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10,929 results for “community”
Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico
Two of the most pervasive human impacts on ecosystems are alteration of global nutrient budgets and changes in the abundance and identity of consumers. Fossil fuel combustion and agricultural fertilization have doubled and quintupled, respectively, global pools of nitrogen and phosphorus relative to pre-industrial levels. In spite of the global impacts of these human activities, there have been no globally coordinated experiments to quantify the general impacts on ecological systems. This experiment seeks to determine how nutrient availability controls plant biomass, diversity, and species composition in a desert grassland. This has important implications for understanding how future atmospheric deposition of nutrients (N, S, Ca, K) might affect community and ecosystem-level responses. This study is part of a larger coordinated research network that includes more than 40 grassland sites around the world. By using a standardized experimental setup that is consistent across all study sites, we are addressing the questions of whether diversity and productivity are co-limited by multiple nutrients and if so, whether these trends are predictable on a global scale. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and and foliage, over time and incoporates growth as well as loss to death and decomposition. To measure this change the vegetation variables, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. Volumetric measurements are made using vegetation data from permanent plots (SEV231, "Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet NPP Quadrat Sampling") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Alaska 2020 update for USGS G19AP00019: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2020 uses earthquake travel-time and ambient noise group velocity data to update Eberhart-Phillips et al. (2006: AKEP2006)</p> <p> </p> <p>The model is provided in a table: vlAKEP2020xyzltlnSFDRE.tbl.txt</p> <p>and in the simul output from velocity inversion: vlAKEP2020.out.txt</p> <p>Map plots of Vp and Vp/Vs are also provided, with lines denoting limits of adequate data.</p> <p> </p> <p>Velocity Inversion Procedure Notes for AKEP2020 model</p> <p> </p> <p>The 2006 AK model and the 2020 updated model both use Transverse Mercator coordinate transformation with central meridian= -150 and counterclockwise rotation of 162.1. Earth-flattening transformation is used for velocity during ray-tracing. The depths are relative to sea-level and station elevations are used. For the group-velocity data, the surface is taken as the 30-km median filtered topography.</p> <p>Velocity with the 3D gridded model is defined by linearly interpolating between nodes.</p> <p>A gradational inversion approach was used with earthquake and shot travel-time data, and group velocity observations for periods 6-15 s. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p> </p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G19AP00019. Note that an earlier model, AKEP2018, from the first year of this funded project was reported in Eberhart-Phillips et al. (2019).<br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey</p>
Scholarly journals publishing articles by family and community physicians in Brazil, up to December 2018
<p>This is the dataset of manuscript titled "In which journals do family and community physicians in Brazil publish? The <em>Trajetórias MFC</em> project". There are two spreadsheets: the dataset proper and the data dictionary. See the manuscript for background.</p> <p>All spreadsheets are in the CSV (comma-separated values) format, delimited with semicolons and encoded in UTF-8 with the byte-order mark (BOM). The spreadsheets can be opened with desktop or Web application software (LibreOffice Calc, Microsoft Excel, Google Sheets) or with statistical software such as R.</p> <p>A <a href="https://zenodo.org/record/3905255">previous version</a> of this dataset was used in a <a href="https://doi.org/10.1101/19005744">preprint</a>. This version should be cited by an upcoming article.</p> <p>See also the <a href="https://doi.org/10.1136/fmch-2020-000321">article</a>, <a href="https://doi.org/10.5281/zenodo.3376310">dataset</a> and <a href="https://doi.org/10.5281/zenodo.3381576">supplementary table</a> for an earlier milestone, about the postgraduate education of family and community physicians in Brazil.</p>
Data from: Investigating the impact of street lighting changes on garden moth communities
