Datasets:

rfr2003
/

Geo_Benchmark

language:
  - en
license: other
pretty_name: Geo Benchmark
task_categories:
  - text-generation
configs:
  - config_name: GKMC
    data_files:
      - split: test
        path: GKMC/test-*
  - config_name: GeoQuery_place
    data_files:
      - split: train
        path: GeoQuery_place/train-*
      - split: validation
        path: GeoQuery_place/validation-*
      - split: test
        path: GeoQuery_place/test-*
  - config_name: GeoQuery_regression
    data_files:
      - split: train
        path: GeoQuery_regression/train-*
      - split: validation
        path: GeoQuery_regression/validation-*
      - split: test
        path: GeoQuery_regression/test-*
  - config_name: GeoQuestions1089_YN
    data_files:
      - split: test
        path: GeoQuestions1089_YN/test-*
  - config_name: GeoQuestions1089_coord
    data_files:
      - split: test
        path: GeoQuestions1089_coord/test-*
  - config_name: GeoQuestions1089_place
    data_files:
      - split: test
        path: GeoQuestions1089_place/test-*
  - config_name: GeoQuestions1089_regression
    data_files:
      - split: test
        path: GeoQuestions1089_regression/test-*
  - config_name: GeoSQA
    data_files:
      - split: train
        path: GeoSQA/train*
      - split: test
        path: GeoSQA/test*
      - split: validation
        path: GeoSQA/val*
  - config_name: GridRoute
    data_files:
      - split: test
        path: GridRoute/test-*
  - config_name: MsMarco
    data_files:
      - split: test
        path: MsMarco/test-*
      - split: train
        path: MsMarco/train-*
      - split: validation
        path: MsMarco/validation-*
  - config_name: NY-POI
    data_files:
      - split: test
        path: NY-POI/test-*
  - config_name: PPNL_multi
    data_files:
      - split: test
        path: PPNL_multi/test-*
      - split: train
        path: PPNL_multi/train-*
      - split: validation
        path: PPNL_multi/validation-*
  - config_name: PPNL_single
    data_files:
      - split: test
        path: PPNL_single/test-*
      - split: train
        path: PPNL_single/train-*
      - split: validation
        path: PPNL_single/validation-*
  - config_name: SpartUN
    data_files:
      - split: test
        path: SpartUN/test-*
      - split: train
        path: SpartUN/train-*
      - split: validation
        path: SpartUN/validation-*
  - config_name: SpatialEvalLLM
    data_files:
      - split: test
        path: SpatialEvalLLM/test-*
  - config_name: TourismQA
    data_files:
      - split: test
        path: TourismQA/test-*
      - split: train
        path: TourismQA/train-*
      - split: validation
        path: TourismQA/validation-*
dataset_info:
  - config_name: GKMC
    features:
      - name: question_id
        dtype: int64
      - name: answer
        dtype: string
      - name: scenario
        dtype: string
      - name: question
        dtype: string
      - name: A
        dtype: string
      - name: B
        dtype: string
      - name: C
        dtype: string
      - name: D
        dtype: string
    splits:
      - name: test
        num_bytes: 1055828
        num_examples: 1600
    download_size: 510919
    dataset_size: 1055828
  - config_name: GeoQuery_place
    features:
      - name: question
        dtype: string
      - name: answer
        list: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: train
        num_bytes: 60643
        num_examples: 346
      - name: validation
        num_bytes: 4301
        num_examples: 33
      - name: test
        num_bytes: 29436
        num_examples: 184
    download_size: 34701
    dataset_size: 94380
  - config_name: GeoQuery_regression
    features:
      - name: question
        dtype: string
      - name: answer
        list: float64
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: train
        num_bytes: 13826
        num_examples: 182
      - name: validation
        num_bytes: 1201
        num_examples: 17
      - name: test
        num_bytes: 7038
        num_examples: 89
    download_size: 16126
    dataset_size: 22065
  - config_name: GeoQuestions1089_YN
    features:
      - name: question_id
        dtype: int64
      - name: question
        dtype: string
      - name: answer
        list: bool
      - name: answer_type
        list: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: test
        num_bytes: 13860
        num_examples: 181
    download_size: 9116
    dataset_size: 13860
  - config_name: GeoQuestions1089_coord
    features:
      - name: question_id
        dtype: int64
      - name: question
        dtype: string
      - name: answer
        list:
          list: float64
      - name: answer_type
        list: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: test
        num_bytes: 7407
        num_examples: 84
    download_size: 6981
    dataset_size: 7407
  - config_name: GeoQuestions1089_place
    features:
      - name: question_id
        dtype: int64
      - name: question
        dtype: string
      - name: answer
        list: string
      - name: answer_type
        list: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: test
        num_bytes: 4377028
        num_examples: 455
    download_size: 1899119
    dataset_size: 4377028
  - config_name: GeoQuestions1089_regression
    features:
      - name: question_id
        dtype: int64
      - name: question
        dtype: string
      - name: answer
        list: float64
      - name: answer_type
        list: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: test
        num_bytes: 23075
        num_examples: 234
    download_size: 12517
    dataset_size: 23075
  - config_name: GeoSQA
    features:
      - name: question_id
        dtype: int64
      - name: scenario_id
        dtype: int64
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
      - name: scenario
        dtype: string
      - name: question
