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4,243 results for “seasonality”
Assessment of the condition of winter crops on the basis of Planet data; season 2017/2018
<p>NDVI determined on the basis of images of Planets from the dates 17.10.2017 and 13.04.2018, were used to study the assessment of wintering of crops. Acquisition of data before and after winter rest allows to assess the condition of winter crops. Available data come from the research area of the Kujawsko-Pomorskie voivodeship.</p>
Assessment of the condition of winter crops before winter dormancy on the basis of Sentinel-2 data; season 2018
<p>NDVI determined on the basis of images of Sentinel-2 from the dates 15 and 18.10.2018, were used to study the assessment of the winter crop before winter dormancy. Data were used to assess the degree of development and density of plants. The data was used to study the correlation with Planet.</p>
Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection
<p>Several layers describing density of surface water / streams projected to the <a href="https://en.wikipedia.org/wiki/Goode_homolosine_projection">Good Homolosine projection</a>. List of layers included:</p> <ul> <li>hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro">MERIT Hydro</a>,</li> <li>hyd_river.density_gloric_p = rasterized <a href="https://www.hydrosheds.org/page/gloric">Global River Classification (GLORIC)</a> DB,</li> <li>lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's <a href="https://global-surface-water.appspot.com/">Global Surface Water</a>,</li> <li>lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the <a href="https://lcviewer.vito.be/">Copernicus LC100 map</a>,</li> <li>lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on <a href="https://doi.org/10.1594/PANGAEA.892657">Tootchi et al. (2019)</a>,</li> <li>Goode_Homolosine_domain_250m.tif = map domain prepared by <a href="https://doi.org/10.5281/zenodo.1475152">Luís de Sousa</a>,</li> <li>tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,</li> </ul> <p>Important notes: Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/WaterDensity">here</a></strong>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated.</p> <p>These maps were develop in connection to the <a href="http://www.OpenLandMap.org">OpenLandMap.org</a> initiative.</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>hyd = theme: hydrology and water dynamics,</li> <li>log1p.upstream.area = variable: log(X+1)*10 of the upstream area,</li> <li>merit.hydro = determination method: MERIT Hydro,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: surface,</li> <li>2017 = time reference: period 2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement"
<p>Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement" submitted to The Cryosphere.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations
<p>This dataset contains the reduced Io spectra used in the paper "Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations" (doi: 10.1016/j.icarus.2024.116151). There are 150 spectra, spanning from 2001 to 2023. These spectra are described in Table 1 of the paper.</p> <p>The spectra in the data file are listed in date order. For each spectrum, we first provide the date (YYMMDD format) and the mean Io central longitude at the time of the observation. This is then followed by the spectrum. Column 1 is the wavelength, in units of microns. Column 2 is the Io spectrum, which has been divided by a Callisto spectrum, flattened in order to correct for any residual continuum slope, and then normalized such that the continuum level is 1. </p>
Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall
<h1>Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon.</h1>
Monthly, Seasonal and Yearly Net Primary Productivity (NPP) data of India from 2003-2020 modelled using the CASA model
<p>We estimated monthly Net Primary Productivity (NPP) at 1km spatial resolution for India using the Carnegie-Ames-Stanford Approach (CASA) Model. The CASA is a light use efficiency (LUE) based model that simulates NPP driven by remote sensing and meteorological data inputs. NPP is calculated as a product of the light use efficiency (LUE) and absorbed photosynthetically active radiation (APAR). The seasonal and annual data are prepared by aggregating the monthly data. The India Meteorological Department (IMD) recognizes four seasons in India based on climate conditions. The four seasons are defined as Winter (January-February), Pre-monsoon (March-May), Monsoon (June-September), and Post-monsoon (October-December). The seasonal data is prepared according to the above classification of the seasons. </p>
Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023
<p>The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023.</p> <p>Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis. </p> <p>Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.</p>
Data and code for "Negative effects of allelopathic plant invasion intensify as the growth season progresses"
