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Pointers

Reviewer Guide and Checklists

Reviewers, along with the Submission Guide, please use the following checklists as guidance for your review:

Overall Impression of the Submission

Please give the submitter and other reviewers your overall impression of the dataset and supplementary materials

Data Organisation

The dataset is organized according to the following principles:

  • The data is “Tidy” (if appropriate to the data type), as described by Hadley Wickham in 2014.
  • The data is human readable
  • The data is presented in a plain text or appropriate database format
  • The dataset is not too small or unnecessarily large for the intended lesson type

Documentation

The documentation provided for the dataset:

  • Is provided in a readme.txt file
  • Is well organized and understandable to a potential user

The documenation includes the following elements, or their exclusion is warranted given the context:

  • Overview and provenance
  • Use cases
  • Keywords
  • File/Variable Descriptions
  • Codes
  • Hazards
  • Processing
  • Citation

License

To eliminate barriers to reuse, datasets in the collection should be released into the public domain, i.e. published with the Creative Commons Zero waiver (CC0). We will also accept datasets published with the Creative Commons Attribution 4.0 International license (CC-BY), but we encourage submitters to consider that CC-BY is not considered appropriate for licensing data and the license may have the unintended effect of making it more difficult for others to reuse the data.

  • The dataset has been assigned CC-0 license in a data.txt file
  • The documentation for the dataset has been assigned a CC-BY license in a documentation.txt file
  • Any code or software provided with the dataset has been assigned an MIT license in a code.txt file
  • The license is clearly labelled and in a LICENSES folder

Supplementary Materials and Information

  • Any supplementary documentation (e.g. data dictionaries) provided is well organized and understandable to a potential user
  • Any other supplementary materials (e.g. research articles, reference lists, etc.) are appropriately labelled and human readable

Code:

  • Code is sufficiently internally documented
  • Code is written in a preferred language (R, Python)
  • Code is presented in a non-preferred language for reasons that are well described
  • References are included for related or “upstream” software and the references (e.g. URLs, DOIs) are functional

Teaching Materials:

  • Teaching materials are presented in a generalized way that allows people unfamiliar wtih the data and/or material to teach it with relatively little preparation time
  • Teaching materials are presented in an open format (e.g. plain text, pdf, R Markdown, Jupyter Notebook)