ARXIV:2602.14285 · DATASET FOR SCHOLARLY RESEARCH · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

FMMD: A multimodal open peer review dataset based on F1000Research

arXiv

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research. In parallel, research on automated and AI-assisted peer review has proliferated.

METHOD

Full abstract

Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) systems are increasingly incorporated into real-world manuscript evaluation. In parallel, research on automated and AI-assisted peer review has proliferated. Despite this momentum, empirical progress remains constrained by several critical limitations in existing datasets. While reviewers routinely evaluate figures, tables, and complex layouts to assess scientific claims, most existing datasets remain overwhelmingly text-centric. This bias is reinforced by a narrow focus on data from computer science venues. Furthermore, these datasets lack precise alignment between reviewer comments and specific manuscript versions, obscuring the iterative relationship between peer review and manuscript evolution. In response, we introduce FMMD, a multimodal and multidisciplinary open peer review dataset curated from F1000Research. The dataset bridges the current gap by integrating manuscript-level visual and structural data with version-specific reviewer reports and editorial decisions. By providing explicit alignment between reviewer comments and the exact article iteration under review, FMMD enables fine-grained analysis of the peer review lifecycle across diverse scientific domains. FMMD supports tasks such as multimodal issue detection and multimodal review comment generation. It provides a comprehensive empirical resource for the development of peer review research.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. By providing explicit alignment between reviewer comments and the exact article iteration under review, FMMD enables fine-grained analysis of the peer review lifecycle across…

WHY NOW

Dataset for Scholarly Research moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainFMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.14285 · DATASET FOR SCHOLARLY RESEARCH · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

FMMD: A multimodal open peer review dataset based on F1000Research

arXiv

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research. In parallel, research on automated and AI-assisted peer review has proliferated.

METHOD

Full abstract

RESULT

WHY NOW

Dataset for Scholarly Research moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainFMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.14285

FMMD: A multimodal open peer review dataset based on F1000Research

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 33% coverage.

What was readable

linkedon filenot materializedderived fallback26 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research. In parallel, research on automated and AI-assisted peer review has proliferated.

METHOD

RESULT

WHY NOW

Dataset for Scholarly Research moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research. In parallel, research on automated and AI-assisted peer review has proliferated.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) systems are increasingly incorporated into real-world manuscript evaluation. In parallel, research on automated and AI-assisted peer review has proliferated.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. By providing explicit alignment between reviewer comments and the exact article iteration under review, FMMD enables fine-grained analysis of the peer review lifecycle across diverse scientific domains.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Dataset for Scholarly Research moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Dataset for Scholarly Research

Competitors

not indexed

Competitive landscape

FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.

Segment

Dataset for Scholarly Research

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.14285 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(26)

What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review

2025Sangkeun Jung, Goun Pyeon et al.

How to Bridge the Gap Between Modalities: Survey on Multimodal Large Language Model

2025Shezheng Song, Xiaopeng Li et al.

Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025

2025Nitya Thakkar, Mert Yuksekgonul et al.

A Multidisciplinary Multimodal Aligned Dataset for Academic Data Processing

2025Haitao Song, Hong Xu et al.

Large language models for automated scholarly paper review: A survey

2025Zhenzhen Zhuang, Jiandong Chen et al.

Agent Reviewers: Domain-specific Multimodal Agents with Shared Memory for Paper Review

2025Kai Lu, Shixiong Xu et al.

Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey.

2024Jeremy Y. Ng, S. Maduranayagam et al.

AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing

2024Huawei Ji, Cheng Deng et al.

SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding

2024Sihang Li, Jian Huang et al.

Inconsistencies in TeX-Produced Documents

2024Jovyn Tan, Manuel Rigger

Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis

2024Jianxiang Yu, Zichen Ding et al.

MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding

2024Zekun Li, Xianjun Yang et al.

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

2024Jiangshu Du, Yibo Wang et al.

ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation

2024Chufan Shi, Cheng Yang et al.

RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance

2024Paulo Henrique Couto, Quang Phuoc Ho et al.

cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers

2024Anirudh S. Sundar, Jin Xu et al.

LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers

2024Abdur Rahman Bin Md Faizullah, Ashok Urlana et al.

Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models

2024Lei Li, Yuqi Wang et al.

An Analysis of Tasks and Datasets in Peer Reviewing

2024Moritz Staudinger, Wojciech Kusa et al.

A survey on multimodal large language models

2023Shukang Yin, Chaoyou Fu et al.

Showing 20 of 26 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkSPM-Bench: Benchmarking Large Language Models for Scanning Probe Microscopy

5.0

Extension

Builds On ThisAre we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction

3.0

Builds On ThisOn the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

3.0

Builds On ThisReimagining Peer Review Process Through Multi-Agent Mechanism Design

3.0

Commercially relevant

Higher ViabilityPRAIB: Peer Review AI Benchmark of Behaviour of LLM-Assisted Reviewing

6.0

Higher ViabilityAI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot

8.0

Higher ViabilityCan AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future

7.0

Higher ViabilitySciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning

6.0

Higher ViabilityAPRES: An Agentic Paper Revision and Evaluation System

7.0

Higher ViabilityPaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers

7.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.14285
Route: /paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research
Active tab: read
Artifact: fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research/paper-pack
REST build passport API/api/v1/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "2511a234-8426-4686-84ae-f4e9f01470fa",
  "arxiv_id": "2602.14285",
  "canonical_route": "/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research",
  "endpoints": {
    "paper_pack": "/api/v1/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research/paper-pack",
    "build_passport": "/api/v1/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

