Spyglass is a framework for reproducible and shareable neuroscience research
Scientific progress depends on reliable and reproducible results. Progress can be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data involve bespoke formats and software that make replication and reuse of data difficult. To address these challenges, we created Spyglass, an open-source data management and analysis…
Scientific advancement hinges on dependable and reproducible findings. Progress can be hastened when data are exchanged and reassessed to explore novel questions. Present methods for storing and processing neural data entail custom formats and software, rendering replication and reuse of data challenging. Addressing these issues, we developed Spyglass, an open-source data management and analysis framework constructed in Python.
Spyglass offers reproducible workflows for typical neuroscience analyses and sharing of raw data, intermediate analyses, and ultimate results within and among laboratories. Spyglass employs the Neurodata Without Borders (NWB) standard and incorporates pipelines for spectral filtering, spike sorting, pose tracking, and neural decoding.
Spyglass can be adapted to apply current and emerging pipelines to datasets from various sources. We illustrate these capabilities within the scope of a cross-laboratory replication by employing advanced state space decoding algorithms on publicly accessible data. New users can experiment with Spyglass on a Jupyter Hub provided by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.
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