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Item Bibliothek aus Sicht der Nutzerinnen und Nutzer(2020-11) Dippelhofer, SebastianErläuterungen zum Datensatz finden sich in der Publikation: Dippelhofer, Sebastian (2014). Das Bibliothekssystem der Universität Gießen: Erwartungen und Wünsche ihrer Besucher/innen; eine quantitativ-empirische Bestandsaufnahme (urn:nbn:de:hebis:26-opus-109749). Es handelt sich um eine csv.-Datei, die bspw. in SPSS einlesbar ist.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 Data and Code for "Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ∆∆G‡ versus enantiomeric excess"(2023-10-13) Ruth, Marcel; Institute of Organic Chemistry, Justus Liebig University; Institute of Chemistry, TU BerlinThis repository contains all datasets that were used to evaluate the difference between ee and ΔΔG‡ modeling in enantioselective organocatalytic reactions. The scripts and notebooks used are also included to elucidate our modeling process. All descriptor and fingerprint-based models are included in "descriptorbased_parametric_models-repeated.ipynb". The evaluations and hyperparameter optimizations by our graph neural network are split into several small scripts and helper functions (basically all Python files). Article abstract: The modeling of catalytic, enantioselective reactions is pivotal for chiral drug development, green chemistry, and industrial applications. While ligand-based and quantitative structure-activity relationships have a long history, the limitations of these methods, including inadequate representation of reaction dynamics and physical constraints, have become increasingly evident. With the rise of machine learning due to increased computational power, the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict reactions. Here we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using instead physically grounded differences Gibbs free activation energies (ΔΔG‡). We outline key benefits, such as enhanced modeling performance using ΔΔG‡, escaping physical limitations, addressing temperature effects, managing non-linear error propagation, adjusting for data distributions, and how to deal with unphysical predictions. For this endeavor, we gathered ten datasets from the literature covering very different reaction types, e.g., hydrogenation, Suzuki-, and Heck-reactions for 2761 data points. We evaluated fingerprint, descriptor, and graph neural network based models. Our results highlight the distinction in performance among varying model complexities and emphasize the importance of choosing suitable metrics for accurate and robust chemical modeling.Item Data and Code for "Designing the Next Better Catalyst Utilizing Machine Learning with a Key-Intermediate Graph: Differentiating a Methyl from an Ethyl Group"(2023-11-15) Pereira, Oliver; Ruth, Marcel# Dataset and Scripts Overview ## General Overview This dataset includes a series of Python scripts and Jupyter notebooks that are primarily focused on the analysis, modeling, and visualization of chemical data. The scripts encompass various aspects of data preprocessing, including graph-based transformations, model definitions for Graph Neural Networks (GNNs) and Feedforward Neural Networks (FFNNs), training utilities like early stopping, and comprehensive workflows for training, evaluating, and visualizing model performance. ## File Descriptions ### Python Scripts 1. **CV_cat-subs.py**: Script for training and evaluating a machine learning model using cross-validation. 2. **LOOCV_CV.py**: Similar to CV_cat-subs.py but employs Leave-One-Out Cross-Validation for model evaluation. 3. **config.py**: Configuration file containing optimal parameters for the models. 4. **eval_mol_representation.py**: Evaluates molecular representations using various machine learning models. 5. **hpo_cbs.py**: Hyperparameter optimization script for tuning Graph Neural Network models. 6. **models.py**: Defines neural network models, including Graph Neural Networks. 7. **preprocessing_data_new.py**: Preprocesses the dataset, preparing it for analysis and modeling. 8. **preprocessing_graph_new.py**: Prepares graph-based data representations, essential for GNNs. 9. **screening_models.py**: Provides additional model definitions for various machine learning tasks. 10. **training.py**: Contains utilities for model training, including an early stopping mechanism. ### Jupyter Notebooks 1. **CV_plots.ipynb**: Focuses on data visualization, particularly for cross-validation results. 2. **main.ipynb**: A comprehensive notebook covering data preprocessing, model training, evaluation, and visualization. ## Dataset Structure: "CBS_10-04-2023.csv" The dataset "CBS_10-04-2023.csv" is a key component of this collection. It includes various chemical properties and molecular structures relevant to the domain of cheminformatics. The structure of the dataset comprises columns that detail different chemical entities (catalyst, substrate, product), reaction conditions, and results. This dataset is used extensively throughout the scripts and notebooks for preprocessing, analysis, and model training. Understanding its structure is crucial for interpreting the results and for any further modification or analysis. ## Usage Instructions To use these scripts and notebooks, ensure you have Python installed along with necessary libraries like Pandas, NumPy, Torch, Torch Geometric, and RDKit. Each script can be executed independently, provided the required data files are available in the specified paths. The Jupyter notebooks can be run in a sequence for a complete end-to-end workflow. ## Variable and Function Explanations - Variables and functions within the scripts and notebooks are named to reflect their purpose in data processing, modeling, or visualization tasks. Specific domain-related variables, such as those handling chemical properties or molecular structures, are used in accordance with standard practices in cheminformatics. ## Additional Notes - These scripts and notebooks are tailored for chemical data analysis and may require domain-specific understanding for optimal usage and interpretation of results.Item Data and Code for "Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies"(2023-02-02) Ruth, Marcel; Institute of Organic ChemistryThe datasets, models, and scripts were created to achieve an accurate prediction of the increment of single-point energies between density functional theory (DFT) and wavefunction-based methods, which led to our submitted article: 'A Machine Learning Approach for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies'. We used the ORCA quantum chemical package to compute the geometries of each species at the B3LYP-D3(BJ)/cc-pVTZ level of theory. The optimized structure was subsequently employed for single-point (SP) computations at the DLPNO-CCSD(T)/cc-pVTZ and CCSD(T)/cc-pVTZ levels of theory. All data were extracted from the calculations and compiled in the provided .csv files. With the datasets and prediction scripts, it is possible to forecast the differences in single-point (SP) energies between the B3LYP-D3(BJ)/cc-pVTZ and DLPNO-CCSD(T)/cc-pVTZ (for monomers and dimers) levels of theory, as well as to the CCSD(T)/cc-pVTZ level of theory for monomers. The datasets can be opened and read with any text editor. The Pytorch models can be loaded and manipulated as usual (https://pytorch.org/tutorials/beginner/saving_loading_models.html). The prediction can be made by installing a suitable Python environment and setting the code line: test_database = f'TestDatabase_{mode}.csv' to the desired dataset for prediction. The format and column names of the file should match the uploaded dataset files. Once the line is modified, a prediction can be generated using the following command, for example, “python gen_predictions_CCSDt.py”.Item Data for "Current extinction rate in European freshwater gastropods greatly exceeds that of the late Cretaceous mass extinction"(2021-03-24) Neubauer, Thomas A.The dataset contains the primary data underyling the diversification analyses in Neubauer et al. (2021), as well as the results of the iucn_sim analyses on current species extinction rates.Item Data for "Direct genetic effects, maternal genetic effects and maternal genetic sensitivity on prenatal heat stress for calf diseases and corresponding genomic loci in German Holsteins"(2022-04-20) Yin, TongThe aim of this study was to infer the effects of heat stress (HS) during late gestation of dams on direct and maternal genetic parameters for pneumonia (PNEU, 112,563 observations), diarrhea (DIAR, 176,904 observations) and omphalitis (OMPH, 176,872 observations) in Holstein calves kept in large-scale co-operator herds. The genotype dataset included 41,135 SNPs from 19,247 male and female cattle. Temperature-humidity indices (THI) during the last eight weeks of pregnancy were calculated, using the climate data from the nearest public weather station for each herd. Heat load effects were considered for average weekly THI larger than 60, and THI lower than 60 were treated equally. Phenotypically, regression coefficients of calf diseases on prenatal THI during the last eight weeks of gestation were estimated in eight consecutive runs. Strongest detrimental effects on PNEU and DIAR due to prenatal HS were identified for the last week of pregnancy (WK1). Thus, only WK1 was considered in ongoing genetic analyses. In an advanced model considering prenatal HS, random regression coefficients on THI in WK1 nested within maternal genetic effects (maternal slope effects for heat load) were considered to infer maternal sensitivity in response to prenatal THI alterations. Direct heritabilities from the advanced model ranged from 0.10 (THI 60) to 0.08 (THI 74) for PNEU, and were close to 0.16 for DIAR. Maternal heritabilities for PNEU increased from 0.03 to 0.10 along the THI gradient. For DIAR, the maternal heritability was largest (0.07) at the minimum THI (THI = 60), and decreased to 0.05 at THI 74. Genetic correlations smaller than 0.80 for PNEU and DIAR recorded at THI 60 with corresponding diseases at THI 74 indicate genotype by climate interactions for maternal genetic effects. Genome-wide associations studies (GWAS) were performed using de-regressed proofs of genotyped sires for direct genetic, maternal genetic and maternal slope effects. 