Datasets and Benchmarks Track: Call for Papers

Background

As the premier international forum for data mining researchers and practitioners from academia, industry, and government, the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2025) is excited to announce the launch of the Datasets and Benchmarks Track. This new track aims to serve as a premier venue for the presentation of high-quality datasets, benchmarks, and tools that are essential for advancing research and applications in data science, data mining, and data-centric machine learning. It also provides a forum for discussing best practices and standards for dataset creation and benchmark development, ensuring ethical and responsible use.

Important Dates

All deadlines are end-of-day in the Anywhere on Earth (AoE) time zone.

Submission Site

We will use OpenReview to manage the submissions and reviewing. Submissions will not be made public on OpenReview during the reviewing period.

All listed authors must have an up-to-date OpenReview profile. Here is information on how to create an OpenReview profile. Note OpenReview’s moderation policy for newly created profiles:

The OpenReview profile will be used to handle conflict of interest and paper matching. Incomplete OpenReview profile is sufficient ground for desk rejection. 

To be considered complete, each author profile must be properly attributed with the following mandatory fields: current and past institutional affiliation (going back at least 5 years), homepage, DBLP (if there is prior publication), ORCID, Advisors and Recent Publications (if any). In addition, other fields such as Google Scholar, LinkedIn, Semantic Scholar, Advisees and Other Relations should be entered wherever applicable. Abstracts and papers can be submitted through OpenReview.

Objective

The Datasets and Benchmarks Track is dedicated to fostering the development, sharing, and evaluation of datasets and benchmarks that are valuable for the KDD community. We seek contributions that introduce novel datasets, propose new benchmarks, or offer tools and methodologies for dataset creation, curation, and evaluation. The track supports open science by encouraging the submission of open-source libraries and tools that accelerate research in data science and machine learning.

Evaluation Criteria

Submissions will be reviewed with the same rigor as the main KDD conference but tailored to the specific needs and challenges of datasets and benchmarks. The key evaluation criteria include:

Relationship to KDD

Submissions to the track will be part of the main KDD conference, presented alongside the main conference papers. Accepted papers will be officially published in the KDD proceedings.

Scope of Submissions

We welcome submissions in the following categories:

Submission Guidelines

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Ethical Standards and Reproducibility

KDD 2025 emphasizes ethical research practices and reproducibility. Submissions must adhere to ethical guidelines, including considerations around data privacy, consent, and bias mitigation. The reviews will not be publicly visible during the review phase but will be published post-decision. Accepted datasets should be accessible to reviewers and can be released publicly at a later date.

Program Committee Co-Chairs

Email: KDD25-benchmark-chairs@acm.org

Ambuj Singh (UCSB)

Haixun Wang (EvenUp)