Webinar 2

A total of 20 participants attended the webinar

1 Webinar Slides

2 Poll results

1. What is your level of expertise with RDM?

A: I know very little (completely new to the topic): 11 responses (64 %)

B: I know some basics about RDM, but still have many questions: 3 responses (18%)

C: I am aware of the importance of RDM but still have specific questions: 1 resp. (6%)

D: I’m quite knowledgeable about RDM, just curious about the seminar: 2 responses (12%)

2. Would you trust (and use) data created by others?

A: No, I have to know exactly how data are created and the only way to ensure this is by making sure I create it myself:  2 responses (11 %)

B: I am not sure: 2 responses (11 %)

C: Yes, but there are limitations:, for example, I would only use raw data, I need to do all secondary analysis myself: 10 responses (56 %)

D: Yes, I am regularly using data from others because it means I don‘t have to repeat certain steps – in my community shared data are well organized and curated: 4 responses (22 %)

3. Do you know what a research data management plan is?

Yes:  3 responses (30 %)

No:  14 responses (70 %)

3. Questions raised by the audience

Q.1 Do you have materials for further reading?

Q.2 Do you have any special advice for qualitative data?

Q.3 The grey circle seems to depend strongly on the research field and the community (tools, data sets, data base). The others are quite general, right?

Q.4 How works this Markup language? is possible have in the future a tutorial to know more about that?

4. Speaker’s comments and references for further reading

Use and re-use of research data sets

The video shown during the webinar makes the point that using well curated and checked secondary dataset is beneficial simply because the level of resources that go into the creation of secondary data are usually much larger than the level of resources that an individual researcher will have at his or her disposal. Centrally created data are easy to find and re-use. But it is important to ensure the quality of the data is adequate.

The video was taken from data management tool MANTRA, from the EDINA Centre at the University of Edinburgh. This is a very useful resource with step-by-steps modules and tutorials to all the basics about the management of digital data: https://mantra.edina.ac.uk/

RDM aspects to consider when conducting qualitative research

When dealing with qualitative data it is important to know your data sources in advance so that you can estimate the quality of the data being obtained. If you are creating your own datasets, it is important that you establish thorough QA checks which covers all relevant data creation steps and that you give these a realistic time allocation.

If you are working with textual materials, then focusing on good transcription methods is essential and ensuring you have thorough procedures for the anonymization/pseudonimization of your personal data.

Webinar 3 of this series will deal with this subject in further details but here is some good advice on the matter from the finish social science data archive

If you are working with a combination of data (audio files, videos, transcriptions, interview data, etc.) a good directory structure and file convention rules will help you navigate through your data. We have dealt with these issues in this webinar (see slides 21 and 22 webinar 2 slides) today.

Another important aspect is to ensure you have solid metadata which describes your research outputs. For qualitative research you can already achieve a lot with basic standards like the DC but there are a number of more complex standards for qualitative research such as the QuDEx standard or special adaptations for qualitative research based on basic documentation initiatives like the DDI.

Markup language and tutorials

As already mentioned, it is a good idea if you use markup language for your documentation processes and for collaborating. Many collaborative tools use markup. Here is a selection of mostly-free tools which you download and use locally on your computer to start creating markup text:

MacDown (Mac OS)

Typora (Windows)             

Remarkable (Linux)

If you prefer to collaborate online, there are also a number of free to use alternatives such as HackMD and StackEdit.

For a good introduction to Markdown you may wish to start with this github tutorial

Further reading

Introduction to research data management:

UKDA Guide to good practice: Managing and sharing research data

Fair Data and Research data management Plans FOSTER

Data management plans (DMPs)

During the webinar data management plans were defined as valuable instruments which can help you streamline and make your data management more efficient. At the library we have dedicated a section of our open science website to discussing the importance of DMPs. In these pages you will find examples of DMP from ongoing academic projects at the MLU.

As shown in the slides (s. 30) of the webinar, it is important to know that while the tools and specific requirements for practicing a good RDM may vary according to your disciplines, the general approach and principles will apply to all disciplines. It is important for you to know that the university via institutions such as the library, its legal department, the graduate academy, the ITZ and others can support you in a number of ways with your research management requirements. Do get in touch if you have further specific RDM questions.   

Dr. Roberto Cozatl | Open Science Team | openscience@bibliothek.uni-halle.de | 02.10.2020

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