Data Challenge is one of the most important parts of ChinaVis. We invite researchers, developers and amateurs who use their most effective visual analytics techniques and tools to complete visual analytics tasks. Data Challenge is designed to help participants better evaluate the effectiveness of their techniques and tools in solving complex problems. At the same time, we would like to advance the development of the field of visualization and visual analytics in China.

COVID-19 epidemic ravaged many countries around the world. On March 11, 2020, the World Health Organization (WHO) officially announced the COVID-19 as a global pandemic. In the course of the global fight against the COVID-19, an unprecedented amount of large-scale data has been generated. Big data analysis techniques can assist in discovering the process of virus transmission, monitoring the development of the epidemic and allocating relief materials, so as to better carry out epidemic prevention and control work. Visual analysis, as an important method of big data analysis, combines data processing, visual representation and interactive analysis, so that machine intelligence and human intelligence are deeply integrated, providing effective basis and guidance for analysis and decision-making in epidemic prevention and control. The 7th ChinaVis Data Challenge 2020 calls for epidemic-related visual analysis works from researchers, university students and faculty, enterprises and data visual analysis amateurs. Let us make full use of the state-of-the-art technology to guard people's life and the peace of the society together.

Submission entries are required to explore and discover the hidden patterns and insight based on data related to the COVID-19 or other major public health events, using visual analysis techniques and methods. Including the following topics:

(1) Spatio-temporal analysis of the epidemic: Using visual analysis techniques, analyze the spatio-temporal distribution pattern of the epidemic, monitor the development of the epidemic and evaluate the preventive and control measures.

(2) Analysis of epidemic propagation patterns: Using visual analysis techniques, fully correlate multi-source data (e.g., migration and movement data, population age structure, urban and rural population distribution, etc.), analyze epidemic propagation patterns, compare differences in propagation of different countries or regions, detect abnormal propagation events, and formulate control strategies.

(3) Epidemic prediction and public opinion monitoring: using visual analysis techniques, predict the epidemic development trend and milestones, analyze the dynamic evolution of social media topics and emotions, and perceive the trend of social public opinion.

(4) Pathological study of COVID-19: Using scientific visualization, information visualization and visual analysis techniques to analyze the molecular structure of virus, analyze viral gene sequences, and construct pathological knowledge graph to support pathological study.

(5) Analysis of the potential impact of the epidemic and secondary disasters: Using visual analysis techniques to fully correlate multi-source data (e.g., economic indicators, material procurement, online education, etc.), assess the impact of the epidemic on the economy, enterprise, education and training, and prevent and control the occurrence of secondary disasters such as difficulties in returning to work and production, imbalance of material supply and demand, and trauma of the people.

(6) Customized topics: Participants can choose their own topics of interest and use visual analysis techniques to solve problems related to the epidemic.

Students and teachers from universities (including higher vocational schools), researchers in the research institutes, developers and designers of enterprises, and amateurs are all welcome to participate in the challenge. Participants are required to take part in Data Challenge in the form of team, which consists of no more than 5 participants. Student teams may have 1 or 2 extra advisers.

The participant whose name ranking the 1st in the team is designated as the leader who is responsible for the communications. Individual teams that represent themselves but not their affiliations including university, research institute or enterprise should indicate themselves as amateurs teams. The rule for the team name is as follow: “leader’s affiliation (or Amateur if participants are all amateurs)-leader’s name”, for instance, “TianJin University-Zhang San” (or “Amateur-Li Si”).

Participants should sign up for the data via online registration:https://jiandaoyun.com/f/5e770ba62f08d500063c1d22

• Registration information should include: Corresponding Email, Team name, Submission Title, Participant Names (Supervisor, Leader and Members), Phone number, E-mails, Affiliations, Degree and Titles.
• Notes: One email can only be used for register for one team, which is the identifier for the team. If you forgot the identifier, you can inquiry it with your registered email.

Participants are required to submit the following items: (1) Submission Document (2) Video (3) Paper (Optional).

All submissions should be submitted through on-line links. In case of the high peak of submissions, you can choose either of the following entrance. Please avoid the last minute submission.

