Participation Agreement
We look forward to your participation in the DataFEWSion (FEWS) Traineeship Program, a National Science Foundation (NSF) funded National Research Traineeship (NRT) through Iowa State University. As a DataFEWSion Trainee, you will be part of a cohort of students with diverse backgrounds who have expressed a desire to address the complexities around the Food, Energy and Water Systems FEWS nexus.
This document describes the expectations and benefits of being a DataFEWSion Trainee. Please read and sign this agreement, also together with your major professor, read and sign the attached Agreement between your Major Professor and you. Please return these documents by April 15, 2020, to Dr. Sarah Ryan 3004 Black Engineering, 2529 Union Drive, Ames, IA 50011-2030 or via email to datafewsion@iastate.edu.
As an NSF NRT DataFEWSion Trainee, I understand and agree that I will fully participate and complete:
- Coursework to obtain the graduate-level certificate in Data-Driven Food, Energy and Water Decision Making
- Fully engage and participate in the Learning Community by:
- Attending the 2-year series of monthly workshops focused on professional development and the FEWS nexus.
- Attending, participating and leading weekly small group meeting, including peer reviews
- Create and maintain a web presence through an e-portfolio or other web-based design
- Present a verbal or visual presentation at the annual DataFEWSion Symposium
- In the second year, be a peer mentor to the incoming cohort of DataFEWSion Trainees (Applicable to assistantship trainees only)
Coursework
|
Course Title |
Semester |
Credits |
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|
Core Course |
|
|
||
|
Fundamental of FEWS nexus |
A B E 690: Key Performance Indicators for Agriculture (offered in spring) |
|
2 |
|
|
Communication |
Gr St 566: Communications in Science (offered in fall) |
|
1 |
|
|
Electives (select one course from each knowledge area) |
|
|||
|
Data Acquisitions, visualization and analytics |
3 |
|||
|
Complex system modeling for Decision support |
3 |
|||
|
Economics, Policy, Sociology of FEWS |
3 |
|||
Complete the below worksheet of anticipated classes and dates for the Data-Driven Food, Energy & Water Decision Making Certificate. See attached Approved Coursework electives.
This section is only applicable to assistantship trainees only.
I further understand that as an NSF NRT DataFEWSion Stipend Trainee I am eligible for the following benefits over a 12-month academic period:
- $34,000 Stipend
- Full tuition waiver
- Health insurance
Conditions
After the conclusion of the first year of the NSF NRT DataFEWSion Traineeship program, continued support will be arranged by the student’s home department at the stipend level determined by the department. Note that most departmental assistantships are lower than the NSF NRT financial package. Please be prepared to adjust.
As a DataFEWSion Trainee, you are required to maintain good academic standing as a full-time student in a Ph.D. program with an overall GPA of 3.00 or better in graduate coursework and satisfactory progress on your research as determined by your major professor. In addition, should you transfer from the Ph.D. to an M.S. program of study during your 12 months of financial support, you will forfeit further NSF NRT DataFEWSion financial support as described in the benefits.
NSF NRT DataFEWSion Traineeship Program Participation Agreement:
I am fully aware of the nature and extent of my participation in the NSF NRT DataFEWSion Traineeship Program as stated above. I agree to fulfill all the requirements and fully participate in the program. In addition, I acknowledge that I will be given a signed copy of this participation agreement.
Attachment 1: Agreement between Major Professor and DataFEWSion Trainee
The DataFEWSion Traineeship Program is a National Science Foundation (NSF) funded National Research Traineeship (NRT) program at Iowa State University. The vision of DataFEWSion is to build a community of researchers and professionals who are equipped to explore, develop and implement effective data-driven models for decision making that are relevant to all the stakeholders around the FEWS nexus.
The NSF NRT DataFEWSion Traineeship is a 2-year program, available through a competitive process, to selected M.S. & Ph.D. applicants who have been accepted into the ISU Graduate College with a sponsoring department. It is expected that their dissertation research topic is consistent with the DataFEWSion vision.
The NSF NRT DataFEWSion Traineeship programs offer the Trainee a foundation of technical skills, professional development, and communication needed to address complexities around the FEW nexus in the 21st century. In exchange they have agreed to participate and complete the following:
- Coursework to obtain the graduate-level certificate in Data-Driven Food, Energy and Water Decision Making
- Fully engage and participate in the Learning Community by:
- Attending the two year series of monthly workshops with the focus of professional development and the FEWS nexus.
- Attending, participating and leading weekly small group meeting, with the goal of developing leadership skills through peer reviews, sharing collaborative information, and preparing for the annual DataFEWSion Symposium.
- Create and maintain a web presence through an e-portfolio or other web-based design
- Present a verbal or visual presentation at the annual DataFEWSion Symposium
- In the second year, be a peer mentor to the incoming cohort of DataFEWSion Trainees (Applicable to assistantship trainees only)
The NSF NRT DataFEWsion Trainee is required to maintain good academic standing as a full-time student in an M.S. or Ph.D. program in your department with an overall GPA of 3.00 or better in graduate coursework and satisfactory progress on their research.
By signing below the Trainee and Major Professor acknowledges that they have discussed the above topics and agreed to the coursework and other expectations of the program.
By signing below, you as the Major Professor indicate a willingness to work with the Trainee and acknowledges the expectations of the program.