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724 results for “Germans”
German Index of Socioeconomic Deprivation (GISD)
<p>Der German Index of Socioeconomic Deprivation (GISD) ist ein am Robert Koch-Institut entwickelter Index zur Erfassung regionaler sozioökonomischer Benachteiligung. Er wird verwendet, um regionale sozioökonomische Ungleichheiten in der Gesundheit sichtbar zu machen und Ansatzpunkte zur Erklärung regionaler Unterschiede in der Gesundheit aufzeigen zu können. Mit dem GISD wird es möglich, sozioökonomische Unterschiede in den Gesundheitschancen, Krankheits- und Sterberisiken in Deutschland auch dann zu untersuchen, wenn die betreffenden Gesundheitsdaten auf individueller Ebene keine Information zum sozioökonomischen Status enthalten. Für die Generierung des GISD werden Information der Bildungs-, Beschäftigungs- und Einkommenssituation in Kreisen und Gemeinden aus der Datenbank INKAR verwendet. Er wird auf der Ebene der Gemeinden generiert und wird für die Raumbezüge Gemeinden, Gemeindeverbände, Stadt- und Landkreise, Raumordnungsregionen, NUTS-2 und Postleitzahlbereiche bevölkerungsgewichtet aggregiert bereitgestellt. Die Gewichtung der Indikatoren wird über Hauptkomponentenanalysen innerhalb der Teildimensionen vorgenommen. Die aktuell verfügbaren Daten beziehen sich auf den Gebietsstand 31.12.2021 und enthalten Werte von 1998 bis 2021.</p>
German image spectral library of urban surface materials
<p>The German image spectral library consists of 5102 labelled image spectra of urban surface materials covering the spectral wavelength range between 455 nm and 2449 nm. The spectra have been extracted from high resolution imaging spectroscopy data (HyMap) acquired over the German cities of Dresden (18/05/1999, 01/08/2000, 20/07/2003), Potsdam (18/05/1999) and Munich (17/06/2007, 25/06/2007). This image data package ensures the collection of the most typical urban surface materials including their variations due to different illumination, alteration, observation conditions, regional specifications and data processing characteristics.</p> <p>The collection was done in two main steps: (1) manual collection of spectrally pure urban surface material pixels from the Dresden and Potsdam data sets including additional information, such as the results of field investigations, a field spectral library and color infrared aerial imagery (Heiden et al., 2007 ) and subsequent reduction for redundant pixel spectra; (2) spectral dissimilarity analysis to include and label meaningful unknow spectra from the Munich data set (Jilge et al. 2017 ). </p> <p>The image spectra are labelled based on three sets of spectra labels: one for EAGLE land cover (EAGLE_LCC, consult the “Explanatory Documentation of the EAGLE Concept” from the Copernicus Land website) , one for generalized material groupings (GENLIB_LCH_BuC_MG) and one for more detailed artificial material type (GENLIB_LCH_BuC_AMT).</p> <p>While every effort was made to ensure accurate information, this data set is presented "as is" without warranties of any kind. The authors accept no liability or responsibility to any person as a consequence of any reliance upon the data presented here. The user assumes all responsibility and risk for the use of this data.</p>
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017
<p>The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.</p> <p>Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>.</p> <p>Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).</p> <p>DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`.</p> <p>These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`).</p> <p>For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01.<br> For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.</p> <p>Raw registry data for versions 1 and 2 is available in `raw-registries.zip`.</p> <p>Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.</p> <p>Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`.<br> Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`.</p> <p>Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.</p> <p>All dates are provided as `yyyy-mm-dd`.</p> <p>Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`.</p> <p>Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>.</p> <p><strong>References:</strong></p> <p>Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a></p> <p>Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a><br> </p>
From the collective to the individual: transformation processes at the transition from the 4th to the 3rd millennium BC in the German low mountain zone