        dtype: string
      - name: A
        dtype: string
      - name: B
        dtype: string
      - name: C
        dtype: string
      - name: D
        dtype: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: train
        num_bytes: 2371495
        num_examples: 2644
      - name: validation
        num_bytes: 571713
        num_examples: 628
      - name: test
        num_bytes: 768839
        num_examples: 838
    download_size: 1350888
    dataset_size: 3712047
  - config_name: GridRoute
    features:
      - name: matrix_size
        dtype: int64
      - name: start
        list: int64
      - name: end
        list:
          list: int64
      - name: obstacles_coords
        list:
          list: int64
      - name: path
        list:
          list: int64
    splits:
      - name: test
        num_bytes: 439500
        num_examples: 300
    download_size: 16947
    dataset_size: 439500
  - config_name: MsMarco
    features:
      - name: question_id
        dtype: int64
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: passages
        list:
          - name: is_selected
            dtype: int64
          - name: passage_text
            dtype: string
          - name: url
            dtype: string
      - name: __index_level_0__
        dtype: int64
    splits:
      - name: test
        num_bytes: 10883874
        num_examples: 2907
      - name: train
        num_bytes: 90927375
        num_examples: 23513
      - name: validation
        num_bytes: 16159504
        num_examples: 4149
    download_size: 58681842
    dataset_size: 117970753
  - config_name: NY-POI
    features:
      - name: long-term_check-ins
        list:
          list: string
      - name: recent_check-ins
        list:
          list: string
      - name: candidates
        list:
          list: string
      - name: ground_truth
        list: string
    splits:
      - name: test
        num_bytes: 9070607
        num_examples: 1347
    download_size: 3818269
    dataset_size: 9070607
  - config_name: PPNL_multi
    features:
      - name: matrix_size
        dtype: int64
      - name: world_description
        dtype: string
      - name: world
        list:
          list: int64
      - name: obstacles_coords
        list:
          list: int64
      - name: start
        list: int64
      - name: end
        list:
          list: int64
      - name: n_goals
        dtype: int64
      - name: path
        list:
          list: int64
      - name: agent_as_a_point
        dtype: string
      - name: agent_has_direction
        dtype: string
      - name: distribution
        dtype: string
    splits:
      - name: test
        num_bytes: 80282702
        num_examples: 55440
      - name: train
        num_bytes: 76667038
        num_examples: 53440
      - name: validation
        num_bytes: 9587004
        num_examples: 6680
    download_size: 13201821
    dataset_size: 166536744
  - config_name: PPNL_single
    features:
      - name: matrix_size
        dtype: int64
      - name: world_description
        dtype: string
      - name: world
        list:
          list: int64
      - name: obstacles_coords
        list:
          list: int64
      - name: start
        list: int64
      - name: end
        list:
          list: int64
      - name: n_goals
        dtype: int64
      - name: path
        list:
          list: int64
      - name: agent_as_a_point
        dtype: string
      - name: agent_has_direction
        dtype: string
      - name: distribution
        dtype: string
    splits:
      - name: test
        num_bytes: 15738553
        num_examples: 19044
      - name: train
        num_bytes: 12749254
        num_examples: 16032
      - name: validation
        num_bytes: 1594684
        num_examples: 2004
    download_size: 1341236
    dataset_size: 30082491
  - config_name: SpartUN
    features:
      - name: scenario_id
        dtype: string
      - name: question_id
        dtype: string
      - name: scenario
        dtype: string
      - name: question
        dtype: string
      - name: candidates_answers
        list: string
      - name: answer
        list: string
      - name: type
        dtype: string
      - name: k_hop
        dtype: int64
    splits:
      - name: test
        num_bytes: 3597916
        num_examples: 5551
      - name: train
        num_bytes: 24431833
        num_examples: 37095
      - name: validation
        num_bytes: 3562581
        num_examples: 5600
    download_size: 3174385
    dataset_size: 31592330
  - config_name: SpatialEvalLLM
    features:
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: struct_type
        dtype: string
      - name: size
        dtype: string
      - name: k_hop
        dtype: string
      - name: seed
        dtype: string
      - name: description_level
        dtype: string
    splits:
      - name: test
        num_bytes: 1091123
        num_examples: 1400
    download_size: 211349
    dataset_size: 1091123
  - config_name: TourismQA
    features:
      - name: question
        dtype: string
      - name: city
        struct:
          - name: coord
            list: float64
          - name: name
            dtype: string
      - name: tagged_locations
        list: string
      - name: tagged_locations_lat_long
        list:
          list: float64
      - name: answers_names
        list: string
      - name: answers_adresses
        list: string
      - name: answers_sum_reviews
        list: string
      - name: answers_reviews
        list:
          list: string
      - name: answers_lat_longs
        list:
          list: float64
    splits:
      - name: test
        num_bytes: 7601034
        num_examples: 2173
      - name: train
        num_bytes: 74876719
        num_examples: 19960
      - name: validation
        num_bytes: 7348256
        num_examples: 2119
    download_size: 45129970
    dataset_size: 89826009

