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readme

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+
+---
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- 10K<n<100K
+tasks:
+- named-entity-recognition
+- token-classification
+pretty_name: COPIOUS
+tags:
+- named-entity-recognition
+- biodiversity
+- species-occurrence
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+  - split: validation
+    path: dev.jsonl
+  - split: test
+    path: test.jsonl
+---
+
+# Dataset Card for COPIOUS
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Format Conversion](#format-conversion)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [COPIOUS: A gold standard corpus of named entities... (Biodiversity Data Journal)](https://doi.org/10.3897/BDJ.7.e29626)
+- **Paper:** [COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature](https://doi.org/10.3897/BDJ.7.e29626)
+- **Point of Contact:** [Sophia Ananiadou](mailto:[email protected])
+
+### Dataset Summary
+
+Species occurrence records are critical in the biodiversity domain. However, a key challenge has been the lack of a single, consolidated corpus that includes all the entity types needed to extract this information from scientific literature. To address this gap, the **COPIOUS corpus** (Corpus of named entities towards extracting Species Occurrence) was created.
+
+This dataset serves as a gold standard, annotated with a wide range of entities relevant to biodiversity science. It was developed as part of the broader COPIOUS project, which aims to build a knowledge repository of Philippine biodiversity by applying text mining to scientific texts. The corpus is manually annotated with five key entity categories: **Taxon**, **Geographical Location**, **Habitat**, **Temporal Expression**, and **Person** names.
+
+### Supported Tasks and Leaderboards
+
+- **Tasks:** Named Entity Recognition, Token Classification
+- **Leaderboards:** N/A
+
+### Languages
+
+The text in the dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+An example from the `train` split:
+
+```json
+{
+  "id": "100785_English_120409_32367238_1954",
+  "tokens": ["FAMILY", "SERRANIDAE", "—", "SCHULTZ", "..."],
+  "ner_tags": [0, 1, 0, 0, ...]
+}
+```
+
+### Data Fields
+
+- `id`: A unique identifier for the example (string).
+- `tokens`: A list of tokens (list of strings).
+- `ner_tags`: A list of integer IDs corresponding to the NER tags. The mapping is:
+
+```json
+{
+  "0": "O",
+  "1": "B-Taxon",
+  "2": "I-Taxon",
+  "3": "B-Geographical_Location",
+  "4": "I-Geographical_Location",
+  "5": "B-Habitat",
+  "6": "I-Habitat",
+  "7": "B-Temporal_Expression",
+  "8": "I-Temporal_Expression",
+  "9": "B-Person",
+  "10": "I-Person"
+}
+```
+
+### Data Splits
+
+The corpus consists of 668 documents split as follows:
+
+| Split     | Documents |
+|-----------|-----------|
+| train     | 534       |
+| validation| 67        |
+| test      | 67        |
+
+## Dataset Creation
+
+### Curation Rationale
+
+The corpus was created to be a sufficiently reliable and sizeable resource for training and evaluating Named Entity Recognition (NER) tools in the biodiversity domain, specifically for the task of species occurrence extraction.
+
+### Source Data
+
+The data consists of 668 documents randomly selected from over 169,000 English-language pages downloaded from the Biodiversity Heritage Library (BHL).
+
+### Annotations
+
+The dataset was manually annotated by two experts in biology using the Argo workbench. 200 documents were double-annotated to ensure quality, achieving an overall Inter-Annotator Agreement (IAA) of 81.86% F-score.
+
+### Format Conversion
+
+For this Hugging Face Hub version, the dataset was prepared as follows:
+- **Local Sourcing:** The data was sourced from a local copy because the original download link on the NaCTeM website was inactive.
+- **Conversion to JSONL:** The original data was annotated in the Brat Standoff Format (.txt + .ann files). It has been converted to the standard JSON Lines (.jsonl) format to align with Hugging Face Hub standards.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This corpus can be used to develop text mining tools that automatically locate species occurrences in literature, which is useful for monitoring species distribution and preserving biodiversity.
+
+### Other Known Limitations
+
+- **Annotation Complexity:** The IAA for the Habitat entity type was the lowest (47.07% F-score), indicating this category is complex and challenging for both human annotators and automated systems.
+- **Source Text Quality:** The source documents from BHL contain a large number of OCR errors, which may adversely affect the performance of NER models trained on this data.
+
+## Additional Information
+
+### Dataset Curators
+
+Nhung T.H. Nguyen, Roselyn S. Gabud, and Sophia Ananiadou.
+
+### Licensing Information
+
+The dataset is distributed under the Creative Commons Attribution License (CC BY 4.0).
+
+### Citation Information
+
+```bibtex
+@article{nguyen2019copious,
+  author = {Nguyen, Nhung T.H. and Gabud, Roselyn S. and Ananiadou, Sophia},
+  title = {{COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature}},
+  journal = {Biodiversity Data Journal},
+  volume = {7},
+  pages = {e29626},
+  year = {2019},
+  doi = {10.3897/BDJ.7.e29626}
+}
+```