Up to $1 million in total project funding available
LOI Applications close October 30, 2026

The initiative is intended to advance outstanding biological or translational science in primary tauopathies while also strengthening the long-term Tau Consortium Learning Research Network (LRN). Competitive projects should address an important scientific question and show why AI-enabled collaboration, interoperable infrastructure, or autonomous or semi-autonomous workflows provide a meaningful advantage over traditional approaches.

The LRN is RCF’s evolving approach to creating a more connected, reusable, and AI-enabled scientific research environment. Its goal is to make it easier for researchers and, increasingly, AI-enabled tools to build upon data, methods, analyses, software, workflows, and knowledge generated across different projects and laboratories. Rather than a single database, software platform, or AI model, the LRN is envisioned as an ecosystem of interoperable scientific resources and capabilities that can work together to accelerate discovery.

No. Applicants should propose outstanding science addressing important questions in primary tauopathies. RCF is particularly interested in projects that also generate datasets, tools, workflows, standards, or other resources that can be reused beyond the individual project and potentially become part of the broader LRN.

Proposals should address an important biological or translational question applicable to the primary tauopathies. Examples of relevant outcomes include advances in disease mechanisms, diagnosis, patient stratification, biomarkers, or treatment, including new or refined therapeutic hypotheses and targets. The strongest fit will combine scientific significance with a credible AI-enabled and collaborative strategy.

Teams and funding

Proposals should address an important biological or translational question applicable to the primary tauopathies. Examples of relevant outcomes include advances in disease mechanisms, diagnosis, patient stratification, biomarkers, or treatment, including new or refined therapeutic hypotheses and targets. The strongest fit will combine scientific significance with a credible AI-enabled and collaborative strategy.

The lead applicant must direct an independent scientific or technical program, laboratory, engineering team, data resource, or equivalent research unit and must be eligible to receive and administer funding through the applicant’s institution or organization. Eligible leads may include faculty investigators at the assistant professor level or above, as well as qualified scientific, engineering, nonprofit, or industry leaders with demonstrated independence and relevant expertise.

No. This is an open program. Applications are encouraged by current Tau Consortium investigators and by investigators, engineers, data scientists, and technical leaders who are not currently affiliated with the Tau Consortium.

Applicants may be affiliated with eligible academic, medical, research, nonprofit, or commercial organizations anywhere in the world. The organization must be able to receive and administer the award. If awarded, for profit organizations will require additional due diligence and post-award reporting.

No, collaboration is encouraged, but not mandatory, and collaborating laboratories or technical teams may be from the same or different institutions. The LOI asks applicants to indicate whether the application involves more than one laboratory or technical team. Applicants should explain why the proposed team structure is appropriate for the scientific question and how collaboration will improve the work.

A proposal may include up to four participating laboratories or technical teams. The LOI identifies one coordinating Principal Investigator and provides space for up to three additional participating PIs or team leads, for a maximum of four participating teams.

The maximum is $250,000 per participating laboratory or technical team, with a maximum total project budget of $1,000,000. The requested amount should be appropriate to the number of participating teams and the proposed scope.

No. Indirect costs will not be awarded.

The LOI requires a budget upload using a provided template. Program duration is not to exceed 12 months.

RCF will issue a single award to the Coordinating PI’s institution. The lead institution will be responsible for establishing and administering subawards to participating institutions, as applicable. The $250,000 maximum per participating laboratory or technical team applies regardless of whether funds are provided through the prime award or a subaward. Indirect costs are not allowable on either the prime award or subawards.

AI should be integral to the scientific or collaborative approach, not an add-on. The research plan should explain how AI will be used, why it provides a meaningful advantage over traditional approaches, and why the approach is technically feasible. AI integration and technical feasibility are explicit LOI review criteria.

No. The program encourages, but does not require, development of reusable AI agents, digital laboratory notebook templates, analysis pipelines, workflow containers, APIs, and related tools. A project may instead use AI to accelerate discovery, analyze data, support collaboration, or generate and test hypotheses, provided the role of AI is meaningful and well justified.

New datasets should be FAIR (findable, accessible, interoperable, and reusable) and should be well curated and structured for broad scientific reuse, including use by AI tools. AI-ready data should be sufficiently structured, documented, curated, and accessible to support computational analysis and appropriate use by AI-enabled tools. As appropriate, data should be placed in suitable repositories and accompanied by sufficient metadata, ontologies, data dictionaries, and documentation. Harmonized datasets, benchmark datasets, and reusable data standards are encouraged when they would benefit the field.

A reusable resource is an output that can provide scientific value beyond the immediate project that created it. Examples may include well-curated datasets, analysis pipelines, software, APIs, AI agents, benchmark datasets, knowledge graphs, laboratory workflows, data standards, models, or documented experimental and analytical methods.

No. Not all project outputs must be open source, although open-source development is encouraged when appropriate. Applicants should propose dissemination approaches that support interoperability, community adoption, scientific reuse, and long-term benefit. All data, systems, and programs generated under the award should be made available for appropriate scientific reuse within six months of project completion, subject to applicable privacy, consent, licensing, institutional, and regulatory requirements.

The plan should identify expected datasets, software, AI agents, APIs, workflows, or other outputs and explain how they could contribute to the LRN. It should address interoperability, documentation, data access and sharing, storage size estimates, integration readiness, and community adoption. Awardees will also be expected to coordinate with the LRN technical team, participate in relevant Tau Consortium and LRN working groups, and support integration of funded resources.

The LOI form requests the project title; Coordinating PI name, email, and institution; information on participating PIs or team leads; a non-confidential lay abstract of no more than 350 words; a research plan of no more than two pages including figures; an LRN Contribution and Integration Plan of no more than one-half page; a budget upload; current biosketches or brief CVs for the Coordinating PI and each participating PI or team lead; and an optional references upload of no more than one page.

No. It is a separate upload.

The criteria that will be used include: potential to advance understanding, diagnosis, stratification, or treatment of primary tauopathies; AI integration and technical feasibility; innovation; data management and sharing; degree of collaboration; potential to strengthen neurodegenerative research; potential to create enduring scientific capabilities for the LRN and beyond; and the quality and feasibility of the LRN Contribution and Integration Plan.

Applicants selected through the competitive LOI process will be invited to submit a full proposal. LOIs will be accepted from September 15, 2026 to October 30^th, 2026 at 5 pm ET. Notifications for application invitations will be sent out by December 11, 2026 and all applications will be due by 5 pm ET on February 12, 2027. Note: secure AI-assisted tools may be used to organize, analyze, and support scientific review of applications. Any AI-generated analysis will be advisory, and final funding decisions will be made by human reviewers.