A Scientist's Nightmare: Software Problem Leads to Five Retractions

@article{Miller2006ASN,
  title={A Scientist's Nightmare: Software Problem Leads to Five Retractions},
  author={Greg Miller},
  journal={Science},
  year={2006},
  volume={314},
  pages={1856 - 1857}
}
  • G. Miller
  • Published 22 December 2006
  • Education
  • Science
Due to an error caused by a homemade data-analysis program, on page 1875, Geoffrey Chang and his colleagues retract three Science papers and report that two papers in other journals also contain erroneous structures. (Read more.) 
Five Recommended Practices for Computational Scientists Who Write Software
TLDR
It could be many years before a consolidated handbook of software engineering techniques and approaches is available, but computational scientists can look to the practices of other scientists who write successful software.
Guidelines for data analysis scripts
TLDR
Some guidelines are presented that help keep analysis code well organized, easy to understand and convenient to work with as data analysis pipelines are getting longer and more complicated.
"Can I Implement Your Algorithm?": A Model for Reproducible Research Software
TLDR
A new open platform for scientific software development is proposed which effectively isolates specific dependencies from the individual researcher and their workstation and allows faster, more powerful sharing of the results of scientific software engineering.
Towards "Reproducibility-as-a-Service"
TLDR
A new open automated platform for scientific software development is proposed which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results.
Storing Reproducible Results from Computational Experiments using Scientific Python Packages
TLDR
Storing metadata along with results is important in implementing reproducible research and it is readily achievable using scientific Python packages, and the particular use cases are pinpointed.
Make researchers revisit past publications to improve reproducibility
TLDR
This work proposes one way of reducing scientific irreproducibility by asking authors to revisit their previous publications and provide a commentary after five years, believing that this measure will alert authors not to over sell their results and will help with better planning and execution of their experiments.
In Search of Elegance in the Theory and Practice of Computation
TLDR
Two models for data-centric workflows are presented: the first based on business artifacts and the second on Active XML, and it is argued that Active XML is strictly more expressive, based on a natural semantics and choice of observables.
Reproducibility in Research: Systems, Infrastructure, Culture
TLDR
A high-level prototype open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results is proposed.
Towards a more reproducible ecology
TLDR
The keys to a greater level of reproducibility in ecology are to establish analytical protocols that are robust and transparent, to faithfully document the analytical process including any failed attempts, and to ensure that the storage and acquisition of data is documented and includes the appropriate metadata.
Software Testing and the Naturally Occurring Data Assumption in Natural Language Processing
TLDR
This paper compares code coverage using a suite of functional tests and using a large corpus and finds that higher class, line, and branch coverage is achieved with structured tests than with even a very large corpus.
...
1
2
3
4
5
...