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zenodo44/100

Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 1: 2000 - 2004) derived from ERA5-Land data

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month; DD = day): <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code> Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Daily Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/6147830">https://zenodo.org/record/6147830</a>

opencc-by-sa-4.0Mar 2022View details →

zenodo44/100

Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 2: 2005 - 2009) derived from ERA5-Land data

opencc-by-sa-4.0Mar 2022View details →

zenodo44/100

Annual maps of cropland abandonment, land cover, and other derived data for time-series analysis of cropland abandonment

This archive contains raw annual land cover maps, cropland abandonment maps, and accompanying derived data products to support: <blockquote> Crawford C.L., Yin, H., Radeloff, V.C., and Wilcove, D.S. 2022. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. Science Advances <a href="https://doi.org/10.1126/sciadv.abm8999">doi.org/10.1126/sciadv.abm8999</a>. </blockquote> An archive of the analysis scripts developed for this project can be found at: <a href="https://github.com/chriscra/abandonment_trajectories">https://github.com/chriscra/abandonment_trajectories</a> (<a href="https://doi.org/10.5281/zenodo.6383127">https://doi.org/10.5281/zenodo.6383127</a>). Note that the label "_2022_02_07" in many file names refers to the date of the primary analysis. "dts” or “dt” refer to “data.tables," large .csv files that were manipulated using the data.table package in R (Dowle and Srinivasan 2021, <a href="http://r-datatable.com/">http://r-datatable.com/</a>). “Rasters” refer to “.tif” files that were processed using the raster and terra packages in R (Hijmans, 2022; <a href="https://rspatial.org/terra/">https://rspatial.org/terra/</a>; <a href="https://rspatial.org/raster">https://rspatial.org/raster</a>). Data files fall into one of four categories of data derived during our analysis of abandonment: observed, potential, maximum, or recultivation. Derived datasets also follow the same naming convention, though are aggregated across sites. These four categories are as follows (using “age_dts” for our site in Shaanxi Province, China as an example): <ol> <li>observed abandonment identified through our primary analysis, with a threshold of five years. These files do not have a specific label beyond the description of the file and the date of analysis (e.g., shaanxi_age_2022_02_07.csv);</li> <li>potential abandonment for a scenario without any recultivation, in which abandoned croplands are left abandoned from the year of initial abandonment through the end of the time series, with the label “_potential” (e.g., shaanxi_potential_age_2022_02_07.csv);</li> <li>maximum age of abandonment over the course of the time series, with the label “_max” (e.g., shaanxi_max_age_2022_02_07.csv);</li> <li>recultivation periods, corresponding to the lengths of recultivation periods following abandonment, given the label “_recult” (e.g., shaanxi_recult_age_2022_02_07.csv).</li> </ol>   This archive includes multiple .zip files, the contents of which are described below: <ul> <li>age_dts.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for, as of that year, also referred to as length, duration, etc.), for each year between 1987-2017 for all 11 sites. These maps are stored as .csv files, where each row is a pixel, the first two columns refer to the x and y coordinates (in terms of longitude and latitude), and subsequent columns contain the abandonment age values for an individual year (where years are labeled with "y" followed by the year, e.g., "y1987"). Maps are given with a latitude and longitude coordinate reference system. Folder contains observed age, potential age (“_potential”), maximum age (“_max”), and recultivation lengths (“_recult”) for all sites. Maximum age .csv files include only three columns: x, y, and the maximum length (i.e., “max age”, in years) for each pixel throughout the entire time series (1987-2017). Files were produced using the custom functions "cc_filter_abn_dt()," “cc_calc_max_age()," “cc_calc_potential_age(),” and “cc_calc_recult_age();” see "_util/_util_functions.R."</li> <li>age_rasters.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for), for each year between 1987-2017 for all 11 sites. Maps are stored as .tif files, where each band corresponds to one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Folder contains observed age, potential age (“_potential”), and maximum age (“_max”) rasters for all sites. Maximum age rasters include just one band (“layer”). These rasters match the corresponding .csv files contained in "age_dts.zip.”</li> <li>derived_data.zip - summary datasets created throughout this analysis, listed below.