Veille dédiée aux Sciences de l'Information et des Bibliothèques // Collecting and Sharing research papers in Library and Information science ISSN 2429-3938
Authors : Tobias Weber, Dieter Kranzlmüller
Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management.
The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.
Auteur : Joachim Schöpfel
Nous proposons une réflexion sur le rôle des normes et standards dans la gestion des données de la recherche, dans l’environnement de la politique de la science ouverte.
A partir d’une définition générale des données de la recherche, nous analysons la place et la fonction des normes et standards dans les différentes dimensions du concept des données. En particulier, nous nous intéressons à trois aspects faisant le lien entre le processus scientifique, l’environnement réglementaire et les données de la recherche : les protocoles éthiques, les systèmes d’information recherche et les plans de gestion des données.
A l’échelle internationale, nous décrivons l’effet normatif des principes FAIR qui, par la mobilisation d’autres normes et standards, créent une sorte de « cascade de standards » autour des plateformes et entrepôts, avec un impact direct sur les pratiques scientifiques.
Author : Kevin B. Read
Background
Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted.
Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown.
Case Presentation
Leveraging my academic library’s experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop.
This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library’s existing data support program.
The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop.
Conclusions
The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work.
Attendees also articulated how they would implement the main competencies they learned from the workshop into their work.
For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.
Authors : Rae A. Earnshaw, Mohan de Silva, Peter S. Excell
In contrast with practice in recent times past, computational and data intensive processes are increasingly driving collaborative research in science and technology.
Large amounts of data are being generated in experiments or simulations and these require real-time, or near real-time, analysis and visualisation. The results of these evaluations need to be validated and then published quickly and openly in order to facilitate the overall progress of research on a national and international basis.
Research is increasingly undertaken in large teams and is also increasingly interdisciplinary as many of the major research challenges lie at the boundaries between existing disciplines.
The move to open access for peer reviewed publications is rapidly becoming a required option in the sector. At the same time, communication and dissemination procedures are also utilising non-traditional forms facilitated by burgeoning developments in social networking.
It is proposed that these elements, when combined, constitute a paradigm shift in the model of research and the dissemination of research results.
Alternative location : http://aetic.theiaer.org/archive/v3/v3n1/p1.html
Authors: Mingfang Wu, Fotis Psomopoulos, Siri Jodha Khalsa, Anita de Waard
As data repositories make more data openly available it becomes challenging for researchers to find what they need either from a repository or through web search engines.
This study attempts to investigate data users’ requirements and the role that data repositories can play in supporting data discoverability by meeting those requirements.
We collected 79 data discovery use cases (or data search scenarios), from which we derived nine functional requirements for data repositories through qualitative analysis.
We then applied usability heuristic evaluation and expert review methods to identify best practices that data repositories can implement to meet each functional requirement.
We propose the following ten recommendations for data repository operators to consider for improving data discoverability and user’s data search experience:
1. Provide a range of query interfaces to accommodate various data search behaviours.
2. Provide multiple access points to find data.
3. Make it easier for researchers to judge relevance, accessibility and reusability of a data collection from a search summary.
4. Make individual metadata records readable and analysable.
5. Enable sharing and downloading of bibliographic references.
6. Expose data usage statistics.
7. Strive for consistency with other repositories.
8. Identify and aggregate metadata records that describe the same data object.
9. Make metadata records easily indexed and searchable by major web search engines.
10. Follow API search standards and community adopted vocabularies for interoperability.
Author : Fernando Rios
Many large research universities provide research data management (RDM) support services for researchers. These may include support for data management planning, best practices (e.g., organization, support, and storage), archiving, sharing, and publication.
However, these data-focused services may under-emphasize the importance of the software that is created to analyse said data. This is problematic for several reasons.
First, because software is an integral part of research across all disciplines, it undermines the ability of said research to be understood, verified, and reused by others (and perhaps even the researcher themselves).
Second, it may result in less visibility and credit for those involved in creating the software.
A third reason is related to stewardship: if there is no clear process for how, when, and where the software associated with research can be accessed and who will be responsible for maintaining such access, important details of the research may be lost over time.
This article presents the process by which the RDM services unit of a large research university addressed the lack of emphasis on software and source code in their existing service offerings.
The greatest challenges were related to the need to incorporate software into existing data-oriented service workflows while minimizing additional resources required, and the nascent state of software curation and archiving in a data management context.
The problem was addressed from four directions: building an understanding of software curation and preservation from various viewpoints (e.g., video games, software engineering), building a conceptual model of software preservation to guide service decisions, implementing software-related services, and documenting and evaluating the work to build expertise and establish a standard service level.
URL : Incorporating Software Curation into Research Data Management Services
Alternative location : http://www.ijdc.net/article/view/608/529
Authors : Joachim Schöpfel, Coline Ferrant, Francis Andre, Renaud Fabre
Purpose
The purpose of this paper is to present empirical evidence on the opinion and behaviour of French scientists (senior management level) regarding research data management (RDM).
Design/methodology/approach
The results are part of a nationwide survey on scientific information and documentation with 432 directors of French public research laboratories conducted by the French Research Center CNRS in 2014.
Findings
The paper presents empirical results about data production (types), management (human resources, IT, funding, and standards), data sharing and related needs, and highlights significant disciplinary differences.
Also, it appears that RDM and data sharing is not directly correlated with the commitment to open access. Regarding the FAIR data principles, the paper reveals that 68 per cent of all laboratory directors affirm that their data production and management is compliant with at least one of the FAIR principles.
But only 26 per cent are compliant with at least three principles, and less than 7 per cent are compliant with all four FAIR criteria, with laboratories in nuclear physics, SSH and earth sciences and astronomy being in advance of other disciplines, especially concerning the findability and the availability of their data output.
The paper concludes with comments about research data service development and recommendations for an institutional RDM policy.
Originality/value
For the first time, a nationwide survey was conducted with the senior research management level from all scientific disciplines. Surveys on RDM usually assess individual data behaviours, skills and needs. This survey is different insofar as it addresses institutional and collective data practice.
The respondents did not report on their own data behaviours and attitudes but were asked to provide information about their laboratory. The response rate was high (>30 per cent), and the results provide good insight into the real support and uptake of RDM by senior research managers who provide both models (examples for good practice) and opinion leadership.