a>" [2]. Use of such simulated data allows for the comparison of the assembled contigs to the sequences from which the reads were derived, while excluding the effects of unknown variation; including that of sequencing error and poor coverage. The CStone project page is located at: https://sourceforge.net/projects/cstone/</p> <p>Related software to this project are:<br> 1. <a href="http://sourceforge.net/projects/cstone/">CStone</a> <<br> 2. <a href="http://sourceforge.net/projects/csreadgen/">CSReadGen</a><br> 3. <a href="https://sourceforge.net/projects/cview/">CView</a><br> 4. <a href="https://sourceforge.net/projects/chimsim/">ChimSim</a><br> 5. <a href="https://sourceforge.net/projects/tvscript/">TVScript</a></p> <p>A related poster discussing the the identification of chimerism during assembly is available <a href="https://doi.org/10.5281/zenodo.6022493">here</a> and one discussing the effects of chimerism is available <a href="https://doi.org/10.5281/zenodo.6023170">here</a>.</p> <p>General details of the project are available <a href="https://cibio.up.pt/en/projects/de-novo-based-sequence-assembly-of-next-generation-sequence-data-without-chimeras-improved-annotation-gene-expression-profiles-and-haplotype-br-reconstruction/">here</a>.</p> <p> </p> <p><strong>References</strong></p> <p>1. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48: D682–D688. doi:10.1093/NAR/GKZ966</p> <p>2. Linheiro R, Archer J. <em>CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure.</em> PLOS Comput Biol. 2021;17: e1009631.</p>
RESONATE case study disturbance information
<p>Disturbance information for the RESONATE case studies: <a href="https://efi.int/projects/resonate">https://efi.int/projects/resonate</a>. The data was compiled from existing disturbance and agent attribution data that was extended to the year 2020 within the RESONATE project.</p> <p>Folter 'agent classification': Raster layers with four values: 0=no disturbance, 1=harvest, 2=bark beetle/storm, 3=fire<br> Folder 'disturbance_summaries': Rummary tables with year, agent, disturbance area (ha) and disturbance rate (percent of forest area)<br> Folder 'disturbances': Raster layers with disturbance year (0=no disturbance)<br> Folder 'forest_masks': Forest makes (1=no forest/2=forest) to calculate disturbance ratesF<br> older 'outlines': Outlines of case studies as vector files </p> <p>All spatial files are in EPSG:3035.</p>
Data and code accompanying "'Safe spaces' and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023
<p>Data and code accompanying "‘Safe spaces’ and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023</p>
Annual crop yields of the CS1 Study Case
<p>Database on main crop and secondary crop yields of the CS1 Study case for the period 2019-2021 </p>
Estimated densities of activity from: Characterising diel activity patterns to design conservation measures: Case study of European bat species
<p>Estimated densities of activity calculated in <em>"Characterising diel activity patterns to design conservation measures: case study of European bat species"</em> (see Materials and Methods for more information on these calculations).</p> <p>The files named "<em>densitiyAllYear</em>" correspond to the estimated densities based on our entire dataset.</p> <p>The files named "<em>densitiySpring</em>" correspond to the estimated densities based on a subset of our dataset comprising only monitoring carried out between 1 March and 21 June.</p> <p>The files named "<em>densitiySummer</em>" correspond to the estimated densities based on a subset of our dataset comprising only monitoring carried out between 22 June and 21 August.</p> <p>The files named "<em>densitiyAutumn</em>" correspond to the estimated densities based on a subset of our dataset comprising only monitoring carried out between 22 August and 31 October.</p> <p>The first column of each file (<em>"PercentageNight"</em>) corresponds to the percentage of the night elapsed (0 % = sunset time, 100 % = sunrise time), the second to the estimated activity density (<em>"Density"</em>).</p>
Supplementary material to: Using UAV-based photogrammetry coupled with in-situ fieldwork and U-Pb geochronology to decipher multi-phase deformation processes: A case study from Sarclet, Inner Moray Firth Basin, UK.
