ALVSCE Data Science Incubator

Request for Proposals

Background

The mission of the CCT Data Science Team is to enable data-intensive research and computing for The University of Arizona’s Division of Agriculture, Life and Veterinary Sciences, and Cooperative Extension (ALVSCE). ALVSCE includes the College of Agriculture and Life Science (CALS), the Arizona Experiment Station (AES), the Cooperative Extension System (CES), and the College of Veterinary Medicine (VetMed).

We are now formally launching the ALVSCE Data Science Incubator. This is an ongoing opportunity for researchers in ALVSCE to work with our team to understand and take advantage of opportunities enabled by new and emerging data resources and computing methods.

Our team of data scientists and software engineers provides expertise in state-of-the-art technologies and methods alongside domain expertise. These include statistics, data synthesis, data management, GIS, simulation modeling, analytics, cloud and cluster computing, software design and engineering, visualization, and more.

The Data Science Incubator will provide up to ten full-time days of support for ALVSCE projects spread over three months to work on focused, intensive, collaborative projects.

We invite short proposals (1-2 pages) for a data-intensive research collaboration focusing on a research problem with clearly defined outcomes. This support is funded by USDA Hatch funds, and can be leveraged to develop funding for longer term collaborations.

Expectations

Each project will entail close collaborative work between a researcher working on the project and a member of the CCT Data Science team staff, who will schedule regular meetings with project members. We find that collaboration in shared virtual or physical spaces is essential for success, providing technical engagement and opportunities for “cross-pollination” among multiple concurrent projects.

Incubator projects are not “for-hire” contracts. The project lead and team members will work in collaboration with the data science team members. Project leads are responsible for successful project completion, with the CCT - Data Science team providing guidance on methods, technologies, and best practices as well as general software engineering and data management. It is expected that the project lead and/or project team members will assume full responsibility of the project once the incubator work is completed. This could be through having needs fully met, developing new capacity, and / or funding longer term support from the CCT - Data Science team staff.

Data scientists who make substantial and substantive contributions to incubator projects should be acknowledged or included as co-authors as appropriate.

Eligibility

This opportunity is open to all ALVSCE researchers and staff.

To Apply:

Applicants should prepare and submit a short project proposal describing the science goals and any expected technical challenges. The ideal proposal clearly identifies the datasets involved, the questions to be answered, explains how the technical components of the project are critical to delivering exciting new findings, and who from the proposing team will be working directly on the project. 

You are encouraged to develop the proposal in collaboration with one or more members of the Data Science team.

The application should contain the following information, and you can find a template here. There is a (soft) two page limit that does not include list of participants or references:

  • Name, affiliation, and email of
    • project lead
    • primary point of contact (if different from above; e.g. lead can be PI; point of contact can be grad student or postdoc)
    • other collaborators
  • Brief project summary
    • Background and Objectives - can rely heavily on links and references
    • Key science questions
    • Technical challenges
  • Approach 
    • Description of any data, software, or computing resources that will be used or developed in the project
    • for data: approximate size, formats, location, and any privacy and access restrictions.
    • for software: methods or libraries that are being or could be used
  • Expected outcomes
    • Scientific Products (including manuscripts, white papers, data, protocols, software)
    • Longer term research and funding opportunities
    • Skills development 
    • Any other contributions 
  • Plans for project sustainability beyond this support period
  • List of relevant citations and resources

Upload your proposal here.

Selection Criteria

Proposals will be evaluated based on the following criteria:

  • Potential for development of new skills and capacity in ALVSCE
  • Availability and engagement of someone from the proposing group
  • Skills of the CCT Data Science team research staff
  • Plans for long term sustainability and impact
  • Clarity and appropriateness of scope
  • Potential for measurable outcomes, including publications and grants

Examples of past projects

  • Making an analysis reusable, automated, and scalable
  • Open science makeover: review and implement best practices with a lab or project to improve data and software management
  • Designing and optimizing databases and APIs
  • Custom statistical modeling including meta-analysis, hierarchical modeling, and partially missing or imbalanced data.
  • Synthesis of prior research through meta-analysis
  • Experimental design
  • Visualization and interactive web applications

 

Contacts:

Other support we provide

Program History and Acknowledgements:

This program is based on the University of Washington eScience Institute Incubator program and we have used text from that program with permission. This is also inspired by David LeBauer’s experience with the National Center for Supercomputing Applications Fellowship Program.