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- ItemAdvancing Research Data Management in Universities of Science and Technology(Meyrin : CERN, 2020-02-13) Björnemalm, Matthias; Cappellutti, Federica; Dunning, Alastair; Gheorghe, Dana; Goraczek, Malgorzata Zofia; Hausen, Daniela; Hermann, Sibylle; Kraft, Angelina; Martinez Lavanchy, Paula; Prisecaru, Tudor; Sànchez, Barbara; Strötgen, RobertThe white paper ‘Advancing Research Data Management in Universities of Science and Technology’ shares insights on the state-of-the-art in research data management, and recommendations for advancement. A core part of the paper are the results of a survey, which was distributed to our member institutions in 2019 and addressed the following aspects of research data management (RDM): (i) the establishment of a RDM policy at the university; (ii) the provision of suitable RDM infrastructure and tools; and (iii) the establishment of RDM support services and trainings tailored to the requirements of science and technology disciplines. The paper reveals that while substantial progress has been made, there is still a long way to go when it comes to establishing “advanced-degree programmes at our major universities for the emerging field of data scientist”, as recommended in the seminal 2010 report ‘Riding the Wave’, and our white paper offers concrete recommendations and best practices for university leaders, researchers, operational staff, and policy makers. The topic of RDM has become a focal point in many scientific disciplines, in Europe and globally. The management and full utilisation of research data are now also at the top of the European agenda, as exemplified by Ursula von der Leyen addressat this year’s World Economic Forum.However, the implementation of RDM remains divergent across Europe. The white paper was written by a diverse team of RDM specialists, including data scientists and data stewards, with the work led by the RDM subgroup of our Task Force Open Science. The writing team included Angelina Kraft (Head of Lab Research Data Services at TIB, Leibniz University Hannover) who said: “The launch of RDM courses and teaching materials at universities of science and technology is a first important step to motivate people to manage their data. Furthermore, professors and PIs of all disciplines should actively support data management and motivate PhD students to publish their data in recognised digital repositories.” Another part of the writing team was Barbara Sanchez (Head of Centre for Research Data Management, TU Wien) and Malgorzata Goraczek (International Research Support / Data Management Support, TU Wien) who added:“A reliable research data infrastructure is a central component of any RDM service. In addition to the infrastructure, proper RDM is all about communication and cooperation. This includes bringing tools, infrastructures, staff and units together.” Alastair Dunning (Head of 4TU.ResearchData, Delft University of Technology), also one of the writers, added: “There is a popular misconception that better research data management only means faster and more efficient computers. In this white paper, we emphasise the role that training and a culture of good research data management must play.”
- ItemDiscussion on Existing Standards and Quality Criteria in Nanosafety Research : Summary of the NanoS-QM Expert Workshop(Zenodo, 2021) Binder, Kunigunde; Bonatto Minella, Christian; Elberskirchen, Linda; Kraegeloh, Annette; Liebing, Julia; Petzold, Christiane; Razum, Matthias; Riefler, Norbert; Schins, Roel; Sofranko, Adriana; van Thriel, Christoph; Unfried, KlausThe partners of the research project NanoS-QM (Quality- and Description Standards for Nanosafety Research Data) identified and invited relevant experts from research institutions, federal agencies, and industry to evaluate the traceability of the results generated with the existing standards and quality criteria. During the discussion it emerged that numerous studies seem to be of insufficient quality for regulatory purposes or exhibit weaknesses with regard to data completeness. Deficiencies in study design could be avoided by more comprehensive use of appropriate standards, many of which already exist. The use of Electronic Laboratory Notebooks (ELNs) that allow for early collection of metadata and enrichment of datasets could be one solution to enable data re-use and simplify quality control. Generally, earlier provision and curation of data and metadata indicating their quality and completeness (e.g. guidelines, standards, standard operating procedures (SOPs) that were used) would improve their findability, accessibility, interoperability, and reusability (FAIR) in the nanosafety research field.
- ItemAnalysis of Knowledge Tracing performance on synthesised student data(Hannover : Technische Informationsbibliothek, 2024) Pagonis, Panagiotis; Hartung, Kai; Wu, Di; Georges, Munir; Gröttrup, SörenKnowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data.
