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910 results for “pollution”
Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations
<p>This dataset provides data on typical pollutant removal efficiencies, evaluation critera and cost estimation for 37 common wastewater treatment unit processes. The data is based on literature research, expert workshops and estimations.</p> <p>The following files are available:</p> <ul> <li>Pollutant removal efficiencies, evaluation criteria and cost estimations for wastewater treatment unit processes (dataset) - Microsoft Excel:<br> Database of pollutant removal efficiencies for 11 paramameters, cost estimation and evaluation criteria.</li> <li>Pollutant Removal Efficiencies for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Evaluation Criteria for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Cost Estimation for Wastewater Treatment Unit Processes (Dataset) - pdf</li> </ul> <p> </p> <p> </p>
What Is Polluting Delhi's Air? A Review from 1990 to 2022
<p>This supplementary information for open use is part of the publication, "<a href="https://doi.org/10.3390/su15054209">What is Polluting Delhi's Air? A Review from 1990 to 2022</a>" This paper offers insight by reviewing the influence of Delhi’s urban growth since 1990 on pollution levels and sources and the evolution of technical, institutional, and legal measures to control emissions in the National Capital Region of Delhi..</p> <p><strong>The databases and the documents uploaded here are the following</strong></p> <p>Available ambient air quality monitoring data for Delhi</p> <ul> <li>cpcb_delhi_data_2006-2018-raw-cleaned.rar - This is CPCB data from 2006 to 2018 as raw and cleaned files. Raw data is at 15 min internals, which needs some qa/qc checks before use. The cleaned data is hourly. For data cleaning, all null points, all negative points, integer values equal to 9999, 999, 1985, 985, 915, 515, 1200, 1000, 2000, 380, 3800, 675, and 718 were excluded. These values were recognized after searching the raw data for patterns. Instances of sudden jumps, which occur due to malfunctioning of instruments were recognized using running means. </li> <li>1999-2006-CPCB ITO-Hourly.xlsx - This is hourly data from the ITO station only</li> <li>NAMP data for 2011 to 2015 is hosted here - https://doi.org/10.5281/zenodo.6925200</li> <li>Graph-Composite-1989-2022.xlsx -- This is a composite of all the annual average data along with the worksheet to make the image in the preview.</li> </ul> <p>Support documents</p> <ul> <li>1997-CPCB-White-Paper-on-Delhi-Air-Pollution.pdf</li> <li>2011-CPCB-Source-Apportionment-Report-Extracts.pdf</li> <li>2015-04 Infograph Delhi Banning Vehicles to Control AP.jpg</li> <li>2016-03 Inforgraph Delhi Odd Even Emissions.jpg</li> <li>2019-09-Infograph-Delhi-Odd-Even-Buses.jpg</li> <li>NCAP-Planned-Source-Apportionment-studies.pdf</li> <li>SIM-41-2021-Data-Resources-for-Energy-Emissions-Analysis.pdf</li> </ul> <p>Reanalysis fields from WUSTL</p> <ul> <li>wustl_delhi_1998-2021.csv - This data is at 0.01 degree resolution for the city airshed. The long-lat represent the grid mid-point. </li> <li>wustl_delhi_1998-2021.png - This is a composite image of data extracted for all the years</li> </ul> <p>Satellite data extracts</p> <ul> <li>satellite-modis_terra_aod_delhi-covidperiod.xlsx</li> <li>satellite-modis_terra_aod_delhi-longerperiod.xlsx</li> <li>satellite-omi_no2_delhi-covidperiod.xlsx</li> <li>satellite-tropomi_o3_delhi.xlsx</li> <li>satellite-tropomi_so2_delhi.xlsx</li> </ul>
Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China
<p>Here presented the forcasted results of Potential Morbidity Risk Index (PMRI) for the personal patients with allerigc rhinitis and the public health administrations, and these results are supplied to the published paper of "Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China".</p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Data and analysis scripts for: Lung adenocarcinoma promotion by air pollutants
