P0L3/CliReNER_v_1_1_28_SILVER

CliReNER Silver: Named Entity Recognition for Climate Research

Dataset Summary

CliReNER silver is a fine-grained Named Entity Recognition (NER) dataset designed specifically for the climate change research domain. Recognizing the lack of broad-coverage, expert-annotated resources in Climate NLP, this dataset provides sentence-level annotations across 28 distinct entity types (27 domain-specific types + Other).

The dataset is derived from 50 full-text peer-reviewed scientific publications covering diverse topics in climate science. Sentences were pre-annotated using GLiNER and rigorously curated by the authors using a flat NER schema (nested entities are excluded).

This silver dataset consists of 1,027 sentences split into train (803), validation (106), and test (118). It is intended primarily for training and fine-tuning models. For high-fidelity, multi-expert evaluation, please refer to our companion dataset, CliReNER_gold (annotated by 12 domain experts) or pre-expert-annotation version CliReNER_{gold author annotations}.

Dataset Structure

Data Instances

A typical instance in the dataset represents a single sentence from a scientific paper, tokenized and tagged using the standard BIO (Begin, Inside, Outside) format.

{
  "id": "doc_12-sent_45",
  "text": "The increase in surface water temperature affects the local ecosystem.",
  "tokens": ["The", "increase", "in", "surface", "water", "temperature", "affects", "the", "local", "ecosystem", "."],
  "ner_tags": [0, 0, 0, 1, 2, 2, 0, 0, 0, 11, 0]
}

Data Fields

• id: A unique string identifier for the sentence.
• text: The original raw string of the sentence.
• tokens: A list of strings representing the tokenized sentence.
• ner_tags: A list of integers representing the BIO tags for each token. There are 57 tags in total (0 for O, and 1-56 representing the B- and I- tags for the 28 entity types).

Entity Typology (28 Classes)

The dataset covers a highly granular taxonomy designed for climate change literature:

• Geographical Feature, Body of Water, Meteorological Phenomenon, Natural Phenomenon, Natural Disaster, Physical Phenomenon.
• Organism, Ecosystem, Body Part, Disease.
• Chemical, Energy Source, Quantity, Mathematical Expression.
• Organization, Person, Location, Method, Measuring Device, Satellite, Physical Artefact, Intellectual Artefact, Policy, System, Asset, Field of Study, Time Period.
• Other.

(For complete definitions of each entity type, please refer to Appendix B of the associated paper).

Dataset Creation

• Source Data: Sentences were sampled from a corpus of 50 publications drawn from 25 peer-reviewed journals (curated by Poleksić and Martinčić-Ipšić, 2024).
• Annotation Process: We formulated a flat NER schema to reduce cognitive load and model complexity, meaning overlapping or nested entities were resolved to the most relevant span (e.g., in "gridded rain gauge data", the full span is marked as Intellectual Artefact, subsuming the inner Measuring Device). Pre-annotation was performed via GLiNER, followed by manual author correction in Label Studio.

Limitations and Biases

• Class Imbalance: Due to the natural distribution of scientific text, some classes (e.g., Chemical, Organism, Time Period) are highly frequent, while others (e.g., Asset, Body Part, Natural Disaster) appear sparsely.
• Flat Schema: The dataset constrains entity overlap. Discontinuous and nested entities are not captured, which inherently results in some loss of granular information.
• Silver Standard: This specific split (silver) was author-annotated. While highly accurate, it does not possess the multi-annotator consensus methodology applied to the gold test set.

Usage

You can easily load the dataset using the Hugging Face datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("P0L3/CliReNER_v_1_1_28_SILVER")

# View the first training example
print(dataset["train"][0])

# Access the label names (string representations of the integers)
label_names = dataset["train"].features["ner_tags"].feature.names
print(label_names)

Citation Information

@misc{poleksic2026named,
  author       = {Poleksić, Andrija and Martinčić-Ipšić, Sanda},
  title        = {Named Entity Recognition for Climate Change Research},
  year         = {2026},
  howpublished = {Research Square},
  note         = {Preprint}
}