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5,794 results for “exposures”
Imidacloprid exposure through soil and its effect on Anthophora plumipes, a ground-nesting bee
<p>These are the raw data files is associated with the manuscript titled "Neonicotinoid exposure through soil and its effect on Anthophora plumipes, a ground-nesting bee".</p>
Data from: Do the health benefits of boiling drinking water outweigh the negative impacts of increased indoor air pollution exposure?
<p><strong>Background: </strong>Billions of the world's poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.</p> <p><strong>Objectives: </strong>This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.</p> <p><strong>Methods: </strong>We calculated the total change in disability-adjusted life years (DALYs) from indoor air pollution (IAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies. </p> <p><strong>Results: </strong>Boiling drinking water reduced the diarrhea disease burden by a mean of 1110 DALYs and 368 DALYs per 10,000 people for adults and children <5 years in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, apart from fewer avoided DALYs in children due to different demographics. In both countries, for households with high baseline IAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in IAP DALYs due to the log-linear dose-response curves. Boiling, even at low effectiveness, was associated with <em>net </em>DALY reductions for medium- and high-risk water, even if using unclean stoves/fuels. Replacing traditional stoves with improved stoves coupled with effective boiling practices significantly reduced total DALYs. </p> <p><strong>Discussion: </strong>Boiling water generally resulted in a net decrease in DALYs. Future efforts should empirically measure health outcomes from IAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.</p>
Figure 1 in Exposure of the snail Potamopyrgus antipodarum to herbicide boosts output and survival of parasite infective stages
Figure 1. Mean (±SE) number of cercariae emerging per day from individual snails, P. antipodarum, exposed to either control water, low, medium or high concentrations of the herbicide glyphosate. Data are shown separately for the trematodes C. parvum (N = 6 snails for each treatment), Apatemon sp. (N = 3), and an undescribed renicolid species from two localities (Tomahawk Lagoon, N = 12; Lake Waihola, N = 9). In each case, different letters on the bars indicate mean values that are significantly different (Tukey–Kramer tests, P <0.05).
Figure 5A–I in Caecidae (Mollusca: Gastropoda) from late Miocene exposures of the "Imperial" Formation in Riverside County, California
Figure 5A–I. Caecum roederi Raines, Powell and LaFollette n. sp.: A–B, Holotype, LACMIP 43085.1, LACMIP Type 14894; A, profile view; B, ventral view; C, Paratype, UF 330224, ventral view; D, Paratype, LACMIP 43085.2, LACMIP Type 14895, profile view; E, Paratype, LACMIP 43085.3 Scale bar=500μm.
Figure 4 in Caecidae (Mollusca: Gastropoda) from late Miocene exposures of the "Imperial" Formation in Riverside County, California
Figure 4. Composite section of "Imperial" Formation in and around Super Creek, east of Whitewater Canyon, Riverside County, CA (LACMIP loc. 43085). Stratigraphy in this section is approximate as different sections show slightly different sequences of rocks. The red stars mark where in the section the specimens were collected by Patrick LaFollette and Mark Roeder between 2010 and 2015.
Figure 3 in Caecidae (Mollusca: Gastropoda) from late Miocene exposures of the "Imperial" Formation in Riverside County, California
Figure 3. Exposures of the "Imperial" Formation along and adjacent to Super Creek, Riverside County, California as mapped by Matti et al. (1985). SC2–SC6 represents measured sections used to compile the composite stratigraphic section used here. The Caecidae described here are all from a narrow stratigraphic interval marked by a star.
Figure 1 in Caecidae (Mollusca: Gastropoda) from late Miocene exposures of the "Imperial" Formation in Riverside County, California
Figure 1. Locality map showing outcrops of the Imperial Formation s.l. in southern California. Outcrops of the late Miocene to Pliocene Imperial Formation s.s. include: 1) Edom Hill/ Indio Hills/Willis Palms/Thousand Palms Canyon (Powell 1985, Powell 1986, Powell et al. 2011), 2) the southern Santa Rosa Mountains (King et al. 2002), 3) Travertine Point (Powell 2008), 4) Ocotillo Wells State Vehicle Recreation area (Powell 1993), 5) Superstition Mountain (Morton 1977), 6) Fish Creek Mountains (Dibblee 1954, Morton 1977), 7) Coyote Mountains (Hanna 1926), and 8) Yuha Buttes (Hanna 1926). Outcrops of the late Miocene "Imperial" Formation s.l. occur at 9) Lions Canyon (Bramkamp 1935, Powell 1986), 10) Super Creek (Bramkamp 1935, Powell 1985, Powell 1986, Powell and LaFollette 2012), and 11) Garnet Hill (Powell 1985, Powell 1986, Rymer et al. 1995, Powell 1995).
