Recipient Organization
UNIVERSITY OF WASHINGTON
4333 BROOKLYN AVE NE
SEATTLE,WA 98195
Performing Department
Environmental & Forest Science
Non Technical Summary
We aim to accelerate the bridging of the gap between sustainable forest management and data sciences by establishing leadership focused, diverse graduate fellowship in SEFS at UW. We will use evidence based experiential learning frameworks, harnessing big data and apply those to large scale integrated social and biological issues faced by the forest and natural resourcesindustries. We focus on Targeted Expertise Shortage Areas of Data Science, to enable management through geospatial data building, analysis, and research communications. This approach responds to the USDA Strategic Goals: (5) Strengthen the stewardship of private lands through technology and research and (6) Foster productive and sustainable use of our National Forest System Lands. This is responsive to the NNF goals of providing traineeship programs meeting the national need to develop scientific and professional expertise in Natural Resources, and Human Sciences. We harness engagement with stakeholders on the board of the PFC. The objectives of the fellowship framework will holistically integrate stakeholder driven applied research by selecting projects defined by the PFC private, public, and non-profit sector stakeholders. The development of knowledge and intellectual abilities will be achieved through focused courses and stakeholder engagement. The experiential learning will include the project and potential for summer internship with the stakeholders, in which the projects can be further explored and refined. We will focus on communication through scientific writing, public speaking, and other innovative engagement opportunities. Intellectual growth will be complimented with professional development, leadership training, and mentorship to advance the personal effectiveness of the Fellows.
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
The goal of the proposed program is to accelerate bridging the gap between sustainable forest management and DS by establishing a leadership driven and diverse graduate DS Fellowship program in SEFS at the UW within the USDA 'Data Science' Targeted Expertise Shortage Areas (TESA). The Fellowships trainees will use evidence-based experiential learning frameworks, harnessing big data theory and methods developed in the geosciences, and apply those to large-scale integrated social and biological issues faced by the forest and natural resources industries. The DS TESA will focus on enabling forest management through geospatial big data compilation, analysis, and research communications. This approach responds to the USDA Strategic Goals: (5) Strengthen the stewardship of private lands through technology and research and (6) Foster productive and sustainable use of our National Forest System Lands. This is responsive to the NNF goals of providing traineeship programs to develop scientific and professional expertise in Natural Resources and Human Sciences.Both forestry and DS exhibit a diversity gap. This gap is best exemplified in environmental and natural resource sciences by lack of ethnic and racial diversity (Ruf, 2020) and in the DS realm by lack of gender diversity (Forbes, 2017). The lack of diversity amplified by equity and inclusion issues lessens our ability to ask important questions and solve large scale problems (Hofstra et al., 2020). Thus, it is critical to amplify our efforts to alleviate racism and unconscious biases in these disciplines, to truly harness the strengths bolstered by diverse mindsets and skills. A very recent approach dedicated to facilitating and supporting diversity, equity, and inclusion (DEI) in environmental and DS fields is the 2019 launch of the Environmental Data Science Inclusion Network (EDSIN). The priority mentorship areas identified by EDSIN in support of progress include accessibility, allies, data ethics, education and training, collaboration, cultural change, professional development, recruitment, and retention and relevance. We adopt these as critical aspects of our diversity-driven Fellowship endeavor. Intellectual growth needs to be complemented with professional development, leadership training, and mentorship to advance the personal effectiveness of this new workforce. By integrating the above themes, the following key steps aim towards achieving our goal:1. Building critical knowledge and skills in a diverse workforce of 6 Master Level Student Fellows (6 FTE) to apply modern DS analysis techniques and tools to advance and accelerate environmental research and applications.2. Harnessing experiential learning through stakeholder-driven applied research projects.3. Complementing intellectual growth with professional development, leadership training, and mentorship to advance the personal effectiveness of the Fellowship trainees, lead by four of the faculty PI's at 0.25 FTE each.The proposed leadership-driven and DEI-focused graduate Fellowship program is designed to meet the critical education gap between the intersection of DS and sustainable forest management, through the tools of geospatial sciences. Upon completion, the trainees will be equipped to tackle modern environmental challenges using bid data, machine learning, statistical inference, and visualization techniques. The proposed Fellowship program provides trainees with a foundation in DS and is designed for, but not limited to, students with minimal or no background in DS, computer science, or coding. The Fellowship program will especially focus on recruitment from underrepresented groups and will develop leadership in food and agricultural sciences (forestry and environment resource management).
Project Methods
The following key steps aim towards achieving our goal:1. Building critical knowledge and skills in a diverse workforce to apply modern DS analysistechniques and tools to advance and accelerate environmental research and applications.2. Harnessing experiential learning through stakeholder-driven applied research projects.3. Complementing intellectual growth with professional development, leadership training, andmentorship to advance the personal effectiveness of the Fellowship trainees.Core competencies that each Fellow will be expected to attain come from a combination of course work and experiential learning requirements. Fellows will complete courses in at least three of the four areas below. Each area will have a list of current courses offered within SEFS and other departments on the UW Seattle campus that will satisfy the requirement in that area. A minimum of 11 credits is required (3) from each of the three areas, and multiple credits can be earned for eScience seminar - 2 credits each). The 11 total credits will count toward the minimum number of credits required for the SEFS MS. The areas of focus will include:• Software Development for DS with at least 6 current courses identified at UW.• Statistical and Machine Learning with 18 course options in SEFS and UW.• Data Management and Data Visualization with 10 course options at UW, half within SEFS.• SEFS Requirements courses that include DS content but are environmental in application.In addition to the Experiential Learning project, further discussed below, Fellows will be required to participate quarterly and present once in the weekly 1-hour eScience Community Seminar. The Fellows will be required to fulfill all the standard SEFS and graduate school degree requirements, where SEFS requirements may be simultaneously fulfilled with the Fellowship requirements. Performance in these elements will be evaluated using a variety of techniques described below, and used as part of the Fellowship outcome measures.