---
license: odc-by
task_categories:
- token-classification
language:
- en
tags:
- climate
pretty_name: Climate Change NER
size_categories:
- n<1K
---
# Dataset Card for Climate Change NER

The Climate Change NER is an English-language dataset containing 534 abstracts of climate-related papers. They have been sourced from the Semantic Scholar Academic Graph "abstracts" dataset. The abstracts have been manually annotated by classifying climate-related tokens in a set of 14 categories.

## Dataset Details

### Dataset Description

We introduce a comprehensive dataset for developing and evaluating NLP models tailored towards understanding and addressing climate-related topics across various domains.
The **Climate Change NER** is a manually curated dataset of 534 abstracts from climate-related articles. They have been extracted from the [Semantic Scholar Academic Graph](https://www.semanticscholar.org/product/api) (_abstracts_ dataset) 
by using a seed set of climate-related keywords. The abstracts were annotated by tagging relevant tokens with the IOB format (inside, outside, beginning) using 14 differents categories.

- **Curated by:** Birgit Pfitzmann
- **Shared by [optional]:** Cesar Berrospi Ramis
- **Language(s) (NLP):** English
- **License:** [Open Data Commons Attribution License (ODC-By)](https://opendatacommons.org/licenses/by/1-0/)

### Dataset Sources [optional]

- **Paper [optional]:** [More Information Needed]

## Uses

This dataset is primarly intended for evaluating Named Entity Recognition (NER) tasks.

## Dataset Structure

### Data Instances

Each instance consists of several lines representing a document abstract and its annotation. The beginning of an instance is marked by a line starting with `-DOCSTART-`
and containing a unique hash of the document across the entire dataset. Each token from the abstract is included in a separate line with one of the following labels:
- `O` if the token is not part of any named entity instance
- `B` if the token is the beginning of a named entity instance
- `I` if the token is inside of a named entity instance

If the token is marked with `O` or `I`, the entity type is added. A maximum of one entity type can be assigned to a token. The possible types are:
`assets`, `climate-named`, `datasets`, `ghg`, `hazards`, `impacts`, `mitigations`, `models`, `nature`, `observations`, `organisms`, `organizations`, `problems`, and `properties`.

```
-DOCSTART- -X- O O cc560f5c553e1b60e054abe1578227ff

Non O
- O
identifiable O
RHH O
emergency O
department O
data O
and O
climate O
data O
from O
the O
Australian B-climate-organizations
Bureau I-climate-organizations
of I-climate-organizations
Meteorology I-climate-organizations
were O
obtained O
for O
the O
period O
2003 O
- O
2010 O
. O
```

### Data Splits

The Climate Change NER dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 1.0.0 of the dataset.

| Dataset Split | Number of Instances in Split |
| ------------- | ---------------------------- |
| Train         | 382                          |
| Validation    |  77                          |
| Test          |  75                          |


## Dataset Creation

### Curation Rationale


[More Information Needed]

### Source Data

The source data are abstracts from the Semantic Scholar Academic Graph Dataset (the _abstracts_ dataset).
This collection is licensed under ODC-BY. (https://opendatacommons.org/licenses/by/1.0/)\n\nBy downloading this data you acknowledge that you have read and agreed to all the terms in this license.\n\nATTRIBUTION\nWhen using this data in a product or service, or including data in a redistribution, please cite the following paper:

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

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[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

The dataset was annotated by Birgit Pfitzmann

[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@misc{https://doi.org/10.48550/arxiv.2301.10140,
  title = {The Semantic Scholar Open Data Platform},
  author = {Kinney, Rodney and Anastasiades, Chloe and Authur, Russell and Beltagy, Iz and Bragg, Jonathan and Buraczynski, Alexandra and Cachola, Isabel and Candra, Stefan and Chandrasekhar, Yoganand and Cohan, Arman and Crawford, Miles and Downey, Doug and Dunkelberger, Jason and Etzioni, Oren and Evans, Rob and Feldman, Sergey and Gorney, Joseph and Graham, David and Hu, Fangzhou and Huff, Regan and King, Daniel and Kohlmeier, Sebastian and Kuehl, Bailey and Langan, Michael and Lin, Daniel and Liu, Haokun and Lo, Kyle and Lochner, Jaron and MacMillan, Kelsey and Murray, Tyler and Newell, Chris and Rao, Smita and Rohatgi, Shaurya and Sayre, Paul and Shen, Zejiang and Singh, Amanpreet and Soldaini, Luca and Subramanian, Shivashankar and Tanaka, Amber and Wade, Alex D. and Wagner, Linda and Wang, Lucy Lu and Wilhelm, Chris and Wu, Caroline and Yang, Jiangjiang and Zamarron, Angele and Van Zuylen, Madeleine and Weld, Daniel S.},
  publisher = {arXiv},
  year = {2023},
  doi = {10.48550/ARXIV.2301.10140},
  url = {https://arxiv.org/abs/2301.10140}
}
```

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

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## More Information [optional]

[More Information Needed]


## Dataset Card Contact

Cesar Berrospi Ramis [@ceberam](https://github.com/ceberam))