
# Dataset Card for Dataset Name

Here is a filled out dataset card for the SciGraphQA dataset:

\## Dataset Description  

- **Homepage:** https://github.com/findalexli/SciGraphQA

- **Repository:** https://huggingface.co/datasets/alexshengzhili/SciGraphQA-295K-train 

- **Paper:** https://arxiv.org/abs/2308.03349

- **Leaderboard:** N/A

- **Point of Contact Alex Li alex.shengzhi@gmail.com:**

\### Dataset Summary

SciGraphQA is a large-scale synthetic multi-turn question-answering dataset for scientific graphs. It contains 295K samples of open-vocabulary multi-turn question-answering dialogues about graphs from 290K academic papers. The dataset was created by using the Palm-2 API to generate dialogues conditioned on rich textual context including paper titles, abstracts, captions, paragraphs mentioning the figure.

\### Supported Tasks and Leaderboards

- Scientific graph question answering
- Visual question answering
- Multi-modal reasoning

Please see our paper for leaderboard

\### Languages

English

\## Dataset Structure  

\### Data Instances

Each data instance contains:

- Paper title 
- Paper abstract
- Figure caption
- Paragraph mentioning the figure
- Multi-turn question-answer conversation (2.23 turns on average)

\### Data Fields

- `title`: Paper title
- `abstract`: Paper abstract 
- `caption`: Figure caption
- `paragraph`: Paragraph mentioning the figure
- `questions`: List of question strings 
- `answers`: List of answer strings

\### Data Splits

- Training data: 295K samples 
- Validation data: N/A
- Test data: 3K samples

\## Dataset Creation  

\### Curation Rationale

This dataset was created to provide a large-scale benchmark for training and evaluating multi-modal models on scientific graph question answering.

\### Source Data

Figures, captions, paragraphs and metadata were sourced from 290K academic papers on ArXiv focused on Computer Science and Machine Learning.

\#### Initial Data Collection and Normalization

Figures were extracted using PDFFigures 2.0. Captions and paragraphs were extracted using regular expressions and heuristic rules.

\#### Who are the source language producers?

The source data consists of academic papers written in English by researchers in computer science and machine learning. 

\### Annotations

\#### Annotation process 

The multi-turn question-answer dialogues were generated using the Palm-2 conversational API conditioned on the sourced data context. The quality was validated by rating a subset with GPT-4.

\#### Who are the annotators?

The dialogues were automatically generated by Palm-2, an AI system developed by Anthropic.

\### Personal and Sensitive Information

The source academic papers may contain limited personal information about the authors such as name, affiliation, email. No other personal or sensitive information is included in this dataset.

\## Considerations for Using the Data  

\### Social Impact of Dataset

This dataset presents minimal social risks since it contains only synthetic dialogues about scientific graphs and related metadata sourced from public academic papers.

\### Discussion of Biases

The dialogues reflect the characteristics and limitations of the Palm-2 system used to generate them. There may also be biases inherent in the academic source material.

\### Other Known Limitations

The dataset focuses specifically on computer science and machine learning papers. Performance on scientific graphs from other domains may differ.

\## Additional Information

\### Dataset Curators

Shengzhi Li, Nima Tajbakhsh

\### Licensing Information

This dataset is licensed under the MIT license.

