Open Research Data

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https://www.weizenbaum-library.de/handle/id/381

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Auflistung Open Research Data nach Titel

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    A categorized multimodal TikTok dataset
    (2023) Wedel, Lion
    This dataset encompasses 11242 entries of 5137 unique videos listed between the 31st of July and the 4th of August on the TikTok explore page (https://www.tiktok.com/explore). The page was accessed via a German IP address without being logged in. The data has been collected via the 4CAT Toolkit and the Zeeschuimer browser extension. The dataset contains the category and multimodal embeddings for each video. **Intended Purpose** The dataset is primarily intended for proof-of-concept studies, as a toy dataset to teach or to be used for seminar papers by students. Given the lack of a clear definition for each category by TikTok, the focus of such work might be to explore those definitions or to conduct work with a focus on methods. The multimodal embeddings allow for directly applying unsupervised and supervised machine learning techniques. **Contents** The dataset consists of four zipped .csv files: * – metadata.zip * – text_embeddings.zip * – audio_embeddings.zip * – video_embedding.zip **For further details, please consult the Data Report** (datenbericht_v2.pdf).
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    Appendix to “Your social ties, your personal public sphere, your responsibility”
    (2022) Gagrčin, Emilija
    ### Appendix Vignette 1: Private Profile Vignette 2: Public Page
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    Attachment to the article “From Insult to Hate Speech: Mapping Offensive Language in German User Comments on Immigration”
    (2021) Paasch-Colberg, Sünje; Strippel, Christian; Trebbe, Joachim; Emmer, Martin
    ### English Translation of German User Comments Attachment to the article “From Insult to Hate Speech: Mapping Offensive Language in German User Comments on Immigration” as part of the issue “Dark Participation in Online Communication: The World of the Wicked Web”, edited by Thorsten Quandt (University of Münster, Germany). #### Disclaimer This document contains both the German original and the English translation of all user comments quoted in the article. In order to ensure the anonymity of the authors, the original German quotations were modified. However, the parts that are relevant to the argument or example remain unchanged. Due to the nature of this document, the user comments presented here contain potentially offensive and upsetting terms, particularly racist and islamophobic. They are solely used as examples to illustrate the results of this research and do not reflect the views of the authors in any way.
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    Boulianne, Shelley (2021). Pathways to Environmental Activism in Four Countries. figshare. Dataset.
    (2022) Boulianne, Shelley
    YouthEnviroActivism Sept2021.sav YouthEnviroActivism Sept2021.sps YouthEnviroActivism Sept2021.spv YouthEnviroActivism Sept2021.xls
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    Data of the paper: Clickbait or conspiracy? How Twitter users address the epistemic uncertainty of a controversial preprint
    (Center for Open Science, 2022-06-22) Franzreb, Carlos; Schimmler, Sonja; Bauer, Mareike Fenja
    This project contains sources related with the paper „Clickbait or conspiracy? How Twitter users address the epistemic uncertainty of a controversial preprint“:
    Scripts
    Scripts used to retrieve Tweets and to analyze/visualize them.

    Quantitative Analysis: Data
    + tweets.json: All Tweets of the relevant users as nodes and their relationships (retweet, quote or reply) as edges.
    + users_clustered.json: Users as nodes and their follow-relationships as edges, clustered with the Leiden algorithm.
    + follower_network.json: JSON file corresponding to Figure 1.
    + interaction_network.json: JSON file corresponding to Figure 2

