Video Presentation (YouTube)
Abstract:
This evidence-based practice paper presents outcomes on workshop participants’ perception of the usability of data visualization worksheets utilized in a critical data visualization workshop. Training students to think critically about data is a process that takes practice. Thinking critically about data requires understanding the data, its provenance and context, and the best way to represent insights contained in the data. A 5-day workshop on teaching critical and ethical approaches to data visualization was hosted by the Center for Science of Information at Purdue University in June 2019. The motivation for the workshop was to provide an introduction to data science through the lens of critical data visualization. In addition to instruction and hands-on data visualization and data manipulation exercises supported by activity worksheets, workshop participants were exposed to small group discussion and active-learning focused team science and data visualization ethics modules. This three-prong approach provides the context in which the data visualization activity worksheets were utilized. This paper focuses solely upon the results of a usability survey of data visualization activity worksheets to introduce the data visualization process and reports students’ perception of the worksheets to introduce the data visualization process to novice users. Overall participant perception of the worksheet method was positive: 61% thought the method was easy to use, felt most people would learn to use the method quickly, felt confident using the worksheet method after using the worksheets for multiple visualization challenges and indicated they would like to use the method frequently when applying the data visualization process. This work is funded by The National Science Foundation (Award #CCF-0939370). Next steps involve integrating team science and data visualization ethics activities into future iterations of the data visualization activity sheets and usability survey.

Abstract:
An international community of practice of young scholars has developed as a result of longitudinal efforts emphasizing multi-institutional interdisciplinarity. Results demonstrate significant impacts on student capacity for interdisciplinary engagement. The Information Frontiers program is offered as a model for establishing similar education and research programs.
With a goal of training the next generation of scholars in the emerging topic of the science of information, a national level Information Frontiers program was designed to introduce diverse cohorts of students to data and information science processes while fostering research team best practices crossing domains and institutions. In eight years of the program, student-led teams represent 24 universities and 22 distinct domains. Supporting educational efforts from the program have involved 4,000 students from 125 universities internationally. A community of practice (Wenger et. al., 2002) has emerged.
Results show significant and positive relationships between community-based research collaborations and scholarly outputs. Multi-year student team collaborations have resulted, several with generational impacts further multiplying the results. Given careful consideration of support and training, with viable pathways for crossing domains and institutions, our results demonstrate that students can achieve collaboration success typically shown only at the faculty level (Leahey, 2016). Student members of the community report significant impacts on their capacity for engaging in interdisciplinary discussions and research. As such, the Information Frontiers program can be offered as a model for establishing similar education and research programs.

Abstract:
Dedicated training was designed and offered annually to introduce diverse cohorts of students and early-career scientists to first principles and concepts from data analysis, while also working within interdisciplinary teams. Participants completed a pre-workshop online four-week Introduction to R course. The week-long workshop emphasized handson tutorials with techniques for data wrangling and visualization including data scraping, parsing, cleaning, and analysis while also fostering interdisciplinary team science. Diverse backgrounds and experience were prioritized during the selection of participants, along with disciplinary interests from the full spectrum of STEM disciplines and beyond. Teams were organized around real-world, data-driven research projects. Students from statistics, math, and computer science domains were matched with students from engineering, life sciences, and liberal arts. Multi-institutional interdisciplinary teams received funds for continuing collaborative research with the goal of co-publishing results. Outcomes demonstrate that participants gain tangible data science skills and knowledge. Further, the interdisciplinary team experiences result in successful long-term student collaborations across institutions and topic domains at the nexus of data science.

Abstract:
The Center for Science of Information, a national NSF Science & Technology Center, has developed an integrated Information Frontiers education and diversity program to successfully broaden participation at undergraduate, graduate, and postdoctoral levels. Outcomes from eight years of the program have revealed ten defining characteristics that lead to broadening participation across STEM disciplines in the context of data science and information science domains. Specific activities include mentoring, leadership roles, team science collaborations, unconscious bias training, networking, STEM outreach, and recruitment strategy. The results, lessons learned, and supporting program characteristics will be presented.

Abstract:
From 2011-2017 the Center for Science of Information (NSF-STC), with a goal of fostering a community of practice (Wenger et. al., 2002), emphasized research collaborations across eleven member universities. This paper focuses on graduate student collaborations and research teams. Pathways for collaboration were developed. Guiding questions included:
1. Is there a relationship between collaboration and scholarly outputs?
2. Do factors of research funding source, university, gender, or length of Center membership influence collaborations?
3. What lessons can be learned from student research team formation and interactions, and their ability to address interdisciplinary questions?
4. To what extent can a community of young scholars with large geographic distribution productively collaborate together?