Wolfgang Müller adminproject_administratorasset_housekeeper

Research Groups: Playground, Scientific Databases and Visualisation

Institutions: Heidelberg Institute for Theoretical Studies

https://orcid.org/0000-0002-4980-3512
Hadas Leonov

Research Groups: Playground, Scientific Databases and Visualisation

Institutions: Heidelberg Institute for Theoretical Studies

Olga Krebs

Research Groups: Scientific Databases and Visualisation

Institutions: Heidelberg Institute for Theoretical Studies

Ulrike Wittig

Research Groups: Scientific Databases and Visualisation

Institutions: Heidelberg Institute for Theoretical Studies

https://orcid.org/0000-0002-9077-5664
Provenance Core Data Set: A Minimal Information Model for Data Provenance in Biomedical Research

Abstract (Expand)

The exchange, dissemination, and reuse of biological specimens and data have become essentialfor life sciences research. This requires standards that enable cross-organizational documentation, traceability, … and tracking of data and its corresponding metadata. Thus, data provenance, or the lineage of data, is an important aspect of data management in any information system integrating data from different sources [1]. It provides crucial information about the origin, transformation, and accountability of data, which is essential for ensuring trustworthiness, transparency, and quality of healthcare data [2]. For biological material and derived data, a novel ISO standard was recently introduced that specifies a general concept for a provenance information model for biological material and data and requirements for provenance data interoperability and serialization [3,4]. However, a specific standard for health data provenance is currently missing. In recent years, there has been a growing need for developing a minimal core data set for representing provenance information in health information systems. This paper presents a Provenance Core Data Set (PCDS), a generalized data model that aims to provide a set of attributes for describing data provenance in health information systems and beyond. 

Authors: Ulrich Sax, Christian Henke, Christian Dräger, Theresa Bender, Alessandra Kuntz, Martin Golebiewski, Hannes Ulrich, Mattias Löbe

Date Published: 7th Sep 2023

Publication Type: Journal

NFDI4Health Local Data Hubs for Finding and Accessing Health Data

Abstract (Expand)

To support federated data structuring and sharing for sensitive health data from clinical trial, epidemiological and public health studies in the context of the German National Research Data Infrastructure … for Personal Health Data (NFDI4Health), we have developed Local Data Hubs (LDHs) based on the FAIRDOM-SEEK platform. Those LDHs connect to the German Central Health Study Hub (CSH) to make the health data searchable and findable. This decentralised approach supports researchers to make health studies with their data FAIR (Findable, Accessible, Interoperable and Reusable), and at the same time fully preserves data protection for sensitive data.

Authors: Frank Meineke, Martin Golebiewski, Xiaoming Hu, Toralf Kirsten, Matthias Löbe, Sebastian Klammt, Ulrich Sax, Wolfgang Müller

Date Published: 7th Sep 2023

Publication Type: Proceedings

RDA COVID-19 Recommendations and Guidelines

Abstract

This is the final version of the Recommendations and Guidelines from the RDA COVID19 Working Group, and has been endorsed through the official RDA process.

Author: RDA COVID-19 Working Group

Date Published: 28th May 2020

Publication Type: Manual

Data Formats for Systems Biology and Quantitative Modeling

Abstract (Expand)

Data standards support the reliable exchange of information, the interoperability of tools, and the reproducibility of scientific results. In systems biology standards are agreed ways of structuring, … describing, and associating models and data, as well as their respective parts, graphical visualization, and information about applied experimental or computational methods. Such standards also assist with describing how constituent parts interact together, or are linked, and how they are embedded in their environmental and experimental context. Here the focus will be on standards for formatting models and their content, and on metadata checklists and ontologies that support modeling.

Author: Martin Golebiewski

Date Published: 2019

Publication Type: Journal

Ten Simple Rules for FAIR Sharing of Experimental and Clinical Data with the Modeling Community

Abstract (Expand)

Science continues to become more interdisciplinary and to involve increasingly complex data sets. Many projects in the biomedical and health-related sciences follow or aim to follow the principles of …rinciples of FAIR data sharing, which has been demonstrated to foster collaboration, to lead to better research outcomes, and to help ensure reproducibility of results. Data generated in the course of biomedical and health research present specific challenges for FAIR sharing in the sense that they are heterogeneous and highly sensitive to context and the needs of protection and privacy. Data sharing must respect these features without impeding timely dissemination of results, so that they can contribute to time-critical advances in medical therapy and treatment. Modeling and simulation of biomedical processes have become established tools, and a global community has been developing algorithms, methodologies, and standards for applying biomedical simulation models in clinical research. However, it can be difficult for clinician scientists to follow the specific rules and recommendations for FAIR data sharing within this domain. We seek to clarify the standard workflow for sharing experimental and clinical data with the simulation modeling community. By following these recommendations, data sharing will be improved, collaborations will become more effective, and the FAIR publication and subsequent reuse of data will become possible at the level of quality necessary to support biomedical and health-related sciences.

Authors: Matthias König, Jan Grzegorzewski, Martin Golebiewski, Henning Hermjakob, Mike Hucka, Brett Olivier, Sarah Keating, David Nickerson, Falk Schreiber, Rahuman Sheriff, Dagmar Waltemath

Date Published: 19th Nov 2021

Publication Type: Journal

Publication Policy of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) and the NFDI4Health Task Force COVID-19

Abstract

Not specified

Authors: Birte Lindstädt, Aliaksandra Shutsko, Martin Golebiewski, Dennis-Kenji Kipker, Vanessa Lettieri, Sophie Klopfenstein, Carina Vorisek, Matthias Löbe, Carsten Oliver Schmidt

Date Published: 11th Feb 2022

Publication Type: Manual

NFDI4Health – Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten

Abstract (Expand)

Epidemiologische und klinische Studien sind standardisiert und gut dokumentiert, jedoch erfüllen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien … nicht in ausreichendem Maße. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkörpern und zugehörigen 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 Qualität 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 Einwilligungserklärungen der Studienteilnehmenden erfordert, was jedoch ihre Wiederverwendbarkeit einschränkt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknüpfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies möglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmöglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIR-Prinzipien vorantreiben und die Interoperabilität von IT-Lösungen für Metadatenrepositorien stärken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote für Training und Ausbildung, um der Herausforderung der Umsetzung der Lösungen in den Universitäten und Forschungseinrichtungen zu begegnen. Schließlich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestärkt.

Authors: Juliane Fluck, Birte Lindstädt, Wolfgang Ahrens, Oya Beyan, Benedikt Buchner, Johannes Darms, Ralf Depping, Jens Dierkes, Hubertus Neuhausen, Wolfgang Müller, Hajo Zeeb, Martin Golebiewski, Markus Löffler, Matthias Löbe, Frank Meineke, Sebastian Klammt, Holger Fröhlich, Horst Hahn, Matthias Schulze, Tobias Pischon, Ute Nöthlings, Ulrich Sax, Harald Kusch, Linus Grabenhenrich, Carsten Oliver Schmidt, Dagmar Waltemath, Sebastian Semler, Juliane Gehrke, Toralf Kirsten, Fabian Praßer, Sylvia Thun, Lothar Wieler, Iris Pigeot

Date Published: 28th Jul 2021

Publication Type: Misc

Toward a common standard for data and specimen provenance in life sciences

Abstract (Expand)

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and … knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Authors: Rudolf Wittner, Petr Holub, Cecilia Mascia, Francesca Frexia, Heimo Müller, Markus Plass, Clare Allocca, Fay Betsou, Tony Burdett, Ibon Cancio, Adriane Chapman, Martin Chapman, Mélanie Courtot, Vasa Curcin, Johann Eder, Mark Elliot, Katrina Exter, Carole Goble, Martin Golebiewski, Bron Kisler, Andreas Kremer, Simone Leo, Sheng Lin‐Gibson, Anna Marsano, Marco Mattavelli, Josh Moore, Hiroki Nakae, Isabelle Perseil, Ayat Salman, James Sluka, Stian Soiland‐Reyes, Caterina Strambio‐De‐Castillia, Michael Sussman, Jason R. Swedlow, Kurt Zatloukal, Jörg Geiger

Date Published: 18th Apr 2023

Publication Type: Journal

ISO 20691:2022 Biotechnology — Requirements for data formatting and description in the life sciences

Abstract (Expand)

This document specifies requirements for the consistent formatting and documentation of data and corresponding metadata (i.e. data describing the data and its context) in the life sciences, including … biotechnology, and biomedical, as well as non-human biological research and development. It provides guidance on rendering data in the life sciences findable, accessible, interoperable and reusable (F-A-I-R). This document is applicable to manual or computational workflows that systematically capture, record or integrate data and corresponding metadata in the life sciences for other purposes. This document provides formatting requirements for both primary experimental or procedural data obtained manually and machine derived data. This document also describes requirements for storing, sharing, accessing, interoperability and reuse of data and corresponding metadata in the life sciences. This document specifies requirements for large quantities of data systematically obtained from automated high throughput workflows in the life sciences, as well as requirements for large-scale and small-scale data sets obtained by other life science technologies and manual data capture. This document is applicable to many domains in biotechnology and the life sciences including, but not limited to: basic/applied research in all domains of the life sciences, and industrial, medical, agricultural, or environmental biotechnology (excluding for diagnostic or therapeutic purposes), as well as methodology-driven domains, such as genomics (including massive parallel sequencing, metagenomics, epigenomics and functional genomics), transcriptomics, translatomics, proteomics, metabolomics, lipidomics, glycomics, enzymology, immunochemistry, synthetic biology, systems biology, systems medicine and related fields.