<p>This data package accompanies:<br><em>Plummer et al (2016). Investigating the impact of street lighting changes on garden moth communities. Journal of Urban Ecology. DOI 10.1093/jue/juw004</em></p> <p>It contains a copy of the two derived datasets used to complete the analyses presented in the paper. File details:</p> <p><strong>1. ReadMe.txt: </strong>Includes a description of the variables included in each dataset.</p> <p><strong>2. Plummer_JUrbanEcol_2016_BACI_dataset.csv: </strong>A .csv file including two years (2011 & 2013) of macro-moth community data (abundance, richness, diversity) for 18 garden locations in Birmingham, UK. Data are summarised per garden and year, together with data for proximity to street lamp replacement.</p> <p><strong>3. Plummer_JUrbanEcol_2016_light_composition_dataset.csv:</strong> A .csv file including one year (2013) of garden moth community data (abundance, richness, diversity; including macro- and micro-moths) for 18 garden locations in Birmingham, UK. Data are summarised per trapping event, together with associated street lighting and habitat characteristics for each garden. </p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Two-minute averaged net community production in seawater estimated along the Antarctic Circumnavigation Expedition during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>During the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017, seawater O2/Ar ratios were measured underway from the ship's flow-through seawater system. High-resolution net community production (NCP) in units of mmol O2 m-2 day-1 was derived from delta O2/Ar and National Centers for Environmental Prediction (NCEP) reanalysis winds followed by a linear correction for sea-ice.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_ncp_linear_correction.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data reprocessed using different version of sea ice.</p> <ul> <li>data file reprocessed</li> <li>README updated to reflect change in processing</li> <li>change log added to dataset</li> </ul> <p>v1.0 - initial release of dataset</p>
Detecting local variations across metazoan communities in backreef depressions of Reunion Island (Mascarene Archipelago) through environmental DNA survey
<p>The back-reef depressions, or lagoons, of Reunion Island (western Indian Ocean) host a high abundance of organisms living amongst the coral reefs and are critical sites for artisanal fishing, tourism, and shoreline stability for the island. Over time, increasing degradation of Reunionese reefs has been observed due to overexploitation, beach erosion and eutrophication. Efforts to mitigate the impact of these pressures on aquatic organisms include biodiversity surveys primarily performed through visual censuses that can be logistically complex and may unintentionally overlook organisms. Surveys integrating environmental DNA (eDNA) collections have provided rapid biodiversity assessments, while helping to circumvent some limitations of visual surveys. The present study describes the results of an exploratory eDNA survey, which aims to characterize metazoan communities of four Reunionese lagoons located along the west coast of the island. As eDNA surveys first require deliberate study design and optimization for each new context, we sought to establish a modernized workflow implementing specialized equipment to collect and preserve samples to facilitate future studies in these lagoons. During the austral summer of 2023, samples were pumped directly from surface and bottom depths at each site through self-preserving filters which were then processed for DNA metabarcoding using regions of the 12S ribosomal RNA (12S), small ribosomal subunit 18S (18S) and Cytochrome Oxidase I (COI) genes. The survey detected high species richness that varied by site, and in a single collection period, recovered the presence of 60 teleost families and numerous invertebrate taxa, including members of the coral faunal community that are less studied in Reunion. Distinct biological communities were observed at each site, and within a single lagoon, suggesting that these differences are due to site-specific factors (e.g., environmental variables, geographic distance, etc.). Although continued protocol optimization is needed, the present findings demonstrate the successful application of an eDNA-based survey for biodiversity assessment within Reunionese lagoons.</p>
One-hectare fine-scale dataset of a fynbos plant community in the Cape Floristic Region
<p>Cape fynbos, which forms part of the Cape Floristic Region (CFR) of South Africa, a global biodiversity hotspot, is renowned for its high levels of plant species endemism and diversity. This extraordinary ecosystem, characterised by nutrient-poor soils and fire-adapted vegetation, is a treasure trove of endemic flora. However, this fragile system faces increasing threats from habitat loss, climate change, and invasive species. Pristine fynbos, naturally high in plant diversity and which forms a large part of the CFR, presents an ideal opportunity to gather fine-scale data on community assembly patterns. Most fynbos vegetation surveys use a plot size of about 100 m2, with no spatial structures within plots to demarcate individual subplots. Here, a groundbreaking dataset is presented