</li> <li>diff.zip - .csv files for each of our eleven sites containing the year-to-year lagged differences in abandonment age (i.e., length of time abandoned) for each pixel. The rows correspond to a single pixel of land, and the columns refer to the year the difference is in reference to. These rows do not have longitude or latitude values associated with them; however, rows correspond to the same rows in the .csv files in "input_data.tables.zip" and "age_dts.zip." These files were produced using the custom function "cc_diff_dt()" (much like the base R function "diff()"), contained within the custom function "cc_filter_abn_dt()" (see "_util/_util_functions.R"). Folder contains diff files for observed abandonment, potential abandonment (“_potential”), and recultivation lengths (“_recult”) for all sites.</li> <li>input_dts.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment (<a href="https://doi.org/10.1016/j.rse.2020.111873">https://doi.org/10.1016/j.rse.2020.111873</a>). Like “age_dts,” these maps are stored as .csv files, where each row is a pixel and the first two columns refer to x and y coordinates (in terms of longitude and latitude). Subsequent columns contain the land cover class for an individual year (e.g., "y1987"). Note that these maps were recoded from Yin et al. 2020 so that land cover classification was consistent across sites (see below). This contains two files for each site: the raw land cover maps from Yin et al. 2020 (after recoding), and a “clean” version produced by applying 5- and 8-year temporal filters to the raw input (see custom function “cc_temporal_filter_lc(),” in “_util/_util_functions.R” and “1_prep_r_to_dt.R”). These files correspond to those in "input_rasters.zip," and serve as the primary inputs for the analysis.</li> <li>input_rasters.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment. Maps are stored as ".tif" files, where each band corresponds one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Maps are given with a latitude and longitude coordinate reference system. Note that these maps were recoded so that land cover classes matched across sites (see below). Contains two files for each site: the raw land cover maps (after recoding), and a “clean” version that has been processed with 5- and 8-year temporal filters (see above). These files match those in "input_dts.zip."</li> <li>length.zip - .csv files containing the length (i.e., age or duration, in years) of each distinct individual period of abandonment at each site. This folder contains length files for observed and potential abandonment, as well as recultivation lengths. Produced using the custom function "cc_filter_abn_dt()" and “cc_extract_length();” see "_util/_util_functions.R."</li> </ul> derived_data.zip contains the following files: <ul> <li>"site_df.csv" - a simple .csv containing descriptive information for each of our eleven sites, along with the original land cover codes used by Yin et al. 2020 (updated so that all eleven sites in how land cover classes were coded; see below).</li> <li>Primary derived datasets for both observed abandonment (“area_dat”) and potential abandonment (“potential_area_dat”). <ul> <li>area_dat - Shows the area (in ha) in each land cover class at each site in each year (1987-2017), along with the area of cropland abandoned in each year following a five-year abandonment threshold (abandoned for >=5 years) or no threshold (abandoned for >=1 years). Produced using custom functions "cc_calc_area_per_lc_abn()" via "cc_summarize_abn_dts()". See scripts "cluster/2_analyze_abn.R" and "_util/_util_functions.R."</li> <li>persistence_dat - A .csv containing the area of cropland abandoned (ha) for a given "cohort" of abandoned cropland (i.e., a group of cropland abandoned in the same year, also called "year_abn") in a specific year. This area is also given as a proportion of the initial area abandoned in each cohort, or the area of each cohort when it was first classified as abandoned at year 5 ("initial_area_abn"). The "age" is given as the number of years since a given cohort of abandoned cropland was last actively cultivated, and "time" is marked relative to the 5th year, when our five-year definition first classifies that land as abandoned (and where the proportion of abandoned land remaining abandoned is 1). Produced using custom functions "cc_calc_persistence()" via "cc_summarize_abn_dts()". See scripts "cluster/2_analyze_abn.R" and "_util/_util_functions.R." This serves as the main input for our linear models of recultivation (“decay”) trajectories.</li> <li>turnover_dat - A .csv showing the annual gross gain, annual gross loss, and annual net change in the area (in ha) of abandoned cropland at each site in each year of the time series. Produced using custom functions "cc_calc_abn_diff()" via "cc_summarize_abn_dts()" (see "_util/_util_functions.R"), implemented in "cluster/2_analyze_abn.R." This file is only produced for observed abandonment.