Prediction Ledger: Ex-Ante Prediction Tracking for Grant Evidence
Software for labs to record their predictions before a study to prove their results are honest.
A lab-facing SaaS where research groups register their programme's predictions before running studies, log outcomes as they land, and auto-generate a progress report of corroborated novel predictions — the verifiable evidence grant panels ask for at renewal.
Concept
The methodology of scientific research programmes [0] makes a programme's progress measurable in one currency: novel predictions stated in advance and later corroborated. Prediction Ledger is a simple first-party tool that captures that currency as it is produced. Labs register predictions (our model implies X will hold under condition Y) with timestamps before data collection; the tool links each to the study that tests it, records corroborated, refuted, or patched outcomes, and computes a running progress score for the group's programme. At grant renewal, the group exports a one-page report — predictions made, corroborated, and still open — replacing narrative self-justification with a verifiable record. Ledger entries feed directly into the methodology and discussion sections of the group's articles, matching the structured article-development pathways that [1] identifies as the route to rigorous scientific writing.
Why now
[0] shows that separating productive from unproductive science cannot rest on how many people believe a theory or how strongly — it requires a recorded track of risky predictions and their fates, which no current lab tool systematically keeps. Pre-registration exists but stops at single studies; nothing follows the programme-level ledger across years, yet the research programme is exactly the unit [0] says progress must be judged on. [1] documents that explicit, structured methodology is what turns raw work into credible, publishable articles — the ledger supplies the missing structured layer that makes a group's predictive track record visible to funders at renewal time.
Written by GLM · 3 Oct 2026
AI assessment
A focused tool for labs to track long-term predictive success, though it faces significant headwinds from systemic academic incentives that reward 'positive' narratives over rigorous tracking.
- Evidence strength 3/5
- The idea is strongly grounded in the philosophy of science [0, 2] and the need for structured methodology [1], but the supporting 'Full Text' for [3] is irrelevant to the core product concept.
- Market pull 2/5
- While funders (NIH, Max Planck) want better evidence, the primary users (researchers) are incentivized by the 'publish-or-perish' culture to avoid recording refuted predictions, as highlighted in [6, 7, 8].
- Novelty & moat 2/5
- The idea is a direct 'first-party' implementation of the 'Programme Pulse' concept already in the catalog, shifting the data entry from external mining to internal logging.
- Feasibility 5/5
- The product is a straightforward CRUD application with timestamping and reporting functionality, requiring very little technical risk to build an MVP.
- Wedge clarity 4/5
- The 'one-page report for grant renewal' is a sharp, high-value entry point that solves a specific pain point for PIs during the renewal cycle.
- Simplicity / focus 5/5
- The product is highly focused on a single function: a ledger of predictions and outcomes.
Scored by Gemma 4 · 13 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.
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Business analysis
The PESTEL analysis reveals a strong alignment with current institutional pushes for open science and reproducibility, though it faces significant cultural resistance from researchers fearing the exposure of failed predictions. The idea's success depends on transitioning from a voluntary tool to a mandated requirement by major funding bodies like the NIH.
Political2
Economic2
Social2
Technological2
Environmental2
Legal2
The success of the tool depends heavily on the social and political shift toward open science and the regulatory requirements of grant-funding bodies like the NIH. · Generated 2026-10-13 by Gemma 4AI-generatedFull PESTEL Analysis →
Who benefits
- Center for Open Scienceorganization
Operates OSF pre-registration at the single-study level; a programme-level prediction ledger with progress scoring extends its mission and gives registered predictions a long-run payoff.
- U.S. National Institutes of Healthorganization
Requires progress reports in grant renewals; grantees using a prediction ledger submit verifiable corroboration records instead of narrative summaries, making review more efficient.
- Brian Nosekindividual
As executive director of the Center for Open Science and a leading advocate of registered reports, he benefits from a tool that turns pre-registration from a one-off compliance step into a cumulative progress metric researchers want to maintain.
- Max Planck Societyorganization
Its hundreds of institutes compete for renewal funding; a standardized programme progress score gives directors a fair, evidence-based way to demonstrate scientific advance.
Research it builds on
- AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)He, Zhen · 2026 · 49999 citationsAll ideas from this paper →
- A importância da metodologia científica no desenvolvimento de artigos científicosRabbith Ive Carolina Shitsuka Risemberg, Michael Wakin, Ricardo Shitsuka · 2026 · 520 citationsAll ideas from this paper →
- La Metodologia de los programas de investigación científicaImre Lakatos · 2026 · 285 citationsAll ideas from this paper →
- Analysis of 567,758 randomized controlled trials published over 30 years reveals trends in phrases used to discuss results that do not reach statistical significanceWillem M. Otte, Christiaan H. Vinkers, Philippe C. Habets et al. · 2022 · 43 citationsAll ideas from this paper →
- Improving the Efficiency of Grant and Journal Peer Review: Registered Reports FundingMarcus Robert Munafo · 2017 · 35 citationsAll ideas from this paper →
- Is biomedical research self-correcting? Modelling insights on the persistence of spurious scienceDavid Robert Grimes · 2024 · 18 citationsAll ideas from this paper →
- Reproducibility: Has Cancer Biology Failed beyond Repair?John P. A. Ioannidis · 2022 · 3 citationsAll ideas from this paper →
- Is biomedical research self-correcting? Modeling insights on the persistence of spurious scienceDavid Robert Grimes · 2023 · 2 citationsAll ideas from this paper →