Datasets:

DFKI-SLT
/

copious

@@ -1,171 +0,0 @@
----
-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:sophia.ananiadou@manchester.ac.uk)
-### 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}
-}
-```