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600 results for “contamination”
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
Indicators of Contaminant Sources, PFAS, and Water Quality in Ellerbe Creek and New Hope Creek, NC (2019-2022)
Thousands of chemical contaminants are found in urban stream globally. This is a dataset of water quality measures of (1) compounds that are indicative of specific contaminant sources, (2) common water quality measures [trace metals, major ions, nutrients], and (3) PFAS. Sampling was conducted in Ellerbe Creek and New Hope Creek in the Durham and Orange counties of North Carolina. Biweekly and synoptic sampling was undertaken to explore spatial and temporal variation in water concentrations.
Landscape Position Project at North Temperate Lakes LTER: Fish Growth and Mercury Contaminant Data 1998 - 1999
As part of the Landscape Position Project, yellow perch were collected for mercury and isotope analysis by a combination of angling, beach seining, vertical gill net, fyke net and electrofishing in the summers of 1998 and 1999. A total of 86 yellow perch from 25 lakes were analyzed. Scales were used to determine age and length at ages 1 to 3 years. The nitrogen stable isotope signature indicates the relative food-web position of the fish relative to cladocerans collected from the same lake. The N_SIGNATURE value divided by 3.2 gives trophic position relative to cladoceran Sampling Frequency: one survey on each lake in late June through late July of 1998 or 1999 Number of sites: 25
Introduction to Ancient Metagenomics Textbook (Edition 2025): Contamination
<p>Data and conda software environment file for the chapter 'Contamination' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
S12 | NORMANEWS | NormaNEWS for Retrospective Screening of New Emerging Contaminants
<p>This is the collection associated with list S12 NormaNEWS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S12</p> <p>NORMANEWS</p> <p><strong>NormaNEWS for Retrospective Screening of New Emerging Contaminants</strong></p> <p>NormaNEWS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/NormaNEWS_V4_26042017_wDTXSIDs.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/NormaNEWS_V4_26042017_wDTXSIDs.xlsx">XLSX</a> (3/10/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/normanews">NORMANEWS List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/NormaNEWS_V4_InChIKeys.txt">NormaNEWS InChIKeys</a> (8/05/2017)</p> <p><a href="http://www.norman-network.com/?q=node/244">NormaNEWS</a> list provided by Nikiforos Alygizakis, Saer Samanipour and Kevin Thomas.</p> <p>Alygizakis et al 2018, DOI: 10.1021/acs.est.8b00365</p>
LIDAROC dataset 10m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 10m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div> </div> <div> </div>
LIDAROC dataset 5m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <p>This dataset is the 5m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 10m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div>
LIDAROC dataset 20m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 20m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 10m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> </div> <div> </div>
Post-remediation evaluation of contaminated site using geophysical methods: Ortophotomosaic Olkusz (Poland) 20220629
<p>The orthophotomap is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is an orthophotomap with a 2.57 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326).</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs
<p><strong>Abstract</strong></p> <p>To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.</p>
Data for removal kinetics and breakthrough curves of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns [Dataset]
<p>This dataset describes the transport and removal of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns. The experiments aimed at providing sustainable treatment options for relevant persistent, mobile and toxic (i.e., PMT substances) linked to vehicular traffic pollution. We assessed removal for 1H-benzotriazole, N'N-diphenylguanidine, and hexamethoxymethyl-melamine (PMT precursor) in batch and column experiments using pyrogenic carbonaceous adsorbents (e.g., GAC and biochar). Data contain kinetics batch experiments and breakthrough curves for the target contaminants.</p>
Dissolved trace element concentration profiles of micronutrients (Mn, Ni, Cu, Zn, Co) and contaminants (Cd, Pb) in seawater from discrete bottle samples from CCE Process Cruises in the California Current System, 2021 - 2025 (ongoing).
Dissolved trace element is sampled from the trace metal clean rosette. The sample is collected by filtering seawater through a 0.2µm PES filter. The seawater sample is then acidified to pH~1.8 using ultra clean hydrochloric acid and subsequently analyzed using sector-field inductively coupled plasma-mass spectrometry, scanning in low and medium resolution, with either standard curve or isotope dilution methods. The samples are used to develop a description of the distribution of dissolved trace elements in the CCE region.
