Open Science

This work seeks to shift awareness and practice by centering racial equity and community voice within the context of data integration and use that supports power sharing and building across agencies and community members.

This report explains the Executive Order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government (“Equity EO”) launched a whole-of-government effort to incorporate the principle of equity throughout the federal government. Recognizing that the ability to conduct equity assessments and identify and remove barriers to equitable access to government programs is contingent on gathering the necessary data, President Biden ordered the formation of the Equitable Data Working Group. The President directed the Working Group to study existing federal data collection policies, programs, and infrastructure to identify inadequacies and provide recommendations that lay out a strategy for increasing data available for measuring equity and representing the diversity of the American people. The following recommendations are then discussed: make disaggregated data the norm while protecting privacy; catalyze existing federal infrastructure to leverage underused data; build capacity for robust equity assessment for policymaking and program implementation; galvanize diverse partnerships across levels of government and the research community; and be accountable to the American public. Key administration actions that address each of the recommendations are described.

This expert roundtable focuses on how the disaggregation of data, especially race and ethnicity data, can improve equity.

The Commission’s report and recommendations make it clear that in our current system, data on health inequities are divorced from the history and community conditions that shape poor health outcomes.

This guide was developed in response to librarians fielding multiple requests from UMN researchers looking to incorporate anti-racism into their research practices. This guide shares racist research systems and practices, followed by resources for mitigating those problematic systems and practices, but we wholeheartedly acknowledge that this guide is not a “solution” to the issues of racism embedded in research.

Given the significant nature of the Big Data (BD) and Big Data Analytics (BDA), this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.

Background: Multiple factors, including experiences with unethical research practices, have made some Indigenous groups in the United States and Canada reticent to participate in potentially beneficial health-related research. Yet, Indigenous peoples have also expressed a willingness to participate in research when certain conditions related to the components of data management?including data collection, analysis, security and storage, sharing, dissemination, and withdrawal?are met. A scoping review was conducted to better understand the terms of data management employed in healthrelated research involving Indigenous communities in the United States and Canada. Methods: PubMed, Embase, PsychINFO, and Web of Science were searched using terms related to the populations and topics of interest. Results were screened and articles deemed eligible for inclusion were extracted for content on data management, community engagement, and community-level research governance. Results: The search strategy returned 734 articles. 31 total articles were extracted,
of which nine contained in-depth information on data management and underwent detailed extraction. All nine articles reported the development and implementation of data management tools, including research ethics codes, data-sharing agreements, and biobank access policies. These articles reported that communities were involved in activities and decisions related to data collection (n=7), data analysis (n=5), data-sharing (n=9), dissemination (n=7), withdrawal (n=4), and development of data management tools (n=9). The articles also reported that communities had full or shared ownership of (n=5), control over (n=9), access to (n=1), and possession of data (n=5). All nine articles discussed the role of community engagement in research and communitylevel research governance as means for aligning the terms of data management with the values, needs, and interests of communities. Conclusions: There is need for more research and improved reporting on data management in health-related research involving Indigenous peoples in the United States and Canada. Findings from this review can provide guidance for the identification of data management terms and practices that may be acceptable to Indigenous communities considering participation in health-related research.

This publication presents the final recommendations of an external advisory group AcademyHealth convened to help shape a sustainable, action-oriented strategy to address diversity, equity, & inclusion in the field

For this follow-up volume, Do No Harm Guide: Additional Perspectives on Data Equity, we handed the pen to data experts and practitioners whose voices have been traditionally underrepresented.

This resource is intended to provide state policymakers with a suite of state approaches to address health disparities ? from targeted to cross-agency comprehensive strategies. Each section below comprises best practices and state
examples from a geographically diverse set of states to guide state policymakers to the opportunity that best suits their states? needs and capacity. Also included is a lexicon, recognizing the importance of common definitions in this domain, along with links to key tools and resources.

This expert roundtable focuses on how the COVID-19 pandemic brought to light the serious and pervasive data gaps facing marginalized groups and what cross-cutting themes the panels found in their work.

Self-governance of science was supposed to mean freedom of inquiry, but it also ended up serving the business model of scientific publishers while undermining the goals of science policy.

The Lancet REWARD (REduce research Waste And Reward Diligence) Campaign invites everyone involved in biomedical research to critically examine the way they work to reduce waste and maximize efficiency.

Examines the measurement of sex, gender identity, & sexual orientation to produce recommendations for specific measures that can be used in surveys & research, administrative, clinical, & other health settings.

We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective.

A guide to support funders seeking to develop and implement a preprint policy. The toolkit includes benefits of preprints for funders, guidlines on how to incorporate and review the use of preprints into current policies, and a preprint policy text document

Until recently, there were no preprint servers for medical and health sciences. In this post, Harlan Krumholz and Joseph Ross, co-founders of medRxiv, describe the benefits of preprints and highlight features of their preprint server for health sciences launched in June 2019.

This brief introduces the Belmont Report?s principles and provides selected principle-aligned practices and resources to help data experts at all levels integrate the principles into their work and move toward more equitable data practice.

The Commission’s report and recommendations make it clear that in our current system, data on health inequities are divorced from the history and community conditions that shape poor health outcomes.

This article identifies where the technology and data sectors can contribute skills, expertise, and assets in support of innovative workforce models and augment the development of public health workforce competencies.

Our aim is to lay the groundwork for an ethically responsible development of AI in the domains of health research, clinical practice and public health.

This article summarizes some of the challenges around data sharing and reuse and identifies where the technology and data sectors can contribute to fill current gaps to promote interoperability and data stewardship.

In this session, presenters Ninez Ponce and Juanita Chinn describe approaches to improving data collection and analysis with Asian/Pacific Islander, Native American people, and other under-represented groups.

This article summarizes what data should be included and identifies where the technology and data sectors can contribute to fill current gaps to measure equity, positive health, and well-being.

What we?re realizing as a community, is that we?re leaving an enormous amount of value on the table, and that if we can do a better job of capturing, preserving, and making available more of the research workflow, we?ll drive better transparency and reliability of the research conclusions, and improve efficiency and the return on the investment we make in research funding.

If we seek to improve community and population health through effective interventions targeting social and environmental contexts, who will drive the change? On the basis of our experience in Durham County, North Carolina, we have learned that successful interventions must rely, at least in part, on stakeholders outside the public health and health care sectors, including those in business, education, philanthropy, nonprofit organizations, community development, and government.