While small labs produce much of the fundamental experimental research in Material Science and Engineering (MSE), little is known about their data management and sharing practices and the extent to which they promote trust in and transparency of the published research. In this research, a case study is conducted on a leading MSE research lab [at MIT] to characterize the limits of current data management and sharing practices concerning reproducibility and attribution. The workflows are systematically reconstructed, underpinning four research projects by combining interviews, document review, and digital forensics. Then, information graph analysis and computer-assisted retrospective auditing are applied to identify where critical research information is unavailable orat risk.
Data management and sharing practices in this leading lab protect against computer and disk failure; however, they are insufficient to ensure reproducibility or correct attribution of work,especiallywhen a group member withdraws before the project completion.Therefore, recommendations for adjustments in MSE data management and sharing practices are proposed to promote trustworthiness and transparency by adding lightweight automated file-level auditing and automated data transfer processes.
https://doi.org/10.2218/ijdc.v18i1.940
| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |
