Menoci: lightweight extensible web portal enhancing data management for biomedical research projects

Suhr, Markus; Lehmann, Christoph U.; Bauer, Christian R.; Bender, Theresa; Knopp, Cornelius; Freckmann, Luca; Hansen, Björn Öst; Henke, Christian; Aschenbrandt, Georg; Kühlborn, Lea K; Rheinländer, Sophia; Weber, Linus; Marzec, Bartlomiej; Hellkamp, Marcel; Wieder, Philipp; Kusch, Harald; Sax, Ulrich; Nussbeck, Sara Y.

doi:10.1186/s12859-020-03928-1

Cited by 9 publications

(18 citation statements)

References 34 publications

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“…We selected the generic Drupal framework with a great potential to combine readily available web portal functionality with the possibility to add specific modules for special requirements. A comparison of functional categories of menoci and other RDM frameworks (such as OpenBis or Seek) has been described previously [12].…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the implementation of our self-developed Drupal-based menoci Research Data Platform (RDP) [12], essential core and driver of the requirements engineering according to Pohl [13] was the close collaboration with selected pilot experts and early adopters of a standardized documentation workflow. Most of the projects make use of the two service projects in the CRC 1002 (S01, "In Vivo and In Vitro Disease Models", S02 "High resolution fluorescence microscopy and integrative data analysis").…”

Section: Requirements Analysis Concept Development and Implementationmentioning

confidence: 99%

“…For each selected data type, modules were designed and integrated into the Drupal-based menoci framework. The decision process and comparison of alternative frameworks as well as detailed technical aspects of the implementations are described by Suhr et al [12]. In brief, the software development process followed the agile development paradigm integrating user (selected pilot experts and early adopters) feedback and continuous evaluation within the development cycles.…”

Section: Requirements Analysis Concept Development and Implementationmentioning

confidence: 99%

“…Complementing the catalogue modules described above, menoci workflow modules were designed on the one hand to enable the deep documentation and teaching of complex experimental approaches and research procedures, and on the other hand for planning, conducting and data storing of two different imaging techniques: ALMN and Mouse Echocardiography. A more detailed description of the technical workflow employed in these modules can be found in Suhr et al [12]. In short, a request for an imaging experiment can be put forth by researchers which will then be performed or supported by a corresponding service project of the CRC 1002.…”

Section: Workflow Support Modulesmentioning

confidence: 99%

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Management of Metadata Types in Basic Cardiological Research

Kusch

Kossen

Suhr

et al. 2021

Studies in Health Technology and Informatics

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Introduction: Ensuring scientific reproducibility and compliance with documentation guidelines of funding bodies and journals is a topic of greatly increasing importance in biomedical research. Failure to comply, or unawareness of documentation standards can have adverse effects on the translation of research into patient treatments, as well as economic implications. In the context of the German Research Foundation-funded collaborative research center (CRC) 1002, an IT-infrastructure sub-project was designed. Its goal has been to establish standardized metadata documentation and information exchange benefitting the participating research groups with minimal additional documentation efforts. Methods: Implementation of the self-developed menoci-based research data platform (RDP) was driven by close communication and collaboration with researchers as early adopters and experts. Requirements analysis and concept development involved in person observation of experimental procedures, interviews and collaboration with researchers and experts, as well as the investigation of available and applicable metadata standards and tools. The Drupal-based RDP features distinct modules for the different documented data and workflow types, and both the development and the types of collected metadata were continuously reviewed and evaluated with the early adopters. Results: The menoci-based RDP allows for standardized documentation, sharing and cross-referencing of different data types, workflows, and scientific publications. Different modules have been implemented for specific data types and workflows, allowing for the enrichment of entries with specific metadata and linking to further relevant entries in different modules. Discussion: Taking the workflows and datasets of the frequently involved experimental service projects as a starting point for (meta-)data types to overcome irreproducibility of research data, results in increased benefits for researchers with minimized efforts. While the menoci-based RDP with its data models and metadata schema was originally developed in a cardiological context, it has been implemented and extended to other consortia at GÃűttingen Campus and beyond in different life science research areas.

