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Mon Nov 18 2024 - Fri Nov 22 Past

Mon - Fri 18 Nov 2024 - 22 Nov 2024Past

By DKZ.2R: The Carpentries Workshop: Introduction to the Unix Shell, Git, and GitLab

Workshop Aachen (RWTH Aachen University)

Workshop Unix Shell Git GitLab Carpentries

The DKZ.2R presents, as a part of our “Trainings” work package, a Carpentries Workshop on the 18th and 22nd of November on the topic of “Introduction to the Unix Shell, Git, and GitLab”. This is an official Carpentries Workshop and will be hosted on-site at RWTH Aachen University.

Workshop material is available online and will be presented by official Carpentries instructors, who will guide you through the concepts with the help of hands-on exercises and personalized support. The course is designed for beginners and is open to participants from all domains. No prior knowledge is required. If you are interested in taking part in the workshop, you can sign up here.

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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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Announcement - Call for participation

A. Janz
Consulting , Data , RDM , HPC , Data Science , Artificial Intelligence , Machine Learning

Update (June 24, 2024)

The call for participation is now open! Read more

Upcoming!

The Data Literacy Center Rhine-Rhur is issuing a call for participation in its “rent-an-expert” project! This is a great opportunity for PhD students and early postdocs who are working on research projects that involve data science, artificial intelligence, high performance computing and simulation, to get free support from our expert consultants.

Support can take the form of short- or long-term consulting, depending on the needs of the project. More info will be available shortly!

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: