scholarly journals Data Curation through Catalogs: A Repository-Independent Model for Data Discovery

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Helenmary Sheridan ◽  
Anthony J. Dellureficio ◽  
Melissa A. Ratajeski ◽  
Sara Mannheimer ◽  
Terrie R. Wheeler
Keyword(s):  
Community Of Practice ◽  
Academic Libraries ◽  
Gold Standard ◽  
Research Data ◽  
Data Curation ◽  
Viable Alternative ◽  
Potential Solution ◽  
Data Discovery ◽  
Data Repositories ◽  
Independent Model

Institutional data repositories are the acknowledged gold standard for data curation platforms in academic libraries. But not every institution can sustain a repository, and not every dataset can be archived due to legal, ethical, or authorial constraints. Data catalogs—metadata-only indices of research data that provide detailed access instructions and conditions for use—are one potential solution, and may be especially suitable for "challenging" datasets. This article presents the strengths of data catalogs for increasing the discoverability and accessibility of research data. The authors argue that data catalogs are a viable alternative or complement to data repositories, and provide examples from their institutions' experiences to show how their data catalogs address specific curatorial requirements. The article also reports on the development of a community of practice for data catalogs and data discovery initiatives.

scholarly journals Data Soup Webinar, December 16, 2021: hosted by the Data Curation Network and the Journal of eScience Librarianship

2021 ◽  
pp. e1232
Keyword(s):  
Research Data ◽  
Data Curation ◽  
Special Issue ◽  
Data Discovery ◽  
Longitudinal Surveys ◽  
Subject Areas ◽  
Independent Model

Data Soup is a collaboration between the Journal of eScience Librarianship (JeSLIB) and the Data Curation Networkto host a series of community focused webinars/discussions to exchange practices for curating research data of different formats or subject areas among data curators. The lineup of the inaugural webinar includes the following speakers and topics from the recent JeSLIB Special Issue: Data Curation in Practice: Creating Guidance for Canadian Dataverse Curators: Portage Network’s Dataverse Curation Guide Alexandra Cooper, Michael Steeleworthy, Ève Paquette-Bigras, Erin Clary, Erin MacPherson, Louise Gillis, and Jason Brodeur, https://escholarship.umassmed.edu/jeslib/vol10/iss3/2; Active Curation of Large Longitudinal Surveys: A Case Study Inna Kouper, Karen L. Tucker, Kevin Tharp, Mary Ellen van Booven, and Ashley Clark, https://doi.org/10.7191/jeslib.2021.1210; Data Curation through Catalogs: A Repository-Independent Model for Data Discovery Helenmary Sheridan, Anthony J. Dellureficio, Melissa A. Ratajeski, Sara Mannheimer, and Terrie R. Wheeler, https://doi.org/10.7191/jeslib.2021.1203.

scholarly journals A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question"

2019 ◽  
Vol 5 ◽  
Author(s):  
Matthew Murray ◽  
Megan O'Donnell ◽  
Mark Laufersweiler ◽  
John Novak ◽  
Betty Rozum ◽  
...  
Keyword(s):  
United States ◽  
Academic Libraries ◽  
The United States ◽  
Research Data ◽  
The State ◽  
Executive Summary ◽  
Data Repositories ◽  
Data Services ◽  
Survey Questions ◽  
State Of Research

This report shares the results of a Spring 2018 survey of 35 academic libraries in the United States in regard to the research data services (RDS) they offer. An executive summary presents key findings while the results section provides detailed information on the answers to specific survey questions related to data repositories, metadata, workshops, and polices.

scholarly journals Introduction to the Special JeSLIB Issue on Data Curation in Practice

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Cynthia Hudson Vitale ◽  
Jake R. Carlson ◽  
Hannah Hadley ◽  
Lisa Johnston
Keyword(s):  
Research Integrity ◽  
Scientific Communication ◽  
Research Data ◽  
Data Curation ◽  
Research Projects ◽  
Academic Institutions ◽  
Data Repositories ◽  
Communication Processes ◽  
Potential Impact

