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8,375 results for “nationalism”
Fig. 1 in Late Pleistocene Birds from Binagada (Azerbaijan) in Collection of the National Museum of Natural History (Kyiv, Ukraine)
Fig. 1. Map of Azerbaijan showing the location of the Binagada asphalt lake.
Fig. 2 in Home Range Of The Spur-Thighed Tortoise, Testudo Graeca (Testudines, Testudinidae), In The National Park Of El-Kala, Algeria
Fig. 2. View of the study area with different habitats.
Fig. 1 in Home Range Of The Spur-Thighed Tortoise, Testudo Graeca (Testudines, Testudinidae), In The National Park Of El-Kala, Algeria
Fig. 1. Location of the study site in the National Park of El Kala, in north-eastern Algeria.
Fig. 3 in Home Range Of The Spur-Thighed Tortoise, Testudo Graeca (Testudines, Testudinidae), In The National Park Of El-Kala, Algeria
Fig. 3. Tortoises locations (A) in relation to Dwarf palm distribution (B) on the study site.
Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations.
<p>This dataset underpins the study "Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations".</p> <p>The study provides insights into energy supply and demand, power generation, investments and total system costs, SD7 indicators, job creation as well as carbon dioxide emissions for each African nation (48 in total).</p> <p>An energy systems model enhanced with geospatial data was developed to evaluate energy supply requirements to cover the energy needs of the African continent during the period 2015-2030 and achieve universal access by 2030. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS and the geospatial electrification outlook (GEP). The objective function is to minimise the total energy system costs. </p> <p>The results can be found https://doi.org/10.5281/zenodo.6468262</p>
Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations
<p>The attached modeling results underpin the study "Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations".</p> <p>The study provides insights into energy supply and demand, power generation, investments and total system costs, SD7 indicators, job creation as well as carbon dioxide emissions for each African nation (48 in total).</p> <p>An energy systems model enhanced with geospatial data was developed to evaluate energy supply requirements to cover the energy needs of the African continent during the period 2015-2030 and achieve universal access by 2030. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS and the geospatial electrification outlook (GEP). The objective function is to minimise the total energy system costs. </p> <p>The TEMBA model produces aggregate energy, and detailed power system results in each country in the African continent. The power sector results are also reported with power pool aggregation.</p> <p>The OSeMOSYS model and input data used to produce these results can be found at JoPapp/jrc_temba: v1.0.2 [Data set]. Zenodo.https://doi.org/10.5281/zenodo.6468278 (Authors: Ioannis Pappis. (2021)).</p>
Six new species of Pristimantis (Anura: Strabomantidae) from Llanganates National Park and Sangay National Park in Amazonian cloud forests of Ecuador
<p><strong>Fig. S1. Phylogenetic relationships of <em>Pristimantis</em> species showing <em>Pristimantis anaiae</em> sp. nov., <em>Pristimantis glendae</em> sp. nov., <em>Pristimantis kunam</em> sp. nov., <em>Pristimantis resistencia</em> sp. nov., <em>Pristimantis venegasi</em> sp. nov. and <em>Pristimantis tamia</em> sp. nov relations.</strong></p> <p>Maximum likelihood tree obtained for genes 16S, 12S, RAG1 and ND1. Support values are on the corresponding branches: aLRT values above the slash and bootstrap below. The phylogeny was derived from an analysis of 4155 bp for of mitochondrial (gene fragments 12S and 16S) and nuclear (gene fragments RAG1) DNA sequences for 235 terminals. For each specimen, museum number or, in unavailable, GenBank accession number is shown, as well as its locality. Outgroup is not shown. Abbreviations: CCS = confirmed candidate species, UCS = unconfirmed candidate species, ECU = Ecuador.</p> <p> </p> <p><strong>Figure S3. Phylogenetic relationships, based on mitochondrial genes only, showing <em>Pristimantis </em>new species relations.</strong> </p> <p>Maximum likelihood tree obtained for mitochondrial genes 12S, 16S and ND1. Support values are on the corresponding branches: aLRT values above the slash and bootstrap below. The phylogeny was derived from an analysis of 3573 bp for 235 samples of mitochondrial genes DNA sequences. For each specimen, museum number or, in unavailable, GenBank accession number is shown, as well as its locality. Outgroup is not shown. Abbreviations: CCS = confirmed candidate species, UCS = unconfirmed candidate species, ECU = Ecuador. In red, the inconsistencies found.</p>
A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019
<p>Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO<sub>2</sub> emissions from fossil fuel combustion and industry (FFI), CH<sub>4</sub> emissions, N<sub>2</sub>O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO<sub>2</sub> emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.</p>