Dataset Card for Geo-Benchmark

Dataset Description

Homepage: https://github.com/Rfr2003/GeoBenchmark
Repository: https://github.com/Rfr2003/GeoBenchmark
Paper:
Point of Contact: rodrigo.ferreira-rodrigues@utoulouse.fr

Dataset Summary

Geo-Benchmark aims to assess Large Language Models' (LLM) geographical abilities across a multitude of tasks. It is built from 12 datasets split across 8 differents tasks:

Knowledge/Coordinates Prediction : GeoQuestions1089
Knowledge/Yes|No questions: GeoQuestions1089
Knowledge/Regression questions: GeoQuestions1089, GeoQuery
Knowledge/Place Prediction: GeoQuestions1089, GeoQuery, Ms Marco
Reasoning/Scenario Complex QA: GeoSQA, GKMC
Reasoning/Spatial Reasoning: SpartUN, StepGame, SpatialEvalLLM
Application/POI Recommendation: TourismQA, NY-QA
Application/Path Finding: GridRoute, PPNL

These datasets have been preprocessed in order to be easily accessible.

import datasets

dataset = datasets.load_dataset("rfr2003/Geo_Benchmark", "GeoSQA")

Supported Tasks and Leaderboards

The dataset is used for Text Generation.

Languages

All datasets are in English (en).

Dataset Structure

As this dataset contains very heterogenous tasks, almost every dataset as a different data structure.

Data Instances

TO DO

Data Fields

TO DO

Data Splits

Category	Tasks	Datasets	Train	Dev	Test
Knowledge	Coordinates Prediction	GeoQuestions1089	–	–	84
	Yes/No questions	GeoQuestions1089	–	–	181
	Regression	GeoQuestions1089 GeoQuery	– 180	– 17	234 88
	Place prediction	GeoQuestions1089 GeoQuery MS-Marco	– 348 23 513	– 32 4 149	455 184 2 907
──────────	──────────	──────────	──────────	──────────	──────────
Reasoning	Scenario Complex QA	GeoSQA GKMC	– –	– –	4 110 1 600
	Spatial Reasoning	SpatialEvalLLM SpartUN StepGame	– 37 095 50 000	– 5 600 5 000	1 400 5 551 100 000
──────────	──────────	──────────	──────────	──────────	──────────
Application	POI Recommendation	TourismQA NY-QA	19 960 –	2 119 –	2 173 1 347
	Path Finding	bAbI (task 19) GridRoute PPNL	9 000 – 69 472	1 000 – 8 684	1 000 300 74 484
──────────	──────────	──────────	──────────	──────────	──────────
Total	–	–	236 290	29 942	176 628

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

Thanks for all the authors of the all the datasets. If you use this Benchmark, please cite their work too.

