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5,794 results for “Exposure”
Named Entity Corpus for Occupational Substance Exposure Assessment
<p>This is a corpus consisting of selected sections (i.e., <em>Abstract, Methods</em> and <em>Results</em>) of scientific research articles concerning occupational exposures to two different types of substance, i.e., diesel exhaust (51 articles) and respirable crystalline silica (RCS) (50 articles). The article sections have been annotated by experts in the field with 6 categories of named entities (NEs) relevant to the assessment of occupational substance exposures, particularly in the context of Job Exposure Matrices (JEMs).</p> <p>The corpus is available in two different formats, <a href="https://brat.nlplab.org/standoff.html">brat standoff format</a> and JSON. </p> <p>The corpus and associated NER models are described in more detail in the following atricle, which should be cited if you use the corpus: </p> <p>Thompson, P., Ananiadou, S., Basinas I., Brinchmann, B. C., Cramer, C., Galea, K. S., Ge, C., Georgiadis, P., Kirkeleit, J., Kuijpers, E., Nguyen, N., Nuñez, R., Schlünssen, V., Stokholm, Z. A., Taher, E. A., Tinnerberg, H., Van Tongeren, M. and Xie, Q. (2024).<a href="https://doi.org/10.1371/journal.pone.0307844"> </a><a href="https://doi.org/10.1371/journal.pone.0307844">Supporting the working life exposome: annotating occupational exposure for enhanced literature search</a>. PLoS ONE 19(8): e0307844</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1839 (Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures)
Title: Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures <br>Species: Homo sapiens <br>Number of samples: 2705 <br>Number of named analytes: 10 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=61 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1407 (Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study)
Title: Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study <br>Species: Homo sapiens <br>Number of samples: 157 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=2 <br>
Pan-European exposure maps and uncertainty estimates from HANZE v2.0 model, 1870-2020
<p>This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files:</p> <p>- CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP)</p> <p>- Pop: population</p> <p>- GDP: gross domestic product (2020 euros)</p> <p>- FA: fixed asset value (2020 euros)</p> <p>- imp: imperviousness density (%)</p> <p>Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods.</p> <p>Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables.</p> <p>For detailed description of the files, see the documentation provided with the code.</p> <p>This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.</p>
Noise exposure at ultrasound-related industrial workplaces and public sites
<p>The dataset contains single measurements at different public sites and workplaces in Europe. The data has been used or obtained in the context of the project 15HLT03 “EarsII” from the EMPIR-programme.</p> <p>For each measurement metadata is available. This includes the measurement circumstances and involved machinery, a description of the measurement location and noise reduction measures, the microphone position during measurement, and the measurement procedure used to obtain the measurement data.</p> <p>A detailed description of all the quantities contained in the dataset is documented in the accompanying pdf-file.</p>
Exposure to sublethal concentrations of a pesticide or predator cues induces changes in brain architecture in larval amphibians, 2013.
Naturally occurring environmental factors shape developmental trajectories to produce variable phenotypes. Such developmental phenotypic plasticity can have important effects on fitness, and has been demonstrated for numerous behavioral and morphological traits. However, surprisingly few studies have examined developmental plasticity of the nervous system in response to naturally occurring environmental variation, despite accumulating evidence for neuroplasticity in a variety of organisms. Here, we asked whether the brain is developmentally plastic by exposing larval amphibians to natural and anthropogenic factors. Leopard frog tadpoles were exposed to predator cues, reduced food availability, or sublethal concentrations of the pesticide chlorpyrifos in semi-natural enclosures. Mass, growth, survival, activity, larval period, external morphology, brain mass, and brain morphology were measured in tadpoles and after metamorphosis. Tadpoles in the experimental treatments had lower masses than controls, although developmental rates and survival were similar. Tadpoles exposed to predator cues or a high dose of chlorpyrifos had altered body shapes compared to controls. In addition, brains from tadpoles exposed to predator cues or a low dose of chlorpyrifos were narrower and shorter in several dimensions compared to control tadpoles and tadpoles with low food availability. Interestingly, the changes in brain morphology present at the tadpole stage did not persist in the metamorphs. Our results show that brain morphology is a developmentally plastic trait that is responsive to ecologically relevant natural and anthropogenic factors. Whether these effects on brain morphology are linked to performance or fitness is unknown.