FMMD: A multimodal open peer review dataset based on F1000Research

Canonical ID fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research | Route /paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.14285"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "FMMD: A multimodal open peer review dataset based on F1000Research",
  "normalized_query": "2602.14285",
  "route": "/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research",
  "paper_ref": "fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: FMMD: A multimodal open peer review dataset based on F1000Research

/buildability/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research

Watchwatch

Subject: FMMD: A multimodal open peer review dataset based on F1000Research

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research

Paper ref

fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research

arXiv id

2602.14285

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

33%

Hash state

Lineage hash

80a4031463784b66bb593cae89ed31803c00a6555b6efc4f7048cd989401a255

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research#webpage",
      "url": "https://sciencetostartup.com/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research",
      "name": "FMMD: A multimodal open peer review dataset based on F1000Research",
      "description": "FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research#scholarlyArticle",
      "headline": "FMMD: A multimodal open peer review dataset based on F1000Research",
      "description": "FMMD offers a rich, multimodal peer review dataset for AI-enhanced manuscript evaluation research.",
      "url": "https://sciencetostartup.com/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research",
      "sameAs": "https://arxiv.org/abs/2602.14285",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2602.14285"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-02-15T19:36:05.000Z",
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "60a0a21e6eb36e657c2875173132f29b28293aff"
          },
          "url": "https://www.semanticscholar.org/paper/60a0a21e6eb36e657c2875173132f29b28293aff"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3efcf2b9ee86b4398027a3e5af87f2fa842f2172"
          },
          "url": "https://www.semanticscholar.org/paper/3efcf2b9ee86b4398027a3e5af87f2fa842f2172"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "ec64fd9b864a5b525e70f4bf03088522facc0c47"
          },
          "url": "https://www.semanticscholar.org/paper/ec64fd9b864a5b525e70f4bf03088522facc0c47"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1b0ff7f11e6a47429ca6623024a823d234a37d5b"
          },
          "url": "https://www.semanticscholar.org/paper/1b0ff7f11e6a47429ca6623024a823d234a37d5b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d892b1335ad37c4773f98e8282b73d1460a9f2bf"
          },
          "url": "https://www.semanticscholar.org/paper/d892b1335ad37c4773f98e8282b73d1460a9f2bf"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "5eb2bf97b93adad9b0a291d5bb24334b3e72636a"
          },
          "url": "https://www.semanticscholar.org/paper/5eb2bf97b93adad9b0a291d5bb24334b3e72636a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "0505541865634d61d8e7b20c15269703ec4faca4"
          },
          "url": "https://www.semanticscholar.org/paper/0505541865634d61d8e7b20c15269703ec4faca4"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "ba5563821f237eefe6c05d6f6ca97b61fab019ae"
          },
          "url": "https://www.semanticscholar.org/paper/ba5563821f237eefe6c05d6f6ca97b61fab019ae"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "cd93ac14bb79ac43569182929853f0c3c503668d"
          },
          "url": "https://www.semanticscholar.org/paper/cd93ac14bb79ac43569182929853f0c3c503668d"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "9acda8593fb9c70f724f6a7161be348d5a415e36"
          },
          "url": "https://www.semanticscholar.org/paper/9acda8593fb9c70f724f6a7161be348d5a415e36"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e073a2fda0c4d6198ba231c8e9df10f4f2ba1c51"
          },
          "url": "https://www.semanticscholar.org/paper/e073a2fda0c4d6198ba231c8e9df10f4f2ba1c51"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f2c7f69177d7972510e7a026c7fa20c26905a3df"
          },
          "url": "https://www.semanticscholar.org/paper/f2c7f69177d7972510e7a026c7fa20c26905a3df"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "250043fdae97d2ac87245a44923682e7a3decd50"
          },
          "url": "https://www.semanticscholar.org/paper/250043fdae97d2ac87245a44923682e7a3decd50"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "7dfd0f91066a75c88ee86bbea9ccf16ab1ff8334"
          },
          "url": "https://www.semanticscholar.org/paper/7dfd0f91066a75c88ee86bbea9ccf16ab1ff8334"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3392f42d99c42098453e6bfcf2e3f7983d34c8c1"
          },
          "url": "https://www.semanticscholar.org/paper/3392f42d99c42098453e6bfcf2e3f7983d34c8c1"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "fee78e9a61bbb0fd7468a15a142e32e1804b6cf1"
          },
          "url": "https://www.semanticscholar.org/paper/fee78e9a61bbb0fd7468a15a142e32e1804b6cf1"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "56994972adca9319577617345128e46803a4043f"
          },
          "url": "https://www.semanticscholar.org/paper/56994972adca9319577617345128e46803a4043f"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "ebedc4d7a2356090904baba4104ef0832bc236df"
          },
          "url": "https://www.semanticscholar.org/paper/ebedc4d7a2356090904baba4104ef0832bc236df"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "985ebe4b615cd0a23518318081effdff7d3a0bda"
          },
          "url": "https://www.semanticscholar.org/paper/985ebe4b615cd0a23518318081effdff7d3a0bda"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "130e3e810a3300a95277516b5bd0c7e203f491dc"
          },
          "url": "https://www.semanticscholar.org/paper/130e3e810a3300a95277516b5bd0c7e203f491dc"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 5
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Dataset for Scholarly Research"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Dataset for Scholarly Research",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "FMMD: A multimodal open peer review dataset based on F1000Re",
          "item": "https://sciencetostartup.com/paper/fmmd-a-multimodal-open-peer-review-dataset-based-on-f1000research"
        }
      ]
    }
  ]
}

FMMD: A multimodal open peer review dataset based on F1000Research

FMMD: A multimodal open peer review dataset based on F1000Research

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(26)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(26)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline