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1,219 results for “Sand”
SBC LTER: Reef: Kelp Forest Community Dynamics: Cover of bottom substrate and sand depth
These data describe the percent cover of eight bottom substrate types as determined by a uniform point contact method. The type of bottom substrate is recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Percent cover of each substrate type on a transect is estimated as the proportion of the 80 points contacted by that substrate type x 100. In cases where the substrate type is sand, the depth of the sand is measured. These data are part of the SBC LTER’s time series observations of kelp forest dynamics, which began in 2000 and was designed to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset include nine reef sites along the mainland coast of the Santa Barbara Channel and two on the north side of Santa Cruz Island. These 11 sites were chosen to reflect the wide range of oceanographic conditions present in the Channel and proximity to sources of terrestrial runoff. Data are collected at each site once per year in summer and this dataset is updated annually. The time period of data collection varied among the 11 reef sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. The two tables in this data package include: 1) The percent cover of eight bottom substrate types; and 2) the sand depth of each sampling point (sand depth = 0 if substrate type ≠sand).
SBC LTER: Reef: Long-term experiment: Kelp removal: Cover of bottom substrate and sand depth
These data describe the percent cover of eight bottom substrate types within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The type of bottom substrate was recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Percent cover of each substrate type on transect was estimated as the proportion of the 80 points contacted by the substrate type x 100. In cases where the substrate type was sand the depth of the sand was measured. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal. The two tables in this data package include: 1) The percent cover of eight bottom substrate types; and 2) the sand depth of each sampling point (sand depth = 0 if substrate type is not sand)
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Cover of bottom substrate and sand depth
These data describe the percent cover of eight bottom substrate types within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The type of bottom substrate was recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Percent cover of each substrate type on transect was estimated as the proportion of the 80 points contacted by the substrate type x 100. In cases where the substrate type was sand, the depth of the sand was measured. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel. The two tables in this data package include: 1) The percent cover of eight bottom substrate types; and 2) the sand depth of each sampling point (sand depth = 0 if substrate type is not sand)
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
Figure 2: Direct and indirect paths of knowledge transfer to New Zealand to manage sand drifting in the nineteenth and twentieth centuries.
<p>Figure 2 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>sand.wfraction = variable: sand weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Meiofauna higher taxa abundance data from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)
<p>We provide abundance data for meiofauna taxa determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3.</p> <p>Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. The organisms were extracted by decantation over a 32-μm sieve. The total number of individuals per taxon was counted and is presented as individuals per 10 cm<sup>2</sup>.</p> <p>In the framework of our monitoring, samples were primarily taken for a large-scale metabarcoding study on meiofauna communities. One core per station and sampling date was reserved for morphology-based community analyses. Here we present the results for the stations AH01, AH03, AH05, and PAP. We selected these stations because of their location at both ends and in the center of the impacted zone (AH01, AH03, AH05) and at the control site (PAP). The meiofauna (32–1000 µm) was mostly represented by Copepoda, Nematoda, Platyhelminthes, Gastrotricha, and some Annelida. We counted 27445 individuals in total, encompassing 10 higher taxa. We counted copepod nauplii separately due to their small body size. We defined the combined group “Plathyhelminthes+<em>Diurodrilus</em> sp.” because members of the annelid genus <em>Diurodrilus</em> sp. are not distinguishable from Platyhelminthes under the stereomicroscope.</p> <p>Here we present a Table on meiofauna higher taxa counts per 10cm<sup>2</sup> (as xlsx and tab-delimited file; including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth).</p> <p>The meiofauna abundance data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and metabarcoding analyses (see “related works”).</p> <p><strong>Comment: </strong>Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a></p>
Metabarcoding data (number of reads per operational taxonomic unit) from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)