that fully covers 1-hectare of pristine fynbos, systematically gridded into 50 × 50 subplots, each measuring 2 × 2 m, arranged evenly within a square-shaped survey site. Each plot was assigned a unique Y–X coordinate combination. For each plot, all plant species present were recorded, along with their total percentage covers and maximum height values. Total percentage covers were also recorded for bare soil, rock, and termite mounds. This dataset provides a valuable contribution to the field of fynbos ecology, as well as plant community ecology in general, and establishes a benchmark for future one-hectare surveys of similar fynbos vegetation types, delineating the fine-scale composition and structure of fynbos in the CFR. The dataset will be useful for a wide audience, including community and spatial ecologists, plant and environmental scientists, and biodiversity informaticians and statistical ecologists, offering ideal data for testing new metrics of diversity and compositional turnover. Data in Brief, Volume 59, April 2025, 111334: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2025.111334" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2025.111334</span></span></a></p>
3C dataverse: Community capitals, cover crops, & conservation agriculture in the U.S. corn-soybean belt, version 2.2
<p><strong>What? </strong></p> <p>A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.</p> <p><strong>Why? </strong></p> <p>Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.</p> <p><strong>How? </strong></p> <p>Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. </p> <p><strong>What is 'new' or corrected in version 2.2? </strong></p> <p><em>Edited/amended</em>: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. </p> <p><em>Added</em>: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. </p> <p><em>Omitted</em>: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell.</p> <p><strong>CRediT</strong>: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K</p> <p><strong>Acknowledgements</strong>: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance. </p>
Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs
<p>Datasets describing the fungal species diversity, microbial density and acidity of French sourdoughs, phenotypic variation of Kazachstania bulderi and Kazachstania humilis strains as well as the diversity of bread-making practices of 40 bakers and farmers-bakers.The data were collected, analyzed, and reported within the following publication :</p> <p>Elisa Michel, Estelle Masson, Sandrine Bubbendorf, Léocadie Lapicque, Thibault Nidelet, Diego Segond, Stéphane Guézenec, Thérèse Marlin, Hugo deVillers, Olivier Rué, Bernard Onno, Judith Legrand, Delphine Sicard and the participating bakers: <strong>Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs</strong>. PCI Evol. Biol.</p> <p> </p>
Data from: Functional structure of European forest beetle communities is enhanced by rare species
<p>From article abstract:</p> <p><a href="https://doi.org/10.1016/j.biocon.2022.109491">https://doi.org/10.1016/j.biocon.2022.109491</a></p> <p><strong>ABSTRACT</strong></p> <p>Biodiverse communities have been shown to sustain high levels of multifunctionality and thus a loss of species likely negatively impacts ecosystem functions. For most taxa, however, the roles of individual species are poorly known. Rare species, often the most likely to go extinct, may have unique traits leading to unique functional roles. Alternatively, rare species may be functionally redundant, such that their loss would not disrupt ecosystem functions. We quantified the functional role of rare species by using capture records of wood-living (saproxylic) beetle species, combined with recent databases of their morphological and ecological traits, from three regions in central and northern Europe. Using a rarity index based on species’ local abundance, geographic range, and habitat breadth, we used local and regional species removal simulations to examine the contributions of both the rarest and the most common beetle species to three measures of community functional structure: functional richness, functional specialization, and functional originality. In both regional species pools and local communities, all three of these measures declined more rapidly when rare species were removed than under common (or random) species removal scenarios. These consistent patterns across scales and among several forest types give evidence that rare species provide unique functional contributions, and that their loss may disproportionately impact ecosystem functions. This implies that conservation measures targeting rare and endangered species, such as preserving intact forests with dead wood and mature trees, can provide broader ecosystem-level benefits. Experimental research linking functional structure to ecosystem processes should be prioritized to increase our understanding of the functional consequences of species loss and to develop more effective conservation strategies.