</li> </ul> </li> <li>Area summary files (for observed abandonment only) <ul> <li>area_summary_df - Contains a range of summary values relating to the area of cropland abandonment for each of our eleven sites. All area values are given in hectares (ha) unless stated otherwise. It contains 16 variables as columns, including 1) "site," 2) "total_site_area_ha_2017" - the total site area (ha) in 2017, 3) "cropland_area_1987" - the area in cropland in 1987 (ha), 4) "area_abn_ha_2017" - the area of cropland abandoned as of 2017 (ha), 5) "area_ever_abn_ha" - the total area of those pixels that were abandoned at least once during the time series (corresponding to the area of potential abandonment, as of 2017), 6) "total_crop_extent_ha" - the total area of those pixels that were classified as cropland at least once during the time series, 7) "total_area_abn_remaining_2017" - duplicate of "area_abn_ha_2017," the area abandoned as of 2017 (ha), taken from "area_recult_threshold," 8) "total_initial_area_abn" - the sum of the initial area of each cohort of abandonment when it is first classified as "abandoned," i.e., at the 5 year mark (note that this is cumulative, and because it counts those pixels that were abandoned more than once, it is therefore larger than "area_ever_abn_ha"), taken from "area_recult_threshold" 9) "total_area_abn_recultivated_2017" - the area of abandoned land that was recultivated as of 2017 (cumulatively, i.e., "total_initial_area_abn" - "area_abn_ha_2017"), taken from "area_recult_threshold," 10) "proportion_recultivated" - the proportion of all abandoned cropland (including multiple periods per pixel) that was recultivated by 2017, taken from "area_recult_threshold," 11) "area_2017_as_prop_site" - area abandoned as of 2017 as a proportion of the total site area, 12) "area_2017_as_prop_total_crop" - area abandoned as of 2017 as a proportion of the total crop extent, 13) "area_2017_as_prop_crop87" - area abandoned as of 2017 as a proportion of cropland area in 1987, 14) "area_ever_abn_as_prop_site" - area ever abandoned as a proportion of the total site area, 15) "area_ever_abn_as_prop_total_crop" - area ever abandoned as a proportion of the total crop extent, 16) "area_ever_abn_as_prop_crop87" - area ever abandoned as a proportion of cropland area in 1987. See script "1_summary_stats.Rmd."</li> <li>area_recult_threshold - Contains data on the proportion of observed abandoned cropland area that is recultivated by the end of our time series. This includes the area of abandoned cropland as of 2017 ("total_area_abn_remaining_2017") and the sum of the initial area of each cohort of abandonment when it is first classified as abandoned (at year 5; "total_initial_area_abn"). This "total_initial_area_abn" is cumulative, and allows for pixels that were abandoned multiple times during the time series to be counted multiple times. The difference between these two columns yields the "total_area_abn_recultivated_2017," which in turn is used to calculate the "proportion_recultivated," and the (ascending) "order" of sites based on this proportion. This file includes recultivation stats for each site for three abandonment definitions: 5, 7, and 10 years. See script "1_summary_stats.Rmd."</li> <li>abn_lc_area_2017 - Contains the number of pixels and corresponding area (in ha) of abandoned cropland in the year 2017 at each site, according to the land cover class (either woody vegetation [2], or herbaceous vegetation [4]) and the age in 2017 (5 to 30 years). See script "cluster/6_lc_of_abn.R."</li> <li>abn_prop_lc_2017 - Contains the number of pixels and corresponding area (ha) of cropland abandoned in the year 2017 in each land cover type (woody vegetation [2], or herbaceous vegetation [4]). It also shows this area as a proportion of the total area abandoned at each site (i.e., in either land cover class: 2 or 4). See script "cluster/6_lc_of_abn.R."</li> </ul> </li> <li>Carbon <ul> <li>carbon_df – contains the observed and potential carbon accumulation in abandoned croplands in each site in each year (in Mg C), for two abandonment thresholds: 5 years (our default abandonment definition) and 1 year (i.e., no threshold). Each data point corresponds to one of two scenarios (“type” column), either “observed” or “potential.” Carbon accumulation figures are for both the sum of forest and soil carbon at each site in a given year. Carbon accumulation is listed in three columns: 1) “C_up_to_20” contains the total carbon accumulated in those abandoned croplands with abandonment durations between 5 and 20 years. 2) “C_21_30” contains the total carbon accumulation in croplands with durations between 21 and 30 years, which are differentiated in order to account for non-linear carbon accumulation rates in soils over time, and 3) “total_C_Mg” contains the sum of the previous two columns, representing the total carbon accumulated across all abandoned croplands in each year.