<p>Data collected by Clara Drummer, Kiel 2022.</p> <p>Clara Drummer, Vom Kollektiv zum Individuum: Transformationsprozesse am Übergang vom 4. zum 3. Jahrtausend v. Chr. in der Deutschen Mittelgebirgszone. Scales of transformation Bd. 13 (Leiden 2022).https://d-nb.info/1241580332</p> <p>CRC 1266: "Scales of Transformation - Human-Environmental Interaction in Prehistoric and Archaic Societies."<br> "Regional and Local Patterns of 3rd Millennium Transformations of Social and Economic Practic-es in the Central German Mountain Range (D2)" Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 128675135 https://gepris.dfg.de/gepris/projekt/316739879</p> <p>Data for the analyses of the decisive transformation in the Hessian-Westphalian area from the Wartberg society to the Corded Ware groups. The work discusses above all the social aspects of the change. This includes, on the one hand, a more detailed analysis of burial rituals and, on the other hand, the integration of, for example, available aDNA results into the overall analysis.</p>
Virtually Real short film "Sonnenwende" by Timo von Gunten, versions with real and virtual backgrounds, 8min35sec, german, ProRes 1998 × 1080
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These 2 QuickTime files represents both the "real" and "virtual" version of the short Film "Sonnenwende" (2019) by Timo von Gunten. The real version was shot on location using real backgrounds, the virtual version was shot in the studio in front of a green-screen using virtual backgrounds that were added in post production. </p> <p>Running time: 8min35sec. This is the highest quality version available. </p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
Synthesized anthropometric data for the German working-age population
<p>The anthropometric datasets presented here are virtual datasets. The unweighted virtual dataset was generated using a synthesis and subsequent validation algorithm (Ackermann et al., 2023). The underlying original dataset used in the algorithm was collected within a regional epidemiological public health study in northeastern Germany (SHIP, see Völzke et al., 2022). Important details regarding the collection of the anthropometric dataset within SHIP (e.g. sampling strategy, measurement methodology & quality assurance process) are discussed extensively in the study by Bonin et al. (2022).</p><p>To approximate nationally representative values for the German working-age population, the virtual dataset was weighted with reference data from the first survey wave of the Study on health of adults in Germany (DEGS1, see Scheidt-Nave et al., 2012). Two different algorithms were used for the weighting procedure: (1) iterative proportional fitting (IPF), which is described in more detail in the publication by Bonin et al. (2022), and (2) a nearest neighbor approach (1NN), which is presented in the study by Kumar and Parkinson (2018). Weighting coefficients were calculated for both algorithms and it is left to the practitioner which coefficients are used in practice. Therefore, the weighted virtual dataset has two additional columns containing the calculated weighting coefficients with IPF ("WeightCoef_IPF") or 1NN ("WeightCoef_1NN"). Unfortunately, due to the sparse data basis at the distribution edges of SHIP compared to DEGS1, values underneath the 5th and above the 95th percentile should be considered with caution.</p><p>In addition, the following characteristics describe the weighted and unweighted virtual datasets: According to ISO 15535, values for "BMI" are in [kg/m2], values for "Body mass" are in [kg], and values for all other measures are in [mm]. Anthropometric measures correspond to measures defined in ISO 7250-1. Offset values were calculated for seven anthropometric measures because there were systematic differences in the measurement methodology between SHIP and ISO 7250-1 regarding the definition of two bony landmarks: the acromion and the olecranon. Since these seven measures rely on one of these bony landmarks, and it was not possible to modify the SHIP methodology regarding landmark definitions, offsets had to be calculated to obtain ISO-compliant values. In the presented datasets, two columns exist for these seven measures. One column contains the measured values with the landmarking definitions from SHIP, and the other column (marked with the suffix "_offs") contains the calculated ISO-compliant values (for more information concerning the offset values see Bonin et al., 2022). The sample size is N = 5000 for the male and female subsets. The original SHIP dataset has a sample size of N = 1152 (women) and N = 1161 (men). Due to this discrepancy between the original SHIP dataset and the virtual datasets, users may get a false sense of comfort when using the virtual data, which should be mentioned at this point. In order to get the best possible representation of the original dataset, a virtual sample size of N = 5000 is advantageous and has been confirmed in pre-tests with varying sample sizes, but it must be kept in mind that the statistical properties of the virtual data are based on an original dataset with a much smaller sample size.</p>
H2020 Platone German Demonstrator Use Case 1 Asset and Topolgy Data
<p>This dataset asset and topology data of the field test setup. The dataset gives details about:</p> <p>- Low Voltage (LV) network</p> <p>- Tranformer located in the secondary substation</p> <p>- Community Battery Energy Storage System (CBES) connected to the LV-busbar in the 2nd. Substation</p> <p>- PV and number of households located in the energy community</p> <p>- PV installed generation power of the community</p> <p>- Domestic Storages and Inverter</p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Market Data
<p>This dataset contains settings of the Local Energy Management System, that have been set via a Graphical User Interface (GUI). The dataset contain follwowing data:</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Timestamp</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Use Case</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_ID</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Option</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Priority</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Submission_Time</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_Start_Date</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_End_Date</p> <p> </p> <p> </p>
Polifonia Corpus - Books Module Metadata - German Language (Full)
<p>We release the Metadata of the Books module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
COVID-19 German Student Well-being Study (C19 GSWS)
<p><strong>COVID-19 German Student Well-being Study (C19 GSWS)</strong></p> <p>Following the COVID-19 International Student Well-being Study (C19 ISWS; survey phase: May 13<sup>th</sup>, 2020 to May 29<sup>th</sup>, 2020 in 27 European countries coordinated by the University of Antwerp), well-being of university students during the COVID-19 pandemic continued to be the focus of the collaborative COVID-19 German Student Well-being Study (C19 GSWS) which was conducted at five universities in Germany.</p> <p> </p> <p>The five German universities taking part in the study were the Charité – Universitätsmedizin Berlin (PI: Prof. Christiane Stock), the University of Bremen (PI: Dr. Heide Busse), Heinrich-Heine-University Duesseldorf (PI: Prof. Claudia Pischke), University of Siegen (PI: Prof. Claus Wendt) and Martin-Luther University Halle-Wittenberg (PI: Prof. Rafael Mikolajczyk).</p> <p> </p> <p>The following research questions were addressed:</p> <p> </p> <p>- How did university students' (physical and socioeconomic) living conditions and academic workload change during the COVID-19 pandemic?</p> <p>- How were living and study conditions associated with mental health outcomes among university students during the COVID-19 pandemic?</p> <p>- How were living conditions and academic workload associated with health behaviours (e.g., substance use) among university students during the pandemic?</p> <p>- Which attitudes towards COVID-19 vaccination and determinants of vaccination behavior were prevalent r among university students?</p> <p> </p> <p>To answer the research questions, an online survey among university students was conducted at all participating universities from October 27<sup>th</sup>, 2021 to November 14<sup>th</sup>, 2021. The resulting data allow for a description of living conditions, as well as well-being, during the ongoing COVID-19 pandemic in German university student populations.</p> <p> </p> <p>Information about C19 ISWS on Zenodo:</p> <p>https://zenodo.org/communities/c19-isws/?page=1&size=20</p>
Bootstrapped Lexicon of German Verbal Polarity Shifters
