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724 results for “german”
German Fake News Dataset "GermanFakeNC"
<p>"GermanFakeNC" is a German Fake News Corpus including 490 texts which were retrieved from German alternative online media sources. Every fake statement in the text was verified claim-by-claim by authoritative sources (e.g. from local police authorities, scientific studies, the police press office, etc.). The time interval for most of the news is established from December 2015 to March 2018.</p> <p>Steps to reproduce the data are described in the README file.</p> <p>Please cite:</p> <p> </p> <pre>@inproceedings{TPDL_Vogel19, author = {Inna Vogel and Peter Jiang}, title = {Fake News Detection with the New German Dataset "GermanFakeNC"}, booktitle = {Digital Libraries for Open Knowledge - 23rd International Conference on Theory and Practice of Digital Libraries, {TPDL} 2019, Oslo, Norway, September 9-12, 2019, Proceedings}, pages = {288--295}, year = {2019}, url = {https://doi.org/10.1007/978-3-030-30760-8\_25}, doi = {10.1007/978-3-030-30760-8\_25}, }</pre>
German Innsbruck Corpus (GermInnC) 1800-1950
<p><strong>A digital corpus on variation in German (1800-1950)</strong></p> <p>The <em>German Innsbruck Corpus</em> <em>(GermInnC) 1800-1950</em> is a digitised corpus built after the fashion of the <em>German Manchester Corpus (GerManC) 1650-1800</em> (cf. Scheible et al. 2011; Durrell et al. 2012). Hence, the corpus design of the GermInnC is balanced according to period, region and genre.</p> <p>The GermInnC consists of ca. 840,000 tokens, ca. 120,000 per genre (seven in total: Drama, Humanities, Legal texts, Narrative prose, Newspapers, Scientific texts, Sermons). It is subdivided into three periods, 1800-1850, 1851-1900 und 1901-1950, as well as five regions, North German, West Central German, East Central German, West Upper German (including Switzerland), East Upper German (including Austria).</p> <p>The corpus can be retrieved in a raw version, a lemmatised, fully-annotated version, or an “all data” file (including metadata annotation of file names and periods) for further import and processing. The <em>Stuttgart Tag Set </em>(STTS) and the POS-Tagger <em>TreeTagger </em>was used for linguistic annotation.</p> <p>Two documentation files (word and excel, both included in the download package), provide a more detailed description of the corpus and the digitisation.</p> <p>The corpus may be of interest to all scholars working on the history of the German language, standardisation of German, variation and change, historical sociolinguistics, and Germanic linguistics.</p> <p>The corpus was generously funded by the early career funding of the University of Innsbruck (October 2018 through September 2019).</p>
German Eltec Corpus
<pre># ELTeC-deu This is the German novel collection for the ELTeC, the European Literary Text Collection, produced by the COST Action Distant Reading for European Literary History (CA16204, https://distant-reading.net). ## Release notes General information about ELTeC releases is available at https://github.com/COST-ELTeC/ELTeC. The ELTeC-deu collection as it stands contains 100 novels encoded at level 1. The corpus composition criteria are fulfilled. ## Contributors * Collection editor(s): Fotis Jannidis, Leo Konle, Carolin Odebrecht, an algorithm * Sources: Textgrid Repository, Deutsches Textarchiv ## Licence All texts included in this collection are in the public domain. The textual markup is provided with a Creative Commons Attribution International 4.0 licence (CC 0, https://creativecommons.org/publicdomain/zero/1.0/).</pre>
Fig. 1 in Standard deviation of carabid size in Western German forest succession - a complex picture
Fig. 1. Male (squares) and female (circles) elytrae lengths of C. violaceus (mean ± SD).
Fig. 1 in Whale lice (Isocyamus deltobranchium & Isocyamus delphinii; Cyamidae) prevalence in odontocetes off the German and Dutch coasts - morphological and molecular characterization and health implications
Fig. 1. Map of German and Dutch coast with sampling locations of harbour porpoises and pilot whale.
