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2,532 results for “at present”
SGS-LTER Standard Met Data: 1986-09-12-2010 CR21x Level 2 Meteorological Data on the Central Plains Experimental Range, Nunn, Colorado, USA 1986 - present, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Additional information and referenced materials can be found:http://hdl.handle.net/10217/82146.
Trend surface analysis of the 300 Years-Before Present stratigraphic horizon and the Holocene/Pleistocene boundary of the Virginia Coast Reserve
This data set contains sediment grain-size distribution and organic matter data obtained from thirty vibracore samples collected along transects crossing Hog Island Bay, VA prior to 1997. Â Analysis was conducted at various depths along each core.
NOAA 6-Minute Tidal Heights for Wachapreague, VA, 1996-2000 and 2005-present
Data is 6-minute interval predicted and observed tide levels at NOAA/NOS/CO-OP station 8631044, Wachapreague, Virginia. Station is operated by the Virginia Institute of Marine Science (VIMS) Eastern Shore Lab. Data were acquired from the NOAA/NOS Center for Operational Oceanographic Products and Services (CO-OPS) web site (http://tidesandcurrents.noaa.gov/) for station ID 8631044 (Wachapreague, Virginia) using the CO-OPS API for data retrieval. Separate comma-delimited files were retrieved for predicted and observed water level data, one month of data per file, and the data combined using R.
Data presented in "Long Rotational Coherence Times of Molecules in a Magnetic Trap"
<p>Data presented in our paper "Long Rotational Coherence Times of Molecules in a Magnetic Trap". The files give the data that appears in figures 2 and 3 of the paper.</p>
Data files associated with the k-nearest neighbor global prediction of isopachs for present to middle Miocene
<p>This compressed dataset (Dataset 1) includes five folders. Each folder contains the observed data, final predictors and final predictions used for global predictions of isopachs from present to mid-Miocene aged sediments. The name of each folder corresponds to the name of the isopach for which the data is related to. For example, the folder named “Isopach0.0_1.8” contains all the data for the 0-1.8-million-year-old isopach.</p> <p> </p> <p>All grids contained within this supplemental material are netCDF4 file format. The grid pitch for all “.nc” files is uniformly at 5-arc minute denoted by “.5m”. Grids are cell-centered sized 4320 x 2160. Within each folder, there contains observed data used in the prediction. This observed data is in centimeters logarithmic base 10 units. Additionally, within each folder there is a final k-NN prediction file and standard deviation (i.e. uncertainty) file. The units for both the prediction file and uncertainty file are in meters.</p> <p> </p> <p>Collectively, there are 85 unique predictor grids. Predictor grid file names adhere to the naming conventions outlined below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension (.nc).</p> <p> </p> <p><strong>Possible interfaces from the top – down:</strong></p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p> </p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), and longitude (and derivatives).</p> <p> </p> <p>Units referenced are as follows:</p> <p> </p> <p>M - meters</p> <p>MS - meters per second</p> <p>DEG – degree</p> <p>DD – decimal degrees</p> <p>M_ASL - meters above sea level (i.e. meters referenced to sea level)</p> <p>TGC_YR-1 - terragram of carbon per year</p> <p>TGYR - terragram per year</p> <p>PSU – percent salinity units</p> <p>MLL – milliliters per liter</p> <p>PCTSAT – percent saturation</p> <p>C – degree centigrade</p> <p>PDW – percent dry weight</p> <p>MCML -micromole per milliliter</p> <p>KG_M-3 – kilogram per cubic meter</p> <p>MG_M-3 – milligram per cubic meter</p> <p>MG_CM-2 - milligram of carbon per square meter</p> <p>MOL_M3-1 – moles per cubic meter</p> <p> </p> <p> </p> <p> </p> <p>Predictor statistics grids are calculated within a given radius (e.g. 10km, 50km, 100km, 125km, 200km, 250km, 500km) of the respective cell-centered value. The statistics grids include mean (.men), the absolute value of the common logarithm (.alg) and the mean of the common logarithm (.mlg). Additionally, some grids are a weighted count (.wct) for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p> </p> <p>Some grids are denoted by “DECADAL_AVERAGE” or “MISSION_MEAN”. These respective markings denote the values within the grid represent the average of values within a decade (10 year) or over the entire mission of the instrument (e.g. reflectance values from satellite mission). Further, an “s” or “x” in addition to the source name indicates additional conditioning on the final grid was required. The “x” simply indicates upsampling or extension to polar regions was performed via various interpolation techniques (e.g. bilinear or machine learning). The “s” indicates there was smoothing (i.e. averaging over set radii) required to produce a qualitatively geologically reasonable global dataset.