BrainLiner: A Platform for Neurophysiological Data Sharing and Manipulation

Makoto Takemiya (ATR Computational Neuroscience Laboratories, Department of Neuroinformatics), Satoshi Murata (ATR Computational Neuroscience Laboratories, Department of Neuroinformatics), Yukiyasu Kamitani (ATR Computational Neuroscience Laboratories, Department of Neuroinformatics)

Sharing data may help advance neuroscience by increasing the availability of data to discover unknown patterns and to develop new theories. Contemporary neuroscience is data-driven and often relies on machine-learning techniques that build statistical models from relationships between stimuli and brain activity data. Free access to large amounts of data is required for large-scale, data-driven approaches. However, collecting data to test new algorithms can be a time-consuming process since terms of use for data have to be agreed upon and often arbitrary data formats understood. Though many database projects exist to share data, these projects are often hampered by usability and/or data licensing issues. Also, many databases use custom file formats that make using data more difficult, and do not provide tools for free-text search or data processing. To better facilitate sharing of neurophysiological data, as part of a Japanese Ministry of Education, Culture, Sports, Science and Technology strategic research program for brain sciences, we created BrainLiner: a suite of web- and desktop-based tools for users to search for, share, and edit data.

Our web portal and search engine at allows data creators to login and upload data, while data consumers can search for, preview, and download data. Data is licensed with a Creative Commons license, providing data uploaders with the ability to specify how their work is cited, while allowing downloaders to freely use the data. Additionally, our platform uses Neuroshare as a common data file format, allowing users to download data without having to worry about multiple file types. To assist using our common file format, we have created converters from common neurophysiological data formats to Neuroshare, as well as a Neuroshare reader. The incentive for users to upload data to our web portal is to increase the visibility of their research and to promote citations of previously published works. We are also implementing a feature to allow users to automatically apply a set of common filters to the data they upload, which may also provide some incentive for users to share their data.

To increase the usability of data, we are providing data-editing tools that are focused on improving the efficiency of processing data, especially for experimental paradigms used in brain-machine interface (BMI) research, where a researcher creates statistical models relating subjects’ brain activity data to presented stimuli. In this paradigm, brain activity data needs to be time-aligned with stimuli in order to train a model on the effects that stimuli have on brain activity. To facilitate this, we have created desktop-based software to align neurophysiological data on a time axis. Our software tools enable users to read a variety of file formats, including Neuroshare, drag data on a time axis to quickly align neuropysiological data and stimuli, and output data to the Neuroshare file format used by our web portal. Future work will involve using a web-based API to upload data directly from the desktop tools to the BrainLiner web portal, thus allowing users to readily share their data. We are offering our desktop software completely free as an open source project hosted on, with the hope that other groups can freely extend our functionality to meet the needs of the community.

Through providing the BrainLiner web- and desktop-based tools, we hope to help connect users with the data they need, as well as to assist researchers who perform experiments to process their data. By acting as a catalyst for facilitating data sharing among neuroscientists, we are working to make it easier for experimentalists to promote their work and for theorists to develop new algorithms that will enrich the scientific community.

Preferred presentation format: Demo
Topic: Infrastructural and portal services

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