Food Systems Data Equity Framework

The data equity framework outlined below is based on 7 categories identified by We All Count. It is for anyone who plans to utilize the data shared on this website, collect additional data for their own projects or provide funding for others to collect data. We ask you to consider the information outlined below as use, analyze, interpret and share your work.

Funding Sources for Data Collection

  • Collecting data can be an expensive process. Many researchers must seek outside funding to support their research. Funding for this kind of work can come from federal, state, local or tribal governments, non-profit foundations, private companies, individual donors, universities, etc.
  • Each funder, no matter their affiliation, has some sort of motivation for funding a particular research project. It is important for one to consider the potential motivations and priorities of the funder and also determine the level of influence that particular funder has on the research that is being conducted.
  • When using and analyzing data sets, try to ground truth it by looking at other research previously conducted in this area or reviewing related peer-reviewed journal articles. Is it consistent with other research? If not, is there an explanation for the inconsistencies?
  • Work your way through the next 6 sections of this framework (motivation; project design; data collection and usage; analysis; conveying your findings; and, communication and distribution) with the following consideration – is this designed in a fair and transparent manner with the only motivation being accurate and thorough data collection or could the funding source have influenced the development of the project to best fit their desired needs?


  • Be clear about your motivation for engaging in data analysis. Are you applying for a grant? Providing a progress report? Providing information to your community? Advocating for a topic of interest? Identify your “why”.
  • Taking the time to identify your motivation will not only lead to more focused data analysis, but will also help ensure you effectively incorporate equity into your project from the start.
  • If equity is a focus of your project, it is important to define what you mean by equity and have a clear definition underpinning your work.
  • Clarity around your motivation for data analysis also helps manage the complexity inherent in food systems. Many of us are examining “wicked problems” so it is important to keep individual data analysis projects focused on your specific “why”.
  • Seek to develop shared project goals in partnership with community members. Shared motivations can help to facilitate authentic community engagement.

Project Design

  • Have you considered equity throughout the design process: methodology, survey and instrument design, use of [primary and secondary] data, chosen sample population? Your choices will determine how truly equitable your results might be.
  • Make sure you are using a food systems lens when designing your project—that is, how might barriers, challenges or inequities within the food system impact the way in which you design your project or research?
  • Consider which groups of people you might seek for the study:
    • What is the diversity of the population? What languages do they speak? Would they feel most comfortable speaking with someone by phone, through email, in-person, as part of a focus group? Would it be better for a trusted community member to interview this population instead of a researcher?
    • If researching a specific program or policy: who might have been excluded from this program in the past? How might specific policies have discouraged individuals from participating in certain programs or have impacted their ability to access resources (i.e. farm loans, insurance, access to farmable land)?
      • For example, if your project involves increasing access to local food and you have secondary data about Supplemental Nutrition Assistance Program (SNAP, formerly food stamps) participants, you would only be able to articulate results related to those who were eligible for, applied for, and received SNAP benefits—not all individuals and households in need of food assistance. You would also want to understand and consider the barriers to applying for SNAP (which can differ by state) and fears about public perceptions related to accessing these funds (which has been shown to lessened participation in this program).
    • Use community advisors who can help to ensure that community input is gathered at all stages of the project and in a way where the community feels actively engaged and heard through the process.

Data Collection and Usage

Our research briefs offer recommendations on which additional data measures would be most useful to collect/construct to further inform the food landscape.

Primary Data Collection

  • Seek to include community members in the design of your instruments and in your data collection process. Consider partnerships with community organizations who are well-positioned to assist with recruitment and training.
  • Take steps to use plain language and be concise and clear with the wording of your survey questions. Be sure not to make any assumptions about the sample population when developing your questions.
  • Seek out data collection procedures that respect cultural traditions. This might include the use of interviews or focused groups, which allow for oral history and storytelling, rather than a survey.
  • Take steps to reduce community participant burden. This includes reducing the time required for participation and providing adequate compensation for time involved.
  • Engage with community members or organizations you are wishing to research. Tell them about the goals of your research and learn about their goals and needs. Design a mutually beneficial project and once the research has been completed, return to the survey participants with the research or tools that could help the community further their goals.
  • Only include questions in your survey that is relevant to your work or project. For example, do not ask about income level or level of education if it is not a data point that is pertinent to your research question.