Research data curation is a set of scientific communication processes and activities that support the ethical reuse of research data and uphold research integrity. Data curators act as key collaborators with researchers to enrich the scholarly value and potential impact of their data through preparing it to be shared with others and preserved for the long term. This special issues focuses on practical data curation workflows and tools that have been developed and implemented within data repositories, scholarly societies, research projects, and academic institutions.

scholarly journals Curating Humanities Research Data: Managing Workflows for Adjusting a Repository Framework

1970 ◽  
Vol 12 (2) ◽  
Author(s):  
Hagen Peukert
Keyword(s):  
Heterogeneous Data ◽  
Research Data ◽  
Data Curation ◽  
Management Problem ◽  
Strategic Positioning ◽  
Data Repositories ◽  
Standard Case ◽  
Traditional Sense ◽  
Definition Of ◽  
Data Subject

Handling heterogeneous data, subject to minimal costs, can be perceived as a classic management problem. The approach at hand applies established managerial theorizing to the field of data curation. It is argued, however, that data curation cannot merely be treated as a standard case of applying management theory in a traditional sense. Rather, the practice of curating humanities research data, the specifications and adjustments of the model suggested here reveal an intertwined process, in which knowledge of both strategic management and solid information technology have to be considered. Thus, suggestions on the strategic positioning of research data, which can be used as an analytical tool to understand the proposed workflow mechanisms, and the definition of workflow modules, which can be flexibly used in designing new standard workflows to configure research data repositories, are put forward.

scholarly journals Data Curation in Practice: Extract Tabular Data from PDF Files Using a Data Analytics Tool

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Allis J. Choi ◽  
Xuying Xin
Keyword(s):  
Data Analytics ◽  
Research Data ◽  
Data Curation ◽  
Data Repository ◽  
State University ◽  
Tabular Data ◽  
Data Discovery ◽  
Penn State ◽  
Efficient Data ◽  
General Data

Data curation is the process of managing data to make it available for reuse and preservation and to allow FAIR (findable, accessible, interoperable, reusable) uses. It is an important part of the research lifecycle as researchers are often either required by funders or generally encouraged to preserve the dataset and make it discoverable and reusable. This has been especially important as the Open Access (OA) policy is being implemented in many institutions across the nation. In facilitating research data discovery and enhancing its easier reuse, an efficient data repository and its data curation play key roles. In this article, we briefly discuss the local institutional repository at Penn State University and the general data curation practices we adopt for the deposited files and datasets, then we focus on a data analytics tool that has recently been applied to extract tabular data from PDF files. This is an enhancement to the existing data curation practices as it adds additional tabular data to deposits with PDF files where tables are often embedded and not easily reused.

scholarly journals Data Stewardship: Environmental Data Curation and a Web-of-Repositories

2009 ◽  
Vol 4 (2) ◽  
pp. 12-27 ◽  
Author(s):  
Karen S. Baker ◽  
Lynn Yarmey
Keyword(s):  
Scientific Communication ◽  
Research Data ◽  
Environmental Data ◽  
Data Curation ◽  
Data Repository ◽  
Data Repositories ◽  
Reference Collection ◽  
Inclusive Approach ◽  
Data Stewardship ◽  
Organizational Element

Scientific researchers today frequently package measurements and associated metadata as digital datasets in anticipation of storage in data repositories. Through the lens of environmental data stewardship, we consider the data repository as an organizational element central to data curation. One aspect of non-commercial repositories, their distance-from-origin of the data, is explored in terms of near and remote categories. Three idealized repository types are distinguished – local, center, and archive - paralleling research, resource, and reference collection categories respectively. Repository type characteristics such as scope, structure, and goals are discussed. Repository similarities in terms of roles, activities and responsibilities are also examined. Data stewardship is related to care of research data and responsible scientific communication supported by an infrastructure that coordinates curation activities; data curation is defined as a set of repeated and repeatable activities focusing on tending data and creating data products within a particular arena. The concept of “sphere-of-context” is introduced as an aid to distinguishing repository types. Conceptualizing a “web-of-repositories” accommodates a variety of repository types and represents an ecologically inclusive approach to data curation.

scholarly journals Towards a Symbiotic Relationship Between Academic Libraries and Disciplinary Data Repositories: A Dryad and University of Michigan Case Study

