(Hyper)active Data Curation: A Video Case Study from Behavioral Science.

Kasey C. Soska, Melody Xu, Sandy L. Gonzalez, Orit Hertzberg, Catherine S Tamis-LeMonda, R. Gilmore, K. Adolph
{"title":"(Hyper)active Data Curation: A Video Case Study from Behavioral Science.","authors":"Kasey C. Soska, Melody Xu, Sandy L. Gonzalez, Orit Hertzberg, Catherine S Tamis-LeMonda, R. Gilmore, K. Adolph","doi":"10.31234/OSF.IO/89RCB","DOIUrl":null,"url":null,"abstract":"Video data are uniquely suited for research reuse and for documenting research methods and findings. However, curation of video data is a serious hurdle for researchers in the social and behavioral sciences, where behavioral video data are obtained session by session and data sharing is not the norm. To eliminate the onerous burden of post hoc curation at the time of publication (or later), we describe best practices in active data curation-where data are curated and uploaded immediately after each data collection to allow instantaneous sharing with one button press at any time. Indeed, we recommend that researchers adopt \"hyperactive\" data curation where they openly share every step of their research process. The necessary infrastructure and tools are provided by Databrary-a secure, web-based data library designed for active curation and sharing of personally identifiable video data and associated metadata. We provide a case study of hyperactive curation of video data from the Play and Learning Across a Year (PLAY) project, where dozens of researchers developed a common protocol to collect, annotate, and actively curate video data of infants and mothers during natural activity in their homes at research sites across North America. PLAY relies on scalable standardized workflows to facilitate collaborative research, assure data quality, and prepare the corpus for sharing and reuse throughout the entire research process.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":"10 3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of escience librarianship","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31234/OSF.IO/89RCB","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Video data are uniquely suited for research reuse and for documenting research methods and findings. However, curation of video data is a serious hurdle for researchers in the social and behavioral sciences, where behavioral video data are obtained session by session and data sharing is not the norm. To eliminate the onerous burden of post hoc curation at the time of publication (or later), we describe best practices in active data curation-where data are curated and uploaded immediately after each data collection to allow instantaneous sharing with one button press at any time. Indeed, we recommend that researchers adopt "hyperactive" data curation where they openly share every step of their research process. The necessary infrastructure and tools are provided by Databrary-a secure, web-based data library designed for active curation and sharing of personally identifiable video data and associated metadata. We provide a case study of hyperactive curation of video data from the Play and Learning Across a Year (PLAY) project, where dozens of researchers developed a common protocol to collect, annotate, and actively curate video data of infants and mothers during natural activity in their homes at research sites across North America. PLAY relies on scalable standardized workflows to facilitate collaborative research, assure data quality, and prepare the corpus for sharing and reuse throughout the entire research process.
(超)主动数据处理:来自行为科学的视频案例研究。
视频数据非常适合研究重用和记录研究方法和发现。然而,对于社会和行为科学的研究人员来说,视频数据的管理是一个严重的障碍,在这些领域,行为视频数据是逐节获得的,数据共享不是常态。为了消除在发布时(或之后)进行事后管理的繁重负担,我们描述了主动数据管理的最佳实践,即在每次数据收集后立即对数据进行管理和上传,以便在任何时候只需按一下按钮即可实现即时共享。事实上,我们建议研究人员采用“过度活跃”的数据管理方式,公开分享他们研究过程的每一步。database提供了必要的基础设施和工具,这是一个安全的、基于web的数据库,专为主动管理和共享个人身份视频数据和相关元数据而设计。我们提供了一个来自Play(全年游戏和学习)项目的视频数据过度活跃管理的案例研究,在该项目中,数十名研究人员开发了一种通用协议,用于收集、注释和积极管理北美各地研究地点的婴儿和母亲在家中自然活动期间的视频数据。PLAY依靠可扩展的标准化工作流程来促进协作研究,确保数据质量,并为整个研究过程中的共享和重用准备语料库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
审稿时长
16 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信