<p>Initial release for TT23_ms_data.</p> <p>Repository contains data and code for "Negative effects of allelopathic plant invasion intensify as the growth season progresses" by Perkowski et al. (in prep). Manuscript plots and tables are also included in release.</p>
Seasonal cycle in sea level across the coastal zone
<p>Data supplement for manuscript "Seasonal cycle in sea level across the coastal zone" by Rui Ponte, Michael Schindelegger, submitted to <em>Earth and Space Science</em>, 2024.</p> <p> </p> <p><strong>Content:</strong></p> <p>This repository contains amplitude and phase estimates for the mean annual (Sa) and semiannual (Ssa) oscillations in sea level, as observed by satellite altimetry and tide gauges. We also provide related determinations of Sa/Ssa in manometric sea level, steric sea level, and in several secondary phenomena that are typically considered as corrections to altimetric sea levels or tide gauge observations (e.g., inverted barometer effect, vertical land motion):</p> <ul> <li><em>altimetric.tar.gz</em>: Sa/Ssa in gridded DUACS and MEaSUREs altimetry, and the regional X-TRACK-L2P along-track product (<a href="https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/regional/x-track-sla/x-track-l2p-sla-version-2022.html" target="_blank" rel="noopener">10.24400/527896/a01-2022.020</a>); all standard altimetry corrections have been applied to these data.</li> <li><em>tgauges_harmonics.da</em>t: Sa/Ssa estimates at 747 globally distributed tide gauges from the GESLA-3 database (<a href="https://gesla787883612.wordpress.com/">https://gesla787883612.wordpress.com/</a>). The harmonics are not corrected for the inverted barometer effect.</li> <li><em>manometric_steric.tar.gz</em>: Gridded Sa/Ssa estimates in manometric sea level, derived from GSFC GRACE 1° mascons, and steric sea level, as deduced from a hydrographic atlas (WOA2023).</li> <li><em>corrections.tar.gz</em>: Sa/Ssa contributions to the oceanic inverted barometer, the astronomical tides, and vertical land motion, along with an estimate for the annual pole tide signal.</li> </ul> <p>The respective files contain a few more relevant specifications such as data sources, underlying grids, and time periods considered for the analysis.</p> <p> </p> <p><strong>Important notes</strong>:</p> <ul> <li>Phases are referred to the vernal equinox, and NOT the beginning of the calendar year. See Ray et al. (2021) for a pertinent discussion.</li> <li>Amplitudes of the corrective fields are in (mm), all other amplitudes are in (cm).</li> </ul> <p> </p> <p><strong>Terms of usage:</strong></p> <ul> <li>If you use the DUACS or MEaSUREs seasonal cycle estimates or the corrective fields, please cite: Ray, R.D., Loomis, B.D. & Zlotnicki, V. (2021). The mean seasonal cycle in relative sea level from satellite altimetry and gravimetry. <em>Journal of Geodesy</em>, 95, 80. <a href="https://doi.org/10.1007/s00190-021-01529-1">https://doi.org/10.1007/s00190-021-01529-1</a>.</li> <li>If you use any of the other datasets (i.e., seasonal cycle in X-TRACK, tide gauges, manometric and steric sea level), please cite: Ponte, R.M. & Schindelegger, M. (2024). Seasonal cycle in sea level across the coastal zone. <em>ESS Open Archive</em> [preprint]. doi: <a href="https://essopenarchive.org/users/534050/articles/1225967-seasonal-cycle-in-sea-level-across-the-coastal-zone" target="_blank" rel="noopener">10.22541/essoar.172736425.52711759/v1</a>.</li> </ul> <p>----</p> <p>Contact: M. Schindelegger (schindelegger@igg.uni-bonn.de)</p> <p> </p>
Dataset from : Browsing is a strong filter for savanna tree seedlings in their first growing season
<p>1: Newly germinated seedlings are vulnerable to biomass removal but usually have at least six months to grow before they are exposed to dry-season fires, a major disturbance in savannas. In contrast, plants are exposed to browsers from the time they germinate, making browsing potentially a very powerful bottleneck for establishing seedlings. 2: Here we assess the resilience of seedlings of 10 savanna tree species to top-kill during the first 6 months of growth. Newly-germinated seeds from four dominant African genera from across the rainfall gradient were planted in a common garden experiment at the Wits Rural Facility and clipped at 1 cm when they were ~2, 3, 4, and 5 months old. Survival, growth, and key plant traits were monitored for the following 2.5 years. 3: Seedlings from environments with high herbivory pressure survived top-kill at a younger age than those from low-herbivore environments, and more palatable genera had higher herbivore-tolerance. Most individuals that survived were able to recover lost biomass within 12 months, but the clipping treatment affected root mass fraction and branching patterns. 4: Synthesis: The impact of early browsing as a demographic bottleneck can be predicted by integrating information on the probability of being browsed and the probability of surviving a browse event. Establishment limitation through early-browsing is an under-recognised constraint on savanna tree species distributions. Data may be used without requesting permission after the the publication of the paper</p>
Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020
<p>Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being:</p> <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> <p>Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track.</p> <p>The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows:</p> <p><span>\(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\)</span></p> <p>For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.</p>
Data and code for: Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, Suricata suricatta
<p>Data and code to go with our publication "Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, <em>Suricata suricatta", </em>Nature Communications (2021).</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em>****** DATA ******</em></p> <p><strong>meerkat_16S_data.tar.gz</strong> # 16S V4 amplicon sequences sequenced on an Illumina MiSeq platform using primer pair 515F and 806R, including all faecal samples, controls, and sand samples. Sequence identifiers and basic metadata are in <strong>sequence_identifiers.csv.</strong></p> <p><strong>sequence_identifiers.csv </strong># Simple metadata and identifiers for all sequences/samples (what type of sample/sequencing run, etc), required for QIIME2 processing of the raw fasta.gz files contained in meerkat_16S_data.tar.gz. It contains a column for whether the sample was included in the final analysis. Does not include sample biological metadata as generating this data requires access to Kalahari Meerkat Project database. Biological metadata for samples included in the final analysis are instead provided in <strong>processed_data_phyloseq.RDS </strong>and can be accessed via <em>phyloseq::sample_data(processed_data_phyloseq)</em>.</p> <p><strong>processed_data_phyloseq.RDS</strong> # Phyloseq object containing the processed data used in the presented analysis. Contains data for 1109 samples, and includes the ASV table, the taxonomic classification, the phylogenetic tree, and the sample metadata used in the analysis.</p> <p><strong>technical_replicate_data_phyloseq.RDS</strong> # Phyloseq object containing data from the 16 technical replicates.</p> <p><strong>pilot_study_data_phyloseq.RDS</strong> # Phyloseq object containing data from the pilot study on captive meerkats.</p> <p><em>****** CODE ******</em></p> <p><strong>CODE1_QIIME_script.R</strong> # QIIME2 script to generate ASV table, taxonomy, and phylo tree from <strong>meerkat_16S_data.tar.gz. </strong>Requires a reference taxonomy (SILVA) and a reference phylogeny (SEPP) for taxonomic and phylogenetic placements.</p> <p><strong>CODE2_processing_QIIME_output.Rmd</strong> # R markdown script that processes the QIIME2 output generated by <strong>CODE1_QIIME_script.R</strong>. Does not generate meerkat metadata as this requires access to the Kalahari Meerkat Project database. This metadata is provided in <strong>processed_data_phyloseq.RDS.</strong></p> <p><strong>CODE3_data_analysis_script.Rmd </strong># R markdown script that generates data and figures presented in paper, using data from <strong>processed_data_phyloseq.RDS, technical_replicate_data_phyloseq.RDS, </strong>and<strong> pilot_study_data_phyloseq.RDS.</strong></p> <p><em>****** R MARKDOWN REPORTS ******</em></p> <p>The following reports are html files that show the code output for the two RMD files above.</p> <p><strong>RMARKDOWN_data_processing.html </strong># R markdown report for<strong> CODE2_processing_QIIME_output.Rmd</strong></p> <p><strong>RMARKDOWN_data_analysis.html </strong># R markdown report for <strong>CODE3_data_analysis_script.Rmd</strong></p> <p>*****************************</p> <p>For general queries, unexpected errors and/or inconsistencies, please contact riselya@gmail.com.</p> <p> </p>
Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code
<p>Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)</p>
Supplementary Information to: "Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman"
<p>This dataset contains supplementary information required to understand and reproduce the study detailed in our manuscript titled "<em>Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman</em>" which was submitted for publication to Palaeogeography, Palaeoclimatology, Palaeoecology.</p>
Winter Season Spectral Snowpack Albedo Data For the Caldor and Creek Fires
