Quantifying the Reuse Barrier in Published Omics Data: Dispersal, Cost, and Feasibility Models for Agent-Supported Retrieval, Reanalysis, and Synthesis

In the paper's words

Most published omics data remain functionally inaccessible for computational reuse: the information needed to reproduce a reported result is dispersed across article main text, supplementary files, and code repositories, and intermediate data such as protein abundance tables are shared irregularly if at all [1]. Existing descriptions of this problem are qualitative; no published model states what the dispersal costs a reuser or what an agent changes. This paper supplies that model.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. A derived information-theoretic bound on the reanalysis specification, giving Hartley entropy 83.05 bits at 25 fields and a Shannon-entropy comparison (Section 4.2).

   from 1. Introduction

2. The agent achieves its $\approx 2.93\times$ success improvement at essentially the same expected visit count ($6.20$ vs $6.25$), because retries are triggered only on the 20% of first attempts that fail.

   from 4.5 Agent reproduction with $r = 2$ retries

3. End-to-end, with $k = 5$: $$P_{\text{rep}}(5, 0.8, 0) = 0.8^5.$$ Computing step by step: $0.8^2 = 0.64$; $0.8^3 = 0.64 \times 0.8 = 0.512$; $0.8^4 = 0.512 \times 0.8 = 0.4096$; $0.8^5 = 0.4096 \times 0.8 = 0.32768$. $$P_{\text{rep}}(5, 0.8, 0) = 0.32768.$$ Under these assumptions a manual reproduction attempt succeeds about 32.8% of the time; roughly two of every three attempts fail.

   from 4.4 Manual reproduction (no retries, $r = 0$)

4. Holding $k=5$, $r=0$, manual success is $p^5$: $p=0.6$: $0.6^5 = 0.07776$ $p=0.8$: $0.32768$ $p=0.95$: $0.95^5 = 0.7737809375$ The barrier is jointly driven: even a highly reliable reuser ($p = 0.95$) succeeds only 77.4% of the time at $k=5$, while a moderately reliable agent with retries at $p=0.8$ reaches 96.0%.

   from 4.7 Sensitivity to per-location reliability p

5. The analysis draws on the multi-agent systems literature for the coordination structure [2], [8], on the distributed-systems literature for the reliability question that arises when a pipeline is assembled from independently fallible retrieval steps [4], [5], and on the information-theoretic analysis of biological specification [11] for the bound on what metadata can capture.

   from 1. Introduction

6. A dispersal model of a published study with closed-form expressions for reproduction success probability and expected retrieval cost, evaluated numerically under stated assumptions: manual success 0.32768 versus agent success 0.9606 at $k = 5, p = 0.8, r = 2$ (Sections 3 and 4.4–4.5).

   from 1. Introduction

How it is done

1. We formalize a published study as a set of reproduction-critical artifacts dispersed over document locations, derive closed-form expressions for reproduction success probability and expected retrieval cost, derive a token-and-call cost model for the retrieval stage of an agent pipeline, and derive an information-theoretic bound on the metadata an agent must extract.

   from Abstract

2. The analysis draws on the multi-agent systems literature for the coordination structure [2], [8], on the distributed-systems literature for the reliability question that arises when a pipeline is assembled from independently fallible retrieval steps [4], [5], and on the information-theoretic analysis of biological specification [11] for the bound on what metadata can capture.

   from 1. Introduction

3. The direct object of this paper is the omics data discovery agents framework, which presents an agentic approach to the retrieval, reanalysis, and synthesis of published omics data and documents the core observation motivating everything here: most published data remain functionally inaccessible for computational reuse because reproduction-critical information is dispersed across main text, supplementary files, and code repositories, with intermediate data such as protein abundance files shared irregularly when at all [1].

   from 2. Background and Related Work

4. The output record $O_3$ — the extracted protocol — is the pivotal artifact: it is the metadata specification on which $S_4$ depends, and Section 4.2 bounds its information content.

   from 3.1 Pipeline formalization

What would falsify it

1. A statement of which observations would falsify the central claims, a limitations list naming every assumption, and a practical pathway from the analytical result to a first measurement (Sections 6 and 7).

   from 1. Introduction

2. What would falsify the main result.

   from 6. Discussion

3. The main result — that dispersal breadth dominates the barrier and retries substitute for per-location reliability — is falsified if a measured corpus shows either of the following.

   from 6. Discussion

Where the claim stops

1. The claim's strength is exactly the strength of its assumptions, which are stated assumptions of this analysis rather than measurements.

   from 6. Discussion

2. The result rests on the following assumptions, each with its sensitivity: Independence of artifact retrievals. $P_{\text{rep}} = P_{\text{loc}}^k$ assumes failures are uncorrelated.

   from 6. Discussion

3. The model treats retrieval as success/failure and does not score partial or incorrect retrieval, semantic misinterpretation, or cross-artifact inconsistency; Section 4.8 prices redundancy but does not model error rates of sources.

   from 6. Discussion

Where to read

1. Abstract204 words · 1 min
2. 1. Introduction565 words · 2 min
3. 2. Background and Related Work1,042 words · 5 min
4. 3. Methods735 words · 3 min
5. 4. Analysis1,458 words · 6 min
6. 5. Results386 words · 2 min
7. 6. Discussion668 words · 3 min
8. 7. Practical Implications and Engineering Pathways843 words · 4 min
9. 8. Conclusion268 words · 1 min
10. Provenance and AI Involvement197 words · 1 min