Étiquette : research data

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On a Quest for Cultural Change – Surveying Research Data Management Practices at Delft University of Technology

Authors : Heather Andrews Mancilla, Marta Teperek, Jasper van Dijck, Kees den Heijer, Robbert Eggermont, Esther Plomp, Yasemin Turkyilmaz-van der Velden, Shalini Kurapati

The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties.

The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward’, embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018.

The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty.

Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories.

The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results.

Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels.

URL : On a Quest for Cultural Change – Surveying Research Data Management Practices at Delft University of Technology

DOI : http://doi.org/10.18352/lq.10287

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Skills, Standards, and Sapp Nelson’s Matrix: Evaluating Research Data Management Workshop Offerings

Authors : Philip Espinola Coombs, Christine Malinowski, Amy Nurnberger

Objective

To evaluate library workshops on their coverage of data management topics.

Methods

We used a modified version of Sapp Nelson’s Competency Matrix for Data Management Skills, a matrix of learning goals organized by data management competency and complexity level, against which we compared our educational materials: slide decks and worksheets.

We examined each of the educational materials against the 333 learning objectives in our modified version of the Matrix to determine which of the learning objectives applied.

Conclusions

We found it necessary to change certain elements of the Matrix’s structure to increase its clarity and functionality: reinterpreting the “behaviors,” shifting the organization from the three domains of Bloom’s taxonomy to increasing complexity solely within the cognitive domain, as well as creating a comprehensive identifier schema.

We appreciated the Matrix for its specificity of learning objectives, its organizational structure, the comprehensive range of competencies included, and its ease of use. On the whole, the Matrix is a useful instrument for the assessment of data management programming.

URL : Skills, Standards, and Sapp Nelson’s Matrix: Evaluating Research Data Management Workshop Offerings

Alternative location : https://escholarship.umassmed.edu/jeslib/vol8/iss1/6/

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EN

The Definition of Reuse

Authors : Stephanie van de Sandt, Sünje Dallmeier-Tiessen, Artemis Lavasa, Vivien Petras

The ability to reuse research data is now considered a key benefit for the wider research community. Researchers of all disciplines are confronted with the pressure to share their research data so that it can be reused.

The demand for data use and reuse has implications on how we document, publish and share research in the first place, and, perhaps most importantly, it affects how we measure the impact of research, which is commonly a measurement of its use and reuse.

It is surprising that research communities, policy makers, etc. have not clearly defined what use and reuse is yet.

We postulate that a clear definition of use and reuse is needed to establish better metrics for a comprehensive scholarly record of individuals, institutions, organizations, etc.

Hence, this article presents a first definition of reuse of research data. Characteristics of reuse are identified by examining the etymology of the term and the analysis of the current discourse, leading to a range of reuse scenarios that show the complexity of today’s research landscape, which has been moving towards a data-driven approach.

The analysis underlines that there is no reason to distinguish use and reuse. We discuss what that means for possible new metrics that attempt to cover Open Science practices more comprehensively.

We hope that the resulting definition will enable a better and more refined strategy for Open Science.

URL : The Definition of Reuse

DOI : http://doi.org/10.5334/dsj-2019-022

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Establishing, Developing, and Sustaining a Community of Data Champions

Authors : James L. Savage, Lauren Cadwallader

Supporting good practice in Research Data Management (RDM) is challenging for higher education institutions, in part because of the diversity of research practices and data types across disciplines.

While centralised research data support units now exist in many universities, these typically possess neither the discipline-specific expertise nor the resources to offer appropriate targeted training and support within every academic unit.

One solution to this problem is to identify suitable individuals with discipline-specific expertise that are already embedded within each unit, and empower these individuals to advocate for good RDM and to deliver support locally.

This article focuses on an ongoing example of this approach: the Data Champion Programme at the University of Cambridge, UK.

We describe how the Data Champion programme was established; the programme’s reach, impact, strengths and weaknesses after two years of operation; and our anticipated challenges and planned strategies for maintaining the programme over the medium- and long-term.

URL : Establishing, Developing, and Sustaining a Community of Data Champions

DOI : http://doi.org/10.5334/dsj-2019-023

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Data Sharing Practices among Researchers at South African Universities

Authors : Siviwe Bangani, Mathew Moyo

Research data management practices have gained momentum the world over. This is due to increased demands by governments and other funding agencies to have research data archived and shared as widely as possible.

This paper sought to establish the data sharing practices of researchers in South Africa. The study further sought to establish the level of collaboration among researchers in sharing research data at the university level.

The outcomes of the survey will help the researchers to develop appropriate data literacy awareness programmes meant to stimulate growth in data sharing practices for the benefit of research, not only in South Africa, but the world at large.

A survey research method was used to gather data from willing public universities in South Africa. A similar study was conducted in other countries such as the United Kingdom, France and Turkey but the Researchers believe that circumstances in the developed world may differ with the South African research environment, hence the current study.

The major finding of this study was that most researchers preferred to use data produced by others but less keen on sharing their own data.

This study is the first of its kind in South Africa which investigates data sharing practices of researchers from multi-disciplinary fields at the university level and will contribute immensely to the growing body of literature in the area of research data management.

URL : Data Sharing Practices among Researchers at South African Universities

DOI : http://doi.org/10.5334/dsj-2019-028

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The Landscape of Rights and Licensing Initiatives for Data Sharing

Authors : Sam Grabus, Jane Greenberg

Over the last twenty years, a wide variety of resources have been developed to address the rights and licensing problems inherent with contemporary data sharing practices.

The landscape of developments is this area is increasingly confusing and difficult to navigate, due to the complexity of intellectual property and ethics issues associated with sharing sensitive data.

This paper seeks to address this challenge, examining the landscape and presenting a Version 1.0 directory of resources. A multi-method study was pursued, with an environmental scan examining 20 resources, resulting in three high-level categories: standards, tools, and community initiatives; and a content analysis revealing the subcategories of rights, licensing, metadata & ontologies.

A timeline confirms a shift in licensing standardization priorities from open data to more nuanced and technologically robust solutions, over time, to accommodate for more sensitive data types.

This paper reports on the research undertaking, and comments on the potential for using license-specific metadata supplements and developing data-centric rights and licensing ontologies.

URL : The Landscape of Rights and Licensing Initiatives for Data Sharing

DOI : http://doi.org/10.5334/dsj-2019-029

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Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives

Authors : Charles Vesteghem, Rasmus Froberg Brøndum, Mads Sønderkær, Mia Sommer, Alexander Schmitz, Julie Støve Bødker, Karen Dybkær, Tarec Christoffer El-Galaly, Martin Bøgsted

Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance.

The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives.

For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively.

For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society’s standard.

For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.

URL : Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives

DOI : https://doi.org/10.1093/bib/bbz044