<p>Code for "Lung adenocarcinoma promotion by air pollutants" manuscript</p> <p>egfrm_lc_incidence</p> <ul> <li>Epidemiological analyses of EGFRm lung cancer incidence and PM2.5 levels in England (NHS England), South Korea and Taiwan.</li> </ul> <p>ukbb</p> <ul> <li>Epidemiological analyses of lung cancer incidence and PM2.5 levels in England, using the UKBB data set.</li> </ul> <p>mouse_RNA_seq</p> <ul> <li>Analysis of RNA-seq data derived from lung tumour tissue of pollution-exposed mice.</li> </ul> <p>mouse_WGS</p> <ul> <li>Analysis of WGS data derived from lung tumour tissue of pollution-exposed mice.</li> </ul> <p>normal_lung</p> <ul> <li>Analysis of ddPCR for EGFRm from normal lung tissue from the TRACERx and PEACE cohorts.</li> <li>Analysis of Duplex-seq data from normal lung tissue from the PEACE and BDRE cohorts.</li> </ul>
Data for: Heavy metal pollution impacts soil bacterial community structure and antimicrobial resistance at the Birmingham 35th Avenue Superfund Site
<p>The data in this archive are the results of a study on the impact of heavy metals (HMs) on the soil microbiota of an urban Superfund site in Alabama. HMs are known to modify bacterial communities both in the laboratory and in situ. Consequently, soils in HM-contaminated sites such as the U.S. Environmental Protection Agency (EPA) Superfund sites are predicted to have altered ecosystem functioning, with potential ramifications for the health of organisms, including humans, that live nearby. Further, several studies have shown that heavy metal-resistant (HMR) bacteria often also display antimicrobial resistance (AMR), and therefore HM-contaminated soils could potentially act as reservoirs that could disseminate AMR genes into human-associated pathogenic bacteria. To explore this possibility, topsoil samples were collected from six public locations in the zip code 35207 (the home of the North Birmingham 35th Avenue Superfund Site) and in six public areas in the neighboring zip code, 35214. 35027 soils had significantly elevated levels of the HMs As, Mn, Pb, and Zn, and sequencing of the V4 region of the bacterial 16S rRNA gene revealed that elevated HM concentrations correlated with reduced microbial diversity and altered community structure. While there was no difference between zip codes in the proportion of total culturable HMR bacteria, bacterial isolates with HMR almost always also exhibited AMR. Metagenomes inferred using PICRUSt2 also predicted significantly higher mean relative frequencies in 35207 for several AMR genes related to both specific and broad-spectrum AMR phenotypes. Together, these results support the hypothesis that chronic HM pollution alters the soil bacterial community structure in ecologically meaningful ways and may also select for bacteria with increased potential to contribute to AMR in human disease.</p>
Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations - Dataset
<p>This repository contains the data used for the analysis of the paper "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations (PNC)" which is under submission.</p> <p> </p> <p>The experimental conditions and the instruments used are detailed in Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. <em>Sensors</em> <strong>2020</strong>, <em>20</em>, 2219. https://doi.org/10.3390/s20082219</p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p> </p> <p>sensors_raw.csv contains the following headers:</p> <ul> <li>Bin0 to Bin15: Alphasense OPC-R1 particle number concentrations for different size bins</li> <li>Bin[1-3-5-7]MToF: mean time of flight of particles within the corresponding size bins of the Alphasense OPC-R1</li> <li>Checksum: checksum of the Alphasense OPC-R1</li> <li>SFR: sample flow rate of the Alphasense OPC-R1</li> <li>Humidity: relative humidity measured by the Alphasense OPC-R1</li> <li>Temperature: temperature measured by the Alphasense OPC-R1</li> <li>SamplingPeriod: sampling period of the Alphasense OPC-R1</li> <li>gr03um, gr05um, gr10um, gr25um, gr50um, gr100um: PNC measured by the Plantower PMS5003</li> <li>n05, n1, n25, n4, n10: PNC measured by the Sensirion SPS30</li> <li>humidity: relative humidity measured by a Sensirion SHT-3x</li> <li>temperature: temperature measured by a Sensirion SHT-3x</li> <li>sensor: id of the sensors</li> <li>site: name of the air quality monitor hosting the sensors</li> <li>exp: name of the experiment conducted</li> <li>source: source used to generate PM (incense or candle)</li> <li>variation: whether the sensors were exposed to stable or peak concentrations of PM pollution</li> <li>date: date in format yyyy-mm-dd HH:MM:SS</li> </ul> <p>For more explanations about the fields of individual sensors, please refer to their manual (Alphasense OPC-R1: https://kolegite.com/EE_library/datasheets_and_manuals/sensors/OPC/072-0500_OPC-R1_manual_issue_1_250219.pdf ; Plantower PMS5003: https://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf ; Sensirion SPS30: https://sensirion.com/products/catalog/SPS30/)</p> <p> </p> <p>ops.csv and ops.rds contains the readings from the OPS with the following cut sizes for the bins:</p> <ul> <li>Bin 1 Cut Point (um),0.300</li> <li>Bin 2 Cut Point (um),0.374</li> <li>Bin 3 Cut Point (um),0.465</li> <li>Bin 4 Cut Point (um),0.579</li> <li>Bin 5 Cut Point (um),0.721</li> <li>Bin 6 Cut Point (um),0.897</li> <li>Bin 7 Cut Point (um),1.117</li> <li>Bin 8 Cut Point (um),1.391</li> <li>Bin 9 Cut Point (um),1.732</li> <li>Bin 10 Cut Point (um),2.156</li> <li>Bin 11 Cut Point (um),2.685</li> <li>Bin 12 Cut Point (um),3.343</li> <li>Bin 13 Cut Point (um),4.162</li> <li>Bin 14 Cut Point (um),5.182</li> <li>Bin 15 Cut Point (um),6.451</li> <li>Bin 16 Cut Point (um),8.031</li> <li>Bin 17 Cut Point (um),10.000</li> </ul> <p>nanotracer.csv and nanotracer.rds contain the measurements from the Nanotracer:</p> <ul> <li>N.1.: particles/cm3</li> <li>dp_avg.1.: mean diameter of the particles (nm)</li> <li>P.1.:</li> <li>S_al.1.: Lung Deposited Surface Area in um2/cm3</li> </ul> <p> </p> <p>experimental_conditions.csv and experimental_conditions.rds contain the end dates and start dates of each of the experiment conducted.</p> <p> </p> <p> </p> <p>"pm100_cf1","pm10_cf1","pm25_cf1"</p> <p> </p> <p> </p>
Lithium Pollution of White Dwarfs and Other Secrets of MORDOR (CSV File of MORDOR Survey Objects)
<p>CSV file containing the Gaia DR2 data for the MORDOR Survey from Benjamin C. Kaiser's Ph.D. Dissertation. It also contains the spectral types and SED types that were identified.</p> <p>If you use this data please cite my dissertation, which should be accessible via the UNC Chapel Hill Library in some way. You should probably also cite Gaia DR2 if you use anything other than my spectral types pretty much because all the rest of the data is from Gaia DR2.</p>
Water pollution in Höfen and COVID-19 cases in Austria
<p>Dataset used for the project "Analysis of correlation between water pollution in Höfen and Covid-19 cases in Austria, 2020"</p>
Analysis of correlation between water pollution in Höfen and Covid-19 cases in Austria, in 2020 dataset
<p>Dataset about water pollution in Höfen and COVID-19 cases in Austria, in 2020. The measured water pollution corresponds to the daily COVID-19 cases and deaths.</p>
Figure 5 in Response of marine microalgae Phaeodactylum tricornutum, Prorocentrum cordatum and Gyrodinium fissum to complex pollution of Sevastopol bays (Black Sea)
Figure 5. Influence of the polluted waters of the Sevastopol area to P. tricornutum (I), P. cordatum (II) and G. fissum (III): a) – inhibition effect, b) stimulated effect and c) absence of effect.
Figure 4 in Response of marine microalgae Phaeodactylum tricornutum, Prorocentrum cordatum and Gyrodinium fissum to complex pollution of Sevastopol bays (Black Sea)
Figure 4. Dynamics of the cells abundance in the cultures of P. tricornutum (a), P. cordatum (b) and G. fissum (c) in control (1), on the water of the mussel farm area (2), Artillery Bay (3) and Sevastopol Bay (4) in October 2020.
Figure 1 in Response of marine microalgae Phaeodactylum tricornutum, Prorocentrum cordatum and Gyrodinium fissum to complex pollution of Sevastopol bays (Black Sea)
Figure 1. Map of the seawater sampling stations location: 1 – mussel farm area, 2 – Artillery Bay, 3 – Sevastopol Bay.
Figure 3 in Response of marine microalgae Phaeodactylum tricornutum, Prorocentrum cordatum and Gyrodinium fissum to complex pollution of Sevastopol bays (Black Sea)
Figure 3. Dynamics of the cells abundance in the cultures of P. tricornutum (a), P. cordatum (b) and G. fissum (c) in control (1), on the water from the mussel farm area (2), Artillery Bay (3) and Sevastopol Bay (4) in September 2020.
