Recipient Organization
VIRGINIA POLYTECHNIC INSTITUTE
(N/A)
BLACKSBURG,VA 24061
Performing Department
AAEC
Non Technical Summary
We inhabit a world where data are ubiquitous. Businesses, government agencies, and other public and private entities and organizations harness a variety of data sources and data-centric tools. These data, and the associated tools used to analyze them, are in turn used to improve decision making and to guide the formation, implementation, and performance benchmarking of strategic policy initiatives. These observations go far in explaining the rapid growth in data-related jobs across all sectors of the global economy. An immediate implication is there is a need to offer more hands-on, experiential training in data science to students. Specifically, there is a need to offer data science training to answer questions across the food-agricultural nexus: from ensuring continued agricultural productivity to ensuring the safety and affordability of nutritious foods to ensuring vibrant, healthy, and prosperous rural communities. By building on a research, project-based approach to teaching data science methods to a broad and inclusive set of students, we propose to lead a summer experiential program, DATA Analytics in Community and Rural Economics (DATA-ACRE). Our proposed program fully integrates training in data science methods with policy issues relevant to agriculture and rural communities.The DATA-ACRE internship program is an immersive, ten-week summer program that takes place on Virginia Tech's Blacksburg campus. The program is led by a multidisciplinary team of faculty members (i.e., faculty mentors). The goal of the internship is to provide students with a hands-on, minds-on experiential learning opportunity that demonstrates how disparate types and forms of data are used to inform public policy and decision-making relevant to agriculture and rural communities. The students are expected to fully engage in this team-based learning exercise during the entire ten-week program. The program starts by splitting the students into several teams. Each team works on one or more assigned data-intensive research projects throughout the ten-week period. During the first two weeks, all students receive training in basic data science methods. After that, the training becomes more customized, with advanced topics pertaining to using, for example, GIS data, web scraping, supervised and unsupervised machine learning applied to structured and unstructured data sets, and other advanced statistical methods being covered as warranted. The training will be delivered through a combination of on-site instruction by Virginia Tech (VT) faculty members. In addition, the on-site instruction is supplemented with virtual, synchronous training coordinated with the University of Virginia. In addition, various training materials (e.g., videos, handouts, etc.) developed as part of the USDA-NIFA-AFRI-006609 FACT: Three-State Data Science for the Public Good Coordinated Innovation project are made available to students at the integrated DSPG web resource. While the training will occur during the mornings, the student teams will begin work in the afternoons with their graduate student peer mentor and faculty mentors to design and implement their research projects. At the end of ten weeks, the students will have worked as part of a team to design, implement, and execute at least one and usually two policy-relevant research project(s). Each team will presents its findings in a poster session at the Data Science for the Public Good Symposium, held in early August in Arlington, Virginia.The DATA-ACRE internship program will help shape the agricultural workforce in three fundamental ways. First, it will help us establish a pipeline of underserved and underrepresented students from the social, agricultural, agribusiness, and data-related sciences from our network of nine small and (or) HBCU and HSI universities and colleges across the United States. For various reasons, these students might not otherwise have opportunities to be exposed to data analytics methods and training. In this way, we will increase the diversity of students who will participate in our novel program aimed at developing the future agricultural and rural community workforce. Second, the our team-based, project-focused program will provide all participating undergraduate students with an opportunity to experience how data science tools are applied to meaningful research problems, and especially problems confronting agriculture and rural communities. The hands-on, minds-on applied research experiences we offer help students understand how to formulate a research question and then how to obtain, use, analyze, and present data to address the question. In addition, they develop the skills necessary to summarize and present their work in meaningful ways to policymakers, decision-makers, and the public. Third, the student-stakeholder project collaboration involving Extension specialists and other stakeholders provides an important form of professional mentoring for the student interns. That is, students will gain insights into potential career opportunities in the agricultural and public policy sectors.In summary, the skills taught to the students involved with this project will address such themes as the creation and use of open data, data-driven decision-making for the agricultural sector, and notably, agricultural science policy leadership.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