- ItemATMODAT Standard v3.0(Hamburg : DKRZ, 2020) Gasnke, Anette; Kraft, Angelina; Kaiser, Amandine; Heydebreck, Daniel; Lammert, Andrea; Höck, Heinke; Thiemann, Hannes; Voss, Vivien; Grawe, David; Leitl, Bernd; Schlünzen, K. Heinke; Kretzschmar, Jan; Quaas, JohannesWithin the AtMoDat project (Atmospheric Model Data), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. The ATMODAT standard includes concrete recommendations related to the maturity, publication and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine readable landing pages are a core element of this standard, and should hold and present discipline-specific metadata on simulation and variable level. This standard is an updated and translated version of "Bericht über initialen Kernstandard und Kurationskriterien des AtMoDat Projektes (v2.4)
- ItemTrain-the-Trainer Konzept zum Thema Forschungsdatenmanagement - Version 3.1(Meyrin : CERN, 2020-12-18) Biernacka, Katarzyna; Buchholz, Petra; Danker, Sarah Ann; Dolzycka, Dominika; Engelhardt, Claudia; Helbig, Kerstin; Jacob, Juliane; Neumann, Janna; Odebrecht, Carolin; Wiljes, Cord; Wuttke, UlrikeIm Rahmen des BMBF-Projekts FDMentor wurde ein deutschsprachiges Train-the-Trainer Programm zum Thema Forschungsdatenmanagement (FDM) erstellt, das nach Projektende durch Mitglieder der UAG Schulungen/Fortbildungen der DINI/nestor-AG Forschungsdaten ergänzt und aktualisiert wurde. Die behandelten Themen umfassen sowohl die Aspekte des Forschungsdatenmanagements als auch didaktische Einheiten zu Lernkonzepten, Workshopgestaltung und eine Reihe von didaktischen Methoden. Die nun veröffentlichte dritte, überarbeitete und erweiterte Version des Train-the-Trainer-Konzepts enthält Einheiten zu Methoden und Materialien für Online-Veranstaltungen. Erste Erfahrungen aus bereits online durchgeführten Train-the-Trainer-Workshops sind zusätzlich in das Konzept eingeflossen. Die mit dieser Version eingeführten didaktischen Methoden für Online-Veranstaltungen sollen die geschulten Trainer*innen dabei unterstützen, ihre Schulungsangebote auch im virtuellen Raum lebendig und interaktiv zu gestalten und dient somit auch der weitergehenden Information der bereits geschulten Teilnehmer*innen. An English version of the "Train-the-Trainer Konzept zum Forschungsdatenmanagement" is available under https://doi.org/10.5281/zenodo.4071471
- ItemKnowledge Graphs - Working Group Charter (NFDI section-metadata) (1.2)(Genève : CERN, 2023) Stocker, Markus; Rossenova, Lozana; Shigapov, Renat; Betancort, Noemi; Dietze, Stefan; Murphy, Bridget; Bölling, Christian; Schubotz, Moritz; Koepler, OliverKnowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains.
- ItemMetadatenschema für Schulungsmaterialien zum Thema Forschungsdatenmanagement(Meyrin : CERN, 2020-04-27) Biernacka, Katarzyna; Danker, Sarah Ann; Engelhardt, Claudia; Helbig, Kerstin; Hendriks, Sonja; Jacob, Juliane; Jagusch, Gerald; Lanza, Giacomo; Leone, Claudio; Meier, Kristin; Neumann, Janna; Odebrecht, Carolin; Peters, Karsten; Rehwald, Stephanie; Rex, Jessica; Senft, Matthias; Strauch, Annette; Thiemann, Kathrin; Trautwein-Bruns, Ute; Wiljes, Cord; Wuttke, Ulrike; Ziedorn, FraukeDas Dokument enthält ein Metadatenschema für Schulungsmaterialien zum Thema Forschungsdatenmanagement. Dieses Schema wurde von der UAG Schulungen/Fortbildungen der DINI/nestor AG Forschungsdaten erstellt und bei der Materialsammlung von FDM-Schulungsmaterialien unter https://rs.cms.hu-berlin.de/uag_fdm/ umgesetzt.
- ItemBridging the Gap Between (AI-) Services and Their Application in Research and Clinical Settings Through Interoperability: the OMI-Protocol(Hannover : Technische Informationsbibliothek, 2024-02) Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Grönke, Ana; Seletkov, Dmitrii; Kotter, Elmar; Nensa, Felix; Wehrle, Julius; Kaufmes, Kevin; Scherer, Lucas; Nolden, Marco; Boeker, Martin; Schmidt, Marvin; Pelka, Obioma; Braren, Rickmer; Stump, Shura-Roman; Graetz, Teresa; Pogarell, Tobias; Susetzky, Tobias; Wieland, Tobias; Parmar, Vicky; Wang, YuanbinArtificial Intelligence (AI) in research and clinical contexts is transforming the areas of medical and life sciences permanently. Aspects like findability, accessibility, interoperability, and reusability are often neglected for AI-based inference services. The Open Medical Inference (OMI) protocol aims to support remote inference by addressing the aforementioned aspects. Key component of the proposed protocol is an interoperable registry for remote inference services, which addresses the issue of findability for algorithms. It is complemented by information on how to invoke services remotely. Together, these components lay the basis for the implementation of distributed inference services beyond organizational borders. The OMI protocol considers prior work for aspects like data representation and transmission standards wherever possible. Based on Business Process Modeling of prototypical use cases for the service registry and common inference processes, a generic information model for remote services was inferred. Based on this model, FHIR resources were identified to represent AI-based services. The OMI protocol is first introduced using AI-services in radiology but is designed to be generalizable to other application domains as well. It provides an accessible, open specification as blueprint for the introduction and implementation of remote inference services.
