Archives des mots-clés : research data

A Campus Partnership to Foster Compliance with Funder Mandates

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Authors : Jeff R. Broadbent, Andrea Payant, Kevin Peterson, Betty Rozum, Liz Woolcott

Data from federally funded research must now be made publicly accessible and discoverable. Researchers must adhere to guidelines established by federal agencies, and universities must be prepared to demonstrate compliance with the federal mandate.

At Utah State University, the Office of Research and Graduate Studies and the Merrill-Cazier Library partnered to facilitate data sharing and create an audit trail demonstrating compliance with the terms of each researcher’s award.

This systematic approach uses existing resources such as the grant management system, the institutional repository (IR), and the Library online catalog. This paper describes our process and the first eight months of implementation.

URL : http://digitalcommons.usu.edu/lib_pubs/274/

Research Data Management Services in Academic Libraries in the US: A Content Analysis of Libraries’ Websites

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Authors : Ayoung Yoon, Teresa Schultz

Examining landscapes of research data management services in academic libraries is timely and significant for both those libraries on the front line and the libraries that are already ahead.

While it provides overall understanding of where the research data management program is at and where it is going, it also provides understanding of current practices and data management recommendations and/or tool adoptions as well as revealing areas of improvement and support.

This study examined the research data (management) services in academic libraries in the United States through a content analysis of 185 library websites, with four main areas of focus: service, information, education, and network.

The results from the content analysis of these webpages reveals that libraries need to advance and engage more actively to provide services, supply information online, and develop educational services.

There is also a wide variation among library data management services and programs according to their web presence.

URL : http://crl.acrl.org/index.php/crl/article/view/16788/18346

The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model

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Authors : John L. Faundeen, Vivian B. Hutchison

This paper details how the U.S. Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies and data management applications.

Starting with an extensive literature review of existing data lifecycle models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined.

Refinements and reviews spanned two years, leading to finalization of the model and documentation in a formal agency publication1.

The Model serves as a critical framework for data management policy, instructional resources, and tools. The Model helps the USGS address both the Office of Science and Technology Policy (OSTP)2 for increased public access to federally funded research, and the Office of Management and Budget (OMB)3 2013 Open Data directives, as the foundation for a series of agency policies related to data management planning, metadata development, data release procedures, and the long-term preservation of data.

Additionally, the agency website devoted to data management instruction and best practices (www2.usgs.gov/datamanagement) is designed around the Model’s structure and concepts. This paper also illustrates how the Model is being used to develop tools for supporting USGS research and data management processes.

URL : http://escholarship.umassmed.edu/jeslib/vol6/iss2/4/

 

Building a Disciplinary, World‐Wide Data Infrastructure

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Authors: Françoise Genova, Christophe Arviset, Bridget M. Almas, Laura Bartolo, Daan Broeder, Emily Law, Brian McMahon

Sharing scientific data with the objective of making it discoverable, accessible, reusable, and interoperable requires work and presents challenges being faced at the disciplinary level to define in particular how the data should be formatted and described.

This paper represents the Proceedings of a session held at SciDataCon 2016 (Denver, 12–13 September 2016). It explores the way a range of disciplines, namely materials science, crystallography, astronomy, earth sciences, humanities and linguistics, get organized at the international level to address those challenges. T

he disciplinary culture with respect to data sharing, science drivers, organization, lessons learnt and the elements of the data infrastructure which are or could be shared with others are briefly described. Commonalities and differences are assessed.

Common key elements for success are identified: data sharing should be science driven; defining the disciplinary part of the interdisciplinary standards is mandatory but challenging; sharing of applications should accompany data sharing. Incentives such as journal and funding agency requirements are also similar.

For all, social aspects are more challenging than technological ones. Governance is more diverse, often specific to the discipline organization. Being problem‐driven is also a key factor of success for building bridges to enable interdisciplinary research.

Several international data organizations such as CODATA, RDA and WDS can facilitate the establishment of disciplinary interoperability frameworks. As a spin‐off of the session, a RDA Disciplinary Interoperability Interest Group is proposed to bring together representatives across disciplines to better organize and drive the discussion for prioritizing, harmonizing and efficiently articulating disciplinary needs.