Figure 7A–C in Caecidae (Mollusca: Gastropoda) from late Miocene exposures of the "Imperial" Formation in Riverside County, California
Figure 7A–C. Meioceras nitidum: A–B, LACMIP 43085.13, LACMIP 43085.14, profile views; C, LACMIP 43085.13, ventral view. Scale bar=500μm.
Figure 2 in Caecidae (Mollusca: Gastropoda) from late Miocene exposures of the "Imperial" Formation in Riverside County, California
Figure 2. Correlation chart of the stratigraphic sections including the "Imperial" Formation s.l. from west to east, at Lion Canyon, Super Creek, Garnet Hill, and the Imperial Formation s.s. in the Indio Hills, Riverside County, and in Imperial and San Diego counties from Split Mountain Gorge/Vallecito area, and the Coyote Mountains. Note the age difference between the "Imperial" Formation in Riverside County (exception for the Indio Hills) and the Imperial Formation in Imperial and San Diego counties. The Imperial Formation in the Indio Hills correlates with the type exposures in Imperial and San Diego counties. The ages of all boundaries are approximate. Numbers represent numerical age determinations: 1) Matti et al., 1985 (6.04±0.18 Ma and 5.94±0.18 Ma); 2) Peterson, 1975 (about 10 Ma); 3) Rymer et al., 1994, 1995 (between 8 and 7.6 Ma); 4) McNabb et al. 2017 (3.0-2.6 Ma); 5) Ruisaard 1979 (24.8 to about 15 Ma). Abbreviations: cong. = conglomerate; fm=formation; lac beds = unnamed lacustrine beds; ss = sandstone. In part after McDougall (2008).
Figure 6A–C in Caecidae (Mollusca: Gastropoda) from late Miocene exposures of the "Imperial" Formation in Riverside County, California
Figure 6A–C. Caecum brasilicum: A–B, LACMIP 43085.7, LACMIP 43085.8, profile views; C, LACMIP 43085.9, ventral view. Scale bar=500μm
Post‐processed data and analysis codes for the research "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"
<p>[Earth's Future] Oh et al. "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"</p> <p>1. Information for Raw datasets<br>- The data of eight global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) can be accessed at https://esgf-node.llnl.gov/search/cmip6/, <br> and can also be accessed in Eyring et al. (2016). <br>- The NOAA OISST high resolution dataset can be obtained in Reynolds et al. (2007) or via https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. <br>- The five ocean mask dataset can be obtained from https://reccap2-ocean.github.io/regions/. </p> <p>2. Information for Software<br>- The raw data in this study were analyzed using Fortran 90, R version 4.0.3, and Grads version 2.2.1.<br>- The Fortran 90 can be accessed at https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html#fortran. <br>- The R version 4.0.3 is available from https://cran.r-project.org/bin/windows/base/old/4.0.3/. <br>- The Grads version 2.2.1 can be downloaded from http://cola.gmu.edu/grads/downloads.php.</p> <p>3. Information for Post-Processed data and Codes used in this work.<br>Please find each folder and the relevant post-processed dataset and codes.</p>
Understanding the risks of co-exposures in a changing world: A case study of dual monitoring of the biotoxin domoic acid and Vibrio spp. in Pacific oyster
<div> <p><span>Assessing the co-occurrence of multiple health risk factors in coastal ecosystems is challenging due to the complexity of multi-factor interactions and limited availability simultaneously collected data. Understanding co-occurrence is particularly important for risk factors that may be associated with or occur in similar environmental conditions. In marine ecosystems, the co-occurrence of harmful algal bloom toxins and bacterial pathogens within the genus <em>Vibrio </em>may impact both ecosystem and human health. This study examined the co-occurrence of <em>Vibrio</em> spp. and domoic acid (DA) produced by the harmful algae <em>Pseudonitzschia</em> by 1) analyzing existing California Department of Public Health monitoring data for <em>V. parahaemolyticus</em> and DA in oysters; 2) and conducting a one-year seasonal monitoring of these risk factors across two Southern California embayments. Existing public health monitoring efforts in the state were robust for individual risk factors; however, it was difficult to evaluate the co-occurrence of these risk factors in oysters due to