</p> <p> </p> <p>Appropriate reference naming marker (bold) listed below including example file name (italics), original data source, and date of last access.</p> <p> </p> <p><strong>CRUST1 </strong></p> <p><em>e.g. CM_MANTLE_DEN_KGM3_CRUST1s.5m.nc</em></p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p> </p> <p><strong>GVP</strong></p> <p><em>e.g. GL_VOLCANO_GVP.r10km.wct.5m.nc</em></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p> </p> <p><strong>PLATES</strong></p> <p><em>e.g. GL_DIST_TO_PLATE_BOUNDARY_KM_PLATES.5m.nc</em></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p> </p> <p><strong>ORNL</strong></p> <p><em>e.g. GL_RIVERMOUTH_TSS_TGYR-1_ORNL.5m.nc</em></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), ISLSCP Initiative II Collection. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p> </p> <p><strong>Woa13x</strong></p> <p><em>e.g. SF_AVG_SEA_DENSITY_KGM3_DECADAL_MEAN_woa13x.5m.nc</em></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), NOAA Atlas NESDIS 72, Technical Ed. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p> </p> <p><strong>KIM </strong></p> <p><em>e.g. SF_SEAMOUNTS_KIM.r10km.wct.5m.nc</em></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, Geophysical Journal International, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p> </p> <p><strong>HYCOM</strong></p> <p><em>e.g. SF_CURRENT_EAST_MS_2012_12_HYCOMx.5m.nc</em></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p> </p> <p><strong>NCEDC</strong></p> <p><em>e.g. SF_SHALLOW_QUAKES_NCEDC.r10km.wct.5m.nc</em></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p> </p> <p><strong>Wei2010x</strong></p> <p><em>e.g. SS_BIOMASS_BACTERIA_LOG10_MGCM2_WEI2010x.5m.nc</em></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. PLoS ONE,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p> </p> <p><strong>NGA_egm2008</strong></p> <p><em>e.g. SS_GEOID_M_ABOVE_WGS84_NGA_egm2008.5m.nc</em></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). The EGM2008 Global Gravitational Model, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p> </p> <p><strong>WAVEWATCH3x</strong></p> <p><em>e.g. SS_WAVE_DIRECTION_DEG_2012_12_WAVEWATCH3x.5m.nc</em></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p> </p> <p><strong>Lee</strong></p> <p><em>e.g. SF_TOC_PDW_LEE.5m.nc</em></p> <p>Lee, T. R., Wood, W. T., & Phrampus, B. J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. Global Biogeochemical Cycles, 33(1), 37-46. https://doi.org/10.1029/2018GB005992 Last access: 12/2018.</p> <p> </p> <p><strong>SRTM15+V2</strong></p> <p><em>e.g. GL_ELEVATION_M_ASL_SRTM15+V2.5m.nc</em></p> <p>Tozer, B. , D. T. Sandwell, W. H. F. Smith, C. Olson, J. R. Beale, and P. Wessel, Global bathymetry and topography at 15 arc seconds: SRTM15+, Accepted Earth and Space Science, August 3, 2019. Last Access: 08/2019.</p> <p> </p> <p><strong>GLOBSED_Straume</strong></p> <p><em>e.g. GL_TOT_SED_THICK_M_GLOBSED_Straume.5m.nc</em></p> <p>Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. Geochemistry, Geophysics, Geosystems, 20(4), 1756–1772. https://doi.org/10.1029/2018GC008115. Last Access: 10/2019.</p> <p> </p> <p><strong>Goyetx</strong></p> <p><em>e.g. SS_MIXED_LAYER_DEPTH_MAX_M_Goyetx.5m.nc</em></p> <p>Goyet, C., Healy, R., Ryan, J., and Kozyr, A. Global Distribution of Total Inorganic Carbon and Total Alkalinity below the Deepest Winter Mixed Layer Depths. United States: N. p., 2000. Web. doi:10.2172/760546. Last Access: 10/2013.</p> <p> </p> <p><strong>MODIS_Aqua</strong></p> <p><em>e.g. SS_CHLOROPHYLL_LOG_MG_M3_MODIS_Aqua_MISSION_MEANx.5m.nc</em></p> <p>Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless, D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34( 4), 710– 714. Last Access: 10/2017.</p> <p> </p> <p><strong>SACD_Aquarius</strong></p> <p><em>e.g. SS_WINDSPEED_MS-1_SACD_Aquarius_MISSION_MEANx.5m.nc</em></p> <p>Fore, A. G., Yueh, S. H., Tang, W., Hayashi, A. K., & Lagerloef, G. S. (2013). Aquarius wind speed products: Algorithms and validation. IEEE Transactions on Geoscience and Remote Sensing, 52( 5), 2920– 2927. Last Access: 10/2017.</p> <p> </p> <p> </p>
LIBER FIM4L Working Group Presentation to UN Libraries
<p><strong>The Chair of LIBER's <a href="https://libereurope.eu/strategy/research-infrastructures/fim4l/">FIM4L Working Group</a>, Jos Westerbeke, was recently invited to present the group's work on work on federated authentication with Single Sign-On (SSO) to the UN-Links Working Group.</strong></p> <p></p> <p>UN libraries were interested in the work of LIBER's FIM4L group because, together, they are actively searching for the best technology to provide users with easy, seamless and secure remote access to electronic library resources.</p> <p></p>
Dataset and script for the manuscript Lobet et al. 2020 presenting the QuoVidi web interface
