SubmissionDetails

Regulations of the competition. Any matters not covered herein shall be decided by the committee after deliberation.

1 Eligibility & Teaming

• Participation is open to individuals or teams. Each team may have at most 3 members (including the team lead). An extra supervisor is allowed and optional.
• Only one registration per team is required. There is no restriction on members’ institutions.
• All-student teams receive a scoring advantage (see Prizes). An all-student team means all members are enrolled students (undergraduate, master’s, or PhD) on the submission deadline.
• Each participant (including individuals) may join at most one team. Duplicate participation may result in disqualification.

2 Evaluation Process

The competition has two stages:

• Stage 1 The submitted datasets and reports will be reviewed by the committees, and the Top 5 teams will be selected.
• Stage 2 These Top 5 teams will be invited to an online defense, where they will present their datasets. The final winners will be determined based on this defense.

3 Review & Compliance Screening

• Administrative/compliance screening: A preliminary check for license, privacy, safety, and accessibility. Submissions failing this step will not enter the review process.
• Reviewer assignment: Each valid submission is randomly assigned to 2-3 reviewers who score independently. Reviewers must declare conflicts of interest and recuse as needed.
• Review approach: Scoring will be based on the submitted dataset and report. Reviewers will verify the availability of datasets and code as necessary, and may conduct spot checks or request further clarifications.

4 Submission requirements

Expression of Interest submission opens untill Dec.15,2025.

Bonus score 0.25 for participants submitting EoI before the EoI deadline.

Part 1 DATASET(s)

1) Datasets. 2) README document(s). Introduction of the datasets

Part 1 upload to IEEEDataPort submit dataset. Submission must comply with DataPort’s upload and publication policies.

Part 2 REPORT(s)

1) A brief introduction (max 600 words) document, including

• Link to the uploaded datasets on the IEEE DataPort website
• Targeted topic/use case (e.g., load/generation forecasting, power flow calculation, fault diagnosis, market analytics, power electronics data, etc.).
• What types of PES domain challenges does your dataset address?
• What types of challenges in AI model training/learning/implementation does your dataset address?
• Why is it (are they) good dataset(s)?

2) Data description article. Article Formatting follows the IEEE Data Descriptions journal Descriptor Articles format. Recommended length of 4 pages with a max of 6 pages. Descriptor articles must have and only have these sections (without numbering) the order below. Please highlight the performance of AI model(s) trained using your dataset(s) where relevant.

• Abstract
• Background
• Collection Methods and Design
• Validation and Quality
• Records and Storage
• Insights and Notes
• Source Code and Scripts
• Acknowledgements and Interests
• References

More details in Descriptor Articles Author Guide - IEEE Data Descriptions.

Please use provided templates for Part 2 REPORT(s) 1) & 2).

Reports in PDF format only, and send to the competition email dppesdatacompetition@gmail.com with subject “team name + DOI of submitted datasets in DataPort”. Do not submit Part 2 to DataPort during the competition.

5 IP, Legal, Ethics & Safety

• Teams must ensure they hold the necessary rights to their submissions and do not infringe third-party rights.
• Data, code, and documentation must carry explicit licenses. Third-party components must cite sources and licenses and ensure lawful redistribution and Competition use.
• Teams retain their IP. Organizers and reviewers have a fair-use right for competition-related publicity, review, and scholarly communication (including screenshots, abstracts, and links).
• No PII or sensitive critical-infrastructure details; apply appropriate de-identification/differential privacy where necessary and document it.
• Data sources must be lawful and compliant; assess and disclose potential risks (bias, discrimination, misuse).