S62 | NORMANEWS2 | NormaNEWS2: Retrospective Screening of New Emerging Contaminants
<p>This is the collection associated with list S62 NORMANEWS2 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>List of suspects provided by many contributors to <a href="https://www.norman-network.net/?q=node/327">NormaNEWS2</a>, collated by Kevin Thomas and colleagues at UQ.</p> <p>The Norman Early Warning System (NormaNEWS) is a collaborative activity aimed at members active in non-target analysis. The concept of NormaNEWS is that when one group identifies a new contaminant of emerging concern identification criteria are sent to other members of the group who use retrospective analysis techniques to check their own samples. This way we can rapidly establish the occurrence of newly identified compounds of emerging concern across Europe and beyond. NormaNEWS is lead by Kevin Thomas at NIVA (Norway) / University of Queensland (Australia) as part of the <a href="http://www.normandata.eu/?q=node/252"><strong>Non-target screening cross-working group activity</strong></a> of the NORMAN network.</p> <p><strong>What is NormaNEWS and how does it work</strong></p> <p>The first round of the collaborative NormaNEWS joint activity in 2016 successfully demonstrated the usefulness of the retrospective screening of high resolution mass spectrometric data in establishing the spatial and temporal occurrence of newly identified compounds of emerging concern. The results of this pilot study are presented in <a href="https://pubs.acs.org/doi/abs/10.1021/acs.est.8b00365?journalCode=esthag">Alygizakis <em>et al.</em>, ES&T, DOI: 10.1021/acs.est.8b00365</a>. The list of contaminants screened can be found on the <a href="http://www.norman-network.com/?q=node/236">NORMAN Suspect Exchange</a> and the <a href="https://comptox.epa.gov/dashboard/chemical_lists/normanews">CompTox Chemistry Dashboard</a>.</p> <p>To build on the first study, the NORMAN network has decided to launch NormaNEWS 2 as part of the activities of the <a href="http://www.normandata.eu/?q=node/252">NTS Cross-Working Group Activity</a> (<a href="http://www.norman-network.net/sites/default/files/files_private/JoinProgramme2018/NORMAN%20JPA%202018_final_Feb2018.pdf">NORMAN JPA 2018</a> and NORMAN JPA 2019).</p> <p>In NormaNEWS 2 we wish to further develop this approach to cover many more contaminants of emerging concern, include a broader range of matrices, and significantly increase temporal and spatial coverage.</p> <p>While NORMAN members are encouraged to participate in NormaNEWS, laboratories outside the NORMAN network are also welcome to participate. </p>
Supplementary data: The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.
<p>Supporting information for the article "<strong>The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.</strong>"</p> <p>This provides all the R script and .csv files for each dataset. </p>
Open-Ended Coaxial Cable Measurements in Diesel-Contaminated soil during Bioremediation
<p>Complex dielectric permittivity was measured over a broad frequency range (200 MHz to 20 GHz) with Keysight 85070D dielectric probe kit connected to a network analyzer (Keysight N5227A), on a series of 6 soil microcosms contaminated with diesel oil, before and after a process of bioremediation. This technology measures separately the real and the imaginary component of complex dielectric permittivity, which are meaningful properties of soil.</p>
Data associated to "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis"
<p>Data for replication of main results in "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis". The folder "data_estim" contains all necessary data to replicate all estimations in the article (see the R code "codes_cbs-cost") with three .csv files: dvf_estim.csv, dvfbasol_estim.csv and cell200_simulation.csv. The variable names in these files are as follow:</p><p> </p><p>Identifier Variables:</p><p>- IDMUTATION: identifier for each transacted property</p><p>- comm_code: identifier for each commune defined in 2021</p><p>- admin_code: identifier for urban areas defined in 2021</p><p>- iris2014_code: identifier for each neighborhood defined in 2014</p><p>- cell200_code: identifier for each 200-meters gredded cells</p><p>- dvf_x: longitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- dvf_y: latitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- basol_code: identifier for each CBS (only reported in dvfbasol_estim.csv)</p><p>- anneemut: year of transaction for each property</p><p> </p><p>Dependent Variable:</p><p>- pm2: price in euro per square meter of transacted properties</p><p> </p><p>Interest Variables:</p><p>- areaha_basol250: area in hectare of CBS between 0 and 250 meters from transacted property</p><p>- areaha_basol500: area in hectare of CBS between 250 and 500 meters from transacted property</p><p>- areaha_basol1000: area in hectare of CBS between 500 and 1000 meters from transacted property</p><p>- areaha_basol2000: area in hectare of CBS between 1000 and 2000 meters from transacted property</p><p>- areaha_basol3000: area in hectare of CBS between 2000 and 3000 meters from transacted