A newly published dataset and an accompanying Data Descriptor article by Chen, Morales-Gregorio, et al., titled ‘1024-channel electrophysiological recordings in macaque V1 and V4 during resting state,’ are now available for use by researchers around the world. https://www.nature.com/articles/s41597-022-01180-1
The dataset, collected at the Netherlands Institute for Neuroscience and prepared together with collaborators at Juelich Research Center (Germany), consists of electrophysiology data that was recorded from the visual cortex (V1 and V4) of two monkeys, from 1024 recording sites simultaneously during the resting state, and also includes supporting datasets obtained while the monkeys performed visual tasks.
The data provide a picture of neuronal activity across large regions of the visual cortex at an unprecedented spatial and temporal resolution, with high-density receptive field coverage (>900 recording sites across the V1 and V4 representations of the central 8 degrees of visual angle).
This dataset could allow other scientists to derive new fundamental neuroscientific insights into underlying activity that influences the processing of visual information in our brain. Potential applications include correlation analysis, large-scale modelling, and the spread of spontaneous activity across the cortex in waves. Part of this dataset has already been successfully used to investigate the neural correlates of the BOLD signal obtained via non-invasive imaging, yielding a recent publication in eLife which compares population RF estimates obtained with our multi-channel electrophysiology data and fMRI-generated BOLD activity (Klink et al., 2021).
Additionally, the dataset could be used as teaching material and serve as a template for future publications of large electrophysiology datasets, providing standardized methods and tools for the description, preparation, and organization of both data and metadata, contributing to the era of open data sharing and collaboration.
The dataset has been uploaded to the open-access data-sharing platform, G-Node Infrastructure (GIN, https://gin.g-node.org/; DOI: https://doi.org/10.12751/g-node.i20kyh). It adheres to the FAIR principles, using common standards to ensure interoperability, while providing detailed metadata information for reproducibility. All metadata are organized into a unified hierarchical structure, using the open metadata markup language (odML) (https://g-node.github.io/python-odml/), a human- and machine-readable file format for reproducible metadata management in electrophysiology. The dataset has been published under an open-access Creative Commons Attribution license (CC-BY) licence for data and metadata, and a BSD-3-Clause license for the software.
The dataset is accompanied by a Data Descriptor article in Scientific Data (DOI: 10.1038/s41597-022-01180-1). The article includes a thorough description of the scientific insights that could be obtained from the data, data formats, and methods of data acquisition and processing, with sections on Data Records (describing data usage and file formats); Technical Validation (to validate the data for usability and correctness, using cutting-edge methods to identify artifacts); and Code Availability (describing the scripts used for data collection, processing and analysis).
To facilitate data interoperability and accessibility, scripts for data processing and analysis are provided in both Matlab and Python formats, allowing ease of access for researchers regardless of whether they prefer to use open-source or proprietary programming languages.
This project was funded by the Dutch Research Council (NWO), the European Union FP7, the European Union Horizon 2020 Framework Programme for Research and Innovation, the European Union Horizon 2020 Future and Emerging Technologies, and the German Research Foundation (Deutsche Forschungsgemeinschaft).