30 suggestive and 2 significant SNPs were identified from GWAS. 43 genes located close to the suggestive SNPs (±100 kb) were annotated as potential candidate genes. Three biological processes were inferred on the basis of the identified potential candidate genes, addressing the negative regulation of the viral life cycle, innate immune response and protein ubiquitination. Hence, genetics of prenatal heat stress mechanisms are associated with immune physiology and disease resistance mechanisms.Item Data for "Diversity, biogeography, and evolution of European freshwater gastropods through time: a voyage across scales"(2023-01-31) Neubauer, Thomas A.The dataset contains the taxonomic, geographic, and stratigraphic data on freshwater mollusks used in the habilitation thesis of T. A. Neubauer at JLU (2023). The dataset is divided into four parts: 1. species occurrence data for fossil European and North American freshwater gastropods from the Triassic to the Pleistocene. This includes information on geography, taxonomy/systematics, stratigraphy, and literature sources. The information was acquired over the past 10 years from the primary literature and constantly updated. Parts were published in previous papers by the author. 2. A species list of all fossil and extant fresh- and brackish-water Mollusca stored in the online database MolluscaBase (https://molluscabase.org/), as of 3 January 2023. 3. Distribution data in the form of geographic polygon names for all taxa in (2), as of 19 December 2022. 4. Fossil age data for all taxa in (2), as of 19 December 2022. Disclaimer: Data from MolluscaBase are used and stored with kind permission. MolluscaBase and its parent, the World Register of Marine Species (WoRMS, https://www.marinespecies.org/), is constantly updated and thus contain the most recent available information on the species and associated distribution and age information.Item Data for "Extinction risk is linked to lifestyle in freshwater gastropods"(2021-08-17) Neubauer, Thomas A.The dataset contains the primary species distribution data for Miocene to extant European freshwater gastropods, as well as the presence-absence data for 130 lakes, used in the analyses in Neubauer & Georgopoulou (2021).Item Data for "Factor Structure and Psychometric Properties of the German Version of the Family Expressiveness Questionnaire – the FEQ-GR"(2021-04-07) Zehtner, Raphaela Isabella; Bäurle, Cosima L.; Walter, Bertram; Stark, Rudolf; Hermann, AndreaRaw data, analysis syntax/code and output for the translation, construction and validation of the Familiy Expressiveness Questionnaire (FEQ-GR).Item Data for "Genome-wide scan for selective sweeps identifies novel loci associated with resistance to mastitis in German Holstein cattle"(2021-10-29) Abbasi Moshaii, Bita; Hossein Moradi, Mohammad; Rahimi-Mianji, Ghodratollah; Ardeshir, Nejati-Javaremi; König, SvenThe data include the materials for the research article "Genome-wide scan for selective sweeps identifies novel loci associated with resistance to mastitis". The aim of this study was to identify the genomic regions associated with mastitis, using the genotypes from the national project of Holstein dairy cattle in Germany. The samples were genotyped with Bovine 50K SNP chip. Based on random residual effects of the mastitis diagnoses, 133 healthy and sick samples from 13290 genotyped dairy cows were selected, respectively. Selection signatures between cows in healthy and sick groups were detected using XP-EHH statistic. Detailed analyses and methods can be found in the article. The data used in this study are presented in the following three files: PhenoMast.txt: Mastitis records of the 13276 genotyped cows. First row includes column names, i.e., cow ID, herd, year-season, mastitis diagnoses. GenoHealth.txt: Genotypes of the 133 healthy cows. First column is cow ID and following 45613 columns are genotypes of the 133 cows (one SNP marker per column). GenoSick.txt: Genotypes from the 133 sick cows. First column is cow ID and following 45613 columns are genotypes of the 133 cows (one SNP marker per column).Item Data for "Patterns of enzyme activities and nutrient availability within biocrusts under increasing aridity in Negev desert"(2022-01-31) Drahorad, Sylvie; Heinze, Stefanie; Department of Geography, Soil Science and Soil Ecology, Ruhr-Universität BochumThis dataset contains the raw data of the open access article "Patterns of enzyme activities and nutrient