property</p><p>- area250_indpro: area in hectare of CBS with industrial manufacturing activities between 0 and 250 meters from transacted property</p><p>- area500_indpro: area in hectare of CBS with industrial manufacturing activities between 250 and 500 meters from transacted property</p><p>- area1000_indpro: area in hectare of CBS with industrial manufacturing activities between 500 and 1000 meters from transacted property</p><p>- area2000_indpro: area in hectare of CBS with industrial manufacturing activities between 1000 and 2000 meters from transacted property</p><p>- area3000_indpro: area in hectare of CBS with industrial manufacturing activities between 2000 and 3000 meters from transacted property</p><p>- area250_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 0 and 250 meters from transacted property</p><p>- area500_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 250 and 500 meters from transacted property</p><p>- area1000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 500 and 1000 meters from transacted property</p><p>- area2000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 1000 and 2000 meters from transacted property</p><p>- area3000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 2000 and 3000 meters from transacted property</p><p>- area250_othact: area in hectare of CBS with other or unknown activities between 0 and 250 meters from transacted property</p><p>- area500_othact: area in hectare of CBS with other or unknown activities between 250 and 500 meters from transacted property</p><p>- area1000_othact: area in hectare of CBS with other or unknown activities between 500 and 1000 meters from transacted property</p><p>- area2000_othact: area in hectare of CBS with other or unknown activities between 1000 and 2000 meters from transacted property</p><p>- area3000_othact: area in hectare of CBS with other or unknown activities between 2000 and 3000 meters from transacted property</p><p>- areaha_specific250: area in hectare of CBS specific to a unique CBS between 0 and 250 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific500: area in hectare of CBS specific to a unique CBS between 250 and 500 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific1000: area in hectare of CBS specific to a unique CBS between 500 and 1000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific2000: area in hectare of CBS specific to a unique CBS between 1000 and 2000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p> </p><p>Robustness Variables:</p><p>- pm2mean_iris: average transaction price per square meter of neighborhood IRIS</p><p>- shpoorhouse: share in percentage of poor households </p><p>- dvfschool_nb250: number of schools within 250 meters of property</p><p>- dvfschool_nb500: number of schools within 500 meters of property</p><p>- dvfschool_nb1000: number of schools within 1000 meters of property</p><p>- dvfschool_nb2000: number of schools within 2000 meters of property</p><p>- dvfschool_nb3000: number of schools within 3000 meters of property</p><p>- dvfroad_nb250: number of road connections within 250 meters of property</p><p>- dvfroad_nb500: number of road connections within 500 meters of property</p><p>- dvfroad_nb1000: number of road connections within 1000 meters of property</p><p>- dvfroad_nb2000: number of road connections within 2000 meters of property</p><p>- dvfroad_nb3000: number of road connections within 30000 meters of property</p><p>- dvfrail_nb250: number of railway stations within 250 meters of property</p><p>- dvfrail_nb500: number of railway stations within 500 meters of property</p><p>- dvfrail_nb1000: number of railway stations within 1000 meters of property</p><p>- dvfrail_nb2000: number of railway stations within 2000 meters of property</p><p>- dvfrail_nb3000: number of railway stations within 3000 meters of property</p><p> </p><p>Control Variables:</p><p>- center_dist: distance in kilometers of transacted property from urban area center</p><p>- sterr: surface area in square meter of parcel of each property</p><p>- sbati: surface area in square meter of building surfaces</p><p>- vente_cla: transaction through a classical process (binary variable)</p><p>- vente_adj: transaction through adjudicated process (binary variable)</p><p>- vente_ech: transaction through special exchange process (binary variable)</p><p>- vente_exp: transaction through expropriation process (binary variable)</p><p>- vente_efa: transaction before completion (binary variable)</p><p>- nblocmai: number of houses in each transaction</p><p>- nblocapt: number of apartments in each transaction</p><p>- nblocdep: number of building dependencies in each transaction</p><p>- nblocact: number of properties for commercial purpose in each transaction</p><p>- nbapt1pp: number of apartment with 1 room in each transaction</p><p>- nbapt2pp: number of apartment with 2 rooms in each transaction</p><p>- nbapt3pp: number of apartment with 3 rooms in each transaction</p><p>- nbapt4pp: number of apartment with 4 rooms in each transaction</p><p>- nbapt5pp: number of apartment with 5 and more rooms in each transaction</p><p>- nbmai1pp: number of house with 1 room in each