This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.3389/978-2-88945-421-1 in citations.
Sharing Electrophysiological Data and Metadata on HBP Platforms – An Example Collaboratory Workflow
Sharing Electrophysiological Data and Metadata on HBP Platforms – An Example Collaboratory Workflow
Introduction: The Human Brain Project (HBP) 1 aims at creating and operating aEuropean scientific Research Infrastructure for the neurosciences. A main goal is togather, organise and disseminate data describing the brain and its diseases on the basisof experimental as well as simulated data. Therefo...
Saved in:
Personal Name(s): | Sprenger, Julia (Corresponding author) |
---|---|
Yegenoglu, Alper / Grün, Sonja / Denker, Michael | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Imprint: |
2018
|
DOI: |
10.3389/978-2-88945-421-1 |
Conference: | 1st HBP Student Conference - Transdisciplinary Research Linking Neuroscience, Brain Medicine and Computer Science, Vienna (Austria), 2017-02-08 - 2017-02-10 |
Document Type: |
Abstract |
Research Program: |
The Human Brain Project Human Brain Project Specific Grant Agreement 2 Human Brain Project Specific Grant Agreement 1 Connectivity and Activity |
Publikationsportal JuSER |
Introduction: The Human Brain Project (HBP) 1 aims at creating and operating aEuropean scientific Research Infrastructure for the neurosciences. A main goal is togather, organise and disseminate data describing the brain and its diseases on the basisof experimental as well as simulated data. Therefore a lot of effort is put into the develop-ment of tools for data registration, storage, access and sharing. The most prominent datatype available through the HBP to date are anatomical data and data describing singlecell dynamics. However, the need to include experimental and simulated large-scalefunctional data, and in particular, electrophysiological activity data, has been widelyrecognized. Such data are primarily created in neuronal network simulations as a corepart of the HBP effort. An adapted strategy for data curation is needed, as the estab-lished workflows are not yet considering the integration of electrophysiological data.Another goal of the HBP is to provide a platform to facilitate collaborative research.For this the Collaboratory 2 has been set up—a web portal which provides a com-mon online workspace (Collab) for all members of a collaboration team. The Collabcombines tools which are developed in the context of the HBP platforms and by thisprovides access to high performance computing (HPC), simulation tools and shareddatasets. In particular, it is thought to act as a platform for interactive data analysis.Next to specialized tools which can be integrated or developed for the Collab, genericanalysis can be performed by using Python Jupyter Notebooks 3 .Motivation: Thus, data used in the HBP must be prepared in two respects: (i) inte-gration into the HBP databases and (ii) use in analysis processes on the Collab. Dueto the diversity of data (types) in electrophysiological experiments, standardized dataand metadata models, and tools operating on these models, have only started to bedeveloped (Denker and Grün, 2016; Zehl et al., 2016). A crucial step in further advanc-ing and disseminating these efforts, and to ensure that individual components canbe efficiently linked, is to embed these tools into workflows that recreate the actualscientific routine of a research project.Methods: Here we consider the combination of 4 open source projects attempt toaddress these issues:– Neo provides a generic standardized representation for electrophysiological data,which is able to interface with a range of electrophysiological data formats (Garciaet al., 2014).– The Electrophysiology Analysis Toolkit (Elephant) offers methods ranging fromthe analysis of spike data to population signals, e.g., local field potentials. Elephantis based on the Neo data representation format 4 .– The open metadata Markup Language (odML) is based on XML and offers ahierarchical structure to store metadata related to electrophysiological experiments(Grewe et al., 2011).– NIX is a file format designed to combine electrophysiological data and metadatain a single, standardized format 5 , and is linked to both the Neo and odML datamodels.Results and Discussion: Here we show in 3 stages how these open source programs caninteract to form a structured, comprehensible workflow for electrophysiological spikedata. Firstly, we demonstrate the loading of data and metadata and their integrationinto a single data representation. For this we start with the conversion of the raw datainto a Neo object, which is then further annotated with metadata information. Toobtain the metadata information, primary metadata are first converted to the odMLformat using the odMLtables tool 6 , which is then queried for annotating the Neoobject. In a second stage, the final Neo object is saved as NIX file, which preserves thedata-metadata relations formed in the Neo structure. In a last stage, the data structurefrom the NIX file is used for exemplary analysis of the spiking activity using Elephant.In addition to the implementation of such a workflow in Python for use on a localmachine, we demonstrate the setup of the workflow on the Collaboratory of the HBPand indicate how the interaction of multiple collaboration partners can benefit fromthe workflow realized in this setting. In addition, we discuss how the data, in particularthe odML-based metadata, can be used for integration in the registration processesdeveloped by the Neuroinformatics platforms. |