Browsing by Subject "Data sharing"

Now showing 1 - 6 of 6
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    Advancing Monogenic Diabetes Research and Clinical Care by Creating a Data Commons: The Precision Diabetes Consortium (PREDICT)
    (Sage, 2025-01-09) McCullough, Michael E.; Letourneau-Freiberg, Lisa R.; Naylor, Rochelle N.; Greeley, Siri Atma W.; Broome, David T.; Tosur, Mustafa; Kreienkamp, Raymond J.; Cobry, Erin; Rasouli, Neda; Pollin, Toni I.; Udler, Miriam S.; Billings, Liana K.; Desouza, Cyrus; Evans-Molina, Carmella; Birz, Suzi; Furner, Brian; Watkins, Michael; Ott, Kaitlyn; Volchenboum, Samuel L.; Philipson, Louis H.; Pediatrics, School of Medicine
    Monogenic diabetes mellitus (MDM) is a group of relatively rare disorders caused by pathogenic variants in key genes that result in hyperglycemia. Lack of identified cases, along with absent data standards, and limited collaboration across institutions have hindered research progress. To address this, the UChicago Monogenic Diabetes Registry (UCMDMR) and UChicago Data for the Common Good (D4CG) created a national consortium of MDM research institutions called the PREcision DIabetes ConsorTium (PREDICT). Following the D4CG model, PREDICT has successfully established a multicenter MDM data commons. PREDICT has created a consensus data dictionary that will be utilized to address critical gaps in understanding of these rare types of diabetes. This approach may be useful for other rare conditions that would benefit from access to harmonized pooled data.
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    Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations
    (Mary Ann Liebert, 2021) Huie, J. Russell; Mondello, Stefania; Lindsell, Christopher J.; Antiga, Luca; Yuh, Esther L.; Zanier, Elisa R.; Masson, Serge; Rosario, Bedda L.; Ferguson, Adam R.; Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Investigators; Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Participants and Investigators; Psychiatry, School of Medicine
    Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.
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    Data matters: how earth and environmental scientists determine data relevance and reusability
    (2019-05-01) Murillo, Angela P.
    Abstract Purpose – The purpose of this study is to examine the information needs of earth and environmental scientists regarding how they determine data reusability and relevance. Additionally, this study provides strategies for the development of data collections and recommendations for data management and curation for information professionals working alongside researchers. Design/methodology/approach – This study uses a multi-phase mixed-method approach. The test environment is the DataONE data repository. Phase 1 includes a qualitative and quantitative content analysis of deposited data. Phase 2 consists of a quasi-experiment think-aloud study. This paper reports mainly on Phase 2. Findings – This study identifies earth and environmental scientists’ information needs to determine data reusability. The findings include a need for information regarding research methods, instruments and data descriptions when determining data reusability, as well as a restructuring of data abstracts. Additional findings include reorganizing of the data record layout and data citation information. Research limitations/implications – While this study was limited to earth and environmental science data, the findings provide feedback for scientists in other disciplines, as earth and environmental science is a highly interdisciplinary scientific domain that pulls from many disciplines, including biology, ecology and geology, and additionally there has been a significant increase in interdisciplinary research in many scientific fields. Practical implications – The practical implications include concrete feedback to data librarians, data curators and repository managers, as well as other information professionals as to the information needs of scientists reusing data. The suggestions could be implemented to improve consultative practices when working alongside scientists regarding data deposition and data creation. These suggestions could improve policies for data repositories through direct feedback from scientists. These suggestions could be implemented to improve how data repositories are created and what should be considered mandatory information and secondary information to improve the reusability of data. Social implications – By examining the information needs of earth and environmental scientists reusing data, this study provides feedback that could change current practices in data deposition, which ultimately could improve the potentiality of data reuse. Originality/value – While there has been research conducted on data sharing and reuse, this study provides more detailed granularity regarding what information is needed to determine reusability. This study sets itself apart by not focusing on social motivators and demotivators, but by focusing on information provided in a data record.
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    Developing incentives for data stewardship and sharing: Library engagement beyond liaison relationships
    (2014-06-05) Coates, Heather L.; Polley, David E.
    Many of the obstacles slowing the adoption of more democratic dissemination of scholarly products are cultural, not technological. While libraries have extended their technological capacity to new methods of dissemination, we have been less proactive in fostering the cultural change necessary for significant adoption. Two particular groups of constituents and communities of practice have been engaged with the library profession, but the personal contact between faculty and librarians at the institutional level is inconsistent and often hinges upon liaison relationships. This poster will describe opportunities for librarians to engage with institutional units and research communities extending beyond institutional boundaries to advance incentives rewarding new forms of dissemination, including data as a valued community resource. Examples of relating changes in dissemination to various community missions will be provided.
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    Ethical considerations for biobanks serving underrepresented populations
    (Wiley, 2025) Lee, Yoon Seo; Garrido, Nelson Luis Badia; Lord, George; Maggio, Zane Allan; Khomtchouk, Bohdan B.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Biobanks are essential biological database resources for the scientific community, enabling research on the molecular, cellular, and genetic basis of human disease. They are crucial for computational, data-driven biomedical research, which advances precision medicine and the development of targeted therapies. However, biobanks often lack racial and ethnic diversity, with many data sets predominantly comprising individuals of white, primarily northern European, ancestry. Establishing or enhancing biobanks for the inclusion of historically underrepresented populations requires meticulous ethical and social planning beyond logistical, legal, and economic considerations. This guide provides a roadmap for building and sustaining diverse biobanks, emphasizing ethical guidelines and cultural sensitivity. We highlight the importance of obtaining informed consent from donors, respecting their bodily autonomy, and the economic and research benefits of diverse biobanks to enable precision medicine, drug discovery, and industry-academic partnerships. Prioritizing key ethical and social considerations allows biobanks to advance scientific knowledge while upholding the rights and autonomy of underrepresented populations. Diversity in biobank sample collection enhances research outcomes by ensuring findings are representative and applicable to various human population groups, fostering trust, promoting inclusivity, and addressing health disparities while informing health policy. This is vital to ensuring biobanking efforts contribute meaningfully to the advancement of health equity.
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    Image Sharing Technologies and Reduction of Imaging Utilization: A Systematic Review and Meta-analysis
    (Elsevier, 2015-12) Vest, Joshua R.; Jung, Hye-Young; Ostrovsky, Aaron; Das, Lala Tanmoy; McGinty, Geraldine B.; Department of Health Policy and Management, Richard M. Fairbanks School of Public Health
    INTRODUCTION: Image sharing technologies may reduce unneeded imaging by improving provider access to imaging information. A systematic review and meta-analysis were conducted to summarize the impact of image sharing technologies on patient imaging utilization. METHODS: Quantitative evaluations of the effects of PACS, regional image exchange networks, interoperable electronic heath records, tools for importing physical media, and health information exchange systems on utilization were identified through a systematic review of the published and gray English-language literature (2004-2014). Outcomes, standard effect sizes (ESs), settings, technology, populations, and risk of bias were abstracted from each study. The impact of image sharing technologies was summarized with random-effects meta-analysis and meta-regression models. RESULTS: A total of 17 articles were included in the review, with a total of 42 different studies. Image sharing technology was associated with a significant decrease in repeat imaging (pooled effect size [ES] = -0.17; 95% confidence interval [CI] = [-0.25, -0.09]; P < .001). However, image sharing technology was associated with a significant increase in any imaging utilization (pooled ES = 0.20; 95% CI = [0.07, 0.32]; P = .002). For all outcomes combined, image sharing technology was not associated with utilization. Most studies were at risk for bias. CONCLUSIONS: Image sharing technology was associated with reductions in repeat and unnecessary imaging, in both the overall literature and the most-rigorous studies. Stronger evidence is needed to further explore the role of specific technologies and their potential impact on various modalities, patient populations, and settings.