<p>Dataset and scripts used for the figure of the manuscript "QuoVidi : a open-source web application for the organisation of large scale biological treasure hunt" by Guillaume Lobet, Charlotte Descamps, Lola Leveau, Alain Guillet and Jean-François Rees (2020)</p>
Data from: Consequences of past and present harvest management in a declining flyway population of common eiders Somateria mollissima
<p>1. Harvested species population dynamics are shaped by the relative contribution of natural and harvest mortality. Natural mortality is usually not under management control, so managers must continuously adjust harvest rates to prevent overexploitation. Ideally, this requires regular assessment of the contribution of harvest to total mortality and how this affects population dynamics. 2. To assess the impact of hunting mortality on the dynamics of the rapidly declining Baltic/Wadden Sea population of common eiders Somateria mollissima we first estimated vital rates of ten study colonies over the period 1970–2015. By means of a multi-event capture-recovery model we then used the cause of death of recovered individuals to estimate proportions of adult females that died due to hunting or other causes. Finally, we adopted a stochastic matrix population modelling approach based on simulations to investigate the effect of past and present harvest regulations on changes in flyway population size and composition. 3. Results showed that even the complete ban on shooting females implemented in 2014 in Denmark, where most hunting takes place was not enough to stop the population decline given current levels of natural female mortality. Despite continued hunting of males our predictions suggest that the proportion of females will continue to decline unless natural mortality of the females is reduced. 4. Although levels of natural mortality must decrease to halt the decline of this population, we advocate that the current hunting ban on females is maintained while further investigations of factors causing increased levels of natural mortality among females are undertaken. 5. Synthesis and applications. At the flyway scale, continuous and accurate estimates of vital rates and the relative contribution of harvest versus other mortality causes are increasingly important as the population effect of adjusting harvest rates is most effectively evaluated within a model-based adaptive management framework.</p>
Data presented in: Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters
<p>How insects navigate complex odor plumes, where the location and timing of odor packets are uncertain, remains unclear. Here, we imaged complex odor plumes simultaneously with freely-walking flies, quantifying how behavior is shaped by encounters with individual odor packets. We found that navigation was stochastic, and did not rely on the continuous modulation of speed or orientation. Instead, flies turned stochastically with stereotyped saccades, whose direction was biased upwind by the timing of prior odor encounters, while the magnitude and rate of saccades remained constant. Further, flies used the timing of odor encounters to modulate the transition rates between walks and stops. In more regular environments, flies continuously modulate speed and orientation, even though encounters can still occur randomly due to animal motion. We find that in less predictable environments, where encounters are random in both space and time, walking flies instead navigate with random walks biased by encounter timing.</p>
MFS-M-00157 Snow precipitation measured manually (Tretyakov rain gauge), raised bog (ridge-hollow complex), 2010-present
<p>A raine gauge or Russian hydro-meteorological network standard (Tretyakov' construction) was installed as part of manual meteorological station in Mukhrino research polygon in 2010. The bucket installed at 2m height and protected by wind shield. Since 2014 only winter measurements were collected to supplement rain precipitation measurements measured in summer by automatic rain gauges (see also MFS-M-00154, MFS-M-00307, MFS-M-00308 for summer measurements in the same location).</p>
MFS-M-00308 Rain precipitation measured in raised bog (ridge-hollow complex), HOBO rain gauge RG3-M, 2017-present
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station reseach polygon in the raised bog, treed bog community (ryam) in 2017, installation on the ground level (to prevent wind turbulence), measurement frequency - by event. This series is duplicated by MFS-M-00154 and MFS-M-00307 to cover variability and in case of breakdown. See also MFS-M-157 for winter (snow) precipitation at the same site measured manually.</p>
MFS-M-00156 Rain precipitation measured nearby Shapsha field station (in the village), HOBO rain gauge RG3-M, 2016-present