low number of co-monitoring instances between 2015 and 2020. Seasonal co-monitoring of that DA and <em>Vibrio</em> spp. (<em>V. vulnificus</em> or <em>V. parahaemolyticus</em>) at two embayments revealed the co-occurrence of these health risk factors in 35% of sampled oysters in most seasons. Interestingly, both the overall detection frequency and co-occurrence of these risk factors was considerably less frequent in water samples. These findings may in part suggest the slow depuration of <em>Vibrio </em>spp. and DA in oysters as residual levels may be retained. This study expanded our understanding of the simultaneous presence of DA and <em>Vibrio</em> spp. in bivalves and demonstrate the feasibility of co-monitoring different risk factors from the same sample. Individual programs monitoring for different risk factors from the same sample matrix may consider combining efforts to reduce cost, streamline the process, and better understand the prevalence of co-occurring health risk factors.</span></p> </div>
Insights from a Methylome-Wide Association Study of Antidepressant Exposure
<p>Results datasets from the paper: </p> <p><strong>Insights from a Methylome-Wide Association Study of Antidepressant Exposure</strong></p> <p><strong>Methylome-wide association study (MWAS) summary statistics: </strong></p> <p><strong>All individuals: </strong></p> <p>MWAS of prescription-derived antidepressant exposure (n = 7,951): </p> <p> <a href="https://zenodo.org/uploads/14203230" target="_blank" rel="noopener noreferrer">GRM_unadjusted_antidep_pheno1_clean_appt_MOA_ORM_residph_standard_06_10.moa</a></p> <p>MWAS of self-reported antidepressant exposure (n = 16,531):</p> <p> <a href="https://zenodo.org/uploads/14203230" target="_blank" rel="noopener noreferrer">GRM_unadjusted_selfrep_pheno3_MOA_ORM_residph_standard_06_10.moa</a></p> <p><strong>Individuals with a lifetime status of Major Depressive Disorder (MDD, MDD-subgroup): </strong></p> <p>MWAS of prescription-derived antidepressant exposure (n = 792)</p> <p><a href="https://zenodo.org/uploads/14203230" target="_blank" rel="noopener noreferrer">GRM_unadjusted_antidep_pheno2_clean_appt_MOA_ORM_residph_standard_06_10.moa</a></p> <p>MWAS of self-reported antidepressant exposure (n = 2,268):</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/GRM_unadjusted_selfrep_pheno4_MOA_ORM_residph_standard_06_10.moa/content" target="_blank" rel="noopener noreferrer">GRM_unadjusted_selfrep_pheno4_MOA_ORM_residph_standard_06_10.moa</a></p> <p><strong>Downstream functional analyses: </strong></p> <p><strong>Differentially methylated region (DMRFF) analysis:</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">antidep_pheno1_clean_appt_dmr_res.tsv</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">selfrep_pheno3_dmr_res.tsv</a></p> <p><strong>GO Biological Pathway (msigdbr) enrichment:</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_PD.txt</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_SR.txt</a></p> <p><strong>SynGo pathway (web-portal) enrichment:</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_annotations_matching_user_input_PD.xlsx</a></p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_ontologies_with_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_ontologies_with_annotations_matching_user_input_PD.xlsx</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_annotations_matching_user_input_SR.xlsx</a></p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_ontologies_with_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_ontologies_with_annotations_matching_user_input_SR.xlsx</a></p> <p><strong>Tissue enrichment (FUMA GENE2FUNC):</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_PD.txt</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_SR.txt</a></p> <p><strong>Antidepressant exposure methylation profile score (MPS): </strong></p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">GS_AD_MRS_weights.txt</a></p> <p><strong>Source Data:</strong></p> <p>Source data for Figures and Supplementary Figures which do not represent individual-level data.</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">source_data.xlsx </a></p> <p> </p>
Data from: Is exposure to chytrid fungus and ranavirus higher in ponds invaded by American bullfrogs?
