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HFEPX · Eval paper review

Evaluating the Performance of LLMs in Drafting NIH Data Management Plans

Nahid Zeinali, James ONeil, Xuebin Dong, Rebecca Hofstein Grady +4 more

Published

Nov 10, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Nov 10, 2026

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Expert Verification General

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

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Abstract

Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries. To ensure consistent sharing, the National Institutes of Health (NIH) and other funding agencies require applicants to submit data management plans (DMPs) with their proposals. They aim to encourage researchers to consider their data management and sharing strategies early on. However, researchers often face challenges when developing DMPs due to a lack of knowledge about data-sharing practices. Here, we evaluated the performance of Large Language Models (LLMs) in drafting DMPs compliant with the 2023 NIH guidelines. The goal was to investigate whether LLM-supported DMP creation could reduce the burden on researchers and librarians. We compared Llama and GPT performance against NIH-provided DMP samples. We also asked domain experts to evaluate LLM-generated DMPs. Overall, we found that LLMs can accelerate the creation of initial drafts but still require expert review before submission to ensure clarity and completeness. Moreover, open-source LLMs may not be as ready off-the-shelf as commercial ones for drafting funder-compliant DMPs.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Expert Verification

Directly usable for protocol triage.

"Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"We also asked domain experts to evaluate LLM-generated DMPs."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Expert Verification
Rater population
Domain Experts
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries.
  • To ensure consistent sharing, the National Institutes of Health (NIH) and other funding agencies require applicants to submit data management plans (DMPs) with their proposals.
  • They aim to encourage researchers to consider their data management and sharing strategies early on.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Expert verification evaluation

Contribution summary

  • Sharing research data is essential to promote transparency, enable reproducibility, and fuel scientific discoveries.
  • To ensure consistent sharing, the National Institutes of Health (NIH) and other funding agencies require applicants to submit data management plans (DMPs) with their proposals.

Why it matters for eval

  • Here, we evaluated the performance of Large Language Models (LLMs) in drafting DMPs compliant with the 2023 NIH guidelines.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Expert Verification

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.