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Section: Discussionmentioning

confidence: 99%

Section: Requirements Analysis Concept Development and Implementationmentioning

confidence: 99%

Section: Requirements Analysis Concept Development and Implementationmentioning

confidence: 99%

Section: Workflow Support Modulesmentioning

confidence: 99%

See 2 more Smart Citations

Management of Metadata Types in Basic Cardiological Research

Kusch

Kossen

Suhr

et al. 2021

Studies in Health Technology and Informatics

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“…The LIGO Open Science Center (LOSC) ( ) is another initiative, with the intent to facilitate Open Science research in the Astronomy domain by offering a platform to discover and analyse data from the Laser Interferometer Gravitational-wave Observatory (LIGO) ( Vallisneri et al, 2015 ). Archives such as GenBank ( Benson et al, 2012 ) and menoci ( Suhr et al, 2020 ) have also made their data open and accessible to users in the biomedical domain, and EUDAT ( Widmann & Thiemann, 2016 ) is an international initiative to help overcome the challenges related to the reuse of data, by offering a Collaborative Data Infrastructure (CDI) for the research community. Other developments, in the effort to render the technical difficulties linked to the use of secondary data, have been in 2016 with the introduction of the FAIR Guiding Principles ( Wilkinson et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Aligning restricted access data with FAIR: a systematic review

Martorana¹,

Kuhn²,

Siebes³

et al. 2022

PeerJ Computer Science

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Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable, it is still unclear how the notions of FAIR and Openness can be applied in the context of restricted data. Many methods have been proposed in support of the implementation of the principles, but there is yet no consensus among the scientific community as to the suitable mechanisms of making restricted data FAIR. We present here a systematic literature review to identify the methods applied by scientists when researching restricted data in a FAIR-compliant manner in the context of the FAIR principles. Through the employment of a descriptive and iterative study design, we aim to answer the following three questions: (1) What methods have been proposed to apply the FAIR principles to restricted data?, (2) How can the relevant aspects of the methods proposed be categorized?, (3) What is the maturity of the methods proposed in applying the FAIR principles to restricted data?. After analysis of the 40 included publications, we noticed that the methods found, reflect the stages of the Data Life Cycle, and can be divided into the following Classes: Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage and Post Data Usage. We observed that a large number of publications used ‘Access Control‘ and ‘Usage and License Terms’ methods, while others such as ‘Embargo on Data Release’ and the use of ‘Synthetic Data’ were used in fewer instances. In conclusion, we are presenting the first extensive literature review on the methods applied to confidential data in the context of FAIR, providing a comprehensive conceptual framework for future research on restricted access data.

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FAIR sharing of health data: a systematic review of applicable solutions

Guillot,

Bøgsted,

Vesteghem

2023

Health Technol.

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Purpose Data sharing is essential in health science research. This has also been acknowledged by governments and institutions who have set-up a number of regulations, laws, and initiatives to facilitate it. A large number of initiatives has been trying to address data sharing issues. With the development of the FAIR principles, a set of detailed criteria for evaluating the relevance of such solutions is now available. This article intends to help researchers to choose a suitable solution for sharing their health data in a FAIR way. Methods We conducted a systematic literature review of data sharing platforms adapted to health science research. We selected these platforms through a query on Scopus, PubMed, and Web of Science and filtered them based on specific exclusion criteria. We assessed their relevance by evaluating their: implementation of the FAIR principles, ease of use by researchers, ease of implementation by institutions, and suitability for handling Individual Participant Data (IPD). Results We categorized the 35 identified solutions as being either online or on-premises software platforms. Interoperability was the main obstacle for the solutions regarding the fulfilment of the FAIR principles. Additionally, we identified which solutions address sharing of IPD and anonymization issues. Vivli and Dataverse were identified as the two most all-round solutions for sharing health science data in a FAIR way. Conclusions Although no solution is perfectly adapted to share all type of health data, there are work-arounds and interesting solutions to make health research data FAIR.

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Menoci: lightweight extensible web portal enhancing data management for biomedical research projects

Cited by 9 publications

References 34 publications

Management of Metadata Types in Basic Cardiological Research

Management of Metadata Types in Basic Cardiological Research

Aligning restricted access data with FAIR: a systematic review

FAIR sharing of health data: a systematic review of applicable solutions

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