<p>Data associated with manuscript: https://doi.org/10.1007/s10530-025-03648-8<br><br>The spread of emerging infectious diseases (EIDs) and non-native invasive species are interconnected processes driving biodiversity loss. A key example involves Batrachochytrium dendrobatidis (Bd) and ranavirus (Rv), pathogens contributing to global amphibian declines. Their occurrence has been linked to invasive American bullfrogs (Lithobates catesbeianus) serving as asymptomatic vectors. To determine the relationship between Bd and Rv exposure and bullfrogs, we investigated whether pathogen occurrence and environmental loads are correlated with the presence and density of bullfrogs and whether the seasonal variation in Bd exposure is related to bullfrog phenology. We sampled 157 ponds in Belgium, including four with known bullfrog and Bd presence that were monitored monthly for two years, using quantitative environmental DNA (eDNA) barcoding. We validated a duplexed assay that simultaneously targets Bd and Rv, and we quantified eDNA concentrations of bullfrogs, Bd, and Rv serving as proxies for density and pathogen loads. Bd was detected more frequently when bullfrogs were present and both the frequency of detection and environmental loads of Bd increased in ponds with high bullfrog densities. In contrast, Rv detection likelihood and load were not significantly related to bullfrog presence or density. Seasonal fluctuations in Bd loads varied between ponds with and without breeding activity. In the former, Bd was consistently detected, likely due to overwintering tadpoles. In the latter, Bd loads varied throughout the year, with peak loads coinciding with juvenile immigration. Collectively, our findings indicate that amphibian exposure to Bd, but not Rv, is higher in areas and periods where bullfrogs are present and occur at high densities</p>
Large Landslide Exposure in Metropolitan Cities
<p>These datasets (.Rmd, .Rroj., .rds) are ready to use within the R software for statistical programming with the R Studio Graphical User Interface (https://posit.co/download/rstudio-desktop/). Please copy the folder structure into one single directory and follow the instructions given in the .Rmd file. Files and data are listed and described as follows:</p> <p>Main directory files: results_fpath</p> <ul> <li>Code containing statisticla analysis and ploting: 20240927_code.Rmd </li> <li>1_melted_lan_df.rds: Landslide time series database covering 1,085 landslides intersected with settlement footprints from 1985-2015.</li> <li>4_cities_lan.df.rds: City and landslide data for these 1,085 landslides intersected with settlement footprints from 1985-2015.</li> <li>7_zoib_nested_pop_pressure_model: brms statistical model file.</li> <li>ghs_stat_fua_comb.gpkg: Urban center data from the GHSL - Global Human Settlement Layer.</li> </ul> <p>Population estimation files: wpop_files </p> <ul> <li>2015_ls_pop.csv: Estimates of population on landslides using the 100x100 population density grid from the WorldPop dataset.</li> </ul> <p>Steepness and elevation analysis derived from SRTM and processed in Google Earth Engine for landslides, mountain regions and urban centers in cities: gee_files</p> <ul> <li>1_mr_met.csv: Elevation and mean slope for mountain region areas in cities</li> <li>2_uc_met.csv: Elevation and mean slope for urban centers (defined by in the GHSL data) in cities</li> </ul> <p>Standard deviation analysis derived from SRTM and processed in Google Earth Engine for mean slope in mountain regions and urban centers in cities: gee_sd</p> <ul> <li>gee_mr.csv: Mean slope and standard deviation for mountain region</li> <li>gee_uc.csv: Mean slope and standard deviation for urban centers (defined by in the GHSL data)</li> </ul> <p>Summary overview of cities with large landslides used in this study: cities_summary.csv</p> <p>Variables in this file are:</p> <ul> <li> <ul> <li>eFUA_name := City name</li> <li>cntry := Country name</li> <li>cntry_ISO := Country ISO code</li> <li>fua_area := Estimate of total metropolitan city area (km^2)</li> <li>ls_total_area := Total large landslide areas within city (m^2)</li> <li>ls_pct_fua_area := Mapped large landslides\n(% of the metropolitan city area)</li> <li>pop_FUA_2015 := Metropolitan population estimate (2015)</li> <li>pop_ls := Estimate of total number of people on large landslides</li> </ul> </li> </ul>
