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191 results for “Low flows”
Code and data to "Statistical learning and topkriging improve spatio-temporal low-flow estimation"
<p>This data and software supports the manuscript "Statistical learning and topkriging improve spatio-temporal low-flow estimation" (https:://doi.org/<span>10.1029/2024WR038329</span>).</p> <p>The dataset consists of:</p> <ul> <li>all produced predictions of the models (data/predictions.RDS and data/predictions_csv/*)</li> <li>observational data (data/observations.csv)</li> <li>additional catchment data (data/catchment_data.csv) used for presenting the figures</li> <li>state boundaries of Austria as a shape file (data/boundaries.*)</li> <li>partial predictions of a model-based boosting approach (data/partial_predictions.csv)</li> <li>Example output of number of EOF, due to long computational time (data/number_eofs.RDS)</li> <li>IDs of near natural catchments (data/ids_low_flow.csv)</li> </ul> <p>Additionally, the code is provided to:</p> <ul> <li>Compute the number of EOFs (functions/number_eofs.R)</li> <li>Produce all the figures and tables in the paper (scripts/plotting_results.R)</li> </ul>
Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread
<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
"Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest" -- data sets
<p>These files contain the data used in the analysis and production of graphs reported in a manuscript titled "Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest," by Stefan Gronsdahl, R. Dan Moore, Jordan Rosenfeld, Rich McCleary, Rita Winkler. The paper will be published in the journal Hydrological Processes. The file named "readme.txt" explains the contents of the files.</p>
Alya Eulerian LES results fire extinction nozzle low mass flow rate (paraview files)
<p>The present data complements the Deliverable 5.2. Toolchain V2 to couple CFD and multiple droplet and continuous droplet models.It includes a paraview file (*pvd) for the continuous droplet model (Eulerian-Eulerian) with an inlet mass flow rate of 0.05 kg/s. </p> <p>The unstructured mesh employed to simulate the injector system is composed by 81.6 million tetrahedrons that include the internal ow in the nozzle and the discharged atmosphere. Three levels of refinement are considered to characterize the internal ow and the near eld after the expansion, the jet penetration up to 15D (being D=2mm, the diameter of the nozzle) and the surrounding air with mesh resolution of 0.1, 0.3 and 1.5 mm respectively. A conservative level set is used in combination a a second order low dissipation finite element scheme and the LES model Vreman. </p> <p>Considered variables are: </p> <p> <PDataArray type="Float64" Name="AVD32_average"/> : average particle size distribution</p> <p> <PDataArray type="Float64" Name="AVDEN_average"/> : average density</p> <p> <PDataArray type="Float64" Name="AVL _average"/> : average liquid fraction</p> <p> <PDataArray type="Float64" Name="AVL2 _average"/>: average square liquid fraction </p> <p> <PDataArray type="Float64" Name="AVS _average"/>: average surface density</p> <p> <PDataArray type="Float64" Name="AVS0 _average"/>: average surface density 0</p> <p> <PDataArray type="Float64" Name="CON01_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="CON02_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="CON03_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="CON04_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="D32 _average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="DENSI_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="PRESS_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="SIGM0_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="SIGMA_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="TURBU_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="VELOC_average" NumberOfComponents="3"/>: average velocity</p> <p> <PDataArray type="Float64" Name="VISCO_average"/>: not relevant (instantaneous values)</p>
NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR A LOW PRESSURE.
