2016, Kraft, Angelina, Razum, Matthias, Potthoff, Jan, Porzel, Andrea, Engel, Thomas, Lange, Frank, van den Broek, Karina

Disziplinübergreifendes Forschungsdatenmanagement für Hochschulbibliotheken und Projekte zu vereinfachen und zu etablieren – das ist das Ziel von RADAR. Im Sommer 2016 geht mit ‚RADAR – Research Data Repository‘ ein Service an den Start, der Forschenden, Institutionen verschiedener Fachdisziplinen und Verlagen eine generische Infrastruktur für die Archivierung und Publikation von Forschungsdaten anbietet. Zu den Dienstleistungen gehören u. a. die Langzeitverfügbarkeit der Daten mit Handle oder Digital Object Identifier (DOI), ein anpassbares Rollen- und Zugriffsrechtemanagement, eine optionale Peer-Review-Funktion und Zugriffsstatistiken. Das Geschäftsmodell ermutigt Forschende, die anfallenden Nutzungsgebühren des Repositoriums in Drittmittelanträge und Datenmanagementpläne zu integrieren. Publizierte Daten stehen als Open Data zur Nachnutzung wie etwa Data Mining, Metadaten-Harvesting und Verknüpfung mit Suchportalen zur Verfügung. Diese Vernetzung ermöglicht ein nachhaltiges Forschungsdatenmanagement und die Etablierung von Dateninfrastrukturen wie RADAR.

2023, Steinbeck, Christoph, Koepler, Oliver, Herres-Pawlis, Sonja, Bach, Felix, Jung, Nicole, Razum, Matthias, Liermann, Johannes C., Neumann, Steffen

Research data provide evidence for the validation of scientific hypotheses in most areas of science. Open access to them is the basis for true peer review of scientific results and publications. Hence, research data are at the heart of the scientific method as a whole. The value of openly sharing research data has by now been recognized by scientists, funders and politicians. Today, new research results are increasingly obtained by drawing on existing data. Many organisations such as the Research Data Alliance (RDA), the goFAIR initiative, and not least IUPAC are supporting and promoting the collection and curation of research data. One of the remaining challenges is to find matching data sets, to understand them and to reuse them for your own purpose. As a consequence, we urgently need better research data management.

2022, Hulek, Klaus, Teschke, Olaf

[no abstract available]

2021, Hallinan, Dara, Bernier, Alexander, Cambon-Thomsen, Anne, Crawley, Francis P., Dimitrova, Diana, Bauzer Medeiros, Claudia, Nilsonne, Gustav, Parker, Simon, Pickering, Brian, Rennes, Stéphanie

On 16 July 2020, the Court of Justice of the European Union issued their decision in the Schrems II case concerning Facebook's transfers of personal data from the EU to the US. The decision may have significant effects on the legitimate transfer of personal data for health research purposes from the EU. This article aims: (i) to outline the consequences of the Schrems II decision for the sharing of personal data for health research between the EU and third countries, particularly in the context of the COVID-19 pandemic; and, (ii) to consider certain options available to address the consequences of the decision and to facilitate international data exchange for health research moving forward.

2023, Ihle, Cornelius, Trautwein, Dennis, Schubotz, Moritz, Meuschke, Norman, Gipp, Bela

Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources.

2008, Barth, Andreas, Marx, Werner

This study has been carried out to analyze the research field of high-temperature superconductivity and to demonstrate the potential of modern databases and search systems for generating meta-information. The alkaline earth (A2) rare earth (RE) cuprate high-temperature superconductors as a typical inorganic compound family and the corresponding literature were analyzed by scientometric methods. The time dependent overall number of articles and patents and of the publications related to specific compound subsets and subject categories are given. The data reveal a significant decrease of basic research activity in this research field. The A2 RE cuprate species covered by the CAS compound file were analyzed with respect to the occurrence of specific elements in order to visualize known and unknown substances and to identify characteristic patterns. The quaternary and quinternary cuprates were selected and the number of compound species as a function of specific combinations of A2 and RE elements is given. The Cu/O and RE/A2 ratios of the quaternary cuprate species as a function of A2 and RE atoms are shown. In addition, the research landscape of the MgB2 related publications was established using STN AnaVist, an analysis tool recently developed by STN International.

2021, Dessì, Danilo, Recupero, Diego Reforgiato, Sack, Harald

Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task.

2023, Hallinan, Dara, Boehm, Franziska, Külpmann, Annika Iris, Elson, Malte

Psychological research often involves the collection and processing of personal data from human research participants. The European General Data Protection Regulation (GDPR) applies, as a rule, to psychological research conducted on personal data in the European Economic Area (EEA)—and even, in certain cases, to psychological research conducted on personal data outside the EEA. The GDPR elaborates requirements concerning the forms of information that should be communicated to research participants whenever personal data are collected directly from them. There is a general norm that informed consent should be obtained before psychological research involving the collection of personal data directly from research participants is conducted. The information required to be provided under the GDPR is normally communicated in the context of an informed consent procedure. There is reason to believe, however, that the information required by the GDPR may not always be provided. Our aim in this tutorial is thus to provide general practical guidance to psychological researchers allowing them to understand the forms of information that must be provided to research participants under the GDPR in informed consent procedures.

2020, Hallinan, Dara

Broad consent-the act of gaining one consent for multiple potential future research projects-sits at the core of much current genomic research practice. Since the 25th May 2018, the General Data Protection Regulation (GDPR) has applied as valid law concerning genomic research in the EU and now occupies a dominant position in the legal landscape. Yet, the position of the GDPR concerning broad consent has recently been cause for concern in the genomic research community. Whilst the text of the GDPR apparently supports the practice, recent jurisprudence contains language which is decidedly less positive. This article takes an in-depth look at the situation concerning broad consent under the GDPR and-despite the understandable concern flowing from recent jurisprudence-offers a positive outlook. This positive outlook is argued from three perspectives, each of which is significant in defining the current, and ongoing, legitimacy and utility of broad consent under the GDPR: The principled, the legal technical, and the practical. © 2020 The Author(s).

2021, Cohl, Howard S., Teschke, Olaf, Schubotz, Moritz

This paper reports on the recently launched zbMATH Links API. We discuss its potential based on the initial link partner, the National Institute of Standards and Technology Digital Library of Mathematical Functions. As the API provides machine readable data in the links, we show how one can use data from both sources for further analysis. To exemplify the simplicity, we also show how one can use zbMATH’s link data in Jupyter notebooks.