</li> <li>soc_mean – contains mean soil organic carbon accumulation rates for years 1-20 and years 21-80, derived from Sanderman et al. 2020 (in Mg C; <a href="https://doi.org/10.7910/DVN/HA17D3">https://doi.org/10.7910/DVN/HA17D3</a>). These values correspond to accumulation rates in croplands upon abandonment and regeneration to natural vegetation (Sanderman et al. 2020’s “rewilding” scenario). These mean values are calculated across those pixels identified as cropland by Sanderman et al. 2020 at each site. Mean values in year 20 and 80 are contained in columns “mean_soc_20” and “mean_soc_80” respectively, and the annualized rate over the first 20 years and the subsequent years 21 through 80 are contained in columns “mean_annual_soc_1_20” and “mean_annual_soc_21_80” respectively.</li> </ul> </li> <li>Decay model data – two R data files containing data products for our linear models of abandonment recultivation trajectories. <ul> <li>decay_endpoints_files – an R data file (.rds) containing seven data products produced as part of our common endpoint analysis, which calculated mean trajectories for each site across a range of common endpoints, ensuring that means were based on coefficient estimates derived from a consistent number of observations for each cohort. These files are: <ul> <li>common_endpoint_dat – a .csv containing subsets of “persistence_dat” for each “endpoint” (7 through 29).</li> <li>endpoint_n – a .csv describing, for each endpoint, the corresponding number of observations per cohort (“n_obs”), the number of cohorts (“n_cohorts”), the total number of observations across cohorts included (“total_obs”), and the cohorts that meet the endpoint threshold (“cohorts”).</li> <li>coef_l3_endpoints – corresponding model coefficients for our primary model (“l3”) parameterized by the range of subsets across endpoints.</li> <li>augment_endpoints – fitted values (i.e., model predictions) for linear models produced across the full range of endpoint subsets.</li> <li>fitted_endpoints – a simplified .csv containing the mean linear and log coefficients for each site at each endpoint, and the corresponding predicted proportion remaining abandoned through time (based on the “age,” or duration, of abandonment).</li> <li>time_to_endpoints – a .csv containing, for mean trajectories for each endpoint at each site, the estimated time required for a given amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%).</li> <li>endpoint_half_lives – a .csv containing the half-lives calculated for the mean trajectories for each endpoint at each site.</li> </ul> </li> <li>decay_mod_archive - an R data file (.rds) containing eleven data products derived from linear models of abandonment recultivation ("decay"): <ul> <li>lm_mega_lin_log_lin_l – the primary linear model produced in our analysis. This model is referred to as “lin_log_lin” (or “l3”) because the model predicts linear persistence (“lin”) as a function of a log term of time (“log”) and a linear term of time (“lin”). “mega” refers to the fact that this model is run for the full dataset, pooled across all 11 sites.</li> <li>coef_l3_mega – a .csv containing model coefficients for our primary linear model of recultivation (“lin_log_lin”, or “l3”), with a single row each for the linear term of time and the log term of time, for 26 cohorts at 11 sites.</li> <li>mean_coef_l3_mega – a data frame containing the mean coefficient values for the log and linear terms of time across cohorts at each site. This also contains the mean of the low and high coefficient estimates, based on the 95% confidence interval.</li> <li>half_lives_all_cohorts_l3 – half-lives calculated for each cohort at each site, for our primary model.</li> <li>half_life_mean_coefs_l3 – half-lives calculated based on the mean trajectory for each site (based on the mean log coefficients and mean linear coefficients across all cohorts), for our primary model.</li> <li>mod_AIC_mega – Akaike Information Criterion (AIC) values for all tested model specifications.</li> <li>fitted_combo – fitted values (i.e., model predictions) for our primary model (“l3”) and a series of alternative model specifications (“l3_trim” – excluding cohorts with fewer than 5 observations; “lin_log” – a model including only one log time term; “log2_lin” – in which the log of persistence is predicted by log and linear time terms; and “l3_no_cohort” – our primary model, predicting linear persistence as a function of log time and linear time, but without cohort-level fixed effects).</li> <li>time_to_combo – contains the estimated time required for a certain amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%). See script "2_decay_models.Rmd." These values are calculated for a range of alternative model specifications ("l3_trim", “lin_log”, "log2_lin", and "l3_no_cohort"; see above).</li> </ul> </li> </ul> </li> <li>Length data – includes “_distill_df” files and “mean_length_df” files for observed, potential, and recultivation. <ul> <li>length_distill_df - .csvs containing the number ("freq") of abandonment periods of a specific "length" of time (i.e., age) at each site over the course of the entire time series. Derived from the "length" files in "length.zip." See script "cluster/5_distill_lengths.R."