Submission Entry 1:https://jiandaoyun.com/f/5e77357179e2040006b08a76

Submission Entry 2:https://jiandaoyun.com/f/5e79a6a2dfa6c000069c8464

• Information including: team name, corresponding email, identifier of the team, keywords of the submissions, the name of the submission and the content.
• You can update the submission with the unique identifier any time before the deadline. Only the last updated submission would be considered.

Submission Requirement Details:

(1) Submission Document: participants are required describe their submissions with appropriate images and descriptions using visual analytics, and the description should be in Word or PDF format. Please refer to the recommend document template provided by the organizer.

(2) Video: participants are required to ensure that there is a video available to explain the whole process of visual analytics. The duration of the video is expected to be within 5 minutes and limited to 50Mb.

(3) Paper (Optional): participants are encouraged to summarize the characteristics of the visual analytics solution into a paper with no more than 2 pages. The paper should be in Word or PDF format and be consistent with the content arrangement required by ChinaVis. Only award-winners are REQUIRED to submit the papers. There would be follow-up notifications.

(4) Naming Rule And Size Limit: all files are required to follow the naming rules: “team name-document type”, for example: “TianJin University-Zhang San-Submission Document.pdf”, “TianJin University-Zhang San-Video.mp4”, “TianJin University-Zhang San-Paper.pdf”. All files required should be included in a folder and be zipped into a single compressed file in the standard RAR or ZIP format, and the size of the compressed file cannot exceed 80 Metabytes.

(5) Participants Are Recommended To Visit “Overview Of Previous Years’ ChinaVis Data Challenges” For More Instructions. Award-winning entries of VAST Challenge organized by IEEE VIS Conference can be found in this repository:http://www.cs.umd.edu/hcil/varepository/benchmarks.php, which would also provide some hints and guidance. Paper “ChinaVis Data Challenge from 2015 to 2017” can be also referred for information of data challenge.

All entries submitted will be evaluated by visual analytics experts and domain experts for the comprehensive judgment. It mainly focuses on the theme and potential usage for the epidemic analysis, effectiveness of interaction design, the usage of open data, social impact, novelty and scalability with respect to visual analytics.

All valid entries will be evaluated after the submission deadline. [Strict] for all entries submitted after the submission deadline will NOT be considered. We are not responsible for the missing, postpone and materials broken due to any technical issues including computer, internet and mobile network.

The following prizes will be proportionally awarded by Data Challenge Committee according to the judgment results: 1st Prize, 2nd Prize, 3rd Prize, honorable mention Award and single subject award. The winning teams will be awarded with certificates and prizes at the ChinaVis conference. Some of the winning teams will be invited to give a presentation of their entries at the conference.

Submission document template:《ChinaVis2020 Challenge Work Description Document Recommendation Template》

(1) The entries submitted must not violate relevant laws and regulations and shall not infringe copyrights of others. Participants are responsible for all caused by intellectual property disputes.

(2) Participants can use open-source or commercial data analytics and visualization software, for example DataV,TableaU, R, Excel, etc. Participants are encouraged to use software development tools to design and implement their own visual analytics solutions, with the library of D3、ECharts、AntV, Processing, etc. Note that participants should specify on the submission document what kind of development tools, open-source or commercial software they have used.

(3) Participants are requested to collect their own data related to the COVID-19 epidemic and are encouraged to use officially published and publicly available data sets, e.g., The data report from National Health Commission of the People’s Republic of China、Open data source of COVID-19 、 Daily data of COVID-19 of China. The use of data does not infringe the intellectual property or other rights of any third party. In the event of discovery or verification by the right holder, the responsibility for such discovery rests with the participant who used the data. Participants are requested to clearly describe the source of data, data format and data rigour in the description of their work.

(4) Award-winning teams are required to have at least one person registration for ChinaVis 2020.

(5) Award-winning teams are required to make a poster of their entries and attend the poster session of ChinaVis 2020.

(6) The intellectual property rights of the entries belong to the contestants. The Organizer reserves the right to use the entries, information about the entries, team information for promotional materials, authorized media releases, official website browsing and downloading, exhibition (including touring exhibitions) and other activities.

Huijie Zhang, Northeast Normal University

Zhuo Zhang, Qi An Xin Technology Group

Siming Chen, University of Bonn

Ying Zhao, Central South University

2019 ChinaVis Data Challenge

2018 ChinaVis Data Challenge

2017 ChinaVis Data Challenge