This working group studies the cultural, educational, legal, and technological barriers to reproducible and open research. Through example, we document and demonstrate what advantages reproducibility has for the scientific process and how individuals and teams can improve their productivity by adopting tools and workflows that support reproducibility.
As science becomes more data driven, software plays an increasingly important role. However, faculty, students, and postdocs in many scientific domains are not equipped to develop and deliver the advanced software they require. The charge of this working group was to fill this gap, with an emphasis on bridging the culture of academic research with that of open source software.
Successful adoption of data science will require several linked efforts. Domain scientists need training in the foundations of data science, including programming, statistics, and reproducible computational science, while methodological scientists need training to work productively in domain areas. This working group addresses these needs through a combination of activities, including workshops and bootcamps.
The current system for career advancement in research universities, which is heavily weighted toward publication, often does not align with what makes a modern data scientist successful. This working group aims at identifying and promoting alternative metrics and career paths that lead to growth and advancement opportunities for scientists who do not fit the typical academic mold but are critical to its success.
