15 research outputs found
FAIR health data in the national and international data space
Get PDFZusammenfassung Gesundheitsdaten haben in der heutigen datenorientierten Welt einen hohen Stellenwert. Durch automatisierte Verarbeitung können z. B. Prozesse im Gesundheitswesen optimiert und klinische Entscheidungen unterstützt werden. Dabei sind Aussagekraft, Qualität und Vertrauenswürdigkeit der Daten wichtig. Nur so kann garantiert werden, dass die Daten sinnvoll nachgenutzt werden können. Konkrete Anforderungen an die Beschreibung und Kodierung von Daten werden in den FAIR-Prinzipien beschrieben. Verschiedene nationale Forschungsverbünde und Infrastrukturprojekte im Gesundheitswesen haben sich bereits klar zu den FAIR-Prinzipien positioniert: Sowohl die Infrastrukturen der Medizininformatik-Initiative als auch des Netzwerks Universitätsmedizin operieren explizit auf Basis der FAIR-Prinzipien, ebenso die Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten oder das Deutsche Zentrum für Diabetesforschung. Um eine FAIRe Ressource bereitzustellen, sollte zuerst in einem Assessment der FAIRness-Grad festgestellt werden und danach die Priorisierung für Verbesserungsschritte erfolgen (FAIRification). Seit 2016 wurden zahlreiche Werkzeuge und Richtlinien für beide Schritte entwickelt, basierend auf den unterschiedlichen, domänenspezifischen Interpretationen der FAIR-Prinzipien. Auch die europäischen Nachbarländer haben in die Entwicklung eines nationalen Rahmens für semantische Interoperabilität im Kontext der FAIR-Prinzipien investiert. So wurden Konzepte für eine umfassende Datenanreicherung entwickelt, um die Datenanalyse beispielsweise im Europäischen Gesundheitsdatenraum oder über das Netzwerk der Observational Health Data Sciences and Informatics zu vereinfachen. In Kooperation mit internationalen Projekten, wie z. B. der European Open Science Cloud, wurden strukturierte FAIRification-Maßnahmen für Gesundheitsdatensätze entwickelt.Abstract Health data are extremely important in today’s data-driven world. Through automation, healthcare processes can be optimized, and clinical decisions can be supported. For any reuse of data, the quality, validity, and trustworthiness of data are essential, and it is the only way to guarantee that data can be reused sensibly. Specific requirements for the description and coding of reusable data are defined in the FAIR guiding principles for data stewardship. Various national research associations and infrastructure projects in the German healthcare sector have already clearly positioned themselves on the FAIR principles: both the infrastructures of the Medical Informatics Initiative and the University Medicine Network operate explicitly on the basis of the FAIR principles, as do the National Research Data Infrastructure for Personal Health Data and the German Center for Diabetes Research. To ensure that a resource complies with the FAIR principles, the degree of FAIRness should first be determined (so-called FAIR assessment), followed by the prioritization for improvement steps (so-called FAIRification). Since 2016, a set of tools and guidelines have been developed for both steps, based on the different, domain-specific interpretations of the FAIR principles. Neighboring European countries have also invested in the development of a national framework for semantic interoperability in the context of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Concepts for comprehensive data enrichment were developed to simplify data analysis, for example, in the European Health Data Space or via the Observational Health Data Sciences and Informatics network. With the support of the European Open Science Cloud, among others, structured FAIRification measures have already been taken for German health datasets
Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review
Get PDFBackground: Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.
Objective: This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.
Methods: The Arksey and O’Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
Results: A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic.
Conclusions: This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing.
International Registered Report Identifier (IRRID): RR2-10.2196/2250
Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review
Get PDFData stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic
Research in medical education - chances and challenges : international conference, 20th - 22nd May 2009, Heidelberg ; congress abstracts
No full textFilmic machines and animated monsters: retelling Frankenstein in the digital age
Get PDFFrankensteinian monsters have appeared on our screens since the early days of cinema. Indeed, across the history of film we see Mary Shelley’s “hideous progeny” rewritten as alchemical creations, animated corpses, lumbering fiends, robots, cyborgs, replicants, dinosaurs, artificial intelligences and digital constructions. In particular, Shelley’s text shares its speculative depiction of a posthuman future with fantastic and science-fictional cinema of the digital age. At the same time, posthuman bodies are being created by filmmakers. New possibilities in the digital imaging of human presence – from the replacement of actors with computer-generated imagery to the quest for photorealism in digital animation – themselves evoke the Frankenstein tale and consequently make interesting contributions to the evolving Frankenstein myth.
This thesis investigates the retelling of Frankenstein in popular cinema of the digital age. Through close analysis of a series of chosen texts, I examine the figure of the Frankensteinian monster and his/her/its equivalents in today’s popular culture: posthuman figures who negotiate uneasily with the organic world, boundary creatures who both define and unsettle our understandings of human being. I consider the way the tale, its themes and characters have both endured and evolved over time. I also examine the way these new filmic “machines” and animated “monsters” embody crucial problems associated with the technologies that screen them and the media that contain them.
