German National Library of Medicine

COVID-19 posed one of the greatest challenges to individuals and societies worldwide in recent decades. Public health research, epidemiological and clinical studies were essential to track the spread of SARS-CoV-2 responsible for the pandemic and its variants, to better understand the consequences for health and social life, and to identify effective treatment and vaccination methods. Such studies provided policy makers, industry, health care providers, and society with an empirical basis for containing and managing the pandemic and for making decisions that were based on the most recent data. Therefore, the COVID-19 pandemic excellently illustrates the relevance of data sharing and the importance of providing an effective infrastructure. From the researchers’ perspective, there were significant challenges associated with this request. In a very short time, numerous projects, studies, and networks had emerged to investigate the pandemic, making it increasingly difficult to maintain an overview. Such an overview would have been essential to coordinate research activities, avoid unplanned duplication of research, and to implement studies in a harmonized manner.

<p>One of the purposes of the NFDI4Health is to make data collected in health-related studies FAIR. The first step towards this is the publication of comprehensive information about the context and accessibility of the data (metadata set). In addition, study documents contain further detailed information needed for the correct interpretation of the collected data and should therefore be published as well.</p> <p>The Publication Policy describes the requirements for the publication of research data from health-related studies in the NFDI4Health Study Hub. Researchers or research groups wanting to make their studies discoverable and corresponding research data FAIR should fulfil these requirements.</p>

For all research data collected, data descriptions and information about the corresponding variables are essential for data analysis and reuse. To enable cross-study comparisons and analyses, semantic interoperability of metadata is one of the most important requirements. In the area of clinical and epidemiological studies, data collection instruments such as case report forms (CRFs), data dictionaries and questionnaires are critical for metadata collection. Even though data collection instruments are often created in a digital form, they are mostly not machine readable; i.e., they are not semantically coded. As a result, the comparison between data collection instruments is complex. The German project NFDI4Health is dedicated to the development of national research data infrastructure for personal health data, and as such searches for ways to enhance semantic interoperability. Retrospective integration of semantic codes into study metadata is important, as ongoing or completed studies contain valuable information. However, this is labor intensive and should be eased by software. To understand the market and find out what techniques and technologies support retrospective semantic annotation/enrichment of metadata, we conducted a literature review. In NFDI4Health, we identified basic requirements for semantic metadata annotation software in the biomedical field and in the context of the FAIR principles. Ten relevant software systems were summarized and aligned with those requirements. We concluded that despite active research on semantic annotation systems, no system meets all requirements. Consequently, further research and software development in this area is needed, as interoperability of data dictionaries, questionnaires and data collection tools is key to reusing and combining results from independent research studies.

This handbook aims to support higher education institutions with the integration of FAIR-related content in their curricula and teaching. It was written and edited by a group of about 40 collaborators in a series of six book sprint events that took place between 1 and 10 June 2021. The document provides practical material, such as competence profiles, learning outcomes and lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021.

<p>This is the final report of the Q-Aktiv project ("Quantitative analysis of the dynamics of the scientific, economic and societal impact of research activities and networks"). Based on the observation that innovations often emerge at the interfaces of disciplines, the project team developed a machine-learning-based method that is intended to identify an interdisciplinary convergence of research fields ("scientific convergence") at an early stage on the basis of various information. It is tested with data from the life sciences.</p>

doch erfullen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien nicht in ausreichendem Mase. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkorpern und zugehorigen Dokumenten sowie einen FAIRen Zugang zu diesen erleichtert. Dazu werden die Auffindbarkeit und der Zugang zu strukturierten Gesundheitsdaten aus Registern, administrativen Gesundheitsdatenbanken, klinischen und epidemiologischen sowie Public Health-Studien verbessert und die Qualitat und Harmonisierung der zugrundeliegenden Daten optimiert.Eine weitere Herausforderung entsteht durch die Verwendung personenbezogener Gesundheitsdaten. Diese sind hoch sensibel, so dass ihre Nutzung restriktive Datenschutzbestimmungen und informierte Einwilligungserklarungen der Studienteilnehmenden erfordert, was jedoch ihre Wiederverwendbarkeit einschrankt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknupfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies moglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmoglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIR-Prinzipien vorantreiben und die Interoperabilitat von IT-Losungen fur Metadatenrepositorien starken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote fur Training und Ausbildung, um der Herausforderung der Umsetzung der Losungen in den Universitaten und Forschungseinrichtungen zu begegnen. Schlieslich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestarkt.

In this paper, we report on the outputs and adoption of the Agrisemantics Working Group of the Research Data Alliance (RDA), consisting of a set of recommendations to facilitate the adoption of semantic technologies and methods for the purpose of data interoperability in the field of agriculture and nutrition. From 2016 to 2019, the group gathered researchers and practitioners at the crossing point between information technology and agricultural science, to study all aspects in the life cycle of semantic resources: conceptualization, edition, sharing, standardization, services, alignment, long term support. First, the working group realized a landscape study, a study of the uses of semantics in agrifood, then collected use cases for the exploitation of semantics resources-a generic term to encompass vocabularies, terminologies, thesauri, ontologies. The resulting requirements were synthesized into 39 "hints" for users and developers of semantic resources, and providers of semantic resource services. We believe adopting these recommendations will engage agrifood sciences in a necessary transition to leverage data production, sharing and reuse and the adoption of the FAIR data principles. The paper includes examples of adoption of those requirements, and a discussion of their contribution to the field of data science.

Negative feedback regulation, that is the ability of a gene to repress its own synthesis, is the most abundant regulatory motif known to biology. Frequently reported for transcriptional regulators, negative feedback control relies on binding of a transcription factor to its own promoter. Here, we report a novel mechanism for gene autoregulation in bacteria relying on small regulatory RNA (sRNA) and the major endoribonuclease, RNase E. TIER-seq analysis (transiently-inactivating-an-endoribonuclease-followed-by-RNA-seq) revealed ~25,000 RNase E-dependent cleavage sites in Vibrio cholerae, several of which resulted in the accumulation of stable sRNAs. Focusing on two examples, OppZ and CarZ, we discovered that these sRNAs are processed from the 3’ untranslated region (3’ UTR) of the oppABCDF and carAB operons, respectively, and base-pair with their own transcripts to inhibit translation. For OppZ, this process also triggers Rho-dependent transcription termination. Our data show that sRNAs from 3’ UTRs serve as autoregulatory elements allowing negative feedback control at the post-transcriptional level.

Zusammenfassung Geschichten zu erzählen ist ein uraltes Bedürfnis von Menschen. Wie die wissenschaftliche Analyse von Sagen, Mythen, Romanen und Filmen zeigt, lässt sich die Struktur der Geschichten schlussendlich auf fünf Kapitel reduzieren, die dem Plot der klassischen Heldenreise entsprechen. Menschen, die nach „ETWAS“ streben, berühren die Rezipienten, da sie sich mit den Protagonisten identifizieren. Im Stil der Heldenreise lassen sich faszinierende Geschichten aus der Wissenschaft erzählen.

Since 2013 archiving rules for medical print journals in Germany are in existence. Out of it an extensive cooperation with libraries evolved.