<p>We provide a bootstrapped lexicon of German verbal polarity shifters. Our lexicon covers 2595 verbs of GermaNet. Polarity shifter labels are given for each word lemma. All labels were assigned by an expert annotator who is a native speaker of German.</p> <p><strong>Data</strong></p> <p>The data consists of two lists of GermaNet verbs annotated for whether they cause shifting:</p> <ol> <li><code>verbal_shifters.gold_standard.txt</code>: The initial gold standard (§3) of 2000 randomly sampled verbs.</li> <li><code>verbal_shifters.bootstrapping.txt</code>: The bootstrapped 595 verbs (§5.3) that were labelled as shifters by our best classifier and then manually annotated.</li> </ol> <p><strong>Format</strong></p> <p>Each line contains a verb and its label, separate by a whitespace.</p> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Marc Schulder, Michael Wiegand, Josef Ruppenhofer (2018). <strong>"Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German"</strong>. <em>Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018)</em>. Santa Fe, New Mexico, USA, August 20 - August 26, 2018. <a href="https://doi.org/10.5281/zenodo.3365694">DOI: 10.5281/zenodo.3365694</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p>
Stories on Open Educational Practices in German Higher Education
<p><strong>Stories on Open Educational Practices in German Higher Education by Sigrid Fahrer, </strong><a href="#_oewao57q7xt8"><strong>Tamara Heck</strong></a><strong>, </strong><a href="#_fronuh2cauex"><strong>Ronny Röwert</strong></a><strong>, </strong><a href="#_oshco7u6xx6k"><strong>Naomi Truan</strong></a></p> <p><em>citation suggestion: </em>Fahrer, S., Heck, T., Röwert, R., Truan, N. (2022). Stories on Open Educational Practices in German Higher Education. Data set on autoethnographic reflections. <a href="https://doi.org/10.5281/zenodo.7326390">https://doi.org/10.5281/zenodo.7326390</a></p> <p>The stories are part of the autoethnographic reflections of the four practitioners. They are based on the following research papers:</p> <ul> <li>Cronin, C. (2017). Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education. The International Review of Research in Open and Distributed Learning, 18(5). <a href="https://doi.org/10.19173/irrodl.v18i5.3096">https://doi.org/10.19173/irrodl.v18i5.3096</a></li> <li>Hegarty, B. (2015). Attributes of Open Pedagogy: A Model for Using Open Educational Resources. Educational Technology, 55(4), 3–13. <a href="https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf">https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf</a></li> <li>Mayrberger, K. (2020). Open Educational Practices (OEP) in Higher Education. In M. A. Peters (Ed.), Springer eBook Collection. Encyclopedia of Educational Philosophy and Theory (pp. 1–7). Springer. <a href="https://doi.org/10.1007/978-981-287-532-7_710-1">https://doi.org/10.1007/978-981-287-532-7_710-1</a>.</li> <li>Wiley, D., & Hilton III, J. L. (2018). Defining OER-Enabled Pedagogy. The International Review of Research in Open and Distributed Learning, 19(4). <a href="https://doi.org/10.19173/irrodl.v19i4.3601">https://doi.org/10.19173/irrodl.v19i4.3601</a></li> </ul>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
EPOCHAL (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms): Daily questionnaire (English and German)
<p>This questionnaire was developed for the EPOCHAL study (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms). The study was sponsored and led by Swiss TPH in Basel, Switzerland and approved by the local ethics committee (Ethikkomission Nordwest- und Zentralschweiz EKNZ, project ID 2021-00151). Written informed consent was obtained from every participant prior to study inclusion. </p> <p>This is the "daily questionnaire" which was administered 10 times for each participant on different days during the pollen season. It includes questions about the following topics:<br> 1) Overall health status<br> 2) Allergic symptoms: nose, eyes, lungs<br> 3) Sleep, mood and quality of life<br> 4) Medication use<br> 5) Time spent outdoors<br> 6) Daily covariate information: coffee and alcohol intake, eating, smoking, vigorous exercise<br> 7) Blood pressure<br> 8) Comments</p> <p>The questionnaire is also available in German under the same DOI.</p> <p>Please note that this questionnaire was administered electronically in a browser, and contains:</p> <ul> <li>Form logic, which determines the relevance of some questions based on previous answers. Affected questions are typically shown in grey color.</li> </ul>
EPOCHAL (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms): Nurse home visit form (English and German)
