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How many times have you got frustrated, because your code from last month refuses to run and your past self didn't bother to leave you any guidelines? Are you scared that one day a colleague will ask you the data/results of a paper, and you will have to excavate them out of a folder named 'allDataResultsVersion8bFinalizedRecheckedPleaseEnd'? Are you concerned that your contributions will not have much impact, because people may not be able to access them properly? If yes, you are in the same shoes with me, when I started my PhD! In this talk, I present how I embraced reproducibility to avoid some of these common problems. I explain some best practices, simple tricks and habits I have learned throughout my doctoral research, which improved the quality of my research, made it reusable by others (and myself), and hence improved the accessibility and visibility of my work. I give practical examples in where I failed (and then gradually improved) on organizing, developing, versioning, documenting, licensing and publishing the research material (data, code, experimental setups and results etc.). I also introduce some of the available software tools and services, which would help you to achieve these goals. I hope that this talk will convince you to consider reproducibility as a major criteria in academic research, and give you a head start on achieving this objective.
code quality, licensing, experimental setup, code coverage, packaging, submodule, digital object identifier, git, documentation, Zenodo, version control, commit, dataset, semantic versioning, reproducibility, issue tracking, academic publication, branch, collaboration, results, milestone, github, software development, code, open research, unit testing, continous integration
code quality, licensing, experimental setup, code coverage, packaging, submodule, digital object identifier, git, documentation, Zenodo, version control, commit, dataset, semantic versioning, reproducibility, issue tracking, academic publication, branch, collaboration, results, milestone, github, software development, code, open research, unit testing, continous integration
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 8 |

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