scholarly journals A Report of Data-Intensive Capability, Institutional Support, and Data Management Practices in Social Sciences

2016 ◽  
Vol 11 (1) ◽  
pp. 156 ◽  
Author(s):  
Wei Jeng ◽  
Liz Lyon
Keyword(s):  
Social Sciences ◽  
Data Management ◽  
Data Science ◽  
Management Practices ◽  
Information Science ◽  
Institutional Support ◽  
Current Data ◽  
Management Support ◽  
Model Framework ◽  
Data Intensive

We report on a case study which examines the social science community’s capability and institutional support for data management. Fourteen researchers were invited for an in-depth qualitative survey between June 2014 and October 2015. We modify and adopt the Community Capability Model Framework (CCMF) profile tool to ask these scholars to self-assess their current data practices and whether their academic environment provides enough supportive infrastructure for data related activities. The exemplar disciplines in this report include anthropology, political sciences, and library and information science. Our findings deepen our understanding of social disciplines and identify capabilities that are well developed and those that are poorly developed. The participants reported that their institutions have made relatively slow progress on economic supports and data science training courses, but acknowledged that they are well informed and trained for participants’ privacy protection. The result confirms a prior observation from previous literature that social scientists are concerned with ethical perspectives but lack technical training and support. The results also demonstrate intra- and inter-disciplinary commonalities and differences in researcher perceptions of data-intensive capability, and highlight potential opportunities for the development and delivery of new and impactful research data management support services to social sciences researchers and faculty. 

scholarly journals A gestão dos dados de pesquisa no âmbito da comunidade dos pesquisadores vinculados aos programas de pós-graduação brasileiros na área da Ciência da Informação: desvendando as práticas e percepções associadas ao uso e reuso de dados | The management of research data within the community of researchers linked to Brazilian post-graduate programs in the area of Information Science: unveiling the practices and perceptions associated with the use and reuse of data

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Guilherme Ataíde Dias ◽  
Renata Lemos Dos Anjos ◽  
Débora Gomes De Araújo
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Management Practices ◽  
Information Science ◽  
Graduate Programs ◽  
Research Data ◽  
Formal Control ◽  
Community Of Researchers ◽  
E Mail ◽  
Science Information

RESUMO A pesquisa investigou as práticas e percepções associadas com a gestão de dados pelos pesquisadores na pós-graduação brasileira na área da Ciência da Informação (CI). O instrumento de pesquisa utilizado foi um questionário semiestruturado, enviado por e-mail para 341 pesquisadores vinculados aos programas de pós-graduação brasileiros em CI. Os dados obtidos foram analisados através de técnicas de estatística descritiva e análise temática. Verificou-se que as práticas de gestão de dados conduzidas pelos pesquisadores precisam ser aprimoradas e que eles possuem postura favorável com relação ao compartilhamento de dados, desde que exista algum controle formal sobre os mesmos.Palavras-chave: Dados de Pesquisa; Compartilhamento de Dados de Pesquisa; Ciência da Informação; Tecnologia da Informação.ABSTRACT The research investigated the practices and perceptions associated with data management by researchers in Brazilian postgraduate programs in the Information Science (IC) area. A semi-structured survey was used as the research instrument, it was sent by e-mail to 341 researchers linked to the Brazilian postgraduate programs in CI. The data was analyzed through descriptive statistics techniques and thematic analysis. It was found that the data management practices conducted by the researchers need to be improved and that they have a favorable approach regarding data sharing, provided there is some formal control over them.Keywords: Research Data; Research Data Sharing; Information Science; Information Technology.

scholarly journals Scaling by Optimising: Modularisation of Data Curation Services in Growing Organisations

2021 ◽  
Vol 16 (1) ◽  
pp. 20
Author(s):  
Hagen Peukert
Keyword(s):  
Data Management ◽  
Data Science ◽  
Economies Of Scale ◽  
Management Practices ◽  
Management Science ◽  
Data Curation ◽  
Digital Data ◽  
Digital Management ◽  
Increasing Demand ◽  
The Cost

After a century of theorising and applying management practices, we are in the middle of entering a new stage in management science: digital management. The management of digital data submerges in traditional functions of management and, at the same time, continues to recreate viable solutions and conceptualisations in its established fields, e.g. research data management. Yet, one can observe bilateral synergies and mutual enrichment of traditional and data management practices in all fields. The paper at hand addresses a case in point, in which new and old management practices amalgamate to meet a steadily, in part characterised by leaps and bounds, increasing demand of data curation services in academic institutions. The idea of modularisation, as known from software engineering, is applied to data curation workflows so that economies of scale and scope can be used. While scaling refers to both management science and data science, optimising is understood in the traditional managerial sense, that is, with respect to the cost function. By means of a situation analysis describing how data curation services were applied from one department to the entire institution and an analysis of the factors of influence, a method of modularisation is outlined that converges to an optimal state of curation workflows.

