This framework builds on the assessment of federal scientific integrity policies and practices described in the January 2022 report, Protecting the Integrity of Government Science, and draws from extensive input from federal agencies, as well as from across sectors, including academia, the scientific community, public interest groups, and industry. It has several key components that federal departments and agencies will use to improve scientific integrity policies and practices, including:
The framework requires all agencies to designate a scientific integrity official, and agencies that fund, conduct, or oversee research to designate a chief science officer, and it establishes the National Science and Technology Council (NSTC) Subcommittee on Scientific Integrity to oversee implementation of the framework, and evaluate agency progress.
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The framework was developed following a robust effort to study and improve scientific integrity policies and outcomes, and extensive engagement with stakeholders inside and outside of the federal government starting in May 2021. This process included engaging 30 federal agencies, and processing feedback from over 1,000 individuals and organizations through three listening sessions, three roundtables, and two requests for information.
Strong policies and effective practices protecting scientific integrity are essential for the development of evidence-based policies. By bolstering these policies and practices across the federal government, this first-of-its-kind framework will strengthen the ability of agencies and federal scientists to produce critical scientific information for evidence-based policymaking that can help make our nation healthier, safer, more prosperous, and more secure.
The public meeting notices/agendas provide details on each of the focus group meetings. All CFCC meetings were open to the public, and there were opportunities for public comment at each meeting. Audio recordings of each of the CFCC meetings are available upon request by emailing healtheducationframework@cde.ca.gov or calling 916-324-2452.
In computer systems, a framework is often a layered structure indicating what kind of programs can or should be built and how they would interrelate. Some computer system frameworks also include actual programs, specify programming interfaces, or offer programming tools for using the frameworks. A framework may be for a set of functions within a system and how they interrelate; the layers of an operating system; the layers of an application subsystem; how communication should be standardized at some level of a network; and so forth. A framework is generally more comprehensive than a protocol and more prescriptive than a structure.
The MIT Framework creates a mechanism for ensuring scholarly research outputs are openly and equitably available to the broadest and most inclusive audience possible, while also providing valued services to our community. The vision we seek to advance through the application of this framework is one in which enduring, abundant, equitable, and meaningful access to scholarship serves to empower and inspire humanity.
The Resilient PNT Conformance Framework was sponsored by the U.S. Department of Homeland Security (DHS) Science and Technology (S&T) Directorate and developed in coordination with industry and federal agency partners. It provides guidance for defining expected behaviors in resilient PNT equipment, with the goal of facilitating development and adoption of those behaviors through a common framework that enables improved risk management, determination of appropriate mitigations, and decision making by end-users.
This framework focuses on resilience and is relevant to both equipment vendors and critical infrastructure end-users. It applies to equipment that outputs PNT solutions, including PNT systems of systems, integrated PNT receivers, and PNT source components (such as GNSS chipsets). The framework is outcome-based and contains four levels of resilience so that end-users can select a level that is appropriate for their needs.
While California is engaging in litigation to restore its authority to protect the public health of its residents, it has finalized with six participating automakers individual bilateral agreements based upon the Framework unveiled last year.
Automakers who voluntarily agreed to the framework agreements are BMW of North America (including Rolls Royce for purposes of the agreement), Ford, Honda, Volkswagen Group of America (including VW and Audi), and Volvo.
Under the framework agreements, gasoline and diesel cars and light trucks will get cleaner through 2026 at about the same rate as the former Obama-era program, preventing hundreds of millions of tons of greenhouse gas emissions over the lifetime of the agreements.
Feedback on this CSF 2.0 Public Draft, as well as the related Implementation Examples draft, may be submitted to cyberframework@nist.gov by November 4, 2023 by 11:59 pm ET Monday, November 6, 2023.
The framework is a vehicle to encourage NIMHD- and NIH-supported research that addresses the complex and multi-faceted nature of minority health and health disparities, including research that spans different domains of influence (biological, behavioral, physical/built environment, sociocultural environment, health care system) as well as different levels of influence (individual, interpersonal, community, societal) within those domains.
The framework also provides a classification structure that facilitates analysis of the NIMHD and NIH minority health and health disparities research portfolios to assess progress, gaps, and opportunities. Examples of factors are provided within each cell of the framework (e.g., family microbiome within the interpersonal-biological cell). These factors are not intended to be exhaustive. Health disparity populations, as well as other features of this framework, may be adjusted over time.
To help managers ensure accountability and responsible use of artificial intelligence (AI) in government programs and processes, GAO developed an AI accountability framework. This framework is organized around four complementary principles, which address governance, data, performance, and monitoring. For each principle, the framework describes key practices for federal agencies and other entities that are considering, selecting, and implementing AI systems. Each practice includes a set of questions for entities, auditors, and third-party assessors to consider, as well as procedures for auditors and third- party assessors.
GAO's objective was to identify key practices to help ensure accountability and responsible AI use by federal agencies and other entities involved in the design, development, deployment, and continuous monitoring of AI systems. To develop this framework, GAO convened a Comptroller General Forum with AI experts from across the federal government, industry, and nonprofit sectors. It also conducted an extensive literature review and obtained independent validation of key practices from program officials and subject matter experts. In addition, GAO interviewed AI subject matter experts representing industry, state audit associations, nonprofit entities, and other organizations, as well as officials from federal agencies and Offices of Inspector General.
The Framework offered here is called a framework intentionally because it is based on a cluster of interconnected core concepts, with flexible options for implementation, rather than on a set of standards or learning outcomes, or any prescriptive enumeration of skills. At the heart of this Framework are conceptual understandings that organize many other concepts and ideas about information, research, and scholarship into a coherent whole. These conceptual understandings are informed by the work of Wiggins and McTighe,2 which focuses on essential concepts and questions in developing curricula, and also by threshold concepts3 which are those ideas in any discipline that are passageways or portals to enlarged understanding or ways of thinking and practicing within that discipline. This Framework draws upon an ongoing Delphi Study that has identified several threshold concepts in information literacy,4 but the Framework has been molded using fresh ideas and emphases for the threshold concepts. Two added elements illustrate important learning goals related to those concepts: knowledge practices,5 which are demonstrations of ways in which learners can increase their understanding of these information literacy concepts, and dispositions,6 which describe ways in which to address the affective, attitudinal, or valuing dimension of learning. The Framework is organized into six frames, each consisting of a concept central to information literacy, a set of knowledge practices, and a set of dispositions. The six concepts that anchor the frames are presented alphabetically:
The framework is guided by the principal policy objectives of the United States as laid out in the Executive Order on Ensuring Responsible Development of Digital Assets (March 9, 2022) and tailored to reflect the international aspects of our work:
Regional and Bilateral Engagements: The United States will identify where existing regional and bilateral engagements can be strengthened and, where appropriate, ramp up engagement with new partners to achieve our objectives with respect to digital assets. The United States will use these engagements under a coordinated framework for prioritization across departments and agencies to explore potential opportunities and risks of digital assets, engage in information sharing, drive the adoption and implementation of policies including with respect to AML/CFT; and provide technical assistance, where appropriate. The United States will explore opportunities for joint experimentation on digital assets technologies, market innovations and CBDCs, with this core set of allies and partners to increase our shared learning about ways to develop systems that meet our shared policy objectives. e24fc04721