| AVS 66th International Symposium & Exhibition | |
| New Challenges to Reproducible Data and Analysis Focus Topic | Monday Sessions |
| Session RA+AS+NS+SS-MoA |
| Session: | Quantitative Surface Analysis II/Big Data, Theory and Reproducibility |
| Presenter: | Anne Plant, National Institute of Standards and Technology (NIST) |
| Authors: | A. Plant, National Institute of Standards and Technology (NIST) J. Elliott, NIST R. Hanisch, National Institute of Standards and Technology (NIST) |
| Correspondent: | Click to Email |
Ideally, data should be shareable, interpretable, and understandable within the scientific community. There are many challenges to achieving this, including the need for high quality documentation and a shared vocabulary. In addition, there is a push for rigor and reproducibility that is driven by a desire for confidence in research results. We suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility.