| --- |
| 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 |
|
|
| [](https://github.com/ethicalabs-ai/Echo-DSRN/) |
| [](https://opensource.org/licenses/Apache-2.0) |
| [](https://www.python.org/downloads/) |
| [](https://openaire-2026.ethicalabs.ai/) |
| [](https://innovation.openaire.eu/component/content/article/openaire-ai-hackathon.html) |
|
|
| [](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} |
| } |
| ``` |
|
|
