Research Data Management Infrastructure Research Articles

Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems

Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named “CaosDB”).

Consolidating Research Data Management Infrastructure: Towards Sustainable Digital Scholarship

The sustainability of digital research outputs, particularly in the humanities where these frequently comprise archives of digital cultural heritage material, has always offered a challenge to the researchers and institutions who have responsibility for them. The amount of upfront care, effort, and funding that goes into developing a research project during the active (and funded) research phase is rarely replicated within the post-project maintenance and curation of the delivered digital assets or archives. What often defines the sustainability of a research project and its archive is a combination of research method and expected life span for the digital collection. Innovation in research data design is often at the expense of its longevity. But this does not need to be so. The tradeoff between longevity and functionality is a false dichotomy. Yet what is clear is that care and consideration in planning the research data storage or archive for a project can make a big difference. A data management plan that meets grant funder requirements is asked for for many research projects, but it is more than simply a funding document. Good research data management ensures that outputs are available online for years to come and available for future research and innovation. This article offers a practical insight to the methods being employed at the University of Oxford to support Digital Humanities scholars (and beyond) safeguard their digital legacy for future generations.

BAM Data Store

As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.

Development of research data management infrastructure in Kanazawa University

Development of research data management infrastructure in Kanazawa University

Automating Research Data Management Using Machine-Actionable Data Management Plans

Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.

Practical considerations for a library's research data management services: the case of the National Institutes of Health Library.

Objective:This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services.Methods:We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library's RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle.Results:The findings show that the two models' crosstab framework can provide an overview of an institution's current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers' needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users.Conclusion:These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services.

AN OPEN SCIENCE APPROACH TO GIS-BASED PALEOENVIRONMENT DATA

Abstract. Paleoenvironmental studies and according information (data) are abundantly published and available in the scientific record. However, GIS-based paleoenvironmental information and datasets are comparably rare. Here, we present an Open Science approach for creating GIS-based data and maps of paleoenvironments, and Open Access publishing them in a web based Spatial Data Infrastructure (SDI), for access by the archaeology and paleoenvironment communities. We introduce an approach to gather and create GIS datasets from published non-GIS based facts and information (data), such as analogous maps, textual information or figures in scientific publications. These collected and created geo-datasets and maps are then published, including a Digital Object Identifier (DOI) to facilitate scholarly reuse and citation of the data, in a web based Open Access Research Data Management Infrastructure. The geo-datasets are additionally published in an Open Geospatial Consortium (OGC) standards compliant SDI, and available for GIS integration via OGC Open Web Services (OWS).

AN OPEN SCIENCE APPROACH TO GIS-BASED PALEOENVIRONMENT DATA

Paleoenvironmental studies and according information (data) are abundantly published and available in the scientific record. However, GIS-based paleoenvironmental information and datasets are comparably rare. Here, we present an Open Science approach for creating GIS-based data and maps of paleoenvironments, and Open Access publishing them in a web based Spatial Data Infrastructure (SDI), for access by the archaeology and paleoenvironment communities. We introduce an approach to gather and create GIS datasets from published non-GIS based facts and information (data), such as analogous maps, textual information or figures in scientific publications. These collected and created geo-datasets and maps are then published, including a Digital Object Identifier (DOI) to facilitate scholarly reuse and citation of the data, in a web based Open Access Research Data Management Infrastructure. The geo-datasets are additionally published in an Open Geospatial Consortium (OGC) standards compliant SDI, and available for GIS integration via OGC Open Web Services (OWS).

Knowledge management, research data management, and university scholarship

Purpose– The purpose of this paper is to synthesize existing research on research data management (RDM), academic scholarship and knowledge management and provide a conceptual framework for an institutional research data management support-system (RDMSS) for systems development, managerial and academic use.Design/methodology/approach– Viewing RDMSS from multiple theoretical perspectives, including data management, knowledge management, academic scholarship and the practice-based perspectives of knowledge and knowing, this paper conceptually explores the systems’ elements needed in the development of an institutional RDM service by considering the underlying data discovery and application issues, as well as the nature of academic scholarship and knowledge creation, discovery, application and sharing motivations in a university environment.Findings– The paper provides general criteria for an institutional RDMSS framework. It suggests that RDM in universities is at the very heart of the knowledge life cycle and is a central ingredient to the academic scholarships of discovery, integration, teaching, engagement and application.Research limitations/implications– This is a conceptual exploration and as a result, the research findings may lack generalisability. Researchers are therefore encouraged to further empirically examine the proposed propositions.Originality/value– The broad RDMSS framework presented in this paper can be compared with the actual situation at universities and eventually guide recommendations for adaptations and (re)design of the institutional RDM infrastructure and knowledge discovery services environment. Moreover, this paper will help to address some of the identified underlying scholarship and RDM disciplinary divides and confusion constraining the effective functioning of the modern day university’s RDM and data discovery environment.

Book Review: Delivering Research Data Management Services: Fundamentals of Good Practice

Delivering Research Management Services: Fundamentals of Good Practice. Edited by Graham Pryor, Sarah Jones, and Angus Whyte. London: Facet, 2014. 242 p. $99.95 softcover (ISBN 978-1-85604-933-7). Delivering Research Management Services proposes to build awareness of the need for a research data management (RDM) service infrastructure and explain how to set up such a service (including technological and human resources as well as securing institutional support). It accomplishes this through five chapters authored individually by the three editors, all currently or formerly affiliated with the United Kingdom's (UK) Digital Curation Centre (DCC). It discusses three case studies from institutions that have successfully launched RDM services in the United States, UK, and Australia; and two case studies from national programs in the UK. book provides in-depth information about high-level considerations for RDM services; it does not provide instruction in best practices for managing data. An earlier publication by the same lead editor, Managing Research Data, may prove more useful for those seeking best practices information. (1) Since this book demonstrates the value in planning ahead through advocacy and capacity-building before tackling the preservation and provision of access to research data, it may be a good first read. authors have extensive knowledge of and experience on this subject, yet write clearly enough for novices to comprehend. readers need to have a high tolerance for acronyms, since every agency, service, and tool seems to have one. Each chapter provides enough context to stand on its own, but this reviewer would have preferred that the book had been more tightly edited to facilitate cohesion across chapters. For instance, the authors recommend that Case Study 5 (chapter 10) be read in conjunction with chapter 5, begging the question of why Case Study 5 was not positioned as Case Study 1 (chapter 6). Such sequencing would have naturally achieved the authors' suggestion. book lacks a concluding chapter summing up the best practices, but it is left to readers to draw their own conclusions based on their institutional settings. Indeed, institutions in the UK seem to have an advantage in terms of governmental funding and shared infrastructure. In contrast, US institutions must either step up advocacy and coordination efforts at a national level or find smaller-scale solutions to the challenge of research data management. Examples of US universities' current and emerging practices relating to RDM services can be found in the Association of Research Libraries' SPEC Kit 334: Research Management Services. (2) We can learn much from our UK counterparts and their advancements. Many of the institutions cited in this book developed RDM services incrementally, starting with pilot projects and repurposing existing resources while advocating for the allocation of additional resources. Several authors advised including the costs and risks of doing nothing in that advocacy. In chapter 5, The Range and Components of RDM Infrastructure and Services, Sarah Jones describes DUDs, or Data centers Under Desks, created by research groups attempting to do their own data management at low cost. She notes, However, while the upfront costs may be only a fraction of those quoted by central services, the risk of data loss and security breaches are significantly higher, potentially leading to far greater costs in the long run (98). …

Research Libraries’ New Role in Research Data Management, Current Trends and Visions in Denmark

The amount of research data is growing constantly, due to new technology with new potentials for collecting and analysing both digital data and research objects. This growth creates a demand for a coherent IT-infrastructure. Such an infrastructure must be able to provide facilities for storage, preservation and a more open access to data in order to fulfil the demands from the researchers themselves, the research councils and research foundations.This paper presents the findings of a research project carried out under the auspices of DEFF (Danmarks Elektroniske Fag- og Forskningsbibliotek - Denmark’s Electronic research Library)[i] to analyse how the Danish universities store, preserve and provide access to research data. It shows that they do not have a common IT-infrastructure for research data management. This paper describes the various paths chosen by individual universities and research institutions, and the background for their strategies of research data management. Among the main reasons for the uneven practices are the lack of a national policy in this field, the different scientific traditions and cultures and the differences in the use and organization of IT-services.This development contains several perspectives that are of particular relevance to research libraries. As they already curate digital collections and are active in establishing web archives, the research libraries become involved in research and dissemination of knowledge in new ways. This paper gives examples of how The State and University Library’s services facilitate research data management with special regard to digitization of research objects, storage, preservation and sharing of research data.This paper concludes that the experience and skills of research libraries make the libraries important partners in a research data management infrastructure.