The Pollution from Obsolete Issue Report: An Empirical Study
<p>The Pollution from Obsolete Issue Report: An Empirical Study</p> <p>Project summary</p> <p>In software development, programmers use issue trackers to manage their maintenance issues and record valuable maintenance details in issue reports. Based on these issue reports, programmers have enhanced code comprehension and researchers have mined knowledge from issue reports to assist various programming tasks. Although issue reports are useful, some of them can be obsolete, in that their corresponding commits are overwritten or rolled back, with the evolution of software. The obsolete issue reports can invalidate their references and descriptions, and can have far-reaching impacts on the approaches built on them.</p> <p>To deepen the understanding of obsolete issue reports, we conducted the first empirical study to analyze obsolete issue reports. We consider that an issue report is obsolete if its revisions are partially or totally removed in later commits. To measure how an issue report becomes obsolete, we define an obsolete ratio of an issue report as its deleted lines over all its modified lines. In this paper, we build a tool, ICLINKER, to inspect the obsolete issue reports and calculate the obsolete ratios. With ICLINKER, we analyze 70,180 commits and 46,257 issue reports that are collected from five Apache projects. Taking them as our inputs, we explore four research questions, which concern the distributions, the references, and the explanations of obsolete issue reports. Our findings on these research questions enrich the knowledge of obsolete issue reports, and some are even counterintuitive. For example, we find that obsolete issue reports are mixed with other issue reports. As another example, we find that only a small portion of issue reports are mentioned in code comments, but about half of them are obsolete. Based on our results, we analyze some directions that are worthy of exploration.</p> <p>Our identified obsolete ratios</p> <p>We identified the obsolete ratios of the issue reports from five projects. Their obsolete ratios are as follows: calcite.txt, cassandra.txt, derby.txt, hbase.txt, and hive.txt.</p> <p>Our Dataset</p> <p>Our dataset is stored in the data folder.</p>
Data of "High concentration of atmospheric sub-3 nm particles in polluted environment of eastern China: new particle formation and traffic emission"
<p>The attached data is the measurement data at SORPES station in Yangtze Rive Delta of China. The data is for analysis and figures in the study of "High concentration of atmospheric sub-3 nm particles in polluted environment of eastern China: new particle formation and traffic emission". Currently the manuscript is submitted to JGR-A.</p>
Sex-specific effects of psychoactive pollution on behavioural individuality and plasticity in fish
<p>The global rise of pharmaceutical contaminants in the aquatic environment poses a serious threat to ecological and evolutionary processes. Studies have traditionally focused on the collateral (average) effects of psychoactive pollutants on ecologically-relevant behaviours of wildlife, often neglecting effects among and within individuals, and whether they differ between males and females. We tested whether psychoactive pollutants have sex-specific effects on behavioural individuality and plasticity in guppies (<em>Poecilia</em> <em>reticulata</em>), a freshwater species that inhabits contaminated waterways in the wild. Fish were exposed to fluoxetine (Prozac) for two years across multiple generations before their activity and stress-related behaviour were repeatedly assayed. Using a Bayesian statistical approach that partitions the effects among and within individuals, we found that males—but not females—in fluoxetine-exposed populations differed less from each other in their behaviour (lower behavioural individuality) than unexposed males. In sharp contrast, effects on behavioural plasticity were observed in females—but not in males—whereby exposure to even low levels of fluoxetine resulted in a substantial decrease (activity) and increase (freezing behaviour) in the behavioural plasticity of females. Our evidence reveals that psychoactive pollution has sex-specific effects on the individual behaviour of fish, suggesting that males and females might not be equally vulnerable to global pollutants.</p>
High-Temperature Electrothermal Remediation of Multi-Pollutants in Soil
<p>Source data for our publication entitled "High-Temperature Electrothermal Remediation of Multi-Pollutants in Soil"</p>
Full-scale MBR coupled with PAC for the removal of 232 OMPs: operating conditions, conventional pollutants, concentration of OMPs, UHPLC-QTOF-MS analysis