\### Citation Information

```
@misc{li2023scigraphqa,
  title={SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs}, 
  author={Shengzhi Li and Nima Tajbakhsh},
  year={2023},
  eprint={2308.03349},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```

\### Contributions

We welcome contributions to improve the dataset! Please open an issue or pull request on the GitHub repository.
license:mit arxiv:2308.03349 region:us
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TAGS
#license-mit #arxiv-2308.03349 #region-us 


# Dataset Card for Dataset Name

Here is a filled out dataset card for the SciGraphQA dataset:

\## Dataset Description  

- Homepage: URL

- Repository: URL 

- Paper: URL

- Leaderboard: N/A

- Point of Contact Alex Li alex.shengzhi@URL:

\### Dataset Summary

SciGraphQA is a large-scale synthetic multi-turn question-answering dataset for scientific graphs. It contains 295K samples of open-vocabulary multi-turn question-answering dialogues about graphs from 290K academic papers. The dataset was created by using the Palm-2 API to generate dialogues conditioned on rich textual context including paper titles, abstracts, captions, paragraphs mentioning the figure.

\### Supported Tasks and Leaderboards

- Scientific graph question answering
- Visual question answering
- Multi-modal reasoning

Please see our paper for leaderboard

\### Languages

English

\## Dataset Structure  

\### Data Instances

Each data instance contains:

- Paper title 
- Paper abstract
- Figure caption
- Paragraph mentioning the figure
- Multi-turn question-answer conversation (2.23 turns on average)

\### Data Fields

- 'title': Paper title
- 'abstract': Paper abstract 
- 'caption': Figure caption
- 'paragraph': Paragraph mentioning the figure
- 'questions': List of question strings 
- 'answers': List of answer strings

\### Data Splits

- Training data: 295K samples 
- Validation data: N/A
- Test data: 3K samples

\## Dataset Creation  

\### Curation Rationale

This dataset was created to provide a large-scale benchmark for training and evaluating multi-modal models on scientific graph question answering.

\### Source Data

Figures, captions, paragraphs and metadata were sourced from 290K academic papers on ArXiv focused on Computer Science and Machine Learning.

\#### Initial Data Collection and Normalization

Figures were extracted using PDFFigures 2.0. Captions and paragraphs were extracted using regular expressions and heuristic rules.

\#### Who are the source language producers?

The source data consists of academic papers written in English by researchers in computer science and machine learning. 

\### Annotations

\#### Annotation process 

The multi-turn question-answer dialogues were generated using the Palm-2 conversational API conditioned on the sourced data context. The quality was validated by rating a subset with GPT-4.

\#### Who are the annotators?

The dialogues were automatically generated by Palm-2, an AI system developed by Anthropic.

\### Personal and Sensitive Information

The source academic papers may contain limited personal information about the authors such as name, affiliation, email. No other personal or sensitive information is included in this dataset.

\## Considerations for Using the Data  

\### Social Impact of Dataset

This dataset presents minimal social risks since it contains only synthetic dialogues about scientific graphs and related metadata sourced from public academic papers.

\### Discussion of Biases

The dialogues reflect the characteristics and limitations of the Palm-2 system used to generate them. There may also be biases inherent in the academic source material.

\### Other Known Limitations

The dataset focuses specifically on computer science and machine learning papers. Performance on scientific graphs from other domains may differ.

\## Additional Information

\### Dataset Curators

Shengzhi Li, Nima Tajbakhsh

\### Licensing Information

This dataset is licensed under the MIT license.

\



\### Contributions

We welcome contributions to improve the dataset! Please open an issue or pull request on the GitHub repository.
# Dataset Card for Dataset Name

Here is a filled out dataset card for the SciGraphQA dataset:

\## Dataset Description  

- Homepage: URL

- Repository: URL 

- Paper: URL

- Leaderboard: N/A

- Point of Contact Alex Li alex.shengzhi@URL:

\### Dataset Summary

SciGraphQA is a large-scale synthetic multi-turn question-answering dataset for scientific graphs. It contains 295K samples of open-vocabulary multi-turn question-answering dialogues about graphs from 290K academic papers. The dataset was created by using the Palm-2 API to generate dialogues conditioned on rich textual context including paper titles, abstracts, captions, paragraphs mentioning the figure.

\### Supported Tasks and Leaderboards

- Scientific graph question answering
- Visual question answering
- Multi-modal reasoning

Please see our paper for leaderboard

\### Languages

English

\## Dataset Structure  

\### Data Instances

Each data instance contains:

- Paper title 
- Paper abstract
- Figure caption
- Paragraph mentioning the figure
- Multi-turn question-answer conversation (2.23 turns on average)

\### Data Fields

- 'title': Paper title
- 'abstract': Paper abstract 
- 'caption': Figure caption
- 'paragraph': Paragraph mentioning the figure
- 'questions': List of question strings 
- 'answers': List of answer strings

\### Data Splits

- Training data: 295K samples 
- Validation data: N/A
- Test data: 3K samples

\## Dataset Creation  

\### Curation Rationale

This dataset was created to provide a large-scale benchmark for training and evaluating multi-modal models on scientific graph question answering.

