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958 results for “Data quality”
Monthly city-level water quality data of China from 2014 to 2020.
Open the record for dataset details and reuse information.
Global Water Quality Data: Historical Nitrogen and Phosphorus Loads and Concentrations Simulated by CoSWAT-WQ v1.0
<p>Output data on global TN and TP water quality and scripts for the historical period (1995 - 2016) generated by CoSWAT-WQ v1.0. Please check the readme file for data description. The data includes river flow, , nitrogen and Phosphorus loads and concetrations at monthly timestep.</p>
Exemplary landscape planning results using different qualities of LU/LC data to derive implications for practical planning.
<p>This Dataset consist of two GIS Shape files showing examples of common landscape planning activities, namely the an assessment of the climate protection function and planning towards locating environmental protection measures using different data basis. Using overlay operation it is possible to use descriptive statistics to assess uncertainties and inconsistencies between different land use / land cover data sets and highlight their implications for practical planning.</p> <p>Once published, the results of such analysis can be seen in the related publication "Uncertainties in land use data may have substantial effects on environmental planning recommendations: a plea for careful consideration. Submitted to PLOS ONE."</p>
Supplementary Table 1. Raw data of egg quality parameters for 990 egg samples with ATOL (Animal Trait Ontology for Livestock) descriptors, as function of hen age, pen no. and genotype in 15 replicates.
<p>Data table of egg quality parameters</p>
Air-quality data simulated over Great Paris area
<p>High local simulated concentrations of NO2, PM10, O3 and PM2.5 over Paris at surface for selected dates for 2018.</p>
Data from: Can attraction to and competition for high-quality habitats shape breeding propensity?
<p>This archive contains the data analysed in the paper 'Can attraction to and competition for high-quality habitats shape breeding propensity?' by Acker et al. The dataset is provided as a .Rdata file ('dataset.Rdata'), and full description of the content is provided in the file 'README.txt'. All additional details are available in the main text and appendices of the paper.</p>
High quality rainfall data set for Australia's northern Murray-Darling Basin
<p>Monthly precipitation data were obtained from the Bureau of Meteorology’s homogeneous climate record through its climate change site network. (http://www.bom.gov.au/climate/change/?ref=ftr#tabs=Tracker&tracker=site-networks). These data have undergone complex quality control to address inconsistencies and errors. There are 8 precipitation stations comprising Augathella, Cunnamulla, Normandy, Miles, Surat, Bellata, Bingara and Curlewis. Additionally, monthly mean maximum temperature (TMax) and mean minimum temperature (TMin) data were obtained for Charleville, Miles, Thargomindah, St. George. Moree, Inverell, Walgett and Gunnedah from the Bureau of Meteorology’s climate change site network. </p>
Data set of "Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils"
<p>The uploaded data set contains all the measurements collected during the research that led to the publication of " "Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils"</p>
Evaluation data for "Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution)"
<p>This zip file contains data for performing Global InMAP model runs and evaluations. To the extent that any of the data is covered by third party licenses, it is the responsibility of the user to follow the terms of those licenses. A description of the contents of this directory is below:</p> <p>measurements.csv<br> Vetted global dataset of ground-level annual-average measurements of total PM2.5 and species (pNO3, pSO4, pNH4) compiled from monitoring networks, used for model performance evaluation. Data sources are: World Health Organization (Global), European Environment Agency (Europe), National Air Pollution Surveillance Program (Canada), Environmental Protection Agency (United States of America), Central Pollution Control Board (India), Australian Government State of the Environment (Australia), and Acid Deposition Monitoring Network In East Asia (EANET) (East Asia).</p> <p>population directory<br> Population count data is from the Gridded Population of The World (v4.10) projected to year 2020. The data is in 15x15 arcminute grids, except for in grid cells where the population is above 80,000, where the population data is 30x30 arcseconds.</p> <p>GlobalInMAPData_v1.ncf<br> Regular-grid Global InMAP input data for the year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from GEOS-Chem v.11-01 simulation outputs with the 'inmap preproc' command.