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1,803 results for “Annuals”
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Norway
<p>This dataset contains the results of the surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Iceland
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021 – monthly maps
<p>Here you can find the monthly maps referred to in the 'Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021' by EFSA. Figure legend for all figures: Monthly observations and samples from wild birds on the EFSA list of target species for 2021 by NUTS3 region. The green colour scale represents the number of wild bird observations from the target species, as per data provided by the EuroBirdPortal project. The black dots represent the number of wild bird samples from target species tested within the countries' avian influenza passive surveillance programmes. Wild bird samples reported at NUTS2 level are not shown on these maps.</p>
Historical Annual Revenue of Energy Storage on European Electricity Markets
<p>This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration). It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE; Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming". </p> <p>The following information is provided:</p> <p>Country: Code of the specific market (also the filename)</p> <p>Currency: The currency in which the results are expressed</p> <p>Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged. </p> <p>Efficiency: Ratio between the amount of discharged and charged energy during a full cycle.</p> <p>Year: From January 1st to December 31st.</p> <p>The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros. </p> <p>For any questions or further requirements, please feel free to get in touch with the authors.</p>
Ancillary data for article "The general formulation for runoff components estimation and attribution at mean annual time scale"
<p>The mean annual (1960-1990) precipitation and estimated model parameters wetting potential (Wp), vaporization potential (Vp) and upper limit of <span>the portion remaining after precipitation (</span>Up) of 312 catchments over China.</p>
Global bare soil, photosynthetic and non-photosynthetic vegetation fraction annual at 500 m resolution
<p>Annual mean and std for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023. The dataset was obtained from: <a href="https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html">https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html</a> (monthly values; 320GB in total). Mean and std was derived using terra package in R using functions "mean" and "std" from 12 monthly values; missing values were ignored during derivation.</p> <p>Note: the Global Vegetation Fractional Cover Product (GVFCP) v3.1 (<a href="https://doi.org/10.1016/j.agee.2021.107719">Hill and Guerschman, 2022</a>) is derived from spectral unmixing of all seven optical bands from the 500 m MODIS (Moderate Resolution Imaging Spectroradiometer) Nadir BRDF (Bidirectional Reflectance Distribution Function)-adjusted Reflectance Product (NBAR, MCD43A4 Collection 6). A similar dataset has been produced by <a href="https://doi.org/10.5194/essd-16-1333-2024">Sun et al., (2024)</a>, covering period 2001–2022. Below is the sample code explaining how were the mean, max and std derived.</p> <pre><code>## Download from: https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html ## wget -e robots=off -nH --cut-dirs 4 -nc -r -l5 -A '*.nc' -R 'catalog*' -I /thredds/fileServer/,/thredds/catalog/ 'https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html' ## 6857 tiles library(terra) modis.tiles = list.files("/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", pattern = glob2rx("*.nc")) mod.lst = unique(sapply(modis.tiles, function(i){strsplit(i, "\\.")[[1]][4]})) str(mod.lst) ## 272 ## aggregate per year per tile nc_tile <- function(i, year, dir.x="/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", mc.cores=parallel::detectCores()){ require(terra) if(year == 2023 | year == 2024){ in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".061.nc") } else { in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.nc") } bs.filenames = paste0("./modis-fc/bs_", c("mean", "max", "std"), "/FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.tif") dg = terra::rast(in.filename) if(any(!file.exists(bs.filenames))){ bs = dg["bare_soil"] ## 12 months dg.m = app(bs, fun=mean, na.rm=TRUE, filename=bs.filenames[1], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.x = app(bs, fun=max, na.rm=TRUE, filename=bs.filenames[2], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.s = app(bs, fun=sd, na.rm=TRUE, filename=bs.filenames[3], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) } } ## run in parallel for(year in 2001:2024){ x = parallel::mclapply(sample(mod.lst), function(i){try( nc_tile(i, year=year, mc.cores = 2) )}, mc.cores = 40) tmpFiles(remove=TRUE) }</code></pre>
