Towards Effective Foraging by Data Scientists to Find Past Analysis Choices. Kery, M. B., John, B. E., O'Flaherty, P., Horvath, A., & Myers, B. A. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19, pages 1–13, Glasgow, Scotland Uk, 2019. ACM Press. ![Towards Effective Foraging by Data Scientists to Find Past Analysis Choices [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Data scientists are responsible for the analysis decisions they make, but it is hard for them to track the process by which they achieved a result. Even when data scientists keep logs, it is onerous to make sense of the resulting large number of history records full of overlapping variants of code, output, plots, etc. We developed algorithmic and visualization techniques for notebook code environments to help data scientists forage for information in their history. To test these interventions, we conducted a think-aloud evaluation with 15 data scientists, where participants were asked to nd speci c information from the history of another person’s data science project. e participants succeed on a median of 80% of the tasks they performed. e quantitative results suggest promising aspects of our design, while qualitative results motivated a number of design improvements. e resulting system, called Verdant, is released as an open-source extension for JupyterLab.
@inproceedings{kery_towards_2019,
address = {Glasgow, Scotland Uk},
title = {Towards {Effective} {Foraging} by {Data} {Scientists} to {Find} {Past} {Analysis} {Choices}},
isbn = {978-1-4503-5970-2},
url = {http://dl.acm.org/citation.cfm?doid=3290605.3300322},
doi = {10.1145/3290605.3300322},
abstract = {Data scientists are responsible for the analysis decisions they make, but it is hard for them to track the process by which they achieved a result. Even when data scientists keep logs, it is onerous to make sense of the resulting large number of history records full of overlapping variants of code, output, plots, etc. We developed algorithmic and visualization techniques for notebook code environments to help data scientists forage for information in their history. To test these interventions, we conducted a think-aloud evaluation with 15 data scientists, where participants were asked to nd speci c information from the history of another person’s data science project. e participants succeed on a median of 80\% of the tasks they performed. e quantitative results suggest promising aspects of our design, while qualitative results motivated a number of design improvements. e resulting system, called Verdant, is released as an open-source extension for JupyterLab.},
language = {en},
urldate = {2019-12-09},
booktitle = {Proceedings of the 2019 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems} - {CHI} '19},
publisher = {ACM Press},
author = {Kery, Mary Beth and John, Bonnie E. and O'Flaherty, Patrick and Horvath, Amber and Myers, Brad A.},
year = {2019},
pages = {1--13},
annote = {Excluded because it lacks an analytical part
.},
file = {Kery et al. - 2019 - Towards Effective Foraging by Data Scientists to F.pdf:C\:\\Users\\conny\\Zotero\\storage\\ED52JBCY\\Kery et al. - 2019 - Towards Effective Foraging by Data Scientists to F.pdf:application/pdf}
}
Downloads: 0
{"_id":"779vAdTJLFdGy3ZdM","bibbaseid":"kery-john-oflaherty-horvath-myers-towardseffectiveforagingbydatascientiststofindpastanalysischoices-2019","authorIDs":[],"author_short":["Kery, M. B.","John, B. E.","O'Flaherty, P.","Horvath, A.","Myers, B. A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Glasgow, Scotland Uk","title":"Towards Effective Foraging by Data Scientists to Find Past Analysis Choices","isbn":"978-1-4503-5970-2","url":"http://dl.acm.org/citation.cfm?doid=3290605.3300322","doi":"10.1145/3290605.3300322","abstract":"Data scientists are responsible for the analysis decisions they make, but it is hard for them to track the process by which they achieved a result. Even when data scientists keep logs, it is onerous to make sense of the resulting large number of history records full of overlapping variants of code, output, plots, etc. We developed algorithmic and visualization techniques for notebook code environments to help data scientists forage