The SONATA data format for efficient description of large-scale network models

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale mod...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PLoS computational biology 2020-02, Vol.16 (2), p.e1007696-e1007696
Hauptverfasser: Dai, Kael, Hernando, Juan, Billeh, Yazan N, Gratiy, Sergey L, Planas, Judit, Davison, Andrew P, Dura-Bernal, Salvador, Gleeson, Padraig, Devresse, Adrien, Dichter, Benjamin K, Gevaert, Michael, King, James G, Van Geit, Werner A H, Povolotsky, Arseny V, Muller, Eilif, Courcol, Jean-Denis, Arkhipov, Anton
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page e1007696
container_issue 2
container_start_page e1007696
container_title PLoS computational biology
container_volume 16
creator Dai, Kael
Hernando, Juan
Billeh, Yazan N
Gratiy, Sergey L
Planas, Judit
Davison, Andrew P
Dura-Bernal, Salvador
Gleeson, Padraig
Devresse, Adrien
Dichter, Benjamin K
Gevaert, Michael
King, James G
Van Geit, Werner A H
Povolotsky, Arseny V
Muller, Eilif
Courcol, Jean-Denis
Arkhipov, Anton
description Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.
doi_str_mv 10.1371/journal.pcbi.1007696
format Article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2377705416</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A615716315</galeid><doaj_id>oai_doaj_org_article_96cd62f444274b59b61eee3ed7ac4d6c</doaj_id><sourcerecordid>A615716315</sourcerecordid><originalsourceid>FETCH-LOGICAL-c694t-41fcc354466d03971a5b9a1dcd38f39920add808e588857d29f72929e2518313</originalsourceid><addsrcrecordid>eNqVkk1vEzEQhlcIREvhHyBYwQUOCfb6Y9cXpKgCGilKJZq75dizicPuOrWdAv8eL7utkooL8sHW-JnX74wny15jNMWkxJ927uA71Uz3em2nGKGSC_4kO8eMkUlJWPX06HyWvQhhh1A6Cv48OyMFEgVi9DxbrraQ31wvZ6tZblRUee18q2K_5VDXVlvoYm4gaG_30boud3XeKL-BSdCqgbyD-NP5H3nrDDThZfasVk2AV-N-ka2-flldXk0W19_ml7PFRHNB44TiWmvCKOXcICJKrNhaKGy0IVVNRPKmjKlQBayqKlaaQtRlIQoBBcMVweQiezvI7hsX5NiJIAtSlmUqC_NEzAfCOLWTe29b5X9Lp6z8G3B-I5WPVjcgBdeGFzWltCjpmok1xwBAwJRKU8N10vo8vnZYt2B06ohXzYno6U1nt3Lj7mTyUhGGksC7QcCFaGXQNoLeatd1oKPEvBAMswR9HKDtI-2r2UL2MVQITDDmd339H0ZH3t0eIETZ2qChaVQH7tA3glNECBY0oe8fof9u13SgNulPpe1qlwrRaRlobXIKtU3xGcesTPSx2zEhMRF-xY06hCDnN9__g12esnRgtXcheKgfWoGR7Of-3r7s516Oc5_S3hx_0UPS_aCTP97L-30</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2377705416</pqid></control><display><type>article</type><title>The SONATA data format for efficient description of large-scale network models</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS)</source><source>PubMed Central</source><creator>Dai, Kael ; Hernando, Juan ; Billeh, Yazan N ; Gratiy, Sergey L ; Planas, Judit ; Davison, Andrew P ; Dura-Bernal, Salvador ; Gleeson, Padraig ; Devresse, Adrien ; Dichter, Benjamin K ; Gevaert, Michael ; King, James G ; Van Geit, Werner A H ; Povolotsky, Arseny V ; Muller, Eilif ; Courcol, Jean-Denis ; Arkhipov, Anton</creator><contributor>Marinazzo, Daniele</contributor><creatorcontrib>Dai, Kael ; Hernando, Juan ; Billeh, Yazan N ; Gratiy, Sergey L ; Planas, Judit ; Davison, Andrew P ; Dura-Bernal, Salvador ; Gleeson, Padraig ; Devresse, Adrien ; Dichter, Benjamin K ; Gevaert, Michael ; King, James G ; Van Geit, Werner A H ; Povolotsky, Arseny V ; Muller, Eilif ; Courcol, Jean-Denis ; Arkhipov, Anton ; Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States) ; Marinazzo, Daniele</creatorcontrib><description>Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1007696</identifier><identifier>PMID: 32092054</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Applications programming ; BASIC BIOLOGICAL SCIENCES ; Biological computing ; Biology and Life Sciences ; Brain ; Brain - physiology ; Brain Mapping ; Cognitive Sciences ; Computational Biology ; Computational Biology - methods ; Computational neuroscience ; Computer and Information Sciences ; Computer Simulation ; Construction ; Databases, Factual ; Format ; Humans ; Interfaces ; Life Sciences ; Medicine and Health Sciences ; Models, Neurological ; Nervous system ; Neurobiology ; Neurons ; Neurons - physiology ; Neurons and Cognition ; Neurophysiology ; Neurosciences ; Physical Sciences ; Programming Languages ; Psychology and behavior ; Reproducibility of Results ; Research and Analysis Methods ; Scale models ; Simulation ; Software ; Supervision ; Time series</subject><ispartof>PLoS computational biology, 2020-02, Vol.16 (2), p.e1007696-e1007696</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c694t-41fcc354466d03971a5b9a1dcd38f39920add808e588857d29f72929e2518313</citedby><cites>FETCH-LOGICAL-c694t-41fcc354466d03971a5b9a1dcd38f39920add808e588857d29f72929e2518313</cites><orcidid>0000-0002-2147-5895 ; 0000-0003-4309-8266 ; 0000-0002-8221-7988 ; 0000-0001-8911-2321 ; 0000-0001-5200-4992 ; 0000-0003-0906-8389 ; 0000-0002-2915-720X ; 0000-0002-8562-8213 ; 0000-0001-5963-8576 ; 0000-0002-9351-1461 ; 0000-0002-7547-3297 ; 0000-0002-4793-7541 ; 0000-0003-1106-8310 ; 0000-0003-0071-3265 ; 0000-0001-5725-6910 ; 000000022915720X ; 0000000157256910 ; 0000000282217988 ; 0000000293511461 ; 0000000152004992 ; 0000000275473297 ; 0000000285628213 ; 0000000159638576 ; 0000000343098266 ; 0000000311068310 ; 0000000300713265 ; 0000000221475895 ; 0000000189112321 ; 0000000309068389 ; 0000000247937541</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058350/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058350/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23849,27907,27908,53774,53776,79351,79352</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32092054$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-02913116$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1629515$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><contributor>Marinazzo, Daniele</contributor><creatorcontrib>Dai, Kael</creatorcontrib><creatorcontrib>Hernando, Juan</creatorcontrib><creatorcontrib>Billeh, Yazan N</creatorcontrib><creatorcontrib>Gratiy, Sergey L</creatorcontrib><creatorcontrib>Planas, Judit</creatorcontrib><creatorcontrib>Davison, Andrew P</creatorcontrib><creatorcontrib>Dura-Bernal, Salvador</creatorcontrib><creatorcontrib>Gleeson, Padraig</creatorcontrib><creatorcontrib>Devresse, Adrien</creatorcontrib><creatorcontrib>Dichter, Benjamin K</creatorcontrib><creatorcontrib>Gevaert, Michael</creatorcontrib><creatorcontrib>King, James G</creatorcontrib><creatorcontrib>Van Geit, Werner A H</creatorcontrib><creatorcontrib>Povolotsky, Arseny V</creatorcontrib><creatorcontrib>Muller, Eilif</creatorcontrib><creatorcontrib>Courcol, Jean-Denis</creatorcontrib><creatorcontrib>Arkhipov, Anton</creatorcontrib><creatorcontrib>Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</creatorcontrib><title>The SONATA data format for efficient description of large-scale network models</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.