Global cropland extent (fractions) annual 2000-2022 at 250 m and 1 km
<p>Global cropland extent annual for 2000-2022 based on the <a href="https://glad.umd.edu/dataset/croplands">Potapov et al. (2021)</a>. Cropland defined as: land used for annual and perennial herbaceous crops for human consumption, forage (including hay), and biofuel. Perennial woody crops, permanent pastures, and shifting cultivation are excluded from the definition. The original 30-m resolution data (0/1 values) was interpolated from time-series 2003, 2007, 2011, 2015, 2019 to annual values 2000 to 2022 using linear interpolation. All values shown are in principle fractions 0-100%. The 30-m and 100-m resoluton images (COGs) are too large for Zenodo but you can access them from URLs in the filenames_openlandmap_cropland.txt file. See for example (drop the URL in QGIS):</p> <ul> <li>https://s3.eu-central-1.wasabisys.com/openlandmap/layers30m/cropland_glad.potapov.et.al_p_30m_s_20030101_20031231_go_epsg.4326_v20240624.tif (3.2GB)</li> <li>https://s3.eu-central-1.wasabisys.com/openlandmap/layers100m/cropland_glad.potapov.et.al_p_100m_s_20030101_20031231_go_epsg.4326_v20240624.tif (2.3GB)</li> </ul> <p><strong>Disclaimer</strong>: linear interpolation has limited accuracy and is basically only used to gap-fill the missing years. The remaining missing values in the maps can be ALL consider to be 0 value for cropland. A more detailed up-to-date cropland map of the world is provided by <a href="https://doi.org/10.5194/essd-15-5491-2023">van Tricht et al., (2023)</a>, however only single year (2021) has been mapped at 10-m resolution within the <a href="../doi/10.5281/zenodo.7875104">WorldCereal project</a>.</p> <p>The temporal interpolation was implemented using terra package ii.e. using the following fuction:</p> <pre><code>library(terra) y.l = c(2003, 2007, 2011, 2015, 2019) out.years = 2000:2022 i = parallel::mclapply(y.l, function(x){system(paste0('gdal_translate Global_cropland_', x, '.vrt Global_cropland_', x, '.tif -co TILED=YES -co BIGTIFF=YES -co COMPRESS=DEFLATE -co ZLEVEL=9 -co BLOCKXSIZE=1024 -co BLOCKYSIZE=1024 -co NUM_THREADS=8 -co SPARSE_OK=TRUE -a_nodata 255 -scale 0 1 0 100 -ot Byte'))}, mc.cores = length(y.l)) ## land mask at 1 deg (100x100km) ---- x = parallel::mclapply(y.l, function(x){system(paste0("gdal_translate Global_cropland_", x, ".tif Global_cropland_", x, "_1d.tif -tr 1 1 -r average -co BIGTIFF=YES -ot Byte -co NUM_THREADS=10"))}, mc.cores = length(y.l)) ## 2 hrs g1 = terra::rast(paste0("Global_cropland_", y.l, "_1d.tif")) gs = sum(g1, na.rm=TRUE) plot(gs) gs.p <- as.polygons(gs, values = TRUE, extent=FALSE, dissolve=FALSE, na.rm=TRUE) ## Input layers: r = terra::rast(paste0("Global_cropland_", y.l, ".tif")) int.mc = function(r, tile, y.l, out.years=2000:2022){ bb = paste(as.vector(ext(tile)), collapse = ".") if(any(!file.exists(paste0("./tmp/", out.years, "/Global_cropland_", out.years, "_", bb, ".tif")))){ r.t = terra::crop(r, ext(tile)) ## each tile is 16M pixels r.x = as.data.frame(r.t, xy=TRUE, na.rm=FALSE) rs = rowSums(r.x[,-c(1:2)], na.rm=TRUE) ## if sum is == 0 means no cropland throughout the time-series sel = which(rs>0) ## extract complete values: r.x0 = r.x[sel,-c(1:2)] r.x0[is.na(r.x0)] = 0 ## interpolate between values: t1s = as.data.frame(t(apply(r.x0, 1, function(y){ try( approx(y.l, as.vector(y), xout=out.years, rule=2)$y ) }))) ## write to GeoTIFFs t1s$x <- r.x$x[sel]; t1s$y <- r.x$y[sel] ## convert to RasterLayer: r.x = rast(t1s[,c("x","y",paste0("V", 1:length(out.years)))], type="xyz", crs="+proj=longlat +datum=WGS84 +no_defs") for(j in 1:length(out.years)){ writeRaster(r.x[[j]], filename=paste0("./tmp/", out.years[j], "/Global_cropland_", out.years[j], "_", bb, ".tif"), gdal=c("COMPRESS=DEFLATE"), datatype='INT1U', NAflag=0, overwrite=FALSE) } } } ## test it: #int.mc(r, tile=gs.p[1000], y.l) ## run in parallel ---- ## takes 12 hrs... 1TB RAM i = parallel::mclapply(sample(1:length(gs.p)), function(x){try( int.mc(r, tile=gs.p[x], y.l) )}, mc.cores = 70) </code></pre>
