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Building skill-sets, confidence, and interest for diverse scientific careers in the biological and biomedical sciences.

ABSTRACT: Many biological science PhD graduates are increasingly pursuing careers outside of academia. Subsequently, PhD training programs are increasing their efforts to broaden their awareness of diverse career opportunities, with a firm knowledge of the skills necessary for success. At Yale University, for two semesters we have offered a new course for graduate students in the biological sciences titled "Skills Development for Diverse Scientific Careers" (BBS 550b). This course addressed career-related topics not covered in any curriculum at Yale such as how to run clinical trials, the business side of biotech, how to convert CVs into resumes, and resilience for early career scientists. We sought to better equip students to think broadly about their career options by exposing them to non-academic biomedical career avenues. Furthermore, the course fulfilled a gap in current curricular offerings to prepare students for multiple science career trajectories. Results on a pre-post course survey demonstrated increases in students' interest for, knowledge of, and confidence in securing a position in multiple nontraditional career sectors. Intentional course design can provide an adequate foundation to broaden awareness of myriad career options available to bioscientists. Broadening student knowledge and interest levels will contribute substantially to developing a robust scientific workforce.

SUBMITTER: Claydon J

PROVIDER: S-EPMC8664047 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: There is a growing need for efficient and integrated access to databases provided by diverse institutions. Using a linked data design pattern allows the diverse data on the Internet to be linked effectively and accessed efficiently by computers. Previously, we developed the Allie database, which stores pairs of abbreviations and long forms (LFs, or expanded forms) used in the life sciences. LFs define the semantics of abbreviations, and Allie provides a Web-based search service for researchers to look up the LF of an unfamiliar abbreviation. This service encounters two problems. First, it does not display each LF's definition, which could help the user to disambiguate and learn the abbreviations more easily. Furthermore, there are too many LFs for us to prepare a full dictionary from scratch. On the other hand, DBpedia has made the contents of Wikipedia available in the Resource Description Framework (RDF), which is expected to contain a significant number of entries corresponding to LFs. Therefore, linking the Allie LFs to DBpedia entries may present a solution to the Allie's problems. This requires a method that is capable of matching large numbers of string pairs within a reasonable period of time because Allie and DBpedia are frequently updated. RESULTS: We built a Linked Open Data set that links LFs to DBpedia titles by applying key collision methods (i.e., fingerprint and n-gram fingerprint) to their literals, which are simple approximate string-matching methods. In addition, we used UMLS resources to normalise the life science terms. As a result, combining the key collision methods with the domain-specific resources performed best, and 44,027 LFs have links to DBpedia titles. We manually evaluated the accuracy of the string matching by randomly sampling 1200 LFs, and our approach achieved an F-measure of 0.98. In addition, our experiments revealed the following. (1) Performances were similar independently from the frequency of the LFs in MEDLINE. (2) There is a relationship (r2 = 0.96, P < 0.01) between the occurrence frequencies of LFs in MEDLINE and their presence probabilities in DBpedia titles. CONCLUSIONS: The obtained results help Allie users locate the correct LFs. Because the methods are computationally simple and yield a high performance and because the most frequently used LFs in MEDLINE appear more often in DBpedia titles, we can continually and reasonably update the linked dataset to reflect the latest publications and additions to DBpedia. Joining LFs between scientific literature and DBpedia enables cross-resource exploration for mutual benefits.

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Building skill-sets, confidence, and interest for diverse scientific careers in the biological and biomedical sciences.

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