Dataset (raw questionnaire data and variable importance results) supplementing the publication "Does Gender Really Matter? How Demographics and Site Characteristics Influence Behavior and Attitudes of German Small-Scale Private Forest Owners"
<p>The dataset contains</p> <ul> <li>a translation of the questionnaire,</li> <li>the questionnaire raw data, and</li> <li>the results of the variable importance analysis</li> </ul> <p>used in the publication "Does Gender Really Matter? How Demographics and Site Characteristics Influence Behavior and Attitudes of Small-Scale Private Forest Owners".</p>
Table 1 in German CULex pipienS biotype MoLeStUS and CULex torrentiUM are vector-competent for Usutu virus
<p><b>Table 1</b> Infection, dissemination, and transmission rates of mosquitoes infected with the German USUV Africa 2 strain</p><table><tbody><tr><th><b>Blood meal virus titer (TCID</b> <b>50</b> <b>/ml)</b></th><th><b>Mosquito species</b></th><th><b>Dpi</b></th><th><b>Infection rate (%) (95% CI)</b></th><th><b>Mean viral load bodies (viral copies/µl of total RNA)</b></th><th><b>Dissemination rate (%) (95% CI)</b></th><th><b>Mean viral load legs plus wings (viral copies/µl of total RNA)</b></th><th><b>Transmission rate (%) (95% CI)</b></th></tr></tbody><tbody><tr><th>High titer 10 7.4</th><td><i>Culex pipiens</i> biotype <i>molestus</i> a</td><td>14</td><td>8/10 (80.0) (44.4–97.5)</td><td>6.9 × 10 5</td><td>3/8 (37.5) (8.5–75.5)</td><td>9.0 × 10 3</td><td>3/3 (100) (29.2–100)</td></tr><tr><th></th><td></td><td>21</td><td>4/6 (66.7) (22.3–95.7)</td><td>5.6 × 10 5</td><td>4/4 (100) (39.7–100)</td><td>1.5 × 10 4</td><td>3/4 (75.0) (19.4–99.4)</td></tr><tr><th></th><td><i>Cx.pipiens</i> biotype <i>molestus</i> b</td><td>16</td><td>13/16 (81.3) (54.4–96.0)</td><td>1.9 × 10 6</td><td>13/13 (100) (75.3–100)</td><td>7.8 × 10 4</td><td>2/13 (15.4) (1.9–45.4)</td></tr><tr><th></th><td></td><td>21</td><td>8/10 (80.0) (44.4–97.5)</td><td>8.1 × 10 5</td><td>8/8 (100) (63.1–100)</td><td>7.8 × 10 4</td><td>4/8 (50.0) (15.7–84.3)</td></tr><tr><th></th><td><i>Aedes aegypti</i> d</td><td>14</td><td>0/53 (0) (0–6.7)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><th></th><td></td><td>21</td><td>4/22 (18.2) (5.2–40.3)</td><td>2.3 × 10 5</td><td>1/4 (25.0) (0.6–80.6)</td><td>5.5 × 10 3</td><td>0/1 (0) (0–97.5)</td></tr><tr><th>Low titer 10 5.1</th><td><i>Cx.pipiens</i> biotype <i>molestus</i> a</td><td>14</td><td>2/36 (5.6) (0.7–18.7)</td><td>1.2 × 10 2</td><td>0/2 (0) (0–84.2)</td><td>NA</td><td>NA</td></tr><tr><th></th><td></td><td>21</td><td>1/19 (5.3) (0.7–18.7)</td><td>5.4 × 10 1</td><td>0/1 (0) (0–84.2)</td><td>NA</td><td>NA</td></tr><tr><th></th><td><i>Cx.torrentium</i> c</td><td>14</td><td>1/8 (12.5) (0.3–52.7)</td><td>2.8 × 10 1</td><td>0/1 (0) (0–97.5)</td><td>NA</td><td>NA</td></tr><tr><th></th><td></td><td>21</td><td>1/8 (12.5) (0.3–52.7)</td><td>3.9 × 10 6</td><td>1/1 (100) (2.5–100)</td><td>4.7 × 10 4</td><td>1/1 (100) (2.5–100)</td></tr></tbody></table><p>Transmission rates include results from the saliva inoculation on Vero cells and from the RT-qPCRs of cell culture supernatants.All mosquitoes were incubated for 14/16 or 21 days.Absolute quantification of virus copies/µl of total RNA was performed via an RT-qPCR-based calibration curve</p><p><i>CI</i> confidence interval, <i>dpi</i> days post infection, <i>NA</i> not applicable</p><p><sup>a</sup> <i>Cx.pipiens</i> biotype <i>molestus</i> laboratory colony from“Wendland,” Lower Saxony,Germany</p><p><sup>b</sup> <i>Cx.pipiens</i> biotype <i>molestus</i> laboratory colony from Novi Sad,the Republic of Serbia</p><p><sup>c</sup> <i>Cx.torrentium</i> field-collected colony near Berlin and Bonn,North Rhine-Westphalia,Germany</p><p><sup>d</sup> <i>Ae. aegypti</i> laboratory colony from Malaysia (Bayer CropScience,Langenfeld,Germany)</p>
DWUG DE Resampled: Diachronic Word Usage Graphs for German
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for German. Uses were sampled for the target words from the <a href="https://zenodo.org/doi/10.5281/zenodo.5543723">DWUG DE</a> dataset and from the same source corpora. DWUG DE Resampled can thus be seen as a small-scale replication of DWUG DE. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p> <p>Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. <a href="https://aclanthology.org/2021.emnlp-main.567/">DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages</a>. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.</p>
DiscoWUG: Discovered Diachronic Word Usage Graphs for German
<p>This data collection contains discovered diachronic Word Usage Graphs (WUGs) for German. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>Note:</p> <ul> <li>The date given for each word use does not correspond to the exact date of the document from which the use was sampled but only to the midpoint of the respective time period (1800-1899, 1946-1990), as the exact date was not available in the SemEval corpora.</li> </ul> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Sinan Kurtyigit, Maike Park, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde. 2021. <a href="https://aclanthology.org/2021.acl-long.543/">Lexical Semantic Change Discovery</a>. Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing.</p> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p>
DWUG DE Sense: A data set of historical word sense annotations in German
<p>This data collection contains a subset of <a href="https://zenodo.org/record/5543723">DWUG DE</a> word usage data annotated with classical word sense definitions (<em>DWUG DE Sense</em>, see <code>data/*/judgments_senses.csv</code>). From these annotations aggregated and cleaned sense labels were derived (<code>labels/*/labels_senses.csv</code>). From these labels we derived additional binary semantic proximity labels between use pairs ('0' for different sense, '1' for same sense, <code>labels/*/labels_proximity.csv</code>) and change labels reflecting sense changes between the two time periods from which word usages were sampled (<code>stats/*/stats_groupings.csv</code>).</p> <p>The sense labels were derived from the sense annotation by removing instances where not at least 2/3 annotators agree on the label (<code>maj_2</code>/<code>maj_3</code>). Note that the binary proximity labels were <em>derived</em> from the sense annotation, and not directly judged by humans (in contrast to other <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUG data sets</a>). Note that consequently also the change scores EARLIER, LATER and COMPARE were not calculated directly from human judgments, but from the inferred binary proximity labels. Please find the code aggregating and cleaning the data, deriving proximity labels and deriving change labels in the <a href="https://github.com/Garrafao/WUGs">WUG repository</a>.</p> <p>Please find more information on the provided data in the paper referenced below.</p> <p>Version: 1.0.1, 01.11.2024. Correct or remove some normalization and lemmatization errors in the uses. Updated references.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Frank D. Zamora-Reina, Felipe Bravo-Marquez, Nikolay Arefyev. 2024. <a href="https://doi.org/10.1007/s10579-024-09771-7">Sense Through Time: Diachronic Word Sense Annotations for Word Sense Induction and Lexical Semantic Change Detection</a>. Language Resources and Evaluation.</p> <p>Dominik Schlechtweg. 2023. <a href="http://dx.doi.org/10.18419/opus-12833">Human and Computational Measurement of Lexical Semantic Change</a>. PhD thesis. University of Stuttgart.</p>
German DBnary archive in original Lemon format
