Methods and Technologies for Research- and Metadata Management in Collaborative Experimental Research

Preuß, Nils; Staudter, Georg; Weber, Moritz; Anderl, Reiner; Pelz, Peter F.

doi:10.4028/www.scientific.net/amm.885.170

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Scientific data are understandably complex and costly [11]. Government agencies are seeking greater transparency and accountability in data management and are promoting data accessibility and exploitability beyond organizations.…”

Section: Introductionmentioning

confidence: 99%

From simulation to dissemination: automation of data and metadata management

Arif

Schade

Lehfeldt

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Working with Computational Fluid Dynamics (CFD) can produce numerous datasets, which contain different physical parameters and study settings. Management and dissemination of such data benefits greatly from a comprehensive data description (metadata), ideally a specialized Metadata Information System (MIS), and adequate long-term storage (data warehouse). To avoid the manual creation of rival metadata, an automation method has been developed, which adds metadata automatically by the simulation and post-processing programs. The automation method described here is an example for data curation suitable for a professional work environment. This method begins with the collection and creation of metadata and ends with the dissemination and publication of the data. This procedure reduces the challenging amount of tedious and error-prone workload, avoids redundancy, enhances efficiency, and is thus a means of quality assurance. Basis for the automation process is an open-source metadata information system (MIS) which has been adapted to the techno-scientific demand of simulation metadata. It stores the metadata in an SQL database and provides Open Geospatial Consortium (OGC) compliant services and communication interface. A hierarchical metadata management concept was initiated for efficient management of numerous datasets. Subsequently, the metadata validation and dissemination are automated via a middleware, which compiles metadata in an XML file, imports the metadata into the MIS and transfers the data to a long-term repository via a Representational State Transfer (REST) interface. Finally, the data and metadata are interlinked and published.

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Section: Introductionmentioning

confidence: 99%

From simulation to dissemination: automation of data and metadata management

Arif

Schade

Lehfeldt

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

Fostering Research Data Management in Collaborative Research Contexts: Lessons learnt from an ‘Embedded’ Evaluation of ‘Data Story’

Mosconi

Carvalho

Syed

et al. 2023

Comput Supported Coop Work

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Research Data Management (RDM) practices -spanning the curation, storage, sharing, and reusability of research data -are foundational to the Open Science (OS) agenda. Because of that, many and various funding institutions are increasingly demanding active engagement of researchers in them. Nevertheless, recent studies suggest that RDM practices are not yet properly integrated into daily research work ows, nor supported by any tools researchers typically use. In this paper, we introduce evaluation results of a technological aid for implementing a design concept called 'Data Story', drawing on ideas from digital and data storytelling. This concept has been devised to facilitate the appropriation of RDM practices by researchers working mainly with qualitative data in their daily work ows. It integrates traditional data curation approaches with a more narrative, contextual, and collaborative organizational layer that can be thought of as a 'story'. Our ndings come from a long-term 'embedded' evaluation of the concept and show: (1) the potential bene ts of engaging with a Data Story for RDM; (2) Data Curation issues and learning opportunities; and (3) a broader set of issues and concerns that remain unaddressed in the current state of play. Our contribution, based on lessons learnt, is to provide a new design approach for RDM and for new collaborative research data practices, one grounded in narrative structures, capable to negotiate between top-down policies and bottom-up practices, which supports 're ective' learning opportunities -with and about data -of many kinds.

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Fostering Research Data Management in Collaborative Research Contexts: Lessons learnt from an ‘Embedded’ Evaluation on designing a ‘Data Story’

Mosconi

Carvalho

Syed

et al. 2022

Preprint

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Research Data Management (RDM) practices – spanning the curation, storage, sharing, and reusability of research data – are foundational to the Open Science (OS) agenda. Because of that, many and various funding institutions are increasingly demanding active engagement of researchers in them. Nevertheless, recent studies suggest that RDM practices are not yet properly integrated into daily research workflows, nor supported by any tools researchers typically use. In this paper, we introduce evaluation results of a technological aid for implementing a design concept called ‘Data Story’, drawing on ideas from digital and data storytelling. This concept has been devised to facilitate the appropriation of RDM practices by researchers working mainly with qualitative data in their daily workflows. It integrates traditional data curation approaches with a more narrative, contextual, and collaborative organizational layer that can be thought of as a ‘story’. Our findings come from a long-term ‘embedded’ evaluation of the concept and show: (1) the potential benefits of engaging with a Data Story for RDM; (2) Data Curation issues and learning opportunities; and (3) a broader set of issues and concerns that remain unaddressed in the current state of play. Our contribution, based on lessons learnt, is to provide a new design approach for RDM and for new collaborative research data practices, one grounded in narrative structures, capable to negotiate between top-down policies and bottom-up practices, which supports ‘reflective’ learning opportunities - with and about data - of many kinds.

show abstract

Methods and Technologies for Research- and Metadata Management in Collaborative Experimental Research

Cited by 3 publications

References 20 publications

From simulation to dissemination: automation of data and metadata management

From simulation to dissemination: automation of data and metadata management

Fostering Research Data Management in Collaborative Research Contexts: Lessons learnt from an ‘Embedded’ Evaluation of ‘Data Story’

Fostering Research Data Management in Collaborative Research Contexts: Lessons learnt from an ‘Embedded’ Evaluation on designing a ‘Data Story’

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