Global Mangrove Watch: Annual Mangrove Extent
<p>To improve the resolution and local relevance of the Global Mangrove Watch (GMW) baseline, a new layer has been created for 2020. Using Copernicus Sentinel-2 satellite imagery, processed to a pixel resolution of 10 m, the mangrove extent has been completely remapped and revised with many areas which were not previously mapped now included within the new map. This has increased the spatial resolution of the mapping from a pixel resolution of 25 m to 10 m, allowing finer features to be mapped, such as fringing and riverine mangroves.</p> <p><span>Using the same pre-processed Sentinel-2 data as used for the ESA WorldCover 2020 map (Zanaga et al., 2021), over 30,000 machine learning models were trained to classify mangroves using over 5 million reference points for mangrove and non-mangrove classes. The reference points were generated using the existing CoastTrain reference dataset (Murray et al., 2022), sampling from the GMW v3.0 mangrove extent layers, user feedback and extensive quality assurance and refinement during the production of the classification. </span></p>
waldmonitoring.ch: NDVI difference rasters for annual forest change in Switzerland (Sentinel 2 based): 2016 - 2023
<p><strong>NDVI difference rasters for annual forest change in Switzerland (Sentinel 2 based): 2016 - 2023<br></strong></p> <p><em><strong>Date format:</strong></em> GeoTIFF<br><em><strong>Data type: </strong></em>Int16 - Sixteen bit signed integer*<br><em><strong>Spatial Resolution</strong></em>: 10 x 10 m<br><em><strong>Spatial Extent: </strong></em>Switzerland and Liechtenstein, masked with swisstopo swissTLM3D Forest Mask (2021) <br><em><strong>Coordinate Reference System</strong></em>: EPSG:2056 - CH1903+ / LV95, Swiss. Obl. Mercator<br><br>*:<em> NDVI Difference Values (-1 to 1) are multiplied by 10'000 to allow using Integer 16 bit vs. Float 32 bit while maintaining a precision of 5 digits. The values have to be interpreted accordingly: -10'000 means an NDVI difference of -1, +10'000 an NDVI difference of +0.<br><br></em></p> <p>The NDVI difference rasters for annual forest change in Switzerland are created by using Sentinel 2 based NDVI composites (Normalized Difference Vegetation Index). The code for the generating method can be found in the <a href="https://github.com/HAFL-WWI/Digital-Forest-Monitoring/tree/main/methods/use-case1">waldmonitoring-repository</a>, the method itself is also described and translated in further detail in the <a href="https://wiki.waldmonitoring.ch/index.php/Use_Case_1_-_J%C3%A4hrliche_Waldver%C3%A4nderungen">corresponding waldmonitoring-wiki</a>: For the automatic detection of areas of change, the differences between two years were examined using the NDVI. In order to automatically filter out cloudy images, the maximum NDVI value of all available images of the summer months (June - August) was used for each pixel (10 x 10 m). During this time, practically all the vegetation is green. This results in almost cloud-free, annual raster images with the maximum NDVI ("NDVI maximum composite"). The difference between two years is formed from these composites. The difference values accordingly reflect the strength of the change. </p> <p>As an example for interpretation, values of -0.1 or smaller (closer to -1.0) indicate strong forest changes (e.g. clearing), whereas positive values indicate vegetation regeneration or re-greening of previously unvegetated areas. Using a threshold value (we suggest -0.06 for forest applications), areas with considerable negative change can be separated out and be vectorized (converted to polygons) to create a dataset that can be queried.</p>
The SPARC water vapour assessment II: Comparison of annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour observed from satellites
<p>Here we provide a NetCDF data set that contains the amplitudes and phases for the annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour as observed by 30 satellite data sets. In addition, we combine the results from all data sets to provide average amplitudes and the corresponding standard deviations, among other.</p> <p>The content description of the NetCDF file looks as follows:</p> <p>netcdf results.amt-10-1111-2017 {<br> dimensions:<br> dataset = 30 ;<br> string_length = 60 ;<br> latitude = 37 ;<br> bands = 2 ;<br> altitude = 59 ;<br> variables:<br> char dataset_short(string_length, dataset) ;<br> dataset_short:standard_name = "data set" ;<br> dataset_short:long_name = "data set name" ;<br> dataset_short:description = "short label of data set" ;<br> char dataset_long(string_length, dataset) ;<br> dataset_long:standard_name = "data set" ;<br> dataset_long:long_name = "data set name" ;<br> dataset_long:description = "long label of data set" ;<br> double latitude(latitude) ;<br> latitude:standard_name = "latitude" ;<br> latitude:units = "degree_north" ;<br> latitude:minimum_value = "-90" ;<br> latitude:maximum_value = "90" ;<br> latitude:axis = "Y" ;<br> latitude:_CoordinateAxisType = "Lat" ;<br> double latitude_bands(bands, latitude) ;<br> latitude_bands:units = "degree_north" ;<br> double altitude(altitude) ;<br> altitude:standard_name = "altitude" ;<br> altitude:long_name = "pressure levels" ;<br> altitude:units = "hPa" ;<br> altitude:axis = "Z" ;<br> altitude:_CoordinateAxisType = "Alt" ;<br> double tropopause(latitude) ;<br> tropopause:standard_name = "tropopause" ;<br> tropopause:long_name = "tropopause pressure" ;<br> tropopause:description = "climatological tropopause pressure based on MERRA reanalysis data 2000 - 2014" ;<br> tropopause:units = "hPa" ;</p> <p>// global attributes:<br> :summary = "this file contains the results published in Lossow et al. (2017)" ;<br> :url = "https://www.atmos-meas-tech.net/10/1111/2017/amt-10-1111-2017.html" ;<br> :project = "second SPARC water vapour assessment (WAVAS-II)" ;<br> :creator_name = "Stefan Lossow & Farahnaz Khosrawi" ;<br> :creator_email = "stefan.lossow@kit.edu & farahnaz.khosrawi@kit.edu" ;<br> :creator_email_supplemental = "stefan.lossow@yahoo.se & f.khosrawi@gmail.com" ;<br> :value_for_nodata = "NaN" ;<br> :date_created = "20190105T112425Z" ;</p> <p>group: AO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the AO variation" ;<br> amplitude:description = "regression model is given by Eq. (1) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the AO variation" ;<br> phase:description = "regression model is given by Eq. (1) in the manuscript; phase calculation based on Eq. (3)" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (1) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-6,6] months interval by adding +/- 12 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group AO</p> <p>group: SAO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the SAO variation" ;<br> amplitude:description = "regression model is given by Eq. (4) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the SAO variation" ;<br> phase:description = "regression model is given by Eq. (4) in the manuscript; phase calculation based on Eq. (3)" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (4) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-3,3] months interval by adding +/- 6 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group SAO</p> <p>group: QBO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the QBO variation" ;<br> amplitude:description = "regression model is given by Eq. (5) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the QBO variation" ;<br> phase:description = "regression model is given by Eq. (5) in the manuscript; phase is derived as the shift of the QBO regression fit for which the correlation with the Singapore (1N, 104E) winds at 50 hPa maximises" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (5) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-14,14] months interval by adding +/- 28 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group QBO<br> }</p> <p> </p>
Compound annual growth rate for software: replication package
