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Learning from the Past: Approaches for Reproducibility in Computational Neuroscience · Study

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ChapterLearning from the Past: Approaches for Reproducibility in Computational Neuroscience

Sharon M. Crook2013

Chapter2013

Learning from the Past: Approaches for Reproducibility in Computational Neuroscience

Sharon M. Crook · Andrew P. Davison · Hans Ekkehard Pleßer

61 citations·0/0 files stored·Springer eBooks

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Scientific Computing and Data Management Research Data Management Practices Undergraduate Neuroscience Education and Research

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References

2005Why Most Published Research Findings Are False 2005Why Most Published Research Findings Are False 2005Scientific workflow management and the Kepler system 2007Explaining the Brain 2007Explaining the brain : mechanisms and the mosaic unity of neuroscience 2005Taverna: lessons in creating a workflow environment for the life sciences 2004Extending Ourselves 2005Extending ourselves: computational science, empiricism, and scientific method

Cited by

2020Evaluating the Reproducibility of Physiological Stress Detection Models 2018Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience 2022Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis 2019Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge 2017Reproducibility and Comparability of Computational Models for Astrocyte Calcium Excitability

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2018Reproducibility in Scientific Computingarticle · 6 shared references 2018Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detailarticle · 5 shared references 2019A Survey on Collecting, Managing, and Analyzing Provenance from Scriptsarticle · 5 shared references 2021Reasons, challenges, and some tools for doing reproducible transportation researcharticle · 5 shared references 2011A Universal Identifier for Computational Resultsother · 5 shared references 2018“Reproducible” Research in Mathematical Sciences Requires Changes in our Peer Review Culture and Modernization of our Current Publication Approacharticle · 5 shared references

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Year2013

Typechapter

Published2013-01-01

VenueSpringer eBooks

Languageen

Citations61

Files0 public / 0

ShelfScientific Computing and Data Management

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doi10.1007/978-1-4614-1424-7_4

openalex_workW64130124

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2018

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

2018“Reproducible” Research in Mathematical Sciences Requires Changes in our Peer Review Culture and Modernization of our Current Publication Approach

2013An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook