A Data Science Support Team for ALVSCE at The University of Arizona
Creators: name: LeBauer, David
name: Guo, Jessica
name: Riemer, Kristina
Publisher: name: Zenodo
Year: 2022
Subjects: data science
open science
agricultural experiment station
cooperative extension
scientific research
training

description: This proposal for an Experiment Station Project to support the CCT-Data Science Team was submitted to the USDA NIFA Capacity Grants through the University of Arizona in July 2022. The plan language summary submitted to NIFA follows. The Issue and why it is important: Data science is transforming agricultural research and its applications. Increasingly, large and complex datasets are being collected and analyzed to inform management of crops, livestock and natural resources. When large datasets are paired with new methods in computing, reproducibility, statistics, and machine learning, data science has immense potential to transform agriculture and natural resource sectors. However, there is a gap between what these new technologies could enable and the skills that researchers need in order to effectively apply them to accelerate their research. Goal and objectives: The goal of this proposal is to support a developing data science team that improves data science capacity of researchers at University of Arizona, which will result in clear and measurable improvement in research outputs, through skill building, individualized research assistance, collaboration, and networking. This proposed project will support the Communications &amp; Cyber Technologies Data Science Team (CCT Data Science) within the Division of Agriculture, Life &amp; Veterinary Sciences, and Cooperative Extension (ALVSCE) at the University of Arizona. ALVSCE includes the College of Agriculture and Life Sciences, The Arizona Experiment Station, and Cooperative Extension, and is hereafter referred to collectively as CALS. CALS includes ten colleges, six agricultural research stations, and twenty three cooperative extension offices across Arizona. CALS represents diverse areas of research and extension, and supports the educational, agricultural, and economic needs of the people of Arizona. CALS has prioritized developing data-intensive research and associated capacity for all career stages. This need has been a common theme across multiple internal reports that directly inform our group's priorities. CALS Communications &amp; Cyber Technologies (CCT) is co-located with CALS administration on the University of Arizona campus. This proposal will fund a data science team in CCT to serve CALS by helping provide researchers with tools, training, and one-on-one assistance to implement data science approaches in their research. CCT Data Science has three core objectives: Enabling research helping scientists overcome barriers to the use of data and computational methods, and facilitating collaboration among scientists, engineers, statisticians, and computer scientists. Training researchers individually and in small groups on tools, workflows, and approaches that we use and develop in Objective 1. Community building and outreach by facilitating communication, collaboration, and skills sharing among researchers interested in data science. Target audiences and how they will benefit: The CCT Data Science Team will train the next generation of students and practitioners who will discover and apply new methods to support agriculture, industry, and the people of Arizona. Specifically, this team will enable research, provide training, and coordinate outreach activities that will elevate the data science capacity of researchers and trainees in CALS and the communities we serve. In this way we will amplify the effectiveness of CALS research on stakeholders and partners in Arizona. This will position the college and state to become leaders in the development and application of new technologies. How our activities lead to the outcomes described in the goal statement or objectives. We will build data science capacity through enabling research, training, and community building. We will do this by working with researchers and extension agents to identify opportunities and bottlenecks, developing and communicating technical solutions, and finally applying and teaching these new approaches. Methodology: Describe the ways in which the project will be conducted, with emphasis on the general scientific methods and any unique aspects or significant departures from usual methods. The proposed program will enable and advance a diverse range of research and extension across CALS and beyond. We use an agile approach that allows us to continually evolve to meet the challenges and opportunities that arise and support new people and projects. We organize this work around our three core objectives: Enabling research, Training, and Building Community. I. Enabling Research and Extension CCT Data Science enables research and extension through a number of specific activities aimed at building data science capacity within CALS. We hold open office hours weekly and by appointment. Office hours provide an easy and low pressure environment for people to ask questions, solve problems, and have open ended conversations. We developed the Data Science Incubator program to support projects that require an extended effort. In this program, researchers and extension agents propose a clear scope of work with specific and measurable outcomes that leverage our capacity. The process involves multiple stages. First, the pre-proposal stage includes community engagement and brainstorming, background research, project planning, and refining proposals. Then the active phase of the project may include a range of support. This phase may include conducting novel analyses or scaling analysis pipelines, aggregating and curating data, developing new methods, and creating custom dashboards. Post-project follow up can include finalizing publications and other scientific outputs, writing proposals to support further work, and longer term maintenance, check-ins, and teaching the skills applied or learned. We also support proposal writing and join as co-PIs or senior personnel to support data management, novel analysis, cyberinfrastructure and training. We prioritize proposals led by CALS faculty and occasionally lead proposals that advance data science. These proposals primarily provide salary support for our group members. II. Training Data science training helps bridge the gap between current capabilities and the adoption of new methods and tools. Researchers are often unaware of modern computing methods or unable to decide which methods to learn. Increasingly, researchers are expected by funding agencies, journals, and their scientific communities to adopt open science approaches. Our training includes both shorter targeted workshops and multi-part workshop series. We host monthly workshops or demonstrations on topics that meet the specific and requested needs of researchers. These Workshop Wednesdays focus on providing skills that are immediately applicable to the participants' work. We developed and will continue to teach a workshop series called Reproducibility and Data Science Skills that provides a foundation of computational research skills and best practices in scientific computing. We meet with a group of researchers repeatedly over several months, teaching project management and documentation, intermediate programming, and version control. This series provides skills used in our more advanced workshops and training more effective collaborators, including as our incubator and proposal writing partners. We will develop a formal assessment system to evaluate the efficacy of our training and to identify needs that we can meet through further training. We will also participate in existing and create new communities of practice around data science skills. We model open science by providing our curriculum and workshop recordings online and will continue to coordinate with other groups and events on campus. To build community, we encourage and provide communication channels for our trainees and workshop participants to work together. We also identify advanced and enthusiastic workshop participants who can be helpers at our workshops and train them to teach others. Group members will grow our skill sets through professional development and will keep up with cutting edge computational tools and data science approaches. We learn as needed to support projects, and we will take training to improve our technical, teaching, and assessment skills, and will attend conferences to keep up with modern trends in the science domains we support and the data and computing methods we use and teach. III. Community Building and Outreach We build, coordinate with, and train communities. We cultivate individual expertise and facilitate cross-fertilization across research domains. In this way we amplify the reach of CCT Data Science across CALS and beyond. CCT Data Science takes active leadership roles among the data science community at The University of Arizona. Our members have served on the organizing committee and presented at conferences on campus and in scientific and computing communities. While CCT Data Science primarily serves the needs of CALS resesarchers, we coordinate with other data science and research computation entities on campus. We foster collaborations with other UA data science groups, including mentoring the CALS data science ambassador(s), and co-teaching workshops with our colleagues. We develop and run events that promote data science in CALS as new opportunities arise, including organizing workshops and forums focused on specific topics and techniques where researchers can share their work, learn from each other, and find new collaborations. research. We will team up with departmental data science programs to work synergistically toward a suite of learning opportunities that bridge from the theoretical to the applied. We will also collaborate with the Data Science Institute to support faculty interested in expanding or revising their courses to include new programming languages and methods. CCT Data Science embraces a broad and inclusive definition of a data scientist. We are developing two programs to encourage the CALS community to engage with data science solutions. We will hold a CALS Data Demo Day at which participants identify a specific problem along with any approaches or barriers they face that would benefit from ideas and collaborations. We will also offer a summer data management fellowship. CALS mentors seeking to reorganize their lab or project will be paired with one of our trainees who can help researchers implement data management and publication practices that make their work more accessible to the public and to the research community in accordance with evolving expectations of publishers and funding agencies.