<p>We provide metabarcoding data (number of reads per operational taxonomic unit, OTU) determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 and 16 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3.<br>Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. <br>Three cores (2 cores at T0) per sampling date were taken for metabarcoding analyses. The organisms were extracted by decantation over a 32-μm sieve. Genomic DNA was extracted from the filters using the DNeasy PowerSoil pro kit (Qiagen). Realtime-PCR was performed to amplify V1&V2, two hypervariable regions of 18S rDNA gene. The sequencing run was performed using the MiSeq Reagent Nanokit v2 (250 cycles paired end) on an Illumina MiSeq platform at the DZMB Metabarcoding lab in Wilhelmshaven, Germany. High-resolution amplicon sequence variants (ASVs) were obtained and compared to the NCBI database to assign taxonomic information to each ASV. The target meiofauna ASVs were further classified into operational taxonomic units (OTUs) with a 3% cut-off threshold using the statistical software R.</p> <p>Here, we present two Tables (as xlsx and tab-delimited files):<br>(1) the taxonomic description of the 843 OTUs and their assigned ID number;<br>(2) the number of reads per OTU per sample (including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth).</p> <p>The metabarcoding data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and meiofauna abundances (see “related works”).</p> <p><strong>Comment: </strong>Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a></p>
Global peatland, bare rock and bare sand extent at 100 m to 1 km spatial resolution based on multisource data
<p>Ensemble estimate of the global distribution of <a href="https://en.wikipedia.org/wiki/Peatland">peatlands</a> / extent (<strong>peatland.extent_wri.gfw.peatgrids_p</strong>). This is a simple average from three (3) sources of data:</p> <ol> <li><a href="https://data.globalforestwatch.org/datasets/gfw::global-peatlands/about">WRI Global Peatlands extent map</a> at 30-m (250-m effective);</li> <li><a href="https://doi.org/10.5281/zenodo.12559238">PEATGRIDS</a> at 1-km;</li> <li><a href="https://globalpeatlands.org/new-online-global-peatland-map-asian-peatlands-story-map-presenting-best-peatlands-mapping">Global Peatlands Map 2.0</a> produced by the Global Peatlands Initiative;</li> </ol> <p>The average between the three sources is an extent map with value 0–100%. The refence period is 2000–2020, although probably most of data is based on pre 2010. For more details about the source data please refer to the cited references below.</p> <p>Bare rock and bare sand estimates are based on the following two sources of data:</p> <ol> <li><a href="https://land.copernicus.eu/en/products/global-dynamic-land-cover">Copernicus GLC land cover</a> at 100-m for 2015 and 2019;</li> <li><a href="https://lcz-generator.rub.de/global-lcz-map">Local Climate zones</a> map at 100-m for 2018;</li> </ol> <p>Two classes are considered: (1) probability of occurrence of bare rock (<strong>bare.rock_glc.gfz_p</strong>), (2) probability of occurrence of bare sand i.e. shifting sand (<strong>bare.soil.sand_glc.gfz_p</strong>). We recommend using only the 1-km data for spatial modeling.</p> <p>The time-series of bare areas (<strong>bare.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. </p>
Indicative distribution map for Ecosystem Functional Group M1.7 Subtidal sand beds
<p>This archive contains indicative distribution maps and profiles for <strong>M1.7 Subtidal sand beds</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Data for removal kinetics and breakthrough curves of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns [Dataset]
<p>This dataset describes the transport and removal of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns. The experiments aimed at providing sustainable treatment options for relevant persistent, mobile and toxic (i.e., PMT substances) linked to vehicular traffic pollution. We assessed removal for 1H-benzotriazole, N'N-diphenylguanidine, and hexamethoxymethyl-melamine (PMT precursor) in batch and column experiments using pyrogenic carbonaceous adsorbents (e.g., GAC and biochar). Data contain kinetics batch experiments and breakthrough curves for the target contaminants.</p>
Data from Sand aggradation alters biofilm standing crop and metabolism in a low-gradient Lake Superior tributary
We conducted a comparative study of biofilm standing crop and metabolism in the Salmon Trout River, a tributary of Lake Superior where watershed disturbances have led to 3-fold increases in streambed fine sediments, predominately sand, in the past decade. We compared biofilm standing crop and metabolism rates using light–dark chambers in reaches where substrate consisted of predominately exposed rock or sand substrates. This data archive includes rates of primary production and respiration, biomass measurements from chambers, and benthic standing crop and water chemistry data collected from the same river sites over the course of a summer. All data were published in Journal of Great Lakes research in 2015, https://doi.org/10.1016/j.jglr.2015.09.004
Long-term monitoring of peatlands located near oil sands mining activities surrounding Fort McMurray, Alberta, Canada (2009-Present)
Oil sands mining activities in the Fort McMurray region of Alberta, Canada, have led to increased atmospherically deposited nitrogen (N) and sulfur (S), with N steadily increasing over time and S peaking in 2009, then decreasing with the installation of scrubbers on upgrader stacks. Ecosystems (such as ombrotrophic bogs) near these mining activities see an increase to their depositional load. These peatlands are isolated from groundwater and receive inputs only from precipitation, making them uniquely susceptible to changing depositional scenarios. To evaluate the effect of oil sands development on bogs in this area, since 2009, we have collected and analyzed porewater (pH, conductivity, NH 4 + -N, NO 3 - -N, SO 4 2- -S, and total dissolved N), N and S as represented in extractions of ion exchange resin precipitation collectors (NH 4 + -N, NO 3 - -N, SO 4 2- -S), samples of new growth from the most dominant plant species (C, N, and S, with Ca, Mg, K, and P analyzed in later years), and have recorded annual growth of vegetation. For a majority of the years, we have sampled at least 3 times (June, July, and August). Some sites have burned and have been replaced by others, however, collections are on-going and data from these collections are uploaded as they are published.