transaction</p><p>- nbmai2pp: number of house with 2 rooms in each transaction</p><p>- nbmai3pp: number of house with 3 rooms in each transaction</p><p>- nbmai4pp: number of house with 4 rooms in each transaction</p><p>- nbmai5pp: number of house with 5 and more rooms in each transaction</p><p>- pm2mean_comm: average transaction price in euro per square meter of commune</p><p>- dvfmonument_nb500: number of historical monuments between 0 and 500 meters from transacted property</p><p>- dvfmonument_nb1000: number of historical monuments between 500 and 1000 meters from transacted property</p><p>- dvfmonument_nb2000: number of historical monuments between 1000 and 2000 meters from transacted property</p><p>- dvfindus_nb500: number of active industrial sites between 0 and 500 meters from transacted property</p><p>- dvfindus_nb1000: number of active industrial sites between 500 and 1000 meters from transacted property</p><p>- dvfindus_nb2000: number of active industrial sites between 1000 and 2000 meters from transacted property</p><p>- sh_apt: share of apartments in neighborhood IRIS</p><p>- sh_1945: share in percentage of properties with a building age before 1945</p><p>- sh_1970: share in percentage of properties with a building age before 1970</p><p>- sh_1990: share in percentage of properties with a building age before 1990</p><p>- sh_ap90: share in percentage of properties with a building age between 1990 and 2015</p><p>- sh_2015: share in percentage of properties with a building age after 2015</p><p>- clc1000_urbanhousing: share in percentage of land within 1000 meters of transacted properties with housing</p><p>- clc1000_urbanpark: share in percentage of land within 1000 meters of transacted properties with urban parks</p><p>- clc1000_recreation: share in percentage of land within 1000 meters of transacted properties with recreative activities</p><p>- clc1000_industrial: share in percentage of land within 1000 meters of transacted properties with industrial activities</p><p>- clc1000_transport: share in percentage of land within 1000 meters of transacted properties with transport infrastructures</p><p>- clc1000_nature: share in percentage of land within 1000 meters of transacted properties with natural land use</p><p>- clc1000_agr: share in percentage of land within 1000 meters of transacted properties with agricultural land use</p><p>- clc1000_forest: share in percentage of land within 1000 meters of transacted properties with forest</p><p>- clc1000_water: share in percentage of land within 1000 meters of transacted properties with water</p><p> </p><p> </p>
Dataset: Assessing Background Contamination of Sample Tubes used in Human Biomonitoring by Non-targeted Liquid Chromatography–High Resolution Mass Spectrometry
<p>Data set of the Publication: </p> <div> <div>Krauss, Martin, Carolin Huber, Tobias Schulze, Martina Bartel-Steinbach, Till Weber, Marike Kolossa-Gehring, und Dominik Lermen (2024): Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry. <em>Environment International</em> 183: 108426. <a href="https://doi.org/10.1016/j.envint.2024.108426">https://doi.org/10.1016/j.envint.2024.108426</a>.</div> </div> <p>- raw LC-HRMS data in mzML format for positive and negative mode.</p> <p>- merged MS/MS spectra of whole data set after MZMine 2.53 processing in mgf format.</p> <p> </p>
Point and Nonpoint Proportion of Potentially Contaminated Supply (PPCS) for 116 United States cities
<p>Point and nonpoint PPCS metrics (and additional metrics) computed for 116 United States cities using gamut (Geospatial Analytics for Multisectoral Urban Teleconnections)---<a href="https://doi.org/10.5281/zenodo.5590217">https://doi.org/10.5281/zenodo.5590217</a>.</p> <p>These results are described in the following publication:</p> <p>Turner, S.W.D., Rice, J., Nelson, K., Vernon, C., McManamay, R., Dickson, K., and Marston, L. (accepted manuscript) Comparison of potential drinking water source contamination across one hundred U.S. cities. <em>Nature Communications</em>.</p> <p> </p>
S83 | CCL5 | Contaminant Candidate List CCL 5 (Draft)
<p>This is the collection associated with list S83 | CCL5 | Contaminant Candidate List CCL 5 (Draft) on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>The CCL is a list of contaminants that are currently not subject to any proposed or promulgated US primary drinking water regulations, but are known or anticipated to occur in public water systems. From: <a href="https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5">https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5</a>.</p> <p>Updates: v0.1.1: updated notes to link to Microcystins list: <a href="https://doi.org/10.5281/zenodo.5665355">https://doi.org/10.5281/zenodo.5665355</a>.</p>
Dataset underlying the publication: "Organic contaminants in bio-based fertilizer treated soil: Target and suspect screening approaches" DOI: 10.1016/j.chemosphere.2023.139261
<p>Dataset underlying the publication "Organic contaminants in bio-based fertilizer treated soil: Target and suspect screening approaches" DOI: 10.1016/j.chemosphere.2023.139261.</p>
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.