Datasets:

nicolauduran45
/

horizon_clusters_annotated

dataset_info:
  features:
    - name: title
      dtype: string
    - name: abstract
      dtype: string
    - name: doctype
      dtype: string
    - name: cluster
      sequence:
        sequence: string
    - name: intervention_area
      sequence:
        sequence: string
    - name: id
      dtype: string
  splits:
    - name: train
      num_bytes: 12305011
      num_examples: 7647
    - name: test
      num_bytes: 426545
      num_examples: 229
  download_size: 7015556
  dataset_size: 12731556
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

ESPON Annotated Scientific Publications Dataset

This dataset consists of annotated scientific publications with the following fields:

id: Unique identifier for the record
title: Title of the publication
abstract: Abstract of the publication
cluster_responses: List of cluster annotations (may be empty in test)
area_responses: List of area of intervention annotations (may be empty in test)

Splits

Train: Contains full annotations for all fields (cluster, area, and possibly others)
Test: Contains labels only for cluster and area of intervention

Category Names

Cluster Labels (6 unique):

Civil Security for Society
Climate, Energy and Mobility
Culture, Creativity and Inclusive Society
Digital, Industry and Space
Food, Bioeconomy, Natural Resources, Agriculture and Environment
Health

Area of Intervention Labels (38 unique):

Advanced Materials
Advanced computing and big data
Agriculture, forestry and rural areas
Artificial intelligence and robotics
Bio-based innovation systems in the bioeconomy
Biodiversity and natural resources
Buildings and industrial facilities in energy transition
Circular Industries
Circular systems
Clean, safe and accessible transport and mobility
Climate science and solutions
Communities and cities
Culture, cultural heritage and creativity
Cybersecurity
Democracy and Governance
Disaster-resilient societies
Emerging enabling technologies
Energy storage
Energy supply
Energy systems and grids
Environmental and social health determinants
Environmental observation
Food systems
Healthcare systems
Health throughout the life course
Infectious diseases, including poverty-related and neglected diseases
Industrial competitiveness in transport
Key digital technologies
Manufacturing technologies
Net-zero and less polluting Industries
Next generation internet
Non-communicable and rare diseases
Protection and security
Seas, oceans and inland waters
Smart mobility
Social and economic transformations
Space, including Earth observation
Tools, technologies and digital solutions for health and care, including personalised medicine

Annotation Methodology

The training data was annotated using an ensemble of large language models.
Specifically:

Initial predictions were generated independently by:
- Meta-Llama-3.1-8B-Instruct
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
For each item, the final label was determined by majority vote among these models.
In cases where there was no majority (i.e., a tie), a tie-breaker model (microsoft/WizardLM-2-8x22B) was used to select the final label.

This approach aims to increase annotation reliability by leveraging multiple state-of-the-art models and resolving uncertainty with a strong tie-breaker model.

Number of Samples

Split	Number of samples
Train	7,647
Test	229

Usage

from datasets import load_dataset

dataset = load_dataset('nicolauduran45/horizon_clusters_annotated')