</li> <li>mean_length_df - .csvs with the mean, median, and standard deviation, for each site, for both "all" lengths or just the "max" length per pixel, and for a range of abandonment definitions (1, 3, 5, 7, and 10 years). Derived from "length_distill_df." See script "1_summary_stats.Rmd."</li> </ul> </li> <li>Duration summary files – includes “summary_stats_all_sites” and “summary_stats_all_sites_pooled,” for observed and potential abandonment, and recultivation periods following abandonment. <ul> <li>“summary_stats_all_sites” - A simple .csv derived from "mean_length_df" files containing summary stats across the 11 sites. This includes the mean of the mean abandonment duration ("length", in years) for each of our 11 sites ("mean_of_means"), the standard deviation of these site mean abandonment lengths ("sd_of_means"), the mean of the standard deviation at each site ("mean_of_sds"), the mean median ("mean_of_medians"), and the mean number of abandonment periods ("mean_n_abn_periods"). Note that length "all" indicates that these stats account for all periods (including multiple per pixel), rather than just the max duration per pixel. See script "1_summary_stats.Rmd."</li> <li>“summary_stats_all_sites_pooled” - A summary .csv similar to "summary_stats_all_sites," but calculated by pooling all distinct periods of abandonment across all eleven sites, and then calculating the mean, median, and standard deviation of abandonment duration. See script "1_summary_stats.Rmd."</li> </ul> </li> <li>Comparing annual approach to identifying abandonment to a two-timepoint (“2yr”) approach: <ul> <li>abn_2yr_ages_df - Contains the age of former croplands identified as "abandoned" using a two-timepoint method (i.e., 2017 - 1987), where age values (as of 2017) are derived from our map of abandonment identified using the full annual time series. This includes the area in hectares (ha), in each age class (along with the number of pixels), at each of our 11 sites. This dataset is used to calculate the percent of cropland "abandonment" identified using the two-year method that is actually too "young," i.e., less than 5 years old, and therefore not truly abandonment according to our five-year abandonment definition</li> <li>abn_2yr_overestimation - Compares the area (in hectares) of cropland abandonment at each site identified with our full annual time series (and a five-year abandonment definition) and the "abandonment" identified using a two-timepoint method (2017-1987). This also includes the percent difference in area between the two methods, the Jaccard similarity of the areas identified as abandonment, and the percent of "young" (i.e., <5-year-old) "abandonment" identified by the two-timepoint method.</li> </ul> </li> </ul> Input land cover maps: As noted, the file "input_rasters.zip" contain the raw annual land cover maps for eleven sites generated by: <blockquote> Yin, H., A. Brandão, J. Buchner, D. Helmers, B. G. Iuliano, N. E. Kimambo, K. E. Lewińska, E. Razenkova, A. Rizayeva, N. Rogova, S. A. Spawn, Y. Xie, and V. C. Radeloff. 2020. Monitoring cropland abandonment with Landsat time series. Remote Sensing of Environment 246:111873. https://doi.org/10.1016/j.rse.2020.111873 </blockquote> These land cover maps served as raw inputs for this project and form the basis of the analysis. All land cover maps have a resolution of 30-m and exist for each year from 1987 through 2017. The exceptions are Nebraska / Wyoming (1986-2018) and Wisconsin (1987-2018); these additional years were excluded from our analysis of abandonment duration. Land cover categories in these maps are coded as follows: <ol> <li>Non-vegetated area (e.g., water, urban, barren land)</li> <li>Woody vegetation (e.g., forests)</li> <li>Cropland</li> <li>Herbaceous vegetation (e.g., grassland)</li> </ol> Site file names correspond to the following geographic locations: <ul> <li>belarus = Vitebsk, Belarus / Smolensk, Russia</li> <li>bosnia_herzegovina = Bosnia & Herzegovina</li> <li>chongqing = Chongqing, China</li> <li>goias = Goiás, Brazil</li> <li>iraq = Iraq</li> <li>mato_grosso = Mato Grosso, Brazil</li> <li>nebraska = Nebraska / Wyoming, USA</li> <li>orenburg = Orenburg, Russia / Uralsk, Kazakhstan</li> <li>shaanxi = Shaanxi/Shanxi, China</li> <li>volgograd = Volgograd, Russia</li> <li>wisconsin = Wisconsin, USA</li> </ul> This dataset is minimally altered from Yin et al. 2020.  However, land cover codes were updated for five sites (Iraq, Nebraska/Wyoming, Orenburg/Uralsk, Volgograd, and Wisconsin) in order to maintain consistency in how land cover was coded across all sites. The original land cover codes (matching Yin et al. 2020) are described in the file "site_df.csv" and are as follows: <ol> <li>Iraq: 1 Non-vegetated;  2 Cropland;  3 Woody;  4 Herbaceous</li> <li>Nebraska / Wyoming (USA):  1 Cropland;  2 Woody;  3 Non-vegetated;  4 Herbaceous</li> <li>Orenburg, Russia / Uralsk, Kazakhstan:  1 Non-vegetated;  2 Cropland;  3 Herbaceous;  4 Woody</li> <li>Volgograd (Russia):  1 Non-vegetated;  2 Cropland;  3 Herbaceous;  4 Woody</li> <li>Wisconsin (USA):  1 Cropland;  2 Herbaceous;  3 Woody;  4 Non-vegetated</li> </ol>

