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  - [Dataset Description](#dataset-description)
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  - [Dataset Summary](#dataset-summary)
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  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- - [Languages](#languages)
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  - [Dataset Structure](#dataset-structure)
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- - [Data Instances](#data-instances)
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  - [Data Fields](#data-fields)
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  - [Data Splits](#data-splits)
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  - [Dataset Creation](#dataset-creation)
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- - [Curation Rationale](#curation-rationale)
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- - [Source Data](#source-data)
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  - [Annotations](#annotations)
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- - [Personal and Sensitive Information](#personal-and-sensitive-information)
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- - [Considerations for Using the Data](#considerations-for-using-the-data)
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- - [Social Impact of Dataset](#social-impact-of-dataset)
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- - [Discussion of Biases](#discussion-of-biases)
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- - [Other Known Limitations](#other-known-limitations)
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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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  ## Dataset Description
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- - **Homepage:**
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- - **Repository:**
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- - **Paper:**
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  - **Leaderboard:**
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  - **Point of Contact:**
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  ### Dataset Summary
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- [More Information Needed]
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- ### Supported Tasks and Leaderboards
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- [More Information Needed]
 
 
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- ### Languages
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- [More Information Needed]
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  ## Dataset Structure
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- ### Data Instances
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- [More Information Needed]
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  ### Data Fields
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Data Splits
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- [More Information Needed]
 
 
 
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  ## Dataset Creation
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- ### Curation Rationale
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- [More Information Needed]
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- ### Source Data
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- #### Initial Data Collection and Normalization
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- [More Information Needed]
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- #### Who are the source language producers?
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- [More Information Needed]
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  ### Annotations
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  #### Annotation process
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- [More Information Needed]
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- #### Who are the annotators?
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- [More Information Needed]
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- ### Personal and Sensitive Information
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- [More Information Needed]
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- ## Considerations for Using the Data
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- ### Social Impact of Dataset
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- [More Information Needed]
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- ### Discussion of Biases
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- [More Information Needed]
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- ### Other Known Limitations
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- [More Information Needed]
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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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- License: CDLA-Permissive-1.0
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- [More Information Needed]
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  ### Citation Information
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Contributions
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- Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
 
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  - [Dataset Description](#dataset-description)
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  - [Dataset Summary](#dataset-summary)
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  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
 
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  - [Dataset Structure](#dataset-structure)
 
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  - [Data Fields](#data-fields)
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  - [Data Splits](#data-splits)
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  - [Dataset Creation](#dataset-creation)
 
 
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  - [Annotations](#annotations)
 
 
 
 
 
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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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  ## Dataset Description
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+ - **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
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+ - **Repository:** https://github.com/DS4SD/DocLayNet
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+ - **Paper:** https://doi.org/10.1145/3534678.3539043
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  - **Leaderboard:**
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  - **Point of Contact:**
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  ### Dataset Summary
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+ DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
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+ 1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
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+ 2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
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+ 3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
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+ 4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
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+ 5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
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+ ### Supported Tasks and Leaderboards
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+ We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
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  ## Dataset Structure
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  ### Data Fields
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+ DocLayNet provides four types of data assets:
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+
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+ 1. PNG images of all pages, resized to square `1025 x 1025px`
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+ 2. Bounding-box annotations in COCO format for each PNG image
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+ 3. Extra: Single-page PDF files matching each PNG image
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+ 4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
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+ The COCO image record are defined like this example
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+ ```js
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+ ...
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+ {
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+ "id": 1,
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+ "width": 1025,
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+ "height": 1025,
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+ "file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
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+
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+ // Custom fields:
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+ "doc_category": "financial_reports" // high-level document category
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+ "collection": "ann_reports_00_04_fancy", // sub-collection name
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+ "doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
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+ "page_no": 9, // page number in original document
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+ "precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
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+ },
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+ ...
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+ ```
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+ The `doc_category` field uses one of the following constants:
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+
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+ ```
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+ financial_reports,
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+ scientific_articles,
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+ laws_and_regulations,
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+ government_tenders,
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+ manuals,
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+ patents
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+ ```
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+
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  ### Data Splits
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+ The dataset provides three splits
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+ - `train`
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+ - `val`
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+ - `test`
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  ## Dataset Creation
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  ### Annotations
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  #### Annotation process
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+ The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
 
 
 
 
 
 
 
 
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+ #### Who are the annotators?
 
 
 
 
 
 
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+ Annotations are crowdsourced.
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  ## Additional Information
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  ### Dataset Curators
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+ The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
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+ You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
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+
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+ Curators:
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+ - Christoph Auer, [@cau-git](https://github.com/dolfim-ibm)
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+ - Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
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+ - Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
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+ - Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
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  ### Licensing Information
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+ License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
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  ### Citation Information
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+
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+ ```bib
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+ @article{doclaynet2022,
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+ title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
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+ doi = {10.1145/3534678.353904},
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+ url = {https://doi.org/10.1145/3534678.3539043},
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+ author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
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+ year = {2022},
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+ isbn = {9781450393850},
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+ publisher = {Association for Computing Machinery},
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+ address = {New York, NY, USA},
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+ booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
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+ pages = {3743–3751},
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+ numpages = {9},
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+ location = {Washington DC, USA},
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+ series = {KDD '22}
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+ }
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+ ```
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  ### Contributions
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+ Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/dolfim-ibm) for adding this dataset.