Web Science and Digital Libraries Research Group

On December 7 and 8 I attend the ACM Workshop on Reproducibility in Publication in NYC as part of my role as a member of the ACM Publications Board and co-chair (with Alex Wade ) of the Digital Library Committee.  The purpose of this workshop was to gather input from the various ACM SIGs about the approach to reproducibility and "artifacts", objects supplementary to the conventional publication process.  The workshop was attended by 50+ people, mostly from the ACM SIGs but also included representatives from other professional societies and repositories and hosting services.  A collection of the slides presented at the workshop and a summary report are being worked on now, and as such this trip report is mostly my personal perspectives on the workshop; I'll update with slides, summary, and other materials as they become available. This was the third such workshop that had been held, but it was the first for me since I joined the Publications Board in September of 201

Source: Dr. Roger Peng (2011). Reproducible Research in Computational Science . Science 334: 122 Have you ever needed to look back at a program and research data from lab work performed last year, last month or maybe last week and had a difficult time recalling how the pieces fit together? Or, perhaps the reasoning behind the decisions you made while conducting your experiments is now obscure due to incomplete or poorly written documentation.  I never gave this idea much thought until I enrolled in a series of Massive Open Online Courses (MOOCs) offered on the Coursera platform. The courses, which I took during the period from August to December of 2014, were part of a nine course specialization in the area of data science. The various topics included R Programming , Statistical Inference and Machine Learning . Because these courses are entirely free, you might think they would lack academic rigor. That's not the case. In fact, these particular courses and others on Courser