<p>This questionnaire was developed for the EPOCHAL study (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms). The study was sponsored and led by Swiss TPH in Basel, Switzerland and approved by the local ethics committee (Ethikkomission Nordwest- und Zentralschweiz EKNZ, project ID 2021-00151). Written informed consent was obtained from every participant prior to study inclusion. </p> <p>This is the "nurse home visit form" which was administered 6 times for each participant during weekly home visits by our study nurses during the pollen season. It includes questions about the following topics:<br> 1) Potential for Covid-19 infection, changes in vaccination status<br> 2) Overall health status<br> 3) Allergic symptoms: nose, eyes, lungs<br> 4) Sleep, mood and quality of life<br> 5) Medication use<br> 6) Time spent outdoors<br> 7) Daily covariate information: coffee and alcohol intake, eating, smoking, vigorous exercise<br> 8) Blood pressure measurements<br> 9) Heart rate variability recording<br> 10) Exhaled nitric oxide measurements<br> 11) Pulmonary function testing (spirometry)<br> 12) Comments</p> <p>The questionnaire is also available in German under the same DOI.</p> <p>Please note that this questionnaire was administered electronically on a tablet, and contains:</p> <ul> <li>Form logic, which determines the relevance of some questions based on previous answers. Affected questions are typically shown in grey color.</li> <li>Instructions (in bold blue font) to the participant/study nurse to guide the process of data collection (e.g. “please hand over the tablet to the participant/nurse”).</li> <li>Warnings (in large red font) and directions (in large grey font) to warn nurses against performance of spirometry measurements if contraindications were present. For example, when the nurse entered high blood pressure in Topic #8, or when recent surgery was indicated in Topic #11. Warnings and directions also flag incidental findings (e.g., high blood pressure ≥160 mmHg (systolic) or ≥100 mmHg (diastolic) requiring urgent action.</li> </ul>
German ZIP codes, Kreisschlüssel (Administration Unit), Kreis, Inhabitant per ZIP, City Names, responsible Arbeitsagentur (Social Agency
<p>This dataset from 2019 contains all German ZIP codes, city names associated with it, Kreisschlüssel (Administration Unit ID) Kreis, (Administration Unit), Bundesland (State), Inhabitants, responsible Arbeitsagentur (Social Agency). Note that especially the PLZ ZIP Codes and the responsible Arbeitsagentur change from time to time due to administrative reasons.</p>
Manually Annotated Instances of Ich ('I') from the German KoLas Corpus
<p>Dataset used in Andresen/Knorr (2020). The dataset comprises 360 instances of <em>ich</em> ('I') taken from the German learner corpus KoLaS (Andresen/Knorr 2017, see <a href="http://hdl.handle.net/11022/0000-0001-B732-8">http://hdl.handle.net/11022/0000-0001-B732-8</a> for full corpus access) and manually annotated with categories taken from Steinhoff (2007).</p> <p>Column descriptions:</p> <ul> <li>document: name of the document by which it can be found in the KoLaS corpus</li> <li>code_annotator1 - code_annotator4: Annotations by four annotators. Possible values: Verfasser-<em>Ich</em> (author <em>I</em>), Forscher-<em>Ich</em> (researcher <em>I</em>), Erzähler-<em>Ich</em> (narrator <em>I</em>)</li> <li>max_agreement_freq: Highest number of anntators that agreed on one label</li> <li>max_agreement_label: Label on which the highest number of annotators agreed</li> <li>context_before: 150 characters of context before the match</li> <li>match: the match itself (either <em>ich</em> or <em>Ich</em>)</li> <li>context_after: 150 characters of context after the match</li> </ul> <p><strong>References</strong></p> <p>Andresen M, Knorr D. KoLaS – Ein Lernendenkorpus in der Schreibberatungsausbildung einsetzen. <em>Zeitschrift Schreiben</em>. Published online July 5, 2017:10-17.</p> <p>Andresen M, Knorr D. Exploring the Use of the Pronoun I in German Academic Texts with Machine Learning. In: Burghardt M, Müller-Birn C, eds. <em>Methoden und Anwendungen der Computational Humanities</em>. Lecture Notes in Informatics (LNI). Gesellschaft für Informatik; 2020.</p> <p>Steinhoff T. Zum ich-Gebrauch in Wissenschaftstexten. <em>Zeitschrift für germanistische Linguistik</em>. 2007;35(1-2):1–26.</p>
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
Virtually Real short film "LUX" by Wendy Pillonel, versions with real and virtual backgrounds, 7min18sec, swiss-german, ProRes 1998 × 1080
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These 2 QuickTime files represents both the "real" and "virtual" version of the short Film "Lux" by Wendy Pillonel. The real version was shot on location using real backgrounds, the virtual version was shot in the studio in front of a green-screen using virtual backgrounds that were added in post production. </p> <p>Running time: 7min18sec. This is the highest quality version available. </p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.