scholarly journals Methods for Open and Reproducible Materials Science

2019 ◽  
Author(s):  
Sara L Wilson ◽  
Micah Altman ◽  
Rafael Jaramillo
Keyword(s):  
Data Management ◽  
Data Science ◽  
Management Practices ◽  
Materials Science ◽  
Critical Role ◽  
Structured Interview ◽  
Open Science ◽  
Manuscript Submission ◽  
Attitudes And Practices ◽  
Data Stewardship

Data stewardship in experimental materials science is increasingly complex and important. Progress in data science and inverse-design of materials give reason for optimism that advances can be made if appropriate data resources are made available. Data stewardship also plays a critical role in maintaining broad support for research in the face of well-publicized replication failures (in different fields) and frequently changing attitudes, norms, and sponsor requirements for open science. The present-day data management practices and attitudes in materials science are not well understood. In this article, we collect information on the practices of a selection of materials scientists at two leading universities, using a semi-structured interview instrument. An analysis of these interviews reveals that although data management is universally seen as important, data management practices vary widely. Based on this analysis, we conjecture that broad adoption of basic file-level data sharing at the time of manuscript submission would benefit the field without imposing substantial burdens on researchers. More comprehensive solutions for lifecycle open research in materials science will have to overcome substantial differences in attitudes and practices.

scholarly journals Support Your Data: A Research Data Management Guide for Researchers

2018 ◽  
Vol 4 ◽  
pp. e26439 ◽  
Author(s):  
John Borghi ◽  
Stephen Abrams ◽  
Daniella Lowenberg ◽  
Stephanie Simms ◽  
John Chodacki
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Current Data ◽  
Research Data Management

Researchers are faced with rapidly evolving expectations about how they should manage and share their data, code, and other research materials. To help them meet these expectations and generally manage and share their data more effectively, we are developing a suite of tools which we are currently referring to as "Support Your Data". These tools, which include a rubric designed to enable researchers to self-assess their current data management practices and a series of short guides which provide actionable information about how to advance practices as necessary or desired, are intended to be easily customizable to meet the needs of a researchers working in a variety of institutional and disciplinary contexts.

scholarly journals Modifying researchers’ data management practices

IFLA Journal ◽  
2016 ◽  
Vol 42 (4) ◽  
pp. 253-265 ◽  
Author(s):  
Susan Hickson ◽  
Kylie Ann Poulton ◽  
Maria Connor ◽  
Joanna Richardson ◽  
Malcolm Wolski
Keyword(s):  
Data Management ◽  
Best Practice ◽  
Management Practices ◽  
Social Science Research ◽  
Science Research ◽  
Current Data ◽  
Research Centre ◽  
Data Collection And Analysis ◽  
Management Plans

Data is the new buzzword in academic libraries, as policy increasingly mandates that data must be open and accessible, funders require formal data management plans, and institutions are implementing guidelines around best practice. Given concerns about the current data management practices of researchers, this paper reports on the initial findings from a project being undertaken at Griffith University to apply a conceptual (A-COM-B) framework to understanding researchers’ behaviour. The objective of the project is to encourage the use of institutionally endorsed solutions for research data management. Based on interviews conducted by a team of librarians in a small, social science research centre, preliminary results indicate that attitude is the key element which will need to be addressed in designing intervention strategies to modify behaviour. The paper concludes with a discussion of the next stages in the project, which involve further data collection and analysis, the implementation of targeted strategies, and a follow-up activity to assess the extent of modifications to current undesirable practices.

scholarly journals Dealing with Data: Science Librarians’ Participation in Data Management at Association of Research Libraries Institutions

2014 ◽  
Vol 75 (4) ◽  
pp. 557-574 ◽  
Author(s):  
Karen Antell ◽  
Jody Bales Foote ◽  
Jaymie Turner ◽  
Brian Shults
Keyword(s):  
Data Management ◽  
Academic Libraries ◽  
Data Science ◽  
Management Plan ◽  
Funding Agency ◽  
Management Support ◽  
Data Repositories ◽  
Institutional Repositories ◽  
Research Libraries ◽  
Association Of Research Libraries

As long as empirical research has existed, researchers have been doing “data management” in one form or another. However, funding agency mandates for doing formal data management are relatively recent, and academic libraries’ involvement has been concentrated mainly in the last few years. The National Science Foundation implemented a new mandate in January 2011, requiring researchers to include a data management plan with their proposals for funding. This has prompted many academic libraries to work more actively than before in data management, and science librarians in particular are uniquely poised to step into new roles to meet researchers’ data management needs. This study, a survey of science librarians at institutions affiliated with the Association of Research Libraries, investigates science librarians’ awareness of and involvement in institutional repositories, data repositories, and data management support services at their institutions. The study also explores the roles and responsibilities, both new and traditional, that science librarians have assumed related to data management, and the skills that science librarians believe are necessary to meet the demands of data management work. The results reveal themes of both uncertainty and optimism—uncertainty about the roles of librarians, libraries, and other campus entities; uncertainty about the skills that will be required; but also optimism about applying “traditional” librarian skills to this emerging field of academic librarianship.

