Datasets:

ethicalabs
/

Research-Intent-Collab

---
license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
  - multilingual
tags:
  - research-intent
  - golden-dataset
  - openaire
pretty_name: Research Intent — Collaborative Golden Dataset
---

# Research Intent — Collaborative Golden Dataset

[![GitHub](https://img.shields.io/badge/GitHub-ethicalabs.ai-black.svg)](https://github.com/ethicalabs-ai/Echo-DSRN/)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![Live Demo](https://img.shields.io/badge/Live_Demo-openaire--2026.ethicalabs.ai-brightgreen.svg)](https://openaire-2026.ethicalabs.ai/)
[![OpenAIRE Hackathon](https://img.shields.io/badge/OpenAIRE-AI_Hackathon_2026-orange.svg)](https://innovation.openaire.eu/component/content/article/openaire-ai-hackathon.html)

[![OpenAIRE Graph Evaluator](https://img.youtube.com/vi/9LHmmzLK3zs/0.jpg)](https://www.youtube.com/watch?v=9LHmmzLK3zs)

[**▶️ Watch the Video**](https://www.youtube.com/watch?v=9LHmmzLK3zs)

A curated, consensus-labelled dataset for research paper intent classification,
built from the LLM-as-Judge annotations of the Echo-DSRN collaborative platform
(OpenAIRE AI Hackathon 2026).

## OpenAIRE AI Hackathon 2026 🇪🇺

This dataset is part of **ethicalabs.ai**'s entry in the [OpenAIRE AI Hackathon 2026](https://innovation.openaire.eu/component/content/article/openaire-ai-hackathon.html), co-organised by OpenAIRE and Alien Intelligence — a 12-week open science build challenge.

**[🔴 Live Demo → openaire-2026.ethicalabs.ai](https://openaire-2026.ethicalabs.ai/)**

The live application classifies research papers in real time using this model, with publication metadata streamed from the [OpenAIRE Graph API](https://graph.openaire.eu) under **CC BY 4.0**.

---

## Labels

- **Methodology** — introduces a new algorithm, model architecture, or method
- **Dataset** — primarily contributes a new dataset, benchmark, or annotation scheme
- **Review** — survey, systematic review, or position paper
- **Applied** — applies existing methods to a domain problem
- **Theoretical** — mathematical or formal analysis
- **Unclassifiable** — garbled, non-English, or non-academic content

## Golden labelling

The golden label per paper is the **majority-vote consensus** across all
LLM judge models (plus human curator annotations where available). Papers
flagged as low-quality by **more than one** judge are labelled
**Unclassifiable** instead of being discarded — turning a quality filter into
a detection signal for non-academic content.

## Splits

The dataset ships with stratified `train` / `val` / `test` splits
(80/10/10). Stratification preserves the label distribution across splits,
with explicit safeguarding so the rare **Dataset** class (≈2% of records)
appears in every split.

## Data structure

Consensus label per paper — no ChatML messages and no judge rationale, so
consumers build their own training format from `title` + `description` +
`label`.

```json
{
  "doi": "10.48550/arXiv.2106.01345",
  "title": "...",
  "description": "abstract text",
  "label": "Dataset",
  "flag_count": 0,
  "model_votes": {
    "Gemma-4-E4B-it-GGUF": "Dataset",
    "Qwen3.5-4B-GGUF": "Methodology"
  }
}
```

- `label` — the golden consensus label
- `flag_count` — number of judges that flagged quality issues
- `model_votes` — per-model predictions used for the consensus

> For ChatML conversation format with inline reasoning, see the companion
> dataset `ethicalabs/Research-Intent-Judge`.

## Files

- `golden_train.jsonl` — 7,999 records
- `golden_val.jsonl` — 1,001 records
- `golden_test.jsonl` — 1,001 records
- `golden_stats.json` — label distribution, split sizes, flag counts

## Provenance

Built from `ethicalabs/Research-Intent-Judge` via
`scripts/export_golden_dataset.py` in the
[OpenAIRE-AI-Research-Evaluator](https://github.com/ethicalabs-ai/OpenAIRE-AI-Research-Evaluator)
repository. Paper metadata originates from arXiv and the OpenAIRE Graph
(CC BY 4.0).

## License

CC-BY 4.0.

## Citation

If you reuse this work, please cite:

```bibtex
@software{echo_dsrn_research_intent_clf,
  author = {Massimo Roberto Scamarcia},
  title = {OpenAIRE Graph Evaluator: Research paper classifier and LLM-as-Judge pipeline},
  year = {2026},
  url = {https://huggingface.co/collections/ethicalabs/openaire-ai-hackathon-2026}
}
```