\### Source Data

Figures, captions, paragraphs and metadata were sourced from 290K academic papers on ArXiv focused on Computer Science and Machine Learning.

\#### Initial Data Collection and Normalization

Figures were extracted using PDFFigures 2.0. Captions and paragraphs were extracted using regular expressions and heuristic rules.

\#### Who are the source language producers?

The source data consists of academic papers written in English by researchers in computer science and machine learning. 

\### Annotations

\#### Annotation process 

The multi-turn question-answer dialogues were generated using the Palm-2 conversational API conditioned on the sourced data context. The quality was validated by rating a subset with GPT-4.

\#### Who are the annotators?

The dialogues were automatically generated by Palm-2, an AI system developed by Anthropic.

\### Personal and Sensitive Information

The source academic papers may contain limited personal information about the authors such as name, affiliation, email. No other personal or sensitive information is included in this dataset.

\## Considerations for Using the Data  

\### Social Impact of Dataset

This dataset presents minimal social risks since it contains only synthetic dialogues about scientific graphs and related metadata sourced from public academic papers.

\### Discussion of Biases

The dialogues reflect the characteristics and limitations of the Palm-2 system used to generate them. There may also be biases inherent in the academic source material.

\### Other Known Limitations

The dataset focuses specifically on computer science and machine learning papers. Performance on scientific graphs from other domains may differ.

\## Additional Information

\### Dataset Curators

Shengzhi Li, Nima Tajbakhsh

\### Licensing Information

This dataset is licensed under the MIT license.

\



\### Contributions

We welcome contributions to improve the dataset! Please open an issue or pull request on the GitHub repository.
TAGS
#license-mit #arxiv-2308.03349 #region-us 
 # Dataset Card for Dataset Name

Here is a filled out dataset card for the SciGraphQA dataset:

\## Dataset Description  

- Homepage: URL

- Repository: URL 

- Paper: URL

- Leaderboard: N/A

- Point of Contact Alex Li alex.shengzhi@URL:

\### Dataset Summary

SciGraphQA is a large-scale synthetic multi-turn question-answering dataset for scientific graphs. It contains 295K samples of open-vocabulary multi-turn question-answering dialogues about graphs from 290K academic papers. The dataset was created by using the Palm-2 API to generate dialogues conditioned on rich textual context including paper titles, abstracts, captions, paragraphs mentioning the figure.

\### Supported Tasks and Leaderboards

- Scientific graph question answering
- Visual question answering
- Multi-modal reasoning

Please see our paper for leaderboard

\### Languages

English

\## Dataset Structure  

\### Data Instances

Each data instance contains:

- Paper title 
- Paper abstract
- Figure caption
- Paragraph mentioning the figure
- Multi-turn question-answer conversation (2.23 turns on average)

\### Data Fields

- 'title': Paper title
- 'abstract': Paper abstract 
- 'caption': Figure caption
- 'paragraph': Paragraph mentioning the figure
- 'questions': List of question strings 
- 'answers': List of answer strings

\### Data Splits

- Training data: 295K samples 
- Validation data: N/A
- Test data: 3K samples

\## Dataset Creation  

\### Curation Rationale

This dataset was created to provide a large-scale benchmark for training and evaluating multi-modal models on scientific graph question answering.

\### Source Data

Figures, captions, paragraphs and metadata were sourced from 290K academic papers on ArXiv focused on Computer Science and Machine Learning.

\#### Initial Data Collection and Normalization

Figures were extracted using PDFFigures 2.0. Captions and paragraphs were extracted using regular expressions and heuristic rules.

\#### Who are the source language producers?

The source data consists of academic papers written in English by researchers in computer science and machine learning. 

\### Annotations

\#### Annotation process 

The multi-turn question-answer dialogues were generated using the Palm-2 conversational API conditioned on the sourced data context. The quality was validated by rating a subset with GPT-4.

\#### Who are the annotators?

The dialogues were automatically generated by Palm-2, an AI system developed by Anthropic.

\### Personal and Sensitive Information

The source academic papers may contain limited personal information about the authors such as name, affiliation, email. No other personal or sensitive information is included in this dataset.

\## Considerations for Using the Data  

\### Social Impact of Dataset

This dataset presents minimal social risks since it contains only synthetic dialogues about scientific graphs and related metadata sourced from public academic papers.

\### Discussion of Biases

The dialogues reflect the characteristics and limitations of the Palm-2 system used to generate them. There may also be biases inherent in the academic source material.

\### Other Known Limitations

The dataset focuses specifically on computer science and machine learning papers. Performance on scientific graphs from other domains may differ.

\## Additional Information

\### Dataset Curators

Shengzhi Li, Nima Tajbakhsh

\### Licensing Information

This dataset is licensed under the MIT license.

\



\### Contributions

We welcome contributions to improve the dataset! Please open an issue or pull request on the GitHub repository.
passage: TAGS
#license-mit #arxiv-2308.03349 #region-us 
