Research Data in Current Research Information Systems

Authors : Joachim Schöpfel, Hélène Prost,Violaine Rebouillat

The paper provides an overview of recent research and publications on the integration of research data in Current Research Information Systems (CRIS) and addresses three related issues, i.e. the object of evaluation, identifier schemes and conservation.

Our focus is on social sciences and humanities. As research data gradually become a crucial topic of scientific communication and evaluation, current research information systems must be able to consider and manage the great variety and granularity levels of data as sources and results of scientific research.

More empirical and moreover conceptual work is needed to increase our understanding of the reality of research data and the way they can and should be used for the needs and objectives of research evaluation.

The paper contributes to the debate on the evaluation of research data, especially in the environment of open science and open data, and will be helpful in implementing CRIS and research data policies.

URL : http://archivesic.ccsd.cnrs.fr/sic_01331537

Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research

Authors : Samiya Khan, Kashish A. Shakil, Mansaf Alam

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research.

In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems.

This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

URL : https://arxiv.org/abs/1606.01808

Revisiting the Data Lifecycle with Big Data Curation

Author : Line Pouchard

As science becomes more data-intensive and collaborative, researchers increasingly use larger and more complex data to answer research questions.

The capacity of storage infrastructure, the increased sophistication and deployment of sensors, the ubiquitous availability of computer clusters, the development of new analysis techniques, and larger collaborations allow researchers to address grand societal challenges in a way that is unprecedented.

In parallel, research data repositories have been built to host research data in response to the requirements of sponsors that research data be publicly available. Libraries are re-inventing themselves to respond to a growing demand to manage, store, curate and preserve the data produced in the course of publicly funded research.

As librarians and data managers are developing the tools and knowledge they need to meet these new expectations, they inevitably encounter conversations around Big Data. This paper explores definitions of Big Data that have coalesced in the last decade around four commonly mentioned characteristics: volume, variety, velocity, and veracity.

We highlight the issues associated with each characteristic, particularly their impact on data management and curation. We use the methodological framework of the data life cycle model, assessing two models developed in the context of Big Data projects and find them lacking.

We propose a Big Data life cycle model that includes activities focused on Big Data and more closely integrates curation with the research life cycle. These activities include planning, acquiring, preparing, analyzing, preserving, and discovering, with describing the data and assuring quality being an integral part of each activity.

We discuss the relationship between institutional data curation repositories and new long-term data resources associated with high performance computing centers, and reproducibility in computational science.

We apply this model by mapping the four characteristics of Big Data outlined above to each of the activities in the model. This mapping produces a set of questions that practitioners should be asking in a Big Data project

URL : Revisiting the Data Lifecycle with Big Data Curation

Alternative location : http://www.ijdc.net/index.php/ijdc/article/view/10.2.176

Exploring the opportunities and challenges of implementing open research strategies within development institutions

This research proposal calls for support for a pilot project to conduct open data pilot case studies with eight (8) IDRC grantees to develop and implement open data management and sharing plans.

The results of the case studies will serve to refine guidelines for the implementation of development research funders’ open research data policies. The case studies will examine the scale of legal, ethical and technical challenges that might limit the sharing of data from IDRC projects including issues of:

  • Privacy, personally identifiable information and protection of human subject
  • Protection of intellectual property generated from projects or potential for financial risks for projects or institutions
  • Challenges in the local legal environment, including ownership of data
  • Ethical issues in releasing or sharing of indigenous and community knowledge, and the relationship between project participants and investigators particularly in the context of historical expropriation of resources
  • Local and global issues of capacity and expertise in the management and sharing of data

The duration of the current project will be fifteen (16) months, commencing September 2015 and ending in December 2016. The project will focus on auditing the data being produced by the participating projects, supporting the development of data management and sharing plans, and surfacing and cataloguing issues that arise.

URL : Exploring the opportunities and challenges of implementing open research strategies within development institutions

Alternative location : http://rio.pensoft.net/articles.php?id=8880

 

Assessment of and Response to Data Needs of Clinical and Translational Science Researchers and Beyond

Objective and Setting

As universities and libraries grapple with data management and “big data,” the need for data management solutions across disciplines is particularly relevant in clinical and translational science (CTS) research, which is designed to traverse disciplinary and institutional boundaries.

