scholarly journals The Linguistic Data Life Cycle, Sustainability of Data, and Principles of Solid Data Management

2022 ◽  
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Linguistic Data ◽  
Data Life Cycle

scholarly journals Research data management and services: Resources for novice data librarians

2017 ◽  
Vol 78 (5) ◽  
pp. 274 ◽  
Author(s):  
Sarah Barbrow ◽  
Denise Brush ◽  
Julie Goldman
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Academic Libraries ◽  
Research Data ◽  
Research Data Management ◽  
Academic Fields ◽  
Data Life Cycle ◽  
Management Plans ◽  
Share Data

Research in many academic fields today generates large amounts of data. These data not only must be processed and analyzed by the researchers, but also managed throughout the data life cycle. Recently, some academic libraries have begun to offer research data management (RDM) services to their communities. Often, this service starts with helping faculty write data management plans, now required by many federal granting agencies. Libraries with more developed services may work with researchers as they decide how to archive and share data once the grant work is complete.

scholarly journals Research Data Services at ETH-Bibliothek

IFLA Journal ◽  
2016 ◽  
Vol 42 (4) ◽  
pp. 284-291 ◽  
Author(s):  
Ana Sesartic ◽  
Matthias Töwe
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Service Providers ◽  
Scientific Practice ◽  
Research Data ◽  
Data Services ◽  
Mutual Learning ◽  
Data Life Cycle ◽  
Long Term Preservation

The management of research data throughout its life-cycle is both a key prerequisite for effective data sharing and efficient long-term preservation of data. This article summarizes the data services and the overall approach to data management as currently practised at ETH-Bibliothek, the main library of ETH Zürich, the largest technical university in Switzerland. The services offered by service providers within ETH Zürich cover the entirety of the data life-cycle. The library provides support regarding conceptual questions, offers training and services concerning data publication and long-term preservation. As research data management continues to play a steadily more prominent part in both the requirements of researchers and funders as well as curricula and good scientific practice, ETH-Bibliothek is establishing close collaborations with researchers, in order to promote a mutual learning process and tackle new challenges.

Research data management: a conceptual framework

2016 ◽  
Vol 65 (4/5) ◽  
pp. 226-241 ◽  
Author(s):  
Dimple Patel
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Conceptual Framework ◽  
Data Privacy ◽  
Research Data ◽  
Content Type ◽  
Institutional Level ◽  
Research Data Management ◽  
Data Life Cycle ◽  
Effective Manner

Purpose Research data management (RDM) is gaining a lot of momentum in the present day and rightly so. Research data are the core of any research study. The findings and conclusions of a study are entirely dependent on the research data. Traditional publishing did not focus on the presentation of data, along with the publications such as research monographs and especially journal articles, probably because of the difficulties involved in managing the research data sets. The current day technology, however, has helped in making this task easier. The purpose of this paper is to present a conceptual framework for managing research data at the institutional level. Design/methodology/approach This paper discusses the significance and advantages of sharing research data. In the spirit of open access to publications, freeing research data and making it available openly, with minimal restrictions, will help in not only furthering research and development but also avoiding duplication of efforts. The issues and challenges involved in RDM at the institutional level are discussed. Findings A conceptual framework for RDM at the institutional level is presented. A model for a National Repository of Open Research Data (NRORD) is also proposed, and the workflow of the functioning of NRORD is also presented. Originality/value The framework clearly presents the workflow of the data life-cycle in its various phases right from its creation, storage, organization and sharing. It also attempts to address crucial issues in RDM such as data privacy, data security, copyright and licensing. The framework may help the institutions in managing the research data life-cycle in a more efficient and effective manner.

scholarly journals Knowledge and Practice changes following a student data focused data management education program.

2021 ◽  
Author(s):  
Tina M Griffin
Keyword(s):  
Life Cycle ◽  
Graduate Students ◽  
Data Management ◽  
Management Education ◽  
Management Program ◽  
Management Skills ◽  
Student Knowledge ◽  
Data Life Cycle ◽  
Knowledge And Practice ◽  
Faculty Mentors

Introduction It is known that graduate students work with research data more intimately than their faculty mentors. Because of this, much data management education is geared toward this population. However, student learning has predominantly been assessed through measures of satisfaction and attendance rather than through evaluating knowledge and skills acquired. This study attempts to advance assessment efforts by asking students to report their knowledge and practice changes before, immediately after, and six months following education. Methods Graduate students in STEM and Health sciences disciplines self-enrolled in an eight-week data management program that used their research projects as the focus for learning. Three surveys were administered (pre, post, and six months following) to determine changes in students’ knowledge and practices regarding data management skills. The survey consisted of approximately 115 Likert-style questions and covered major aspects of the data life cycle. Results & discussion Overall students increased their data management knowledge and improved their skills in all areas of the data life cycle. Students readily adopted practices for straightforward tasks like determining storage and improving file naming. Students improved but struggled with tasks that were more involved like sharing data and documenting code. For most of these practices, students consistently implemented them through the six month follow up period. Conclusion Impact of data management education lasts significantly beyond immediate instruction. In depth assessment of student knowledge and practices indicates where this education is effective and where it needs further support. It is likely that this effect is due to the program length and focus on implementation.

Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia

2019 ◽  
Vol 49 (2-3) ◽  
pp. 108-116 ◽  
Author(s):  
Michelle A Krahe ◽  
Julie Toohey ◽  
Malcolm Wolski ◽  
Paul A Scuffham ◽  
Sheena Reilly
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Research Data ◽  
Academic Institution ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Convenience Sample ◽  
Research Data Management ◽  
Data Life Cycle ◽  
Medical Researchers

Background: Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research. Objective: This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia. Method: A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle. Results: Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams. Conclusion: Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group. Implications: This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.

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 The Medical Library Association Guide to Data Management for Librarians

2017 ◽  
Vol 105 (3) ◽  
Author(s):  
Mary A. Wickline
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Health Sciences ◽  
National Institutes Of Health ◽  
National Library ◽  
Academic Health ◽  
Medical Library ◽  
Hospital Environments ◽  
Data Life Cycle

Lisa Federer, AHIP, of the National Institutes of Health (NIH), along with authors from the National Library of Medicine (NLM) and highly reputable universities, introduces readers to the theoretical and data life cycle aspects of data management and then offers practical examples from academic health sciences and hospital environments.

scholarly journals Best practice data life cycle approaches for the life sciences

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1618 ◽  
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

Throughout 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.

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