Documentation From User Experience

• Marvin Lorber
• Documentation , Software , RDM

This post is a condensed version of a talk at our Data Compentcy College

If you regularly use scientific software written by others, or tried to replicate interesting research that relies on software, you have probably also invested weeks of work to solve a software problem or even given up on a software because of missing documentation. Finding a project that might be the solution to your problem and then failing to run the code is frustrating. Being unable to run a project you have built yourself years ago is even worse. Having experienced all those setbacks myself in the past I want to use this post to channel that frustration to fuel solutions for better documentation for our current and future projects.

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Do's and Don'ts in Research Data Management

• C. Hillen
• Data , RDM

Research Data Management Do’s and Don’ts - Step up your RDM skills!

1. Structuring and naming your folders There is an easy way to make your data findable for you and your team: establish a folder structure which makes sense for you and your working group as well as naming conventions for your folders.

Don’t:

Paul and Suzie
»Guideline
>application
»version2_final
»v.3
»review
»3rd.version
>JD
»qn
»0-1

Instead do:

000_int_orga
»01_application
»02_review 120_questionaires
»01_qualitative »02_quantitative 130_data
»01_qualitative »02_quantitative

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How To: Good Scientific Practice

• C. Hillen
• Data , RDM , good scientific practice

“Scientific integrity forms the basis for trustworthy research”, so it says in the Guidelines for Safeguarding Good Research Practice of the DFG, the German Research Foundation. As a major funder of research in Germany the DFG, as well as many other funders of research in Germany and the European Union, requires researchers to follow a certain set of rules conducting their research. These rules are called “good scientific practice” and have to be followed by researchers to be viable for funding. According to the guidelines researchers are required to “document all information relevant to the production of a research result as clearly as is required by and is appropriate for the relevant subject area to allow the result to be reviewed and assessed”. But good scientific practice is not done by documenting your research. It also includes i.a. protecting the personality rights of your subjects and handling research data in an appropriate manner by e.g. “back(-ing) up research data and results made publicly available, as well as the central materials on which they are based and the research software used, by adequate means according to the standards of the relevant subject area, and retain them for an appropriate period of time.” This is where Research Data Management (RDM) comes in. Of course RDM is much more than just creating a backup of your data on a USB-Stick and handing it over to anyone asking for it. “Good scientific practice” in RDM follows the FAIR principles:

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