Training for Research Careers in Biomedical Informatics and Data Science Supported by the National Library of Medicine

2020 ◽  
pp. 13-22
Author(s):  
Valerie Florance
Keyword(s):  
Data Science ◽  
Biomedical Informatics ◽  
National Library ◽  
Research Careers

scholarly journals Biomedical informatics meets data science: current state and future directions for interaction

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Philip R O Payne ◽  
Elmer V Bernstam ◽  
Justin B Starren
Keyword(s):  
Big Data ◽  
Data Science ◽  
Clinical Decision Making ◽  
Social Issues ◽  
Biomedical Informatics ◽  
Scientific Discovery ◽  
Topic Area ◽  
Future Directions ◽  
Current State ◽  
Challenges And Opportunities

Abstract There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. These publications argue that there are substantial benefits resulting from the use of data-centric approaches to solve complex biomedical problems, including an acceleration in the rate of scientific discovery, improved clinical decision making, and the ability to promote healthy behaviors at a population level. In addition, there is an aligned and emerging body of literature that describes the ethical, legal, and social issues that must be addressed to responsibly use big data in such contexts. At the same time, there has been growing recognition that the challenges and opportunities being attributed to the expansion in DS often parallel those experienced by the biomedical informatics community. Indeed, many informaticians would consider some of these issues relevant to the core theories and methods incumbent to the field of biomedical informatics science and practice. In response to this topic area, during the 2016 American College of Medical Informatics Winter Symposium, a series of presentations and focus group discussions intended to define the current state and identify future directions for interaction and collaboration between people who identify themselves as working on big data, DS, and biomedical informatics were conducted. We provide a perspective concerning these discussions and the outcomes of that meeting, and also present a set of recommendations that we have generated in response to a thematic analysis of those same outcomes. Ultimately, this report is intended to: (1) summarize the key issues currently being discussed by the biomedical informatics community as it seeks to better understand how to constructively interact with the emerging biomedical big data and DS fields; and (2) propose a framework and agenda that can serve to advance this type of constructive interaction, with mutual benefit accruing to both fields.

scholarly journals Biomedical informatics and data science: evolving fields with significant overlap

2017 ◽  
Vol 25 (1) ◽  
pp. 2-3 ◽  
Author(s):  
Patricia Flatley Brennan ◽  
Michael F Chiang ◽  
Lucila Ohno-Machado
Keyword(s):  
Data Science ◽  
Biomedical Informatics

scholarly journals Massive open online courses on biomedical informatics

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 180
Author(s):  
Cender Udai Quispe-Juli ◽  
Victor Hugo Moquillaza-Alcántara ◽  
Katherine Arapa-Apaza
Keyword(s):  
United States ◽  
Observational Study ◽  
Data Science ◽  
Developing World ◽  
Online Courses ◽  
Biomedical Informatics ◽  
The United States ◽  
Massive Open Online Courses ◽  
Common Language ◽  
Massive Open Online

This study aimed to identify the characteristics of massive open online courses (MOOCs) related to biomedical informatics offered in several plataforms. We conducted an observational study on specialized MOOCs platforms to find courses related to biomedical informatics, in 2018. Our search identified 67 MOOCs on biomedical informatics. The majority of MOOCs were offered by Coursera (71.6%, 48/67), English was the most common language (95.5%, 64/67). The United States developed the majority of courses (73.1%, 49/67), with the vast majority of MOOCs being offered by universities (94%, 63/67). The majority of MOOCs were in bioinformatics (56.7%, 38/67) and data science (47.7%, 32/67). In conclusion, the MOOCs on biomedical informatics were focused in bioinformatics and data science, and were offered in English by institutions in the developing world.

scholarly journals Don Lindberg’s outreach legacy at the National Library of Medicine

2021 ◽  
pp. 1-9
Author(s):  
Elliot R. Siegel
Keyword(s):  
Health Information ◽  
Biomedical Informatics ◽  
Information Access ◽  
National Library ◽  
Outreach Programs ◽  
Associate Director ◽  
The U.S ◽  
Made In

Friends and colleagues of Donald A.B. Lindberg M.D. came together to give tribute to his extraordinary contributions during his tenure (1984–2015) as Director of the U.S. National Library of Medicine (NLM). Dr. Lindberg died in 2019. The book, Transforming biomedical informatics and health information access: Don Lindberg and the U.S. National Library of Medicine. includes four sections. The ten edited chapters in section three (the Outreach section) are briefly summarized in this overview. As Associate Director for Health Information Programs Development, Elliot R. Siegel Ph.D. coordinated NLM’s outreach programming under Dr. Lindberg’s leadership from its inception in 1989 to his own retirement in 2010. Dr. Lindberg’s legacy at NLM is one of new possibilities imagined, significant changes made in the mission and ethos of a venerable institution, and numerous successes achieved in a variety of settings and contexts. Like so much else Dr. Lindberg accomplished, these Outreach programs that profoundly changed the character of NLM would likely not have occurred without him. He made a difference.

scholarly journals Developing the Librarian Workforce for Data Science and Open Science

2020 ◽  
Author(s):  
Lisa Federer ◽  
Sarah C. Clarke ◽  
Maryam Zaringhalam
Keyword(s):  
Data Science ◽  
Information Science ◽  
Open Science ◽  
Library And Information Science ◽  
National Library ◽  
Academic Settings

(Printer friendly version available in supplementary materials.) This report summarizes the discussions and major themes that emerged from a 1.5-day workshop on Developing the Librarian Data Science and Open Science Workforce held April 15-16, 2019 at the National Library of Medicine (NLM). The workshop aimed to identify the set of skills that librarians will need to advance work in data science and open science (DS/OS). Workshop participants included practitioners who provide DS/OS services, as well as faculty from schools of library and information science who are advancing DS/OS in academic settings.

A Quantitative Ranking of the Biomedical Informatics Serials

1995 ◽  
Vol 34 (04) ◽  
pp. 397-410 ◽  
Author(s):  
J. Kaalaas-Sittig ◽  
D. F. Sittig
Keyword(s):  
Biomedical Informatics ◽  
Evaluation Criteria ◽  
Computing Methods ◽  
Medical Decision ◽  
National Library ◽  
Interlibrary Loan ◽  
Publication Delay ◽  
Library Use ◽  
Resource Library ◽  
Total Circulation

Abstract:We have developed a quantitative serial ranking system based on multiple citation analysis techniques, library use statistics, expert opinion, and selected distinguishing publication characteristics. Evaluation criteria categories include: average Science Citation Index (Impact Factor, Immediacy Index, Total citations) rankings from 1987 to 1992; citation source counts of multiple “core” biomedical informatics publications; a questionnaire sent to American College of Medical Informatics Fellows; publication delay; distinguishing characteristics (e. g., subscription cost, total circulation, year established, places indexed, affiliation with a professional society, major biomedical resource library holdings); and the total number of interlibrary loan requests to the U. S. National Library of Medicine. The top serials were Computers and Biomedical Research, MD Computing, Methods of Information in Medicine, Medical Decision Making and Computers in Biology and Medicine.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
Sign in / Sign up

Export Citation Format

Share Document