Update README.md

README.md

@@ -1,9 +1,126 @@
 ---
 license: cc-by-nc-4.0
 ---
+# Dataset Card for TexPrax
 
-# TexPrax 
-This dataset contains dialogues collected from German factory workers. They are expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme.
+## Table of Contents
+- [Table of Contents](#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)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage: [https://texprax.de/](https://texprax.de/)**
+- **Repository: [https://github.com/UKPLab/TexPrax](https://github.com/UKPLab/TexPrax)**
+- **Paper: [https://arxiv.org/abs/2208.07846](https://arxiv.org/abs/2208.07846)**
+- **Leaderboard: n/a**
+- **Point of Contact: [Ji-Ung Lee](http://www.ukp.tu-darmstadt.de/)**
+
+### Dataset Summary
+
+This dataset contains dialogues collected from German factory workers at the _Center for industrial productivity_ ([CiP](https://www.prozesslernfabrik.de/)). The  dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.
+
+### Supported Tasks and Leaderboards
+
+This dataset supports the following tasks:
+
+* Sentence classification
+* Named entity recognition
+* Dialog generation (so far not evaluated)
+
+### Languages
+
+German
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data was generated by workers at the [CiP](https://www.prozesslernfabrik.de/)
+The data was collected in three rounds. First, 
+
+#### Who are the source language producers?
+
+German factory workers working at the [CiP](https://www.prozesslernfabrik.de/)
+
+### Annotations
+
+#### Annotation process
+
+##### Token level
+Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.
+
+##### Sentence level
+Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the [TexPrax demo paper](https://arxiv.org/abs/2208.07846).
+
+
+#### Who are the annotators?
+
+##### Token level
+Researchers working at the CiP.
+
+##### Sentence level
+The factory workers themselves.
+
+### Personal and Sensitive Information
+
+This dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
 
 You can download the data via:
 
@@ -15,7 +132,32 @@ dataset = load_dataset("UKPLab/TexPrax", "ner") # use the ner tag for named enti
 ``` 
 Please find more information about the code and how the data was collected on [GitHub](https://github.com/UKPLab/TexPrax).
 
-## Other information
+### Dataset Curators
+
+Curation is managed by our [data manager](https://www.informatik.tu-darmstadt.de/ukp/research_ukp/ukp_research_data_and_software/ukp_data_and_software.en.jsp) at UKP.
+
+### Licensing Information
+
+[CC-by-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
+
+### Citation Information
+
+Please cite this data using: 
+
+```
+@article{stangier2022texprax,
+  title={TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation},
+  author={Stangier, Lorenz and Lee, Ji-Ung and Wang, Yuxi and M{\"u}ller, Marvin and Frick, Nicholas and Metternich, Joachim and Gurevych, Iryna},
+  journal={arXiv preprint arXiv:2208.07846},
+  year={2022}
+}
+```
+
+### Contributions
+
+Thanks to [@Wuhn](https://github.com/Wuhn) for adding this dataset.
+
+## Tags
 
 annotations_creators:
 - expert-generated