The entire field of particle physics is set to switch to open-access publishing, a milestone in the push to make research results freely available to readers.
Ph.D. dissertation, Department of Computer Science, Stanford University, 2012: "By understanding the unique challenges faced during research programming, it becomes possible to apply techniques from dynamic program analysis, mixed-initiative recommendation systems, and OS-level tracing to make research programmers more productive. This dissertation characterizes the research programming process, describes typical challenges faced by research programmers, and presents five software tools that I have developed to address some key challenges."
When a cancer study is published in a prestigious peer-reviewed journal, the implication is the findings are robust, replicable, and point the way toward eventual treatments. Consequently, researchers scour their colleagues' work for clues about promising avenues to explore. Doctors pore over the pages, dreaming of new therapies coming down the pike. Which makes a new finding that nine out of 10 preclinical peer-reviewed cancer research studies cannot be replicated all the more shocking and discouraging.
A blog post from C. Titus Brown on how he and his co-authors were able to make a paper they wrote replicable.
As part of this project, in collaboration with Philippe Bonnet, we are using (and extending) our infrastructure to support the SIGMOD Repeatability effort. Below are some case studies that illustrate how authors can create provenance-rich and reproducible papers, and how reviewers can both reproduce the experiments and perform workability tests: packaging an experiment on a distributed database system (link in title).
This site serves as a repository for experiments related to database research. Currently, it supports the submission and review of results published at PVLDB and ACM Sigmod.