    Qualitative Analysis: Data
    Replies and quotes of the Tweets that are used in the qualitative analysis.
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    Digitalization and organizational change during the Covid-19 crisis (Data and Codebook)
    (Weizenbaum Institute, 2024) Krzywdzinski, Martin; Butollo, Florian; Bovenschulte, Marc; Nerger, Michael
    The data set is based on a survey of companies conducted to investigate the extent to which the pandemic led to a strategic reorientation of digitalization measures in companies. The aim was to investigate whether digitalization measures were newly established and intensified, in which areas digitalization took place, and how the digitalization measures differed depending on the company’s level of digitalization, sector, and size. #### Methods The survey was realized as CATI by the market research company Hopp Marktforschung with the support of VDI/VDE Innovation+Technik GmbH (Marc Bovenschulte and Michael Nerger). The survey was conducted in Germany in two waves: in July and August 2021 and September and October 2022. The survey included 540 companies in the first wave and 605 companies in the second wave. 120 companies participated in both waves. The respondents belonged to the top and middle management of the companies (e.g. head of division). The survey was conducted in two waves in order to examine differences between companies' approaches at the beginning and later in the course of the pandemic and to check whether companies’ strategies solidified over time or whether they were just short-term reactions.
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    FAIREST Metrics and Assessment Data
    (zenodo, 2021-11-08) d’Aquin, Mathieu; Kirstein, Fabian; Schimmler, Sonja; Oliveira, Daniela; Urbanek, Sebastian

    This data supplements the article “FAIREST: A Framework for Assessing Research Repositories”. In the article, we introduce the FAIREST principles, an extension of the well-known FAIR principles. Along these principles, we provide comprehensive metrics for assessing and selecting solutions for building digital repositories for research artefacts. The metrics are based on two pillars:

    1. an analysis of established features and functionalities, drawn from existing solutions,
    2. a literature review on general requirements for digital repositories for research artefacts and related systems.

        3. We further describe an assessment of 11 widespread solutions, with the goal to provide an overview of the current landscape of research data repository solutions, identifying gaps and research challenges to be addressed. The solutions are:

        • – ResearchGate
        • – Academia.edu
        • – Zenodo
        • – arXiv
        • – Bibsonomy
        • – Figshare
        • – CKAN
        • – DSpace
        • – Invenio
        • – Dataverse
        • – EPrints

            • Overview of the data

            01 FAIREST Assessment Metrics and Solutions (All-in-one).xlsx

            This Excel file includes both the assessment metrics and the results for the 11 solutions


            02 FAIREST Assessment Metrics.csv

            The assessment metrics as CSV


            XX FAIREST Assessment XXX.csv

            Assessment result for the respective solution


            14 FAIREST Assessment Template.xlsx

            A template to apply the metrics to an individual solution

            Note: Fill in your assessment in column F and get the result at the bottom of the sheet