Big in Japan, Zimbabwe or Brazil – global reach and national preferences for open access books
<p>When we consider the main differences in publication practices between the Humanities and Social Sciences (HSS) and Science Technology and Mathematics (STM) there are two aspects that almost always get mentioned: the prominence of books and the role of local languages. Not surprisingly, the work of HSS researchers written in in other languages than English is more often linked to more regional concerns or a more regional community. Simply put, an author using Dutch will have a different audience in mind than an author writing in English.</p> <p>The OAPEN Library contains thousands of open access monographs in dozens of languages and sees global usage. While books in English are in the majority – over 60% of the collection – this still leaves a large collection of publications in other languages. If a global audience can freely choose from this collection, will there be a preference for global subjects or will more regional concerns take preference? This explorative research will look at the most popular books from 100 countries and try to determine the level of regional interest.</p> <p>We will examine the preference of global readers in a systematic manner. Based on the ten most downloaded books from 100 countries during a 12 month period , the focus on regional topics will be measured in two ways: the amount of books written in non-English languages, and the amount of English language books that mention the country.</p>
Glacier Bay National Park glacial rock avalanche inventory (1984-2020)
<p>An inventory of supraglacially deposited rock avalanches that occurred in Glacier Bay National Park, Alaska, between 1984 and 2020.</p> <p>Reference: Smith, W.D., Dunning, S.A., Ross, N., Telling, J., Jensen, E.K., Shugar, D.H., Coe, J.A. and Geertsema, M. (2023) Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska. <em>Geomorphology</em>, doi: <a href="https://doi.org/10.1016/j.geomorph.2023.108591">https://doi.org/10.1016/j.geomorph.2023.108591</a></p>
Dataset of wild spotted hyenas from 3 clans in the Serengeti National Park
<p><span><span>Host</span> <span>immune</span> <span>defenses</span> <span>are</span> <span>important</span> <span>components of</span> <span>host-parasite</span> <span>interactions</span> <span>that affect</span> <span>the</span> <span>outcome</span> <span>of</span> <span>infection and may have fitness consequences for hosts when increased allocation of resources to immune responses undermines other essential life processes. Research on host-parasite</span> <span>interactions in large free-ranging wild mammals is currently hampered</span> <span>by</span> <span>a</span> <span>lack of verified non-invasive assays.</span> <span><span>We</span></span> <span>successfully</span> <span>adapted</span> <span>existing</span> <span>assays</span> <span>to measure innate and adaptive immune responses produced by the gastrointestinal mucosa in spotted</span> <span>hyena</span> <span>(</span><i><span>Crocuta</span></i><span><i> </i></span><i><span>crocuta</span></i><span>) faeces, including</span> <span>enzyme-linked immunosorbent</span> <span>assays</span> <span>(ELISAs)</span><span><span>, </span></span><span>to</span> <span>quantify</span><b> </b><span>faecal</span> <span>immunoglobulins (total</span> <span>IgA, total IgG) and</span> <span>total</span> <span>faecal</span> <span>O-linked</span> <span>oligosaccharides</span> <span>(mucin).</span> <span>We investigated the</span> <span><span>effect</span></span> <span>of infection load by an energetically costly hookworm (</span><i><span>Ancylostoma</span></i><span>), parasite richness, host age,</span> <span>sex, year of sampling and clan membership on</span> <span>immune</span> <span>responses</span> <span>and asked</span> <span>whether high investment in immune responses</span> <span>during</span> <span>early</span> <span>life</span> <span>affects longevity</span><span><span> in individually known spotted hyenas in the Serengeti National Park, Tanzania.</span></span><span> F</span><span><span>aecal concentrations of IgA, IgG and mucin</span></span> <span>increased with </span><i><span>Ancylostoma</span></i><span> egg load and were higher in juveniles</span> <span>than in</span> <span>adults. Females had higher mucin concentrations than males. Juvenile females had higher IgG concentrations than juvenile males whereas adult females had lower IgG concentrations than males. High</span> <span><span>IgA</span></span><span><span> concentrations </span></span><span>during</span> <span>the</span> <span>first</span> <span>year</span> <span>of</span> <span>life</span> <span>was linked to</span> <span>reduced </span><span><span>longevity</span></span><span><span> after controlling for</span></span> <span>age</span> <span>at</span> <span>sampling</span> <span>and</span> <i><span>Ancylostoma</span></i><span><i> </i></span><span>egg</span><span><span> load</span></span><span>. Our</span> <span>study demonstrates that the use of non-invasive methods can increase knowledge on the complex relationship between gastrointestinal parasites and host local immune responses in wild large mammals and reveal fitness-relevant effects of these responses.</span></span></p>
