Update README.md

README.md

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----
-license: gpl-3.0
----
+---
+language:
+- de
+- en
+license: gpl-3.0
+size_categories:
+- 10k<n<100k
+tasks:
+- named-entity-recognition
+- token-classification
+---
+
+# Dataset Card for BotanicalNER
+
+### Dataset Summary
+BotanicalNER is a Named Entity Recognition dataset for scientific and vernacular plant names in German and English. The dataset was created for a master thesis project at the University of Zurich focusing on identifying and disambiguating plant names across multiple text genres to extract and preserve (ethno-)botanical knowledge.
+
+The dataset contains text with plant name entities marked in IOB format, enabling the training of NER models to recognize both scientific names (e.g., "Urtica dioica") and vernacular plant names (e.g., "nettle") in natural text.
+
+For a full description of the dataset, please refer to the master thesis: Meraner, I. 2019. "Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names." Master Thesis, Institute of Computational Linguistics, University of Zurich.
+
+### Supported Tasks and Leaderboards
+- **Named Entity Recognition**: The dataset is designed for training and evaluating NER models for plant name recognition.
+- **Botanical Knowledge Extraction**: Can be used as a resource for systems aiming to extract botanical knowledge from text.
+
+### Languages
+The dataset supports two languages:
+- German (`de`)
+- English (`en`)
+
+## Dataset Structure
+
+### Data Instances
+An example from the German dataset:
+```json
+{
+  "id": "0",
+  "tokens": ["Die", "Brennnessel", "(", "Urtica", "dioica", ")", "ist", "eine", "wichtige", "Heilpflanze", "."],
+  "pos_tags": ["ART", "NN", "$(", "NE", "NE", "$)", "VAFIN", "ART", "ADJA", "NN", "$."],
+  "ner_tags": ["O", "B-Vernacular", "O", "B-Scientific", "I-Scientific", "O", "O", "O", "O", "O", "O"]
+}
+```
+
+### Data Fields
+- `id`: Unique identifier for each sentence, a `string` feature.
+- `tokens`: The tokens/words in the sentence, a sequence of `string`s.
+- `pos_tags`: Part-of-speech tags for each token, a sequence of `string`s.
+- `ner_tags`: Named entity recognition tags in IOB format, a sequence of `ClassLabel` features with the following classes:
+  - `O`: Outside any entity
+  - `B-Scientific`: Beginning of a scientific plant name
+  - `I-Scientific`: Inside a scientific plant name
+  - `B-Vernacular`: Beginning of a vernacular plant name
+  - `I-Vernacular`: Inside a vernacular plant name
+
+### Data Splits
+The dataset includes multiple splits for both German and English. The training data is a silver-standard corpus, while the test and fungi splits are gold-standard.
+
+| Split | German Files | English Files | Description                                        |
+|-------|--------------|---------------|----------------------------------------------------|
+| train | 4            | 4             | Silver standard training data from multiple sources|
+| test  | 1            | 1             | Gold standard test fold                            |
+| fungi | 1            | 1             | Gold standard fungi testset for in-domain evaluation|
+
+Training data sources include:
+- Plant blog corpus
+- Wikipedia abstract corpus
+- TextBerg subcorpus
+- Botanical literature corpus
+
+## Dataset Creation
+The dataset was created to address the need for robust plant name recognition across different text genres. The training data was collected from multiple sources, tokenized, and POS-tagged using TreeTagger. The training data was automatically annotated using dictionary-based methods (silver standard), while the test data was manually annotated and verified (gold standard).
+
+### Personal and Sensitive Information
+The dataset does not contain personal or sensitive information as it focuses on botanical and scientific content.
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+This dataset can have a positive social impact by:
+- Enabling better extraction and preservation of botanical knowledge.
+- Supporting biodiversity research and conservation efforts.
+- Assisting in ethnobotanical knowledge preservation.
+
+### Discussion of Biases
+Potential biases include geographic bias in plant names, source bias from different text genres, and domain bias from the specific sources used. Most training data also uses automatic annotation which may contain errors.
+
+## Additional Information
+
+### Dataset Curators
+Isabel Meraner, Institute of Computational Linguistics, University of Zurich (Switzerland), 2019.
+
+### Licensing Information
+The dataset is licensed under the GNU General Public License v3.0.
+
+### Citation Information
+```
+@mastersthesis{meraner2019grasping,
+    title={Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names},
+    author={Isabel Meraner},
+    year={2019},
+    school={Institute of Computational Linguistics, University of Zurich},
+    note={Available at: [https://github.com/IsabelMeraner/BotanicalNER](https://github.com/IsabelMeraner/BotanicalNER)}
+}
+```