Auflistung nach Forschungsgruppen "Digitalisierung der Wissenschaft"

Gerade angezeigt 1 - 16 von 16
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    Citizen Science and the Dissolution of Inequalities in Scientific Knowledge Production
    (Weizenbaum Institute, 2019) Wünsche, Hannes; Schimmler, Sonja
    Recently, a larger public has started to critically discuss scientific knowledge and its role in political decision making. In this discussion, scientific and civic epistemologies are put into connection with each other. Just as post-democratic theory argues in relation to political decisions, the production of scientific knowledge is criticized as a non-inclusive process, too. The Citizen Science movement tries to resolve this deficit by involving citizens into research. In this paper, we introduce agency as an analytical category into the discussion, focussing on how participants are represented in Citizen Science. We highlight the interdependencies between the degree of agency granted to the participants in Citizen Science projects and the degree of their representation in knowledge production.
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    Das Öffnen und Teilen von Daten qualitativer Forschung
    (Weizenbaum Institute, 2020) Steinhardt, Isabel; Fischer, Caroline; Heimstädt, Maximilian; Hirsbrunner, Simon David; İkiz-Akıncı, Dilek; Kressin, Lisa; Kretzer, Susanne; Möllenkamp, Andreas; Porzelt, Maike; Rahal, Rima-Maria; Schimmler, Sonja; Wilke, René; Wünsche, Hannes
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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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    Editorial: Volume 1, Issue 1
    (Weizenbaum Institute, 2021) Emmer, Martin; Krasnova, Hanna; Krzywdzinski, Martin; Metzger, Axel; Schimmler, Sonja; Ulbricht, Lena; Neuberger, Christoph
    The Weizenbaum Journal of the Digital Society is an open access journal and could not function any other way, because we see digitalization as a process that changes traditional forms of communication and cooperation, which raises the questions of control of data, information and knowledge anew. We look forward to contributions about the conditions, forms and consequences of the digitalization of society and its sub-sectors such as politics, business, science, labor, the public, civil society, law and culture. The digitalization of society has many facets: the disruptive transformation of the world of work, radical changes in the economic and innovation systems, new forms of learning and the restructuring of educational systems, the transformation of public space through digital media and platforms, changes in the way democracies function, massive challenges for the legal system and the planning and design of technical infrastructures. In light of these developments, the question arises as to how social actors can shape the digital transformation while safeguarding the foundations for individual and societal self-determination.
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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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    Growing Open Science with the Combined Potential of Citizen Science and Auto Science
    (Weizenbaum Institute, 2019) Schimmler, Sonja; Kirstein, Fabian; Urbanek, Sebastian; Wünsche, Hannes; Hauswirth, Manfred
    In this paper, we present our ideas on how to best support researchers in every phase of the research process when dealing with their research data. We propose a Research Data Portal as the central data infrastructure. With the help of this portal, a researcher can easily manage and update his or her research data, share it with collaborators, and reach out to the public. We further propose a Citizen Science Portal, which includes some new and innovative concepts and methods. In this portal, Citizen Science and Auto Science concepts are applied, and support to bring together the best of both worlds is provided. Citizen Science promises to entail the individual (scientists and hobby scientists) to help with research. Auto Science is meant to help analyze research data, e.g., to help publish the data and to help improve its quality, by applying methods from artificial intelligence.
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    IDS as a Foundation for Open Data Ecosystems
    (Springer International Publishing, 2022) Kirstein, Fabian; Bohlen, Vincent; Otto, Boris; Ten Hompel, Michael; Wrobel, Stefan
    Open data is a popular and flourishing concept. The availability of open and structured data is the foundation of new business models, citizen engagement, and scientific research. However, open data still faces many issues to unfold its full potential, including usability, quality, legal, privacy, strategic, and technical barriers. In addition, the public sector remains its main provider, while industry stakeholders are still reluctant to participate in open data ecosystems. In this article, we present an architecture to overcome these drawbacks by utilizing the concepts, specifications, and technologies provided by International Data Spaces. We developed a prototype to demonstrate and evaluate the practical adoption of our architecture. Our work shows that IDS can act a vital foundation for open data ecosystems. The presented solution is available as open source software.
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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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    Mythos Blockchain: Zwischen Hoffnung und Realität
    (Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS, 2021) Kirstein, Fabian; Altenbernd, Anton; Lämmel, Philipp
    Vertrauen schaffen, vermittelnde Institutionen oder auch gleich den Staat ersetzen und das Internet revolutionieren – die mit dem »Mythos Blockchain« verbundenen Ideen schienen keine Grenzen zu kennen. Gut vier Jahre nach dem ersten ÖFIT-White Paper zum Thema wird es Zeit für eine Bestandsaufnahme, was von den Ideen übriggeblieben ist. Zusammen mit unseren Kollegen vom Weizenbaum-Institut geben wir einen Überblick über die Entwicklungen der letzten Jahre. Ein gemeinsames Verständnis vom Stand der Technologie ist wichtig, um die beispielhaften Anwendungsfälle analysieren zu können. Daraus werden Schlüsselfaktoren kristallisiert und Handlungsempfehlungen für den zukünftigen Einsatz der vielfältigen Technologiebausteine abgeleitet.