Data from: Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories
<p><strong>Objective</strong>: Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions.</p> <p><strong>Materials and Methods</strong>: We compared gaps and similarities between diagnostic test manufacturers' recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.</p> <p><strong>Results</strong>: Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P &lt; .0001).</p> <p><strong>Discussion</strong>: The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability. </p> <p><strong>Conclusion</strong>: The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.</p>
First Street Foundation's National Flood Adaptation Database
<p>In order to create a national model with complete coverage of the contiguous United States, the First Street Foundation Flood Model relies on nationally available data that consistently represents the hydrologic conditions of the United States. However, most of this data represents the natural environment better than the modifications made by human activity that impact hydrology and therefore flooding. To make this model as accurate a representation of actual flood risk as possible, First Street has spent considerable time and effort to build a database of “grey” and “green” infrastructure and adaptation projects that affect the flow of water and therefore flooding.</p> <p>Grey Flood control projects include a variety of traditional infrastructure solutions like levees, pump stations, and flood control channels. Many green infrastructure projects also contribute to flood reduction, such as wetland restoration, floodable open space, retention basins, and creek rehabilitation projects. The First Street Foundation Flood Model also records and accounts for climate adaptation projects such as beach renourishment projects that are designed to counteract the effects of rising seas.</p> <p>This data is collected from state, county, and city agencies across the United States. It is digitized by drawing the area for which a structure is providing flood protection and assigned a level of protection provided. These service areas can range in size from a few residential blocks to a small city. Each feature is associated with one or more sources of flooding for which it provides protection. Estimates of the level of protection are based on the return period to which it will continue to function.</p> <p>This data is accounted for in the hydraulic and hydrologic model in several different ways. In most places, water is blocked within the model from entering a service area. This is similar to how a levee operates in real life, blocking water from entering a given location and pushing it somewhere else. Importantly, water is not removed from the model. In other cases like various runoff reduction green infrastructure projects, the service area of the project represents a change in the underlying soil classification within the hydrologic model. This replicates the way green infrastructure projects reduce runoff from their service area. In still other projects such as pump stations, the service area represents an adjustment to the flow of water in an area. Just as a pump station has a certain flowrate at which it will continue to be effective, even in an event beyond its design standard, this is represented in the model by removing an amount of flooding from an area equivalent to the modeled flow in the project’s service area at the design standard. So a pump station designed for a 5 year event would always remove the equivalent of the 5 year event, even in a 100 year event.</p> <p>You can download a sample of the database through Zenodo. If you wish to acquire the adaptation database, you can do so by reaching out to First Street Foundation <a href="https://firststreet.dev/register">here</a>.</p>
Land-use fluxes: data from global models and national inventories
<p>This file includes the data from Supplementary Table 1 of Grassi et al. (ESSD, submitted), for the 42 countries having a managed forest area greater than 10 Million ha. The data includes: </p> <p>(i) areas of managed forest, used in this study and based on country data</p> <p>(ii) The CO2 fluxes (2001-2020 average) from global models - i.e. bookkeeping models (BMs) and Dynamic Global Vegetation Models (DGVMs) -, and from a collection of National GHG inventories (NGHGIs) for LULUCF, forest land, deforestation, and other fluxes (organic soils, cropland, grassland etc.). </p> <p>BM values are averages of three models and DGVM values are averages of 17 models, consistent with the Global Carbon Budget 2021 (<a href="https://priv-bx-myremote.tech.ec.europa.eu/articles/14/1917/2022/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/articles/14/1917/2022/</a>). Values for NGHGIs are from <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a> </p> <p>For further methodological details, see Grassi et al. (ESSD, submitted):</p> <p>Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, San1, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Matthew J. McGrath, Julia Nabel, Benjamin Poulter, Simone Rossi, Anthony P. Walker, Wenping Yuan, Xu YueJulia Pongratz. Mapping land-use fluxes for 2001-2020 from global models to national inventories. ESSD (submitted)</p>
