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Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term composited Normalized Difference Vegetation Index (NDVI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
### overview This data package consists of multiple decades of normalized difference vegetation index (NDVI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). To serve as a proxy measurement of vegetation greenness and productivity across years and seasons, NDVI was derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Tree Canopy Leaf Area Index in CRUI Land Use Project at Harvard Forest 1997
Numerous variables related to land use disturbance and recovery processes can influence forest composition and structure. We’ve measured differences in forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill. None of the sites had noticeable canopy gap disturbance at the time of the measurements. Leaf area index (LAI) was measured with an LAI-2000 plant canopy analyzer (Li-Cor, Inc., Lincoln, NE) at all 77 edge and interior intersection points in the 30 m x 50 m permanent plot (7 columns x 11 rows) in 5 of our 6 land use sites. Under-canopy measurements were made in each site over 25-30 minutes during midday hours (11:00-2:30 EST) on overcast days near solstice (June 13, 18). The under-canopy readings were contrasted with an open-sky measurement taken in an open field near the Harvard Forest headquarters just before beginning data collection in each site. LAI averaged 3.98 and ranged from 2.28 to 5.93 across all sites. W1 had the highest site-level mean (4.62) and maximum (5.93) LAI while S2 had the lowest values (mean = 3.41, max = 4.55). The woodlot also showed the greatest spatial variation as measured by C.V., while plow #1 showed the least variation.
Leaf Area Index at Harvard Forest HEM and LPH Towers since 1998
Leaf area index (LAI) measurements are made to detect changes in forest leaf area that are important in understanding forests’ carbon dioxide (CO2) uptake and water use. Measurements of LAI in different forest types are useful understanding differences between forests in CO2 uptake and water use, while variation in LAI in the same forest type over time helps to explain interannual variation and long-term trends in carbon storage and water use. Within a single year, especially for deciduous forests, seasonal changes in LAI are very important in determining the forest’s cycle of CO2 uptake and water use. LAI measurements at Harvard Forest were begun in the old-growth hemlock stand in 1998 in order to understand and develop a predictive model for its carbon exchange, and were begun at the Little Prospect Hill site in 2002 to better understand CO2 uptake and carbon storage at this site, which were measured by the eddy-covariance method beginning in 2002. Due to a lack of personnel, multiple annual measurements of LAI to examine seasonal change in leaf area did not begin until 2007.
WSC - Leaf area index (LAI) at various points within Wibu field site, 2012-2014
Leaf area index (LAI) measurements collected at various points within the Wibu field site between 2012-2014. Measurements were collected approximately weekly from plant emergence until appr. 1 month past the onset of senescence. The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons; therefore, these are all LAI values for corn. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site and use of the LAI data.
Air Quality Index (AQI) data from PurpleAir sensor at H.J. Andrews Experimental Forest LTER
This dataset contains hourly air quality and meteorological measurements collected from a PurpleAir sensor deployed at the H.J. Andrews Experimental Forest Long Term Ecological Research (LTER) site. The data includes four variables: timestamp (in Pacific Time), relative humidity (%), temperature (°C), and particulate matter concentrations (PM2.5 in µg/m³ using the CF=1 correction factor). The sensor provides continuous monitoring of local air quality conditions, with particular focus on fine particulate matter that can impact ecosystem health and visibility. Data are recorded at hourly intervals and timestamped in ISO 8601 format with UTC offset. PM2.5 values are reported using PurpleAir's CF=1 (Correction Factor 1) algorithm, which is optimized for atmospheric particulate matter. This dataset supports long-term environmental monitoring objectives at the Andrews Forest LTER and provides baseline air quality data for research on atmospheric conditions, wildfire smoke impacts, and climate-ecosystem interactions in Pacific Northwest forest ecosystems.
Leaf Area Index every 15 cm of 1m x 1m chamber flux and point frame plots and sites where dataloggers monitored photosynthetically active radiation (PAR) above, within and below Salix pulchra and Betula nana canopies during the growing season at the Toolik Field Station in AK, Summer 2012.
Leaf area index (LAI) measurements were taken with the Delta-T SunScan wand every 15 cm from the ground to above the canopy under both direct and diffuse light. conditions The data includes all outputs from the SunScan wand: time of measurement, transmitted light, spread of photosynthetically active radiation (PAR) sensors, beam fraction, and zenith angle. These measurements were taken for 1m x 1m chamber flux and point frame plots sampled in tall Salix pulchra and Betula nana shrub canopies as well as sites monitored remotely by PAR sensors situated above, within, and below tall shrub canopies at the Toolik Field Station in the summer of 2012.
Summary of three different Leaf Area Index (LAI) methodologies of 19 1m x 1m point frame plots sampled near the LTER Shrub plots at Toolik Field Station in AK the summer of 2012.
