Open AccessPosted Content10.1101/2021.03.30.437358

DataJoint Elements: Data Workflows for Neurophysiology

- 30 Mar 2021

TL;DR: DataJoint Elements as mentioned in this paper provides modular designs for assembling complete workflow solutions to organize data and computations for common neurophysiology experiments using the DataJoint framework to integrate data collection and analysis in collaborative workflows.

Abstract: A new resource—DataJoint Elements—provides modular designs for assembling complete workflow solutions to organize data and computations for common neurophysiology experiments. The designs are derived from working solutions developed in leading research groups using the open-source DataJoint framework to integrate data collection and analysis in collaborative workflows.

1. What are the contributions in "Datajoint elements: data workflows for neurophysiology" ?

The project aims to provide an efficient approach to managing scientific data workflows—the multi-step methods for data collection, preparation, processing, and analysis—that researchers must perform in the course of an experimental study.. For defining and deploying the workflows, the project relies on DataJoint: the open-source library for MATLAB and Python enabling streamlined interactions with relational databases and bulk data stores [ 1 ].. Yet new 1 was not certified by peer review ) is the author/funder.

Figure 4: Challenges in planning data-intensive projects and their solutions in the DataJoint ecosystem.

Figure 5: The Calcium Imaging Element embedded in a larger workflow. It performs data ingest, motion correction cell segmentation and classification, fluorescent trace extraction, and calcium spike inference (deconvolution), and support for multiple manual curations of the segmentation results.

Figure 1: Scientists use DataJoint from their programming environments (MATLAB or Python) to define the structure of the data pipeline and its computational workflow. The data infrastructure (separated on the right) may be managed on premises or in the cloud. Web interfaces offer convenient interaction with the data pipeline. Diverse configurations are supported.

Figure 2: DataJoint serves as a framework for defining and executing scientific data workflows. It combines principled data management, workflow execution, and flexible queries accessed directly from the popular scientific programming languages MATLAB and Python. A) The graph represents the workflow for a neuroscience experiment where each node is a workflow step with a corresponding table in the database to store its data. Each node also has a class in the host programming language for scientific computing that defines the node and any computations that must be performed to populate the table. B) The Python class definition for one of the nodes in the workflows, RNNFit, defines the callback function, make, which defines the computation.

Figure 3: Data management considerations across the lifecycle of projects featuring shared data pipelines.

Citations

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DataJoint Elements: Data Workflows for Neurophysiology

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1. What are the contributions in "Datajoint elements: data workflows for neurophysiology" ?

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Citations

Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish

The technology, opportunities, and challenges of Synthetic Biological Intelligence

PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research

Facilitating the sharing of electrophysiology data analysis results through in-depth provenance capture.

Reward-reset interval timing drives patch foraging decisions through neural state transitions in dorsomedial striatum

References

The FAIR Guiding Principles for scientific data management and stewardship

Fully integrated silicon probes for high-density recording of neural activity

Toil enables reproducible, open source, big biomedical data analyses

Suite2p: beyond 10,000 neurons with standard two-photon microscopy

Workflows and e-Science: An overview of workflow system features and capabilities

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