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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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    Interviews zu Forschungsdateninfrastrukturen und digitalen Praktiken offener Wissenschaft am Weizenbaum-Institut
    (Zenodo, 2022-02-04) Bauer. Mareike; Wuensche, 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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    Umfrage zu Forschungsdatenmanagement am Weizenbaum-Institut
    (Zenodo, 2021) Toth, Roland; Vuorimäki, Julian; Schimmler, Sonja; Krzywdzinski, Martin; Friesike, Sascha; Neuberger, Christoph; Oellers, Claudia
    This dataset contains responses to a survey on open data and open access amongst members of the Weizenbaum Institute for the Networked Society which ran from 30 August to 21 September 2021. The survey elicited 39 valid responses out of 181 potential respondents working at the institute. Contributors (according to CRediT): Roland Toth Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Validation. Julian Vuorimäki Roles: Conceptualization, Investigation, Project Administration, Validation. Sonja Schimmler Roles: Conceptualization, Supervision. Martin Krzywdzinski Roles: Conceptualization, Supervision. Sascha Friesike Roles: Conceptualization, Supervision. Christoph Neuberger Roles: Conceptualization, Supervision. Claudia Oellers Roles: Conceptualization, Project Administration, Supervision.
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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 00:00:00) 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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    Replication Data for: Message deletion on Telegram: Affected data types and implications for computational analysis
    (Center for Open Science, 2022-11-01 00:00:00) 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.