Secondary Data Usage

  • Take time to acknowledge which populations are represented in the existing data, which populations might be underrepresented, and which populations were left out entirely.
  • Consider supplementing your secondary data with the collection of new primary data (see above).
  • When constructing new data measures from existing data, consider that individual’s identities consist of multiple intersecting factors including race, ethnicity, sexual orientation, gender identity, geographic location, perceived class, and citizenship. Consider the importance of all of these intersectional identities.
  • Be sure to consider and adhere to the laws of data sovereignty, especially when working with indigenous populations.


  • Transparency is essential as you analyze data, especially if your goal is to center equity in your work.
    • Depending on your project, quantitative data analysis may range from calculating averages to developing complex models. Regardless of the type of analysis you conduct it is important to be clear about exactly what you did to the data.
    • If you utilize a complex model your work may require simplifying assumptions. Strive to describe how you selected your analytical approach and developed these assumptions.
    • Take steps to disaggregate data for different populations and sub-groups where possible. Aggregation of data can mask important differences that might be relevant for understanding needs and crafting adequate program and policy solutions.
  • Clarify how decisions you make regarding analysis impact your results.
  • All descriptions of analytical processes should be presented in plain language.
  • Openness in data analysis process is an important part of supporting collective democratic decisions around what food systems look like.

Conveying Your Findings

  • Consider supplementing the data analysis with narratives from community members (using data and stories for example) that illustrate experience and impact where possible.
  • Be transparent about the limitations of your findings and where there are data gaps.
  • Refrain from using outdated labels and naming conventions that are rooted in historical bias. This project decided to keep the fidelity of the data by retaining its variable names and dataset names, but it is encouraged that you use updated terminology in your own report-outs.
  • Consider supplementing the data analysis with narratives from community members (using data and stories) that illustrate experience and impact where possible. Stories from the Field
  • Consider the effects of root causes or the core issues that are contributing to the observational data trends and conditions. Be sure to include them in your narrative. Throughout this data warehouse there are “Narrative Matters” sections for each of the data categories. It is recommended that you read through these sections to gain a better understanding of how the narrative surrounding the interpretation of the data analysis can have a focus on equity.
  • In each of the research briefs, there are “Narrative Matters” sections for each of the data categories. It is recommended that you read through these sections to gain a better understanding of how the narrative surrounding the interpretation of the data analysis can have a focus on equity.   

Communication and Distribution

  • This is where it all really matters—here, you’ll incorporate equity as you make data visualizations, develop dashboards, craft social media posts highlighting results, and write reports and journal articles.
  • Consider multiple report formats and communication channels to ensure that findings are accessible to all audiences.
  • The communication content is key in making sure that your equity standards continue to shine. From We All Count: “Additionally, the equity issues don’t stop at the first bar graph. We’ve seen so many organizations put so much effort into creating equitable data, only to trip at the finish line and prioritize the most privileged people in the way that they communicate it – exactly the opposite of their equity goals.”
  • Describe your standards, how your results came to be (e.g., data analysis, summarization, qualitative analysis), and how you’ve incorporated your equity goals into the food systems work.Explain your intentionality, how you incorporated equity principles and acknowledge where your work could have done better. There will always be more work to be done, more experiences to learn from, and more people who deserve to be heard. Share out where that help is needed so the next person can take on that work.

Additional Considerations for Funders of Research Projects

  • Look to simplify the application process for researchers seeking funding for their work by using plain language and low-tech options.
  • Consider using application criteria that includes the participation of community members as a requirement and that also weights equity criteria appropriately during the review process, so that “equity points” on an application can effectively tip the scale, rather than simply check a box.
  • Distribute funds in a manner that respects the value of community partners’ knowledge and time commitments. (Often times the community partners receive very little compensation in comparison to research partners.)
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