<p>This dataset represents the supplementary material to: Using UAV-based photogrammetry coupled with in-situ fieldwork and U-Pb geochronology to decipher multi-phase deformation processes: A case study from Sarclet, Inner Moray Firth Basin, UK.</p> <p>Appendix A - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘The Haven and The Stack’ locality in 3D PDF format.<br> Appendix B - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘Muiri Geo’ lo-cality in 3D PDF format.<br> Appendix C - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘The Haven and The Stack’ locality in .obj format.<br> Appendix D - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘Muiri Geo’ lo-cality in .obj format.<br> Appendix E - This appendix represents the orthomosaic of Sarclet ‘Muiri Geo’ locality in GeoTiff format.<br> Appendix F - This appendix represents the orthomosaic of Sarclet ‘Muiri Geo’ locality in GeoTiff format.<br> Appendix G - This appendix represents the analytical conditions and geochronology data of the studied samples.</p>
Heterogeneity in the abyssal plains: A case study in the Bering Sea
<p>The abyssal plains are vast areas without large-scale topography that occupy much of the ocean floor. Ecological research in these superficially homogenous regions benefits increasingly from non-destructive visual sampling of epifaunal organisms with imaging technology. We analysed images from ultra high-definition towed camera transects at depths around 3500 m across three stations 100–130 km apart in the Bering Sea to ask whether the density and distribution of visible epifauna indicated any substantial heterogeneity. We identified 71 different megafaunal taxa, of which 24 occurred at only one station. Measurements of the two most abundant faunal elements, the holothurian <em>Elpidia</em> <em>minutissima</em> and xenophyophores, indicated significant differences in local densities and patchy aggregations that were strikingly dissimilar among stations. One station was dominated by xenophyophores, one was relatively depauperate in both target taxa as well as other identified megafauna, and the third station was dominated by <em>Elpidia</em>. This is an unexpected level of variation at relatively small spatial scales (~100 km), within comparable transects in a well-mixed oceanic basin, reinforcing the emerging view that abyssal habitats contain landscape heterogeneity at similar spatial scales to terrestrial continental realms.</p>
Influence of sedimentary environment evolution on fingerprint characteristics of methane isotopes: A case study from Hangzhou Bay
<p><span>To better understand the depositional constraints on the fingerprint characteristics of methane isotopes, we present a set of carbon/hydrogen isotopic data for CH<sub>4</sub>, CO<sub>2</sub>, pore water, carbonates, and total organic carbon (TOC) along a 70-m sedimentary core from Hangzhou Bay, China. The sedimentary facies (Units I, II, and III from upper to bottom) suggested depositional environments of the present estuary, shallow marine, and floodplain-estuary. The values of δD<sub>CH4</sub> displayed similar trends with those of δD<sub>H2O</sub> and Cl<sup>-</sup> concentrations along the depth profiles. The values of δ<sup>13</sup>C<sub>CH4</sub> generally synchronously changed with those of δ<sup>13</sup>C<sub>CO2</sub>. The variation trends of δ<sup>13</sup>C<sub>CH4</sub> and δ<sup>13</sup>C<sub>CO2</sub> were the same with δ<sup>13</sup>C<sub>carbonate</sub> from 10 m to 70 m depth but decoupled above 10 m. Calculations suggested that about 86% of methane was produced through the CO<sub>2</sub> reduction pathway. In this pathway, the hydrogen in CH<sub>4</sub> is from ambient water, while the carbon is from dissolved inorganic carbon. In our study, the low