Datasets and Benchmarks Track: Call for Papers

Datasets and Benchmarks Track: Call for Papers

KDD is the premier Data Science and AI conference, hosting a dedicated track on Datasets & Benchmarks. The conference will take place from <INSERT DATES>, in San Jose, California. KDD has two submission cycles per year. This call details the CFP for the first cycle.

A paper should be submitted to only one of the three KDD tracks (the other two being the Research track and the Applied Data Science track). The important dates for the KDD 2027 First Cycle are:

  • Abstract Deadline: July 19, 2026
  • Paper Deadline: July 26, 2026
  • Author Rebuttal Period: September 29-October 13, 2026
  • Notification: November 14, 2026

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:

  • New profiles created without an institutional email will go through a moderation process that can take up to two weeks. Please take this into account and start the process of creating your account early – the paper deadline will not be extended if your account creation was delayed.
  • New profiles created with an institutional email will be activated automatically.

The OpenReview profile will be used to handle conflict of interest and paper matching. An 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 to that, 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 at this link: KDD 2027 Datasets and Benchmarks Track (Cycle 1) | OpenReview

Scope

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 in the other two tracks of the KDD conference, but the review process will be tailored to the specific needs and challenges of datasets and benchmarks. The key evaluation criteria include:

  • Accessibility: Datasets should be easily accessible to the research community without requiring personal requests. Any associated code or tools must be open source and well-documented.
  • Quality and Documentation: Clear, detailed descriptions of how data was collected, curated, and organized are required. Documentation should include metadata, data collection methods, and any preprocessing steps.
  • Impact: The datasets and benchmarks should demonstrate potential to advance research by addressing gaps, enabling new studies, or enhancing reproducibility and generalizability. Submissions should clearly outline how they can influence future work and contribute significantly to the relevant field.
  • Ethics and Fairness: If applicable, submissions must address pertinent ethical considerations, including data privacy, consent, bias, and potential misuse.

We welcome submissions in the following categories:

  • New Datasets: Original datasets or thoughtfully designed (collections of) datasets based on previously available data, to fill critical gaps, address real-world challenges, or offer unique characteristics that push the boundaries of data science and machine learning research. Original datasets should ideally come with some empirical evaluation results proving the value of the data. Collections of previously available data should only be submitted if they provide bona fide new value, which is not attainable by using the original data (e.g., the data comes with new human annotations).
  • Benchmarks and Benchmarking Tools: New benchmarks, including evaluation methodologies or frameworks, which provide standardized ways to assess model performance on various tasks. Tools for benchmarking model performance across different datasets or domains.
  • Data Generators and Environments: Tools, libraries, or platforms that facilitate the creation of synthetic data or offer new environments for training machine learning models. Synthetic data must be accompanied with a quantification and a discussion of its representativeness, in addition to proving its utility.
  • Advanced Data Collection and Curation Practices: Techniques or methodologies that enhance data collection, organization, and curation.
  • Responsible Dataset Development: Frameworks or methodologies for auditing datasets, identifying significant biases, and developing datasets responsibly.

Submission Guidelines

Submission Deadlines. The submission deadlines are strict and no extensions, regardless of circumstances, will be allowed. Placeholder or dummy abstracts are forbidden, and are grounds for desk rejection of the submission.

Authorship. The ACM has an authorship policy stating who can be considered an author in a submission as well as guidance on the use of generative AI tools. Every person named as the author of a paper must have contributed substantially to the work described in the paper and/or to the writing of the paper and must take responsibility for the entire content of a paper. Any use of generative AI tools must be disclosed and elaborated in the submission form.

Maximum authorship. In the Datasets & Benchmark Track, the number of submissions allowed per author is limited to a maximum of two per cycle. If more than two papers are submitted with the same person listed as an author, the additional papers submitted after the first two (by submission id) will be desk-rejected.

Authorship changes. The full list of author names, including the ordering, must be finalized by submission deadline. There cannot be any addition, removal, or reordering of authors after the submission deadline under any circumstances. The only changes allowed are the correction of spelling mistakes or new affiliation.

Anonymity. The review process for the Datasets & Benchmarks Track will be single-blind. Author names and affiliations should be listed.

