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Data Matters
In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, ...
Planning for Long-Term Use of Biomedical Data
Biomedical research data sets are becoming larger and more complex, and computing capabilities are expanding to enable transformative scientific results. The National Institutes of Health's (NIH's) National Library of Medicine (NLM) has the unique role of ensuring that biomedical research data are findable, accessible, interoperable, and reusable in an ethical manner. Tools that forecast the costs of long-term data preservation could be useful as the cost to curate and manage these data in meaningful ways continues to increase, as could stewardship to assess and maintain data that have future value. The National Academies of Sciences, Engineering, and Medicine convened a workshop on July 11-...
Improving Access to and Confidentiality of Research Data
Improving Access to and Confidentiality of Research Data summarizes a workshop convened by the Committee on National Statistics (CNSTAT) to promote discussion about methods for advancing the often conflicting goals of exploiting the research potential of microdata and maintaining acceptable levels of confidentiality. This report outlines essential themes of the access versus confidentiality debate that emerged during the workshop. Among these themes are the tradeoffs and tensions between the needs of researchers and other data users on the one hand and confidentiality requirements on the other; the relative advantages and costs of data perturbation techniques (applied to facilitate public release) versus restricted access as tools for improving security; and the need to quantify disclosure risksâ€"both absolute and relativeâ€"created by researchers and research data, as well as by other data users and other types of data.
Data Ethics
Data-gathering technology is more sophisticated than ever, as are the ethical standards for using this data. This second edition shows how to navigate this complex environment. Data Ethics provides a practical framework for the implementation of ethical principles into information management systems. It shows how to assess the types of ethical dilemmas organizations might face as they become more data-driven. This fully updated edition includes guidance on sustainability and environmental management and on how ethical frameworks can be standardized across cultures that have conflicting values. There is also discussion of data colonialism, the challenge of ethical trade-offs with ad-tech and analytics such as Covid-19 tracking systems and case studies on Smart Cities and Demings Principles. As the pace of developments in data-processing technology continues to increase, it is vital to capitalize on the opportunities this affords while ensuring that ethical standards and ideals are not compromised. Written by internationally regarded experts in the field, Data Ethics is the essential guide for students and practitioners to optimizing ethical data standards in organizations.
Leveraging Longitudinal Data in Developing Countries
Longitudinal data collection and analysis are critical to social, demographic, and health research, policy, and practice. They are regularly used to address questions of demographic and health trends, policy and program evaluation, and causality. Panel studies, cohort studies, and longitudinal community studies have proved particularly important in developing countries that lack vital registration systems and comprehensive sources of information on the demographic and health situation of their populations. Research using data from such studies has led to scientific advances and improvements in the well-being of individuals in developing countries. Yet questions remain about the usefulness of...
Casebook on Ethical Issues in International Health Research
This casebook collects 64 case studies, each of which raises an important and difficult ethical issue connected with planning, reviewing, or conducting health-related research. The book's purpose is to contribute to thoughtful analysis of these issues by researchers and members of research ethics committees (RECs, known in some places as ethical review committees or institutional review boards), particularly those involved with studies that are conducted or sponsored internationally. This collection is envisioned principally as a tool to aid educational programs, from short workshops on research ethics to in-service learning for scientists and REC members, to formal degree or certificate cou...
Data Science for Undergraduates
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
Social innovation in health monitoring and evaluation framework
This framework was developed in partnership with the Social Innovation in Health Initiative (SIHI), a network convened by the TDR and partners to advance social innovations globally. The framework involved a three-step process, including an open crowdsourcing call for ideas (described here), a scoping review, a series of multisectoral discussions and an adapted Delphi process. Crowdsourcing is an approach in which a group of individuals attempt to solve all or part of a problem, then implement exceptional solutions in the community. The purpose of this document is to provide a monitoring and evaluation framework for social innovations in health. Supporting monitoring and evaluation will help to democratize research and engage more stakeholders to work in partnership with researchers. The evidence generated will help us to understand effectiveness and the potential for sustainability.
