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Open Science Guide

What is Open Science?

Open Science aims to make research and its dissemination available to all, and to promote transparency, inclusiveness, and democratic participation in the scientific process. By following the FAIR principles of ensuring that data, code, and other resources are findable, accessible, interoperable, and reusable, it supports reproducibility and facilitates the reuse of research artefacts.

Learn more about open science and the FAIR principles here (What is open science?) and here (FAIR Principles)

Why Open Science at AutoUI?

While the benefits of open science and open data practices are well understood, the AutoUI community has been slow to adapt. The proportion of papers that are open access and share their data, code, and other research artefacts is only slowly increasing. As a community, we want to change this and accelerate the practice of open science by providing information and guidance to authors on how to publish research artefacts alongside their papers, and how to ensure that these artefacts are useful to everyone.

If you want to learn more, explore the open science artefacts of the AutoUI community, or already have some open datasets from prior work that you would like to promote, visit the AutoUI Open Science Initiative!

How to Achieve the ACM Artefact Available Badge?

Authors who share artefacts on a FAIR platform (see below) will receive an Artifact Available badge for their AutoUI 2025 paper. This badge, reviewed by the Open Science Chairs, certifies that the author’s artefacts are in an archival repository. In the ACM Digital Library, papers with this badge display an icon, are searchable and filterable, and include metadata to enhance discoverability.

When you submit your material for AutoUI 2025, you will see a field on the submission form asking if you want your paper to be considered for the Artifact Available badge. Similar to CHI 2025, we will support Video (e.g., a video demonstration of a hardware prototype or a walkthrough of a virtual world), Audio (e.g., sounds critical to your paper, audio recordings of oral data), Software (e.g., analysis scripts, simulation code), Datasets, and Other artefact types (e.g., survey and interview protocols, pre-registration details).

Regardless of the type of artefacts submitted for consideration for the Artifacts Available badge, they must be freely and publicly accessible through a digital repository at the time of submission.

How to Open Science?

On Which Platforms to Publish My Artefacts?

There are many places where we can publish open access artefacts. It is good practice to keep your data on a FAIR platform. Here are some examples (updated February 2025):

  1. ACM Digital Library (FAIR,https://dl.acm.org). AutoUI is an ACM conference, and the platform allows the sharing of (small) supplementary material with the publication. For greater control and wiki-like functionality, platforms such as OSF may be more suitable. However, you can still include a link in the ACM Digital Library that redirects to your chosen repository.
  2. OSF (FAIR, https://osf.io). Allows to make repos anonymous for review, which can be handy for the submission process. It is a ‘swiss knife’ for publishing artifacts in open access, and the platform allows a great level of flexibility. File versioning is straightforward, and OSF also allows you to pre-register your study. Size Limit: 5GB (private projects) and 50GB (public projects).
  3. Zenodo (FAIR, https://zenodo.org). Data is stored at CERN, which is reliable. It is becoming a common place to share data, which may make it user-friendly. They accept up to 50GB per dataset with an option to have multiple datasets. Size Limit 50GB.
  4. GitHub (not FAIR, https://github.com). Everyone knows how to navigate around a repo on GitHub, which can be an advantage for outreach. But it is not FAIR. What can be practiced is to provide a link to the active project in the manuscript, and then still upload materials to a FAIR platform for reproducibility. Size Limit: 2GB (GitHub Free and Pro).
  5. 4TU.ResearchData (FAIR, https://data.4tu.nl). Local storage for the technical universities of the Netherlands. They do quite a good job of making the management of datasets easy. Likely, there is a similar portal in your institution/country. Size Limit 5GB.
  6. IEEE Dataport (FAIR, “https://ieee-dataport.org/). Research data platform designed to make scientific data openly accessible to all and help researchers and institutions share research, manage their data, and collaborate with peers. Size Limit 2TB with option for extension.
  7. Hugging Face (not FAIR, https://huggingface.co/). A library for easily accessing and sharing datasets for Audio, Computer Vision, and Natural Language Processing (NLP) tasks. Size Limit 500GB.

Anonymisation for peer review

Submissions at AutoUI are anonymous. Hence not only the manuscript but also all data shared should be anonymized as well. The platforms for publishing open artefacts usually give you support in achieving that (e.g., OSF allows making repos anonymous for the review stage).

🏆 AutoUI Open Science Award

This year, for the first time, authors will have the opportunity to win the AutoUI Open Science Award for a paper that makes a substantial contribution to the AutoUI community and the sharing of artefacts. To be considered for the Open Science Award, submissions will be evaluated based on the following key criteria:

  1. Data Organisation – Is the data well-structured, labelled, and formatted in a way that enhances accessibility and usability?
  2. Comprehensive Documentation – Is there clear, detailed, and user-friendly documentation available to guide users in understanding, accessing, and utilizing the data effectively?
  3. Data Quality – Is the data accurate, reliable, and up-to-date? Does it meet high standards of completeness, consistency, and integrity?
  4. Usability & Accessibility – How easy is it for researchers, developers, and the broader community to use and apply the data? Are there tools or interfaces that enhance accessibility?
  5. Openness & Reusability – Is the data openly available under a suitable license that encourages reuse, collaboration, and innovation?
  6. Impact & Relevance – Does the dataset or tool address a significant scientific or societal challenge? Can it drive meaningful advancements in research, technology, or public knowledge?