Highlights from the Era of Open Source Web-Based Tools

Abstract

High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span subdisciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.

A Systematic, Open-Science Framework for Quantification of Cell-Types in Mouse Brain Sections Using Fluorescence Microscopy

“…Earlier neuro-morphometric studies relied on stereology as a method to mitigate variability and inconsistencies ( Haug, 1986 ; Zhao and van Praag, 2020 ) within small regions; however, newer advances and initiatives permit the quantification of cell density across large regions and even the whole brain at an unprecedented speed. In this regard, neuroscience-specific initiatives, such as the Brain Initiative’s functional connectome project, the Allen Brain Institute Reference Atlas, WholeBrain, and BrainGlobe provide excellent resources and tools ( Fürth et al, 2018 ; Anderson et al, 2021 ; Claudi et al, 2021 ). However, barriers such as user-friendliness, proficiency in programming languages, and other inherent restrictions – for example, the requirement for large whole brain section images – still limit the wide application of many of these tools.…”

Large scale similarity search across digital reconstructions of neural morphology

“…Similarity search is available for both human users through the NeuroMorpho.Org graphical interface as well as for programmatic usage as an API, therefore allowing all users to discover morphologically similar neurons efficiently. The now fully automated microservice-based data ingestion pipeline of NeuroMorpho.Org [22] checks for duplicate reconstructions before ingestion as a part of the data quality assurance. This is necessary as reconstructions may unintentionally be resubmitted if they are used in different studies together with novel reconstructions.…”

“…The growth rate of this repository has continuously increased due to a combination of more efficient reconstruction techniques, greater willingness of the neuroscience community to share, and rising open data expectations from funding organizations and scientific publishers [21]. In parallel, the NeuroMorpho.Org internal processing pipeline evolved into a micro-service based architecture, reducing the time from deposition to publication from months to weeks [22]. Within the data standardization workflow, it must be verified that each submitted reconstruction is not a duplicate of any previously submitted specimen.…”

Deep learning based markerless motion tracking as a clinical tool for movement disorders: Utility, feasibility and early experience

“…An additional advantage of DLC is its open-source availability and associated development community Anderson et al, 2021). DLC development is ongoing (Nath et al, 2019), and third-party contributions have enabled realtime tracking (Forys et al, 2020;Kane et al, 2020;Nourizonoz et al, 2020;Schweihoff et al, 2021;Sehara et al, 2021) and 3D reconstruction (Gosztolai et al, 2020;Sheshadri et al, 2020;Dunn et al, 2021;Karashchuk et al, 2021;Zhang et al, 2021).…”