</p> <p> </p> <p><strong>DATASET DESCRIPTION</strong></p> <p>This dataset includes a) beetle capture information and b) beetle trait information from three countries: 1) Norway, 2) Finland, and 3) Germany. </p> <p> </p> <p><strong>FILES</strong></p> <p><strong>readme.txt</strong> -- this has the information from this description section</p> <p><strong>Norway_traits.csv</strong>, <strong>Finland_traits.csv</strong>, <strong>Germany_traits.csv</strong> -- these are the trait files, including all species</p> <p><strong>Norway_sites.species.csv</strong>, <strong>Finland_sites.species.csv</strong>, <strong>Germany_sites.species.csv</strong> -- this has species (rows) by sites (columns); values are the number of beetles caught (for number of traps, dates, and other site covariates, see related dataset: <a href="https://doi.org/10.5061/dryad.tmpg4f50b">https://doi.org/10.5061/dryad.tmpg4f50b</a> and manuscript: <a href="https://doi.org/10.1111/jbi.14272">https://doi.org/10.1111/jbi.14272</a>). Species names follow GBIF taxonomic backbone.</p> <p><strong>Traits_METADATA.csv</strong> -- this has information on all the fields in the trait data</p> <p> </p>
Net community production, nutrients, and hydrographic parameters in the South China Sea in summer 2017
<p>In summer, the Vietnam Offshore Current (VOC) and the Kuroshio intrusion are two important processes provoking considerable environmental fluctuations in the South China Sea (SCS). Net community production (NCP) is an important proxy of biological pump strength and can be estimated based on the dissolved oxygen to argon ratio (O<sub>2</sub>/Ar) in the mixed layer. To determine the influence of the VOC and Kuroshio intrusion on the NCP in the oligotrophic SCS, we conducted high-resolution underway measurements of O<sub>2</sub>/Ar and hydrographic parameters using membrane inlet mass spectrometry (MIMS, HPR-40, Hiden, UK) and multi-parameter water quality logger (RBR Maestro, Canada) during the cruise in the northeastern SCS in summer 2017. NCP in the mixed layer was estimated using the supersaturation of O<sub>2</sub>/Ar (Delta O<sub>2</sub>/Ar) and gas transfer velocity (k). All the underway observation data were compiled into the 5-min interval. To monitor the nutritive fluctuations induced by the VOC and Kuroshio intrusion, we also collected surface water samples from Niskin bottles at sampling stations for the nutrients analysis; the nutrients were then determined by an auto-analyzer. We divided the cruise into three phases (Phase 1, 2, and 3); Phase 1 was dominated by the Kuroshio intrusion, while Phase 3 was influenced by the VOC. Because the upwelling driven by cyclonic eddies and typhoons could introduce considerable uncertainties to the NCP result, we excluded the data obtained in the upwelling regions.</p>
Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs
<p>The dataset includes the outputs of the project: "Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs" funded by the David and Lucille Packard Foundation and awarded to H. Reyes-Bonilla (UABCS). </p> <p>This study analyzed vulnerability of fisheries-dependent coastal communities based on three components: a) adaptive capacity (84 indicators), which reflect the ability of a community to respond and recover after adverse events; b) susceptibility (11 indicators) which was determined based on fishing dependence; and c) exposure (31 indicators) that was evaluated with current environmental data. Future vulnerability was determined for a 2050 horizon and based on two climate change scenarios: SSP126, which represents low emissions, and SSP585, which takes into consideration that the amount of greenhouse gases will continue to increase. These data come from the Coupled Model Intercomparison Project 6 (CMIP6), which serves as the basis for the 6th IPCC report. We evaluated vulnerability using indicators what were available at the local scale.</p>
Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change
<p>Environmental and biodiversity data associated with the article: <br><strong>Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. <em>Oikos. </em></strong>https://doi.org/10.1111/oik.10697</p> <p>Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables.</p> <p>Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file.</p> <ul> <li><strong>Danish Common Bird Monitoring programme</strong> – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. <em>Dansk Ornitologisk Forening</em>.</li> <li><strong>E-OBS European gridded climate data</strong> – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. <em>Journal of Geophysical Research Atmospheres</em>, <em>113</em>(20). https://doi.org/10.1029/2008JD010201</li> <li><strong>AVONET bird traits data</strong> – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. <em>Ecology Letters</em>, <em>25</em>(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul> <p> </p>
Global atmospheric simulation using the Super-Parameterized Community Atmosphere Model
<p>A 1-month subset from a global atmospheric simulation using the Super-Parameterized Community Atmosphere Model (SP-CAM), which implements the Multi-scale Modeling Framework (MMF) described in Khairoutdinov and Randall (2001) and Khairoutdinov et al. (2005). The version of the SP-CAM used here is described in Marchand et al. (2009) and Ovtchinnikov et al (2006), and was run at Pacific Northwest National Laboratory with DOE support. It is based on CAM 3.0 for the global atmospheric component, and uses the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003). This simulation is configured with CAM running the finite volume dynamical core on a 2x2.5 degree latitude-longitude grid with 26 vertical levels. The embedded CRM (SAM) is configured with 64 horizontal columns at 4 km grid spacing with 24 vertical levels (sharing the bottom 24 levels with the CAM grid), and single-moment microphysics. The simulation was initialized on 1 September 1997 and runs through June 2002, forced with observed monthly-mean sea surface temperatures. Only the month of July 2000 is uploaded here, which is what is required to reproduce the results in Hillman et al. (2018).</p>
Alaska 2018 update for USGSG18AP00017: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2018 uses earthquake travel-time and ambient noise group velocity data to update the Alaska 3-D model of Eberhart-Phillips et al. (2006: AK2006), for the USGS project on developing Alaska Community Seismic Velocity Models . This 2018 model will be expanded with additional data in 2019 in the second year of the funded project.</p> <p>Velocity within the 3D gridded model is defined by linearly interpolating between nodes. The inversion solved for Vp and Vp/Vs. The model is provided in a table: vlAKEP2018xyzltlnSFDRE.tbl.txt, with velocity at inversion nodes in cartesian and latitude-longitude coordinates. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G18AP00017. <br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.</p>
Data from: Shift of bacterial and fungal communities upon soil amelioration is driven by carbon degradability of organic amendments
<p>Microbial communities of bacteria and fungi have been analyzed in soil. Agricultural soil was amended with different organic amendments including straw, compost, biogas residues, and biochar, and incubated in the lab. After 6 months, DNA extracted from soil samples was analyzed via Illumia MiSeq DNA sequencing (16S V3V4 for bacteria, ITS1 for fungi) to evaluate changes to the microbial community structure.</p> <p>For details, please see the respective publication (DOI: 10.1007/s44378-024-00012-5).</p>
Dataset: Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery
<div> <div> <div> <p>This repository containts the datasets from the article "Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery" (Submitted). The dataset was used in a workflow used to create saltmarsh maps for the Algarve region (South Portugal), focused on the 4 main costal systems of the region: Alvor, Arade, Ria Formosa and Guadiana.</p> <p> </p> <p>For a description of the methodology see the article [link] and Github repo [link].</p> <p> </p> <h1>Repository content</h1> <h2>1. system-masks.zip</h2> <p>Contains 4 <code>geojson</code>files with a polygon which delimits the areas included in the study. The files are named after the respective systems that they delimit. Any region outside of these polygons were not used in the analysis.</p> <p><strong>CRS</strong> - EPSG:4326</p> <h2>2. manual-clean-up-masks.gpkg</h2> <p>Polygons which were manually created to mask out (exclude) pixels which were classified as saltmarsh, but are clearly not.</p> <p>File contains a single layer with 52 polygons and one variable.</p> <p><strong>Variables:</strong></p> <ul> <li>system [<em>string</em>] - Which system the polygon delimits</li> </ul> <h2>3. saltmarsh-training-data.gpkg</h2> <p>Data used for supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, target classes, or predictor classes.</p> <p>File contains a single layer with 2448 points and 18 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the quadrat was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>train [<em>boolean</em>] - Was quadrat used in the train (TRUE) or test (FALSE) stage of model training?</li> <li>ndvi [<em>numerical</em>] - Normalized Difference Vegetation Index, calculated from the satellite image mosaic as (nir – red) / (nir + red).</li> <li>ndwi_high [<em>numerical</em>] - Normalized Difference Water Index estimated from images at high tide, calculated as (green – nir) / (green + nir)</li> <li>ndwi_low [<em>numerical</em>] - Normalized Difference Water Index estimated from images at low tide, calculated as (green – nir) / (green + nir)</li> <li>subtime [<em>numerical</em>] - Fraction of time that a cell is estimated to be submerged in water over one year.