Formatting Requirements. Submissions must be in English, in double-column format, and must adhere to the ACM template and format (also available in Overleaf). The recommended setting for LaTeX is:

\documentclass[sigconf,review]{acmart}

Submissions must be a single PDF file: 8 (eight) content pages as main paper, followed by references and an optional Appendix that has no page limits. The Appendix can contain details on reproducibility, proofs, pseudo-code, etc. The first 8 pages should be self-contained, since reviewers are not required to read past that. Note that different limits will apply to camera-ready (see below).

Originality and Concurrent Submissions. Submissions must present original work—this means that papers under review at or published in / accepted to any peer-reviewed conference or journal with published proceedings cannot be submitted. Submissions that have been previously presented orally, as posters or abstracts-only, or in non-archival venues with no formal proceedings, including workshops or PhD symposia without proceedings, are allowed. The ACM has a strict policy against plagiarism, misrepresentation, and falsification that applies to all publications.

Resubmission. Papers rejected in the last cycle of KDD D&B track, are not eligible for submission.

Serving as Reviewer. To ensure that all papers receive a sufficient number of high quality reviewers, there is a requirement for authors to contribute to reviewing.

  • Every submission must nominate at least one author who is a qualified reviewer (i.e., authors with at least three papers in KDD or other related conferences). Only if no qualified reviewer exists in the author list, the submission should nominate the best-qualified author for consideration by the PC chairs.
  • Any author listed on two or more papers may be automatically “drafted” (signed up) as a reviewer unless they are already serving as a reviewer, AC, or SAC in any of the three KDD tracks.

Submitting a paper to KDD constitutes an acceptance of the above requirements to serve as a reviewer, and a commitment to carry out the regular reviewing load responsibly. Failure to provide a qualified reviewer when one exists in the author list, or failure to carry out the assigned reviewing duty properly, is grounds for desk rejection of all the submissions co-authored by that individual.

Ethical Use of Data and Informed Consent. Authors are encouraged to include a section on the ethical use of data and/or informed consent of research subjects in their paper, when appropriate. You and your co-authors are subject to all ACM Publications Policies, including ACM’s Publications Policy on Research Involving Human Participants and Subjects. Please ensure all authors are familiar with these policies.

Please consult the regulations of your institution(s) indicating when a review by an Institutional Ethics Review Board (IRB) is needed. Note that submitting your research for approval by such may not always be sufficient. Even if such research has been approved by your IRB, the program committee might raise additional concerns about the ethical implications of the work and include these concerns in its review.

Submissions that do not follow these guidelines or do not view or print properly, will be desk-rejected.

Reviewing Process

Reviewing. Each submission will receive at least three independent reviews, overseen by an Area Chair (AC).

Any use of generative AI tools during the reviewing process must be disclosed in the review form. In particular, verbatim uploading of any passage from the paper being reviewed to any generative AI tool is forbidden.

Rebuttal. Authors will have the chance (but no obligation) to provide a response to each review during the rebuttal period. The ACs will consider the authors’ response to the points raised by the reviewers, as well as discussion among reviewers, to inform acceptance decisions.

Withdrawal. Authors may use the withdrawal button on OpenReview up until the end of the rebuttal period. Beyond that, any request to withdraw must be made to the PC Chairs in writing, and approval for late withdrawal is at the discretion of PC Chairs. If withdrawal is made after reviews have been revealed to authors, the paper will face a 12-month waiting period before it could be submitted to KDD again.

Decision. A range of factors including technical merit, originality, potential impact, quality of execution, quality of presentation, related work, reproducibility of results, and ethics, will be used by the ACs to make a recommendation. The PC Chairs will make the final decisions.

Transparency. By submitting paper(s) to KDD 2027, the authors agree that the original submission, reviews, meta-reviews, and discussions will be made public in OpenReview for all accepted papers.

Conflict of Interest (COI) Policy

All authors and reviewers must declare conflicts of interest in OpenReview. A domain conflict (entered in Education & Career History) must be declared for employment at the same institution or company, regardless of geography/location, currently or in the last 12 months. A personal conflict should be declared when the following associations exist:

  • candidate for employment at the same institution or company
  • co-author on book/paper or co-PI on a funded grant/research proposal in the last 24 months
  • active collaborator
  • family relationship or close personal relationship
  • graduate advisee/advisor relationship, regardless of time elapsed since graduation
  • deep personal animosity

In general, we expect authors, PC, the organizing committee, and other volunteers to adhere to ACM’s Conflict of Interest Policy as well as the ACM’s Code of Ethics and Professional Conduct.

Any transgression that falls short of ethical standards will be investigated and may face disciplinary actions. Such transgressions include, but are not limited to, falsification, dual submission, collusion, pressuring any member of the program committee. Convicted misconduct may result in a 3-year ban from SIGKDD events for all the authors.

By submitting a paper to KDD, authors give consent to the SIGKDD to process and share their submission and other relevant data pertaining to the submission such as authors’ names, affiliations, and email addresses to related conference organizations. This is necessary to help the PC chairs assess the submission and watch for possible CFP violations. Any and all data will be processed by only the respective Program Chairs and the Ethics Committee Members.

Publication and Presentation Policies

Publication. All accepted papers will be allowed the same maximum page length in the proceedings (12 pages, of which up to 9 are content pages), which will be published by ACM and will be accessible via the ACM Digital Library. That is, while we allow one more content page for accepted papers to incorporate reviewer feedback and enhance the quality of their papers, we limit the references and Appendix (combined) to only 3 pages. Accepted papers will require a further revision to meet the requirements of the camera-ready format required by ACM. Camera-ready versions of accepted papers can and should include all information to identify the authors, and should acknowledge any funding received that directly supported the presented research. The rights retained by authors who transfer copyright to ACM can be found here.

Reproducibility. In their submission, authors may refer to a GitHub repository. Though not strictly required, it is highly recommended. After the submission deadline, there will be no further opportunity to share this with reviewers during the review process, as rebuttals and discussions will not allow hyperlinks.

Upon acceptance, authors are required to make their code and data publicly available. We are promoting the use of the “Artifacts Available” badge in ACM Digital Library. If you release any code, dataset, or similar artifact to accompany your paper, and host it in a publicly available, archival repository for research artifacts that provides a Document Object Identifier (DOI), you are welcome to apply for this badge.

There will be two rounds of applications for the badge:

  • Upon submission, authors can pledge that they will make their artifacts available upon publication. This pledge will be revealed to reviewers, who may consider this commitment positively. An accepted paper that later reneges on its pledge may have its acceptance retracted.
  • Upon acceptance, authors who have not made such a pledge during submission, would still be welcome to apply for this badge during the camera-ready preparations.

An artifact evaluation committee will check the artifacts of all accepted papers for availability and relatedness to the paper after the acceptance notification.

Registration. To be included in the proceedings, every accepted paper must be covered by a distinct conference registration, e.g., two multi-authored papers require two registrations, even if they have overlapping authors. This registration must be Full Conference (5-day) registration, at the standard (non-student) in-person rate, payment of which must be completed by the specified deadline. This registration requirement applies universally, regardless of attendance or presentation mode.

Presentation. Every accepted paper must be presented at the conference. Only a portion of accepted papers will be selected to present orally at the conference. No-show papers may be withdrawn from the conference program (and proceedings where it applies).

Official Publication Date. The official publication date is the date the proceedings are made available in the ACM Digital Library. This date for KDD 2027 will be confirmed at a later stage. The official publication date affects the deadline for any patent filings related to published work.

Important update on ACM’s new open access publishing model for 2026 ACM Conferences!

Starting January 1, 2026, ACM will fully transition to Open Access. All ACM publications, including those from ACM-sponsored conferences, will be 100% Open Access. Authors will have two primary options for publishing Open Access articles with ACM: the ACM Open institutional model or by paying Article Processing Charges (APCs). With over 1,800 institutions already part of ACM Open, the majority of ACM-sponsored conference papers will not require APCs from authors or conferences (currently, around 70-75%).

Authors from institutions not participating in ACM Open will need to pay an APC to publish their papers, unless they qualify for a financial or discretionary waiver. To find out whether an APC applies to your article, please consult the list of participating institutions in ACM Open and review the APC Waivers and Discounts Policy. Keep in mind that waivers are rare and are granted based on specific criteria set by ACM.

Program Committee Co-Chairs

Email: KDD27-benchmark-chairs@acm.org

Yan Liu (USC)

Sayan Ranu (Indian Institute of Technology Delhi)

Evgeniy Gabrilovich (Microsoft)

KDD 2027 | San Jose, California