opencc-by-4.0Mar 2022View details →

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ERA5-Land weekly: Surface temperature, weekly time series for Europe at 1 km resolution (2016 - 2020)

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Surface temperature: Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis (starting from Saturday) for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of surface temperature. File naming: Average of daily average: <code>era5_land_ts_avg_weekly_YYYY_MM_DD.tif</code> Max of daily max: <code>era5_land_ts_max_weekly_YYYY_MM_DD.tif</code> Min of daily min: <code>era5_land_ts_min_weekly_YYYY_MM_DD.tif</code> The date in the file name determines the start day of the week (Saturday). Pixel values: °C * 10 Example: Value 302 = 30.2 °C The QML or SLD style files can be used for visualization of the temperature layers. Coordinate reference system: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035) Spatial extent: north: 82N south: 18S west: -32W east: 61E Spatial resolution: 1 km Temporal resolution: weekly Time period: 01/01/2016 - 12/31/2020 Format: GeoTIFF Representation type: Grid Software used: GRASS 8.0 Original ERA5-Land dataset license: <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a> CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a> Other resources: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Contact: mundialis GmbH & Co. KG, info@mundialis.de

opencc-by-sa-4.0Jun 2021View details →

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ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Air temperature (2 m): Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of air temperature (2 m). File naming: Average of daily average: <code>era5_land_t2m_avg_weekly_YYYY_MM_DD.tif</code> Max of daily max: <code>era5_land_t2m_max_weekly_YYYY_MM_DD.tif</code> Min of daily min: <code>era5_land_t2m_min_weekly_YYYY_MM_DD.tif</code> The date in the file name determines the start day of the week (Saturday). Pixel value: °C * 10 Example: Value 44 = 4.4 °C The QML or SLD style files can be used for visualization of the temperature layers. Coordinate reference system: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 1km Temporal resolution: weekly Time period: 01/01/2016 - 12/31/2020 Format: GeoTIFF Representation type: Grid Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Lineage: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used. Original ERA5-Land dataset license: <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a> CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a> Other resources: <a href="https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b">https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b</a> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Contact: mundialis GmbH & Co. KG, info@mundialis.de

opencc-by-sa-4.0Feb 2022View details →

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Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series

This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.

opencc-by-4.0Jan 2022View details →

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MERRIN: MEtabolic Regulation Rule INference from time series data (Docker image and notebooks)

This record contains notebooks and Docker image for reproducing the results of the paper "MERRIN: MEtabolic Regulation Rule INference from time series data" published as part of the ECCB 2022 conference. Notebooks can be executed interactively within the Docker image <code>bioasp/merrin:v1</code> which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code> Also see <a href="https://github.com/bioasp/merrin-covert">https://github.com/bioasp/merrin-covert</a> The Docker image can be executed as follows: <pre><code class="language-bash">docker pull bioasp/merrin:v1 docker run -it --rm -p 8888:8888 bioasp/merrin:v1 </code></pre> then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>. The image can be imported using the command <code>docker load</code> with the image file provided in this record: <pre><code>docker load -i image.tar.gz</code></pre> or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>: <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.6670165</code></pre>

opencc-by-4.0Jun 2021View details →

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Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.

Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.

opencc-by-4.0Jul 2022View details →

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ERA-NUTS: meteorological time-series based on C3S ERA5 for European regions (1980-2021)

# ERA-NUTS (1980-2021) This dataset contains a set of time-series of meteorological variables based on <a href="https://climate.copernicus.eu/climate-reanalysis">Copernicus Climate Change Service (C3S) ERA5 reanalysis</a>. The data files can be downloaded from here while notebooks and other files can be found on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>. This data has been generated with the aim of providing hourly time-series of the meteorological variables commonly used for power system modelling and, more in general, studies on energy systems. An example of the analysis that can be performed with ERA-NUTS is shown <a href="https://youtu.be/zVeF8Dv6jlE">in this video</a>. Important: this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us <a href="mailto:matteo.de-felice@ec.europa.eu">sending an email</a> or opening an Issue in the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>. ## Data The time-series have hourly/daily/monthly frequency and are aggregated following the <a href="https://ec.europa.eu/eurostat/web/nuts/background">NUTS  2016 classification</a>. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries). This dataset contains NUTS0/1/2 time-series for the following variables obtained from the ERA5 reanalysis data (in brackets the name of the variable on the Copernicus Data Store and its unit measure):   - t2m: 2-meter temperature (`2m_temperature`, Celsius degrees)   - ssrd: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)   - ssrdc: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)   - ro: Runoff (`runoff`, millimeters)   - sd: Snow depth (`sd`, meters)   There are also a set of derived variables:   - ws10: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)   - ws100: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)   - CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)   - RH: Relative Humidity (computed following Lawrence, BAMS 2005 and Alduchov & Eskridge, 1996)   - HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the <a href="https://ec.europa.eu/eurostat/cache/metadata/en/nrg_chdd_esms.htm">EUROSTAT definition</a>. For each variable we have 367 440 hourly samples (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for 34/115/309 regions (NUTS 0/1/2).   The data is provided in two formats:   - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` to minimise the size of the files.   - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)   All the CSV files are stored in a zipped file for each variable. ## Methodology The time-series have been generated using the following workflow:   1. The NetCDF files are downloaded from the Copernicus Data Store from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form">ERA5 hourly data on single levels from 1979 to present</a> dataset   2. The data is read in R with the <a href="http://www.meteo.unican.es/climate4R">climate4r</a> packages and aggregated using the function `/get_ts_from_shp` from <a href="https://github.com/matteodefelice/panas">panas</a>. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.   3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R   4. The NetCDF are created using `xarray` in Python 3.8. ## Example notebooks In the folder `notebooks` on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the <a href="https://github.com/kavvkon/enlopy">enlopy</a> package. There are currently two notebooks:   - exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.   - ERA-NUTS-explore-with-widget: explorer interactively the datasets with [<a href="https://jupyter.org/">jupyter</a>]() and <a href="https://ipywidgets.readthedocs.io/en/stable/">ipywidgets</a>. The notebook `exploring-ERA-NUTS` is also available rendered as HTML. ## Additional files In the folder `additional files`on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region. ## License This dataset is released under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0 license</a>. ## Changelog 2022-04-08 Added Relative Humidity (RH) 2022-03-07 Added the missing month in CDD/HDD  2022-02-08 Updated the wind speed and temperature data due to missing months.