Meteorology and soil moisture data collected at multiple frequencies from the C-SAND NPP site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's C-SAND NPP site weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind at 5-minute frequency, and all sensors are measured at 30-minute, hourly and daily frequencies. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; total precipitation; average and total solar incoming and reflectance; average albedo; average net radiation; soil moisture, temperature and conductivity. These are measured and calculated based on 1-second scan rate of all sensors located at an automated weather station, and a nearby soil substation, installed at the site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, air temperature and relative humidity at approximately 2.5m, and solar at approximately 3m. Soil sensors are installed at approximately 10, 20 and 30cm depths.
Grass exposure, mesquite height, and plant greenness at a Jornada Basin LTER sand-sheet site, July and September 2019
Two large wind storms in March and April, 2019 caused severe erosion and exposed the roots of many grass patches in the wind-erodible "sand sheet" of the Jornada LTER. These data are from these wind-affected areas. Field transect measurements of grass cover (line-point intercept, grass plants with exposed roots (1-m belt transect), grass plants with green shoots (1-m belt transect), mesquite (PRGL) height (maximum height of cord on transect for each individual) for 18 transects in the sandy loam "sand sheet" geomorphic surface of Jornada. Also included are histograms of UAV-based measurements of green chromatic coordinate (GCC) and grass patch size (cm2) for individual grass patches within each 1-m belt transect.
Hoofprints in the Sand Supplement S3: ADI & GMM datasheet, TPS and sliders for GMM analysis
<p>Datasheet: Provenience, dating, measurements (mm), astragalar index values, and catalogue numbers for the astragali used in the ADI and GMM analyses (.csv file); thin plate spline coordinates (.tps file) and sliders (.csv file) for use in the GMM analysis. As demonstrated in Harding, S. et al. Hoofprints in the Sand: A Metric Study of Livestock on the Southern Phoenician Coast. In preparation for <em>Quaternary International</em>. </p>
Hoofprints in the Sand Supplement S4: R Code for LSI, ADI, GMM Analyses
<p>R code to reproduce the statistics and graphic plots shown in our study on the maritime mobility of sheep in the Iron Age eastern Mediterranean, as demonstrated in Harding, S. et al. Hoofprints in the Sand: A Metric Study of Livestock on the Southern Phoenician Coast. In preparation for <em>Quaternary International</em>. </p>
Figure 3: Scheme of the procedure adopted for implementing the Sand Dune Acts of 1903/1908, to reclaim the lands affected by sand drifting
<p>Figure 3 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Hoofprints in the Sand Supplement S2: LSI Datasheet
<p>Provenience, dating, measurements (mm), and catalogue numbers for the skeletal elements used in the LSI analysis, as demonstrated in Harding, S. et al. Hoofprints in the Sand: A Metric Study of Livestock on the Southern Phoenician Coast. In preparation for <em>Quaternary International</em>.</p> <p>v.2 changed the spelling of Tel Shiqmona</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.