EXPOS Model for Estimating Topographic Exposure to Wind (R and Python)
The EXPOS model uses a digital elevation model (DEM) to estimate exposed and protected areas for a given hurricane wind direction and inflection angle. The resulting topograhic exposure maps can be combined with output from the HURRECON model to estimate hurricane wind damage across a region. EXPOS is available in R and Python. The R version is available on CRAN as ExposR. The model is an updated version of the original EXPOS model written in Borland Pascal for use with Idrisi (see HF024). For more details and sample datasets, see the project website on GitHub (https://github.com/expos-model).
Grass exposure, mesquite height, and plant greenness at a Jornada Basin LTER sand-sheet site, July and September 2019
Two large wind storms in March and April, 2019 caused severe erosion and exposed the roots of many grass patches in the wind-erodible "sand sheet" of the Jornada LTER. These data are from these wind-affected areas. Field transect measurements of grass cover (line-point intercept, grass plants with exposed roots (1-m belt transect), grass plants with green shoots (1-m belt transect), mesquite (PRGL) height (maximum height of cord on transect for each individual) for 18 transects in the sandy loam "sand sheet" geomorphic surface of Jornada. Also included are histograms of UAV-based measurements of green chromatic coordinate (GCC) and grass patch size (cm2) for individual grass patches within each 1-m belt transect.
A route to school informational intervention for air pollution exposure reduction
<p>iSCAPE Dataset Reference No. = DS_PD_020</p> <p>Following datasets are gathered during the implementation of route to school intervention study in Antwerp (Belgium)</p> <ol> <li>Introductory Questionnaire Responses</li> <li>Feedback Questionnaire Responses</li> </ol>
Metabolic recovery and compensatory shell growth of juvenile Pacific geoduck Panopea generosa following short-term exposure to acidified seawater
<p><strong>METABOLIC RECOVERY AND COMPENSATORY SHELL GROWTH OF JUVENILE PACIFIC GEODUCK <em>PANOPEA GENEROSA</em> FOLLOWING SHORT-TERM EXPOSURE TO ACIDIFIED SEAWATER</strong></p> <p><strong>Samuel J. Gurr<sup>1*</sup>, Brent Vadopalas<sup>2</sup>, Steven B. Roberts<sup>3</sup>, Hollie M. Putnam<sup>1</sup></strong></p> <p><sup>1 </sup>University of Rhode Island, College of the Environment and Life Sciences, 120 Flagg Rd, Kingston, RI 02881 USA</p> <p><sup>2 </sup>University of Washington, Washington Sea Grant, 3716 Brooklyn Ave NE, Seattle, WA 98105 USA</p> <p><sup>3 </sup>University of Washington, School of Aquatic and Fishery Sciences, 1122 NE Boat St, Seattle, WA 98105 USA</p> <p><strong>*Corresponding author:</strong> Fax: Phone:1-401-874-9510 Email: samuel_gurr@uri.edu</p> <p><strong>Abstract</strong></p> <p>While acute stressors can be detrimental, environmental stress conditioning can improve performance. To test the hypothesis that physiological status is altered by stress conditioning, we subjected juvenile Pacific geoduck, <em>Panopea generosa, </em>to repeated exposures of elevated <em>p</em>CO<sub>2</sub> in a commercial hatchery setting followed by a period in ambient common garden. Respiration rate and shell length were measured for juvenile geoduck periodically throughout short-term repeated reciprocal exposure periods in ambient (~550 µatm) or elevated (~2400 µatm) <em>p</em>CO<sub>2</sub> treatments and in common, ambient conditions, five months after exposure. Short-term exposure periods comprised an initial 10-day exposure followed by 14 days in ambient before a secondary 6-day reciprocal exposure. The initial exposure to elevated <em>p</em>CO<sub>2 </sub>significantly reduced respiration rate by 25% relative to ambient conditions, but no effect on shell growth was detected. Following 14 days in common garden, ambient conditions, reciprocal exposure to elevated or ambient <em>p</em>CO<sub>2</sub> did not alter juvenile respiration rates, indicating ability for metabolic recovery under subsequent conditions. Shell growth was negatively affected during the reciprocal treatment in both exposure histories, however clams exposed to the initial elevated <em>p</em>CO<sub>2</sub> showed compensatory growth with 5.8% greater shell length (on average between the two secondary exposures) after five months in ambient conditions. Additionally, clams exposed to the secondary elevated <em>p</em>CO<sub>2 </sub>showed 52.4% increase in respiration rate after five months in ambient conditions. Early exposure to low pH appears to trigger carry-over effects suggesting bioenergetic re-allocation facilitates growth compensation. Life stage-specific exposures to stress can determine when it may be especially detrimental, or advantageous, to apply stress conditioning for commercial production of this long-lived burrowing clam.</p> <p> </p>