</li> <li>coastal_blue [<em>numerical</em>] - Surface reflectance values at 443 nm.</li> <li>blue [<em>numerical</em>] - Surface reflectance values at 490 nm.</li> <li>green_i [<em>numerical</em>] - Surface reflectance values at 531 nm.</li> <li>green [<em>numerical</em>] - Surface reflectance values at 565 nm.</li> <li>yellow [<em>numerical</em>] - Surface reflectance values at 610 nm.</li> <li>red [<em>numerical</em>] - Surface reflectance values at 665 nm.</li> <li>rededge [<em>numerical</em>] - Surface reflectance values at 705 nm.</li> <li>nir [<em>numerical</em>] - Surface reflectance values at 865 nm.</li> </ul> <h2>4. saltmarsh-transect-metadata.csv</h2> <p>Comma-delimited file with information about vegetation sampling transects. Each row represents one transect.</p> <p>File contains 6 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>site [<em>string</em>] - Name of the site within the study system. This was used exclusively to plan transects.</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>date [<em>date yyyy-mm-dd</em>] - Date of transect sampling.</li> <li>notes [<em>string</em>] - Notes taken during transect sampling and which might be relevant to understand data issues.</li> </ul> <h2>5. saltmarsh-vegetation-quadrats.gpkg</h2> <p>Data used for to create vegetation clusters (<code>cluster</code>) and saltmarsh community types (<code>marsh_type</code>). The later was used as the target class in the supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, or presence/absence of species.</p> <p>File contains a single layer with 2448 points and 32 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>distance_from_water <em>[integer]</em> - Distance from start of quadrat, which was the point closes to the water where saltmarsh was found for that transect.</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>Arthrocaulon.macrostachyum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Tripolium.pannonicum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.halimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Cistanche.phelypaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.portulacoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limbarda.crithmoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Juncus.effusus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limoniastrum.monopetalum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Myriolimon.ferulaceum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limonium.vulgare [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Phragmites.australis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Polygonum.maritimum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Puccinellia.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.procumbens [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.europaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Caroxylon.vermiculatum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.fruticosa [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.perennis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Bolboschoenus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Spergularia.bocconei [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Suaeda.vera [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Triglochin.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.montevidensis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> </ul> <h2>6. predicted-map.tif</h2> <p>Geotiff file with a single layer for predicted saltmarsh community. Values are:<br> - <em>no data</em> - Not saltmarsh<br> - <em>1</em> - Low saltmarsh<br> - <em>2</em> - Middle saltmarsh<br> - <em>3</em> - High saltmarsh</p> <p><strong>CRS</strong> - EPSG:32629</p> </div> </div> </div>
AMBON diversity & community composition, data & code
<p>Data and R code for analyzing diversity and community composition of eight assemblages in the Northeast Chukchi Sea in 2015 and 2017. For details of the analysis, results and interpretation, see:</p> <p><em>Mueter, F.J., Iken, K., Cooper, L.W., Grebmeier, J.M., Kuletz, K.J., Hopcroft, R.R., Danielson, S.L., Collins, R.E., Cushing, D. Changes in diversity and species composition across multiple assemblages in the northeast Chukchi Sea during two contrasting years are consistent with borealization. Oceanography (In Press).</em></p>
Offering ART refill through community health workers versus clinic-based follow-up after home-based same-day ART initiation in rural Lesotho: The VIBRA cluster-randomised clinical trial
<p>These are pseudo-anonymised data from the VIBRA randomized trial: "Offering ART refill through community health workers versus clinic-based follow-up after home-based same-day ART initiation in rural Lesotho: The VIBRA cluster-randomised clinical trial". The data dictionary explains the data available in the dataset. Between August 2018 and May 2019, 257 eligible individuals from 117 consenting villages were enrolled from two districts of Lesotho, and followed up for a maximum of 15 months. The protocol was published, doi: 10.1186/s13063-019-3510-5</p>