<p>As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three</p>
Temperature effect on non-Darcian flow in low-permeability porous media
<p>This dataset includes the measured threshold gradients and permeabilities of low permeability porous at 3 temperatures, and the measured hydraulic gradients and flow velocities of different permeabilities at 3 temperatures. The experiment is designed to reveal the temperature effects on the non-Darcian flow in low permeability porous media. </p>
Supplementary data: Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments
<p>The files in this record represent supplementary data for the article titled “Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments” in Water Resources Research. The files contain simulations of the HBV rainfall-runoff model for 14 alpine catchments in Switzerland. The model simulated different water balance components (such as runoff, snow water equivalent and evapotranspiration) for the reference period 1980-2009 and the three scenario periods (2020-2049, 2045-2074 and 2070-2099) using the A1B emission scenario.</p>
Fig. 3 in Transportation of microplastic during high-flow and low-flow seasons in southeastern Black Sea: A modelling approach
Fig. 3 — Snapshots of microplastic distribution on southeastern Black Sea in high-flow (S1, S2, and S3) and low-flow (S4, S5, and S6)
STRATIFICATION EFFECTS ON FLOW HYDRODYNAMICS AND MIXING AT A CONFLUENCE WITH A HIGHLY DISCORDANT BED AND A RELATIVELY LOW VELOCITY RATIO
<p>The effects of temperature induced stratification on flow hydrodynamics, thermal mixing and the capacity of the flow to entrain sediment at a medium-size stream confluence with a highly discordant bed are investigated. To isolate the effects due to differences in the temperature/density of the incoming streams, two simulations were conducted with identical flow conditions (mean velocity ratio VR=2.44, temperature difference between the two streams ΔT=4.7<sup>0 </sup>C). In the first case the Richardson number was Ri=0 (no coupling between the temperature and the momentum equations via the Boussinesq approximation), while in the second simulation Ri=0.67. Even in the Ri=0 case the structure of the mixing interface (MI) was different from the one expected for concordant bed confluences with a similar confluence angle and VR. The MI contained only co-rotating eddies shed in the shear layer forming on the fast speed side of the confluence apex. In the Ri = 0.67 case no wake region was present but a large recirculation eddy formed not far from the confluence apex. In both cases, the flow near the upstream part of the MI was found to be highly 3D and to allow the passage of particles from one side of the confluence to the other. While in the Ri = 0 case mixing was driven by the MI eddies, in the Ri = 0.67 case mixing was controlled by large near-bed intrusions of heavier fluid from the tributary containing colder water and also by the fluid advected in and out of the recirculation eddy.</p>
Low-frequency oscillations of blood flow velocity
<p>figS4a-d.h5 - Intensity profiles obtained by scanning along a line (described by the below attribute) placed along a FITCdextran stained vessel. Record features traces of individual red blood cells from which the blood flow velocity in figure S4 was obtained.<br> File contains single dataset:<br> 1 - Imaging data_200807_111309 -> voltage from PMT collecting FITCdextran fluorescence signal<br> The dataset features two attributes:<br> 1 - Display Time -> hologram dwell time in microseconds<br> 2 - Image Dimentions -> dimensions of the full resolution image used prior the line-scans has been selected <br> 3 - ROI_XY -> coordinates of individual pixels of the scanning trajectory</p> <p> </p>
Upland Hillslope Groundwater Subsidy Affects Low-flow Storage-Discharge Relationship
<p>The package provides data and codes used in the following publication " Upland Hillslope Groundwater Subsidy Affects Low-flow Storage-Discharge Relationship". The publication is now under review in Water Resources Research Journal</p>
Data from: Low coverage genomic data resolve the population divergence and gene flow history of an Australian rain forest fig wasp
Population divergence and gene flow are key processes in evolution and ecology. Model-based analysis of genome-wide datasets allows discrimination between alternative scenarios for these processes even in non-model taxa. We used two complementary approaches (one based on the blockwise site frequency spectrum (bSFS), the second on the Pairwise Sequentially Markovian Coalescent (PSMC)) to infer the divergence history of a fig wasp, Pleistodontes nigriventris. Pleistodontes nigriventris and its fig tree mutualist Ficus watkinsiana are restricted to rain forest patches along the eastern coast of Australia, and are separated into northern and southern populations by two dry forest corridors (the Burdekin and St. Lawrence Gaps). We generated whole genome sequence data for two haploid males per population and used the bSFS approach to infer the timing of divergence between northern and southern populations of P. nigriventris, and to discriminate between alternative isolation with migration (IM) and instantaneous admixture (ADM) models of post divergence gene flow. Pleistodontes nigriventris has low genetic diversity (π = 0.0008), to our knowledge one of the lowest estimates reported for a sexually reproducing arthropod. We find strongest support for an ADM model in which the two populations diverged ca. 196kya in the late Pleistocene, with almost 25% of northern lineages introduced from the south during an admixture event ca. 57kya. This divergence history is highly concordant with individual population demographies inferred from each pair of haploid males using PSMC. Our analysis illustrates the inferences possible with genome-level data for small population samples of tiny, non-model organisms and adds to a growing body of knowledge on the population structure of Australian rain forest taxa.
