Browsing by Author "Hochheiser, Harry"

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    Best practices to evaluate the impact of biomedical research software-metric collection beyond citations
    (Oxford University Press, 2024) Afiaz, Awan; Ivanov, Andrey A.; Chamberlin, John; Hanauer, David; Savonen, Candace L.; Goldman, Mary J.; Morgan, Martin; Reich, Michael; Getka, Alexander; Holmes, Aaron; Pati, Sarthak; Knight, Dan; Boutros, Paul C.; Bakas, Spyridon; Caporaso, J. Gregory; Del Fiol, Guilherme; Hochheiser, Harry; Haas, Brian; Schloss, Patrick D.; Eddy, James A.; Albrecht, Jake; Fedorov, Andrey; Waldron, Levi; Hoffman, Ava M.; Bradshaw, Richard L.; Leek, Jeffrey T.; Wright, Carrie; Pathology and Laboratory Medicine, School of Medicine
    Motivation: Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation. Results: To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress. Availability and implementation: More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website.
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    Informatics education for translational research teams: An unrealized opportunity to strengthen the national research infrastructure
    (Cambridge University Press, 2022-10-28) Mendonca, Eneida A.; Richesson, Rachel L.; Hochheiser, Harry; Cooper, Dan M.; Bruck, Meg N.; Berner, Eta S.; Pediatrics, School of Medicine
    Objective: To identify the informatics educational needs of clinical and translational research professionals whose primary focus is not informatics. Introduction: Informatics and data science skills are essential for the full spectrum of translational research, and an increased understanding of informatics issues on the part of translational researchers can alleviate the demand for informaticians and enable more productive collaborations when informaticians are involved. Identifying the level of interest in different topics among various types of of translational researchers will help set priorities for development and dissemination of informatics education. Methods: We surveyed clinical and translational science researchers in Clinical and Translational Science Award (CTSA) programs about their educational needs and preferences. Results: Researchers from 23 out of the 62 CTSA hubs responded to the survey. 67% of respondents across roles and topics expressed interest in learning about informatics topics. There was high interest in all 30 topics included in the survey, with some variation in interest depending on the role of the respondents. Discussion: Our data support the need to advance training in clinical and biomedical informatics. As the complexity and use of information technology and data science in research studies grows, informaticians will continue to be a limited resource for research collaboration, education, and training. An increased understanding of informatics issues across translational research teams can alleviate this burden and allow for more productive collaborations. To inform a roadmap for informatics education for research professionals, we suggest strategies to use the results of this needs assessment to develop future informatics education.
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    The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species
    (Oxford Journals, 2016-11-26) Mungall, Chris; McMurry, Julie A.; Köhler, Sebastian; Balhoff, James P.; Borromeo, Charles; Brush, Matthew; Carbon, Seth; Conlin, Tom; Dunn, Nathan; Engelstad, Mark; Foster, Erin D.; Gourdine, J.P.; Jacobsen, Julius O.B.; Keith, Dan; Laraway, Bryan; Lewis, Suzanna E.; Xuan, Jeremy N.; Shefchek, Kent; Vasilevsky, Nicole; Yuan, Zhou; Washington, Nicole; Hochheiser, Harry; Groza, Tudor; Smedley, Damian; Robinson, Peter N.; Haendel, Melissa A.
    The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.