Data Science Training: the Essentials

Johns Hopkins University, Baltimore MD

Investigators

Linked publications & trials

Abstract

Project Summary Federal investments of all types over the last 40 years in the battle against the pathological consequences of HIV-1 have given hope that a cure will soon be realized. However, those currently living and aging with HIV continue to present with debilitating health impacts that negatively affect quality of life. In the context of the neurological complications, people with HIV- 1 can also suffer from cardiovascular, pulmonary, bone and kidney disease revealing that multiple body systems continue to be impacted despite anti-retroviral therapy successful in preventing transmission. Harnessing Big Data available through longstanding resources like the National Center for Biotechnology Information (NCBI), and newer sources including the NIH All of Us Initiative will increasingly be more widely included and used for testing of critical hypotheses, many which emerge from laboratory-based research. As discussed in the recent NIH report on the strategic plan for data science, the rapid technological advances and collection of vast amounts of genomic and gene expression and other types of biologic data, it has been long recognized that for biomedical researchers knowledge about varied databanks/repositories, and expertise in the proper handling, analysis, storage, sharing, and reporting of findings in ways that are rigorous and disseminate new knowledge, will be important to improve human health and well-being in a more rapid fashion. In this regard, we recently assembled a workgroup made of faculty in the neurological sciences to discuss how to strengthen training in computational sciences for predoctoral and doctoral trainees. One of the findings that emerged from the discussion was, given the varying degrees of prior preparation/exposure there is disparity in trainees’ ability to explore more advanced concepts in computational neuroscience and little instruction on how to apply the tools to scientific investigations of behavior. For our currently funded R25, we introduced predoctoral trainees to computational neuroscience through lectures. In the proposed supplement we would provide a deeper immersion through four inter-related course modules delivered through a combination of in-person and virtual modalities that maximizes the 10-week summer training period.