Data from: Anthropogenic land-use change intensifies the effect of low flows on stream fishes
1. As ecosystems experience simultaneous disturbances, it is critical to understand how multiple stressors interact to affect ecological change. Land-use change (LUC) and extreme flow events are two important stressors that could interact to affect fish populations. 2. We evaluated the individual and interactive effects of discharge and LUC associated with oil and natural gas development (ONGD) on populations of two stream fishes over a seven-year period. We used repeated-state (i.e., abundance trends) and rate (i.e., colonization and persistence) responses to advance our understanding of flow-ecology relationships in a multiple-stressor framework. 3. Overall, fish abundance, colonization, and persistence declined in association with discharge. The effect of LUC associated with ONGD differed between species, with the abundance of Mottled Sculpin declining and Mountain Sucker increasing. We found both synergistic and antagonistic interactions between discharge and LUC. LUC intensified the effect of low flows for one species and lead to greater variability in responses to flows for the other species. These differences between species' responses are likely due to differences in their physiological tolerances and behavioral adaptations to disturbance. 4. Synthesis and applications. Our research provides empirical evidence for the complex interactions that can arise between discharge and anthropogenic LUC. Management efforts (e.g., silt fences, vegetation replanting, and in-stream restoration) to mitigate the effects of anthropogenic alterations and promote high-quality refuge habitats could help mitigate the effect of hydrologic extremes. Further development of flow-ecology relationships in a multiple-stressor framework will help guide management of stream fishes, and provide a better understanding of the mechanisms underlying flow-ecology relationships for different species.06-Sep-2019
Influence of rotation speed on flow field and hydraulic noise in the conduit of a vertical axial-flow pump under low flow rate condition
<p>The complex flow inside the axial-flow pump device will cause the problem of hydraulic noise, in order to explore the influence law of rotation speed on the internal flow characteristics and hydraulic noise of the axial-flow pump conduit, the combination of Computational Fluid Dynamics (CFD) and Computational Acoustics (CA) is used to numerically solve the flow field and internal sound field in the pump device. The results show that the flow in the elbow inlet conduit is smooth at different rotation speeds, and there is no obvious unstable flow. The higher the rotation speed, the more disordered the flow pattern in the left half of the elbow, which intensifies the unstable flow in the straight outlet conduit. The impeller is the main sound source of the internal hydrodynamic noise of the vertical axial-flow pump device, when the sound source propagates upstream and downstream along the conduit, the Total Sound Source Intensity (TSSI) will gradually decay with the increase of distance, the greater the rotation speed is, the faster the Total Sound Source Intensity (TSSI) will decay. When the rotation speed is increased from 1450 r/min to 2200 r/min, the TSSI in the straight outlet conduit is attenuated by 8.9 dB, 13.9 dB and 16.0 dB respectively, and the TSSI in the elbow inlet conduit is attenuated by 11.0 dB, 13.5 dB and 25.9 dB respectively. The vortex structure in the conduit will induce flow noise and delay the attenuation of TSSI in the propagation process, with the increase of rotation speed, this delay will be more obvious.</p>
Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models [source code]
<p>J. Heiland & Y. Kim, 'Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models', GAMM 2024</p>
Fig. 2 in Transportation of microplastic during high-flow and low-flow seasons in southeastern Black Sea: A modelling approach
Fig. 2 — Mountly mean wind, and current during high-flow (a, b) and low-flow (c, d) seasons
Fig. 1 in Transportation of microplastic during high-flow and low-flow seasons in southeastern Black Sea: A modelling approach
Fig. 1 — Map of the study area and microplastic release point
Data and scripts from: Balanced polymorphism fuels rapid selection in an invasive crab despite high gene flow and low genetic diversity
<p><em>Carcinus maenas</em> is a globally invasive species which spreads and thrives across a range of temperate environments. In the northwestern Pacific, the species has spread across >12 degrees of latitude in 10 years from a single source, following its introduction <35 years ago. Using six locations spanning >1,500 km, we examined genetic structure and selection to temperature using 9,376 Single Nucleotide Polymorphisms (SNPs) derived from cardiac transcriptome sequencing.</p> <p>Data in this repository includes information on sequenced samples (*.csv, *.txt), a cleaned transcriptome assembly after expression filtering (*.fasta), transcriptome annotation from EnTAP (*.tsv), list of transcripts removed from analysis after mapping (*.txt), high-quality SNPs identified from the transcriptome sequencing with GATK (seven files representing different SNP sets used in the analysis; *.vcf), and four custom scripts used in processing SNP data (*.py and *.R).</p> <p>Raw sequence data is archived in GenBank's SRA. 2015-2016 samples: BioProject ID PRJNA690934 and BioSample IDs SAMN17267686–SAMN17267781. 2011 samples: BioProject ID PRJNA283611 and BioSample IDs SAMN03653390–SAMN03653413.</p>
Supporting information for "Kinetics of CN(v=1) reactions with butadiene isomers at low temperature by cw-Cavity Ringdown in a pulsed Laval flow with theoretical modelling of rates and entrance channel branching
<p>This file contains the master equation inputs for all the reactions studied, as well as all the details on stationary points and VRC-TST fluxes necessary to reproduce the simulations. </p>
ScienceDex guides
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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.