scholarly journals GO FAIR Brazil: A Challenge for Brazilian Data Science

2020 ◽  
Vol 2 (1-2) ◽  
pp. 238-245 ◽  
Author(s):  
Luana Sales ◽  
Patrícia Henning ◽  
Viviane Veiga ◽  
Maira Murrieta Costa ◽  
Luís Fernando Sayão ◽  
...  
Keyword(s):  
Data Management ◽  
Nuclear Energy ◽  
Data Science ◽  
Management Practices ◽  
Good Practice ◽  
Open Data ◽  
Management Processes ◽  
Health Domains ◽  
Fair Principles ◽  
Active Implementation

The FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, are recognised worldwide as key elements for good practice in all data management processes. To understand how the Brazilian scientific community is adhering to these principles, this article reports Brazilian adherence to the GO FAIR initiative through the creation of the GO FAIR Brazil Office and the manner in which they create their implementation networks. To contextualise this understanding, we provide a brief presentation of open data policies in Brazilian research and government, and finally, we describe a model that has been adopted for the GO FAIR Brazil implementation networks. The Brazilian Institute of Information in Science and Technology is responsible for the GO FAIR Brazil Office, which operates in all fields of knowledge and supports thematic implementation networks. Today, GO FAIR Brazil-Health is the first active implementation network in operation, which works in all health domains, serving as a model for other fields like agriculture, nuclear energy, and digital humanities, which are in the process of adherence negotiation. This report demonstrates the strong interest and effort from the Brazilian scientific communities in implementing the FAIR principles in their research data management practices.

The Perception of Library and Information Science (LIS) Professionals about Research Data Management Services in University Libraries of Pakistan

Libri ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Murtaza Ashiq ◽  
Qurat Ul Ain Saleem ◽  
Muhammad Asim
Keyword(s):  
Developing Countries ◽  
Data Management ◽  
Management Practices ◽  
Information Science ◽  
Research Data ◽  
Motivational Factors ◽  
Survey Method ◽  
Library And Information Science ◽  
Research Data Management ◽  
Management Services

Abstract Research data management services (RDMS) is considered as an emerging and groundbreaking area for research libraries. A large number of studies focused on researchers’ perspectives of how they perform research data management practices. There are some studies that examine this important area of research from library and information science (LIS) professionals’ context, especially developing countries like Pakistan. Hence, this study addresses the gap and investigate the RDMS training needs, motivational factors, possible hindrances, and key reasons to support RDMS. A survey method was used and a self-developed questionnaire was prepared using Google Docs survey. The questionnaire link was shared with LIS professionals considering purposive sampling technique. The study highlights the main RDMS supporting reasons, needed training areas, best methods to get training, the motivational factors, and possible hindrances while planning and implementing RDMS. This study fills the gap and addresses research data management literature in developing countries’ context, especially Pakistan, and established that RDMS are poorly observed in developing countries and require some drastic steps to be launched and improved. Higher Education Commission/departments, university administrations, and donor agencies take such initiatives that research data should be openly available through repositories and the maximum number of training opportunities should be provided to LIS professionals.

scholarly journals Best Practice Data Life Cycle Approaches for the Life Sciences

2017 ◽  
Author(s):  
Philippa C. Griffin ◽  
Jyoti Khadake ◽  
Kate S. LeMay ◽  
Suzanna E. Lewis ◽  
Sandra Orchard ◽  
...  
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Best Practice ◽  
Data Science ◽  
Management Practices ◽  
Life Sciences ◽  
Heterogeneous Data ◽  
Clear Understanding ◽  
Data Generation ◽  
Data Life Cycle

AbstractThroughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a ‘life cycle’ view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on ‘omics’ datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

scholarly journals Various Algorithms & Techniques Driving Data Science for Big Data

2020 ◽  
Vol 9 (5) ◽  
pp. 725-727
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Science ◽  
Information Science ◽  
Big Business ◽  
Level Data

In basic terms, Big Data1 – when joined with Data Science2 – permit chiefs to gauge and survey fundamentally more data about the nuances of their organizations, and to utilize the data in settling on progressively keen choices. In early 2010, during the period when the development of Big Data was truly increasing noteworthy notification all through the 3Data Management industry, said that it "is advancing into the key reason for rivalry." It has now developed, information volumes proceed to develop, and now the inquiry is never again if it's another pattern and what influences it will have, yet how to use Big Data in significant manners for the venture. Information Science has been around for any longer than Big Data, yet it wasn't until the development of information volumes arrived at contemporary levels that Data Science has become an essential part of big business level Data Management.

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