Savanna plant and soil carbon data from different burn seasons and histories across Mole National Park, Ghana
<p>Aboveground plant pool and belowground (soil plus root) carbon data for a space-for-time substitution survey of different burn seasons and histories across Mole National Park, Ghana. Carbon data was collected to determine the impact of unintentional late growing season wildfires on carbon storage in a protected area dominated by early growing season prescribed burning land management. The methodology and findings from the study are detailed in the below publication:</p> <p>Awuah J, Smith SW, Speed JDM, Graae BJ. 2022. Can seasonal fire management reduce the risk of carbon loss from wildfires in a protected Guinea savanna? Ecosphere, e4283. https://doi. 88 org/10.1002/ecs2.4283 </p> <p>This data repository contains the following data (and descriptive metadata): </p> <p>(1) Study_site_coordinates: locations for 28 sites surveyed in 2016 as part of an ecosystem carbon stock assessment</p> <p>(2) Aboveground_carbon: aboveground tree, shrub, herbaceous vegetation, deadwood and litter carbon stocks estimated from either destructive biomass sampling or allometric equations. Aboveground carbon data are presented per site. </p> <p>(3) LOI_to_carbon_conversion: a subset of soil samples were analysed for both loss on ignition (LOI) and automated dry combustion using an elemental analyser, the latter more accurate for carbon determination and used to correct LOI values. </p> <p>(4) Belowground_carbon: combined soil and root carbon collected collected to a maximum depth of 17 cm, and split into four soil layers (0-2 cm, 2-7 cm, 7-12 cm and 12-17 cm). </p> <p>MCD14DL MODIS Active Fire Detections data used to defined different burn seasons and histories for sites has not been uploaded and is freely available from online sources detailed in the journal article. </p>
National Data Files for Pre-built Sector-coupled Euro-Calliope Model
<p>National time series data derived from the <a href="https://zenodo.org/record/5774988#.YtUQ9-zP3Ph">Sector-coupled Euro-Calliope Pre-built Model</a></p>
How does the selection of National Development Zones affect urban green innovation?
<p><span><span>The launch of the selection process for National Development Zones (NDZs) marked a fundamental change in the construction of development zones, making it an essential position for local authorities to implement high-quality development. Based on the data of prefecture-level cities in China from 2000 to 2018, this paper examines the impact and mechanism of selecting NDZs on urban green innovation through a double-difference spatial durbin model using the selection of NDZs as a "quasi-natural experiment". The study finds that the selection of NDZs can promote green innovation in cities and has a significant window-radiating effect. The heterogeneity test results show that the implementation of the selection policy for development zones in non-old industrial cities, large and medium-sized cities, cities with easy access to transportation, and cities with high market orientation are more likely to promote urban green innovation. At the same time, the higher the level of government governance and the better the level of economic development of the development zones, the more it helps to realize the effects of the selection policy. The results of the mechanism test show that the selection of NDZs has a positive impact on urban green innovation through environmental regulation effects, resource allocation effects, and policy amplification effects. </span></span>Data on development zones are sourced from the China Directory of Development Zones Audit and Announcement (2006) and the China Directory of Development Zones Audit and Announcement (2018 Edition)(http://www.gov.cn/xinwen/2018-03/03/content_5270330.htm); data on patents are sourced from the China Intellectual Property Office (https://www.cnipa.gov.cn/); Other city-level data from China City Statistical Yearbook, China Regional Economic Statistical Yearbook, China Statistical Yearbook (https://data.cnki.net/Yearbook/Navi?type=type&code=A). Our data is collated from the above sources using Python software. </p>
Supporting data (cave length, area and volume) - Bats as ecosystem engineers in iron ore caves in the Carajás National Forest, Brazilian Amazonia
<p>Supporting data for the manuscript "Bats as ecosystem engineers in iron ore caves in the Carajás National Forest, Brazilian Amazonia", in PLOS ONE. Cave length (PH: horizontal projection, in meters), cave area (Área, in square meters) and cave volume (Volume, in cubic meters) for 1,309 caves in the Carajás National Forest area, Pará State, Brazilian Amazonia.</p>