We inhabit a world where data are ubiquitous. Businesses, government agencies, and other public and private entities and organizations have harnessed a variety of data sources and tools to improve decision making and to guide the formation and implementation of strategic policy initiatives. Given the rapid growth in data-related jobs, there is a need to offer more hands-on experiential training in data science. Specifically, there is a need to offer data science training to answer questions across the food-agricultural nexus: from ensuring prosperous rural communities to ensuring continued agricultural productivity to ensuring the safety and affordability of nutritious foods to ensuring vibrant, healthy rural communities. By building on a research, project-based approach to teaching data science methods we propose to lead a summer experiential program, DATA Analytics in Community and Rural Economics (DATA-ACRE). Our proposed program fully integrates training in data science methods with policy issues relevant to agriculture and rural communities.The DATA-ACRE internship program is a ten-week summer program that takes place on Virginia Tech's Blacksburg campus, and that is led by a multidisciplinary team of faculty members (i.e., faculty mentors). The goal of the internship is to provide students with a hands-on, minds-on experiential learning opportunity that demonstrates how disparate types and forms of data are used to inform public policy and decision-making relevant to agriculture. The students are expected to fully engage during the entire ten-week program.The program starts by splitting the students into teams. Each team will then work on one or more assigned projects throughout the program. All students will receive training in basic data science methods and then more customized and advanced training on using, for example, GIS data, web scraping, supervised and unsupervised machine learning applied to structured and unstructured data sets, and other advanced statistical methods. The training will be delivered through a combination of on-site instruction delivered by Virginia Tech (VT) faculty, virtual synchronous training coordinated with the University of Virginia, and by using various training materials developed as part of the USDA-NIFA-AFRI-006609 FACT: Three-State Data Science for the Public Good Coordinated Innovation project--these materials are available at the integrated DSPG web resource. While the training will occur during the mornings, the student teams will begin work in the afternoons with their graduate student peer mentor and faculty mentors to design and implement their research projects. At the end of ten weeks, the students will have worked as part of a team to design, implement, and execute a policy-relevant research project. Each team will then present its finding at the Data Science for the Public Good Symposium, held in early August in Arlington, Virginia.The DATA-ACRE internship program will help shape the agricultural workforce in three fundamental ways. First, it will help us establish a pipeline of students from the social, agricultural, agribusiness, and data-related sciences from our network of nine small and (or) HBCU and HSI universities and colleges whose students might not otherwise be exposed to data analytics methods and training. In this way, we will increase the diversity of students who will participate in our novel program aimed at developing the future agricultural workforce.Second, the team-based, project-focused program we propose to use will provide all participating undergraduates with an opportunity to experience how data science tools are applied to meaningful research problems confronting agriculture and rural communities. The hands-on, minds-on applied research experiences we will offer help students understand how to formulate a research question and then how to obtain, use, analyze, and present data in meaningful ways to policymakers, decisionmakers, and the public.Third, the student-stakeholder project collaboration involving Extension specialists and other stakeholders provides an important form of professional mentoring for the student interns. That is, students will gain insights into potential career opportunities in the agricultural and public policy sectors.In summary, the skills to be taught in the project will address such themes as the creation and use of open data, data-driven decision making for the agricultural sector, and notably, agricultural science policy leadership.The specific objectives of this experiential learning program are:Train 50 US undergraduates, with many coming from HBCUs and HSIs, with the data science skills that are needed to inform local and regional policymaking in agricultural, community, and rural economics.Teach students how to use data to construct meaningful socio-economic measures and indices that inform agricultural, natural resource management, and rural policy.Mentor and guide students in applying these skills to specific yet actionable research projects.Mentor and guide students on the tools and techniques applicable to team science and team-based project workEngage and work with Extension stakeholders and other community leaders to frame relevant research problems and finalize data sources and research strategies.Demonstrate how to plan, conduct, and report team-based, data-centric research that is replicable and open source so that stakeholders can update the analysis to suit their future needs.Mentor and guide students in preparing research reports.Mentoring and providing students with research presentation opportunities.Providing students with professional development and the opportunity to build a peer/professional network.
