EAGER: Computer-Assisted Redaction and Anonymization of Scholarly Communications and Products (CARASCAP)
EAGER: Computer-Assisted Redaction and Anonymization of Scholarly Communications and Products (CARASCAP)
批准号:
2135446
负责人:
Christopher Lee
金额:
$29.99万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-07-15 至 2023-06-30
中文摘要
计算机辅助编辑和匿名学术交流和产品(CARASCAP)项目将产生一个概念验证开源应用堆栈,以帮助研究团队和个人学者识别、记录和编辑其研究产品中的敏感和个人识别信息。个人识别信息(PII)和其他敏感信息的潜在存在严重阻碍了公众获取公共资助研究的数据集和其他产品。如果没有可靠和具有成本效益的程序来识别这些信息,默认的反应往往是无限期地阻止公众获取整个研究产品集合。通过开发新的组件和工具,将迭代编校功能纳入工作流程,以准备供公众传播的数据集,该项目将促进研究数据出版工作的更强大的生态系统。该软件将主要用Python语言开发,麻省理工学院许可,并打包在Python程序包索引(PYPI)上分发。独立的模块将解释和修改源材料的数据结构。在这个原型阶段,该项目将专注于可能对广泛的收藏感兴趣的格式,包括从特定URL、文本格式和现代办公格式(例如,PDF、.odt、.docx、.pst、.ost)处抓取的开放文本。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The Computer-Assisted Redaction and Anonymization of Scholarly Communications and Products (CARASCAP) project will produce a proof-of-concept open-source application stack to assist research teams and individual scholars in identifying, documenting, and redacting sensitive and personally identifying information within their research products. The potential presence of personally identifying information (PII) and other sensitive information is a significant inhibitor to public access to datasets and other products of publicly funded research. Without reliable and cost-effective processes for identifying such information, the default response is most often to indefinitely prevent the public from accessing entire collections of research products. By developing new components and tools for iterative redaction functions incorporated into workflows to prepare datasets for public dissemination, this project will foster a stronger ecosystem of research data publishing efforts. The software will be developed primarily in Python, MIT Licensed, and packaged for distribution on the Python Package Index (PyPI). Independent modules will interpret and modify the source material data structures. For this prototype phase, the project will focus on formats likely to be of interest to a broad range of collections, including open text scraped from web pages at specific URLs, text formats, and modern office formats (e.g., PDF,.odt, .docx, .pst, .ost).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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