Archives des mots-clés : research data management

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/

 

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

The development of a research data policy at Wageningen University & Research: best practices as a framework

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Authors: Hilde van Zeeland, Jacquelijn Ringersma

The current case study describes the development of a Research Data Management policy at Wageningen University & Research, the Netherlands. To develop this policy, an analysis was carried out of existing frameworks and principles on data management (such as the FAIR principles), as well as of the data management practices in the organisation.

These practices were defined through interviews with research groups. Using criteria drawn from the existing frameworks and principles, certain research groups were identified as ‘best-practices’: cases where data management was meeting the most important data management criteria.

These best-practices were then used to inform the RDM policy. This approach shows how engagement with researchers can not only provide insight into their data management practices and needs, but directly inform new policy guidelines.

URL : The development of a research data policy at Wageningen University & Research: best practices as a framework

DOI : http://doi.org/10.18352/lq.10215

Using Peer Review to Support Development of Community Resources for Research Data Management

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Authors : Heather Soyka, Amber Budden, Viv Hutchison, David Bloom, Jonah Duckles, Amy Hodge, Matthew S. Mayernik, Timothée Poisot, Shannon Rauch, Gail Steinhart, Leah Wasser, Amanda L. Whitmire, Stephanie Wright

Objective

To ensure that resources designed to teach skills and best practices for scientific research data sharing and management are useful, the maintainers of those materials need to evaluate and update them to ensure their accuracy, currency, and quality.

This paper advances the use and process of outside peer review for community resources in addressing ongoing accuracy, quality, and currency issues. It further describes the next step of moving the updated materials to an online collaborative community platform for future iterative review in order to build upon mechanisms for open science, ongoing iteration, participation, and transparent community engagement.

Setting

Research data management resources were developed in support of the DataONE (Data Observation Network for Earth) project, which has deployed a sustainable, long-term network to ensure the preservation and access to multi-scale, multi-discipline, and multi-national environmental and biological science data (Michener et al. 2012).

Created by members of the Community Engagement and Education (CEE) Working Group in 2011-2012, the freely available Educational Modules included three complementary components (slides, handouts, and exercises) that were designed to be adaptable for use in classrooms as well as for research data management training.

Methods

Because the modules were initially created and launched in 2011-2012, the current members of the (renamed) Community Engagement and Outreach (CEO) Working Group were concerned that the materials could be and / or quickly become outdated and should be reviewed for accuracy, currency, and quality.

In November 2015, the Working Group developed an evaluation rubric for use by outside reviewers. Review criteria were developed based on surveys and usage scenarios from previous DataONE projects.

Peer reviewers were selected from the DataONE community network for their expertise in the areas covered by one of the 11 educational modules. Reviewers were contacted in March 2016, and were asked to volunteer to complete their evaluations online within one month of the request, by using a customized Google form.

Results

For the 11 modules, 22 completed reviews were received by April 2016 from outside experts. Comments on all three components of each module (slides, handouts, and exercises) were compiled and evaluated by the postdoctoral fellow attached to the CEO Working Group.

These reviews contributed to the full evaluation and revision by members of the Working Group of all educational modules in September 2016. This review process, as well as the potential lack of funding for ongoing maintenance by Working Group members or paid staff, provoked the group to transform the modules to a more stable, non-proprietary format, and move them to an online open repository hosting platform, GitHub.

These decisions were made to foster sustainability, community engagement, version control, and transparency.

Conclusion

Outside peer review of the modules by experts in the field was beneficial for highlighting areas of weakness or overlap in the education modules. The modules were initially created in 2011-2012 by an earlier iteration of the Working Group, and updates were needed due to the constant evolving practices in the field.

Because the review process was lengthy (approximately one year) comparative to the rate of innovations in data management practices, the Working Group discussed other options that would allow community members to make updates available more quickly.

The intent of migrating the modules to an online collaborative platform (GitHub) is to allow for iterative updates and ongoing outside review, and to provide further transparency about accuracy, currency, and quality in the spirit of open science and collaboration.

Documentation about this project may be useful for others trying to develop and maintain educational resources for engagement and outreach, particularly in communities and spaces where information changes quickly, and open platforms are already in common use.

URL : Using Peer Review to Support Development of Community Resources for Research Data Management

DOI : https://doi.org/10.7191/jeslib.2017.1114

Recommended versus Certified Repositories: Mind the Gap

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Authors : Sean Edward Husen, Zoë G. de Wilde, Anita de Waard, Helena Cousijn

Researchers are increasingly required to make research data publicly available in data repositories. Although several organisations propose criteria to recommend and evaluate the quality of data repositories, there is no consensus of what constitutes a good data repository.

In this paper, we investigate, first, which data repositories are recommended by various stakeholders (publishers, funders, and community organizations) and second, which repositories are certified by a number of organisations.

We then compare these two lists of repositories, and the criteria for recommendation and certification. We find that criteria used by organisations recommending and certifying repositories are similar, although the certification criteria are generally more detailed.

We distil the lists of criteria into seven main categories: “Mission”, “Community/Recognition”, “Legal and Contractual Compliance”, “Access/Accessibility”, “Technical Structure/Interface”, “Retrievability” and “Preservation”.

Although the criteria are similar, the lists of repositories that are recommended by the various agencies are very different. Out of all of the recommended repositories, less than 6% obtained certification.

As certification is becoming more important, steps should be taken to decrease this gap between recommended and certified repositories, and ensure that certification standards become applicable, and applied, to the repositories which researchers are currently using.

URL : Recommended versus Certified Repositories: Mind the Gap

DOI: https://doi.org/10.5334/dsj-2017-042

What do data curators care about? Data quality, user trust, and the data reuse plan

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Author : Frank Andreas Sposito

Data curation is often defined as the practice of maintaining, preserving, and enhancing research data for long-term value and reusability. The role of data reuse in the data curation lifecycle is critical: increased reuse is the core justification for the often sizable expenditures necessary to build data management infrastructures and user services.

Yet recent studies have shown that data are being shared and reused through open data repositories at much lower levels than expected. These studies underscore a fundamental and often overlooked challenge in research data management that invites deeper examination of the roles and responsibilities of data curators.

This presentation will identify key barriers to data reuse, data quality and user trust, and propose a framework for implementing reuser-centric strategies to increase data reuse.

Using the concept of a “data reuse plan” it will highlight repository-based approaches to improve data quality and user trust, and address critical areas for innovation for data curators working in the absence of repository support.

URL : What do data curators care about? Data quality, user trust, and the data reuse plan

Alternative location : http://library.ifla.org/id/eprint/1797