Risk assessment on Glycoalkaloids in feed and food: Occurrence data in food and feed submitted to EFSA and dietary exposure assessment for humans
<p><strong>UPDATE to version 2 of this upload:</strong></p> <p>Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided <em>in csv format</em>. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields).</p> <p><strong>Description - Version 1</strong></p> <p><strong>Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans</strong></p> <p>Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA</p> <p>Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data</p> <p>Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion</p> <p>Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow)</p> <p>Table A.5. European Starch Association data on feed and potatoes for starch</p> <p>Table A.6. Details acute assessment across surveys (consumption days only)</p> <p>Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors</p>
Mapping of FoodEx2 Exposure Hierarchy with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives
<p>FoodEx2 is a comprehensive food classification and description system aimed at covering the need to describe food in data collections across different food safety domains. All foods, including beverages and food supplements reported in the EFSA Comprehensive Food Consumption Database are coded with the FoodEx2 Exposure Hierarchy. EFSA developed a mapping of all FoodEx2 basic terms reported in the Comprehensive Database with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives in order to facilitate the assessment of exposure to food additives. In particular, this mapping has been used in the Food Additives Intake Model 2.0 (FAIM), which allows the estimation of chronic dietary exposure to food additives based on use levels proposed for food categories as presented in Annex II (part D) of Regulation (EC) No 1333/2008 on food additives.</p> <p>Some of the food categories, restrictions and/or exceptions presented in the Regulation could have not been mapped with FoodEx2 basic terms and the original food descriptors and/or FoodEx2 facets might have been used to correctly map all eating events. This information might not be available in this table.</p>
Exposure to pesticides data for residents and bystanders, and for environmental risk assessment
<p>In 2014, EFSA has commissioned a study to review and evaluate all published data related to the exposure to pesticides for residents and bystanders and for environmental risk assessment. The aim was to conduct a literature review and to produce a database containing all published data (predominately peer-reviewed publications supplemented by grey-literature) for the last 25-years, which will support the non-dietary exposure assessment to pesticides for bystanders and residents, as well as daily air concentration (vapours and aerosols) of pesticides, drift values from spray, seed and granular applications, and dislodgeable foliar residues.</p> <p>The data has been collated via a systematic and extensive literature review defined and managed according to a pre-defined 'review protocol'. The data was also exported in a format that meets the requirements of the EFSA Data Collection Framework (DCF).</p> <p>Based on quality and relevance criteria, articles and related studies have been selected. For dislodgeable foliar residues the assessment includes 27 articles (containing 49 discrete studies); for air concentrations, 26 articles (containing 84 discrete studies); for resident and bystander exposure, 5 articles (containing 8 discrete studies); and for drift values 55 articles (containing 275 discrete studies). </p> <p>For dislodgeable foliar residues the data retained covered 17 crops (including grass, glasshouse crops, lucerne, and citrus) and 29 pesticides; for air concentrations the data retained covered 21 crops (including fruit, glasshouse crops, ornamentals, grass, vegetables and cereals) and 39 pesticides. For drift values, the data covers a range of crops and landscapes from cereals, grass and turf, orchards, vineyards and regenerated forestry. The vast majority of the data retrieved applies to field studies for liquid spray drift, measured either as ground deposits or collected at various heights and were conducted using fluorescent tracers rather than pesticides. No data was found for microbials (biopesticides). For resident and bystander exposure, many articles were rejected due to the applied inclusion/exclusion criteria.</p>
Air pollution exposure fields for 2020
<p>Air pollution exposure fields for the year 2020 created with a chemical transport model and landuse regression models. ASCII data files in zip form. Windows assignment program that calcualtes exposure concentrations based on location, start date, end date. R codes that perform statistical analysis based on assigned exposures. Note that confidential patient data is <em>not</em> included in the archive.</p>
C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.
