Étiquette : research data management

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Do I-PASS for FAIR? Measuring the FAIR-ness of Research Organizations

Authors : Jacquelijn Ringersma, Margriet Miedema

Given the increased use of the FAIR acronym as adjective for other contexts than data or data sets, the Dutch National Coordination Point for Research Data Management initiated a Task Group to work out the concept of a FAIR research organization.

The results of this Task Groups are a definition of a FAIR enabling organization and a method to measure the FAIR-ness of a research organization (The Do-I-PASS for FAIR method). The method can also aid in developing FAIR-enabling Road Maps for individual research institutions and at a national level.

This practice paper describes the development of the method and provides a couple of use cases for the application of the method in daily research data management practices in research organizations.

URL : Do I-PASS for FAIR? Measuring the FAIR-ness of Research Organizations

DOI : http://doi.org/10.5334/dsj-2021-030

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Between administration and research: Understanding data management practices in an institutional context

Authors : Stefan Reichmann, Thomas Klebel, Ilire Hasani-Mavriqi, Tony Ross-Hellauer

Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance.

This paper presents results of an institutional survey (N = 258) at a medium-sized Austrian university with a STEM focus, supplemented with interviews (N = 18), to give an overview of the state-of-play of RDM practices across faculties and disciplinary contexts.

RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving.

Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline-specific problems need a more targeted approach.

The paper extends the state-of-the-art on RDM practices by combining in-depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.

URL : Between administration and research: Understanding data management practices in an institutional context

DOI : https://doi.org/10.1002/asi.24492

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Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study

Authors: Jitka Stilund Hansen, Signe Gadegaard, Karsten Kryger Hansen, Asger Væring Larsen, Søren Møller, Gertrud Stougård Thomsen, Katrine Flindt Holmstrand

Citizen science (CS) projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. Increasing the value and reuse of CS data has received growing attention with the appearance of the FAIR principles and systematic research data management (RDM) practises, which are often promoted by university libraries.

However, RDM initiatives in CS appear diversified and if CS have special needs in terms of RDM is unclear. Therefore, the aim of this article is firstly to identify RDM challenges for CS projects and secondly, to discuss how university libraries may support any such challenges.

A scoping review and a case study of Danish CS projects were performed to identify RDM challenges. 48 articles were selected for data extraction. Four academic project leaders were interviewed about RDM practices in their CS projects.

Challenges and recommendations identified in the review and case study are often not specific for CS. However, finding CS data, engaging specific populations, attributing volunteers and handling sensitive data including health data are some of the challenges requiring special attention by CS project managers. Scientific requirements or national practices do not always encompass the nature of CS projects.

Based on the identified challenges, it is recommended that university libraries focus their services on 1) identifying legal and ethical issues that the project managers should be aware of in their projects, 2) elaborating these issues in a Terms of Participation that also specifies data handling and sharing to the citizen scientist, and 3) motivating the project manager to good data handling practises.

Adhering to the FAIR principles and good RDM practices in CS projects will continuously secure contextualisation and data quality. High data quality increases the value and reuse of the data and, therefore, the empowerment of the citizen scientists.

URL : Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study

DOI : http://doi.org/10.5334/dsj-2021-025

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How Long Can We Build It? Ensuring Usability of a Scientific Code Base

Authors : Klaus Rechert, Jurek Oberhauser, Rafael Gieschke

Software and in particular source code became an important component of scientific publications and henceforth is now subject of research data management. Maintaining source code such that it remains a usable and a valuable scientific contribution is and remains a huge task.

Not all code contributions can be actively maintained forever. Eventually, there will be a significant backlog of legacy source-code. In this article we analyse the requirements for applying the concept of long-term reusability to source code.

We use simple case study to identify gaps and provide a technical infrastructure based on emulator to support automated builds of historic software in form of source code.

URL : How Long Can We Build It? Ensuring Usability of a Scientific Code Base

DOI : https://doi.org/10.2218/ijdc.v16i1.770

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Doctoral Students’ Educational Needs in Research Data Management: Perceived Importance and Current Competencies

Author : Jukka Rantasaari

Sound research data management (RDM) competencies are elementary tools used by researchers to ensure integrated, reliable, and re-usable data, and to produce high quality research results.

In this study, 35 doctoral students and faculty members were asked to self-rate or rate doctoral students’ current RDM competencies and rate the importance of these competencies.

Structured interviews were conducted, using close-ended and open-ended questions, covering research data lifecycle phases such as collection, storing, organization, documentation, processing, analysis, preservation, and data sharing.

The quantitative analysis of the respondents’ answers indicated a wide gap between doctoral students’ rated/self-rated current competencies and the rated importance of these competencies.

In conclusion, two major educational needs were identified in the qualitative analysis of the interviews: to improve and standardize data management planning, including awareness of the intellectual property and agreements issues affecting data processing and sharing; and to improve and standardize data documenting and describing, not only for the researcher themself but especially for data preservation, sharing, and re-using. Hence the study informs the development of RDM education for doctoral students.

URL : Doctoral Students’ Educational Needs in Research Data Management: Perceived Importance and Current Competencies

DOI : https://doi.org/10.2218/ijdc.v16i1.684

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Open Data Policies among Library and Information Science Journals

Author : Brian Jackson

Journal publishers play an important role in the open research data ecosystem. Through open data policies that include public data archiving mandates and data availability statements, journal publishers help promote transparency in research and wider access to a growing scholarly record.

The library and information science (LIS) discipline has a unique relationship with both open data initiatives and academic publishing and may be well-positioned to adopt rigorous open data policies.

This study examines the information provided on public-facing websites of LIS journals in order to describe the extent, and nature, of open data guidance provided to prospective authors.

Open access journals in the discipline have disproportionately adopted detailed, strict open data policies. Commercial publishers, which account for the largest share of publishing in the discipline, have largely adopted weaker policies. Rigorous policies, adopted by a minority of journals, describe the rationale, application, and expectations for open research data, while most journals that provide guidance on the matter use hesitant and vague language. Recommendations are provided for strengthening journal open data policies.

URL : Open Data Policies among Library and Information Science Journals

DOI : https://doi.org/10.3390/publications9020025

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Data sharing practices and data availability upon request differ across scientific disciplines

Authors : Leho tedersoo, Rainer Küngas, Ester Oras, Kajar Köster, Helen Eenmaa, Äli Leijen, Margus Pedaste, Marju Raju, Anastasiya Astapova, Heli Lukner, Karin Kogermann, Tuul Sepp

Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors’ concerns, requests and reasons for declining data sharing.

Although data sharing has improved in the last decade and particularly in recent years, data availability and willingness to share data still differ greatly among disciplines. We observed that statements of data availability upon (reasonable) request are inefficient and should not be allowed by journals.

To improve data sharing at the time of manuscript acceptance, researchers should be better motivated to release their data with real benefits such as recognition, or bonus points in grant and job applications.

We recommend that data management costs should be covered by funding agencies; publicly available research data ought to be included in the evaluation of applications; and surveillance of data sharing should be enforced by both academic publishers and funders. These cross-discipline survey data are available from the plutoF repository.

URL : Data sharing practices and data availability upon request differ across scientific disciplines

DOI : https://doi.org/10.1038/s41597-021-00981-0