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Digitized workflows and data management

Digitized workflows and data managementCopyright: Sonja Grün / Michael Denker

The workflows that cover the recording of experimental data up to the publication of figures illustrating neuroscientific analysis results are interwoven and complex. Today, electrophysiological experiments combine electrophysiological recordings of hundreds of channels in parallel with behavior under almost natural conditions (e.g. Brochier et al, 2018). Details of the experiment are relevant for the analysis of such rich data, in particular when correlation structures between the neuronal activities are of interest. Unfortunately, current implementations of analysis workflows of electrophysiological experiments are far from being automatized, and software supporting such a goal is largely still in development or missing. In consequence, the level of reproducibility of data analysis is poor compared to other scientific disciplines. Although the problem is well-known and leads to ineffective, unsustainable science, there is yet no solution in terms of a complete, provenance-tracked workflow. We have identified relevant components for such a general workflow (Bouchard et al, 2016; Denker and Grün, 2016), such as e.g. a common software for data analysis or the urgent need for metadata and tools for their acquisition and aggregation (Zehl et al, 2016). Based on these insights we provide rigorously tested solutions as open source software (Rostami et al, 2017). Furthermore we integrate existing tools and contribute to community projects, e.g. such as NEO. In collaboration with colleagues from other disciplines we further develop tools and concepts for such digitized workflows and test their suitability together with experimental partners, and support them in publishing their data (Brochier et al, 2018).


Publications

Gutzen R., von Papen M., Trensch G., Quaglio P., Grün S., Denker M. (2018) Reproducible neural network simulations: statistical methods for model validation on the level of network activity data. Frontiers in Neuroinf DOI: 10.3389/fninf.2018.00090

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. (Data publication) Scientific Data 5:180055 DOI:10.1038/sdata.2018.55.
Data available at https://web.gin.g-node.org/INT/multielectrode_grasp

Rostami V., Ito J., Denker M., Grün S. (2017a). [Re] spike synchronization and rate modulation differentially involved in motor cortical function. Rescience 3. DOI: 10.5281/zenodo.583814.

Senk J., Yegenoglu A., Amblet O., Brukau Y., Davison A., Lester DR., Lührs A., Quaglio P., Rostami V., Rowley A., Schuller B., Stokes AB., van Albada SJ., Zielasko D., Diesmann M., Weyers B., Denker M., Grün S. (2017). A collaborative simulation-analysis workflow for computational neuroscience using HPC. In: Di Napoli E, Hermanns M-A, Iliev H, Lintermann A, Peyser A eds. High-Performance Scientific Computing. Cham: Springer International Publishing, 243–256. DOI: 10.1007/978-3-319-53862-4_21.

Denker M., Grün S. (2016) Designing workflows for the reproducible Analysis of Electrophysiological Datain: Amunts K, Grandinetti L, Lippert T, Petkov N:Brain Inspired Computing, Springer Series Lecture Notes in Computer Science, Vol 10087:58-72. DOI: 10.1007/978-3-319-50862-7_5

Bouchard KE., Aimone JB., Chun M., Dean T., Denker M., Diesmann M., Donofrio DD., Frank LM., Kasthuri N., Koch C., Ruebel O., Simon HD., Sommer FT., Prabhat (2016). High-performance computing in neuroscience for data-driven discovery, integration, and dissemination. Neuron 92:628–631. DOI: 10.1016/j.neuron.2016.10.035.

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.

Badia R., Davison A., Denker M., Giesler A., Gosh S., Goble C., Grewe J., Grün S., Hatsopoulos N., LeFranc Y., Muller J., Pröpper R., Teeters J., Wachtler T., Weeks M., Zehl L. (2015) INCF Program on Standards for data sharing: new perspectives on workflows and data management for the analysis of electrophysiological data. International Neuroinformatics Coordination Facility (INCF) https://www.incf.org/about-us/history/incf-scientific-workshops

Denker M., Einevoll ET., Franke F., Grün S., Hagen E., Kerr J., Nawrot M., Ness TB., Ritz R., Smith L., Wachtler T., Wojcik D. (2014) 1st INCF workshop on validation of analysis methods. International Neuroinformatics Coordination Facility (INCF) https://www.incf.org/about-us/history/incf-scientific-workshops

Pipa G., Riehle A., Grün S. (2007). Validation of task-related excess of spike coincidences based on neuroxidence. Neurocomputing 70:2064–2068. DOI: 10.1016/j.neucom.2006.10.142.

Pazienti A., Grün S. (2006). Robustness of the significance of spike synchrony with respect to sorting errors. Journal of Computational Neuroscience 21:329–342. DOI: 10.1007/s10827-006-8899-7.

Software

http://neuralensemble.org/elephant/

http://neuralensemble.org/neo/

https://github.com/G-Node/python-odml

https://pythonhosted.org/python-odmltables/

Collaborations

  • Prof. Thomas Wachtler, gnode, TU Muenchen www.g-node.org
  • Dr. Andrew Davison, CNRS, Gif Sur Yvette

Funding


Servicemeu

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