availability within biocrusts under increasing aridity in Negev desert". All raw data is listed in two excel sheets. Excel sheet one contains the soil related data provided by the first author at the Justus-Liebig-University Gießen, excel sheet two contains the enzyme data provided by the last author at the Ruhr-Universität Bochum. The soil data presents 5 replicates of biocrust samples at two study sites (S1-S5 and N1-N5). Three samples of each site were choosen for enzyme measurements, each provided as four pseudoreplicates. The methods of data production can be found in the original open access published article "Patterns of enzyme activities and nutrient availability within biocrusts under increasing aridity in Negev desert".Item Data for "Short-term paleogeographic reorganizations and climate events shaped diversification of North American freshwater gastropods over deep time"(2022-01-03) Neubauer, Thomas A.The dataset contains species occurrence data for fossil North American freshwater gastropods from the Late Triassic to the Pleistocene. This includes information on geography, taxonomy/systematics, stratigraphy and literature sources.Item Data for "The Impact of Negative Mood on Event-Related Potentials When Viewing Pornographic Pictures"(2021-04-29) Strahler, JanaRaw data, analysis syntax for the manuscript "The impact of negative mood on event-related potentials when viewing pornographic pictures"Item Data for "The µDose-system: determination of environmental dose rates by combined alpha and beta counting – performance tests and practical experiences"(2021-06-13) Kolb, Thomas; Tudyka, Konrad; Kadereit, Annette; Lomax, Johanna; Poreba, Grzegorz; Zander, Anja; Zipf, Lars; Fuchs, MarkusItem Dataset Russian Booktube collected Aug-Oct. 2021(2022-06-28) Hamidy, ElenaThe data set was created in August-October 2021 as part of a research project on Russian Booktube. Research results will be published in the 14th issue of the peer-reviewed Apparatus Journal (2022:14) in Russian. Data on videos were collected with youtube-dl (development status August 2021). Only officially, public-accessed data were collected. Before collection, video channels were selected through field observation and qualitative and quantitative evaluation of relevant tags. The data for each video from these channels was downloaded in three phases: on 08/23/21, 09/14/21, and 10/07/21. The files EHAMIDY_RUSBOOKTUBE_WITH CLASSIFICATION.xlsx (31,5 MB) and EHAMIDY_RUSBOOKTUBE_WITH CLASSIFICATION.csv (93,1 MB) contain basic video information downloaded on 08/23/21, and information about views downloaded on 09/14/21 and 10/07/21. In addition, the files contain a classification of the videos by titles and tags. Two visualizations are published within the dataset: the visualization of the dataset as an interactive table (EHAMIDY_Booktube_dataset_interactive_table.html) and an interactive graph (EHAMIDY_Booktube_genres.html) showing the number of videos by date and category. Because both excel- and csv-files are large and contain Cyrillic letters they may cause errors if you open them in Excel. In Python, e.g. in Jupyter Notebooks they work perfectly.Item Daten zum DFG-Fortsetzungsantrag Open-Access-Publikationskosten 2025-2027(2024) Ruckelshausen, Florian; Arriens, Edda; Dees, Werner; Derichs, Andrea; Freiberg, Michael; Heit, Alexander; Meyer, HelenaDie Daten wurden im Rahmen des DFG-Fortsetzungsantrags Open-Access-Publikationskosten 2025-2027 erhoben. Der Datensatz enthält die im Sinne des Antrags förderfähigen Artikel bzw. Bücher (corresponding author der JLU, Publikationsjahr 2021-2023, DFG-Projektkontext). Eine genaue Beschreibung der Vorgehensweise bei der Erhebung findet sich in der Readme-Datei.Item Daten zur Umfrage zu sozio-politischen Einstellungen von Geflüchteten in Deutschland(2023-09-27) Jordan, ChristopherDie Daten entstanden im Rahmen einer Master-Thesis an der Professur für Methoden der international vergleichenden Sozialforschung mit dem Titel "Wie können soziale Online-Netzwerke zur Survey-Rekrutierung von Geflüchteten genutzt werden? Das Beispiel sozio-politischer Einstellungen von Geflüchteten in Deutschland". Die Erhebung wurde Anfang 2023 im Zeitraum vom 19.01. bis 02.02. durchgeführt. Zur Rekrutierung wurden personalisierte Werbeanzeigen auf Meta-Plattformen (Facebook und Instagram) genutzt, welche Mitglieder der Zielgruppe zu einem auf LimeSurvey erstellten Fragebogen weiterleiteten. Die Zielgruppe waren erwachsene, ukrainische Flüchtlinge, die aufgrund der russischen Invasion aus der Ukraine geflohen sind und sich aktuell in Deutschland aufhalten. Die Teilnehmendenzahl betrug 395 Teilnehmer. Das hier vorliegende Codebook enthält alle Fragen des in LimeSurvey erstellten und verwendeten Fragebogen. Hierbei handelt es sich um eine englische Übersetzung, da bei der Befragung die Fragen auf ukrainisch und russisch angeboten wurden.
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