opencc-by-4.0Feb 2022View details →

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MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution

Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS data for Europe at 1 km resolution. Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MOD13A1 v006): https://lpdaac.usgs.gov/products/mod13a1v006/ - MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MYD13A1 v006): https://lpdaac.usgs.gov/products/myd13a1v006/ The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 16 days. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". File naming: productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif example: MOD13A1.A2020305_mosaic_1000m_16_days_NDVI.tif The date is Year and Day of Year. Values are NDVI/EVI * 10000. Example: Value 6473 = 0.6473 Projection + EPSG code: ETRS89 / LAEA Europe (EPSG:3035) (EPSG: 3035) Spatial extent: north: 72N south: 30S west: -52W east: 49E Spatial resolution: 1 km Temporal resolution: 16 days Pixel values: NDVI/EVI * 10000 (scaled to Integer; example: value 6473 = 0.6473) Software used: GRASS GIS 8.0 Original dataset license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/myd13a1v006/ Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)

opencc-by-sa-4.0Oct 2021View details →

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MODIS MNDWI, 8-day time series for Europe at 1 km resolution

Modified Normalized Difference Water Index (MNDWI) from MODIS data for Europe at 1 km resolution. Source data: - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v006): https://lpdaac.usgs.gov/products/mod09a1v006/ The corresponding MODIS/Aqua product (MYD09A1 v006) could not be used due to the fact that the Aqua satellite has a number of broken detectors resulting in unreliable data for band 6 (SWIR) measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 8 days. Bad quality pixels (cloud, cloud shadow, dead detector, solar zenith angle too large, etc.) have been masked using the provided quality assurance (QA) layers and appear as "no data". File naming: productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif example: MOD09A1.A2016353_mosaic_1000m_8_days_MNDWI.tif The date is Year and Day of Year. Values are MNDWI * 10000. Example: Value -5099 = -0.5099 Projection + EPSG code: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035) Spatial extent: north: 72N south: 30S west: -52W east: 49E Spatial resolution: 1 km Temporal resolution: 8 days Pixel values: MNDWI * 10000 (scaled to Integer; example: value -5099 = -0.5099) Software used: GRASS GIS 8.0 Original dataset license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/mod09a1v006/ Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)

opencc-by-sa-4.0Oct 2021View details →

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Time series of Agulhas leakage from satellite altimetry. Update of Le Bars et al. 2014.

Agulhas leakage time series updated from Le Bars et al. 2014 and Kelly et al. 2016. Final results are stored in: Trp_track_twb122_barot30W.nc,Trp_track_twb20_barot30W.nc and Trp_track_twb198_barot30W.nc An example of analysis and comparison with older time series is in the Python 3.9 Jupyter notebook: AnalyseOutputs.ipynb (AnalyseOutputs.html to simply visualise) The code and intermediate results are also provided. See README.txt for instructions on how to use it. It was written in 2013 with the NCAR Command Language and Python 2.? but it can still run now with later version of NCL and with Python 2.7. References: Le Bars, D., Durgadoo, J. V., Dijkstra, H. A., Biastoch, A., and De Ruijter, W. P. M.: An observed 20-year time series of Agulhas leakage, Ocean Sci., 10, 601–609, https://doi.org/10.5194/os-10-601-2014, 2014. Kelly, K. A., K. Drushka, L. Thompson, D. Le Bars, and E. L. McDonagh (2016), Impact of slowdown of Atlantic overturning circulation on heat and freshwater transports, Geophys. Res. Lett., 43, doi:10.1002/2016GL069789.

opencc-by-4.0Aug 2022View details →

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A Review Of Metaheuristics in Fuzzy Time Series Applied To Zero Inflated Datasets

