P-RECS 2018

First International Workshop on Practical Reproducible Evaluation of Computer Systems.

June 11, 2018. In conjunction with HPDC’18. In cooperation with:

[In cooperation with ACM](http://www.acm.org) [In cooperation with SIGHPC](http://www.sighpc.org)

This workshop will focus heavily on practical, actionable aspects of reproducibility in broad areas of computational science and data exploration, with special emphasis on issues in which community collaboration can be essential for adopting novel methodologies, techniques and frameworks aimed at addressing some of the challenges we face today. The workshop will bring together researchers and experts to share experiences and advance the state of the art in the reproducible evaluation of computer systems, featuring contributed papers and invited talks.


We expect submissions from topics such as, but not limited to:


Submit (single-blind) via EasyChair. We look for two categories of submissions:


Authors are invited to submit manuscripts in English not exceeding 5 pages of content. The 5-page limit includes figures, tables and appendices, but does not include references, for which there is no page limit. Submissions must use the ACM Master Template (please use the sigconf format with default options).


The proceedings will be archived in both the ACM Digital Library and IEEE Xplore through SIGHPC.


These tools can be used used to automate your experiments (not an exhaustive list): CWL, Popper, ReproZip, Sciunit, Sumatra.

Accepted Papers



07:30-08:45 - Breakfast
08:45-09:00 - Welcome
09:00-10:00 - Keynote (Dr. Fatma Deniz)
10:00-10:30 - Coffee break
10:30-10:45 - Lightning talks
10:45-12:00 - Paper Presentations 1
12:00-13:00 - Lunch (hosted by HPDC)
13:00-14:30 - Paper Presentations 2
14:30-15:00 - Coffee break
15:00-16:00 - Panel (Moderator: Victoria Stodden)
16:00-17:00 - Poster session

Paper Session 1 (chair: Jay Lofstead)

  1. Abdulqawi Saif, Alexandre Merlin, Lucas Nussbaum, and Ye-Qiong Song. An Integrated Experiment Monitoring Framework Standing on Off-The-Shelf Components.
  2. Luís Oliveira, David Wilkinson, Daniel Mossé, and Bruce Childers. Supporting Long-term Reproducible Software Execution.
  3. Quan Pham, Tanu Malik, Dai Hai Ton That, and Andrew Youngdahl. Improving Reproducibility of Distributed Computational Experiments.

Paper Session 2 (chair: Carlos Maltzahn)

  1. Jay Jay Billings. Applying Distributed Ledgers to Manage Workflow Provenance.
  2. David Wilkinson, Luís Oliveira, Daniel Mossé, and Bruce Childers. Software Provenance: Track the Reality Not the Virtual Machine.
  3. Victoria Stodden, Matthew S. Krafczyk, and Adhithya Bhaskar. Enabling the Verification of Computational Results: An Empirical Evaluation of Computational Reproducibility.
  4. Michael A. Sevilla and Carlos Maltzahn. Popper Pitfalls: Experiences Following a Reproducibility Convention.

Keynote Address

Abstract: Establishing generalizable findings from systematic empirical observations is at the core of the modern scientific method. Reproducible research practices can help scientists arrive at results that flourish new theories and technological advances. Importantly, it enables the scientific community to corroborate published results and theories. In my talk, I will start by introducing reproducibility and what it means to aim reproducible research practices. I will then present technical tools and data science practices that can incorporate reproducible research in a scientist’s everyday workflow. I will discuss how we can use these tools and practices to conduct large-scale data exploration and computational science by introducing case studies from data-intensive sciences. I will finish my talk by providing my account of reproducible research, where I will present a web-based replication engine (https://boldpredictions.gallantlab.org) that uses data derived from naturalistic neuroimaging experiments to simulate a variety of language experiments. Bio: Dr. Fatma Deniz is a Moore-Sloan Data Science Fellow in Berkeley Institute for Data Science, a Postdoctoral-Fellow in Dr. Jack Gallant’s laboratory (Helen Wills Neuroscience Institute) at the University of California, Berkeley and a visiting scientist at the Technical University Berlin. She is interested in how sensory information is encoded in the brain and uses machine-learning approaches to fit computational models to large-scale brain data. Her current work focuses on cross-modal language representation in the human brain. She did her PhD in Dr. John-Dylan Haynes’s laboratory at Bernstein Center for Computational Neuroscience and Technical University Berlin, where she studied functional connectivity changes during conscious perception in humans. She got a bachelor’s and master’s degrees in Computer Science from the Technical University Munich. During her master’s work, Dr. Deniz worked with Dr. Christof Koch at Caltech, where she studied visual saliency and automated text detection. As an advocate of reproducible research practices, she is the editor of the book titled “The Practice of Reproducible Research”. In addition, she works on improving Internet security applications using knowledge gained from cognitive neuroscience and Mooney images (mooneyauth.org). Her work is at the intersection between computer science, human cognition, and neuroscience. She is a passionate coder, baker and loves playing the cello.


To register for the workshop, go to the HPDC registration page.

Important Dates


Program Committee


Please address workshop questions to ivo@cs.ucsc.edu.