2014 ◽  
Vol 9 (1) ◽  
pp. 119-131 ◽  
Author(s):  
Katherine G. Akers ◽  
Jennifer A. Green
Keyword(s):  
Academic Libraries ◽  
Medical Data ◽  
Research Data ◽  
Academic Librarians ◽  
Symbiotic Relationship ◽  
University Of Michigan ◽  
Data Repositories ◽  
The University ◽  
Support Research

In addition to encouraging the deposit of research data into institutional data repositories, academic librarians can further support research data sharing by facilitating the deposit of data into external disciplinary data repositories. In this paper, we focus on the University of Michigan Library and Dryad, a repository for scientific and medical data, as a case study to explore possible forms of partnership between academic libraries and disciplinary data repositories. We found that although few University of Michigan researchers have submitted data to Dryad, many have recently published articles in Dryad-integrated journals, suggesting significant opportunities for Dryad use on our campus. We suggest that academic libraries could promote the sharing and preservation of science and medical data by becoming Dryad members, purchasing vouchers to cover researchers’ data submission costs, and hosting local curators who could directly work with campus researchers to improve the accuracy and completeness of data packages and thereby increase their potential for re-use. By enabling the use of both institutional and disciplinary data repositories, we argue that academic librarians can achieve greater success in capturing the vast amounts of data that presently fail to depart researchers’ hands and making that data visible to relevant communities of interest.

scholarly journals Qualitative Data Sharing: Data Repositories and Academic Libraries as Key Partners in Addressing Challenges

2018 ◽  
Vol 63 (5) ◽  
pp. 643-664 ◽  
Author(s):  
Sara Mannheimer ◽  
Amy Pienta ◽  
Dessislava Kirilova ◽  
Colin Elman ◽  
Amber Wutich
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Data Sharing ◽  
Knowledge Production ◽  
Academic Libraries ◽  
Qualitative Data ◽  
Federal Funding ◽  
Research Data ◽  
Data Repositories ◽  
Funding Agencies

Data sharing is increasingly perceived to be beneficial to knowledge production, and is therefore increasingly required by federal funding agencies, private funders, and journals. As qualitative researchers are faced with new expectations to share their data, data repositories and academic libraries are working to address the specific challenges of qualitative research data. This article describes how data repositories and academic libraries can partner with researchers to support three challenges associated with qualitative data sharing: (1) obtaining informed consent from participants for data sharing and scholarly reuse, (2) ensuring that qualitative data are legally and ethically shared, and (3) sharing data that cannot be deidentified. This article also describes three continuing challenges of qualitative data sharing that data repositories and academic libraries cannot specifically address—research using qualitative big data, copyright concerns, and risk of decontextualization. While data repositories and academic libraries cannot provide easy solutions to these three continuing challenges, they can partner with researchers and connect them with other relevant specialists to examine these challenges. Ultimately, this article suggests that data repositories and academic libraries can help researchers address some of the challenges associated with ethical and lawful qualitative data sharing.

scholarly journals Revisiting the Data Lifecycle with Big Data Curation

2016 ◽  
Vol 10 (2) ◽  
pp. 176-192 ◽  
Author(s):  
Line Pouchard
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
High Performance ◽  
Research Data ◽  
Data Curation ◽  
Complex Data ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Data Repositories ◽  
Data Life Cycle

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

scholarly journals Data Curation in Academic Libraries as Part of the Digital Revolution

2019 ◽  
Vol 57 (1A (113A)) ◽  
pp. 28-36
Author(s):  
Tibor Koltay
Keyword(s):  
Data Management ◽  
Academic Libraries ◽  
Research Data ◽  
Data Curation ◽  
Digital Revolution ◽  
Research Data Management

Purpose/Thesis: This paper outlines the role of data curation in the context of Research 2.0 and Research Data Management.Approach/Methods: The argument is based on a non-exhaustive review of the literature.Results and conclusions: Despite the relative vagueness and variety of definitions of data curation, academic libraries should engage in it.Research limitations: The study focused mainly on theoretical writings.Practical implications: The worldwide challenge associated with Research Data Management and data curation. Several countries and institutions have already answered the challenge, but the overall level of its recognition is low, and thus there is a need to raise awareness of its importance.Originality/Value: The premise of the argument is based on the assumption that views on data are changing.

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