δD<sub>CH4</sub> below 44.5 m corresponded to low δD<sub>H2O</sub> and low salinity during the cold and low-sea-level period. The values of δ<sup>13</sup>C<sub>CH4</sub> in Units II and III were correlated with the δ<sup>13</sup>C<sub>carbonates</sub>, which is related to the sedimentary processes. But decoupling of low values of δ<sup>13</sup>C<sub>CH4</sub> and δ<sup>13</sup>C<sub>CO2</sub> from δ<sup>13</sup>C<sub>carbonates</sub> in Unit I may be related to preferential microbial consumption of labile compounds with light carbon isotopic compositions, such as lipids. In short, the variations of the stable carbon and hydrogen isotopic compositions of CH<sub>4</sub> were largely related to sedimentary processes.</span></p>
Why do parents produce small broods of offspring that have lower body mass, survival, and lifetime reproductive success? A case study in a long-lived bird
<p class="MsoNormal"><span>Numerous studies have </span><span>examined the correlation between offspring </span><span>quantity</span><span> and quality</span><span>, and many have found that the most common brood size is often smaller than broods with the highest offspring quality or production. However, the reasons why these small broods with lower offspring quality are produced, are still poorly explained. Using data spanning 29 years, we investigated the effects of brood size on nestlings' body mass and the lifetime fitness for those offspring as adults (as proxies of offspring quality) in the Crested Ibis (<em>Nipponia nippon</em>). We also examined the temporal variation of brood size. We found that overall offspring quality increases with brood size and that individuals from broods of three had the highest quality, as quantified by larger body mass, higher adult survival, and lifetime reproductive success. Furthermore, brood size of an individual pair significantly varied across years, and the proportion of broods containing two offspring increased while broods of three decreased after 2000 when the population dispersed to low-quality habitat. These findings indicate that spatiotemporal variation in resources may impact variation in brood size and subsequent fitness consequences, and that small broods are more common in resource-poor years or low-quality habitats. In contrast, parents with access to high-quality resources produce larger broods of nestlings that achieve higher body mass and subsequently experience higher adult survival and lifetime fitness. This study highlights how variation in life history traits can be influenced by resource condition, and provides an insight into particular habitat that need conservation for Crested Ibis.</span></p>
Case study n°10 : From hazards in Senegal to migrants' integration in the City of Paris, Impact Chain model
<p>The case work led to the development of two correlated impact chains. The <em>first</em> impact chain (“risk sender”) models the components of the decision to migrate for rural Senegalese, accounting for hazard occurrence as well as the exposure and vulnerability. The outcome of the individual arbitrage is migration (internal or international), or immobility (willing or trapped). The <em>second</em> impact chain (“risk receiver”) considers the integration process for international migrants, accounting for the exposure and vulnerability of Paris in multiple dimensions (economic, social, cultural, linguistic, residential).</p>
Artifact for ICSE'23 paper: Using Reactive Synthesis: An End-to-End Exploratory Case Study
<p>Artifact for ICSE'23 paper: Using Reactive Synthesis: An End-to-End Exploratory Case Study </p>
MetUM Simulations of 2nd February Case Study
