The relative citation ratio: what is it and why should medical librarians care?

Bibliometrics is becoming increasingly prominent in the world of medical libraries. The number of presentations related to research impact at the Medical Library Association (MLA) annual meeting has been increasing in past years. Medical centers have been using institutional dashboards to track clinical performance for over a decade, and more recently, these institutional dashboards have included measures of academic performance. This commentary reviews current practices and considers the role for a newer metric, the relative citation ratio

Informationist Support for a Study of the Role of Proteases and Peptides in Cancer Pain

Two supplements were awarded to the New York University Health Sciences Libraries from the National Library of Medicine\u27s informationist grant program. These supplements funded research support in a number of areas, including data management and bioinformatics, two fields that the library had recently begun to explore. As such, the supplements were of particular value to the library as a testing ground for these newer services. This paper will discuss a supplement received in support of a grant from the National Institute of Dental and Craniofacial Research (PI: Brian Schmidt) on the role of proteases and peptides in cancer pain. A number of barriers were preventing the research team from maximizing the efficiency and effectiveness of their work. A critical component of the research was to identify which proteins, from among hundreds identified in collected samples, to include in preclinical testing. This selection involved laborious and prohibitively time-consuming manual searching of the literature on protein function. Additionally, the research team encompassed ten investigators working in two different cities, which led to issues around the sharing and tracking of both data and citations. The supplement outlined three areas in which the informationists would assist the researchers in overcoming these barriers: 1) creating an automated literature searching system for protein function discovery, 2) introducing tools and associated workflows for sharing citations, and 3) introducing tools and workflows for sharing data and specimens

Rigor and reproducibility instruction in academic medical libraries

Background: Concerns over scientific reproducibility have grown in recent years, leading the National Institutes of Health (NIH) to require researchers to address these issues in research grant applications. Starting in 2020, training grants were required to provide a plan for educating trainees in rigor and reproducibility. Academic medical centers have responded with different solutions to fill this educational need. As experienced instructors with expertise in topics relating to reproducibility, librarians can play a prominent role in providing trainings, classes, and events to educate investigators and trainees, and bolstering reproducibility in their communities. Case Presentations: This special report summarizes efforts at five institutions to provide education in reproducibility to biomedical and life sciences researchers. Our goal is to expand awareness of the range of approaches in providing reproducibility services in libraries. Conclusions: Reproducibility education by medical librarians can take many forms. These specific programs in reproducibility education build upon libraries’ existing collaborations, with funder mandates providing a major impetus. Collaborator needs shaped the exact type of educational or other reproducibility support and combined with each library’s strengths to yield a diversity of offerings based on capacity and interest. As demand for and complexity of reproducibility education increases due to new institutional and funder mandates, reproducibility education will merit special attention

Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach

BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. METHODS: Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. RESULTS: The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. CONCLUSIONS: The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum

Research Data Management Teaching Toolkit

<p>This teaching toolkit is designed to be used for a one hour introductory data management class for biomedical researchers. It consists of an instructional guide for teaching the material, a PowerPoint presentation with a script in the accompanying notes to each slide, and a separate evaluation form. This material is built on training material provided in the BD2K funded online research data management educational modules that are freely available here: <u><a href="http://bit.ly/RDM_Modules">http://bit.ly/RDM_Modules</a></u></p

Word embeddings learnt on MEDLINE abstracts

Accompanying a preprint manuscript and code repository, this folder contains both raw text data and learnt word embeddings. The data source is the set of MEDLINE articles published on or after 2000. Preprocessing consists of extraction of each article's title and abstract and some minor text processing. The result is a corpus of 10.5 million documents in a single 14 GB file. word2vec and fastText are used to learn word embeddings on this corpus and three sets of word embeddings are shared here: 1) word2vec skip-gram, 2) word2vec CBOW, and 3) fastText skip-gram. All three sets use the default parameters of the software (e.g. context=5) with the exception of hierarchical softmax optimization and dimension=200. Preprint manuscript: https://arxiv.org/abs/1705.06262 GitHub repository: https://github.com/vincentmajor/ctsa_predictio