Faculty Bibliography

Academic health sciences libraries ("libraries") offer services that span the entire research lifecycle, positioning them as natural partners in advancing clinical and translational science. Many libraries enjoy active and productive collaborations with Clinical and Translational Science Award (CTSA) Program hubs and other translational initiatives like the IDeA Clinical & Translational Research Network. This article explores areas of potential partnership between libraries and Translational Science Hubs (TSH), highlighting areas where libraries can support the CTSA Program's five functional areas outlined in the Notice of Funding Opportunity. It serves as a primer for TSH and libraries to explore potential collaborations, demonstrating how libraries can connect researchers to services and resources that support the information needs of TSH.

OBJECTIVE:Researchers at New York University (NYU) Grossman School of Medicine contacted the Health Sciences Library for help with locating large datasets for reuse. In response, the library developed and maintained the NYU Data Catalog, a public-facing data catalog that has supported not only faculty acquisition of data but also the dissemination of the products of their research in various ways. MATERIALS AND METHODS/METHODS:The current NYU Data Catalog is built upon the Symfony framework with a tailored metadata schema reflecting the scope of faculty research areas. The project team curates new resources, including datasets and supporting software code, and conducts quarterly and annual evaluations to assess user interactions with the NYU Data Catalog and opportunities for growth. RESULTS:Since its launch in 2015, the NYU Data Catalog underwent a number of changes prompted by an increase in the disciplines represented by faculty contributors. The catalog has also utilized faculty feedback to enhance support of data reuse and researcher collaboration through alterations to its schema, layout, and visibility of records. DISCUSSION/CONCLUSIONS:These findings demonstrate the flexibility of data catalogs as a platform for enabling the discovery of disparate sources of data. While not a repository, the NYU Data Catalog is well-positioned to support mandates for data sharing from study sponsors and publishers. CONCLUSION/CONCLUSIONS:The NYU Data Catalog makes the most of the data that researchers share and can be harnessed as a modular and adaptable platform to promote data sharing as a cultural practice.

BACKGROUND/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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.

Background/UNASSIGNED:Over four years of hosting library data workshops, we conducted post-workshop evaluation of attendees' satisfaction with the workshops but not longer-term follow-up. To best allocate library resources and most effectively serve the needs of our users, we sought to determine whether our data workshops were impactful and useful to our community. This paper describes a pilot project to evaluate the impact of data workshops at our academic health sciences library. Case Presentation/UNASSIGNED:We surveyed individuals who signed up for data workshops between 2016 and 2019. Surveys included open-ended and multiple-choice questions, with the goal of having participants describe their motivations for taking the workshop(s) and how they ultimately used what they learned. An analysis of responses using the Applied Thematic Analysis model indicated that the workshops had an impact on the respondents, although the strength of our conclusions is limited by a relatively low response rate. Conclusions/UNASSIGNED:Survey results indicated that our workshops impacted how researchers at our medical center collect and analyze data, supporting the conclusion that we should concentrate our educational efforts on providing skills-based workshops. The low response rate and time-consuming nature of the analysis point toward several improvements for future evaluation efforts, including better tracking of workshop attendees, a shorter survey with fewer open-ended questions, and survey implementation within one year of the workshop date.

Background/UNASSIGNED:Librarians developed a pilot program to provide training, resources, strategies, and support for medical libraries seeking to establish research data management (RDM) services. Participants were required to complete eight educational modules to provide the necessary background in RDM. Each participating institution was then required to use two of the following three elements: (1) a template and strategies for data interviews, (2) the Teaching Toolkit to teach an introductory RDM class, or (3) strategies for hosting a data class series. Case Presentation/UNASSIGNED:Six libraries participated in the pilot, with between two and eight librarians participating from each institution. Librarians from each institution completed the online training modules. Each institution conducted between six and fifteen data interviews, which helped build connections with researchers, and taught between one and five introductory RDM classes. All classes received very positive evaluations from attendees. Two libraries conducted a data series, with one bringing in instructors from outside the library. Conclusion/UNASSIGNED:The pilot program proved successful in helping participating librarians learn about and engage with their research communities, jump-start their teaching of RDM, and develop institutional partnerships around RDM services. The practical, hands-on approach of this pilot proved to be successful in helping libraries with different environments establish RDM services. The success of this pilot provides a proven path forward for libraries that are developing data services at their own institutions.

BACKGROUND:Better research data management (RDM) provides the means to analyze data in new ways, effectively build on another researcher's results, and reproduce the results of an experiment. Librarians are recognized by many as a potential resource for assisting researchers in this area, however this potential has not been fully realized in the biomedical research community. While librarians possess the broad skill set needed to support RDM, they often lack specific knowledge and time to develop an appropriate curriculum for their research community. The goal of this project was to develop and pilot educational modules for librarians to learn RDM and a curriculum for them to subsequently use to train their own research communities. MATERIALS AND METHODS/METHODS:We created online modules for librarians that address RDM best practices, resources and regulations, as well as the culture and practice of biomedical research. Data was collected from librarians through questions embedded in the online modules on their self-reported changes in understanding of and comfort level with RDM using a retrospective pre-post design. We also developed a Teaching Toolkit which consists of slides, a script, and an evaluation form for librarians to use to teach an introductory RDM class to researchers at their own institutions. Researchers' satisfaction with the class and intent to use the material they had learned was collected. Actual changes in RDM practices by researchers who attended was assessed with a follow-up survey administered seven months after the class. RESULTS AND DISCUSSION/CONCLUSIONS:The online curriculum increased librarians' self-reported understanding of and comfort level with RDM. The Teaching Toolkit, when employed by librarians to teach researchers in person, resulted in improved RDM practices. This two-tiered curriculum provides concise training and a ready-made curriculum that allows working librarians to quickly gain an understanding of RDM, and translate this knowledge to researchers through training at their own institutions.

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.

Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document. Recent algorithms such as word2vec are capable of learning semantic meaning and similarity between words in an entirely unsupervised manner using a contextual window and doing so much faster than previous methods. Each word is projected into vector space such that similar meaning words such as "strong" and "powerful" are projected into the same general Euclidean space. Open questions about these embeddings include their utility across classification tasks and the optimal properties and source of documents to construct broadly functional embeddings. In this work, we demonstrate the usefulness of pre-trained embeddings for classification in our task and demonstrate that custom word embeddings, built in the domain and for the tasks, can improve performance over word embeddings learnt on more general data including news articles or Wikipedia.

BACKGROUND: The New York University Health Sciences Library data services team had developed educational material for research data management and data visualization and had been offering classes at the request of departments, research groups, and training programs, but many members of the medical center were unaware of these library data services. There were also indications of data skills gaps in these subject areas and other data-related topics. CASE PRESENTATION: The data services team enlisted instructors from across the medical center with data expertise to teach in a series of classes hosted by the library. We hosted eight classes branded as a series called "Data Day to Day." Seven instructors from four units in the medical center, including the library, taught the classes. A multipronged outreach approach resulted in high turnout. Evaluations indicated that attendees were very satisfied with the instruction, would use the skills learned, and were interested in future classes. CONCLUSIONS: Data Day to Day met previously unaddressed data skills gaps. Collaborating with outside instructors allowed the library to serve as a hub for a broad range of data instruction and to raise awareness of library services. We plan to offer the series three times in the coming year with an expanding roster of classes.