Catching Up to Corporate: A Shift towards Academic Data Governance
| dc.contributor.author | Clark, Jennifer L. | en |
| dc.contributor.author | Hurley, Lori | en |
| dc.contributor.author | Ogier, Andrea | en |
| dc.date.accessioned | 2018-07-25T17:14:31Z | en |
| dc.date.available | 2018-07-25T17:14:31Z | en |
| dc.date.issued | 2014-07-07 | en |
| dc.description.abstract | The recent rise in concern over the implications of big data analysis extends to issues endemic to the research process. Academic researchers, whether using large or small datasets, have always faced challenges in data processing, but the rapidly increasing volume of data has forced the issue of the quality of data management into the limelight. Inconsistent and unreproducible workflows in data transformations and data processing can affect analysis and hinder scientific progress on the big and small scale. Big data has brought these issues to the forefront, but the implications on the credibility of knowledge-based science have a more widespread reach. | en |
| dc.description.notes | Yes, abstract only (Peer reviewed?) | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.orcid | Ogier, AL [0000-0002-2143-6212] | en |
| dc.identifier.uri | http://hdl.handle.net/10919/84389 | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.title | Catching Up to Corporate: A Shift towards Academic Data Governance | en |
| dc.type | Conference proceeding | en |
| dc.type.dcmitype | Text | en |
| dc.type.other | Paper | en |
| pubs.organisational-group | /Virginia Tech | en |
| pubs.organisational-group | /Virginia Tech/Library | en |