PM10, SO2, and NO2 Ambient Air Quality Monitoring Data from India's National Ambient Monitoring Program (NAMP) 2011-2015
<p>India's Central Pollution Control Board (CPCB) operates and maintains the National Ambient Monitoring Program (<a href="https://cpcb.nic.in/about-namp/">NAMP</a>) which includes both continuous and manual ambient monitoring stations. This dataset is a collation of manual monitoring data by day for years 2011, 2012, 2013, 2014, and 2015 for PM10, SO2, and NO2. These stations collect for a maximum of 104 days in a year. This cleaned dataset was utilized for understanding trends and conducting comparisons with modeled concentrations under the APnA city program, published <a href="https://doi.org/10.1016/j.uclim.2018.11.005">here</a> (<a href="https://doi.org/10.1016/j.uclim.2018.11.005">Urban Climate, 2019</a>).<br> <br> Data format - year, month, day, SO2, NO2, PM10, Stn Code, State, City<br> All units - micro-gm/m3 (ug/m3)</p> <p>Official annual summary reports (PDFs) are available <a href="https://cpcb.nic.in/namp-data/">here</a>.</p> <p>For guidelines for ambient and emissions monitoring, summaries of available data, and other resources on monitoring in India, visit <a href="https://urbanemissions.info/resources-energy-emissions-analysis-in-india/#monitoring">https://urbanemissions.info/resources-energy-emissions-analysis-in-india</a></p>
Quantifying nitrogen deposition inputs to cropland: A national scale dataset from 1961 to 2020
<p>Nitrogen (N) deposition is one of the major inputs to cropland and consequently important for the estimation of N Use Efficiency (NUE) for crop production. However, the estimates for N deposition carry large uncertainty, and existing assessments of N budgets and NUE on agricultural land use different estimates of N deposition. To evaluate the uncertainties in existing methods for national scale N deposition estimation and assess their impacts on the resulting NUE estimation for countries around the world, we 1) reviewed existing methods and related data sources for quantifying N deposition inputs to crop production on a national scale; 2) identified the most up–to–date data sources and designed methods to quantify N deposition input to crop production on a national scale; 3) collected N deposition data from observation sites in major countries (e.g., UK, US, and China) to validate the estimated N deposition input; and 4) conducted sensitivity analysis to evaluate how the uncertainties in N deposition affect crop NUE assessment. As a result, we established four estimates for N deposition inputs on cropland for 251 countries around the world during 1961–2020 as combinations of two sets of N deposition maps (ACCMIP<sup>1</sup> and Wang <em>et al</em>.<sup>2-4</sup>) and two sets of cropland maps (HYDE<sup>5</sup> and LUH2<sup>6</sup>). The four products (1. <strong>AH</strong>: ACCMIP and HYDE, 2. <strong>AL</strong>: ACCMIP and LUH2, 3. <strong>WH</strong>: Wang <em>et al</em>. and HYDE, and 4. <strong>WL</strong>: Wang <em>et al</em>. and LUH2) show good agreement in N deposition estimates for the majority of countries, but have large differences in several Asian countries (e.g., China, India, and Pakistan), and the differences are mostly caused by the use of different N deposition maps. According to the comparison with the observation records in China, the deposition estimates based on Wang <em>et al</em>. show a better agreement with the observations. Hence, the authors recommend using product #4 <strong>WL</strong> (Wang <em>et al</em>. and LUH2) as the reference dataset for N deposition in the global assessments of N budgets by countries. </p> <p><strong>References:</strong></p> <ol> <li>Lamarque, J. F. <em>et al</em>. Multi-model mean nitrogen and sulfur deposition from the atmospheric chemistry and climate model intercomparison project (ACCMIP): Evaluation of historical and projected future changes. <em>Atmos. Chem. Phys</em>. <strong>13</strong>, 7997–8018 (2013).</li> <li>Shang, Z. <em>et al</em>. Weakened growth of cropland-N2O emissions in China associated with nationwide policy interventions. <em>Glob. Chang. Biol</em>. <strong>25</strong>, 3706–3719 (2019).</li> <li>Wang, Q. <em>et al</em>. Data-driven estimates of global nitrous oxide emissions from croplands. <em>Natl. Sci. Rev</em>. <strong>7</strong>, 441–452 (2020).</li> <li>Wang, R. <em>et al</em>. Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100. <em>Glob. Chang. Biol</em>. <strong>23</strong>, 4854–4872 (2017).</li> <li>Goldewijk, K. K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene - HYDE 3.2. <em>Earth Syst. Sci</em>. <em>Data</em> <strong>9</strong>, 927–953 (2017).</li> <li>Hurtt, G. C. <em>et al</em>. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6. <em>Geoscientific Model Development </em><strong>13</strong>, (2020).</li> </ol>
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.