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OT-ST-WS-08 | Data preparation

Registration closed

Dr. James Imber, Karl Kortum, Dr. Nikolay Koldunov, Dr. Stephan Kloep, Prof. Betina Hollstein, Dr. Jan-Ocko Heuer

Coding
Interested in looking into characteristics of different types of data?

Data holds the answers to all manner of questions and analysis methods can extract those answers - linking the two is “data preparation”. Anyone interested in working with data will likely need to know at least some of the principles outlined.
Furthermore, cross-discipline data analysis is part of the scientific progress. This workshop offers the opportunity to look into data preparation procedures for different data types and to learn about their individual characteristics:

 

Day 1: General introduction and focus on image data

An understanding of how to approach a new dataset and the typical steps that will be necessary in a variety of contexts. An insight into the preparation of image data and practical experience performing such tasks using Python.

Day 2: Focus on climate model data

This topic will be useful for students that plan to work with models of Earth System components as their primary research tool. It also will be useful for researchers that require information about past or future state of the Earth System as additional parameter in their research (e.g. weather conditions when interview was taken, or clinical study was performed).

Day 3: Focus on clinical data

Clinical data is either collected during the course of ongoing patient care or as part of a formal clinical trial program. To analyse clinical data a sophisticated data management concept including data preparation procedures is needed to comply with data protection regulations.

Day 4: Focus on qualitative data

The term “qualitative data” is used to describe a broad variety of heterogeneous data, including various types of text (e.g. transcripts of interviews or observations), audio, video, picture or material artefacts. From the perspective of “quantitative” research – i.e., the application of statistical methods to standardized numerical data –, qualitative materials just seem to be data that need more structure. But qualitative material is a specific type of data that is usually richer, more context-dependent and more sensitive than quantitative data. On the other hand, qualitative data can be fruitfully analyzed with common tools of quantitative inquiry (e.g. text mining). Thus, this workshop addresses both quantitative and qualitative researchers and aims to introduce them to the particular ethical, legal and practical challenge.

Contents

1. Introduction and image data (James Imber & Karl Kortum)

An introduction to the preparation of data for analysis, beginning with the initial production or acquisition through to an analysis ready dataset. The specific case of image data will then be discussed in more detail using examples from satellite-based Earth Observation.

2. Climate model data (Nikolay Koldunov)

We will cover the following topics:

  • Where to find climate model information
  • netCDF data format
  • Basic types of atmospheric, ocean, land and sea ice data
  • Validation of model data
  • Ways to extract weather and climate information for particular regions and times in past and the future
3. Clinical data (Stephan Kloep)
  • Where health data is collected
    • clinical trials
    • hospital / doctor's office
      • statutory
      • private
    • Medical service of the Health Funds (MDK)
  • Conduction of a clinical trial
    • Planning
    • Evaluation
    • Data handling
  • Data protection vs. data collection
    • Informed consent
    • Legal regulation for the purpose of research
    • Challenges in reusing clinical data
  • How to use health data in a research project?
4. Qualitative data (Betina Hollstein & Jan-Ocko Heuer)

This hands-on workshop will make participants familiar with approaches, challenges, standards and tools for research data management of ‘qualitative’ (i.e., unstructured/non-standardized) data primarily from the social sciences (e.g. transcripts of interviews or focus groups, images, audiovisual data etc.). The focus will be on giving participants ample opportunities to get practical insights and experiences in managing these kinds of person-related and often sensible data.<br /> The following topics will be addressed:</p>

  • What are qualitative data?
  • Why, for Whom and How are qualitative data important?
  • Overview: Process of preparing qualitative data (for analysis)
  • Generating/‘Collecting’ qualitative data
  • Transforming qualitative data
  • Anonymizing/Pseudonymizing qualitative data
  • Contextualizing qualitative data
  • Sharing qualitative data
  • Ensuring quality of qualitative data
  • Summary


Outcomes

1. Introduction and image data

An understanding of how to approach a new dataset and the typical steps that will be necessary in a variety of contexts. An insight into the preparation of image data and practical experience performing such tasks using Python.


2. Climate model data

Basic understanding of strengths and weaknesses of data from Earth System models. Information on what kinds of data on weather and climate are available, how to get it and extract and post-process it.

3. Clinical data

Basic understanding of how clinical data is collected and how it is handled in compliance with data protection regulations in the research context.

4. Qualitative data

Practical insights and experiences with management of qualitative research data.

Prior knowledge


Some exercises (of Day 2) will require a degree of familiarity with the Python programming language. Some specific extension packages will be used but prior knowledge of these will not be assumed. Basic experience with programming on any language would be an advantage.


Requirements

Computer with modern web browser

  • Corti, Louise; van den Eynden, Veerle; Bishop, Libby; Woollard, Matthew (2020): Managing and sharing research data: A guide to good practice. 2nd ed. Los Angeles: SAGE Publications.

When?

06.09.2021, 10:00 - 15:00

07.09.2021, 10:00 - 15:00

08.09.2021, 10:00 - 15:00

09.09.2021, 10:00 - 15:00

 > Break 12:00-13:00 on each day


Where?

Online via VC


Language?

English

Dr. James Imber & Karl Kortum

Researcher at the Remote Sensing Technology Institute of the German Aerospace Center (DLR) as a member of the Synthetic Aperture Radar (SAR) Oceanography group
&
PhD Candidate at the Remote Sensing Technology Institute of the German Aerospace Center (DLR) as a member of the Synthetic Aperture Radar (SAR) Oceanography group

 

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Nikolay Koldunov

Dr. Nikolay Koldunov

Scientist at Alfred Wegener Institute

 

Dr. Stephan Kloep

Data Manager at the Competence Center for Clinical Studies at the University of Bremen

 

Prof. Dr. Betina Hollstein & Dr. Jan-Ocko Heuer

Professor for Microsociology and Qualitative Methods at University Bremen Head of QUALISERVICE, SOCIUM–Research Center on Inequality and Social Policy at University of Bremen
Head of Dept. Methods Research at SOCIUM–Research Center on Inequality and Social Policy at University of Bremen
&
Postdoctoral Researcher, Research Data Center (RDC) Qualiservice, SOCIUM Research Center on Inequality and Social Policy, University of Bremen

 

Other status groups or externals:

Free places will be offered to candidates on the waiting list after registration was closed (one week before the workshop takes place).