ArticleASSETS. Annual ACM Conference on Assistive Technologies2020
IncluSet: A Data Surfacing Repository for Accessibility Datasets.
Article in ASSETS. Annual ACM Conference on Assistive Technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Market shaping via digital public infrastructure for assistive technology in low- and middle-income countries.Frontiers in rehabilitation sciences · 2026Article
- Exploring Collaboration to Center the Deaf Community in Sign Language AI.ASSETS. Annual ACM Conference on Assistive Technologies · 2025Article
- Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
- Blind Users Accessing Their Training Images in Teachable Object Recognizers.ASSETS. Annual ACM Conference on Assistive Technologies · 2022Article
- Data Representativeness in Accessibility Datasets: A Meta-Analysis.ASSETS. Annual ACM Conference on Assistive Technologies · 2022Article
- Sharing Practices for Datasets Related to Accessibility and Aging.ASSETS. Annual ACM Conference on Assistive Technologies · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Datasets and data sharing play an important role for innovation, benchmarking, mitigating bias, and understanding the complexity of real world AI-infused applications. However, there is a scarcity of available data generated by people with disabilities with the potential for training or evaluating machine learning models. This is partially due to smaller populations, disparate characteristics, lack of expertise for data annotation, as well as privacy concerns. Even when data are collected and are publicly available, it is often difficult to locate them. We present a novel data surfacing repository, called IncluSet, that allows researchers and the disability community to discover and link accessibility datasets. The repository is pre-populated with information about 139 existing datasets: 65 made publicly available, 25 available upon request, and 49 not shared by the authors but described in their manuscripts. More importantly, IncluSet is designed to expose existing and new dataset contributions so they may be discoverable through Google Dataset Search.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.