Towards a Unified University Infrastructure: The Data Management Roll-Out at the University of Oxford

Since presenting a paper at the International Digital Curation Conference 2010 conference entitled ‘An Institutional Approach to Developing Research Data Management Infrastructure’, the University of Oxford has come a long way in developing research data management (RDM) policy, tools and training to address the various phases of the research data lifecycle. Work has now begun on integrating these various elements into a unified infrastructure for the whole university, under the aegis of the Data Management Roll-out at Oxford (Damaro) Project.This paper will explain the process and motivation behind the project, and describes our vision for the future. It will also introduce the new tools and processes created by the university to tie the individual RDM components together. Chief among these is the ‘DataFinder’ – a hierarchically-structured metadata cataloguing system which will enable researchers to search for and locate research datasets hosted in a variety of different datastores from institutional repositories, through Web 2 services, to filing cabinets standing in department offices. DataFinder will be able to pull and associate research metadata from research information databases and data management plans, and is intended to be CERIF compatible. DataFinder is being designed so that it can be deployed at different levels within different contexts, with higher-level instances harvesting information from lower-level instances enabling, for example, an academic department to deploy one instance of DataFinder, which can then be harvested by another at an institutional level, which can then in turn be harvested by another at a national level.The paper will also consider the requirements of embedding tools and training within an institution and address the difficulties of ensuring the sustainability of an RDM infrastructure at a time when funding for such endeavours is limited. Our research shows that researchers (and indeed departments) are at present not exposed to the true costs of their (often suboptimal) data management solutions, whereas when data management services are centrally provided the full costs are visible and off-putting. There is, therefore, the need to sell the benefits of centrally-provided infrastructure to researchers. Furthermore, there is a distinction between training and services that can be most effectively provided at the institutional level, and those which need to be provided at the divisional or departmental level in order to be relevant and applicable to researchers. This is being addressed in principle by Oxford’s research data management policy, and in practice by the planning and piloting aspects of the Damaro Project.

Building Research Data Management Infrastructure using Open Source Software

AbstractThe implementation of a research data management infrastructure for a large interdisciplinary research project is presented here, based on well‐established Free and Open Source Software for Geospatial (FOSS4G) products such as MapServer, MapProxy, GeoExt and pyCSW, as well as the (not primarily geospatial) open source technologies Typo3 and CKAN. The presented implementation depends primarily on the demands for research data management infrastructure by the funding research agency. It also aligns to theory and practice in Research Data Management (RDM) and e‐Science. After the research project and related work in the field of RDM are introduced, a detailed description of the architecture and its implementation is given. The article discusses why Open Source and open standards are chosen to implement the infrastructure and provides some suggestions and examples on how to make it easier and more attractive for researchers to upload and publish their primary research data.

An Institutional Approach to Developing Research Data Management Infrastructure

This article outlines the work that the University of Oxford is undertaking to implement a coordinated data management infrastructure. The rationale for the approach being taken by Oxford is presented, with particular attention paid to the role of each service division. This is followed by a consideration of the relative advantages and disadvantages of institutional data repositories, as opposed to national or international data centres. The article then focuses on two ongoing JISC-funded projects, ‘Embedding Institutional Data Curation Services in Research’ (Eidcsr) and ‘Supporting Data Management Infrastructure for the Humanities’ (Sudamih). Both projects are intra-institutional collaborations and involve working with researchers to develop particular aspects of infrastructure, including: University policy, systems for the preservation and documentation of research data, training and support, software tools for the visualisation of large images, and creating and sharing databases via the Web (Database as a Service).