Notably Inaccessible -- Data Driven Understanding of Data Science Notebook (In)Accessibility
Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Goog...
Gespeichert in:
Veröffentlicht in: | arXiv.org 2023-08 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Potluri, Venkatesh Singanamalla, Sudheesh Tieanklin, Nussara Mankoff, Jennifer |
description | Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We perform a large scale systematic analysis of 100000 Jupyter notebooks to identify various accessibility challenges in published notebooks affecting the creation and consumption of these notebooks. Through our findings, we make recommendations to improve accessibility of the artifacts of a notebook, suggest authoring practices, and propose changes to infrastructure to make notebooks accessible. An accessible PDF can be obtained at https://blvi.dev/noteably-inaccessible-paper |
doi_str_mv | 10.48550/arxiv.2308.03241 |
format | Article |
fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2308_03241</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2847580018</sourcerecordid><originalsourceid>FETCH-LOGICAL-a528-ab2c4b49658741b8c27d1b7d59ff00ffc387bdebd92404a245d23260c9da41883</originalsourceid><addsrcrecordid>eNotkD1PwzAYhC0kJKrSH8CEJRYYEuzXduKMVctHpQoGyoYU-SvIJTjFTivy7yltpxvu9OjuELqiJOdSCHKv4q_f5cCIzAkDTs_QCBijmeQAF2iS0poQAkUJQrAR-njpeqXbAS-CMsal5HXrcJbhueoVnke_cwG_B-ti6lWwPnzirjmab8a7YBzeE5zuui98uwh30xPEt74fLtF5o9rkJicdo9Xjw2r2nC1fnxaz6TJTAmSmNBiueVUIWXKqpYHSUl1aUTUNIU1jmCy1ddpWwAlXwIUFBgUxlVWcSsnG6PqIPUyvN9F_qzjU_xfUhwv2iZtjYhO7n61Lfb3utjHsO9UgeSkkIVSyP2UrXcE</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2847580018</pqid></control><display><type>article</type><title>Notably Inaccessible -- Data Driven Understanding of Data Science Notebook (In)Accessibility</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Potluri, Venkatesh ; Singanamalla, Sudheesh ; Tieanklin, Nussara ; Mankoff, Jennifer</creator><creatorcontrib>Potluri, Venkatesh ; Singanamalla, Sudheesh ; Tieanklin, Nussara ; Mankoff, Jennifer</creatorcontrib><description>Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We perform a large scale systematic analysis of 100000 Jupyter notebooks to identify various accessibility challenges in published notebooks affecting the creation and consumption of these notebooks. Through our findings, we make recommendations to improve accessibility of the artifacts of a notebook, suggest authoring practices, and propose changes to infrastructure to make notebooks accessible. An accessible PDF can be obtained at https://blvi.dev/noteably-inaccessible-paper</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2308.03241</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Accessibility ; Computer Science - Computers and Society ; Computer Science - Human-Computer Interaction ; Computer Science - Software Engineering ; Data analysis ; Data science ; Scientific visualization ; Software</subject><ispartof>arXiv.org, 2023-08</ispartof><rights>2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.1145/3597638.3608417$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.48550/arXiv.2308.03241$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Potluri, Venkatesh</creatorcontrib><creatorcontrib>Singanamalla, Sudheesh</creatorcontrib><creatorcontrib>Tieanklin, Nussara</creatorcontrib><creatorcontrib>Mankoff, Jennifer</creatorcontrib><title>Notably Inaccessible -- Data Driven Understanding of Data Science Notebook (In)Accessibility</title><title>arXiv.org</title><description>Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We perform a large scale systematic analysis of 100000 Jupyter notebooks to identify various accessibility challenges in published notebooks affecting the creation and consumption of these notebooks. Through our findings, we make recommendations to improve accessibility of the artifacts of a notebook, suggest authoring practices, and propose changes to infrastructure to make notebooks accessible. An accessible PDF can be obtained at https://blvi.dev/noteably-inaccessible-paper</description><subject>Accessibility</subject><subject>Computer Science - Computers and Society</subject><subject>Computer Science - Human-Computer Interaction</subject><subject>Computer Science - Software Engineering</subject><subject>Data analysis</subject><subject>Data science</subject><subject>Scientific visualization</subject><subject>Software</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotkD1PwzAYhC0kJKrSH8CEJRYYEuzXduKMVctHpQoGyoYU-SvIJTjFTivy7yltpxvu9OjuELqiJOdSCHKv4q_f5cCIzAkDTs_QCBijmeQAF2iS0poQAkUJQrAR-njpeqXbAS-CMsal5HXrcJbhueoVnke_cwG_B-ti6lWwPnzirjmab8a7YBzeE5zuui98uwh30xPEt74fLtF5o9rkJicdo9Xjw2r2nC1fnxaz6TJTAmSmNBiueVUIWXKqpYHSUl1aUTUNIU1jmCy1ddpWwAlXwIUFBgUxlVWcSsnG6PqIPUyvN9F_qzjU_xfUhwv2iZtjYhO7n61Lfb3utjHsO9UgeSkkIVSyP2UrXcE</recordid><startdate>20230807</startdate><enddate>20230807</enddate><creator>Potluri, Venkatesh</creator><creator>Singanamalla, Sudheesh</creator><creator>Tieanklin, Nussara</creator><creator>Mankoff, Jennifer</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230807</creationdate><title>Notably Inaccessible -- Data Driven Understanding of Data Science Notebook (In)Accessibility</title><author>Potluri, Venkatesh ; Singanamalla, Sudheesh ; Tieanklin, Nussara ; Mankoff, Jennifer</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a528-ab2c4b49658741b8c27d1b7d59ff00ffc387bdebd92404a245d23260c9da41883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accessibility</topic><topic>Computer Science - Computers and Society</topic><topic>Computer Science - Human-Computer Interaction</topic><topic>Computer Science - Software Engineering</topic><topic>Data analysis</topic><topic>Data science</topic><topic>Scientific visualization</topic><topic>Software</topic><toplevel>online_resources</toplevel><creatorcontrib>Potluri, Venkatesh</creatorcontrib><creatorcontrib>Singanamalla, Sudheesh</creatorcontrib><creatorcontrib>Tieanklin, Nussara</creatorcontrib><creatorcontrib>Mankoff, Jennifer</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Potluri, Venkatesh</au><au>Singanamalla, Sudheesh</au><au>Tieanklin, Nussara</au><au>Mankoff, Jennifer</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Notably Inaccessible -- Data Driven Understanding of Data Science Notebook (In)Accessibility</atitle><jtitle>arXiv.org</jtitle><date>2023-08-07</date><risdate>2023</risdate><eissn>2331-8422</eissn><abstract>Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We perform a large scale systematic analysis of 100000 Jupyter notebooks to identify various accessibility challenges in published notebooks affecting the creation and consumption of these notebooks. Through our findings, we make recommendations to improve accessibility of the artifacts of a notebook, suggest authoring practices, and propose changes to infrastructure to make notebooks accessible. An accessible PDF can be obtained at https://blvi.dev/noteably-inaccessible-paper</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2308.03241</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2023-08 |
issn | 2331-8422 |
language | eng |
recordid | cdi_arxiv_primary_2308_03241 |
source | arXiv.org; Free E- Journals |
subjects | Accessibility Computer Science - Computers and Society Computer Science - Human-Computer Interaction Computer Science - Software Engineering Data analysis Data science Scientific visualization Software |
title | Notably Inaccessible -- Data Driven Understanding of Data Science Notebook (In)Accessibility |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T05%3A27%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Notably%20Inaccessible%20--%20Data%20Driven%20Understanding%20of%20Data%20Science%20Notebook%20(In)Accessibility&rft.jtitle=arXiv.org&rft.au=Potluri,%20Venkatesh&rft.date=2023-08-07&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2308.03241&rft_dat=%3Cproquest_arxiv%3E2847580018%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2847580018&rft_id=info:pmid/&rfr_iscdi=true |