Shareable, standardised, in-depth descriptions of activity data sets and their origin

Shareable, standardised, in-depth descriptions of activity data sets and their origin

Copyright: Designing workflows for reproducible data acquisition and post-processing. Figure modified from: Zehl et al. (2016).

In the past decades, neuroscientists have witnessed a rapid increase in the complexity and volume of data, including, in particular, the subdomains of electrophysiology and optophysiology. Today, electro- and optophysiological experiments combine recordings of hundreds of channels in parallel with behaviour under increasingly natural conditions. Minute details of the experiment may become relevant for the analysis of such rich data, often in ways that are hardly foreseen during the time when the experiment is conducted. Despite the importance of unambiguous and machine-readable metadata describing the experiments, current workflows for acquisition and post-processing are largely homegrown, use custom data and metadata representations, are difficult to re-use, and rely on error-prone manual intervention. The team addresses the need for defining practical solutions to automate, standardise, and streamline data and metadata acquisition in the experimental laboratory (Zehl et al., 2016). This process leads to the identification and implementation of missing software components (Sprenger et al., 2019) for digitised acquisition workflows that assist scientists in creating high-quality publications of their precious data (Brochier et al, 2018). To expedite bringing concepts of FAIR data management to wide-spread use in the laboratories, the team is among the initial members to spark the NFDI Neuroscience consortium (http://nfdi-neuro.de) as a community for exchange of knowledge on research data management.

Publications for this project are:

  • Brochier T., Zehl L., Hao Y., Duret M., Sprenger J., Denker M., Grün S., Riehle A. (2018) Massively Parallel Recordings in Macaque Motor Cortex during an Instructed Delayed Reach-to-Grasp Task. Scientific Data 5, 180055.
    DOI: 10.1038/sdata.2018.55

  • Sprenger J., Zehl L., Pick J., Sonntag M., Grewe J., Wachtler T., Grün S., Denker M. (2019) odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments. Front. Neuroinform. 13, 62.
    DOI: 10.3389/fninf.2019.00062

  • Zehl L., Jaillet F., Stoewer A., Grewe J., Sobolev A., Wachtler T., Brochier TG., Riehle A., Denker M., Grün S. (2016) Handling Metadata in a Neurophysiology Laboratory. Frontiers in Neuroinformatics 10, 26.
    DOI: 10.3389/fninf.2016.00026
Last Modified: 09.07.2024