ERROR 2022

2nd Workshop on E-science ReseaRch leading tO negative Results
October 10, 2022

ERROR 2022 provides the e-Science community a dedicated and active forum for exchanging cross-discipline experiences on research leading to negative results and lessons learned. The workshop covers both applications and systems areas, including topics in research methodology, reproducibility, the applications/systems interface, social problems in computational science, and other relevant areas.

In conjunction with  
Proceedings with

Workshop Program

Oct 10, Miller Town Hall

Time Event Chair
1:30pm-1:40pm Welcome & Introduction
Ketan Maheshwari
1:40pm-2:40pm Keynote
Brian S Van Essen
Justin Wozniak
2:40pm-3:00pm The Ghost of Performance Reproducibility Past
Srinivasan Ramesh, Mikhail Titov, Matteo Turilli, Shantenu Jha and Allen Malony
3:00pm-3:30pm Break
3:30pm-3:50pm Failure Sources in Machine Learning for Medicine---A Study
Hana Ahmed, Roselyne Tchoua and Jay Lofstead
Rafael Ferreira da Silva
3:50pm-4:10pm Automated metadata extraction: challenges and opportunities
Tyler Skluzacek, Kyle Chard and Ian Foster
4:10pm-4:30pm F*** workflows: when parts of FAIR are missing
Sean Wilkinson, Greg Eisenhauer, Anuj Kapadia, Kathryn Knight, Jeremy Logan, Patrick Widener and Matthew Wolf
4:30pm-4:35pm Concluding Remarks
Ketan Maheshwari

Important Dates

  • July 8, 2022

    July 22, 2022

    Papers Submission
  • August 8, 2022

    Paper Acceptance Notifications
  • August 15, 2022

    Camera-ready Submissions
  • October 10, 2022


All deadlines are Anywhere on Earth (AoE).

Call for Papers

Escience researchers often invest significant time and effort in projects that can have unexpected outcomes. When results are obtained that do not fit the established molds expected by publishing venues, these results can be lost. The ERROR Workshop invites research results from computing and computational projects that encounter unexpected or negative results that would be difficult to report on elsewhere.

Novel hardware technologies and machine learning approaches (among others) are rapidly changing approaches, methods, and scale applied in escience domains. Researchers must deal with this novelty in multiple dimensions, many of which are beyond their control. Consequently, it is likely that some of the obtained results will not be of the expected form: they are negative (deviating from initial hypothesis), abnormal (anomalous to results from similar studies), or otherwise not useful in the established sense.

Under normal circumstances, such negative results and why they were obtained are seldom discussed, analyzed and published. Useful lessons are thus lost to the scientific community. Yet ignoring such results and the process by which they were obtained poses a risk of repetition. The fact that other researchers likely face the same situations and the same pitfalls further increases the cost of research, a cost that would have been avoided if the negative results were brought forward and discussed in-depth within and across communities.

Topics for the workshop

  • Unforeseen technology / problem / technique misfits
  • Institutional policies (on rejected research)
  • Failures and obstacles faced during a successful research work
  • Controversial results because of undiscovered technical glitch
  • Unconventional results which contradict theoretical expectations
  • Discovery of better approaches after significant efforts spent on research
  • Inadequate or misconfigured infrastructure
  • Abnormal and anomalous results
  • Ongoing research with setbacks and lessons learned
  • A hypothesis with one or more limiting assumptions found to be incorrect
  • Discovery of unexpected behavior in hardware, networks or platforms
  • Data size that is too big or too small for the applied technique
  • Implementation of simulation tools based on incorrect physical observations
  • Defect in software design, architecture and/or user interface
  • Software and platform incompatibilities
  • Zero defect software policy and its implications
  • Other topics relevant to the description above

Proceedings Publication

Accepted papers from the workshop will be published as part of eScience 2022 proceedings to be published by the IEEE Computer Society Press, USA and made available online through the IEEE Digital Library.

Paper Submission Guidelines

Authors are invited to submit a maximum of 6-page manuscripts describing original and unpublished work surrounding the aforementioned topics. The format of the paper should be of double column text using single spaced 10 point size on 8.5 x 11 inch pages, as per IEEE 8.5 x 11 manuscript guidelines. Templates are available from this link.
Authors should submit a PDF file that will print on a postscript printer to the easychair conference system.

Keynote Speaker

Brian C. Van Essen

Lawrence Livermore National Laboratory, USA


Ketan Maheshwari (

Oak Ridge National Laboratory, USA

Rafael Ferreira da Silva

Oak Ridge National Laboratory, USA

Tristan Glatard

Concordia University, Canada

Justin Wozniak

Argonne National Laboratory, USA

Daniel S. Katz

University of Illinois at Urbana-Champaign, USA

Program Committee

Tainã Coleman

University of Southern California, USA

Rosa Filgueira

University of St Andrews, UK

Arnaud Legrand

CNRS, France

Ulf Leser

Humboldt-Universitaet zu Berlin, Germany

Loïc Pottier

University of Southern California, USA

Frédéric Suter

Oak Ridge National Laboratory, USA

Sean R. Wilkinson

Oak Ridge National Laboratory, USA

Matthew Wolf

Oak Ridge National Laboratory, USA

Olga Kuchar

Oak Ridge National Laboratory, USA