Find research datasets worth reusing

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2012): Reflectances bands, NDVI and NDWI

<h2>Data information</h2> This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2012. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2010): Reflectances bands, NDVI and NDWI

<h2>Data information</h2> This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2010. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2009): Reflectances bands, NDVI and NDWI

<h2>Data information</h2> This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2009. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2006): Reflectances bands, NDVI and NDWI

<h2>Data information</h2> This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2006. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2002): Reflectances bands, NDVI and NDWI

<h2>Data information</h2> This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2002. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2014): Reflectances bands, NDVI and NDWI

<h2>Data information</h2> This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2014. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

opencc-by-4.0Nov 2021View details →

Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."

Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument." The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea.  This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour.  We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle.  This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.

opencc-by-4.0Nov 2021View details →

Disentangling the effects of jasmonate and tissue loss on the sex allocation of an annual plant

In this study, we explored norms of reaction in sex expression and sex allocation to herbivory in an experiment designed to uncouple its direct (through tissue loss) and indirect effects (due to defensive jasmonate signalling) in hermaphroditic XX females of the wind-pollinated Mercurialis annua. To uncouple the direct and indirect effects of herbivory on the sex expression and to test the role of jasmonate on conditional sex allocation, we conducted a two-factorial experiment manipulating tissue loss (25% chronic defoliation) and plant anti-herbivore defences via the jasmonate pathway (external application of jasmonate), and measured sexual expression in plants with both a male and a female function. The herbivory treatment applied were: For the control treatment (C), leaves were sprayed with a sham solution containing only water and polysorbate until all leaves were wet (see Supplementary Materials for detailed solution formulae). The herbivory treatment (H) consisted of cutting off half of every second leaf on the plant with scissors and spraying plants with a sham solution until all leaves were wet (defoliation resulted in a 25% reduction of total leaf area over the course of the whole plant’s lifetime). In the jasmonate treatment (JA) plants were sprayed with a solution of methyl-jasmonate and polysorbate until all leaves were wet (polysorbate 20 was used to fix the methyl-jasmonate on the sprayed leaves). Finally, the jasmonate and herbivory treatment (JAH) consisted of cutting off half of every other leaf on the plant with scissors and spraying plants with the methyl-jasmonate solution until all leaves were wet. These treatments were applied repeatedly as plants continued to grow, i.e., they represent chronic stress or manipulation. The first round of treatment was applied one week after repotting the plants (25th of November 2019) and then every two weeks over the next 12 weeks (the last treatment was applied on the 2nd of February 2020). On the first round of treatment, when most plants had fewer than six leaves each, we cut off only half a leaf (~10% of the leaf area removed) for plants under the herbivory treatments to avoid seedlings death. Plant sampling consisted of cutting all above-ground plant material of 34 plants per enclosure (N = 272) and recording total height. Plants were then cut in half, lengthwise, creating two distinct segments: top and bottom. The top segment was carefully examined and we counted the number of fruits (immature and mature) and harvested all male flowers using tweezers. Male flowers were stored in paper envelopes, dried and weighed. After phenotyping, plant segments were dried and weighed to obtain plant dry biomass (top + bottom). To estimate seed production, the seeds were isolated from the dried plant materials, stored in paper envelopes and weighed. All materials were dried in an oven at 50°C for at least 14 days and weighed using a digital scale. Variables names and meaning: PlantID: Individual identifier for each plant nb_seeds_estimate.TOP: Number of seeds form the top section of the plant     Biomass.BOTTOM: Dry biomass of the bottom plant section (grams)     Total_biomass: Dry biomass of the whole aboveground plant materials, except for the male flowers     Biomass.TOP:     Dry biomass of the bottom plant section (grams)     seed_mass_total: Dry biomass of the seeds of the whole plant (top+bottom sections) (grams) seed_mass.BOTTOM: Dry biomass of the seeds from the bottom section (grams) seed_nb_total: Number of seeds from the whole plant (top+bottom sections)     Lenght_section.TOP: Length of the top section (cm)     Fruit_number.TOP: Number of fruits present on the top sectioon at the time of harvest nb_seeds_estimate.BOTTOM: Number of seeds from the bottom section Height: Plant height (top+bottom sections) (cm) at the time of harvest Fruit_number.BOTTOM: Number of fruits present on the bottom section at the time of harvest     DPT: Days-post-treatment = the period elapsed between the last treatment application and the plant sampling date. For logistical reasons, our sampling was spread over 14 days by a team of six assistants. Lenght_section.BOTTOM:     Length of the bottom section (cm) Treatment: Herbivory treatments: C=Control; H= 25% chronic tissue loss, JA=exogenous jasmonate application; JAH=tissue loss + jasmonate. Box: Enclosure in which plants were kept. This was a blocking factor with 2 boxes per treatment, each one with 30-32 plants.       Date: sampling date     seed_mass.TOP:     Dry biomass of the seeds on the bottom plant sections (grams) Observer: Identifier for each of the six researchers who sampled plants. We recorder observer identity and included it in our statistical analyses to account for possible biases among assistants. male_fl_mass.TOP: Dry biomass of the male flowers sampled from the top plant section (grams).     nb_fl_estimate.TOP: Number of male flowers present on the top plant section at the time of harvest     male_fl_mass.BOTTOM: Dry biomass of the male flowers sampled from the bottom plant section (grams).     nb_fl_estimate.BOTTOM: Number of male flowers present on the bottom plant section at the time of harvest

opencc-by-4.0Nov 2021View details →

Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide 21-year-long (2000–2020) gap free annual mean AOD, PM2.5 and PM10 concentration data with a 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

opencc-by-4.0Oct 2020View details →

Supplementary Data from, "A Mechanistic Model of Annual Sulfate Concentrations in the U.S."

