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  - [Additional Information](#additional-information)
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  - [Dataset Curators](#dataset-curators)
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  - [Licensing Information](#licensing-information)
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- - [Citation Information](#citation-information)
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  - [Contributions](#contributions)
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  ## Dataset Description
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  ## Dataset Creation
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- ### Curation Rationale
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  ### Source Data
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  #### Initial Data Collection and Normalization
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  #### Who are the source language producers?
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  ### Annotations
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  ### Social Impact of Dataset
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  ### Discussion of Biases
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  ### Other Known Limitations
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  ## Additional Information
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  ### Dataset Curators
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- [More Information Needed]
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  ### Licensing Information
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  ### Contributions
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  - [Additional Information](#additional-information)
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  - [Dataset Curators](#dataset-curators)
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  - [Licensing Information](#licensing-information)
 
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  - [Contributions](#contributions)
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  ## Dataset Description
 
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  ## Dataset Creation
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+ For the full details of how the dataset was created, please refer to our [EMNLP 2022 paper]().
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+ ### Curation Rationale
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+ Science communication is a complex process of translation from highly technical scientific language to common language that lay people can understand. At the same time, the general public relies on good science communication in order to inform critical decisions about their health and behavior. SPICED was curated in order to provide a training dataset and benchmark for machine learning models to measure changes in scientific information at different stages of the science communication pipeline.
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  ### Source Data
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  #### Initial Data Collection and Normalization
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+ Scientific text: S2ORC
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+ News articles and Tweets are collected through Altmetric.
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  #### Who are the source language producers?
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+ Scientists, journalists, and Twitter users.
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  ### Annotations
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  ### Social Impact of Dataset
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+ Models trained on SPICED can be used to perform large scale analyses of science communication. They can be used to match the same finding discussed in different media, and reveal trends in differences in reporting at different stages of the science communication pipeline. It is hoped that this can help to build tools which will improve science communication.
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  ### Discussion of Biases
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+ The dataset is restricted to computer science, medicine, biology, and psychology, which may introduce some bias in the topics which models will perform well on.
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  ### Other Known Limitations
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+ While some context is available, we do not release the full text of news articles and scientific papers, which may contain further context to help with learning the task. We do however provide the paper DOIs and links to the original news articles in case full text is desired.
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  ## Additional Information
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  ### Dataset Curators
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+ Dustin Wright, Jiaxin Pei, David Jurgens, and Isabelle Augenstein
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  ### Licensing Information
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+ MIT
 
 
 
 
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  ### Contributions
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