for information in their history. To test these interventions, we conducted a think-aloud evaluation with 15 data scientists, where participants were asked to nd speci c information from the history of another person’s data science project. e participants succeed on a median of 80% of the tasks they performed. e quantitative results suggest promising aspects of our design, while qualitative results motivated a number of design improvements. e resulting system, called Verdant, is released as an open-source extension for JupyterLab.","language":"en","urldate":"2019-12-09","booktitle":"Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19","publisher":"ACM Press","author":[{"propositions":[],"lastnames":["Kery"],"firstnames":["Mary","Beth"],"suffixes":[]},{"propositions":[],"lastnames":["John"],"firstnames":["Bonnie","E."],"suffixes":[]},{"propositions":[],"lastnames":["O'Flaherty"],"firstnames":["Patrick"],"suffixes":[]},{"propositions":[],"lastnames":["Horvath"],"firstnames":["Amber"],"suffixes":[]},{"propositions":[],"lastnames":["Myers"],"firstnames":["Brad","A."],"suffixes":[]}],"year":"2019","pages":"1–13","annote":"Excluded because it lacks an analytical part .","file":"Kery et al. - 2019 - Towards Effective Foraging by Data Scientists to F.pdf:C\\:\\\\Users\\o̧nny\\\\Zotero\\\\storage\\\\ED52JBCY\\\\Kery et al. - 2019 - Towards Effective Foraging by Data Scientists to F.pdf:application/pdf","bibtex":"@inproceedings{kery_towards_2019,\n\taddress = {Glasgow, Scotland Uk},\n\ttitle = {Towards {Effective} {Foraging} by {Data} {Scientists} to {Find} {Past} {Analysis} {Choices}},\n\tisbn = {978-1-4503-5970-2},\n\turl = {http://dl.acm.org/citation.cfm?doid=3290605.3300322},\n\tdoi = {10.1145/3290605.3300322},\n\tabstract = {Data scientists are responsible for the analysis decisions they make, but it is hard for them to track the process by which they achieved a result. Even when data scientists keep logs, it is onerous to make sense of the resulting large number of history records full of overlapping variants of code, output, plots, etc. We developed algorithmic and visualization techniques for notebook code environments to help data scientists forage for information in their history. To test these interventions, we conducted a think-aloud evaluation with 15 data scientists, where participants were asked to nd speci c information from the history of another person’s data science project. e participants succeed on a median of 80\\% of the tasks they performed. e quantitative results suggest promising aspects of our design, while qualitative results motivated a number of design improvements. e resulting system, called Verdant, is released as an open-source extension for JupyterLab.},\n\tlanguage = {en},\n\turldate = {2019-12-09},\n\tbooktitle = {Proceedings of the 2019 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems} - {CHI} '19},\n\tpublisher = {ACM Press},\n\tauthor = {Kery, Mary Beth and John, Bonnie E. and O'Flaherty, Patrick and Horvath, Amber and Myers, Brad A.},\n\tyear = {2019},\n\tpages = {1--13},\n\tannote = {Excluded because it lacks an analytical part\n.},\n\tfile = {Kery et al. - 2019 - Towards Effective Foraging by Data Scientists to F.pdf:C\\:\\\\Users\\\\conny\\\\Zotero\\\\storage\\\\ED52JBCY\\\\Kery et al. - 2019 - Towards Effective Foraging by Data Scientists to F.pdf:application/pdf}\n}\n\n","author_short":["Kery, M. B.","John, B. E.","O'Flaherty, P.","Horvath, A.","Myers, B. A."],"key":"kery_towards_2019","id":"kery_towards_2019","bibbaseid":"kery-john-oflaherty-horvath-myers-towardseffectiveforagingbydatascientiststofindpastanalysischoices-2019","role":"author","urls":{"Paper":"http://dl.acm.org/citation.cfm?doid=3290605.3300322"},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/JKU-ICG/provenance-survey/master/assets/data/main.bib?token=AFMAFPFPO73YUVEBH3EL22C6YQDJU","creationDate":"2020-05-12T16:18:24.872Z","downloads":0,"keywords":[],"search_terms":["towards","effective","foraging","data","scientists","find","past","analysis","choices","kery","john","o'flaherty","horvath","myers"],"title":"Towards Effective Foraging by Data Scientists to Find Past Analysis Choices","year":2019,"dataSources":["iLdxLZjd62Nd3rAFg"]}