Forschungsdaten
Dauerhafte URI für die Sammlung
Suchen
Auflistung Forschungsdaten nach Auflistung nach DDC "ddc:333.7"
Gerade angezeigt 1 - 4 von 4
Treffer pro Seite
Sortieroptionen
Item cropdata – spatial yield productivity data base for the ten most cultivated crops in Germany from 1989 to 2020 - version 1.0(2022-09-19) Ellsäßer, FlorianThis data set contains productivity data in decitons (100kg) per hectare (dt/ha) on the yield of the following crops (winter wheat, rye and winter mixed crops, spring and winter barley, oat, triticale, silage maize, oilseed rape, potato and sugar beet) for the years 1989 to 2020. The data are available in a (1) gap-filled and (2) a gap-filled and detrended version in the digital formats (a) .csv and (b) .netCDF. Therefore, the data set includes the files (1a) all_crops_gapfilled_with_indicators_v001.csv, (1b) all_crops_productivity_gapfilled_v001.nc4, (2a) all_crops_gapfilled_detrended_with_indicators_v001.csv and (2b) all_crops_productivity_gapfilled_detrended_v001.nc4. The files (1a) and (2a) use the following column headers: year = the harvest year, reg_number = county or city identification number, name = county or city name, description = description of the county or city, state_id = numerical id of the state following the same system than the reg_number, the crop names (winter_wheat, rye, winter_barley, summer_barley, oat, triticale, potato, silage_maize, sugar_beet, winter_oilseed_rape, where rye includes also winter mixed crops) listing the productivity data in dt/ha, the crop name with the _gap_filled ending indicates if this crop productivity value was gap-filled (indicated as True) or taken from an existing data set (indicated as False), geometry = Multipolygon or Polygon with geographic borders of the county or city area. Both files can be opened with MS Excel or Libre office or any other software to open .csv files. The files (1b) and (2b) use three coordinates: longitude, latitude and year. The data variables are similar to (1a) and (2a) including the ten crop productivity variables in (dt/ha) and the indicator of gap filling. However, the gap filled areas are indicated with a 1 and original values are indicated with a 0 here (instead of True and False respectively). Both files can be opened with software such as the MS NetCDF Viewer, however we recommend using the Python xarray package to work with the data. All files were gap-filled using a nearest neighbor gap-filling procedure where neighboring pixels were considered more than the locally typical values with a ratio of 3:1. Detrending was applied using a LOWESS regression, for all time-series where there was a significant trend (CI=95%). This data set is based on multiple data sets that were provided by the federal and state statistical offices of Germany and the Federal Agency for Cartography and Geodesy. The copyright statements and names of institutions are mentioned and listed in the following: For the spatial data set (county borders): © GeoBasis-DE / BKG (2022) - Datenlizenz Deutschland Version 2.0, further raw data from Regionaldatenbank Deutschland were used on federal level: © Statistisches Bundesamt (Destatis), 2022. Further data from individual states were collected and include the following copyright statements: Schleswig-Holstein: © Statistisches Amt für Hamburg und Schleswig-Holstein, Hamburg 2022; Niedersachsen: © Landesamt für Statistik Niedersachsen, Hannover 2020; Nordrhein-Westfalen: © Information und Technik Nordrhein-Westfalen, Düsseldorf 2020; Hessen: © Hessisches Statistisches Landesamt, Wiesbaden, 2022; Rheinland-Pfalz: © Statistisches Landesamt Rheinland-Pfalz, Bad Ems, 2022. Baden-Württemberg:© Statistisches Landesamt Baden-Württemberg, Stuttgart, 2020; Freistaat Bayern: © Bayerisches Landesamt für Statistik, Fürth, 2022; Saarland: © Statistisches Amt des Saarlandes, Saarbrücken; Brandenburg: © Amt für Statistik Berlin-Brandenburg, Potsdam, 2021; Mecklenburg-Vorpommern: © Statistisches Amt Mecklenburg-Vorpommern, Schwerin, 2022, Freistaat Sachsen: © Statistisches Landesamt des Freistaates Sachsen, Kamenz 2022; Sachsen-Anhalt: © Statistisches Landesamt Sachsen-Anhalt, Halle (Saale), 2020; Freistaat Thüringen: © Thüringer Landesamt für Statistik, Erfurt, 2021. Please refer to these institutions and copyright statements when publishing data or products that are based on this data set.Item cropdata – yield anomaly catalogue for the ten most cultivated crops in Germany from 1989 to 2020 - version 1.0(2022-09-19) Ellsäßer, Florian; Justus-Liebig University Gießen, ZEU – Center for International Development and Environmental ResearchThis data set contains a .csv file yield_anomaly_catalogue_v001.csv that lists all areas affected by large or extreme yield anomalies. Large and extreme anomalies are defined according to the Standardized Yield Anomaly Index (SYAI) where “large” is defined as all values between ± 1 standard deviation (std) and ± 2 std and “extreme” values are defined as all values above or below ± 2 stds from the mean. The data set is organized by the following headers: year = harvest year of occurrence; crop = defining the crop type; attribute = where -2 and -1 are extreme and large negative yield anomalies respectively and 1 and 2 are large and extreme positive yield anomalies respectively; size = affected area in km²; relative_size = affected area in relative