Summary of three methods used to estimate the Leaf Area Index (LAI) of 19 1m x 1m plots sampled with a point frame near the LTER Shrub plots at the Toolik Field Station in AK the summer of 2012. The methods used were: (1) exponential relationship between LAI and Normalized Leaf Index (NDVI) as measured above the canopy with a Unispec spectroradiometer; (2) Delta-T SunScan canopy analyzer held at 5 cm above the ground under both direct and diffuse light conditions; (3) pin-drop point frame technique. Where values have been averaged (such as for the NDVI and SunScan measurements), the standard deviation is given. Raw data are available upon request for the Unispec data; the raw SunScan data is available under the file "PF_SunScan_LAI".
Normalized Difference Vegetation Index (NDVI) derived from 2019 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices—Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2019 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, and 2017 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP study area boundary.
Soil-Adjusted Vegetation Index (SAVI) derived from 2019 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices—Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2019 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, and 2017 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP study area boundary.
North Temperate Lakes LTER Northern Highland Lake District Coarse Woody Debris Shoreline Development Index
Coarse woody debris (CWD) is an important, but often neglected, component of lake ecosystems. It is ecologically valuable because it creates littoral habitat complexity but it is susceptible to manipulation by riparian process, in particular removal by property owners. The objective of this study is to determine the spatial scales at which human and environmental factors contribute to coarse woody debris input and output dynamics. Coarse woody debris, boat docks, and riparian trees (with the potential of becoming CWD) around the five lakes of the NTL-LTER site (Trout Lake, Allequash Lake (north basin), Sparkling Lake, Crystal Lake, and Big Muskellunge Lake) were measured in 1996 and 1997. Shorelines were characterized using a qualitative shoreline development index (SDI). The index included: (1) developed and devoid of riparian trees (i.e. lawn, boat launch), (2) few widely spaced trees, (3) some riparian trees and some understory, (4) substantial forest cover, and (5) undeveloped and heavily forested. All 5 lakes were surveyed in June/July 1996. Endpoint positions were determined by greater than 30 dGPS points. Shoreline data were subsequently divided into 10 m segments.
North Temperate Lakes LTER Regional Survey Macrophytes Plant Index 2015 - current
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. The purpose of the macrophyte survey is to identify, and quantify the types of aquatic plants within the various 28 regional survey lakes. The macrophyte survey consists of sampling macrophyte plants using a metal rake attached to a 15ft pole at approximately 140 spatially resolved points on a lake that are spread out in a grid like fashion, equally spaced from each other. Sampling locations were chosen such that the maximum depth at which macrophytes were surveyed was equal to or less than 15ft of water. Macrophyte sampling occurs in the latter part of the summer (after July 10) to ensure that macrophytes have had adequate time to grow and our sampling efforts capture the typical summer macrophyte community in each lake. Macrophyte sampling in these 28 lakes is ongoing and will be repeated approximately once every six years.
Plant richness of the terrestrial ecoregions of the world with a mean aridity index lower than 0.65
<p>Data used to compose the <strong>Figure 1</strong> and the <strong>Table S1</strong> of the paper <strong>Biogeography of Global Drylands</strong>, by Maestre <em>et al</em>. (2021).</p>
Mineral spectral refractive index and bulk optical property dataset for aerosol studies
<p>Version 1.3, updated 11/15/2024.</p> <p>Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx',</p> <p>along with the refractive index data.</p> <p>All refractive index files here have 127 rows (wavelengths) and 27 columns (samples)</p> <p>'kall27_coarse.dat' is the imaginary part of the coarse mode. </p> <p>'kall27_fine.dat' is the imaginary part of the fine mode.</p> <p>'nall27_coarse.dat' is the real part of the coarse mode.</p> <p>'nall27_fine.dat' is the real part of the fine mode.</p> <p>Version 1.2, updated 04/23/2024.<br>Major changes: <br>Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples</p> <p>Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively.</p> <p>The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat".</p> <p>Added supplemental file data in 'Supplemental.tar.gz'.</p> <p>Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz'</p> <p>Version 1.1, updated 03/14/2024.<br>Major changes: <br>Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)";<br>Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area <A>, bulk volume <V>, effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row.<br>Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models.</p> <p>Version 1.0, uploaded 01/02/2024.</p> <p>This database include supplemental data and files for the publication of this paper:</p> <p>Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.</p> <p> </p> <p>*****************************************</p> <p>The supplemental data include:</p> <p>1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files.<br>E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k.</p> <p>The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron.</p> <p>The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations.</p> <p>The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples.</p> <p>2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group).<br>'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite.</p> <p>'Vlog_fine.dat': For the fine mode dust.</p> <p>3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well.</p> <p>4) 'DustSamples.tar.gz' Dust sample refractive index files.<br>The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/.</p> <p>fine: fine mode. coarse: coarse mode.</p> <p>'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt').</p> <p>All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx').</p> <p>The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part.</p> <p>5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices)<br>'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca.</p> <p>'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi).</p> <p>'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat")</p> <p>'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm).</p> <p>6) 'PhaseMatrices.tar.gz' Phase matrices data<br>'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44.</p> <p>'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave).</p> <p>There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data.</p> <p>7) 'Supplemental.tar.gz'</p> <p>We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included.</p> <p>The supplemental bulk property files are named based on the figure number.</p> <p>8) 'AdditionalRefInd.tar.gz'</p> <p>We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models.</p> <p>For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.</p>
Cuneiform Inscriptions Geographical Site Index (CIGS)
<p>The <em>Cuneiform Inscriptions Geographial Site</em> (CIGS) index contains a basic set of primary spatial, toponym, attribute, and external link information on close to 600 archaeological locations where texts written in cuneiform and derived scripts have been found. In use across the wider Middle East from c. 3,400 BCE until 100 CE, cuneiform is one of the earliest and most extensively documented ancient scripts in world history. This resource has been prepared by researchers of the Department of Linguistics and Philology of Uppsala University. The index is intended as a tool for students and researchers in cuneiform studies and related areas and as an aid to cultural heritage managers and educators in communicating and safeguarding this unique body of world written heritage. The index remains under development and is regularly updated. The authors will very much appreciate notices of any omissions, errors, or inaccuracies. For any inquiries, please contact <a href="https://www.katalog.uu.se/profile/?id=N18-1120">Rune Rattenborg</a> (<a href="mailto:rune.rattenborg@lingfil.uu.se">rune.rattenborg@lingfil.uu.se</a>). For further details, see <a href="https://cdli.ucla.edu/pubs/cdlj/2021/cdlj2021_001.html">Rattenborg et al. 2021</a>.</p> <p>The version 1.7 index contains 598 entries with a total twenty-six fields, including one primary ID, one integer field for accuracy, twenty-two string fields with toponyms and links, and two spatial data fields. Coordinates given use the WGS 1984 geographic coordinate reference system (<a href="https://epsg.io/4326">EPSG 4326</a>) and have been truncated to four decimal digits. Site locations have been traced from archaeological gazetteers and web mapping services (e.g. <a href="https://pleiades.stoa.org/">Pleiades</a>, <a href="https://www.geonames.org/">GeoNames</a> and <a href="https://www.openstreetmap.org/">OpenStreetMap</a>) and digitally generated from optical recognition using current and legacy satellite imagery datasets in QGIS 3.x.</p> <p>The version 1.7 data set is updated to correlate with archaeological locations included in the <a href="https://cdli.mpiwg-berlin.mpg.de">Cuneiform Digital Library Initiative</a> table of proveniences (see <a href="https://cdli.mpiwg-berlin.mpg.de/proveniences">https://cdli.mpiwg-berlin.mpg.de/proveniences</a>), migrated July 2023. Please see this resource for later updates to individual records.</p>
German Index of Socioeconomic Deprivation (GISD)
<p>Der German Index of Socioeconomic Deprivation (GISD) ist ein am Robert Koch-Institut entwickelter Index zur Erfassung regionaler sozioökonomischer Benachteiligung. Er wird verwendet, um regionale sozioökonomische Ungleichheiten in der Gesundheit sichtbar zu machen und Ansatzpunkte zur Erklärung regionaler Unterschiede in der Gesundheit aufzeigen zu können. Mit dem GISD wird es möglich, sozioökonomische Unterschiede in den Gesundheitschancen, Krankheits- und Sterberisiken in Deutschland auch dann zu untersuchen, wenn die betreffenden Gesundheitsdaten auf individueller Ebene keine Information zum sozioökonomischen Status enthalten. Für die Generierung des GISD werden Information der Bildungs-, Beschäftigungs- und Einkommenssituation in Kreisen und Gemeinden aus der Datenbank INKAR verwendet. Er wird auf der Ebene der Gemeinden generiert und wird für die Raumbezüge Gemeinden, Gemeindeverbände, Stadt- und Landkreise, Raumordnungsregionen, NUTS-2 und Postleitzahlbereiche bevölkerungsgewichtet aggregiert bereitgestellt. Die Gewichtung der Indikatoren wird über Hauptkomponentenanalysen innerhalb der Teildimensionen vorgenommen. Die aktuell verfügbaren Daten beziehen sich auf den Gebietsstand 31.12.2021 und enthalten Werte von 1998 bis 2021.</p>
Build Up Index - ERA-Interim
<p>The Build Up Index (BUI) is a numeric rating of the total amount of fuel available for combustion. It combines the DMC and the DC.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Initial Spread Index - ERA-Interim
<p>The Initial Spread Index (ISI) is a numeric rating of the expected rate of fire spread. It combines the effects of wind and the FFMC on rate of spread without the influence of variable quantities of fuel.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Fire Weather Index - ERA5 HRES
<p>The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread. </p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (Hersbach et al., 2019), and replaces the homonymous indices based on ERA-Interim (Vitolo et al., 2019). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. </p> <p>The dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately on Zenodo. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). The caliver R package (Vitolo et al. 2017, 2018) contains useful functions to process this dataset. </p> <p>Details: </p> <ul> <li>File format: netcdf4</li> <li>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326)</li> <li>Longitude range: [-180, +180]</li> <li>Latitude range: [-90, +90]</li> <li>Temporal resolution: 1 day (at 12 local noon)</li> <li>Spatial resolution: 0.28 degrees (~31 Km)</li> <li>Spatial coverage: Global</li> <li>Time span: from 1980-01-01 to 2019-06-30</li> <li>Stream: Deterministic forecasts</li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.