Description: Reproducibility and replicability are
hot topics at all levels of research. The National Academies define reproducibility as
"obtaining consistent results using the same data and code as the original
study (synonymous with computational reproducibility)". They define replicability as "obtaining consistent
results across studies aimed at answering the same scientific question using
new data or other new computational methods". Researchers must collect and
organize complete data systematically in order to minimize the impact on
reproducibility and replicability. This seminar will provide you with guidelines on
establishing a data collection plan and organizing your data for statistical
analysis. The data collection portion of this
seminar focuses on brainstorming and creating a formal list of the data fields
required, operational definitions, units, frequency of measurement, etc. It also emphasizes the importance of recording special
events in comments fields that might affect your statistical analysis of
results. The spreadsheet portion of the talk
will help you work smart and not hard by doing it right the first time. No one wants to spend hours copying and pasting data for
input into statistical software. Obviously, such reformatting introduce opportunities to
make errors, compromising data integrity and research quality. After the seminar, attendees will be able to streamline
their data management practices for subsequent studies which rely on
spreadsheet data collection and strengthen their commitment to research R &
R.