Archives des mots-clés : research data

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

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Authors : Kathleen Gregory, Paul Groth, Helena Cousijn, Andrea Scharnhorst, Sally Wyatt

A cross‐disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines.

Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

URL : Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

DOI : https://doi.org/10.1002/asi.24165

Les données de la recherche à l’Université Bordeaux Montaigne : Synthèse d’une enquête qualitative auprès des chercheurs

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Auteur/Author : Julie Duprat

Alors que ces dernières années l’importance de l’ouverture des publications écrites par les chercheurs des universités françaises a été largement abordée, les regards se tournent désormais sur une autre de leurs productions avec les données de la recherche.

Dans ce contexte, l’Université Bordeaux Montaigne, spécialisée en sciences humaines et sociales, souhaite mettre en place un service « données de la recherche » afin d’accompagner ses chercheurs dans la gestion et le partage de leurs données de recherche.

Au préalable du service à venir, une enquête a été menée entre septembre et décembre 2018 auprès des chercheurs de l’Université par la conservatrice-stagiaire Julie Duprat afin de faire remonter les besoins du terrain, dans une logique bottom up.

URL : https://hal.archives-ouvertes.fr/hal-02020141

Methods to Evaluate Lifecycle Models for Research Data Management

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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.

URL : https://arxiv.org/abs/1901.11267

Hors norme ? Une approche normative des données de la recherche

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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.

URL : https://revue-cossi.info/numeros/n-5-2018-processus-normalisation-durabilite-information/730-5-2018-schopfel

Adapting data management education to support clinical research projects in an academic medical center

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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.

URL : Adapting data management education to support clinical research projects in an academic medical center

DOI : https://dx.doi.org/10.5195%2Fjmla.2019.580

Models of Research and the Dissemination of Research Results: the Influences of E-Science, Open Access and Social Networking

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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.

URL : Models of Research and the Dissemination of Research Results: the Influences of E-Science, Open Access and Social Networking

Alternative location : http://aetic.theiaer.org/archive/v3/v3n1/p1.html

Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories

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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.

DOI : http://doi.org/10.5334/dsj-2019-003