The manufacturing efficiency reflects directly on the use of natural resources and leads to a higher environmental impact than needed. Efficiency of an industry can be achieved in many ways, but it always starts with demand management. However some products have erratic and irregular demand patterns as the nature of the usage varies, and this often leads to zero inflated demand datasets, said datasets are difficult to forecast due to the nature of traditional models which usually use moving averages, state of the art machine learning models can achieve good results but use too much data for training. Under this background, this paper investigates the Fuzzy Time Series models and how it evolved from its inception to present time and how the usage of metaheuristics can help with forecasting demand on a small dataset with a high count of zeros, then applies the techniques to other zero inflated dataset to verify its generalization capabilities. Finally another model is applied as comparison.

opencc-by-4.0Sep 2022View details →

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Time Series data from wearable sensors to capture the onset of Fatigue in Runners

The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin. The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here consists of the raw readings from the sensors across the three phases of the run. The data is saved seperately as 'F' for Fatigued, 'NF' for Not Fatigued, and 'BeepTest' for the data collected during the fatiguing process. For the processed and labelled fatigue and non fatigue data, see: https://zenodo.org/records/7997851 Kindly cite one of the following papers when using this data: B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612 B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a>

opencc-by-4.0May 2024View details →

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Temperature and wind speed time series on a 50 km^2 grid in Europe

This spatio-temporal dataset contains weather timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000-2018. It has been generated directly from the MERRA-2 reanalysis dataset. This data serves as an input to the Sector-Coupled Euro-Calliope model and has been generated to match the spatial resolution of the renewable energy generation capacity factors found at https://doi.org/10.5281/zenodo.3891480. temperature.nc: air temperature at 2m above ground in degrees C. tsoil5.nc: soil temperature at layer 5 in degrees C. wind10m.nc: wind speed at 10m above ground in m/s. grid.nc: provides the latitude and longitude of each grid (a.k.a. "site") centroid. This data is also given in every other dataset, but is provided here as a lightweight alternative to align other datasets to the same grid spacing.   Changelog 2024-06-07: <ul> <li>Moved data variables to dataset attributes where the same data was duplicated per gridcell.</li> <li>Added units to file attributes.</li> <li>Updated `tsoil5` variable name from `soil_temperture_5` to `tsoil5`. Now all timeseries data variables have names that match the filename. </li> <li>Updated `tsoil5` variable from Kelvin to degrees C.</li> <li>Updated `tsoil5` variable empty data (when gridcell is not over land) from zero to NaN.</li> <li>Added `grid.nc`.</li> <li>Removed `electricity` variable from `wind10m.nc`, leaving only wind speed as the available timeseries data in the file.</li> </ul>

opencc-by-4.0May 2022View details →

zenodo44/100

Dynamical System Multivariate Time Series

The Dynamical System Multivariate Time Series (DSMTS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system under fully nominal conditions (no outliers or anomalies). The DSMTS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Multivariate Time Series Forecasting especially for industrial processes of complex systems: <ul> <li>Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including: <ul> <li>4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.</li> <li>3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.</li> <li>10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.</li> </ul> </li> <li>5 million timestamps. Sensors readings are at 1Hz sampling frequency. </li> <li>Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.</li> <li>No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.</li> </ul>

opencc-by-4.0Jun 2024View details →

zenodo44/100

CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)

Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: - CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m</a> - CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m</a> Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management. The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices. Processing steps: To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value. File naming: Until December 2020: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> e.g.: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif</code> From January 2021 on: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> e.g.: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif</code> The date within the filename is year and month of aggregated timestamp. NOTE: data for 2023-04 are missing, since they are not available from CLMS Pixel values: 0: Sea 70: Water 255: No water Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 27:17:30N south: 14:43:30N west: 17:04:30W east: 04:48:00W Temporal extent: January 2019 - December 2023 (except: April 2023) Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original dataset license: Generated using European Union's Copernicus Land Monitoring Service information Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Contact: mundialis GmbH & Co. KG, info@mundialis.de

opencc-by-sa-4.0Jun 2024View details →

zenodo44/100

MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)

Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a> The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84. File naming: <code>ndvi_filt_YYYY_MM_01T00_00_00.tif</code> e.g.: <code>ndvi_filt_2023_12_01T00_00_00.tif</code> The date within the filename is year and month of aggregated timestamp. Pixel values: NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473 Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28N south: 14N west: 18W east: 4W Temporal extent: January 2019 - December 2023 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original dataset license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Contact: mundialis GmbH & Co. KG, info@mundialis.de

opencc-by-sa-4.0Jun 2024View details →

zenodo44/100

Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> The resulting relative humidity has been aggregated to monthly averages. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month): <code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code> Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Monthly Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a>

opencc-by-sa-4.0Nov 2023View details →

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Multi-year temperature and humidity time series from ice caves on Mauna Loa, Hawaii

Air temperature and humidity time series from data loggers in two lava tube caves on Mauna Loa, Hawaii 2011-2017.

opencc-by-nc-nd-4.0Jul 2017View details →

ScienceDex guides

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record