<p>The spreadsheet contains data regarding the operation and performance of a full-scale MBR coupled with powdered activated carbon (PAC) added inside the reactor. This hybrid system is chosen to evaluate the removal of 232 organic micropollutants (OMPs) and the potential enhancement in the removal efficiencies with the addition of PAC at a concentration of 0.1 g/L and 0.2 g/L</p><ul><li>First worksheet contains minimum, maximum, and average concentrations of conventional pollutants (COD, BOD, SST, SSV, DOC, UV254, nitrogen compounds, phosphorous, <i>E. coli</i>) in the influent and effluent of the WWTP. Methodologies adopted are also reported.</li><li>Second worksheet reports the operating conditions of the full-scale MBR as well as information about the tubular UF membranes installed. Characteristics of the PAC purchased to perform the experiments regarding the removal of OMPs are also described.</li><li>Third worksheet reports minimum, maximum and average concentration of 232 OMPs in the influent and effluent during:<ol><li>The monitoring period considering only the MBR</li><li>The experimental periods where PAC is added and maintained at a concentration of 0.1g/L and 0.2g/L inside the MBR</li></ol></li><li>Fourth worksheet contains metadata regarding the UHPLC–QTOF–MS analysis performed to evaluate the occurrence of OMPs in the influent and effluent of the WWTP. Sampling, storage and sample preparation is described, followed to LC-ESI-tandem MS analysis. LOD and LOQ are reported.</li></ul>
GIS and Pollution Data: Designating Regional Airsheds for Air Quality Management in India
<p>Full journal article published here<br><strong>Designating Airsheds in India for Urban and Regional Air Quality Management<br></strong><a href="https://doi.org/10.3390/air2030015" target="_blank" rel="noopener">https://doi.org/10.3390/air2030015</a><strong><br></strong></p> <p>[Summary presentation <a href="https://urbanemissions.info/wp-content/uploads/docs/UEinfo-Designating-Airsheds-in-India.pptx">download</a>]</p> <p>Datasets used for proposing India's 15 regional airsheds for air quality management are the following</p> <p>PM2.5 Datasets<br>Raw data source: <a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5">https://sites.wustl.edu/acag/datasets/surface-pm2-5</a></p> <ul> <li>Gridded 0.1 degree resolution source apportionment results from WUSTL's global model simulations<br>File: india_data_pm25_wustl_source_cont_0p1deg.xlsx<br>Aggregated Source definitions used in this presentation <ul> <li>1. DUST = Anthropogenic dust = AFCID</li> <li>2. WINDUST = Wind erosion (dust storms) = WDUST</li> <li>3. WASTE = Waste burning = WST</li> <li>4. RESI = All commercial and residential cooking, lighting, and heating = RCOC + RCOO + RCORbiofuel + RCORcoal + RCORother</li> <li>5. TRANS = All transport (excluding aviation) = ROAD + NRTR + SHP</li> <li>6. POWER = Energy generation = ENEcoal + ENEother</li> <li>7. INDUS = All industries and product use = INDcoal + INDother + SLV</li> <li>8. BIOB = Biomass burning, including forest fires and agricultural waste burning = GFEDoburn + GFEDagburn</li> <li>9. AGR = Agricultural activities (excluding agricultural waste burning) = AGR</li> <li>10. OTHER = All others = OTHER</li> </ul> </li> <li>Gridded 0.1 degree resolution, reanalysis data from WUSTL's global model simulations<br>File: india_data_pm25_wustl_reanalysis_0p1deg.xlsx<br>Time period: 1998 to 2022, annual averages</li> <li>Gridded 0.1 degree achive for monthly averages from WUSTL's global model simulations<br>File: <a href="https://www.urbanemissions.info/wp-content/uploads/misc/IndiaSubcontinent-Gridded-Monthly-WUSTL-v4.rar">Download-44MB</a></li> </ul> <p>Population Datasets<br>Raw data source: <a href="https://landscan.ornl.gov">https://landscan.ornl.gov</a></p> <ul> <li>Gridded 0.1 degree resolution population density data<br>File: india_data_population_2021_0p1deg.xlsx</li> </ul> <p>GIS databases used in this study</p> <ul> <li>ESRI shapefile of 0.1 x 0.1 degree mesh file for the Indian Subcontinent covering longitudes from 67E to 99E and latitudes from 7N to 39N<br>File: india_gis_grids-0.1x0.1deg.rar</li> <li>ESRI shapefile of India administrative level 2 data - 28 states and 8 union territories (as of December 2023)<br>File: india_gis_states28+8_2023.rar</li> <li>ESRI shapefile of India administrative level 3 data - 755 districts (as of December 2023): district23 and states23 codes are re-designed for emissions and pollution mapping and data tracking purposes<br>File: India_gis_districts755_2023.rar (original source: <a href="https://projects.datameet.org/maps">https://projects.datameet.org/maps</a>)</li> <li>ESRI shapefile of India's Agro-Climatic zones<br>File: india_gis_agroclimatic_zones.rar (original source: <a href="https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions">https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions</a> </li> <li>ESRI shapefile of India's meteorological sub-divisions<br>File: india_gis_meteo_subdivisions.rar (original source: <a href="https://mausam.imd.gov.in/">https://mausam.imd.gov.in</a>)</li> </ul>
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.