- ItemA tale of two 'opens': intersections between Free and Open Source Software and Open Scholarship(Charlottesville, VA : Center for Open Science, 2020) Tennant, Jonathan P.; Agrawal, Ritwik; Baždarić, Ksenija; Brassard, David; Crick, Tom; Dunleavy, Daniel J.; Evans, Thomas Rhys; Gardner, Nicholas; Gonzalez-Marquez, Monica; Graziotin, Daniel; Greshake Tzovaras, Bastian; Gunnarson, Daniel; Havemann, Johanna; Hosseini, Mohammad; Katz, Daniel S.; Knöchelmann, Marcel; Lahti, Leo; Madan, Christopher R.; Manghi, Paolo; Marocchino, Alberto; Masuzzo, Paola; Murray-Rust, Peter; Narayanaswamy, Sanjay; Nilsonne, Gustav; Pacheco-Mendoza, Josmel; Penders, Bart; Pourret, Olivier; Rera, Michael; Samuel, John; Steiner, Tobias; Stojanovski, Jadranka; Uribe Tirado, Alejandro; Vos, Rutger; Worthington, Simon; Yarkoni, TalThere is no clear-cut boundary between Free and Open Source Software and Open Scholarship, and the histories, practices, and fundamental principles between the two remain complex. In this study, we critically appraise the intersections and differences between the two movements. Based on our thematic comparison here, we conclude several key things. First, there is substantial scope for new communities of practice to form within scholarly communities that place sharing and collaboration/open participation at their focus. Second, Both the principles and practices of FOSS can be more deeply ingrained within scholarship, asserting a balance between pragmatism and social ideology. Third, at the present, Open Scholarship risks being subverted and compromised by commercial players. Fourth, the shift and acceleration towards a system of Open Scholarship will be greatly enhanced by a concurrent shift in recognising a broader range of practices and outputs beyond traditional peer review and research articles. In order to achieve this, we propose the formulation of a new type of institutional mandate. We believe that there is substantial need for research funders to invest in sustainable open scholarly infrastructure, and the communities that support them, to avoid the capture and enclosure of key research services that would prevent optimal researcher behaviours. Such a shift could ultimately lead to a healthier scientific culture, and a system where competition is replaced by collaboration, resources (including time and people) are shared and acknowledged more efficiently, and the research becomes inherently more rigorous, verified, and reproducible.
- ItemAmoeboid Cell Migration through Regular Arrays of Micropillars under Confinement(New York : Cold Spring Harbor Laboratory, 2022) Sadjadi, Zeinab; Vesperini, Doriane; Laurent, Annalena M.; Barnefske, Lena; Terriac, Emmanuel; Lautenschläger, Franziska; Rieger, HeikoMigrating cells often encounter a wide variety of topographic features—including the presence of obstacles—when navigating through crowded biological environments. Unravelling the impact of topography and crowding on the dynamics of cells is key to better understand many essential physiological processes such as the immune response. We study how migration and search efficiency of HL-60 cells differentiated into neutrophils in quasi two-dimensional environments are influenced by the lateral and vertical confinement and spatial arrangement of obstacles. A microfluidic device is designed to track the cells in confining geometries between two parallel plates with distance h, in which identical micropillars are arranged in regular pillar forests. We find that at each cell-pillar contact event, the cell spends a finite time near the pillar surface, which is independent of the height h and the interpillar spacing e. At low pillar density regime, the directional persistence of cells reduces with decreasing h or e, influencing their diffusivity and first-passage properties. The dynamics is strikingly different at high pillar density regime, where the cells are in simultaneous contact with more than one pillar; the cell velocity and persistence are distinctly higher compared to dilute pillar configurations with the same h. Our simulations reveal that the interplay between cell persistence and cell-pillar interactions can dramatically affect cell diffusivity and, thus, its first-passage properties.