https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc/Head https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc http://www.nanopub.org/nschema#hasAssertion https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc/assertion https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc http://www.nanopub.org/nschema#hasProvenance https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc/provenance https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc/pubinfo https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/kpxl/ios/ds/np/RAGa9FfizKthul3fr0voaPsZBIZrXig81rmyO4_iT6Jcc/assertion http://id.crossref.org/issn/2451-8492 http://purl.org/dc/terms/title Data Science https://doi.org/10.3233/DS-170008 http://purl.org/dc/terms/abstract Modern biomedical research is complex and requires a cross section of experts collaborating using multi-, inter-, or transdisciplinary approaches to address scientific questions. Known as team science, such approaches have become so critical it has given rise to a new field – the science of team science. In biomedical research, data scientists often play a critical role in team-based collaborations. Integration of data scientists into research teams has multiple advantages to the clinical and translational investigator as well as to the data scientist. Clinical and translational investigators benefit from having an invested dedicated collaborator who can assume principal responsibility for essential data-related activities, while the data scientist can build a career developing tools that are relevant and data-driven. Participation in team science, however, can pose challenges. One particular challenge is the ability to appropriately evaluate the data scientist’s scholarly contributions, necessary for promotion. Only a minority of academic health centers have attempted to address this challenge. In order for team science to thrive on academic campuses, leaders of institutions need to hire data science faculty for the purpose of doing team science, with novel systems in place that incentivize the data scientist’s engagement in team science and that allow for appropriate evaluation of performance. Until such systems are adopted at the institutional level, the ability to conduct team science to address modern biomedical research with its increasingly complex data needs will be compromised. Fostering team science on campuses by putting supportive systems in place will benefit not only clinical and translational investigators as well as data scientists, but also the larger academic institution. https://doi.org/10.3233/DS-170008 http://purl.org/dc/terms/date 2017-10-17 https://doi.org/10.3233/DS-170008 http://purl.org/dc/terms/isPartOf http://id.crossref.org/issn/2451-8492 https://doi.org/10.3233/DS-170008 http://purl.org/dc/terms/title The integration of the data scientist into the team: Implications and challenges https://doi.org/10.3233/DS-170008 http://purl.org/ontology/bibo/issue 1-2 https://doi.org/10.3233/DS-170008 http://purl.org/ontology/bibo/volume 1 https://doi.org/10.3233/DS-170008 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/PositionPaper https://orcid.org/0000-0002-6949-2651 http://schema.org/affiliation https://ror.org/00f54p054 https://orcid.org/0000-0002-6949-2651 http://schema.org/email manishad@stanford.edu 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