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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 – supplementary data (for spatial yield productivity data base for the ten most cultivated crops in Germany from 1989 to 2020) - version 1.0(2022-10-06) Ellsäßer, FlorianThis data set is a supplementary data set containing the phenological and agricultural management data expressed using the BBCH-scale and days of the year (DOY) as supplement for the data set: http://dx.doi.org/10.22029/jlupub-7177. It contains the sowing and harvesting dates and other key events of the crop phenology cycle for the following crops: winter wheat, rye and winter mixed crops, spring and winter barley, oat, silage maize, oilseed rape and sugar beet for the years 1989 to 2020. However, not all years or locations are available. The data set is based on the phenological observations of crops from sowing to harvest v006 by the DWD (DWD Climate Data Center (CDC): Phenological observations of crops from sowing to harvest (annual reporters, historical), Version v006, 2019)downloaded from: ftp://opendata.dwd.de/climate_environment/CDC/observations_germany/phenology/annual_reporters/crops ,the station data provided by DWD (DWD Climate Data Center (CDC): Eintrittsdaten verschiedener Entwicklungsstadien landwirtschaftlicherKulturpflanzen von der Bestellung bis zur Ernte (Jahresmelder, historisch), Version v006, 2019) downloaded from: https://opendata.dwd.de/climate_environment/CDC/observations_germany/phenology/annual_reporters/vine/historical/PH_Beschreibung_Phaenologie_Stationen_Jahresmelder.txt , the phase description keys also provided by DWD (DWD Climate Data Center (CDC): Eintrittsdaten verschiedener Entwicklungsstadien landwirtschaftlicherKulturpflanzen von der Bestellung bis zur Ernte (Jahresmelder, historisch), Version v006, 2019) and downloaded from: https://www.dwd.de/DE/klimaumwelt/klimaueberwachung/phaenologie/daten_deutschland/jahresmelder/jahresmelder_beobachtungsprogramm.pdf?__blob=publicationFile&v=5 as well as the Natural Area Data following Meynen and Schmithüsen as provided by Bundesamt für Naturschutz (Bundesamt für Naturschutz, Fachgebiet I 1.2, Naturschutzinformation, Geoinformation, Open Data) and downloaded from: https://ffalle.bfn.de:443/ssf/s/readFile/share/1040/-305838928916869590/publicLink/Naturr%C3%A4umliche%20Gliederung.zip . The county borders were kindly provided by the Bundesamt für Geographie und Geodäsie © GeoBasis-DE / BKG (2022) - Datenlizenz Deutschland Version 2.0. The data are available in the digital formats .csv and .netCDF. The .csv file contains the following column headers crop_type = the crop type, year = the harvest year, bbch = the BBCH value on the BBCH scale ( https://en.wikipedia.org/wiki/BBCH-scale ), reg_number = county or city identification number and doy = day of the year (DOI). This file can be opened e.g. with MS Excel or Libre office or any other software to open .csv files. The files use three coordinates: longitude, latitude and year. The data variables are built as follows: or_bbch_99 where the first two letters are an abbreviation for the crop and the last two letters define the BBCH value.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 Preparation and spectroscopic identification of the cyclic carbon dioxide dimer 1,2-dioxetanedione(2023-06-19) Gerbig, DennisMatrix isolation spectra as ASCII data point files in simple xy-format. Files can be opened and plotted by any combination of text editor and plotting program, respectively. Spectra were recorded on a Bruker Invenio R infrared spectrometer in conjunction with the Bruker OPUS 8.5 SP1 software.Item Raw Microsatellite Genotyping Data for "Spatial and temporal genetic variation of Drosophila suzukii in Germany"(2021-03-12) Petermann, SarahThe data presented here is raw data generated for different analyses in our study titled "Spatial and temporal genetic variation of Drosophila suzukii in Germany", published in the Journal of Pest Science in March 2021 (https://doi.org/10.1007/s10340-021-01356-5). The data is further used in a doctoral thesis, which is still in progress (effective September 2021). The zip file contains three Excel files. The first one is named “001_Fragment_Length_Analysis_raw_data” and it was generated by using the microsatellite external plugin of Geneious Prime 2019.2. Microsatellite genotyping data were exported for further analysis in GenAlex software v.6.41 (Peakall and Smouse, 2012), implemented in Excel. The second file is named “002_Null_allele_detection” and contains the data generated with FreeNA (Chapuis and Estoup, 2007). The third file is named “003_Bottleneck_result” and contains the results obtained with Bottleneck v.1.2.2 (Piry et al., 1999).Item Supplemental Tables for "Associations between days in the close-up group and milk production, transition cow diseases, reproductive performance, culling and behavior, around calving of Holstein dairy cows"(2023-05-09) Venjakob, Peter LennartThe objective of the study was to evaluate the association between days in the close-up group (DINCU) and milk production, early lactation