ArticleNeuroinformatics2008
NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF.
Article in Neuroinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed.
- Automated Proofreading of Digitally Reconstructed Neural Morphology Enhances Accuracy, Scalability, and Standardization.bioRxiv : the preprint server for biology · 2026Article
- Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex.Neuroinformatics · 2025Article
- Dendritome mapping reveals the spatial organization of striatal neuron morphology.Nature neuroscience · 2025Article
- NeuroEditor: a tool to edit and visualize neuronal morphologies.Frontiers in neuroanatomy · 2024Article
- Online conversion of reconstructed neural morphologies into standardized SWC format.Nature communications · 2023Article
- Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification.Scientific reports · 2023Article
- RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations.Frontiers in neuroinformatics · 2022Article
- Efficient metadata mining of web-accessible neural morphologies.Progress in biophysics and molecular biology · 2022Article
- Data Management Schema Design for Effective Nanoparticle Formulation for Neurotherapeutics.AIChE journal. American Institute of Chemical Engineers · 2021Article
- Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations.Bioinformatics (Oxford, England) · 2021Article
- Highlights from the Era of Open Source Web-Based Tools.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Review
- Single-neuron dynamical effects of dendritic pruning implicated in aging and neurodegeneration: towards a measure of neuronal reserve.Scientific reports · 2021Article
- Article
- Spatially resolved dendritic integration: towards a functional classification of neurons.PeerJ · 2020Article
- Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform.Frontiers in neuroinformatics · 2019Article
- NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks.Bioinformatics (Oxford, England) · 2018Article
- An open repository for single-cell reconstructions of the brain forest.Scientific data · 2018Article
- Design and implementation of multi-signal and time-varying neural reconstructions.Scientific data · 2018Article
- A Segmentation Scheme for Complex Neuronal Arbors and Application to Vibration Sensitive Neurons in the Honeybee Brain.Frontiers in neuroinformatics · 2018Article
- Mining Big Neuron Morphological Data.Computational intelligence and neuroscience · 2018Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Neuronal morphology affects network connectivity, plasticity, and information processing. Uncovering the design principles and functional consequences of dendritic and axonal shape necessitates quantitative analysis and computational modeling of detailed experimental data. Digital reconstructions provide the required neuromorphological descriptions in a parsimonious, comprehensive, and reliable numerical format. NeuroMorpho.Org is the largest web-accessible repository service for digitally reconstructed neurons and one of the integrated resources in the Neuroscience Information Framework (NIF). Here we describe the NeuroMorpho.Org approach as an exemplary experience in designing, creating, populating, and curating a neuroscience digital resource. The simple three-tier architecture of NeuroMorpho.Org (web client, web server, and relational database) encompasses all necessary elements to support a large-scale, integrate-able repository. The data content, while heterogeneous in scientific scope and experimental origin, is unified in format and presentation by an in house standardization protocol. The server application (MRALD) is secure, customizable, and developer-friendly. Centralized processing and expert annotation yields a comprehensive set of metadata that enriches and complements the raw data. The thoroughly tested interface design allows for optimal and effective data search and retrieval. Availability of data in both original and standardized formats ensures compatibility with existing resources and fosters further tool development. Other key functions enable extensive exploration and discovery, including 3D and interactive visualization of branching, frequently measured morphometrics, and reciprocal links to the original PubMed publications. The integration of NeuroMorpho.Org with version-1 of the NIF (NIFv1) provides the opportunity to access morphological data in the context of other relevant resources and diverse subdomains of neuroscience, opening exciting new possibilities in data mining and knowledge discovery. The outcome of such coordination is the rapid and powerful advancement of neuroscience research at both the conceptual and technological level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.