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1. Goals for Statistics and Data Science Collaborations

   Vance, Eric A. (January 2020, JSM Proceedings, Statistical Consulting Section)

   Statisticians have a long history of consulting and collaborating with experts from a variety of fields. Now with the rise of data science, collaborating across disciplines is both more important and more prevalent than ever before. This paper examines the goals of statistics and data science collaborations and uses the ASCCR (Attitude-Structure-Communication-Content-Relationship) Framework for Collaboration to connect these goals. Specifically, we propose that a useful way of guiding consultations and collaborations is for statisticians and data scientists to work toward two terminal goals of a collaboration: to make a deep contribution to the field and create a strong relationship with the domain expert. To help in achieving these goals, statisticians and data scientists should strive to achieve three instrumental goals: adopt an attitude of collaboration, provide effective structure for the collaboration, and communicate to create shared understanding. We show how these five goals map onto the ASCCR Frame, how they are connected to each other, and how to have conversations about these goals. The goal of this paper is to show statisticians and data scientists how they can become more effective collaborators by providing motivation for using the ASCCR framework to improve their practice of statistics and data science. 

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2. Forming the Future 2021 ICTP

   Participants, ICTP 2021 (July 2021, The minerals metals materials series)

   Daehn, G; Cao, J; Kinsey, B; Tekkaya, E; Vivek, A; Yoshida, Y (Ed.)

   The technology of plasticity primarily refers to processing techniques based on forming technology and the forming process of various metals generally. Metal forming is a vital component to the production of goods worldwide, however, the underlying theme of ICTP2021 were sustained manufacturing, as it relates to metal forming, with interests paid to the impact on industrial applications and fundamental science underpinnings of the field. This conference covered a range of topical areas in metal forming research, representing the most critical, rapidly growing, innovative subject areas (including, but not limited to, Big Data in metal forming, agile metal forming, and the value of, and limits to, simulation), and will bring together a wide array of researchers in this arena, including scientists, engineers, managers, government program officers, professors, and students from across industry, academia, and government. Sessions ran in parallel with as many as five sessions running congruently. The program also included multiple plenary talks, invited speakers, and a robust poster session. This variety of programming offerings provided a breadth of information through in-depth discussions and interaction opportunities. This meeting was open to all interested individuals, with a mixture of attendees from academic research, industry and educational professions, representing both senior and junior investigators, postdoctoral trainees and students. The nine overarching topical areas explored during this international conference included Metal Forming Processes & Equipment; Joining by Forming and Deformation; Microstructure and Damage Development & Characterization; Big Data and Metal Forming; High Speed and Impulse Forming; Agile Metal Forming; Microstructure development by Forming; Technologies to Speed Innovation; and Value of, and Limits to, Simulation. 

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3. The Q ₁ Q ₂ Q ₃ Workflow for Statistics and Data Science Collaborations

   https://doi.org/10.1080/26939169.2025.2475775  

   Vance, Eric A; Trumble, Ilana M; Alzen, Jessica L; House, Leanna L (May 2025, Journal of Statistics and Data Science Education)

   We develop, advance, and promote a previously existing framework called the Qualitative-Quantitative-Qualitative workflow (Q1Q2Q3, pronounced “Q-Q-Q”) to systematically guide the content of interdisciplinary collaborations and improve the teaching of statistics and data science. The Q1Q2Q3 workflow is designed to help statisticians and data scientists develop skills and techniques for collaboration to work with domain experts across academic fields, industry sectors, and organizations. The Q1Q2Q3 workflow explicitly emphasizes the importance of the qualitative context of a project, as well as the qualitative interpretation of quantitative findings. We explain Q1Q2Q3 and provide guidance for implementing each stage of the workflow. We describe how we teach Q1Q2Q3 within a statistics and data science collaboration course and present data evaluating its effectiveness. We also describe how Q1Q2Q3 can be useful for educators teaching introductory, projects-based, and technical statistics and data science courses. We believe that the Q1Q2Q3 workflow is an easy-to-implement technique that is beneficial and necessary for statistics and data science education and practice. It can be used to weave ethics into each stage of practice so that statisticians and data scientists can successfully transform evidence into action for the benefit of society. 

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4. Editorial: Artificial Intelligence for Data Discovery and Reuse Demands Healthy DataEcosystem and Community Efforts

   Wang, Huajin; Webster, Keith (October 2019, Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse)

   There is great value embedded in reusing scientific data for secondary discoveries. However, it is challenging to find and reuse the large amount of existing scientific data distributed across the web and data repositories. Some of the challenges reside in the volume and complexity of scientific data, others pertain to the current practices and workflow of research data management. AIDR 2019 (Artificial Intelligence for Data Discovery and Reuse) is a new conference that brings together researchers across a broad range of disciplines, computer scientists, tool developers, data providers, and data curators, to share innovative solutions that apply artificial intelligence to scientific data discovery and reuse, and discuss how various stakeholders work together to create a health data ecosystem. This editorial summarizes the main themes and takeaways from the inaugural AIDR '19 conference. 

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5. 2021 Future of Survey Research Conference: Conference Report

   Madson, Gabriel; Cooper, Alexandra (March 2021, 2021 Future of Survey Research Conference)

   Survey research is at a crossroads. For at least half a century, survey data have been essential to government agencies, policy-makers, businesses, and academics across different fields to inform a wide range of critical decisions with far-reaching consequences. Even in an era of “big data,” surveys remain fundamental to understanding and shaping the economy, politics and governance, and society. Yet challenges to conducting high quality surveys are substantial and increasing. Face-to-face interviewing remains the gold standard of survey research, but the rising costs of such interviews are prohibitive. New technologies, techniques, and data sources present opportunities to improve the efficiency and speed of survey data collection and/or reduce its costs but have shortcomings that may exceed their advantages. To examine and develop strategies to address the challenges facing survey research, the Duke Initiative on Survey Methodology hosted a conference January 14th and 15th, 2021, on the Future of Survey Research. This report summarizes the proceedings and highlights key recommendations that resulted. 

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