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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lane, Julia Ingrid |
| Abstract | Businesses and government are using new approaches to decision-making. They are exploiting new streams of (mostly) digital personal data, such as daily transaction records, web-browsing data, cell phone location data, and social media activity; and they are applying new analytical models and tools. Social science researchers, who are not trained in the stewardship of these new kinds of data, must now collect, manage and use them appropriately. There are many technical challenges: disparate datasets must be ingested, their provenance determined and metadata documented. Researchers must be able to query datasets to know what data are available and how they can be used. Datasets must be joined in a scientific manner, which means that workflows need to be traced and managed in such a way that the research can be replicated(Lane, 2017). Computer scientists' expertise is of critical value in many of these areas, but of greatest interest to this group is the facilities in which data on human subjects are stored. The data must be securely housed, and privacy and confidentiality must be protected using the best approaches available. The access and use must be documented to meet the needs of data providers. Yet the technology currently used to provide access to sensitive data is largely artisanal and manual. The stewardship restrictions placed on the use of confidential administrative data prevent the use of best practices for research data management. As a result, links between data sources are rarely validated, results often are not replicated, and connected datasets, results, and methods are not accessible to subsequent researchers in the same field. This is where computer scientists' expertise can come to play in building approaches that will enable sensitive data from different sources to be discovered, integrated, and analyzed in a carefully controlled manner, and that will, furthermore, allow researchers to share analysis methods, results, and expertise in ways not easily possible today. |
| Starting Page | 1 |
| Ending Page | 1 |
| Page Count | 1 |
| File Format | |
| ISBN | 9781450346993 |
| DOI | 10.1145/3078597.3078618 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-06-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Privacy Secure data Confidentiality Social science |
| Content Type | Text |
| Resource Type | Article |
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