Project Methods
The methods that will be useare:1. Recruitment of Cohorts: We will create a website advertising the program and the types of projects we are likely to conduct for the internship. We will also have a link to an online interactive Qualtrics-based application to our program. Together with our college pipeline partners we will start the recruitment process by advertising on listservs and appropriate websites and other college venues in the Fall. We will also host informational webinars that will explain the logistics of the program, the projects we have recently undertaken, and the data science skills acquired by previous program participants. Project personnel will review all applications and together select the undergraduate interns. Selection will be based on merit, enthusiasm, and motivation. We expect that the majority of successful applicants will be students who have completed their junior year before the summer research internship and who are in data analytics and/or the social science fields. In selecting the students to participate in the DATA-ACRE program, a priority will be placed on diversity, including gender, race, and socioeconomic and veteran status.2. Project-Based Experiential Learning: At the beginning of the internship, the undergraduate interns and the primary faculty project mentors are sorted into research teams and assigned a project. The projects are based on pressing problems posed to our faculty through their collaborations with national and international stakeholders. Throughout the ten weeks, the interns will learn to design and execute a research plan. The multidisciplinary composition of our faculty mentors and key personnel ensures that we can address a broad array of issues confronting agriculture and rural communities. The projects envisioned--and based on our two pilot internship programs--include questions on educational disparities, workforce development, economic development initiatives, healthcare access, food security, and poverty.3. Student Training: Our data science training curriculum will be delivered during the mornings over the first two weeks of the program. This format provides an opportunity for undergraduate students to routinely interact with each other and network. The training includes topics in Data Science such as: use of VT's Advanced Research Computing (ARC) infrastructure; accessing data repositories; relevant data topics including integrity, security, and privacy; project reproducibility and project management; introduction to R and RStudio for data analysis and visualizations. The curriculum will be adapted from the training materials developed as part of the USDA-NIFA-AFRI-006609 FACT: Three-State Data Science for the Public Good Coordinated Innovation project--these materials are available at the integrated DSPG web resource.Further Training in Data Analytics will be tailored to the focus primarily on their project-based work. Additional data analytic training will occur, but the type and nature of this training will vary, depending on the needs of the project. Based on our pilot program experience, we expect this training could include learning and reviewing activities such as web-scraping, natural language processing, supervised and unsupervised machine learning, GIS data analysis, advanced data visualization and presentation techniques (e.g., shiny dashboards), or advanced statistical methods including, for example, fixed and random panel data models or logistic regressions.4. Research Presentations: The DATA-ACRE internship program will teach undergraduate students how to write technical papers and present their work both in a digital and symposium format. The internship concludes each summer with the student teams presenting their project summaries at the Data Science for the Public Good Annual Symposium, which is held during the first week in August. The project stakeholders are also invited to these meetings to provide feedback on the research. The mentors will accompany their student teams to the one-day conference and watch them present. Prior to the conference, the teams have opportunities to practice and hone their presentations skills through several brown bag mentoring workshops. These brown bag workshops review for students how to write a succinct and informative research report and how to present data-informed research results to a diverse audience.5. Professional Development: Because our projects are sourced from stakeholders with a vested interest in the findings, the interns interact with the stakeholders through a Data Discovery Workshop. During this workshop, each team listens to the pressing policy issues identified by the agricultural and rural community stakeholders, which may include but will not be limited to Cooperative Extension Agents and Specialists. One advantage of this approach is that students observe firsthand what it is like to have a career in the public sector working on agricultural and rural economic problems at the county, federal, or international level.