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There is great value embedded in reusing scientific data for secondary discoveries. However, it is challenging to find and reuse the large amount of existing scientific data distributed across the web and data repositories. Some of the challenges reside in the volume and complexity of scientific data, others pertain to the current practices and workflow of research data management. AIDR 2019 (Artificial Intelligence for Data Discovery and Reuse) is a new conference that brings together researchers across a broad range of disciplines, computer scientists, tool developers, data providers, and data curators, to share innovative solutions that apply artificial intelligence to scientific data discovery and reuse, and discuss how various stakeholders work together to create a health data ecosystem. This editorial summarizes the main themes and takeaways from the inaugural AIDR '19 conference.
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To Reuse or Not To Reuse?: A Framework and System for Evaluating Summarized Knowledge
As the amount of information online continues to grow, a correspondingly important opportunity is for individuals to reuse knowledge which has been summarized by others rather than starting from scratch. However, appropriate reuse requires judging the relevance, trustworthiness, and thoroughness of others' knowledge in relation to an individual's goals and context. In this work, we explore augmenting judgements of the appropriateness of reusing knowledge in the domain of programming, specifically of reusing artifacts that result from other developers' searching and decision making. Through an analysis of prior research on sensemaking and trust, along with new interviews with developers, we synthesized a framework for reuse judgements. The interviews also validated that developers express a desire for help with judging whether to reuse an existing decision. From this framework, we developed a set of techniques for capturing the initial decision maker's behavior and visualizing signals calculated based on the behavior, to facilitate subsequent consumers' reuse decisions, instantiated in a prototype system called Strata. Results of a user study suggest that the system significantly improves the accuracy, depth, and speed of reusing decisions. These results have implications for systems involving user-generated content in which other users need to evaluate the relevance and trustworthiness of that content.
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- PAR ID:
- 10601532
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 5
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Format(s):
- Medium: X Size: p. 1-35
- Size(s):
- p. 1-35
- Sponsoring Org:
- National Science Foundation
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