terms compared to the total area of Germany; average = average/mean value of all pixels in this affected area. This data set is based on multiple data sets that were provided by the federal and state statistical offices of Germany and the Federal Agency for Cartography and Geodesy. The copyright statements and names of institutions are mentioned and listed in the following: For the spatial data set (county borders): © GeoBasis-DE / BKG (2022) - Datenlizenz Deutschland Version 2.0, further raw data from Regionaldatenbank Deutschland were used on federal level: © Statistisches Bundesamt (Destatis), 2022. Further data from individual states were collected and include the following copyright statements: Schleswig-Holstein: © Statistisches Amt für Hamburg und Schleswig-Holstein, Hamburg 2022; Niedersachsen: © Landesamt für Statistik Niedersachsen, Hannover 2020; Nordrhein-Westfalen: © Information und Technik Nordrhein-Westfalen, Düsseldorf 2020; Hessen: © Hessisches Statistisches Landesamt, Wiesbaden, 2022; Rheinland-Pfalz: © Statistisches Landesamt Rheinland-Pfalz, Bad Ems, 2022. Baden-Württemberg:© Statistisches Landesamt Baden-Württemberg, Stuttgart, 2020; Freistaat Bayern: © Bayerisches Landesamt für Statistik, Fürth, 2022; Saarland: © Statistisches Amt des Saarlandes, Saarbrücken; Brandenburg: © Amt für Statistik Berlin-Brandenburg, Potsdam, 2021; Mecklenburg-Vorpommern: © Statistisches Amt Mecklenburg-Vorpommern, Schwerin, 2022, Freistaat Sachsen: © Statistisches Landesamt des Freistaates Sachsen, Kamenz 2022; Sachsen-Anhalt: © Statistisches Landesamt Sachsen-Anhalt, Halle (Saale), 2020; Freistaat Thüringen: © Thüringer Landesamt für Statistik, Erfurt, 2021. Please refer to these institutions and copyright statements when publishing data or products that are based on this data set.Item Supplemental Material for "First DNA sequencing in Beninese Indigenous Cattle Breeds Captures New Milk Protein Variants"(2021-10) Vanvanhossou, Sèyi Fridaïus Ulrich; Giambra, Isabella Jasmin; Yin, Tong; Brügemann, Kerstin; Dossa, Luc Hippolyte; König, SvenThe data are supplementary materials for the research article "First DNA sequencing in Beninese Indigenous Cattle Breeds Captures New Milk Protein Variants". The aim of the study was to investigate polymorphisms in the milk protein genes CSN1S1, CSN2, CSN1S2, CSN3, LALBA, and LGB, and casein haplotypes in Beninese indigenous cattle. For this purpose, the exon sequences, flanking intron sequences and parts of the 5’-upstream regions of milk protein genes were sequenced in 67 Beninese indigenous cattle. Further details in the methodology and the main findings are provided in the article. The supplementary data include five tables: Table S1. Primer sequences utilized for the sequencing of the six milk protein genes (CSN1S1, CSN2, CSN1S2, CSN3, LALBA, LGB) in the Beninese indigenous cattle breeds; Table S2. List of the bovine miRNA and their corresponding seed sequences retrieved from the TargetScan website and used in “targetscan_60.pl” tool for the detection of miRNA target sites; Table S3. List of the polymorphisms detected within the exon sequences, flanking intron sequences and parts of the 5’-upstream regions of milk protein genes (CSN1S1, CSN2, CSN1S2, CSN3, LALBA, LGB) in Beninese indigenous cattle breeds; Table S4. Distributions of variant types (according to their positions and consequence) de-tected within each of the six milk protein genes (CSN1S1, CSN2, CSN1S2, CSN3, LALBA, LGB) and total in Beninese indigenous cattle breeds; Table S5. Functional consequence of the frameshift insertion of nucleotide A at BTA11:103257980 on the translational reading frame and on mature protein.Item Supplemental Material for "Wealth for Health? Affordability of a Healthy and Sustainable Diet - A Food Basket Study"(2024-03) Arendt, SvenjaThis dataset contains Supplemental Material 4 for the publication "Wealth for Health? Affordability of a Healthy and Sustainable Diet – A Food Basket Study" The file provides: - Assessed prices for conventional and organic food products in REWE with product name, price and quantity - Assessed prices for conventional and organic food products in ALDI Süd with product name, price and quantity - Amounts in g/day for the heavy meat consumption diet - Quantities per day and month for the girl in the reference family - Quantities per day and month for the boy in the reference family - Quantities per day and month for the mother in the reference family - Quantities per day and month for the father in the reference family - Amounts in g/day for the moderate meat consumption diet - Quantities per day and month for the girl in the reference family - Quantities per day and month for the boy in the reference family - Quantities per day and month for the mother in the reference family - Quantities per day and month for the father in the reference family - Amounts in g/day for the light meat consumption diet - Quantities per day and month for the girl in the reference family - Quantities per day and month for the boy in the reference family - Quantities per day and month for the mother in the reference family - Quantities per day and month for the father in the reference family - Calculation of total cost and affordabiliy - Calculation of cost shares - Calculation of the share of vegetables and fruits according to the BMEL - Creation of graphs - References