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3,788 results for “indicators”
Indicative distribution map for Ecosystem Functional Group T3.2 Seasonally dry temperate heath and shrublands
<p>This archive contains indicative distribution maps and profiles for <strong>T3.2 Seasonally dry temperate heath and shrublands</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T7.2 Sown pastures and fields
<p>This archive contains indicative distribution maps and profiles for <strong>T7.2 Sown pastures and fields</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T2.4 Warm temperate laurophyll forests
<p>This archive contains indicative distribution maps and profiles for <strong>T2.4 Warm temperate laurophyll forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group S2.1 Anthropogenic subterranean voids
<p>This archive contains indicative distribution maps and profiles for <strong>S2.1 Anthropogenic subterranean voids</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices
<p>Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research.</p> <p>We provide here a benchmark dataset for time series reconstruction method based on the<strong> <a href="https://hls.gsfc.nasa.gov/">harmonized Landsat Sentinel-2 (HLS)</a> </strong>collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview.</p> <p>Specifically, following the <strong><a href="https://hls.gsfc.nasa.gov/products-description/tiling-system/">relative tiling system</a></strong> shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as:</p> <p><span class="math-tex">\(NDVI = {NIR - Red \over NIR + Red}\)</span></p> <p>only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI.tif</em></p> <p>where <em>TILE_NAME</em> is one between the above specified ones, <em>YYYY</em> is the corresponding year (spanning from 2015 to 2022) and <em>DDD</em> is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of <strong>184</strong> images (23 images for 8 years) that can be easily manipulated, for example using the <strong><a href="https://github.com/scikit-map/scikit-map/tree/master">Scikit-Map library</a></strong> in Python.</p> <p>Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both.</p> <p>The data including the artificial gaps are stored with the naming structure</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI_art_gaps.tif</em></p> <p>following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations. </p> <p>This dataset was used to compare the performance of some gap-filling methods and we provide a <strong><a href="https://github.com/OpenGeoHub/EO-benchmark/blob/main/gap_filling_methods/gap_filling_comparison.ipynb">Jupyter notebook</a></strong> that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619). </p> <p>If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you!</p> <p>References:</p> <ol> <li> <p>Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. <a href="https://doi.org/10.4995/raet.2018.9749">https://doi.org/10.4995/raet.2018.9749</a> </p> </li> <li> <p>Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. <a href="https://doi.org/10.5194/bg-10-4055-2013">https://doi.org/10.5194/bg-10-4055-2013</a> </p> </li> <li> <p>Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. <a href="https://doi.org/10.1016/j.rse.2016.11.023">https://doi.org/10.1016/j.rse.2016.11.023</a> </p> </li> <li> <p>Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.<a href="https://doi.org/10.1016/j.rse.2020.111901"> https://doi.org/10.1016/j.rse.2020.111901</a> </p> </li> <li> <p>Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. <a href="https://doi.org/10.1080/014311600209814">https://doi.org/10.1080/014311600209814</a></p> </li> <li> <p>Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619.<a href="https://doi.org/10.1016/j.compag.2021.106619"> https://doi.org/10.1016/j.compag.2021.106619</a></p> </li> </ol>
Pandemic severity indicator for COVID-19 in Germany dataset
<p>The datasets included in this repository represent a pandemic severity indicator for the COVID-19 pandemic in Germany based on a composite indicator for the years 2020 and 2021. The pandemic severity index consists of three indicators: the incidence of patients tested positive for COVID-19, the incidence of patients with COVID-19 in intensive care, and the incidence of registered deaths due to COVID-19. The datasets have been developed within the CODIFF project (Socio-Spatial Diffusion of COVID-19 in Germany) at Leibniz Insitute for Research on Society and Space. The project received funding by Deutsche Forschungsgemeinschaft (DFG, project number 492338717). The datasets have been used in the following publications, in which further methodological details on the indicator can be found:</p> <ul> <li><a href="https://doi.org/10.1101/2023.02.17.23286084">Stabler, M., & Kuebart, A. (2023). Tempo-spatial dynamics of COVID-19 in Germany: A phase model based on a pandemic severity indicator. <em>medRxiv</em>, 2023-02</a>.</li> <li><a href="https://doi.org/10.1016/j.sste.2023.100605">Kuebart, A., & Stabler, M. (2023). Waves in time, but not in space – An analysis of pandemic severity of COVID-19 in Germany. <em>Spatial and Spatio-temporal Epidemiology</em>, 2023.</a></li> </ul> <p>This repository consists of two files:</p> <p><strong>pandemic_severity_germany </strong></p> <p>This table contains the composite indicator for daily pandemic severity for Germany on the national scale as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the <a href="https://github.com/robert-koch-institut">Robert Koch Institute</a>, the German government agency responsible for disease control and prevention.</p> <p><strong>pandemic_severity_counties</strong></p> <p>This table contains the composite indicator for daily pandemic severity for Germany on the level of the 400 individual counties, as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the <a href="https://github.com/robert-koch-institut">Robert Koch Institute</a>, the German government agency responsible for disease control and prevention. The counties can be identified by name (kreis) or by county identification number (ags5)</p>
Indicative distribution map for Ecosystem Functional Group T1.4 Tropical heath forests
<p>This archive contains indicative distribution maps and profiles for <strong>T1.4 Tropical heath forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Ecological Survey of Central Arizona: a survey of key ecological indicators in parcels of residential areas in the greater Phoenix metropolitan area, ongoing since 2010 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/653/2. The abstract below was extracted from the Level 0 data package and is included for context: The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix, and the surrounding Sonoran desert. The survey is conducted every five years at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified dual-density sampling design. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. In 2010, the survey was expanded to include an assessment of residential parcels overlapping the survey plot at sites in residential areas. Many of the same variables that are measured in the 30m x 30m survey plot are measured in the parcel, including an inventory of perennial plants, and the biovolume of trees. In addition, a detailed assessment of characteristics of the parcel is performed. Investigators interested in data from the broader Ecological Survey of Central Arizona that includes all survey plots should should search the data catalog for 'ecological survey of central arizona' or 'survey 200' to locate those and other data related to the CAP LTER's ESCA.
Ecological Survey of Central Arizona: a survey of key ecological indicators in the greater Phoenix metropolitan area and surrounding Sonoran desert, ongoing since 1999 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/652/3. The abstract below was extracted from the Level 0 data package and is included for context: The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix, and the surrounding Sonoran desert. The survey, formerly known as the survey 200 and renamed to ESCA in 2015, is conducted every five years at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified dual-density sampling design. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. Measurements include an inventory of all plants (identified to the lowest possible taxonomic unit, typically species), plant biovolume, soil coring for physicochemical properties, arthropod sweep-net sampling, photo documentation, and a visual survey of site and area characteristics. The objectives of the survey are to (1) characterize patches in terms of key biotic, physical, and chemical variables, and (2) examine relationships among land use, general plant diversity, native plant diversity, plant biovolume, soil nutrient status, and social-economic indices along an indirect urban gradient. A pilot survey was conducted in 1999, and the first full ESCA was conducted in 2000. The maiden survey in 2000 featured a suite of measurements that were not assessed in later surveys, including data from a portable weather station set up during the field survey at each location, organic matter decomposition, pollen,
Sampling sites where ecological indices were used to assess the impact of different environmental stressors in aquatic environments in Argentina
Dataset is a compilation of all sampling sites of articles where ecological indices were used to assess the impact of different environmental stressors in aquatic environments from Argentina. Points of this dataset were extracted from 78 papers published between 1996 and 2018. We selected articles that use ecological indices to analyze some local environmental problematics or stressors. Using the type of index from each article we performed the kml file, which contained the categorized sampling sites by different symbols according to the ecological index: physico-chemical, biological, geomorphological and multimetric. We carried out a map (shapefile) with all the sampling sites referenced to the ecoregions of Argentina proposed by Burkart (1999).
Vegetation indices calculated from canopy reflectance spectra at four sites along Imnavait Creek, AK during the 2008-2010 growing seasons.
A spectrophotometer was used to scan the canopy vegetation at four sites along Imnavait Creek in the Kuparuk Watershed near Toolik Lake LTER, Alaska. The resulting reflectance spectra were used to calculate average vegetation indices for each site and collection day.
Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona
This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.
The Contributionsof Eye Gaze Fixations and Target-Lure Similarity to Behavioral and fMRI Indices of Pattern Separation and Pattern Completion
Open the record for dataset details and reuse information.
Global Fire Weather Indices - DMC using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - FFMC using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - ISI using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - DC using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - BUI using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - FWI using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - DMC using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</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.