| Title |
Overcoming misattribution to understand open data reuse in smart cities |
| ID_Doc |
41142 |
| Authors |
Mazón, JN; Brennan, R; Helfert, M |
| Title |
Overcoming misattribution to understand open data reuse in smart cities |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/BigData52589.2021.9671577 |
| Abstract |
Smart city governments face several barriers in adopting open data initiatives. Some of these barriers are related to the attribution rights of open licenses, as reusers often misattribute open data. As a result, publishers do not know which open data is reused and for what, thus ignoring the return on investment and hindering the sustainability of open data initiatives. In addition, reusers who do not fully comply the right of attribution of open data licences may face legal problems. To overcome these pitfalls, this paper envisions an approach that aims to extend open data publication standards with elements coming from open-source software development standards, to support reusers in achieving proper attribution of open data. Our approach is a first attempt to both (i) enabling publishers to gather information to understand open data reuse in smart cities, and (ii) supporting reusers to avoid lawsuit issues related to open data license violation. |
| Author Keywords |
open data; misattribution; smart city; publisher; consumer; DCAT; SPDX; SBOM |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000800559506033 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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