<p>There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.</p>
(dataset) On the use of SRIM for calculating arc-dpa exposure
<p>Data and code in support of manuscript:</p><p>Mitsi, E., Koutsomitis, K. & Apostolopoulos, G. "On the use of SRIM for calculating arc-dpa exposure." <i>Nucl. Instrum. Methods Phys. Res., Sect. B</i> (2023) doi:<a href="https://doi.org/10.1016/j.nimb.2023.165145">10.1016/j.nimb.2023.165145</a>, arxiv:<a href="https://arxiv.org/abs/2307.12867"> 2307.12867</a></p><p>Version v1.1 of the dataset adds one new figure and some script files. File "dataset-v1.1.zip" can be extracted on top of "dataset.zip" from v1.0.</p>
Dataset for "ZnO decorated Graphene-based NFC tag for personal NO2 exposure monitoring during a workday"
<p>Dataset with all measurements performed and related to the publication "ZnO decorated Graphene-based NFC tag for personal NO2 exposure<br>monitoring during a workday" Published in Sensors MDPI 2024 by A. Santos and co-workers.</p>
Compilation of parallel measurements comparing the temperatures recorded in Stevenson screens with those recorded in pre-Stevenson screen thermometer exposures
<p>Compilation of parallel measurements comparing the temperatures recorded in Stevenson screens with those recorded in pre-Stevenson screen thermometer exposures. This dataset accompanies Wallis et al. (2024); further details of the dataset and its creation can be found in the attached readme file and Wallis et al. (2024).</p> <p>---</p> <p><strong>References</strong></p> <p>Wallis, E.J., Osborn, T.J., Taylor, M., Jones, P.D., Joshi, M. & Hawkins, E. (2024) Quantifying exposure biases in early instrumental land surface air temperature observations. <em>International Journal of Climatology, </em>https://doi.org/10.1002/joc.8401</p>
Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)
<p><strong>Abstract</strong></p> <p>The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.</p> <p> </p> <p><strong>Description of the data and analytical code</strong></p> <p>In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.</p> <p> </p> <p>For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls. Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.</p> <p> </p> <p>In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM. The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package ‘metafor’.</p> <p> </p> <p><strong>Funding</strong></p> <p>Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).</p> <p> </p> <p><strong>Sharing/Access information</strong></p> <p>Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).</p>
Chronic Ethanol Exposure Produces Sex-Dependent Impairments in Value Computations in the Striatum
<div> <div>These datasets and scripts are organized by figures. All data are stored as .mat format and can be open and manipulated using MATLAB. Scripts are all written in MATLAB and can be ran in MATLAB.</div> <div>There are two ways to run the code to reproduce each figures and statistics.</div> <div>1. Run RUN_ME.m. In this case, the file will automatically excute scripts to load corresponding data and figures.</div> <div>2. Open individual script to load corresponding data and generate statistics and figures.</div> <br> <div>All scripts here have been validated and tested. The system and coding environment is:</div> <div>- Windows 11 24H2</div> <div>- MATLAB 2023a</div> <br> <div>Matlab dependent package (not all are required but those are installed in my environment):</div> <div>- Bioinformatics Toolbox v4.17</div> <div>- Communications Toolbox v8.0</div> <div>- Computer Vision Toolbox v10.4</div> <div>- Curve Fitting Toolbox v3.9</div> <div>- Data Acquisition Toolbox v4.7</div> <div>- Database Toolbox v11.0</div> <div>- Deep Learning HDL Toolbox v1.5</div> <div>- Deep Learning Toolbox v14.6</div> <div>- DSP HDL Toolbox v1.2</div> <div>- Econometrics Toolbox v6.2</div> <div>- Financial Toolbox v6.5</div> <div>- Fixed-point Designer v7.6</div> <div>- Image Processing Toolbox v11.7</div> <div>- MATLAB Coder v5.6</div> <div>- MATLAB Compiler v8.6</div> <div>- MATLAB Compiler SDK v7.2</div> <div>- MATLAB Report Generator v5.14</div> <div>- MATLAB Support for MinGW-w64 C/C++ Compiler v23.1.0</div> <div>- Optimization Toolbox v9.5</div> <div>- Parallel Computing Toolbox v9.5</div> <div>- FR Toolbox v4.5</div> <div>- Signal Integrity Toolbox v1.3</div> <div>- Simulink v10.7</div> <div>- Statistics and Machine Learning Toolbox v12.5</div> <div>- Symbolic Math Toolbox v9.3</div> <div>- Text Analytics Toolbox v1.10</div> <div>- Wavelet Toolbox v6.3</div> </div>
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.