diseases, reproductive performance, culling risk and behaviour around calving. In this investigation 14 different statistical models were calculated to evaluate the association between days in the close-up group (DINCU) and 305-d milk projection of nulliparous (1) and parous (2) cows; between DINCU and the risk for clinical hypocalcemia of parous cows (3); between DINCU and the risk for hyperketonemia of nulliparous (4) and parous (5) cows; between DINCU and the risk for displaced abomasum of parous cows (6); between DINCU and the risk for retained placenta of nulliparous (7) and parous (8) cows, between DINCU and the risk for acute puerperal metritis of nulliparous (9) and parous cows (10); between DINCU and the risk for mastitis in parous cows (11); between DINCU and first service pregnancy risk (12); and between DINCU and culling risk of nulliparous (13) and parous (14) cows. Supplemental Table S1 displays the final linear mixed model evaluating the association between days in the close-up group and predicted 305-d milk projection based on 2nd test day milk production of nulliparous cows (n = 7,985). Supplemental Table S2 displays the final linear mixed model evaluating the association between days in the close-up group and predicted 305-d milk projection based on 2nd test day milk production of parous cows (n = 17,483). Supplemental Table S3 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and predicted risk of clinical hypocalcemia of parous cows (n = 19,641). Supplemental Table S4 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and the predicted risk of hyperketonemia (ß-hydroxybutyrate ≥ 1.2 mmol/L using a cow-side blood BHB test) of nulliparous cows (n = 8,798). Supplemental Table S5 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and the predicted risk of hyperketonemia (ß-hydroxybutyrate ≥ 1.2 mmol/L using a cow-side blood BHB test) of parous cows (n = 19,641). Supplemental Table S6 displays the final multivariable logistic regression model evaluating the association between days in the close-up group (DINCU) and the predicted risk for left displaced abomasum using least square estimates (mean ± SEM) from the generalized linear mixed model of parous cows (n = 19,641). Supplemental Table S7 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and the predicted risk of retained placenta of nulliparous cows (n = 8,798). Supplemental Table S8 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and the predicted risk of retained placenta of parous cows (n = 19,641). Supplemental Table S9 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and the predicted risk of acute puerperal metritis of nulliparous cows (n = 8,798). Supplemental Table S10 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and the predicted risk of acute puerperal metritis of parous cows (n = 19,641). Supplemental Table S11 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and the predicted risk for mastitis within 30 DIM of parous cows (n = 19,641). Supplemental Table S12 displays the final multivariable logistic regression model evaluating the association between days in the close-up group and first service pregnancy risk of parous cows (n = 19,641). Supplemental Table S13 displays the final Cox proportional hazards model evaluating the association between days in the close-up group and predicted probability of culling until 300 DIM in nulliparous cows (n = 8,798). Supplemental Table S14 displays the final Cox proportional hazards model evaluating the association between days in the close-up group and predicted probability of culling until 300 DIM in parous cows (n = 19,641).Item X-ray diffraction data for "Formation of Nucleophilic Allylboranes from Molecular Hydrogen and Allenes Catalyzed by a Pyridonate Borane that Displays Frustrated Lewis Pair Reactivity"(2020-09-14) Becker, Jonathan; Hasenbeck, Max; Gellrich, UrsRaw X-ray diffraction images for the publication M. Hasenbeck, S. Ahles, A. Averdunk, J. Becker, U. Gellrich, Angew. Chem. Int. Ed. 2020, 59, 23885 "Formation of Nucleophilic Allylboranes from Molecular Hydrogen and Allenes Catalyzed by a Pyridonate Borane that Displays Frustrated Lewis Pair Reactivity" https://doi.org/10.1002/anie.202011790. File format is the Bruker sfrm frame format. The data can be processed with the SAINT program by BRUKER AXS or using alternative and/or open source tools as XDS ( http://xds.mpimf-heidelberg.mpg.de/ ) together with sfrmtools ( https://homepage.univie.ac.at/tim.gruene/research/programs/conv/sfrmtools/ ) or FabIO ( Knudsen, E. B., Sorensen, H. O., Wright, J. P., Goret, G. & Kieffer, J. (2013). J. Appl. Cryst. 46, 537-539. ). FabIO provides the following information about the file format: "The bruker format uses 80 char lines in key : value format. In the first 512*5 bytes of the header there should be a HDRBLKS key, whose value denotes how many 512 byte blocks are in the total header. The header is always n*5*512 bytes, otherwise it wont contain whole key: value pairs. Data is stored in three blocks: 1. data (uint8) 2. overflow (uint32) 3. underflow (int32). The blocks are zero padded to a multiple of 16 bits.