Author: Martin Golebiewski

Date Published: 4th Nov 2022

Publication Type: Manual

COVID-19-Forschungsdaten leichter zugänglich machen – Aufbau einer bundesweiten Informationsinfrastruktur

Abstract (Expand)

Public-Health-Forschung, epidemiologische und klinische Studien sind erforderlich, um die COVID-19-Pandemie besser zu verstehen und geeignete Maßnahmen zu ergreifen. Daher wurden auch in Deutschland … zahlreiche Forschungsprojekte initiiert. Zum heutigen Zeitpunkt ist es ob der Fülle an Informationen jedoch kaum noch möglich, einen Überblick über die vielfältigen Forschungsaktivitäten und deren Ergebnisse zu erhalten. Im Rahmen der Initiative „Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten“ (NFDI4Health) schafft die „Task Force COVID-19“ einen leichteren Zugang zu SARS-CoV-2- und COVID-19-bezogenen klinischen, epidemiologischen und Public-Health-Forschungsdaten. Dabei werden die sogenannten FAIR-Prinzipien (Findable, Accessible, Interoperable, Reusable) berücksichtigt, die eine schnellere Kommunikation von Ergebnissen befördern sollen. Zu den wesentlichen Arbeitsinhalten der Taskforce gehören die Erstellung eines Studienportals mit Metadaten, Erhebungsinstrumenten, Studiendokumenten, Studienergebnissen und Veröffentlichungen sowie einer Suchmaschine für Preprint-Publikationen. Weitere Inhalte sind ein Konzept zur Verknüpfung von Forschungs- und Routinedaten, Services zum verbesserten Umgang mit Bilddaten und die Anwendung standardisierter Analyseroutinen für harmonisierte Qualitätsbewertungen. Die im Aufbau befindliche Infrastruktur erleichtert die Auffindbarkeit von und den Umgang mit deutscher COVID-19-Forschung. Die im Rahmen der NFDI4Health Task Force COVID-19 begonnenen Entwicklungen sind für weitere Forschungsthemen nachnutzbar, da die adressierten Herausforderungen generisch für die Auffindbarkeit von und den Umgang mit Forschungsdaten sind.

Authors: Carsten Oliver Schmidt, Juliane Fluck, Martin Golebiewski, Linus Grabenhenrich, Horst Hahn, Toralf Kirsten, Sebastian Klammt, Matthias Löbe, Ulrich Sax, Sylvia Thun, Iris Pigeot, Wolfgang Ahrens, Johannes Darms, Jörg Henke, Xiaoming Hu, Sophie Klopfenstein, Lisa Langnickel, Bianca Lassen-Schmidt, Hermann Pohlabeln, Michael Lieser, Anatol-Fiete Näher, Markus Scholz, Carina Vorisek, Dagmar Waltemath, Hannes Wünsche

Date Published: 1st Sep 2021

Publication Type: Journal

Facilitating Study and Item Level Browsing for Clinical and Epidemiological COVID-19 Studies.

Abstract (Expand)

COVID-19 poses a major challenge to individuals and societies around the world. Yet, it is difficult to obtain a good overview of studies across different medical fields of research such as clinical … trials, epidemiology, and public health. Here, we describe a consensus metadata model to facilitate structured searches of COVID-19 studies and resources along with its implementation in three linked complementary web-based platforms. A relational database serves as central study metadata hub that secures compatibilities with common trials registries (e.g. ICTRP and standards like HL7 FHIR, CDISC ODM, and DataCite). The Central Search Hub was developed as a single-page application, the other two components with additional frontends are based on the SEEK platform and MICA, respectively. These platforms have different features concerning cohort browsing, item browsing, and access to documents and other study resources to meet divergent user needs. By this we want to promote transparent and harmonized COVID-19 research.

Authors: C. O. Schmidt, J. Darms, A. Shutsko, M. Lobe, R. Nagrani, B. Seifert, B. Lindstadt, M. Golebiewski, S. Koleva, T. Bender, C. R. Bauer, U. Sax, X. Hu, M. Lieser, V. Junker, S. Klopfenstein, A. Zeleke, D. Waltemath, I. Pigeot, J. Fluck

Date Published: 27th May 2021

Publication Type: Journal

Towards standardization guidelines for in silico approaches in personalized medicine

Abstract (Expand)

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind … its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.

Authors: Søren Brunak, Catherine Bjerre Collin, Katharina Eva Ó Cathaoir, Martin Golebiewski, Marc Kirschner, Ingrid Kockum, Heike Moser, Dagmar Waltemath

Date Published: 24th Jul 2020

Publication Type: Journal

SEEK: a systems biology data and model management platform.

Abstract (Expand)

BACKGROUND: Systems biology research typically involves the integration and analysis of heterogeneous data types in order to model and predict biological processes. Researchers therefore require tools … and resources to facilitate the sharing and integration of data, and for linking of data to systems biology models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several model repositories. However, this silo-type storage of data and models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and models are essential. Researchers require an environment that will allow the management and sharing of heterogeneous data and models in the context of the experiments which created them. RESULTS: The SEEK is a suite of tools to support the management, sharing and exploration of data and models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, models and results in a configurable system that is available 'off the shelf'. Tools to run model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. CONCLUSION: The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.

Authors: K. Wolstencroft, S. Owen, O. Krebs, Q. Nguyen, N. J. Stanford, M. Golebiewski, A. Weidemann, M. Bittkowski, L. An, D. Shockley, J. L. Snoep, W. Mueller, C. Goble

Date Published: 15th Jul 2015

Publication Type: Journal

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