At the University of Florida Health Science Center Library, a team of librarians undertook an assessment of the research data management needs of CTS researchers, including an online assessment and follow-up one-on-one interviews.

Design and Methods

The 20-question online assessment was distributed to all investigators affiliated with UF’s Clinical and Translational Science Institute (CTSI) and 59 investigators responded. Follow-up in-depth interviews were conducted with nine faculty and staff members.

Results

Results indicate that UF’s CTS researchers have diverse data management needs that are often specific to their discipline or current research project and span the data lifecycle. A common theme in responses was the need for consistent data management training, particularly for graduate students; this led to localized training within the Health Science Center and CTSI, as well as campus-wide training.

Another campus-wide outcome was the creation of an action-oriented Data Management/Curation Task Force, led by the libraries and with participation from Research Computing and the Office of Research.

Conclusions

Initiating conversations with affected stakeholders and campus leadership about best practices in data management and implications for institutional policy shows the library’s proactive leadership and furthers our goal to provide concrete guidance to our users in this area.

URL : Assessment of and Response to Data Needs of Clinical and Translational Science Researchers and Beyond

Alternative location : http://escholarship.umassmed.edu/jeslib/vol5/iss1/2/

Data Management Plan Requirements for Campus Grant Competitions: Opportunities for Research Data Services Assessment and Outreach

Objective

To examine the effects of research data services (RDS) on the quality of data management plans (DMPs) required for a campus-level faculty grant competition, as well as to explore opportunities that the local DMP requirement presented for RDS outreach.

Methods

Nine reviewers each scored a randomly assigned portion of DMPs from 82 competition proposals. Each DMP was scored by three reviewers, and the three scores were averaged together to obtain the final score. Interrater reliability was measured using intraclass correlation.

Unpaired t-tests were used to compare mean DMP scores for faculty who utilized RDS services with those who did not. Unpaired t-tests were also used to compare mean DMP scores for proposals that were funded with proposals that were not funded. One-way ANOVA was used to compare mean DMP scores among proposals from six broad disciplinary categories.

Results

Analyses showed that RDS consultations had a statistically significant effect on DMP scores. Differences between DMP scores for funded versus unfunded proposals and among disciplinary categories were not significant. The DMP requirement also provided a number of both expected and unexpected outreach opportunities for RDS services.

Conclusions

Requiring DMPs for campus grant competitions can provide important assessment and outreach opportunities for research data services.

While these results might not be generalizable to DMP review processes at federal funding agencies, they do suggest the importance, at any level, of developing a shared understanding of what constitutes a high quality DMP among grant applicants, grant reviewers, and RDS providers.

URL : Data Management Plan Requirements for Campus Grant Competitions

DOI : http://dx.doi.org/10.7191/jeslib.2016.1089

Les enjeux de la patrimonialisation et de la réutilisation des données qualitatives de la recherche en Sciences humaines et sociales

Les archives de la recherche sont par nature passionnantes puisqu’elles permettent de comprendre comment les découvertes se font et comment la science évolue de jour en jour. L’arrivée du numérique a fait surgir de nouvelles possibilités pour la diffusion notamment de ces données, mais aussi de nouveaux challenges, en termes d’archivage entre autres.

L’archivage, le partage et la réutilisation des données qualitatives des SHS soulèvent de nombreuses questions et les différents acteurs concernés, les professionnels de l’IST et les chercheurs, peuvent avoir des avis divergents. Comprendre les points de vue de chacun et déterminer dans quelle mesure celles-ci peuvent être compatibles sont les enjeux de ce mémoire.

URL : Les enjeux de la patrimonialisation et de la réutilisation des données qualitatives de la recherche en Sciences humaines et sociales

Alternative location : http://www.enssib.fr/bibliotheque-numerique/notices/66007-les-enjeux-de-la-patrimonialisation-et-de-la-reutilisation-des-donnees-qualitatives-de-la-recherche-en-sciences-humaines-et-sociales