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    Interviews zu Forschungsdateninfrastrukturen und digitalen Praktiken offener Wissenschaft am Weizenbaum-Institut
    (Zenodo, 2022-02-04) Bauer, Mareike; Wünsche, Hannes
    Die Forschungsgruppe „Digitalisierung der Wissenschaft“ begleitet am Weizenbaum-Institut den Aufbau eines Repositoriums für Publikationen und Forschungsdaten. Als Teil der Anforderungsanalyse wurden leitfadengestützte Interviews mit wissenschaftlichen Mitarbeiter*innen des Weizenbaum-Instituts durchgeführt. Ziel dieser war es, deren Erfahrung mit und Anforderungen an Forschungsdateninfrastrukturen zu identifizieren. Dieser Datensatz beinhaltet: \+ Studienreport \+ anonymisierte Interviewtranskripte \+ E-Mail Aufruf \+ Interviewleitfaden \+ Einwilligungserklärung.
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    Online Supplementary Material for “How Right-Wing Populist Comments Affect Online Deliberation on News Media Facebook Pages“
    (2022) Thiele, Daniel; Turnšek, Tjaša
    Appendix A: Literature Review Appendix B: Dictionaries for the Topic of Migration Appendix C: Codebook Appendix D: Automated Text Analysis Appendix E: Summary Statistics for Step 1 and Step 2 Appendix F: Right-wing Populism in Comments by Media Type Appendix G: Regression Tables for Step 1 References in Appendices A-G
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    Replication Data for: Message deletion on Telegram: Affected data types and implications for computational analysis
    (Center for Open Science, 2022-11-01) Bühling, Kilian
    Online supplement for: Buehling, K. (2023). Message deletion on Telegram: Affected data types and implications for computational analysis. Communication Methods and Measures. https://www.doi.org/10.1080/19312458.2023.2183188. Please see the full paper for a description of data and methods.
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    Replication Data for: “Who reports witnessing and performing corrections on social media in the US, UK, Canada, and France?”
    (2024) Tang, Rongwei; Vraga, Emily; Bode, Leticia; Boulianne, Shelley
    These are the replication materials for the article "Who reports witnessing and performing corrections on social media in the US, UK, Canada, and France?" ### Files + HKSMR data.tab + HKSMR syntax.sps
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    Supplemental material for “Beware: Processing of Personal Data—Informed Consent Through Risk Communication”
    (2024) Seiling, Lukas; Gsenger, Rita; Mulugeta, Filmona; Henningsen, Marte; Mischau, Lena; Schirmbeck, Marie
    Appendix A: GDPR content analysis Appendix B: Expert interview questions Appendix C: Results of the systematic qualitative content analysis of expert interviews
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    Supplemental material for “Datafication Markers: Curation and User Network Effects on Mobilization and Polarization During Elections”
    (2023) Gagrčin, Emilija dc.contributor.author Ohme, Jakob dc.contributor.author Buttgereit, Lina dc.contributor.author Grünewald, Felix
    Supplemental material Table A1. Predicting campaign participation (cross-sectional) Table A2. Predicting campaign participation (auto-regressive) Table A3. Predicting vote choice certainty (cross-sectional) Table A4. Predicting vote choice certainty (auto-regressive) Table A5. Predicting turnout (cross-sectional) Table A6. Predicting turnout (auto-regressive) Table A7. Predicting attitude reinforcement (change variable) Table A8. Predicting affective polarization (cross-sectional) Table A8. Predicting affective polarization (cross-sectional)
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    Supplemental Material for “Notable enough? The questioning of women’s biographies on Wikipedia”
    (2024) Martini, Franziska
    Table I. Sample size Table II. Reliability coefficients Table III. Total and relative numbers of biographies nominated for deletion by specific criteria for notability Figure I. Percentage of users and their level of justification by their position and the biography’s gender Figure II. Numbers of German-language Wikipedia biographies by tagged gender category and year of creation Figure III. Number of deletion nominations (N = 396) per logged-in users (N = 158) Figure V. Number of decisions (N = 362) per administrator (N = 35) Figure IV. Number of discussions (N = 461) logged-in users (N = 608) participated in
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    Supplemental Material for “Pandemic protesters on Telegram”
    (2023) Bühling, Kilian; Heft, Annett
    ### Supplemental Material A.1 STM results A.2 Message characteristics A.3 Cluster prevalence of all actor types A.4 Reliability coefficients of manual content analysis A.5 Two-sample t-tests of topic distributions by entity type A.6 Pairwise two-sample t-tests of topic distributions by source type B.1 Codebook B.2 Codebook appendix
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    Supplemental Material for “Unravelling the Role of Data in Industrial Value Chains”
    (2025) Schneidemesser, Lea; Butollo, Florian
    Table 1: Overview of interviewed companies and experts
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    Supplemental material: “Whose ideas are worth spreading? The representation of women and ethnic groups in TED talks”
    (2019)
    S1 Image recognition algorithm S2 Topic modeling S3 Regressions References
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    Supplementary data for “New methodologies for the digital age? How methods (re-)organize research using social media data”
    (2023) Fan, Yangliu; Lehmann, Sune; Blok, Anders
    Data collection Data annotation and Word2Vec model training Table S1. Method list Table S2. Network topological quantities for the journal citation network
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    Supplementary data for “Why, with whom, and how to conduct interdisciplinary research? A review from a researcher’s perspective”
    (2024) Vladova, Gergana dc.contributor.author Haase, Jennifer dc.contributor.author Friesike, Sascha
    Supplementary data Table 1: Overview of the paper included in the data analysis. (Essential papers in bold) Table 2: Overview of the coding scheme
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    Supplementary Information for “Understanding scholar-trajectories across scientific periodicals”
    (2024) Fan, Yangliu; Lehmann, Sune; Blok, Anders
    1. Gender Assignment 2. Tables and Figures 3. The embedding model with five dimensions References