<p>This repository contains data to support the publication "Kilometre-scale simulations of trade-wind cumulus capture processes of mesoscale organization" submitted to JAMES.</p> <p>The data is from Met Office Unified Model simulations of the 2nd February 2020 EUREC4A case study. There are 3 kilometre-scale resolutions (1.1km, 2.2km, 4.4km) nested in ERA5 boundary conditions, and two 100m-scale resolutions (300m, 500m) nested in the 1.1km simulation. The simulations are initialised at 1st February 00:00. Also included are two sensitivity tests with the kilometre-scale simulations run with 1) evaporation of rain switched off and 2) a later start time (2nd February 00:00).</p> <p>The code used to analyse this data is at <a href="https://github.com/leosaffin/moisture_tracers">https://github.com/leosaffin/moisture_tracers</a>.</p> <p>The data is across four folders (full directory structure at the bottom)</p> <p><strong>regridded - </strong>Contains model data regridded to the lowest resolution (4.4km) either fixed on the 100m-scale domain (coarse_grid) or following a Lagrangian trajectory for the kilometre-scale resolutions and sensitivity tests (lagrangian_grid). There are also files with model data following a different trajectory and smaller domain, to be consistent with <a href="https://doi.org/10.1029/2021MS002619">Narenpitak et al. (2021)</a>, for the default kilometre-scale simulations (lagrangian_grid_Ron_Brown).</p> <p><strong>trajectories</strong> - Contains all the trajectories used to extract the domains in "regridded"</p> <p><strong>simulated_satellite</strong> - Contains model simulated brightness temperatures from 11Z-21Z for each resolution simulation. These simulations were run with the same setup but on a different machine and also include a 150m-resolution simulation.</p> <p><strong>full_grid</strong> - Contains 2d model output a T+36 from the 500m and 1.1km simulations which were used to make figure 1 in the paper.</p> <p>Full directory structure:</p> <ul> <li><strong>full_grid</strong> <ul> <li><strong>D100m_500m</strong> <ul> <li><em>model-diagnostics_20200201T0000_T+35.nc</em></li> </ul> </li> <li><strong>km1p1</strong> <ul> <li><em>model-diagnostics_20200201T0000_T+35.nc</em></li> </ul> </li> </ul> </li> <li><strong>regridded</strong> <ul> <li><strong>D100m_300m</strong> <ul> <li><strong>coarse_grid</strong> <ul> <li><em>20200201T0000_D100m_300m_T+{24..48}_coarse_grid.nc</em></li> </ul> </li> </ul> </li> <li><strong>D100m_500m</strong> <ul> <li><strong>coarse_grid</strong> <ul> <li><em>20200201T0000_D100m_500m_T+{24..48}_coarse_grid.nc</em></li> </ul> </li> </ul> </li> <li><strong>km1p1</strong> <ul> <li><strong>coarse_grid</strong> <ul> <li><em>20200201T0000_km1p1_T+{24..48}_coarse_grid.nc</em></li> </ul> </li> <li><strong>lagrangian_grid</strong> <ul> <li><em>20200201T0000_km1p1_T+{01..48}_lagrangian_grid.nc</em></li> <li><em>20200202T0000_km1p1_T+{01..24}_lagrangian_grid.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_no_evap</strong> <ul> <li><em>20200201T0000_km1p1_T+{24..48}_lagrangian_grid_no_evap.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_Ron_Brown</strong> <ul> <li><em>20200201T0000_km1p1_T+{24..48}_lagrangian_grid_Ron_Brown.nc</em></li> </ul> </li> </ul> </li> <li><strong>km2p2</strong> <ul> <li><strong>coarse_grid</strong> <ul> <li><em>20200201T0000_km2p2_T+{24..48}_coarse_grid.nc</em></li> </ul> </li> <li><strong>lagrangian_grid</strong> <ul> <li><em>20200201T0000_km2p2_T+{24..48}_lagrangian_grid.nc</em></li> <li><em>20200202T0000_km2p2_T+{01..24}_lagrangian_grid.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_no_evap</strong> <ul> <li><em>20200201T0000_km2p2_T+{24..48}_lagrangian_grid_no_evap.