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1. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science

   National Academies of Sciences, Engineering ( January 2021 , Publications listing National Academy of Sciences National Academy of Engineering Institute of Medicine National Research Council)

   null (Ed.)

   The effective use of data science - the science and technology of extracting value from data - improves, enhances, and strengthens acquisition decision-making and outcomes. Using data science to support decision making is not new to the defense acquisition community; its use by the acquisition workforce has enabled acquisition and thus defense successes for decades. Still, more consistent and expanded application of data science will continue improving acquisition outcomes, and doing so requires coordinated efforts across the defense acquisition system and its related communities and stakeholders. Central to that effort is the development, growth, and sustainment of data science capabilities across the acquisition workforce. At the request of the Under Secretary of Defense for Acquisition and Sustainment, Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science assesses how data science can improve acquisition processes and develops a framework for training and educating the defense acquisition workforce to better exploit the application of data science. This report identifies opportunities where data science can improve acquisition processes, the relevant data science skills and capabilities necessary for the acquisition workforce, and relevant models of data science training and education. 

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2. Establishing ABET Accreditation Criteria for Data Science

   https://doi.org/10.1145/3408877.3432445  

   Blair, Jean R. ; Jones, Lawrence ; Leidig, Paul ; Murray, Scott ; Raj, Rajendra K. ; Romanowski, Carol J. ( March 2021 , 52nd ACM Technical Symposium on Computer ScienceEducation(SIGCSE’21),)

   null (Ed.)

   Prompted by the skyrocketing demand for data scientists, progress made by the ACM Data Science Task Force on defining data science competencies, and inquiries about data science accreditation, ABET is in the process of developing accreditation criteria for undergraduate data science programs. The effort is led by members of a joint data science criteria subcommittee appointed by ABET’s Computing Accreditation Commission (CAC) and CSAB (the lead society for computing accreditation). Establishing data science accreditation criteria is a notable milestone in the maturing data science discipline, indicating the presence of an accepted body of knowledge, standards of practice, and ethical codes for practitioners. This position paper motivates the effort and discusses prior work towards defining data science education requirements. It describes the ongoing process for creating and obtaining approval of the accreditation criteria, and how feedback was and will be solicited from the computing and statistical communities. The current draft data science criteria, which was approved in July 2020 by the relevant ABET bodies for a year of public review and comment, is presented. These criteria emphasize the three pillars of data science: computing foundations, mathematical/statistical foundations, and experience in at least one data application domain. This report thus serves both to inform and to stimulate the academic discussion needed to finalize appropriate data science accreditation by ABET. 

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3. Keeping Data Science Broad: Negotiating the Digital and Data Divide Among Higher-Education Institutions

   Rawlings-Goss, Renata ; Cassel, Lillian ; Cragin, Melissa ; Cramer, Catherine ; Dingle, Angela ; Friday-Stroud, Shawnta ; Herron, Al ; Horton, Nicholas ; Inniss, Tasha R ; Jordan, Kari ; et al ( January 2018 , Workshop: Bridging the Digital and Data Divide)

   A report summarizing the “Keeping Data Science Broad” series including data science challenges, visions for the future, and community asks. The goal of the Keeping Data Science Broad series was to garner community input into pathways for keeping data science education broadly inclusive across sectors, institutions, and populations. Input was collected from a community input survey, three webinars (Data Science in the Traditional Context, Alternative Avenues for Development of Data Science Education Capacity, and Big Picture for a Big Data Science Education Network available to view through the South Big Data Hub YouTube channel) and an interactive workshop (Negotiating the Digital and Data Divide). Through these venues, we explore the future of data science education and workforce at institutions of higher learning that are primarily teaching-focused. The workshop included representatives from sixty data science programs across the nation, either traditional or alternative, and from a range of institution types including community colleges, Historically Black Colleges and Universities (HBCU’s), Hispanic-Serving Institutions (HSI’s), other minority-led and minority-serving institutions, liberal arts colleges, tribal colleges, universities, and industry partners. 

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4. Roundtable on Data Science Postsecondary Education: A Compilation of Meeting Highlights

   National Academies of Sciences, Engineering ( January 2020 , Publications listing National Academy of Sciences National Academy of Engineering Institute of Medicine National Research Council)

   null (Ed.)

   Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. 

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5. The Critical Importance of Citizen Science Data

   https://doi.org/10.3389/fclim.2021.650760  

   de Sherbinin, Alex ; Bowser, Anne ; Chuang, Tyng-Ruey ; Cooper, Caren ; Danielsen, Finn ; Edmunds, Rorie ; Elias, Peter ; Faustman, Elaine ; Hultquist, Carolynne ; Mondardini, Rosy ; et al ( March 2021 , Frontiers in Climate)

   null (Ed.)

   Citizen science is an important vehicle for democratizing science and promoting the goal of universal and equitable access to scientific data and information. Data generated by citizen science groups have become an increasingly important source for scientists, applied users and those pursuing the 2030 Agenda for Sustainable Development. Citizen science data are used extensively in studies of biodiversity and pollution; crowdsourced data are being used by UN operational agencies for humanitarian activities; and citizen scientists are providing data relevant to monitoring the sustainable development goals (SDGs). This article provides an International Science Council (ISC) perspective on citizen science data generating activities in support of the 2030 Agenda and on needed improvements to the citizen science community's data stewardship practices for the benefit of science and society by presenting results of research undertaken by an ISC-sponsored Task Group. 

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