<p>* Description: The file "Caldor_Creek_Fires_Winter_Snow_Albedo_Spectrometer_Dataset.csv" is a comma-delimited file containing the spectral snowpack albedo measurements from the Caldor and Creek Fires in California and the associated burn severities at the location of each measurement.</p> <p> </p> <p>Data and File Overview</p> <p>======================</p> <p>Summary Metrics</p> <p>---------------</p> <p>* File count: 1</p> <p>* Total file size: 909 KB </p> <p>* Range of individual file sizes: 909 KB </p> <p>* File formats: .csv</p> <p> </p> <p>Naming Conventions</p> <p>------------------</p> <p>* File naming scheme: One file that includes all dates, all burn severities, all wavelengths.</p> <p>* Format(s): Comma-separated value (.csv) file</p> <p>* Size(s): 909 KB</p> <p>* Dimensions: 17,209 rows x 6 columns</p> <p>* Variables:</p> <p> * Measurement_Number: An index of the measurement number, unitless</p> <p> * wavelength: The wavelength of light (units in nanometers) for which albedo sample ranging from 350-2500 nm</p> <p> * Type: Measurement type is albedo in various burn severity environments (_hb is high burn severity, _mb is moderate burn severity, _ub is unburned, with the last letter corresponding to the month (J is January, F is February, A is April). NA corresponds to estimated January unburned data for the Caldor Fire where unburned albedo for April in the Creek Fire was adjusted downwards by 0.04 to account for less grain-size growth (Colbeck 1982, Rev. Geophys., https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/RG020i001p00045). </p> <p> * Albedo: The albedo (unitless), is a measurement of the solar radiation reflected by the snow surface divided by the radiation incident on its surface. In other words, albedo is the fraction of the incident sunlight reflected by the snow.</p> <p> * month: The month when the measurement was taken</p> <p> * burn: Burn severity at measurement location based upon classifications (High, Medium,, Unburned) from the Monitoring Trends in Burn Severity Dataset (https://mtbs.gov/). NA corresponds to the estimated unburned data for the Caldor Fire using April unburned data in the Creek Fire.</p> <p> * Missing data codes: No missing data is presented.</p> <p> </p> <p>Dates and Locations</p> <p>-------------------</p> <p>* Dates of data collection: Surface albedo collected in 27-28 February 2021, 1 April 2021, and 21 January 2022</p> <p>* Geographic locations of data collection: Data collected within the Caldor Fire perimeter in the central Sierra Nevada, California-Nevada (January 2022) and the Creek Fire perimeter, California (February and April 2021).</p> <p> </p> <p>Setup</p> <p>-----</p> <p>* Recommended software/tools to open file: parentage- R Studio; file can be opened using any text editor or programming language (e.g., R Studio, MATLAB, Python, TextMate, Microsoft Excel, etc)</p> <p> </p>
Native and exotic plants play different roles in urban pollination networks across seasons
<p>Datasets for 'Native and exotic plants play different roles in urban pollination networks across seasons' by Zaninotto et al. (2023) in Oecologia.</p>
Supplemental tables for a study of the seasonal Impacts of the Physical Environment on Biogeochemical Cycles in Arctic Lakes of the Mackenzie River Delta
<p>submitted abstract</p> <p>We conducted two- and six-year-long deployments of continuous water samplers (OsmoSamplers) and sensors (Temperature, pressure, light level, dissolved oxygen (DO) and conductivity) in nine lakes within the mid- to outer-delta region of the Mackenzie River and documented biogeochemical fluctuations (Mn, Fe, sulfate, and DO), defined physical processes that that drive such fluctuations, and constrained the impact of lake solutes on annual riverine fluxes. Five lakes were in the mid-delta region near Inuvik, NT, two lakes were in the outer delta, and two lakes were on the Arctic coastal plain and were not impacted by the Mackenzie River. In general, temperature minima occurred in September/October, indicative of ice formation, and distinct hydrostatic pressure (water level) anomalies occurred in May/June associated with ice breakup, lasting for days to months and impacting lake levels up to 4.2 m higher than “normal”. Such anomalies coincide with a dramatic change in solute concentrations. Systematic changes in solute concentrations indicate redox-driven biogeochemical reactions, salt exclusion during ice formation, and continuous to sporadic exchange of river water. Redox reactions were regulated by DO inputs stemming from atmospheric, photosynthetic, and riverine sources. During ice-covered periods dissolved sulfate may be conservative but was generally removed. Manganese and iron concentrations showed phases of production and removal during ice-covered periods, but both were produced overall. Calculated solute fluxes from lake waters alone to the Arctic Ocean may only impact yearly riverine fluxes for solutes that exceed ten times the river concentration prior to ice breakup (e.g., Mn and Fe).</p>
Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons
<p>Dataset comprised of nutrient fluxes (DOC, TDN, NH4, TDN and SRP), optical parameters related to DOM composition (SUVA, spectral slopes and slope ratio), pH, and other nutrient and elemental ratios for passive pan lysimeters installed across terrestrial sites in Pynn's Brook, Newfoundland.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.