<p>The DBnary dataset is an extract of Wiktionary data from many language editions in RDF Format. Until July 1st 2017, the lexical data extracted from Wiktionary was modeled using the lemon vocabulary.</p> <p>This dataset contains the full archive of all DBnary dumps in Lemon format containing lexical information from German language edition, ranging from 30th August 2012 to 1st July 2017.</p> <p>After July 2017, DBnary data has been modeled using the ontolex model and will be available in another Zenodo entry.</p>
Figure 3 in A new stereospondyl from the German Middle Triassic, and the origin of the Metoposauridae
Figure 3. Callistomordax kugleri, skull of the type specimen SMNS 82035 (dorsal view).
German poll data for McMenamin et al., Institutions and Elections, Journalism, 2021.
<p>German poll data (STATA) for McMenamin et al., Journalism, 2021.</p> <p>Code and other datasets for this article also available on Zenodo.</p>
German newspaper data for McMenamin et al., Institutions and Elections, Journalism, 2021.
<p>German newspaper data (in STATA format) from McMenamin et al., Journalism, 2021.</p> <p>Code and other datasets also available on Zenodo.</p>
No seasonal curtailment of the Eurasian Skylark's (Alauda arvensis) breeding season in German heterogeneous farmland
<p><span>The lack of suitable nesting sites is one key driver behind the farmland bird crisis in Europe. Winter cereals become impenetrable for ground-breeding birds like the Eurasian Skylark (<em>Alauda arvensis</em>), curtailing breeding time. Stable Skylark populations depend on multiple breeding attempts per year; thus, the widespread cultivation of winter cereals has strongly contributed to their tremendous decline. Crop diversification is thought to be a potential measure to counteract this development. Therefore, we explored</span> <span>how individual Skylarks respond to the decreasing suitability of winter cereals as nesting habitat in heterogeneous but otherwise conventionally managed farmland. Our study focused on: i) the degree to which Skylarks prematurely cease nesting activity, switch nesting habitats, or breed on linear structures like tramlines. Additionally, we analyzed: ii) if nest success decreases throughout the breeding season and iii) how often Skylarks make a successful breeding attempt per year. We radio-tagged 28 adults in a German population during April 2018 and 2019, tracked half of them for more than 3 months, and measured their breeding success. Additionally, we monitored nests of untagged pairs, resulting in 96 nests found. None, except one tagged individual, stopped breeding activity before July 1st. Home ranges were mainly stable, but Skylarks switched nesting habitats away from winter cereals to crops like sugar beet or set-aside. High-risk nesting sites like corn and linear structures played a minor role in breeding. Overall, Mayfield logistic regressions revealed no seasonal decrease in nest success, and tagged Skylarks had sufficient time to make 1.5 – 1.8 breeding attempts, of which 0.8 were successful. We suggest that heterogeneous farmland in our study area, which enabled diversely composed home ranges, prevented a curtailment of the breeding season. Thus, our study reinforces the need for crop diversification which gives Skylarks a chance to survive in modern farmland. </span></p>
Subset of #MeToo-tweets in English, German, Spanish from 2019 and 2021
<p>The zip contains two tables with tweets on #MeToo in English, Spanish and German from the months 2019-07, 2019-08, 2021-07 and 2021-08. Tweets were mostly annotated with evaluative categories (positive, negative, neutral, ambiguous) and whether they represent a concrete or a meta discourse.</p>
