In today’s world, the need for data is now matched by our ability to obtain it. Remote sensing, multi-channel instruments are interrogating a wide range of environments, including environments hostile to and perhaps essential to, society (e.g., the deep sea). Collective data patterns emerging out of thousands of volunteers with personal devices (e.g., smart phone users, smart watch users) can speak to everything from health and fitness (e.g., heart rate) to climate change (e.g., air temperature typed to date, time and location) to loss of biodiversity and species introductions (e.g., photographs of organisms types to location.)

How these data are collected, stored, collated, proofed, curated, analyzed, visualized and ultimately used to address the questions and complexities we face within environmental change is the subject of the emerging science of “Big Data.” More than mapping, Big Data is building the relational data structures that facilitate problem-solving.

The Integral Environmental Big Data Research Fund targets graduate students in the UW College of the Environment who are incorporating a big data approach to their scholarly work. In this context, a big data project is defined as one where the student is focused on extracting information from large datasets through the use of interesting, innovative computational and analytic approaches, and where the object of the work is an exploration of emergent patterns and/or relationships of scientific interest. Ideally, the work involves multiple data types. Data can be of any type, including model output.

Students and advisers should be able to demonstrate how the requested funding will be used to develop a new student-directed project, or support an existing student-directed extension of a project in which the student is already engaged. The project must be entirely accomplished within the funding year, and a scholarly research product (e.g., manuscript submitted to peer-reviewed journal, presentation at a national or international scientific society conference) must result from the work.

Proposals are limited to a maximum of $4,000. Funding is available immediately upon award. A sub-budget will be set up in the student’s academic unit with fiscal oversight at the department/unit level.

Funding restrictions

Funding can be put towards any legitimate research expense. Examples include, but are not limited to:

  • extended travel to another location (university, research facility) to learn about the dataset from the experts who created it
  • participation in a focused workshop or short course targeting specific analytical skills/approaches to be used in the project
  • computation hardware, software or cloud storage
  • computation time

Funding may not be used for:

  • graduate student stipend or salary, benefits, tuition or quarterly fees associated with enrollment in the graduate program
  • shared equipment (e.g., a small portion of a new computer purchased by the faculty adviser)
  • travel to present results of research, for instance at a national meeting

Note that funding for journal article publication (page charges) up to $500 will be separately awarded upon notice of publication acceptance.


Applications for the 2024-2025 award year are due January 19, 2024. When applying, send required materials to the Dean’s Office (

Required content

The following is a list of required proposal elements. The choice of arrangement and formatting is at your discretion. Your total proposal – exclusive of the cover sheet, budget spreadsheet, references and the letter of support from your adviser – must be no more than 3 pages, single-spaced, 11 point Calibri font with one inch margins. If you have figures or tables, you will need to include them within this space limit. Failure to follow the guidelines relative to content and length will result in a proposal being returned unreviewed.

  • Cover Sheet

    Your name, academic unit (department or school), degree sought, year during the proposed research (i.e., your 4th year in a Ph.D. program); thesis or dissertation title; statement of good academic standing (i.e., you are making satisfactory progress towards your degree); adviser’s name

  • Covid-19 impact

    What is the impact of COVID-19 restrictions (especially travel), if any, on your proposed work?  If there is an impact, please describe your plan for addressing it in order to complete your proposed project.

  • Issue/Question/Hypothesis

    In two sentences (max) please state the specific question or issue your proposed big data research will address.


    A Plain Language Summary (PLS) is a concise, jargon-free paragraph summarizing a scientific study: the context for the work, the major results, and the So What?  The language should be easy for the interested non-specialist, including researchers from other fields and certainly including members of the public. This Plain Language Summary document provides greater detail on crafting your PLS.

  • Data Type(s) and Source(s)

    List your expected data type(s) and source(s).

  • Computational/Analytic Approach(es)

    Briefly outline the method(s) you will apply to the dataset(s) you are working with.

  • Context

    Place your research within a literature or research context.  What work are you advancing with the proposed research?

  • Thesis or Dissertation Context

    How does the proposed work augment your proposed thesis or dissertation work?  Be specific in outlining your current graduate research work and the additional work you are proposing here.

  • Product

    What scholarly product will result from this specific piece of research?  If you are proposing a peer-reviewed journal article, what is your target journal?  If you are proposing a national scientific conference, what is the target society, meeting location, and date?

  • Timeline

    Create a simple timeline for the proposed work, from collection of samples/data, through laboratory analyses, to data synthesis, statistical analysis, and writing/presentation production and submission/delivery.

  • Budget

    Detail your proposed expenses so that a reviewer can easily understand how much each element costs, and how many things you are requesting.  For instance, if you are traveling to a field location, your budget could include mileage charges and total mileage to specific field locations, etc.  The best way to show a budget is in an excel spreadsheet.

  • Budget Justification

    Explain each line of your proposed budget so that a reviewer can understand why you need whatever you are proposing.

  • Unit Concurrence

    A letter from your academic unit agreeing to fiscal oversight of the award

  • Letter of Support

    Your thesis/dissertation adviser must write a letter of support detailing:

    • How the proposed work augments and exceeds what the student is already doing
    • Why current research funding (for instance, grant funding already supporting the student) is not available
    • How the proposed work will result in a significant scientific advance
    • Evidence the student has the skills and ability to carry the proposed work to completion
    • Availability of faculty time and complementary lab resources (e.g., computational facilities) that will be needed for the project
    • Why the proposed scholarly product from the work is appropriate

Proposed review

Proposals will be reviewed by faculty scientists who are experts in big data, and by faculty who are not experts in the field but who are natural scientists.  Please ensure that your language and explanations are general enough for non-experts to understand your meaning.  The majority of your proposal should focus on what you want to do and why, and how it extends what you are already doing, rather than on a review of knowledge to date.  At the same time, resist the urge to explain your methods in great detail – this takes crucial space you will need to explain your idea and its significance.

Each proposal will receive three independent reviews.  Reviewers will score each proposal on:

  • Impact, innovation and significance to the field
  • Impact on the thesis or dissertation work of the student
  • Likelihood of successful completion

A panel of reviewers will discuss all proposals and reviews, and select a maximum of two awardees.  All proposals will receive written feedback.

Project requirements

Any publication or presentation of the work must explicitly acknowledge The Integral Environmental Big Data Research Fund.

One year after the transfer of funds, a one-page report must be submitted to the Dean’s Office (  The report should detail:

  • Expenditures
  • Remaining funds
  • Work completed
  • Scholarly product (including in process and expected date of completion)
  • Brief (2-3 sentences) description of results, including impacts of research
  • Unspent funds will be returned to the Dean’s Office unless a specific proposal for extension (workplan and timeline for extension; reason needed) is submitted and approved.

Integral Environmental Big Data Research Fund recipients are also eligible to receive up to $500 for publication costs (page charges) stemming from the research supported by the Award.  At the time of billing, recipients must submit to the Dean’s Office:

  • An invoice indicating award recipient, faculty adviser, and amount requested
  • The article being published, which must include acknowledgement of The Integral Environmental Big Data Research Fund

About the funders

The Integral Charitable Foundation (ICF) was created in 2021, through a founding grant from the shareholders of Integral Consulting Inc. (Integral), as a vehicle to bring change aligned with their values and fulfill their responsibilities for environmental and social engagement. They are particularly interested in solving challenges related to health and the environment, and in supporting pioneering research in emerging contaminants, water quality, and physical and mathematical sciences, including big data research, analytics, and computer vision. The ICF advances the core elements of their mission by forming partnerships with key academic institutions, their students, and their alumni. Integral is an international science and engineering firm providing multidisciplinary services in health, environment, technology, and sustainability.

Several of Integral’s top scientists and engineers are alumni of the University of Washington and together with the ICF, the firm hopes to deepen its connection with the University and its students. Together, the ICF and Integral seek to do their part in bending the arc toward a more sustainable world.


Recipients of the Integral Environmental Research Fund


  • Sofia Kruszka

    School of Environmental and Forest Sciences (advisor: Brian Harvey)
    Project: Projecting the drivers of forest resilience to climate change with agent-based landscape simulation model iLand

  • Ben Lloyd

    Department of Earth and Space Sciences (advisor: Caroline Strömberg)
    Project: Segmentation and Classification of Grass Silica Short Cell Phytoliths using Convolutional Neural Networks


  • Noah rosenberg

    School of Oceanography (advisor: LuAnne Thompson)
    Project: Drivers of Spatial Variations in the Temporal Spectra of North Atlantic Upper Ocean Temperatures in Models and Proxy Records


  • Nikhil Dadheech

    Department of Atmospheric Sciences (advisor: Alexander Turner)
    Project: Emulating atmospheric transport model through machine learning algorithms to address computational limitations in conventional transport models


    School of Oceanography (advisor: Andrea Ogston)
    Project: Geomorphic Insights from the Ayeyarwady River Delta: Quantifying Channel Response to Landscape-Scale Deforestation


    Department of Atmospheric Sciences (advisor: Cecilia Bitz)
    Project: The third dimension: predicting September sea ice extent using data assimilation techniques and sea ice thickness measurements



    School of Aquatic and Fishery Sciences (advisor: Julian Olden)
    Project: Emerging technologies to assess human benefits from and risks to water resources


  • Amelia duvall

    School of Aquatic and Fishery Sciences (advisor: Sarah Converse)
    Project: The ecology and conservation of seabird populations in the rapidly changing Pacific Ocean


  • apryle craig

    School of Environmental and Forest Sciences (advisor: Aaron Wirsing)
    Project: Behaviorally-mediated trophic cascades: Interactions among wolves, deer, and plants in north central Washington, U.S.A.

  • Ariane ducellier

    Department of Earth and Space Sciences (advisor: Kenneth Creager)
    Project: Data analysis of recordings of slow earthquakes tectonic tremor, low-frequency earthquakes, and slow slip events


  • ryan groussman

    School of Oceanography (advisor: Virginia Armbrust)
    Project: Eukaryotic phytoplankton gene expression in the North Pacific across diel and basin-scale studies

  • Hillary scannell

    School of Oceanography (advisor: LuAnne Thompson)
    Project: Ocean-atmosphere interactions associated with marine heatwave


  • Catherine Kuhn

School of Environmental and Forest Sciences (advisor: David Butman)

Project: Leveraging high-resolution remote sensing and machine learning to monitor climate
change impacts to water quality for the inland waters of the United States.