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3,788 results for “indices”
Landsat-based Spectral Indices for pan-EU 2000-2022 - longterm predictor (2000-2022): Spectral indices P50
<h2><strong>Data Information</strong></h2> <p>This dataset includes long-term P50 values for NDVI, NDWI and BSF.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-03-01/2000-04-30): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-03-01/2000-04-30.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Groundwater dynamics indices
<p>Data set to reproduce results from Heudorfer et al., 2019 (submitted) containing:</p> <ul> <li>45 index values of 751 daily groundwater level time series from Central Europe, raw and scaled.</li> <li>Controls used in chapter 5 and Figure 8</li> </ul> <p>Data is a R data.frame object saved in an uncompressed RDS file. Can be easily read into R with readRDS in <em>base</em> R or read_rds with the packages <em>readr</em>.</p> <p>(to be updated, when paper is published)</p>
Data and Code for "Cell Type-specific Genome Scans of DNA Methylation Diversity Indicate an Important Role for Transposable Elements"
<p>This is a release of the gitlab repository "meta-methylome" (https://gitlab.com/okartal/meta-methylome.git) that, in addition to the code, also contains the resulting genomic data.</p> <p>Extract the directory on the command line using</p> <pre><code class="language-bash">$ tar -xhzvf meta-methylome.tar.gz</code></pre> <p>to preserve the symbolic links.</p>
Deliverable 2.2: Biodiversity indicator database for focus regions
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km<sup>2 </sup>resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism<sup> </sup>index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a valuable first step in describing human-induced changes in vegetation and biodiversity patterns across both South America and Africa.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1969
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1969.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
Hrycyna et al. 2022 - Satellite observations of NO2 indicate legacy impacts of Redlining in US Midwestern cities
<p>This dataset contains remotely sensed estimates of nitrogen dioxide (NO2, via TROPOMI accessed via Google Earth Engine) for HOLC neighborhoods in 11 US Midwestern cities, and corresponding coarse geographic and demographic data of those cities. NO2 data is reported daily for the entire calendar year of 2019, geographic and demographic variables are fixed for each city for the entire year. Each HOLC-graded neighborhood included in this dataset was filtered to be greater than 2 km2. The number of pixels used to calculate the area-weighted mean of NO2 is also reported, as is the area of the neighborhood. The dataset has also been filtered for observations that did not pass quality filters for L3 TROPOMI data. The cities included in the study are: Chicago IL, Milwaukee WI, Saint Paul MN, Minneapolis MN, Indianapolis IN, Cleveland OH, Wichita KS, Greater Kansas City KS and MO, Columbus OH, Detroit MI, and Omaha NE. HOLC neighborhood shapefiles were obtained from the Mapping Inequality project website, hosted by the University of Richmond, and resulting polygons used in analysis were created by dissolving shared boundaries in Google Earth Engine. City populations and population density were obtained from the US 2010 Census data. All data was collected and organized to assess if current day NO2 levels varied with HOLC grades in these major cities.</p> <p> </p> <p>Data was used in the study: Hrycyna et al. (2022) <em>Elementa</em> 10(1):00027 </p> <div> <div><a href="https://doi.org/10.1525/elementa.2022.00027" target="_blank" rel="noopener">https://doi.org/10.1525/elementa.2022.00027</a></div> </div> <p>Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. https://dsl.richmond.edu/panorama/redlining/#loc=5/39.1/-94.58&text=downloads</p> <p><strong>Dataset for all analyses presented in Hrycyna et al. Columns described below:</strong></p> <p>HOLC_grade: A, B, C, D (neighborhood grade categories obtained from Mapping Inequality project, indicate historic HOLC designations of neighborhoods).</p> <p>HOLCAreaKm2: continuous area value in km2 of the HOLC neighborhood polygon, which may be more than one HOLC designated polygon merged from the shapefiles downloaded from Mapping Inequality.</p> <p>pixelcount: integer values of the number of TROPOMI NO2 pixels used to produce the area-weighted mean NO2 value.</p> <p>NO2_mol_m2: area-weighted mean value of TROPOMI NO2 for that HOLC neighborhood polygon in mol m-2</p> <p>system.index: designated date and time boundary of the observation collected via TROPOMI</p> <p>date: date of observation</p> <p>month: month of observation</p> <p>City: city in the US Midwest</p> <p>State: state for the city of focus</p> <p>Population: urban population obtained from 2010 census</p> <p>PopDensity: urban population density obtained from 2010 census, based on modern city boundaries (in people per square miles)</p> <p>CityArea_mi2: Area of the city of interest, in square miles.</p> <p>ln_NO2: natural log transformed NO2 values in mol m-2</p> <p>NO2_DU: NO2 value converted from mol m-2 to DU (Dobsons Units, converted by multiplying 2241.15)</p> <p>NO2_lnDU: natural log transformed NO2 values in DU<br><br></p>
STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.3 Content Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the content level of the new monitoring system. It is part of the D3.2 "Report on additional indicators of monitoring system" of the STAR4BBS Project (Appendix 6.3). The list of indicators are accompanied by guidance notes, the specific sector and value chain to which they apply as well as potential examples of indicators are suggested. </p>
ERA5-Land selected indicators daily aggregates for Africa, 1964
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1964.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1965
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1965.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1966
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1966.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1968
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1968.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1967
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1967.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1959
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1959.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1962
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1962.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1952
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1952.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1960
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1960.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1963
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1963.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1951
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1951.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1956
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1956.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.