Browsing by Subject "Open science"

Now showing 1 - 10 of 11
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    Clearinghouse Standards of Evidence on the Transparency, Openness, and Reproducibility of Intervention Evaluations
    (Springer, 2021) Mayo-Wilson, Evan; Grant, Sean; Supplee, Lauren H.; Epidemiology, Richard M. Fairbanks School of Public Health
    Clearinghouses are influential repositories of information on the effectiveness of social interventions. To identify which interventions are “evidence-based,” clearinghouses review intervention evaluations using published standards of evidence that focus primarily on internal validity and causal inferences. Open science practices can improve trust in evidence from evaluations on the effectiveness of social interventions. Including open science practices in clearinghouse standards of evidence is one of many efforts that could increase confidence in designations of interventions as “evidence-based.” In this study, we examined the policies, procedures, and practices of 10 federal evidence clearinghouses that review preventive interventions—an important and influential subset of all evidence clearinghouses. We found that seven consider at least one open science practice when evaluating interventions: replication (6 of 10 clearinghouses), public availability of results (6), investigator conflicts of interest (3), design and analysis transparency (3), study registration (2), and protocol sharing (1). We did not identify any policies, procedures, or practices related to analysis plan registration, data sharing, code sharing, material sharing, and citation standards. We provide a framework with specific recommendations to help federal and other evidence clearinghouses implement the Transparency and Openness Promotion (TOP) Guidelines. Our proposed “TOP Guidelines for Clearinghouses” includes reporting whether evaluations used open science practices, incorporating open science practices in their standards for receiving “evidence-based” designations, and verifying that evaluations used open science practices. Doing so could increase the trustworthiness of evidence used for policy making and support improvements throughout the evidence ecosystem.
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    Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging
    (Wiley, 2025) Jelescu, Ileana O.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Schilling, Kurt G.; Neurology, School of Medicine
    Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
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    Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition
    (Wiley, 2025) Schilling, Kurt G.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Howard, Amy F. D.; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Jelescu, Ileana O.; Neurology, School of Medicine
    The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
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    Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography
    (Wiley, 2025) Schilling, Kurt G.; Howard, Amy F. D.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Jelescu, Ileana O.; Neurology, School of Medicine
    Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.
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    Establishing open science research priorities in health psychology: a research prioritisation Delphi exercise
    (Taylor & Francis, 2022-10) Norris, Emma; Prescott, Amy; Noone, Chris; Green, James A.; Reynolds, James; Grant, Sean Patrick; Toomey, Elaine; Epidemiology, Richard M. Fairbanks School of Public Health
    Objective Research on Open Science practices in Health Psychology is lacking. This meta-research study aimed to identify research question priorities and obtain consensus on the Top 5 prioritised research questions for Open Science in Health Psychology. Methods and measures An international Delphi consensus study was conducted. Twenty-three experts in Open Science and Health Psychology within the European Health Psychology Society (EHPS) suggested research question priorities to create a ‘long-list’ of items (Phase 1). Forty-three EHPS members rated the importance of these items, ranked their top five and suggested their own additional items (Phase 2). Twenty-four EHPS members received feedback on Phase 2 responses and then re-rated and re-ranked their top five research questions (Phase 3). Results The top five ranked research question priorities were: 1. ‘To what extent are Open Science behaviours currently practised in Health Psychology?’, 2. ‘How can we maximise the usefulness of Open Data and Open Code resources?’, 3. ‘How can Open Data be increased within Health Psychology?’, 4. ‘What interventions are effective for increasing the adoption of Open Science in Health Psychology?’ and 5. ‘How can we increase free Open Access publishing in Health Psychology?’. Conclusion Funding and resources should prioritise the research questions identified here.
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    Feasibility of an Audit and Feedback Intervention to Facilitate Journal Policy Change Towards Greater Promotion of Transparency and Openness in Sports Science Research
    (Springer, 2022-08-02) Hansford, Harrison J.; Cashin, Aidan G.; Bagg , Matthew K.; Wewege, Michael A.; Ferraro , Michael C.; Kianersi , Sina; Mayo-Wilson , Evan; Grant, Sean P.; Toomey, Elaine; Skinner , Ian W.; McAuley , James H.; Lee, Hopin; Jones, Matthew D.; Epidemiology, Richard M. Fairbanks School of Public Health
    Objectives To evaluate (1) the feasibility of an audit-feedback intervention to facilitate sports science journal policy change, (2) the reliability of the Transparency of Research Underpinning Social Intervention Tiers (TRUST) policy evaluation form, and (3) the extent to which policies of sports science journals support transparent and open research practices. Methods We conducted a cross-sectional, audit-feedback, feasibility study of transparency and openness standards of the top 38 sports science journals by impact factor. The TRUST form was used to evaluate journal policies support for transparent and open research practices. Feedback was provided to journal editors in the format of a tailored letter. Inter-rater reliability and agreement of the TRUST form was assessed using intraclass correlation coefficients and the standard error of measurement, respectively. Time-based criteria, fidelity of intervention delivery and qualitative feedback were used to determine feasibility. Results The audit-feedback intervention was feasible based on the time taken to rate journals and provide tailored feedback. The mean (SD) score on the TRUST form (range 0–27) was 2.05 (1.99), reflecting low engagement with transparent and open practices. Inter-rater reliability of the overall score of the TRUST form was moderate [ICC (2,1) = 0.68 (95% CI 0.55–0.79)], with standard error of measurement of 1.17. However, some individual items had poor reliability. Conclusion Policies of the top 38 sports science journals have potential for improved support for transparent and open research practices. The feasible audit-feedback intervention developed here warrants large-scale evaluation as a means to facilitate change in journal policies.
