EAGER: Knowledge and Data Transfer: the Formation of a New Workforce

EAGER：知识和数据传输：新劳动力的形成

• 批准号: 1145888
• 负责人: Christine Borgman
• 金额: $ 9万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1145888&HistoricalAwards=false
• 关键词: EAGER; Knowledge; Data; Transfer; Formation

This is an EAGER proposal to support 2 graduate students in research which responds to an immediate need concerning critical questions focused on transferring, managing, and the organizational issues of large digital datasets. The basis of the project capitalizes on a very important event: The Sloan Digital Sky Survey (SDSS) scientific dataset is being transferred from one repository to two others, from a national laboratory to a university library and also to a university-based group of astronomers - from one kind of workforce to two others. This is a pivotal moment to study in the organization of digital knowledge in astronomy and how that knowledge will be developed. The transfer of the database between the three workforces, although highly planned, will face multiple strategic difficulties that will require members of all workforces to develop a new form of knowledge at the interface of their different practices. Those who become adept at this interface of knowledge and data transfer will possess knowledge crucial to the designers of data repositories and those who make use of them. Understanding changes, organizational structures, identifying differences and managing cultural and technical issues can be extremely informative to proposed approaches in the cross-disciplinary/cross-workforce science of the future.

这是一项迫切的建议，旨在支持2名研究生进行研究，以回应有关大型数字数据集的传输、管理和组织问题的关键问题的迫切需求。该项目的基础是利用一个非常重要的事件：斯隆数字天空调查(SDSS)科学数据集正在从一个储存库转移到另外两个储存库，从一个国家实验室转移到大学图书馆，还转移到一个大学天文学家小组--从一种劳动力转移到另外两种劳动力。这是研究天文学数字知识的组织以及如何发展这一知识的关键时刻。数据库在三支队伍之间的转移虽然计划周密，但将面临多重战略困难，这将要求所有队伍的成员在其不同做法的界面上开发一种新形式的知识。那些熟练掌握知识和数据传输接口的人将拥有对数据储存库设计者和使用者至关重要的知识。了解变化、组织结构、确定差异和管理文化和技术问题，对于未来跨学科/跨劳动力科学提出的方法可能非常有用。