Abstract
Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines.
Highlights
APPROACH GNData addresses the need for comprehensive data management by providing (1) a storage system based on a common data representation that is suitable to organize data and metadata from any electrophysiological experiment, with (2) a general application programming interface (API) so that data access can be integrated in software applications, and (3) client tools in common languages to support and facilitate this integration into the laboratory data workflow
To support the use of the data API for everyday data management in the lab, we provide client libraries that communicate with the server via the GNData API, enabling instant data access from the local computational environment
We presented GNData, a data management system with an open API for electrophysiological data
Summary
Advances in technology and methodology during the past years have dramatically increased the volume and complexity of data recorded in electrophysiological experiments. Obstacles to efficient data management arise from the variety of data formats and constraints of accessing data in proprietary formats, and from the amount and complexity of additional information about the experiment that needs to be collected and stored This additional information, which is commonly called “metadata” despite the fact that it is to large part data supplementing the recorded data (Figure 1), is necessary to reproduce the study and essential for searching, selecting, and analyzing the data. Developing common tools and standardized formats has turned out to be challenging for the area of electrophysiology (Teeters et al, 2013) This field faces an enormous variety in experimental methodology, with a large number of data acquisition systems, file formats that are often vendor-specific and undocumented (Garcia et al, 2014), a variety of electrode configurations, species, preparations, stimuli, and overall experimental paradigms. As long as a comprehensive ontology for this field is missing (Bandrowski et al, 2013), approaches to achieve a common scheme for metadata description must leave sufficient flexibility to account for the variety and heterogeneity of experiments (Grewe et al, 2011)
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