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As much as research data sharing and re-usability is a staple in the open science practices, their value would be hugely diminished if their quality is compromised.
In a time when machine-readability and the related software are getting more and more crucial in science, while data are piling up by the minute, it is essential that researchers efficiently format and structure as well as deposit their data, so that they can make it accessible and re-usable for their successors.
Errors, as in data that fail to be read by computer programs, can easily creep into any dataset. These errors are as diverse as invalid characters, missing brackets, blank fields and incomplete geolocations.
To summarise the lessons learnt from our extensive experience in biodiversity data audit at Pensoft, we have now included a Data Quality Checklist and Recommendations page in the About section of each of our data-publishing journals.
We are hopeful that these guidelines will help authors prepare and publish datasets of higher quality, so that their work can be fully utilised in subsequent research.
At the end of the day, proofreading your data is no different than running through your text looking for typos.
We would like to use the occasion to express our gratitude to Dr. Robert Mesibov, who prepared the checklist and whose expertise in biodiversity data audit has contributed greatly to Pensoft through the years.
For how and why researchers should publish their biodiversity data, see also Strategies and guidelines for scholarly publishing of biodiversity data recently published in the open science journal Research Ideas and Outcomes (RIO).