URL : Building a Disciplinary, World‐Wide Data Infrastructure

DOI : http://doi.org/10.5334/dsj-2017-016

 

Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics

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Authors : Jian Qin, Kevin Crowston, Arden Kirkland

Objective

To support the assessment and improvement of research data management (RDM) practices to increase its reliability, this paper describes the development of a capability maturity model (CMM) for RDM. Improved RDM is now a critical need, but low awareness of – or lack of – data management is still common among research projects.

Methods

A CMM includes four key elements: key practices, key process areas, maturity levels, and generic processes. These elements were determined for RDM by a review and synthesis of the published literature on and best practices for RDM.

Results

The RDM CMM includes five chapters describing five key process areas for research data management: 1) data management in general; 2) data acquisition, processing, and quality assurance; 3) data description and representation; 4) data dissemination; and 5) repository services and preservation.

In each chapter, key data management practices are organized into four groups according to the CMM’s generic processes: commitment to perform, ability to perform, tasks performed, and process assessment (combining the original measurement and verification).

For each area of practice, the document provides a rubric to help projects or organizations assess their level of maturity in RDM.

Conclusions

By helping organizations identify areas of strength and weakness, the RDM CMM provides guidance on where effort is needed to improve the practice of RDM.

URL : Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics

DOI : https://doi.org/10.7191/jeslib.2017.1113

How to responsibly acknowledge research work in the era of big data and biobanks: ethical aspects of the Bioresource Research Impact Factor (BRIF)

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Authors : Heidi Carmen Howard, Deborah Mascalzoni, Laurence Mabile, Gry Houeland, Emmanuelle Rial-Sebbag, Anne Cambon-Thomsen

Currently, a great deal of biomedical research in fields such as epidemiology, clinical trials and genetics is reliant on vast amounts of biological and phenotypic information collected and assembled in biobanks.

While many resources are being invested to ensure that comprehensive and well-organised biobanks are able to provide increased access to, and sharing of biomedical samples and information, many barriers and challenges remain to such responsible and extensive sharing.

Germane to the discussion herein is the barrier to collecting and sharing bioresources related to the lack of proper recognition of researchers and clinicians who developed the bioresource. Indeed, the efforts and resources invested to set up and sustain a bioresource can be enormous and such work should be easily traced and properly recognised.

However, there is currently no such system that systematically and accurately traces and attributes recognition to those doing this work or the bioresource institution itself. As a beginning of a solution to the “recognition problem”, the Bioresource Research Impact Factor/Framework (BRIF) initiative was proposed almost a decade and a half ago and is currently under further development.

With the ultimate aim of increasing awareness and understanding of the BRIF, in this article, we contribute the following: (1) a review of the objectives and functions of the BRIF including the description of two tools that will help in the deployment of the BRIF, the CoBRA (Citation of BioResources in journal Articles) guideline, and the Open Journal of Bioresources (OJB); (2) the results of a small empirical study on stakeholder awareness of the BRIF and (3) a brief analysis of the ethical dimensions of the BRIF which allow it to be a positive contribution to responsible biobanking.

URL : How to responsibly acknowledge research work in the era of big data and biobanks: ethical aspects of the Bioresource Research Impact Factor (BRIF)

Alternative locaton : https://link.springer.com/article/10.1007/s12687-017-0332-6

De l’open data à l’open science : retour réflexif sur les méthodes et pratiques d’une recherche sur les données géographiques

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Auteurs/Authors : Nathalie Pinède, Matthieu Noucher, Françoise Gourmelon, Karel Soumagnac-Colin

Nous mobilisons ici l’expérience d’un projet de recherche en cours pour analyser la façon dont les nouveaux terrains d’expérimentations sur le web, modifient les conditions de la pratique scientifique, des objets aux méthodes, de l’open data à l’open science.

La massification des données géographiques disponibles sur le web reconfigure les dynamiques de recherche selon trois axes de transformation : les objets, les méthodes et les pratiques de recherche. Tout d’abord, nous soulignerons comment les enjeux de pouvoir autour de la cartographie se sont déplacés avec l’avènement du web et de l’open data.

Nous développerons ensuite les impacts en matière de méthodologie de recherche dans un contexte d’approche interdisciplinaire. Enfin, nous montrerons comment ce projet de recherche s’inscrit dans une démarche de type open science.

URL : https://rfsic.revues.org/3200