<p>A raine gauge HOBO RG3-M was installed in Shapsha village in an open space (Shapsha field station) as part of macrofungi long-term plots monitoring in Shapsha vicinity. Installation on the ground level to prevent wind turbulence.</p>
MFS-M-00307 Rain precipitation measured in raised bog (ryam), HOBO rain gauge RG3-M, 2017-present
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station reseach polygon in the raised bog, treed bog community (ryam) in 2017, installation on the ground level (to prevent wind turbulence), measurement frequency - by event. This series is duplicated by MFS-M-00154 and MFS-M-00308 to cover variability and in case of breakdown. See also MFS-M-157 for winter (snow) precipitation at the same site measured manually.</p>
Perceiving tempo in incongruent audiovisual presentation of human motion: Evidence for a visual driving effect
<p>The video set includes 81 audiovisual stimuli which served in the bisection experiment of the study "Perceiving tempo in incongruent audiovisual presentation of human motion: Evidence for a visual driving effect". The auditory sound tracks (bass drum) and visual stimuli (point-light displays of biological motions) each cover the tempo range from 60 to 180BPM, 15BPM per step. Coupling of the stimuli from both modalities results in 81 stimuli in total. </p>
Spectral data presented in Hinrichs J L, Lucey P G. Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil.
<p>In this dataset, we present the spectral data in paper: Hinrichs, J. L., & Lucey, P. G. (2002). Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil. <em>Icarus</em>, <em>155</em>(1), 169-180.</p>
Effects of past and present-day landscape structure on forest soil microorganisms
<p><span><span><span><span><span><span><span><span><span><span><span>Principles of landscape ecology have been built on birds and plant species distribution, but the number of clues is now growing on below-ground organisms, whose dispersal may also be affected by above-ground landscape structure. For communities of microorganisms, the question remains if and how they answer to landscape structure, with or without time lag, and if some groups of microorganisms may react more than others. Here, we investigated if fungi or bacteria diversity is driven by the amount of forest cover in the current or the past landscape. We tested the Habitat Amount Hypothesis (HAH) on ancient forests of Cevennes national park, that were particularly fragmented 150 years ago, and are today surrounded by recent forests. As ancient forests are often more diverse in plant species, we hypothesized that the higher quantity of ancient forests in the landscape, the richer fungal and bacterial communities would be locally. More precisely, we expected that ectomycorrhizal fungi, and pathotrophic fungi, often indicators of mature forests, would be also more sensitive to forest history and therefore to the quantity of ancient forests than bacteria and saprotrophic fungi. We sampled 40 soil cores per 0.5 ha, pooled in 8 composite samples per plot in 27 landscapes and sequenced ITS and 16S marker by Illumina-Mi seq. To identify functional groups of fungi, we relied on their taxonomy and the use of public databases. Our results partly follow the HAH, as fungi richness was positively related with the quantity of ancient forests in the landscape and not by the focal patch size. Ectomycorrhizal and pathotrophic fungi were positively affected by the ancient forest cover, and so were saprotrophic ones, but not bacteria. Local factors also shaped the communities such as soil composition and elevation, confirming classical patterns in soil ecology. Interestingly, past landscape structure better explained fungi communities richness than contemporary landscape, suggesting a time lag in the response of communities to landscape modification and a potential extinction debt. Our results invite to consider below-ground communities in landscape studies and historical ecology, as their structure and functions might be intimately linked with soil and landscape history.</span></span></span></span></span></span></span></span></span></span></span></p>
Wrapped Present
Low poly count present. UVs are a little mismatch on the corners due to bevelling issues, may resolve in the future. Source: Objaverse 1.0 / Sketchfab
Autonomous Mobile Robots: Past, Present and Future of SLAM 2013
<p>“Autonomous Mobile Robots: Past, Present and Future of SLAM” 2013. In:Workshop<br /> at the First RSI/ISM International Conference on Robotics and Mechatronics by<br /> Sharif University of Technology. Presenter: Prof. Hamid D. Taghirad, 2013.</p>
Data presented in "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states"
<p>These are the data presented in our paper "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states" which has been accepted for publication in the New Journal of Physics (as of 07 November 2016).</p>
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
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.