<p>This repository contains the reproducibility package (software and data) for the following paper.</p> <p>Les Hatton, Diomidis Spinellis, and Michiel van Genuchten. The long-term growth rate of evolving software: Empirical results and implications. <em>Journal of Software: Evolution and Process</em>, 29(5), May 2017. <a href="http://dx.doi.org/10.1002/smr.1847">doi:10.1002/smr.1847</a></p> <p>The amount of code in evolving software-intensive systems appears to be growing relentlessly, affecting products and entire businesses. Objective figures quantifying the software code growth rate bounds in systems over a large time scale can be used as a reliable predictive basis for the size of software assets. We analyze a reference base of over 404 million lines of open source and closed software systems to provide accurate bounds on source code growth rates. We find that software source code in systems doubles about every 42 months on average, corresponding to a median compound annual growth rate (CAGR) of 1.21±0.01. Software product and development managers can use our findings to bound estimates, to assess the trustworthiness of road maps, to recognise unsustainable growth, to judge the health of a software development project, and to predict a system’s hardware footprint.</p> <p> </p>
Conference Papers Presented at Semi-Annual Conferences to 2017
<p>A CSV file containing paper title, author, affiliation of all papers presented at semi-annual meetings of the Canadian Network for Economic History from 1965-2017.</p>
Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement"
<p>Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement" submitted to The Cryosphere.</p>
Ensemble projections (+ uncertainties) of contemporary (2012-2031) and future (2081-2100) mean annual plankton/phytoplankton/zooplankton species diversity (and species turn-over in time) for the global surface open ocean.
<p><em><strong>Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). </strong></em></p> <p>The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/).</p> <p>In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Annual land cover maps of Germany based on Sentinel-2 MSI Level 3A (WASP) data
<p>Overview:<br> This annual land cover product is available for the years 2016, 2019, 2020, 2021 for the whole of Germany. It was generated based on Sentinel-2 MSI L3A WASP Data provided by DLR (https://geoservice.dlr.de/data-assets/4hcq6dgkj648.html). For a complete description of the classification procedure please refer to<br> Riembauer, G.; Weinmann, A.; Xu, S.; Eichfuss, S.; Eberz, C.; Neteler, M.: Germany-wide Sentinel-2 based land cover classification and change detection for settlement and infrastructure monitoring. In: Proceedings of the 2021 conference on Big Data from Space (doi:10.2760/125905), 2021.</p> <p>Source data:</p> <ul> <li>Satellite data <ul> <li>German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648</li> </ul> </li> <li>Auxiliary data <ul> <li>European Union, Copernicus Land Monitoring Service, European Environment Agency (EEA), <strong>Copernicus High Resolution Layer: Imperviousness Status Map, 2018 </strong>(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018)</li> <li><strong>OpenStreetMap</strong> Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org</li> <li><strong>S2GLC Map of Europe</strong> (R. Malinowski, S. Lewiński, M. Rybicki, E. Gromny, M. Jenerowicz, M. Krupiński, A. Nowakowski, C. Wojtkowski, M. Krupiński, E. Krätzschmar, and P. Schauer, "Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery," Remote Sensing, vol. 12, no. 21, p. 3523, 2020.)</li> </ul> </li> </ul> <p>File naming:<br> classification_map_germany_[year].tif example: classification_map_germany_2020.tif</p> <p>Projection + EPSG code:<br> WGS 84 / UTM zone 32N (EPSG: 32632)</p> <p>Spatial extent:<br> north: 55:03:38.646483N<br> south: 47:08:24.738401N<br> west: 5:33:47.816647E<br> east: 15:34:24.108516E</p> <p>Spatial resolution:<br> 10 m</p> <p>Format: COG (Cloud-Optimized GeoTIFF)</p> <p>Pixel values:<br> 10: forest<br> 20: low vegetation<br> 30: water<br> 40: built-up<br> 50: bare soil<br> 60: agriculture</p> <p>Temporal coverage:<br> Years 2016, 2019, 2020, 2021</p> <p>Software used:<br> GRASS 7.8, actinia</p> <p>Original dataset license:<br> The Sentinel-2 level 3A data produced and distributed by DLR are based on Copernicus Sentinel-2 level 1C data, which are subject to the following license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf One of the following citations is mandatory for using the provided MAJA/WASP L3A product: German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648 or Contains modified Copernicus Sentinel data, processed by DLR, licensed under CC-BY 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Dataset for: "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)"
