16 lines
772 B
Plaintext
16 lines
772 B
Plaintext
important things before the open-access data
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Introduction into available tools
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Understandable coding
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How to manage different data sources in one experiment (e.g. eye tracking, performance, questionnaire..)
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Upload data before or after publishing a paper? Time mangement
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going over guidelines/best practice on how to name files, folders and data as well as folder structure.
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understanding where redundancy is needed (raw data?) and where to avoid it.
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understanding what should always go into a readme file.
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Cleaning up R code for readability
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how to integrate gitHub in workflow
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Documentation of a final R script
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Where to store data for long-term accessibility (conventions?)
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Steps and when to do what
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How to best arrange the data
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Tools, where I should upload my final data
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