@misc{huang2021retrieverreadermeetsscenariobasedmultiplechoice,
      title={When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions}, 
      author={Zixian Huang and Ao Wu and Yulin Shen and Gong Cheng and Yuzhong Qu},
      year={2021},
      eprint={2108.13875},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2108.13875}, 
}

@inproceedings{finegan-dollak-etal-2018-improving,
    title = "Improving Text-to-{SQL} Evaluation Methodology",
    author = "Finegan-Dollak, Catherine  and
      Kummerfeld, Jonathan K.  and
      Zhang, Li  and
      Ramanathan, Karthik  and
      Sadasivam, Sesh  and
      Zhang, Rui  and
      Radev, Dragomir",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1033/",
    doi = "10.18653/v1/P18-1033",
    pages = "351--360",
}

@inproceedings{data-geography-original
  dataset   = {Geography, original},
  author    = {John M. Zelle and Raymond J. Mooney},
  title     = {Learning to Parse Database Queries Using Inductive Logic Programming},
  booktitle = {Proceedings of the Thirteenth National Conference on Artificial Intelligence - Volume 2},
  year      = {1996},
  pages     = {1050--1055},
  location  = {Portland, Oregon},
  url       = {http://dl.acm.org/citation.cfm?id=1864519.1864543},
}

@misc{huang2019geosqabenchmarkscenariobasedquestion,
      title={GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level}, 
      author={Zixian Huang and Yulin Shen and Xiao Li and Yuang Wei and Gong Cheng and Lin Zhou and Xinyu Dai and Yuzhong Qu},
      year={2019},
      eprint={1908.07855},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1908.07855}, 
}

@misc{li2025gridroutebenchmarkllmbasedroute,
      title={GridRoute: A Benchmark for LLM-Based Route Planning with Cardinal Movement in Grid Environments}, 
      author={Kechen Li and Yaotian Tao and Ximing Wen and Quanwei Sun and Zifei Gong and Chang Xu and Xizhe Zhang and Tianbo Ji},
      year={2025},
      eprint={2505.24306},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2505.24306}, 
}

@article{DBLP:journals/corr/NguyenRSGTMD16,
  author    = {Tri Nguyen and
               Mir Rosenberg and
               Xia Song and
               Jianfeng Gao and
               Saurabh Tiwary and
               Rangan Majumder and
               Li Deng},
  title     = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
  journal   = {CoRR},
  volume    = {abs/1611.09268},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.09268},
  archivePrefix = {arXiv},
  eprint    = {1611.09268},
  timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inbook{placequestions,
author = {Hamzei, Ehsan and Li, Haonan and Vasardani, Maria and Baldwin, Timothy and Winter, Stephan and Tomko, Martin},
year = {2020},
month = {01},
pages = {3-19},
title = {Place Questions and Human-Generated Answers: A Data Analysis Approach},
isbn = {978-3-030-14745-7},
doi = {10.1007/978-3-030-14745-7_1}
}

@inproceedings{aghzal2024can,
  title={Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning},
  author={Aghzal, Mohamed and Plaku, Erion and Yao, Ziyu},
  booktitle={ICLR 2024 Workshop on Large Language Model (LLM) Agents},
  year={2024}
}

@inproceedings{mirzaee-kordjamshidi-2022-transfer,
    title = "Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning",
    author = "Mirzaee, Roshanak  and
      Kordjamshidi, Parisa",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.413",
    pages = "6148--6165",
    abstract = "",
}

@article{yamada2023evaluating,
    title={Evaluating Spatial Understanding of Large Language Models},
    author={Yamada, Yutaro and Bao, Yihan and Lampinen, Andrew K and Kasai, Jungo and Yildirim, Ilker},
    journal={Transactions on Machine Learning Research},
    year={2024}
}