<p>The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)".</p> <p>The following files are here available (all file are georeferenced in EPSG: 3003):</p> <p>- AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure.</p> <p>- D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019.</p> <p>- RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.</p>
Annual Mean Levels of Fine PM10 particules in Valladolid city
<p>Road transport and construction operations are identified as major sources of air pollutants in cities. Airborne particulate matter is associated with harmful effects on human cardiovascular and respiratory health. Particles ≤ 10 microns (PM10), and particularly the finer particles ≤ 2.5 microns (PM2.5) associated with road transport vehicles, are of concern due to their small size; (a micron, or micrometre = one-millionth of a meter: 0.001 millimetre). Green walls (or screens) in urban streets may act as barriers to direct dispersal of pollutants from combustion engine vehicles to pedestrian areas. Particulates may be deposited on the leaf surface of trees or taken up into the leaf surface wax layer, reducing atmospheric particulate concentrations. Monitoring of air quality parameters is complex; involving many potentially interacting variables. Variation in weather conditions; prevailing wind direction and speed; tree species, density, location and structure; and the configuration of built urban infrastructure are among factors which may affect the trajectory and rate of dispersal of particulate pollutants. We aim to compare outdoor air concentrations of PM10 and PM2.5 at child and adult head heights at locations with and without street trees or green walls to evaluate whether these NBS are associated with reduced local concentrations of airborne PM10 and PM2.5.</p>
Annual Mean Levels of Fine PM 2.5 particules in Valladolid city
<p>Road transport and construction operations are identified as major sources of air pollutants in cities. Airborne particulate matter is associated with harmful effects on human cardiovascular and respiratory health. Particles ≤ 10 microns (PM10), and particularly the finer particles ≤ 2.5 microns (PM2.5) associated with road transport vehicles, are of concern due to their small size; (a micron, or micrometre = one-millionth of a meter: 0.001 millimetre). Green walls (or screens) in urban streets may act as barriers to direct dispersal of pollutants from combustion engine vehicles to pedestrian areas. Particulates may be deposited on the leaf surface of trees or taken up into the leaf surface wax layer, reducing atmospheric particulate concentrations. Monitoring of air quality parameters is complex; involving many potentially interacting variables. Variation in weather conditions; prevailing wind direction and speed; tree species, density, location and structure; and the configuration of built urban infrastructure are among factors which may affect the trajectory and rate of dispersal of particulate pollutants. We aim to compare outdoor air concentrations of PM10 and PM2.5 at child and adult head heights at locations with and without street trees or green walls to evaluate whether these NBS are associated with reduced local concentrations of airborne PM10 and PM2.5.</p>
Mean Annual Herbaceous Cover for the Sagebrush Biome, USA (2020 - 2022)
<p><strong>Abstract: </strong>Cheatgrass (Bromus tectorum) and other invasive annual grasses are the single largest threat to sagebrush rangeland health and resilience (Doherty et al. 2022). To address this challenge, NRCS’ Working Lands for Wildlife, the Western Governors Association (WGA), and diverse partners are helping implement a new proactive spatial plan to tackle invasive annuals known as “Defend the Core” (Maestas et al. 2022). Foundational to implementing this new approach is the creation of a common spatial map of invasion severity to guide strategic actions. In 2020, a WGA-led cheatgrass working group an annual herbaceous cover map that summarized the extent of annuals using three remotely-sensed data products for the years 2016 - 2018 (Maestas et al. 2020). This updated product reports annual herbaceous cover for the years 2020 - 2022 using only cover data from the Rangeland Analysis Platform. Data coverage includes all rangelands within the U.S. sagebrush biome. </p> <p><strong>Purpose: </strong>The goal of the annual herbaceous cover map is to support a common spatial strategy for tackling invasive annual grasses across the western U.S. As with all remote sensing-based products, the map presented here is best used alongside local knowledge and data. The map is intended to facilitate cross-boundary regional planning, and it is anticipated that state and local partners will further refine priority areas for management using additional information.</p> <p><strong>Methodology: </strong>This product used the Rangeland Analysis Platform V3 cover product from years 2020, 2021, and 2022. A mean composite was generated from the yearly raster data using the ‘annual herbaceous functional type’ (AFG) layer, representing percent cover of annual forb and grasses. The methodology for producing the cover product is described in Allred et al. 2021. Cover error for AFG in RAP Cover V3 was 7.0% (MAE) and 11.0% (RMSE). More information can be found at <a href="https://rangelands.app/products/">https://rangelands.app/products/</a>. The data is clipped to the extent of the sagebrush biome using the data from Jeffries and Finn (2019). </p> <p>Some important considerations must be kept in mind when using this product. First, the data layer depicts cover for all annual herbaceous species, not just invasive annual grasses. However, annual herbaceous cover is a useful surrogate for invasive annuals on arid rangelands in the sagebrush biome where native annuals typically represent a small proportion of vegetation cover most years. Second, the product reflects modeled predictions, so error must also be considered. This data product is best suited to highlight patterns of invasive annuals where they are known to be widely distributed and cannot be used in isolation to confirm the absence of invasive species. </p> <p><strong>Time Period of Data:</strong></p> <ul> <li>Start Date: 2020-01-01</li> <li>End Date: 2022-12-31</li> </ul> <p><strong>Coordinate Reference System</strong>: Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution is approximately 30m.</p> <p><strong>Data format: </strong>Cloud Optimized GeoTiff</p> <p><strong>Data Value: </strong>Percent (%) annual herbaceous cover</p> <p><strong>Data type: </strong>Byte </p> <p><strong>Nodata value: </strong>255</p> <p><strong>Bounding Coordinates:</strong></p> <ul> <li>West: -122.116081408</li> <li>East: -102.260259357</li> <li>North: 49.0016614443</li> <li>South: 34.2918384983</li> </ul> <p><strong>Keywords:</strong></p> <ul> <li>Terrestrial ecosystems</li> <li>Vegetation</li> <li>Invasive species</li> <li>Grassland ecosystems</li> <li>Remote sensing</li> <li>Grasslands</li> <li>Cheatgrass</li> <li>Great Basin</li> <li>Biota</li> <li>Geoscientific information</li> </ul> <p><strong>Access Constraints: </strong>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> Data are provided "as is" without warranty of any kind, express or implied.</p> <p><strong>Use Constraints:</strong> None. </p> <p><strong>Previous Version(s): </strong>Maestas et al. 2020</p> <p><strong>Data Credit: </strong>University of Montana, USDA-NRCS</p> <p><strong>Data Attribution: </strong>Allred et al. 2021</p>
Data from: Calculating global annual methane increases from satellite data using an ensemble dynamic linear model approach
<p><em><strong>NOTE: This is no official S5P/TROPOMI WFMD XCH4 L3-Dataset.</strong></em></p> <p>This data is used and created by the example code provided in <a href="http://www.doi.org/10.5281/zenodo.8178927">10.5281/zenodo.8178927</a>, which is a supplement to the manuscript <em>'Zonal variability of methane trends derived from satellite data' </em>(Hachmeister et al., 2024 ; 10.5194/acp-24-577-2024). This data can be downloaded to skip the gridding step in the mentioned example code, to avoid downloading the complete input data.</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.