Conference proceeding
Modeling Users' Data Usage Experiences from Scientific Literature
DESIGN, USER EXPERIENCE, AND USABILITY: THEORY, METHODS, TOOLS AND PRACTICE, PT 2, v 6770(2), pp 337-346
01 Jan 2011
Abstract
In the new data-intensive science paradigm, data infrastructures have been designed and built to collect, archive, publish, and analyze scientific data for a variety of users. Little attention, however, has been paid to users of these data infrastructures. This study endeavors to improve our understanding of these users' data usage models through a content analysis of publications related to a frequently cited project in the data-intensive science, Sloan Digital Sky Survey (SDSS). We find that 1) Content analysis of scientific publications could be a complementary method for researchers in HCI community; 2) although SDSS produced a large volume of astronomical data, users did not fully utilize these data; 3) users are not only consumers of scientific data, they are also producers; and 4) studies that can use multiple large scale data sources are relatively rare. Issues of data provenance and usability may prevent researchers from doing research that combines such data sources. Further HCI study of detailed usability issues associated with data infrastructures in the new paradigm is eagerly needed.
Metrics
Details
- Title
- Modeling Users' Data Usage Experiences from Scientific Literature
- Creators
- Jian Zhang - Drexel UniversityChaomei Chen - Drexel UniversityMichael S. Vogeley - Drexel University
- Contributors
- A Marcus (Editor)
- Publication Details
- DESIGN, USER EXPERIENCE, AND USABILITY: THEORY, METHODS, TOOLS AND PRACTICE, PT 2, v 6770(2), pp 337-346
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 10
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics); Physics
- Web of Science ID
- WOS:000302791600039
- Scopus ID
- 2-s2.0-79960494796
- Other Identifier
- 991019170493804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Theory & Methods