OpenAIRE Graph: Dataset for research communities and initiatives
<p>This dataset contains metadata records of the OpenAIRE Graph relevant for the research communities and initiatives collaborating with OpenAIRE and with a public Community Gateway on <a href="https://connect.openaire.eu">OpenAIRE CONNECT</a> as of July 2025.</p> <p>Each file is a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.14891476" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14891476</a></p> <table style="width: 100%; height: 822.939px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;"><strong>Research community name</strong></td> <td style="width: 38.9414%; height: 19.5938px;"><strong>File name</strong></td> <td style="width: 23.1067%; height: 19.5938px;"><strong>URL to the gateway</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Argo France</td> <td style="width: 38.9414%; height: 19.5938px;">argo-france.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://argo-france.openaire.eu/">https://argo-france.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Aurora University Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">aurora.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://aurora.openaire.eu">https://aurora.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Transport Research (EC projects BE OPEN and SciLake)</td> <td style="width: 38.9414%; height: 19.5938px;">beopen.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://beopen.openaire.eu">https://beopen.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">CIVICA Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">civica.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://civica.openaire.eu" target="_blank" rel="noopener">https://civica.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">COVID 19</td> <td style="width: 38.9414%; height: 19.5938px;">covid-19.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://covid-19.openaire.eu">https://covid-19.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">DARIAH EU</td> <td style="width: 38.9414%; height: 19.5938px;">dariah.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dariah.openaire.eu">https://dariah.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Digital Humanities and Cultural Heritage</td> <td style="width: 38.9414%; height: 19.5938px;">dh-ch.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dh-ch.openaire.eu">https://dh-ch.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Virtual Twins in Health (EC project EDITH)</td> <td style="width: 38.9414%; height: 19.5938px;">dth.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dth.openaire.eu">https://dth.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European Digital Innovation Hubs Network ADRIA</td> <td style="width: 38.9414%; height: 19.5938px;">edih-adria.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://edih-adria.openaire.eu">https://edih-adria.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">European Geothermal Research and Innovation Search Engine </td> <td style="width: 38.9414%; height: 39.1875px;">egrise.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://egrise.openaire.eu">https://egrise.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">ELIXIR Greece</td> <td style="width: 38.9414%; height: 19.5938px;">elixir-gr.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://elixir-gr.openaire.eu">https://elixir-gr.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Energy Planning (EC project SciLake)</td> <td style="width: 38.9414%; height: 19.5938px;">energy-planning_1.tar, energy_planning_2.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://energy-planning.openaire.eu/">https://energy-planning.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Energy Research (EC project Enermaps)</td> <td style="width: 38.9414%; height: 19.5938px;">enermaps.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://enermaps.openaire.eu">https://enermaps.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">European University for Smart Urban Coastal Sustainability</td> <td style="width: 38.9414%; height: 39.1875px;">eu-conexus.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://eu-conexus.openaire.eu/">https://eu-conexus.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European University of Technology+</td> <td style="width: 38.9414%; height: 19.5938px;">eut.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://eut.openaire.eu/">https://eut.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">EUTOPIA Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">eutopia.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://eutopia.openaire.eu/">https://eutopia.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">FORTHEM Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">forthem.