These data are used to perform the analysis contained in, "A Mechanistic Model of Annual Sulfate Concentrations in the United States," by Wikle, Hanks, Henneman, and Zigler. This is purely for archival purposes, to facilitate access and replication of the aforementioned analysis. All data were obtained from the following publicly available sources: 1) AMPD Unit Data (U.S. EPA, "Air markets program data," https://ampd.epa.gov/ampd) 2) 2010 U.S. Population Density (U.S.G.S., http://dx.doi.org/10.5066/F74J0C6M) 3) SO4 Concentrations (Randall Martin Atmospheric Composition Analysis Group's North American Regional Estimates, version V4.NA.02, https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03) 4) North American Regional Reanalysis Meteorological Data (NOAA, https://psl.noaa.gov/data/gridded/data.narr.monolevel.html) Code and supplementary material from this analysis are available at: https://github.com/nbwikle/mechanisticSO4-supp_material

opencc-by-4.0Dec 2021View details →

Observation based gridded annual runoff estimates over Victoria, Australia

The dataset provides observation-based interpolated gridded annual runoff estimates over Victoria, Australia during 1982 - 2012.  The methodology extended the R package rtop to allow top-kriging with external drift by employing spatial variability of gridded rainfall estimates. This dataset can be useful to estimate runoff at ungauged or poorly gauged catchments in Victoria. The full paper with the methodology is available at https://mssanz.org.au/modsim2021/papers/K11/weligamage.pdf

opencc-by-4.0Mar 2022View details →

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-4.0May 2022View details →

Long-term annual soil nitrogen surplus across Europe (1850 – 2019)

This dataset consists of annual long-term reconstruction of total N surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 16 N surplus estimates that account for the uncertainties resulting from input data sources and methodological choices in major components of the N surplus.This dataset offers the flexibility of aggregating the N surplus at any spatial scale of relevance to support water and land management strategies.   Data description: 1. Gridded N surplus data (NetCDF format): 16 files, each of them containing 170 years (1850-2019) of gridded data N surplus 2. Aggregated N surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the NUTS ID's. 3. Aggregated N surplus at European river basins (csv format) : 1 file, each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the river basin ID's. Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins) Time period: 1850-2019 Further information: Details/Citation: Long-term annual soil nitrogen surplus across Europe (1850-2019) by M. Batool, F.J.Sarrazin, S. Attinger, N.B.Basu, K.Van Meter and R. Kumar. Further queries regardig these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).

opencc-by-4.0Aug 2020View details →

Annual rain erosion (R) in Brazil

The erosivity data in Brazil. It has a spatial resolution of 30 seconds (~ 1 km²). The data set grid is in GeoTIFF format and corresponds perfectly to WorldClim. It uses the geographic coordinate reference system, with WGS84 projection (EPSG: 4326). Soil is a most important non-renewable natural resource for sustaining life. The rates of soil loss have been increasing. The strength of storms can become a disturbing factor, this water energy is known as rain erosivity, and is a major cause of the loss of sediment and nutrients worldwide. The method of obtaining these values is not simple and is usually one-off and uses the USLE or RUSLE equation. Point values cannot be applied in areas that need to estimate soil losses. And traditional spatialization techniques like kriging, IDW or Thiessen polygons do not represent the variability that actually occurs. Thus, the objective of this article was to model a map of rainfall erosivity for Brazil, with spatial resolution of 30 seconds of arc (~ 1 km²). Using products made available by other articles, GIS techniques and machine learning modeling. Of the 31 pre-selected covariates 8 were used in the modeling, in order of importance, they were: Longitude, Solar Radiation, Annual precipitation (BIO12), Precipitation of the coldest quarter (BIO19), Wind speed, Precipitation of the warmest quarter (BIO18 ) and the annual reference evapotranspiration. After 400 trainings and validations, the model with the best performance indicators was the Random Forest, using the medians, the indices were: NSE of 0.5823, RMSE of 1567.17 MJ.mm/ha.h.ano, MAE of 1135.90 MJ.mm / ha.h.year, nRMSE of 58.50%, ME of -17.76 MJ.mm/ha.h.year and D of 0.8487. The article was submitted for publication. Dados_Erosividade_BR.csv - Data used to model the models. eros_cubist.tif - Erosivity image generated by the cubist model eros_gbm.tif - Image of erosivity generated by the gbm model eros_lm.tif - Erosivity image generated by the linear model eros_rf.tif - Erosivity image generated by the random forest model

Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"

This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld,  M. Finkbeiner,  J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”, Sustainable Energy Fuels, 2022, 6, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>. It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see  <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and  “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Sweden

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Portugal

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Lithuania

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - United Kingdom (Northern Ireland)

This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of: <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> Disclaimer: In accordance with the Agreement on the Withdrawal of the United Kingdom from the EU, and in particular with the Protocol on IE/NI, the EU requirements on data sampling are also applicable to Northern Ireland.

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Italy