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neuPrint: An open access tool for EM connectomics
Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled b...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9350508/ https://www.ncbi.nlm.nih.gov/pubmed/35935535 http://dx.doi.org/10.3389/fninf.2022.896292 |
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author | Plaza, Stephen M. Clements, Jody Dolafi, Tom Umayam, Lowell Neubarth, Nicole N. Scheffer, Louis K. Berg, Stuart |
author_facet | Plaza, Stephen M. Clements, Jody Dolafi, Tom Umayam, Lowell Neubarth, Nicole N. Scheffer, Louis K. Berg, Stuart |
author_sort | Plaza, Stephen M. |
collection | PubMed |
description | Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication—it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components—a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research. |
format | Online Article Text |
id | pubmed-9350508 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93505082022-08-05 neuPrint: An open access tool for EM connectomics Plaza, Stephen M. Clements, Jody Dolafi, Tom Umayam, Lowell Neubarth, Nicole N. Scheffer, Louis K. Berg, Stuart Front Neuroinform Neuroscience Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication—it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components—a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research. Frontiers Media S.A. 2022-07-20 /pmc/articles/PMC9350508/ /pubmed/35935535 http://dx.doi.org/10.3389/fninf.2022.896292 Text en Copyright © 2022 Plaza, Clements, Dolafi, Umayam, Neubarth, Scheffer and Berg. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Plaza, Stephen M. Clements, Jody Dolafi, Tom Umayam, Lowell Neubarth, Nicole N. Scheffer, Louis K. Berg, Stuart neuPrint: An open access tool for EM connectomics |
title | neuPrint: An open access tool for EM connectomics |
title_full | neuPrint: An open access tool for EM connectomics |
title_fullStr | neuPrint: An open access tool for EM connectomics |
title_full_unstemmed | neuPrint: An open access tool for EM connectomics |
title_short | neuPrint: An open access tool for EM connectomics |
title_sort | neuprint: an open access tool for em connectomics |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9350508/ https://www.ncbi.nlm.nih.gov/pubmed/35935535 http://dx.doi.org/10.3389/fninf.2022.896292 |
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