</p> <p>global_inmap_004x003_v1.1.0.gob<br> Global InMAP variable grid resolution input data for coords for year 2016 for use as the "VariableGridData" variable in the InMAP configuration file. It was created with the 'inmap grid' command using GlobalInMAPData_v1.ncf and population.shp.</p> <p>2016_emissions directory<br> Total PM2.5 and precursor emissions to arrive at total PM2.5 concentrations from Global InMAP. Units for polygonized emissions inputs (shapefiles) are short (US) tons/yr, and units for gridded emissions inputs (NetCDF files) are kg/yr.</p> <p>global_emission_changes directory<br> nh3.nc, nox.nc, and sox.nc are gridded emissions for changes in inorganic precursors for comparing Global InMAP and GEOS-Chem. Units are kg/yr. NH4-gc.nc, NIT-gc.nc, and SO4-gc.nc are results for changes in concentrations arising from these changes in emissions for 3 months, 1 month, and 2 months.</p> <p>usa_emission_changes directory<br> Emissions for comparing Global InMAP and US InMAP (described in Tessum et al., 2017).<br> Emissions are derived using the United States National Emissions Inventory (NEI) 2014v.1, processed exactly as in Thakrar et al., 2020.<br> Emissions are coal-powered electricity generation (NEI Source Classification Code: 10100212) and gasoline passenger vehicles (NEI Source Classification Code: 2201210080).<br> Units are ug/s.</p> <p>Tessum, C.W.; Hill, J.D.; Marshall, J.D. InMAP: A model for air pollution interventions. PloS One 2017, 12 (4) e0176131.<br> Thakrar, S.K.; Balasubramanian, S.; Adams, P.J.; Azevedo, I.M.; Muller, N.Z.; Pandis, S.N.; Polasky, S.; Pope III, C.A.; Robinson, A.L.; Apte, J.S.; Tessum, C.W.; Marshall, J.D.; Hill; J.D. Reducing mortality from air pollution in the United States by targeting specific emission sources. Environmental Science & Technology Letters 2020, 7(9), pp.639-645.<br> Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H41V5BX1.</p>
Raw Data for the article: Post-crisis debriefing: A tool for improving quality in the medical emergency team system
<p><strong>Objectives: </strong>To examine clinicians' perception of quality of technical and non-technical response to emergencies and application of post crisis debriefing.</p> <p><strong>Design: </strong>Descriptive, anonymous, self-reporting survey on the needs and perception of a post-crisis debriefing implementation.</p> <p><strong>Setting: </strong>Multi-specialist medical institute in Italy focused on solid organ transplantation and organ failure support.</p> <p><strong>Main outcomes: </strong>Perception of application of guidelines and evaluation of debriefing implementation during in-hospital emergencies.</p> <p><strong>Results: </strong>Response rate to the survey was 25% (148 health care workers). Of all respondents, 86% were employed >10 years, 75% were involved in ≤5 emergencies over the previous year. Resuscitation guidelines were considered fully applied by 55%; 64% of respondents considered the teaching programme as sufficient. Of all participants, 97% were aware of the importance of teamwork dynamics, 79% were aware of the importance of the personal performance, and 52% considered emergencies as valid opportunities for professional growth. Leadership was considered important by 45% of respondents; debriefing implemented by 41%, and considered a potentially useful tool by 85%.</p> <p><strong>Conclusion: </strong>Post-crisis debriefing is a way to diffuse self-reflective and life-long learning culture; it is perceived as a powerful tool for improving quality of the rapid response system by the vast majority of those surveyed.</p> <p><strong>Keywords: </strong>Communication; Crisis resource management; Emergency; ICU; Intensive care medicine; Leadership; Patient safety; Teamwork.</p>
Cluj Airport - Data related to interdependencies and to Quality of Life
<p><strong>Data on interdependencies</strong></p> <p>The case study about interdependencies between noise, emissions (CO2, CO, NOx) was performed within the local context of Cluj-Napoca (Avram Iancu) International Airport (Romania) by COMOTI (Romania).</p> <p><br> Airline data and support was provided by Blue Air Aviation and Getsafe Aviation Solutions and Cluj-Napoca Airport.<br> The sample provided here shows an example of data used to perform analyses in order to outline their effect in terms of noise and emissions. Further, the aim was to inform relevant stakeholders (e.g. airport, airlines, ANSP) about these outcomes and to provide insights about possible methods to protect the population within the area during planning of operations phases.</p> <p><br> This data set contains two examples of some flight data, one related to departure and another to arrival operations.<br> More information about the case study can be read in the publication "D2.11 Recommendations from exemplification case studies summary and implications for BP dissemination" (<a href="https://doi.org/10.5281/zenodo.5604651">https://doi.org/10.5281/zenodo.5604651</a>)</p> <p><strong>Data on Quality of Life</strong></p> <p>The case study about the quality of life around Cluj-Napoca (Avram Iancu) International Airport (Romania) was developed by COMOTI (Romania), in partnership with Cluj-Napoca Airport (Romania), NLR (The Netherlands), ZEUS (Germany) and MMU (United Kingdom). Its aim was to identify possible opportunities of an airport to increase the quality-of-life of residents living near the airport.</p> <p><br> This is a sample of data collected from the population feedback to questions formulated around quality of life dimensions and indicators.</p> <p><br> More information about the case study can be read in the publication "D2.11 Recommendations from exemplification case studies summary and implications for BP dissemination" (<a href="https://doi.org/10.5281/zenodo.5604651">https://doi.org/10.5281/zenodo.5604651</a>).</p>
Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
<p>The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province. </p>
Data from: Freshwater ecological quality assessment of the gold mining Mashcon watershed, Cajamarca - Peru
<p>These data were generated to investigate the aquatic community gradients across different types of anthropogenic impacts (reference, mining, rural and urban) and Andean environmental gradients (headwaters, midstream and downstream) in the Mashcon watershed. </p> <p>The macroinvertebrates' taxa abundance and abundance of traits modalities serve as biological values. The latter in combination with abiotic measurements of the freshwater habitats (i.e. physicochemical water quality and hydromorphology) constitute the ecological data.</p> <p>The values were obtained from river sediments (macroinvertebrates collection), water samples for laboratory analyses, field protocols and in-situ water quality measurements at 40 sites, wherein 6 were downstream of the gold mine's artificially recharged headwaters, 8 sites at near-pristine headwaters tributary streams, 14 sites at midstream rural areas and 12 sites at downstream urban areas (sample grouping information is shown in the Field_protocol.xlsx file).</p>
Madrid datasets (air quality, meteorological and traffic data)
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2020. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (u component, v component, north, northeast, east, southeast, south, southwest, west, northwest), Pressure, Temperature, Humidity, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed.</p>
Datasets and Code for "Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes"
<p>This is the set of data and computer code used for reproducing the results in Jin, Tuo, Tiwari, Bukkapatnam, Aracne-Ruddle, Lighty, Hamza, and Ding, 2022, “Hypothesis tests with functional data for surface quality change detection in surface finishing processes,” <em>IISE Transactions</em>, in press.</p>
Satellite-derived water quality data for Lake Harsha (USA) 2015-2019
This dataset contains satellite-derived water quality (WQ) data of Lake Harsha (USA) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).
Satellite-derived water quality data for Lake Hume (Australia) 2015-2019
<p>This dataset contains satellite-derived water quality (WQ) data of Lake Hume (Australia) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).</p>
Satellite-derived water quality data for Western Water Treatment Plant (Melbourne, Australia) 2015-2019
This dataset contains satellite-derived water quality (WQ) data of Western Water Treatment Plant (Melbourne, Australia) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022).
Satellite-derived water quality data for Lake Mulargia (Sardinia, Italy) 2015-2019
This dataset contains satellite-derived water quality (WQ) data of Lake Mulargia (Sardinia, Italy) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).
Supplementary table of PRIDE datasets analyzed for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"
<p>Our proteomics dataset comes from The PRoteomics IDEntifications (PRIDE) database, the world’s largest data repository of mass spectrometry-based proteomics data. Specifically, we used 633 human proteomics project experiments with a total of 32,546 runs and reanalyzed them using ionbot with an FDR threshold of 0.01 [16], resulting in a total of 154,885,151 peptide spectrum matches for 18,846 proteins. Here is the full list of projects, runs, and general statistics.</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.