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CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries
Big bibliographic datasets hold promise for revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Yet, hosting proprietary and open big bibliographic datasets poses significant difficulties for libraries, both large and small. Libraries face si...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931882/ https://www.ncbi.nlm.nih.gov/pubmed/33693415 http://dx.doi.org/10.3389/fdata.2020.556282 |
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author | Mabry, Patricia L. Yan, Xiaoran Pentchev, Valentin Van Rennes, Robert McGavin, Stephanie Hernandez Wittenberg, Jamie V. |
author_facet | Mabry, Patricia L. Yan, Xiaoran Pentchev, Valentin Van Rennes, Robert McGavin, Stephanie Hernandez Wittenberg, Jamie V. |
author_sort | Mabry, Patricia L. |
collection | PubMed |
description | Big bibliographic datasets hold promise for revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Yet, hosting proprietary and open big bibliographic datasets poses significant difficulties for libraries, both large and small. Libraries face significant barriers to hosting such assets, including cost and expertise, which has limited their ability to provide stewardship for big datasets, and thus has hampered researchers' access to them. What is needed is a solution to address the libraries' and researchers’ joint needs. This article outlines the theoretical framework that underpins the Collaborative Archive and Data Research Environment project. We recommend a shared cloud-based infrastructure to address this need built on five pillars: 1) Community–a community of libraries and industry partners who support and maintain the platform and a community of researchers who use it; 2) Access–the sharing platform should be accessible and affordable to both proprietary data customers and the general public; 3) Data-Centric–the platform is optimized for efficient and high-quality bibliographic data services, satisfying diverse data needs; 4) Reproducibility–the platform should be designed to foster and encourage reproducible research; 5) Empowerment—the platform should empower researchers to perform big data analytics on the hosted datasets. In this article, we describe the many facets of the problem faced by American academic libraries and researchers wanting to work with big datasets. We propose a practical solution based on the five pillars: The Collaborative Archive and Data Research Environment. Finally, we address potential barriers to implementing this solution and strategies for overcoming them. |
format | Online Article Text |
id | pubmed-7931882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79318822021-03-09 CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries Mabry, Patricia L. Yan, Xiaoran Pentchev, Valentin Van Rennes, Robert McGavin, Stephanie Hernandez Wittenberg, Jamie V. Front Big Data Big Data Big bibliographic datasets hold promise for revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Yet, hosting proprietary and open big bibliographic datasets poses significant difficulties for libraries, both large and small. Libraries face significant barriers to hosting such assets, including cost and expertise, which has limited their ability to provide stewardship for big datasets, and thus has hampered researchers' access to them. What is needed is a solution to address the libraries' and researchers’ joint needs. This article outlines the theoretical framework that underpins the Collaborative Archive and Data Research Environment project. We recommend a shared cloud-based infrastructure to address this need built on five pillars: 1) Community–a community of libraries and industry partners who support and maintain the platform and a community of researchers who use it; 2) Access–the sharing platform should be accessible and affordable to both proprietary data customers and the general public; 3) Data-Centric–the platform is optimized for efficient and high-quality bibliographic data services, satisfying diverse data needs; 4) Reproducibility–the platform should be designed to foster and encourage reproducible research; 5) Empowerment—the platform should empower researchers to perform big data analytics on the hosted datasets. In this article, we describe the many facets of the problem faced by American academic libraries and researchers wanting to work with big datasets. We propose a practical solution based on the five pillars: The Collaborative Archive and Data Research Environment. Finally, we address potential barriers to implementing this solution and strategies for overcoming them. Frontiers Media S.A. 2020-11-20 /pmc/articles/PMC7931882/ /pubmed/33693415 http://dx.doi.org/10.3389/fdata.2020.556282 Text en Copyright © 2020 Mabry, Yan, Pentchev, Van Rennes, McGavin and Wittenberg http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Mabry, Patricia L. Yan, Xiaoran Pentchev, Valentin Van Rennes, Robert McGavin, Stephanie Hernandez Wittenberg, Jamie V. CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries |
title | CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries |
title_full | CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries |
title_fullStr | CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries |
title_full_unstemmed | CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries |
title_short | CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries |
title_sort | cadre: a collaborative, cloud-based solution for big bibliographic data research in academic libraries |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931882/ https://www.ncbi.nlm.nih.gov/pubmed/33693415 http://dx.doi.org/10.3389/fdata.2020.556282 |
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