My concern in this project is twofold. Firstly, I seek to map the (changing) relationship between Frankenstein and film. Since the early 1900s, cinema has provided a fertile ground for the retelling of Shelley’s tale. At the same time, cinema itself has always been a sort of Frankensteinian experiment: a means of breathing life into stillness, of constructing and re-constructing human presence, of stitching together fragmented moments to create a semblance of wholeness. In the digital age, this experiment grows and changes: new modes of production are continually being trialled, allowing us to re-create and re-present human presence in new and often bizarre ways. The figure of the Frankensteinian monster confronts and responds to these concerns, embodying and performing the uncanny, spectacular, mechanical, or organic-mechanical nature of screen presence.
Secondly, this thesis reads the Frankensteinian monster as a mythic figure for the digital age. I move towards the assertion that Frankenstein is a tale about the artificial body and its negotiation with a lost or disrupted origin in the organic world, and that this particular problem reverberates strongly in an age of digital representation. The analyses that constitute this thesis contribute to the argument that each time the Frankenstein tale is retold, re-technologised, and re-imagined using new filmic techniques, the problem of the screen body and its troubled origin stories is revisited and complicated
The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)
No full textThis paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.publishe
China's rural development challenges: land tenure reform and local institutional experimentation
Get PDFDespite its unprecedented achievements in rural development, China remains a lower-middle income country. Unsound practices in farmland use and management have contributed to farmland loss, rising social conflicts and deprivation of the landless, which perpetuates rural poverty and land tenure insecurity of the weak and poor. The current hybrid land tenure systems characterized by collective ownership and individual use rights exert both positive and negative effects on land governance. China’s approach to land laws, policies and institutional reforms is characterized by inherent weaknesses which impede the strengthening of peasants’ rights and collective action in the process. With the simplistic assumption on the importance of land tenure to facilitate its transferability and scaled agricultural production, the current reform is undergoing a risky transformation that may backfire. In this sense, the Chinese approach bears resemblances with other countries whose experiences have failed the poor and have produced unintended consequences. In essence, the failure to take into account the livelihoods of the poor especially from sustainable land use perspectives exemplifies their pursuit of short-term gains rather than longer-term solutions to complex rural development issues. The challenges confronting China’s rural development require a renewed understanding of what constitutes an appropriate land tenure system that suits the local conditions of a given community. This needs a holistic study of what kind of land tenure systems exist in China, how they have worked in the past, what their problems are, and how they can be redressed to suit the needs of the poor.
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The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany
Get PDFThe German Center for Diabetes Research (DZD) established a core data set (CDS) of clinical parameters relevant for diabetes research in 2021. The CDS is central to the design of current and future DZD studies. Here, we describe the process and outcomes of FAIRifying the initial version of the CDS. We first did a baseline evaluation of the FAIRness using the FAIR Data Maturity Model. The FAIRification process and the results of this assessment led us to convert the CDS into the recommended format for spreadsheets, annotating the parameters with standardized medical codes, licensing the data set, enriching the data set with metadata, and indexing the metadata. The FAIRified version of the CDS is more suitable for data sharing in diabetes research across DZD sites and beyond. It contributes to the reusability of health research studies
Author Correction: A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.
Get PDFCorrection to: Nature Human Behaviour https://doi.org/10.1038/s41562-021-01173-x, published online 2 August 2021.In the version of this article initially published, the following authors were omitted from the author list and the Author contributionssection for “investigation” and “writing and editing”: Nandor Hajdu (Institute of Psychology, ELTE Eötvös Loránd University, Budapest,Hungary), Jordane Boudesseul (Facultad de Psicología, Instituto de Investigación Científica, Universidad de Lima, Lima, Perú), RafałMuda (Faculty of Economics, Maria Curie-Sklodowska University, Lublin, Poland) and Sandersan Onie (Black Dog Institute, UNSWSydney, Sydney, Australia & Emotional Health for All Foundation, Jakarta, Indonesia). In addition, Saeideh FatahModares’ name wasoriginally misspelled as Saiedeh FatahModarres in the author list. Further, affiliations have been corrected for Maria Terskova (NationalResearch University Higher School of Economics, Moscow, Russia), Susana Ruiz Fernandez (FOM University of Applied Sciences,Essen; Leibniz-Institut fur Wissensmedien, Tubingen, and LEAD Research Network, Eberhard Karls University, Tubingen, Germany),Hendrik Godbersen (FOM University of Applied Sciences, Essen, Germany), Gulnaz Anjum (Department of Psychology, Simon FraserUniversity, Burnaby, Canada, and Department of Economics & Social Sciences, Institute of Business Administration, Karachi, Pakistan).<br/
Author Correction: Child wasting and concurrent stunting in low- and middle-income countries
Get PDFCorrection to: Nature https://doi.org/10.1038/s41586-023-06480-z Published online 13 September 2023
