Universal Grant Application Data Elements

Prior efforts have addressed parts of this problem. In 2003 the Office of Management and Budget standardized the data elements of Standard Form 424 for federal grant applications, and OMB Watch and the National Council of Nonprofit Associations urged a "common application" model like that used for college admissions. In private philanthropy, regional associations of grantmakers, collaborative funder groups, and platforms such as JustFund offer common applications, but each covers only its own participants. The Technology Association of Grantmakers (TAG) has pursued a different approach: a common grantee database and open data standard from which funders pull what they need, without agreeing on a common proposal format. No open, universal set of data elements yet spans government and private grantmakers alike. This plan proposes such a set, comprising a minimal core common to all applications plus a mechanism for funder-specific extensions, so that grantseekers can publish their information once and grantmakers can query it, while each grantmaker retains full discretion over its own priorities and decisions. Because a grant application is essentially a proposed performance plan, the goals, objectives, and performance indicators of proposed projects can be expressed in StratML Part 2 format rather than in narrative prose. StratML Part 2, Performance Plans and Reports (formerly ANSI/AIIM 22:2017) extends the goals and objectives of Part 1 with performance indicators, including target and actual results. Moreover, a grantee's subsequent report is a performance report on the same plan. Thus, a single Part 2 document can serve both purposes: the applicant states target results when applying and records actual results when reporting, enabling common reporting to multiple grantmakers as well as common application. A Part 2 authoring form developed with Claude.ai is available at https://stratml.us/forms/Claude/Part2.html and could be adapted for grant applications. To make authoring as easy as possible to do, ChatGPT has provided a prompting guide that enables users to convert unstructured text into StratML format: https://stratml.us/references/StratML-Prompting-Guide.html This plan complements the U.S. Strategy Data Fellowship initiative of the General Services Administration -- https://stratml.us/docs/USSDF.xml -- which aims to make public-sector strategic plans AI-ready and interoperable in order to improve coordination and execution across governments. Grants are a principal means by which agencies execute their strategies. If applications were expressed in the same machine-readable format as agencies' strategic plans, the alignment of applicants' objectives with grantmakers' goals could be identified automatically. That would extend the Fellowship's cross-government alignment and opportunity identification objectives to the nonprofit organizations it recognizes as stakeholders. The same logic applies to private grantmakers that publish their priorities in StratML format. This plan shares the aim of effective altruism: directing charitable resources where they will do the most good, based on evidence. However, evaluators associated with that movement conduct costly analyses of relatively few organizations, partly because comparable performance data is scarce. By making applicants' intended and actual results available in a standard, machine-readable format, this plan could lower the cost of such comparisons and extend them to many more organizations. Rather than relying on a few evaluators to rank causes and charities, implementation of this plan would empower grantmakers to apply their own values to commonly available data, distinguishing verifiable findings about results from contested judgments about which results matter most. While no measure is perfect, failing to seek objective measures is a prescription for continuous, opinion-based mediocrity. Donors and intended beneficiaries alike have a right to expect better: measures that are continually refined so that they track the outcomes that matter rather than merely those easiest to count. The relationship between effective altruism and verifiable accountability is also addressed, with respect to AI developers, in the plan entitled Verifiable Virtue: https://stratml.us/docs/VV4AI.xml In a LinkedIn post, Vineeta Gupta asks why the financial and operational burden of discovery falls almost entirely on grantseekers rather than funders. She cites MacKenzie Scott's 2024 open call, in which 6,353 organizations applied and 361 were funded, consuming an estimated 60,000 or more hours of unfunded applicants' time. Open calls reach organizations unknown to funders but at great cost to applicants, while invitation-based funding spares applicants but leaves those outside familiar networks invisible. https://lnkd.in/p/egUW7qyk By enabling organizations to publish their information once in a standard, machine-readable format that any funder can query, this plan offers a way out of that dilemma: it shifts the cost of discovery toward funders while reducing that cost through structured data and AI-assisted search. It does not change the odds of success, but it reduces what an unsuccessful application costs and keeps unsuccessful applicants discoverable by other funders. Submitter's Note: This plan was drafted and rendered in StratML format in dialog with Claude.ai. It has been reviewed in the form at https://stratml.us/forms/Claude/Part2.html