The Medical Library Association Guide to Data Management for Librarians

By Wickline, Mary A. | Journal of the Medical Library Association, July 2017 | Go to article overview

The Medical Library Association Guide to Data Management for Librarians


Wickline, Mary A., Journal of the Medical Library Association


The Medical Library Association Guide to Data Management for Librarians. Edited by Lisa Federer. Lanham, MD: Rowman & Littlefield; 2016. 230 p. $125.00. ISBN: 978-1-4422-6426-7. <0>

Big data, data science, data management, data curation - beyond buzz words, these iterations describe the recording, reuse, and long-term care of research data. The need for management and curation also indicates the usefulness and value of libraries and information science to a broad hase of researchers, universities, and clinical scientists.

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. Each chapter has footnotes and a bibliography, as well as pearls of wisdom and recommended reading. The book itself is indexed and logically organized. The theoretical section begins with a chapter from an NLM associate director, Valerie Florance, who maps out the landscape of data needs, organizational or grant mandates, existing datasets that are publicly available, and the recommendations of the NIH Data and Informatics Working Group, which focus on sharing data, supporting methodologies, training the workforce, and funding, commitments.

Chris Eaker's chapter on the impact of poor data management speaks to the reasons every institution and researcher should care deeply about good data management. Eaker begins by citing studies that found that 67% of article retractions were from scientific misconduct and that an inability to replicate studies costs $28 billion per year. The chapter illuminates "what can go wrong"-in planning, data collection, quality assurance, documentation, preservation, and analysis of data - with responses demonstrating specific tools and methods to show "what can be done differently" (pp. 1423).

A dozen years ago, Philip Bourne sought to make the importance of data management clear when he talked about referencing the data and not just the previous analysis in scientific papers [1]. The amount of data now generated from one experiment makes additional analysis a driving, tool for moving science forward. Federer makes that importance concrete with her example in chapter 6 of a computer programmer, using publicly available data, who successfully wrote code aimed at discovering harmful mutations in tumor suppressor genes (benign versus malignant tumor likelihood in BRCAI) through gene expression profiling - who that programmer is offers a delightfully surprising case for why publicly available data are so valuable (p. 69).

Access to data enables verification, replication, and multiple minds working on focused problem solving,. …

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