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://forthem.openaire.eu">https://forthem.openaire.eu</a></td> </tr> <tr> <td style="width: 33.9453%;">[NEW] GoTriple </td> <td style="width: 38.9414%;">gotriple_1.tar, gotriple_2.tar</td> <td style="width: 23.1067%;"><a href="https://gotriple.openaire.eu/">https://gotriple.openaire.eu/</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Heritage Science (EC project IPERION HS)</td> <td style="width: 38.9414%; height: 19.5938px;">heritage-science.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://heritage-science.openaire.eu/">https://heritage-science.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Institut national de recherche en informatique et en automatique (EC project GraspOS)</td> <td style="width: 38.9414%; height: 39.1875px;">inria.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://inria.openaire.eu" target="_blank" rel="noopener">https://inria.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">IPERION HS</td> <td style="width: 38.9414%; height: 19.5938px;">iperionhs.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://iperionhs.openaire.eu">https://iperionhs.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Knowmad Institut</td> <td style="width: 38.9414%; height: 39.1875px;">knowmad_1.tar, knowmad_2.tar, knowmad_3.tar, knowmad_4.tar, knowmad_5.tar, knowmad_6.tar, knowmad_7.tar, knowmad_8.tar, knowmad_9.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://knowmad.openaire.eu/">https://knowmad.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">LifeWathc ERIC</td> <td style="width: 38.9414%; height: 19.5938px;">lifewatch-eric.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://lifewatch-eric.openaire.eu/">https://lifewatch-eric.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European Marine Science</td> <td style="width: 38.9414%; height: 19.5938px;">mes.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://mes.openaire.eu">https://mes.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Atmospheric Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 39.1875px;">neanias-atmospheric.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://neanias-atmospheric.openaire.eu/">https://neanias-atmospheric.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Space Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 19.5938px;">neanias-space.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://neanias-space.openaire.eu/">https://neanias-space.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Underwater Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 39.1875px;">neanias-underwater.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://neanias-underwater.openaire.eu/">https://neanias-underwater.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Netherlands Research Portal</td> <td style="width: 38.9414%; height: 19.5938px;">netherlands_1.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://netherlands.openaire.eu/">https://netherlands.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">[NEW] Neuroscience<br>(EC project SciLake - former Neuroinformatics community is now a subcommunity of Neuroscience)</td> <td style="width: 38.9414%; height: 19.5938px;">neuroscience_1.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://neuroscience.openaire.eu">https://neuroscience.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">North American Studies</td> <td style="width: 38.9414%; height: 39.1875px;">north-american-studies.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://north-american-studies.openaire.eu">https://north-american-studies.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Rural Digital Europe (EC project DESIRA)</td> <td style="width: 38.9414%; height: 39.1875px;">rural-digital-europe.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://rural-digital-europe.openaire.eu/">https://rural-digital-europe.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Sustainable Development Solutions Network - Greece </td> <td style="width: 38.9414%; height: 19.5938px;">sdsn-gr.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://sdsn-gr.openaire.eu/">https://sdsn-gr.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Technological University Network</td> <td style="width: 38.9414%; height: 19.5938px;">tunet.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://tunet.openaire.eu">https://tunet.openaire.eu</a></td> </tr> <tr> <td style="width: 33.9453%;">[NEW] UNITE! University Alliance</td> <td style="width: 38.9414%;">unite.tar</td> <td style="width: 23.1067%;"><a href="https://unite.openaire.eu">https://unite.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">University of the Arctic (UArctic) </td> <td style="width: 38.9414%; height: 19.5938px;">uarctic_1.tar, uarctic_2.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://uarctic.openaire.eu/">https://uarctic.openaire.eu</a></td> </tr> </tbody> </table>
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.