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    The Medical Library Association Data Services Competency: A Framework for Data Science and Open Science Skills Development
    (Medical Library Association, 2020-04) Federer, Lisa; Foster, Erin Diane; Glusker, Ann; Henderson, Margaret; Read, Kevin; Zhao, Shirley; Ruth Lilly Medical Library, School of Medicine
    Increasingly, users of health and biomedical libraries need assistance with challenges they face in working with their own and others' data. Librarians have a unique opportunity to provide valuable support and assistance in data science and open science but may need to add to their expertise and skill set to have the most impact. This article describes the rationale for and development of the Medical Library Association Data Services Competency, which outlines a set of five key skills for data services and provides a course of study for gaining these skills.
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    The case for open research in entomology: reducing harm, refining reproducibility and advancing insect science
    (Agricultural and Forest Entomology, 2024) Cuff , Jordan P.; Barrett , Meghan; Gray , Helen; Fox , Charles; Watt , Allan; Aimé , Emilie
    1. Open research is an increasingly developed and crucial framework for the advancement of science and has seen successful adoption across a broad range of disciplines. Entomology has, however, been slow to adopt these practices compared to many adjacent fields despite ethical and practical imperatives to do so. 2. The grand challenges facing entomology in the 21st century require the synthesis of evidence at global scales, necessitating open sharing of data and research at a pace and scale incompatible with the slow adoption of open research practices. Open science also plays a vital role in fostering trust in research and maximizing use of research outputs, which is ethically crucial for reducing harms to insects. 3. We outline these imperatives and how open research practices can enhance entomological research across a range of contexts. We also highlight the holistic nature of open science across the full research lifecycle through several specific examples of open research practices, which can be adopted easily by individual entomologists. 4. We do, however, argue that the responsibility of promoting, integrating and encouraging open research is most crucially held by publishers, including scholarly societies, which have leveraged widespread adoption in adjacent fields. Entomology must advance quickly to become a leading discipline in the open research transition.
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    The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
    (Oxford University Press, 2021) Haendel, Melissa A.; Chute, Christopher G.; Bennett, Tellen D.; Eichmann, David A.; Guinney, Justin; Kibbe, Warren A.; Payne, Philip R. O.; Pfaff, Emily R.; Robinson, Peter N.; Saltz, Joel H.; Spratt, Heidi; Suver, Christine; Wilbanks, John; Wilcox, Adam B.; Williams, Andrew E.; Wu, Chunlei; Blacketer, Clair; Bradford, Robert L.; Cimino, James J.; Clark, Marshall; Colmenares, Evan W.; Francis, Patricia A.; Gabriel, Davera; Graves, Alexis; Hemadri, Raju; Hong, Stephanie S.; Hripscak, George; Jiao, Dazhi; Klann, Jeffrey G.; Kostka, Kristin; Lee, Adam M.; Lehmann, Harold P.; Lingrey, Lora; Miller, Robert T.; Morris, Michele; Murphy, Shawn N.; Natarajan, Karthik; Palchuk, Matvey B.; Sheikh, Usman; Solbrig, Harold; Visweswaran, Shyam; Walden, Anita; Walters, Kellie M.; Weber, Griffin M.; Zhang, Xiaohan Tanner; Zhu, Richard L.; Amor, Benjamin; Girvin, Andrew T.; Manna, Amin; Qureshi, Nabeel; Kurilla, Michael G.; Michael, Sam G.; Portilla, Lili M.; Rutter, Joni L.; Austin, Christopher P.; Gersing, Ken R.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Materials and methods: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Results: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Conclusions: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
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    The Open Science Movement: Funders, Foundations, and Federal Regulations
    (SocArXiv, 2023) Herzog, Patricia Snell
    The National Science Foundation, National Institute of Health, and National Endowment for the Humanities are among a growing list of federal agencies issuing open science regulations. The NSF states that open data should be publicly available, fully accessible and usable, made available to the widest range of users for the widest range of purposes, and without restrictions placed upon use. Beyond federal agencies, philanthropic and nonprofit organizations are also engaged in perpetuating the movement. With funding from the John Templeton Foundation, the Center for Open Science established the Open Science Framework, and the Open Science of Religion project was launched to advance openness in the science of religion and spirituality. JTF is a founding member of the Open Research Funders Group, which is a partnership among 25 philanthropic organizations committed to open data, including: Gates, Lumina, Sloan, Zuckerberg, Arnold, and Johnson Foundations. Additionally, journals are increasingly required to comply with open data regulations. Yet, questions remain regarding the extent to which qualitative data can ethically be de-identified. The NIH supplemental states that indirect identifiers may pose particular challenges to inferences. Researchers question whether qualitative researchers are ready to share data (Mozersky et al. 2020; 2021). Guidelines and software applications exist to assist with technical aspects of the de-identification process, but broader questions remain regarding whether qualitative researchers can share data without violating the trust of their participants and uphold research ethics for confidentiality.