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    Opening up and Sharing Data from Qualitative Research: A Primer
    (Weizenbaum Institute, 2021) Steinhardt, Isabel; Fischer, Caroline; Heimstädt, Maximilian; Hirsbrunner, Simon David; İkiz-Akıncı, Dilek; Kressin, Lisa; Kretzer, Susanne; Möllenkamp, Andreas; Porzelt, Maike; Rahal, Rima-Maria; Schimmler, Sonja; Wilke, René; Wünsche, Hannes
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    Provenance Management over Linked Data Streams
    (2019) Liu, Qian; Wylot, Marcin; Le Phuoc, Danh; Hauswirth, Manfred
    Provenance describes how results are produced starting from data sources, curation, recovery, intermediate processing, to the final results. Provenance has been applied to solve many problems and in particular to understand how errors are propagated in large-scale environments such as Internet of Things, Smart Cities. In fact, in such environments operations on data are often performed by multiple uncoordinated parties, each potentially introducing or propagating errors. These errors cause uncertainty of the overall data analytics process that is further amplified when many data sources are combined and errors get propagated across multiple parties. The ability to properly identify how such errors influence the results is crucial to assess the quality of the results. This problem becomes even more challenging in the case of Linked Data Streams, where data is dynamic and often incomplete. In this paper, we introduce methods to compute provenance over Linked Data Streams. More specifically, we propose provenance management techniques to compute provenance of continuous queries executed over complete Linked Data streams. Unlike traditional provenance management techniques, which are applied on static data, we focus strictly on the dynamicity and heterogeneity of Linked Data streams. Specifically, in this paper we describe: i) means to deliver a dynamic provenance trace of the results to the user, ii) a system capable to execute queries over dynamic Linked Data and compute provenance of these queries, and iii) an empirical evaluation of our approach using real-world datasets.
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    Pushing the Scalability of RDF Engines on IoT Edge Devices
    (2020) Le-Tuan, Anh; Hayes, Conor; Hauswirth, Manfred; Le-Phuoc, Danh
    Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.
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    Stellungnahme zum Entwurf des Digitalisierungsgesetzes der Landesregierung Schleswig-Holstein, Drucksache 19/3267
    (Weizenbaum Institute, 2021) Müller, Ferdinand; Keiner, Alexandra; Schädlich, Finn; Völzmann, Lisa; Peter, Robert; Schrör, Simon; Efroni, Zohar; Schimmler, Sonja; von Hagen, Prisca
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    Surfing in sound: Sonification of hidden web tracking
    (Georgia Institute of Technology, 2019) Lutz, Otto Hans-Martin; Kröger, Jacob; Schneiderbauer, Manuel; Hauswirth, Manfred
    Web tracking is found on 90% of common websites. It allows online behavioral analysis which can reveal insights to sensitive personal data of an individual. Most users are not aware of the amout of web tracking happening in the background. This paper contributes a sonification-based approach to raise user awareness by conveying information on web tracking through sound while the user is browsing the web. We present a framework for live web tracking analysis, conversion to Open Sound Control events and sonification. The amount of web tracking is disclosed by sound each time data is exchanged with a web tracking host. When a connection to one of the most prevalent tracking companies is established, this is additionally indicated by a voice whispering the company name. Compared to existing approaches on web tracking sonification, we add the capability to monitor any network connection, including all browsers, applications and devices. An initial user study with 12 participants showed empirical support for our main hypothesis: exposure to our sonification significantly raises web tracking awareness.
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    The connection of open science practices and the methodological approach of researchers
    (2022) Steinhardt, Isabel; Bauer, Mareike; Wünsche, Hannes; Schimmler, Sonja
    The Open Science movement is gaining tremendous popularity and tries to initiate changes in science, for example the sharing and reuse of data. The new requirements that come with Open Science poses researchers with several challenges. While most of these challenges have already been addressed in several studies, little attention has been paid so far to the underlying Open Science practices (OSP). An exploratory study was conducted focusing on the OSP relating to sharing and using data. 13 researchers from the Weizenbaum Institute were interviewed. The Weizenbaum Institute is an interdisciplinary research institute in Germany that was founded in 2017. To reconstruct OSP a grounded theory methodology (Strauss in Qualitative Analysis for Social Scientists, Cambridge University Press, Cambridge, 1987) was used and classified OSP into open production, open distribution and open consumption (Smith in Openness as social praxis. First Monday, 2017). The research shows that apart from the disciplinary background and research environment, the methodological approach and the type of research data play a major role in the context of OSP. The interviewees’ self-attributions related to the types of data they work with: qualitative, quantitative, social media and source code. With regard to the methodological approach and type of data, it was uncovered that uncertainties and missing knowledge, data protection, competitive disadvantages, vulnerability and costs are the main reasons for the lack of openness. The analyses further revealed that knowledge and established data infrastructures as well as competitive advantages act as drivers for openness. Because of the link between research data and OSP, the authors of this paper argue that in order to promote OSP, the methodological approach and the type of research data must also be considered
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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.