@inproceedings{10.1145/3459637.3482320,
    author = {Contractor, Danish and Shah, Krunal and Partap, Aditi and Singla, Parag and Mausam, Mausam},
    title = {Answering POI-recommendation Questions using Tourism Reviews},
    year = {2021},
    isbn = {9781450384469},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3459637.3482320},
    doi = {10.1145/3459637.3482320},
    booktitle = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
    pages = {281–291},
    numpages = {11},
    keywords = {large scale qa, poi-recommendation, question answering, real world task, tourism qa},
    location = {Virtual Event, Queensland, Australia},
    series = {CIKM '21}
}


@misc{li2024locationawaremodularbiencoder,
      title={Location Aware Modular Biencoder for Tourism Question Answering}, 
      author={Haonan Li and Martin Tomko and Timothy Baldwin},
      year={2024},
      eprint={2401.02187},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2401.02187}, 
}

@inproceedings{10.1007/978-3-031-47243-5_15,
  title = {Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089},
  author = {Sergios-Anestis Kefalidis, Dharmen Punjani, Eleni Tsalapati, 
         Konstantinos Plas, Mariangela Pollali, Michail Mitsios, 
         Myrto Tsokanaridou, Manolis Koubarakis and Pierre Maret},
  booktitle = {The Semantic Web - {ISWC} 2023 - 22nd International Semantic Web Conference,
            Athens, Greece, November 6-10, 2023, Proceedings, Part {II}},
  year = {2023}
}

@inproceedings{stepGame2022shi,
    title={StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts},
    author={Shi, Zhengxiang and Zhang, Qiang and Lipani, Aldo},
    volume={36},
    url={https://ojs.aaai.org/index.php/AAAI/article/view/21383},
    DOI={10.1609/aaai.v36i10.21383}, 
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    year={2022},
    month={Jun.},
    pages={11321-11329}
}

@inproceedings{Yang_2022, series={SIGIR ’22},
   title={GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation},
   url={http://dx.doi.org/10.1145/3477495.3531983},
   DOI={10.1145/3477495.3531983},
   booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
   publisher={ACM},
   author={Yang, Song and Liu, Jiamou and Zhao, Kaiqi},
   year={2022},
   month=jul, pages={1144–1153},
   collection={SIGIR ’22} 
}
   
@ARTICLE{6844862,
  author={Yang, Dingqi and Zhang, Daqing and Zheng, Vincent W. and Yu, Zhiyong},
  journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, 
  title={Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs}, 
  year={2015},
  volume={45},
  number={1},
  pages={129-142},
  keywords={Tensile stress;Data models;Context modeling;Correlation;Hidden Markov models;Location based social networks;spatial;temporal;tensor factorization;user activity preference;Location based social networks;spatial;temporal;tensor factorization;user activity preference},
  doi={10.1109/TSMC.2014.2327053}
}

@inproceedings{10.1145/3539618.3591770,
    author = {Yan, Xiaodong and Song, Tengwei and Jiao, Yifeng and He, Jianshan and Wang, Jiaotuan and Li, Ruopeng and Chu, Wei},
    title = {Spatio-Temporal Hypergraph Learning for Next POI Recommendation},
    year = {2023},
    isbn = {9781450394086},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3539618.3591770},
    doi = {10.1145/3539618.3591770},
    booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages = {403–412},
    numpages = {10},
    keywords = {graph transformer, hypergraph, next poi recommendation},
    location = {Taipei, Taiwan},
    series = {SIGIR '23}
}

@INPROCEEDINGS{10605522,
  author={Feng, Shanshan and Lyu, Haoming and Li, Fan and Sun, Zhu and Chen, Caishun},
  booktitle={2024 IEEE Conference on Artificial Intelligence (CAI)}, 
  title={Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation}, 
  year={2024},
  volume={},
  number={},
  pages={1530-1535},
  keywords={Accuracy;Large language models;Computational modeling;Buildings;Chatbots;Cognition;Data models;LLMs;Next POI Recommendation;Zero-shot;Spatial-Temporal Data},
  doi={10.1109/CAI59869.2024.00277}
}

Contributions

TO DO

Dataset Card for Geo-Benchmark

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions