Evidence map›Paper›PMID 35187541›Full record

ArticleASSETS. Annual ACM Conference on Assistive Technologies2021

Sharing Practices for Datasets Related to Accessibility and Aging.

Rie Kamikubo, Utkarsh Dwivedi, Hernisa Kacorri

Abstract read
In one paragraph

Article in ASSETS. Annual ACM Conference on Assistive Technologies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Enabling Older Adults to Provide High-quality Activity Labels: Unpacking Accuracy, Precision, and Granularity in Activity Labeling.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025
    Article
  2. AccessShare: Co-designing Data Access and Sharing with Blind People.ASSETS. Annual ACM Conference on Assistive Technologies · 2024
    Article
  3. 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 · 2023
    Article
  4. Data Representativeness in Accessibility Datasets: A Meta-Analysis.ASSETS. Annual ACM Conference on Assistive Technologies · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rie KamikuboCollege of Information Studies University of Maryland, College Park.
Utkarsh DwivediCollege of Information Studies University of Maryland, College Park.
Hernisa KacorriCollege of Information Studies University of Maryland, College Park.

Funding

ACL HHS 90REGE0008
6 · The paper itself

Abstract

Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.

Indexed as

AccessibilitydatasetdisabilityHuman-centered computing → Human computer interaction (HCI)machine learningrepositorySecurity and privacy → Human and societal aspects of security and privacysharing practices

Identifiers

PMID35187541
PMCPMC8855358

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.