Datasets:

nicolauduran45
/

multilingual-research-classification

dataset_info:
  features:
    - name: text
      dtype: string
    - name: label_categories
      dtype: int64
    - name: lang
      dtype: string
  splits:
    - name: train
      num_bytes: 12397135
      num_examples: 17501
    - name: validation
      num_bytes: 2689677
      num_examples: 3751
    - name: test
      num_bytes: 2670478
      num_examples: 3751
  download_size: 10961920
  dataset_size: 17757290
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
task_categories:
  - text-classification
language:
  - en
  - ca
  - es
tags:
  - science

Multilingual Scientific Text Classification Dataset (MAG FoS L1)

Overview

This dataset contains multilingual scientific text samples (Catalan, Spanish, and English) extracted from scientific publications.
Each sample is labeled using Microsoft Academic Graph (MAG) Field of Study — Level 1 categories.

For each publication, the text field is a random selection of:

the title
the abstract
the title followed by the abstract (title + ". " + abstract)

This introduces natural variation and improves model robustness for text classification tasks.

Dataset Structure

Features

Feature	Type	Description
`text`	string	Randomly selected title, abstract, or title + abstract
`label`	string	MAG Field of Study (FoS) Level 1 category
`language`	string	ISO code of publication language (`ca`, `es`, `en`)

Splits

Split	Samples
Train	20,059
Validation	2,507
Test	2,508
Total	25,074

Splits follow an 80/10/10 ratio.

Languages

The dataset includes scientific publications written in:

Catalan (ca)
Spanish (es)
English (en)

Task

Multiclass Scientific Text Classification

Your model should predict the Field of Study (FoS) Level 1 category from a scientific text snippet.
This dataset is suitable for:

multilingual text classification
scientific-domain NLP
domain adaptation
benchmarking multilingual LLMs (mBERT, XLM-R, LLaMA, etc.)
zero-shot or few-shot evaluation

Source

The labels correspond to Level 1 Fields of Study from the Microsoft Academic Graph (MAG) ontology.

Typical categories include (examples):

Chemistry
Physics
Biology
Computer Science
Mathematics
Medicine
Social Sciences
Engineering
Earth Sciences
Environmental Science

The exact label set matches the categories present in the processed data.

Creation Process

Load publication metadata (title, abstract, language, FoS).
Clean and normalize text fields.
Randomly choose one of:
- title
- abstract
- title + abstract
Assign the MAG FoS L1 label.
Perform an 80/10/10 train-validation-test split using HuggingFace datasets.

Usage

Load the dataset

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/YOUR_DATASET_NAME")
print(dataset["train"][0])
Example record
json
Copy code
{
  "text": "Reactividad de CHI3 con radicales O... Las vías de abstracción...",
  "label": "Physical chemistry",
  "language": "es"
}