Data Set: Raman Investigation of In Vivo Radiation Exposure on Melanin in Murine Hair
<p>Updated version contains additional data added during peer review. Files contains Raw Raman spectra collected from the hair of mice irradiataed with gamma rays of specified dose. The time following exposure (in days) that the hair was sampled, the sex of the mouse, and the total dose (Gy) is given for each spectrum. The Raman spectra were collected with excitation wavelengths of 532 nm and 785 nm. The Raman shift labels for each excitation wavelength is given the first row of the data table prior to the raw spectra.</p>
Shaped by the sun: the effect of exposure to sunlight on the evolution of spider bodies
<p>Body temperature strongly influences fitness. Some sun-exposed ectotherms thermoregulate by adjusting body posture according to the sun's position. To evaluate the impact of body shape, size and posture on body temperature, we first built a model combining traditional heat transfer models with models of thermoregulatory postures in spiders. The model indicates that both body size and shape determine thermoregulation efficiency by affecting heat gain via solar irradiance. These estimates corroborate previous empirical studies on spider thermoregulation. We then coupled meteorological data to our heat transfer model. The model predicts that body elongation in large orb-web spiders decreases the risk of high body temperatures. Lastly, we measured the elongation of orb-web spiders across 1,024 species and found that sun-exposed species evolved more elongate bodies than sun-protected species. Overall, our results suggest that thermoregulation influenced the evolution of body shapes of orb-web spiders.</p>
DNA methylation in clonal Duckweed lineages (Lemna minor L.) reflects current and historical environmental exposures.
<p>The following depository contains raw phenotypic data and intermediate DNA methylation data presented in the article <strong>"DNA methylation in clonal Duckweed lineages (<em>Lemna minor </em>L.) reflects current and historical environmental exposures.</strong>" :</p> <p><strong>1) Raw phenotypic data</strong></p> <p>- Frond_area_Phase1_Phase2 -> Frond area measured at different time points (week 1, 6, 7 and 8) during the experiment.</p> <p>- Frond_number_Phase1_Phase2 -> Frond number measured at different time points (week 1, 6, 7 and 8) during the experiment.</p> <p><strong>2) Intermediate files obtained from running the epiGBS2 pipeline. The following files are available:</strong></p> <p>- consensus_cluster.renamed.fa -> epiGBS <em>de novo </em>loci. This file consists of the <em>de novo </em>epiGBS reference sequence file obtained during the <em>de novo </em>reference creation.</p> <p>- methylation.filtMETH -> The filtered DNA methylation data. This data was obtained after filtering the raw DNA methylation data. Cytosines which had a 10X coverage or higher and which were present in 80% of all samples were kept for further analysis.</p> <p>Demultiplexed and raw data were deposited at NCBI: BioProject: <strong>PRJNA883550</strong></p>
Technology pathways could help drive the U.S. West Coast grid's exposure to hydrometeorological uncertainty (Figure Data)
<p>Data used to create figures for:</p> <p>Wessel, J., Kern, J.D., Voisin, N., Oikonomou, K., Haas, J. (2021). “Technology pathways could drive the U.S. West Coast grid's exposure to hydrometeorological uncertainty”.</p> <p>California and West Coast Power System (CAPOW) model is Python based. The model was built to simulate the operations of the major markets comprising the West Coast bulk electric power system: the Mid-Columbia (Mid-C) market, and the California Independent System Operator (CAISO). This version adds future technology pathways, EV adoption, and battery storage.</p> <p>See https://github.com/jawessel/CAPOW_pathways (v1.0 release) for version of CAPOW model used.</p>
High-fidelity simulation of the effects of street trees, green roofs and green walls on the distribution of thermal exposure in Prague-Dejvice
<p>Archive with PALM simulation results. All data were used in paper <a href="https://doi.org/10.1016/j.buildenv.2022.109484">https://doi.org/10.1016/j.buildenv.2022.109484</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.