Traumatic events, post-traumatic stress disorder, and proxy measures for central sensitization in chronic pain patients of a German university outpatient pain clinic
<p>This dataset was acquired at Hannover Medical School, Hannover, Germany. The study complied with the Declaration of Helsinki, and was approved by the local ethics committee. All subjects gave written informed consent, and consent to use their data anonymously for research purposes. The study was registered at ClinicalTrials.gov (NCT05190367).</p> <p>Between February 2019 and July 2020, 914 patients who visited our outpatient pain department gave written consent to use their routinely collected data anonymously for research purposes. Participants were divided into four groups depending on their trauma severity: (1) no trauma; (2) accidental trauma (e.g. illness, accident, natural disaster); (3) interpersonal trauma (e.g. assault, rape, war); (4) PTSD (diagnosed according to ICD-10). Patients fulfilling two or more categories were assigned to the highest group.</p> <p>Data were collected using the SymptomMapper application [1]. Participants provided information about their traumas, current pain intensity (visual analogue scale, VAS (0-100) [2]), mean and maximal pain in the last 4 weeks (VAS (0-100)), sleep impairment (VAS (0-100)), acceptable pain (VAS (0-100)), pain disability index (PDI [3]), pain area (digital drawings), pain widespreadness (widespread pain index, WPI [4], derived from drawings), stress (patient health questionnaire, German version, PHQ-D [5]), anxiety (PHQ-D), depression (PHQ-D), and somatization symptoms (PHQ-D).</p> <p>This dataset contains the raw data as well as necessary scripts for reproducing the results of this study.</p> <p> </p> <p>References<br> ##########</p> <p>[1] Neubert TA, Dusch M, Karst M, Beissner F. Designing a Tablet-Based Software App for Mapping Bodily Symptoms: Usability Evaluation and Reproducibility Analysis. JMIR Mhealth Uhealth 2018;6(5):e127.<br> [2] Dworkin RH, Turk DC, Farrar JT et al. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain 2005;113(1):9-19.<br> [3] Pollard CA. Preliminary Validity Study of the Pain Disability Index. Percept Mot Skills 1984;59(3): 974.<br> [4] Wolfe F, Clauw DJ, Fitzcharles MA et al. The American College of Rheumatology Preliminary Diagnostic Criteria for Fibromyalgia and Measurement of Symptom Severity. Arthritis Care Res 2010;62(5):600-10.<br> [5] Löwe B, Spitzer RL, Zipfel S, Herzog W. Gesundheitsfragebogen für Patienten (PHQ-D). Manual und Testunterlagen (second edition). Pfizer 2002.</p>
Argument-seeking conversations in german
<p>This data set consists of ten conversations. The text files are the unedited transcripts of the conversations. The pdf files contain the conversations divided into their respective subtopics.</p>
Knowledge-driven compound interpretation. A corpus study on German complex nouns headed by -stoff
<p>Dataset for publication "Knowledge-driven compound interpretation. A corpus study on German complex nouns headed by -stoff"</p> <p>To appear in: SKASE Journal of Theoretical Linguistics, ISSN: <a href="https://portal.issn.org/resource/ISSN/1336-782X">1336-782X</a></p> <p>Authors: Olav Mueller-Reichau (University of Leipzig, Germany) and Matthias Irmer (OntoChem GmbH, Halle (Saale), Germany)</p> <p> </p>
Silver Standard of quantified positive, negative and neutral German noun phrases
<p>Repository: silver standard quantified (simple) noun phrases</p> <p>43,842 positive (+), negative (-) or neutral (0) NPs, </p> <p>e.g. "ein notorischer Verführer -5.625" is highly negative (-5.625)</p> <p>see References LREC for a description of the data</p> <p>Format: tsv</p> <p>References:</p> <p>@inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br> </p>
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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.