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_Ron_Brown</strong> <ul> <li><em>20200201T0000_km2p2_T+{24..48}_lagrangian_grid_Ron_Brown.nc</em></li> </ul> </li> </ul> </li> <li><strong>km4p4</strong> <ul> <li><strong>coarse_grid</strong> <ul> <li><em>20200201T0000_km4p4_T+{24..48}_coarse_grid.nc</em></li> </ul> </li> <li><strong>lagrangian_grid</strong> <ul> <li><em>20200201T0000_km4p4_T+{24..48}_lagrangian_grid.nc</em></li> <li><em>20200202T0000_km4p4_T+{01..24}_lagrangian_grid.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_no_evap</strong> <ul> <li><em>20200201T0000_km4p4_T+{24..48}_lagrangian_grid_no_evap.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_Ron_Brown</strong> <ul> <li><em>20200201T0000_km4p4_T+{24..48}_lagrangian_grid_Ron_Brown.nc</em></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>regridded_large_scale</strong> <ul> <li><strong>km1p1</strong> <ul> <li><strong>lagrangian_grid</strong> <ul> <li><em>20200201T0000_km1p1_T+{24..48}_lagrangian_grid_large_scale.nc</em></li> <li><em>20200202T0000_km1p1_T+{01..24}_lagrangian_grid_large_scale.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_no_evap</strong> <ul> <li><em>20200201T0000_km1p1_T+{24..48}_lagrangian_grid_no_evap_large_scale.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_Ron_Brown</strong> <ul> <li><em>20200201T0000_km1p1_T+{24..48}_lagrangian_grid_Ron_Brown_large_scale.nc</em></li> </ul> </li> </ul> </li> <li><strong>km2p2</strong> <ul> <li><strong>lagrangian_grid</strong> <ul> <li><em>20200201T0000_km2p2_T+{24..48}_lagrangian_grid_large_scale.nc</em></li> <li><em>20200202T0000_km2p2_T+{01..24}_lagrangian_grid_large_scale.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_no_evap</strong> <ul> <li><em>20200201T0000_km2p2_T+{24..48}_lagrangian_grid_no_evap_large_scale.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_Ron_Brown</strong> <ul> <li><em>20200201T0000_km2p2_T+{24..48}_lagrangian_grid_Ron_Brown_large_scale.nc</em></li> </ul> </li> </ul> </li> <li><strong>km4p4</strong> <ul> <li><strong>lagrangian_grid</strong> <ul> <li><em>20200201T0000_km4p4_T+{24..48}_lagrangian_grid_large_scale.nc</em></li> <li><em>20200202T0000_km4p4_T+{01..24}_lagrangian_grid_large_scale.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_no_evap</strong> <ul> <li><em>20200201T0000_km4p4_T+{24..48}_lagrangian_grid_no_evap_large_scale.nc</em></li> </ul> </li> <li><strong>lagrangian_grid_Ron_Brown</strong> <ul> <li><em>20200201T0000_km4p4_T+{24..48}_lagrangian_grid_Ron_Brown_large_scale.nc</em></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>simulated_satellite</strong> <ul> <li><em>UMRA3p3_MOapp_20200201T0000Z_HMscale_2D_Hourly_150m_Baseline_goes16_brt.nc</em></li> <li><em>UMRA3p3_MOapp_20200201T0000Z_HMscale_2D_Hourly_300m_Baseline_goes16_brt.nc</em></li> <li><em>UMRA3p3_MOapp_20200201T0000Z_HMscale_2D_Hourly_500m_Baseline_goes16_brt.nc</em></li> <li><em>UMRA3p3_MOapp_20200201T0000Z_KMscale_2D_Hourly_1p1km_Baseline_goes16_brt.nc</em></li> <li><em>UMRA3p3_MOapp_20200201T0000Z_KMscale_2D_Hourly_2p2km_Baseline_goes16_brt.nc</em></li> <li><em>UMRA3p3_MOapp_20200201T0000Z_KMscale_2D_Hourly_4p4km_Baseline_goes16_brt.nc</em></li> </ul> </li> <li><strong>trajectories</strong> <ul> <li><em>trajectories_20200201_km1p1_500m.pkl</em></li> <li><em>trajectories_20200201_km2p2_500m.pkl</em></li> <li><em>trajectories_20200201_km4p4_500m.pkl</em></li> <li><em>trajectories_20200201_km1p1_500m_no_evap.pkl</em></li> <li><em>trajectories_20200201_km2p2_500m_no_evap.pkl</em></li> <li><em>trajectories_20200201_km4p4_500m_no_evap.pkl</em></li> <li><em>trajectories_20200201_km1p1_500m_Ron_Brown.pkl</em></li> <li><em>trajectories_20200201_km2p2_500m_Ron_Brown.pkl</em></li> <li><em>trajectories_20200201_km4p4_500m_Ron_Brown.pkl</em></li> <li><em>trajectories_20200202_km1p1_500m.pkl</em></li> <li><em>trajectories_20200202_km2p2_500m.pkl</em></li> <li><em>trajectories_20200202_km4p4_500m.pkl</em></li> </ul> </li> </ul>
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.