</description><subject>Algorithms</subject><subject>Applications programming</subject><subject>BASIC BIOLOGICAL SCIENCES</subject><subject>Biological computing</subject><subject>Biology and Life Sciences</subject><subject>Brain</subject><subject>Brain - physiology</subject><subject>Brain Mapping</subject><subject>Cognitive Sciences</subject><subject>Computational Biology</subject><subject>Computational Biology - methods</subject><subject>Computational neuroscience</subject><subject>Computer and Information Sciences</subject><subject>Computer Simulation</subject><subject>Construction</subject><subject>Databases, Factual</subject><subject>Format</subject><subject>Humans</subject><subject>Interfaces</subject><subject>Life Sciences</subject><subject>Medicine and Health Sciences</subject><subject>Models, Neurological</subject><subject>Nervous system</subject><subject>Neurobiology</subject><subject>Neurons</subject><subject>Neurons - physiology</subject><subject>Neurons and Cognition</subject><subject>Neurophysiology</subject><subject>Neurosciences</subject><subject>Physical Sciences</subject><subject>Programming Languages</subject><subject>Psychology and behavior</subject><subject>Reproducibility of Results</subject><subject>Research and Analysis Methods</subject><subject>Scale models</subject><subject>Simulation</subject><subject>Software</subject><subject>Supervision</subject><subject>Time series</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVkk1vEzEQhlcIREvhHyBYwQUOCfb6Y9cXpKgCGilKJZq75dizicPuOrWdAv8eL7utkooL8sHW-JnX74wny15jNMWkxJ927uA71Uz3em2nGKGSC_4kO8eMkUlJWPX06HyWvQhhh1A6Cv48OyMFEgVi9DxbrraQ31wvZ6tZblRUee18q2K_5VDXVlvoYm4gaG_30boud3XeKL-BSdCqgbyD-NP5H3nrDDThZfasVk2AV-N-ka2-flldXk0W19_ml7PFRHNB44TiWmvCKOXcICJKrNhaKGy0IVVNRPKmjKlQBayqKlaaQtRlIQoBBcMVweQiezvI7hsX5NiJIAtSlmUqC_NEzAfCOLWTe29b5X9Lp6z8G3B-I5WPVjcgBdeGFzWltCjpmok1xwBAwJRKU8N10vo8vnZYt2B06ohXzYno6U1nt3Lj7mTyUhGGksC7QcCFaGXQNoLeatd1oKPEvBAMswR9HKDtI-2r2UL2MVQITDDmd339H0ZH3t0eIETZ2qChaVQH7tA3glNECBY0oe8fof9u13SgNulPpe1qlwrRaRlobXIKtU3xGcesTPSx2zEhMRF-xY06hCDnN9__g12esnRgtXcheKgfWoGR7Of-3r7s516Oc5_S3hx_0UPS_aCTP97L-30</recordid><startdate>20200224</startdate><enddate>20200224</enddate><creator>Dai, Kael</creator><creator>Hernando, Juan</creator><creator>Billeh, Yazan N</creator><creator>Gratiy, Sergey L</creator><creator>Planas, Judit</creator><creator>Davison, Andrew P</creator><creator>Dura-Bernal, Salvador</creator><creator>Gleeson, Padraig</creator><creator>Devresse, Adrien</creator><creator>Dichter, Benjamin K</creator><creator>Gevaert, Michael</creator><creator>King, James G</creator><creator>Van Geit, Werner A H</creator><creator>Povolotsky, Arseny V</creator><creator>Muller, Eilif</creator><creator>Courcol, Jean-Denis</creator><creator>Arkhipov, Anton</creator><general>Public Library of Science</general><general>PLOS</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><scope>OIOZB</scope><scope>OTOTI</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2147-5895</orcidid><orcidid>https://orcid.org/0000-0003-4309-8266</orcidid><orcidid>https://orcid.org/0000-0002-8221-7988</orcidid><orcidid>https://orcid.org/0000-0001-8911-2321</orcidid><orcidid>https://orcid.org/0000-0001-5200-4992</orcidid><orcidid>https://orcid.org/0000-0003-0906-8389</orcidid><orcidid>https://orcid.org/0000-0002-2915-720X</orcidid><orcidid>https://orcid.org/0000-0002-8562-8213</orcidid><orcidid>https://orcid.org/0000-0001-5963-8576</orcidid><orcidid>https://orcid.org/0000-0002-9351-1461</orcidid><orcidid>https://orcid.org/0000-0002-7547-3297</orcidid><orcidid>https://orcid.org/0000-0002-4793-7541</orcidid><orcidid>https://orcid.org/0000-0003-1106-8310</orcidid><orcidid>https://orcid.org/0000-0003-0071-3265</orcidid><orcidid>https://orcid.org/0000-0001-5725-6910</orcidid><orcidid>https://orcid.org/000000022915720X</orcidid><orcidid>https://orcid.org/0000000157256910</orcidid><orcidid>https://orcid.org/0000000282217988</orcidid><orcidid>https://orcid.org/0000000293511461</orcidid><orcidid>https://orcid.org/0000000152004992</orcidid><orcidid>https://orcid.org/0000000275473297</orcidid><orcidid>https://orcid.org/0000000285628213</orcidid><orcidid>https://orcid.org/0000000159638576</orcidid><orcidid>https://orcid.org/0000000343098266</orcidid><orcidid>https://orcid.org/0000000311068310</orcidid><orcidid>https://orcid.org/0000000300713265</orcidid><orcidid>https://orcid.org/0000000221475895</orcidid><orcidid>https://orcid.org/0000000189112321</orcidid><orcidid>https://orcid.org/0000000309068389</orcidid><orcidid>https://orcid.org/0000000247937541</orcidid></search><sort><creationdate>20200224</creationdate><title>The SONATA data format for efficient description of large-scale network models</title><author>Dai, Kael ; Hernando, Juan ; Billeh, Yazan N ; Gratiy, Sergey L ; Planas, Judit ; Davison, Andrew P ; Dura-Bernal, Salvador ; Gleeson, Padraig ; Devresse, Adrien ; Dichter, Benjamin K ; Gevaert, Michael ; King, James G ; Van Geit, Werner A H ; Povolotsky, Arseny V ; Muller, Eilif ; Courcol, Jean-Denis ; Arkhipov, Anton</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c694t-41fcc354466d03971a5b9a1dcd38f39920add808e588857d29f72929e2518313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Applications programming</topic><topic>BASIC BIOLOGICAL SCIENCES</topic><topic>Biological computing</topic><topic>Biology and Life Sciences</topic><topic>Brain</topic><topic>Brain - physiology</topic><topic>Brain Mapping</topic><topic>Cognitive Sciences</topic><topic>Computational Biology</topic><topic>Computational Biology - methods</topic><topic>Computational neuroscience</topic><topic>Computer and Information Sciences</topic><topic>Computer Simulation</topic><topic>Construction</topic><topic>Databases, Factual</topic><topic>Format</topic><topic>Humans</topic><topic>Interfaces</topic><topic>Life Sciences</topic><topic>Medicine and Health Sciences</topic><topic>Models, Neurological</topic><topic>Nervous system</topic><topic>Neurobiology</topic><topic>Neurons</topic><topic>Neurons - physiology</topic><topic>Neurons and Cognition</topic><topic>Neurophysiology</topic><topic>Neurosciences</topic><topic>Physical Sciences</topic><topic>Programming Languages</topic><topic>Psychology and behavior</topic><topic>Reproducibility of Results</topic><topic>Research and Analysis Methods</topic><topic>Scale models</topic><topic>Simulation</topic><topic>Software</topic><topic>Supervision</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dai, Kael</creatorcontrib><creatorcontrib>Hernando, Juan</creatorcontrib><creatorcontrib>Billeh, Yazan N</creatorcontrib><creatorcontrib>Gratiy, Sergey L</creatorcontrib><creatorcontrib>Planas, Judit</creatorcontrib><creatorcontrib>Davison, Andrew P</creatorcontrib><creatorcontrib>Dura-Bernal, Salvador</creatorcontrib><creatorcontrib>Gleeson, Padraig</creatorcontrib><creatorcontrib>Devresse, Adrien</creatorcontrib><creatorcontrib>Dichter, Benjamin K</creatorcontrib><creatorcontrib>Gevaert, Michael</creatorcontrib><creatorcontrib>King, James G</creatorcontrib><creatorcontrib>Van Geit, Werner A H</creatorcontrib><creatorcontrib>Povolotsky, Arseny V</creatorcontrib><creatorcontrib>Muller, Eilif</creatorcontrib><creatorcontrib>Courcol, Jean-Denis</creatorcontrib><creatorcontrib>Arkhipov, Anton</creatorcontrib><creatorcontrib>Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dai, Kael</au><au>Hernando, Juan</au><au>Billeh, Yazan N</au><au>Gratiy, Sergey L</au><au>Planas, Judit</au><au>Davison, Andrew P</au><au>Dura-Bernal, Salvador</au><au>Gleeson, Padraig</au><au>Devresse, Adrien</au><au>Dichter, Benjamin K</au><au>Gevaert, Michael</au><au>King, James G</au><au>Van Geit, Werner A H</au><au>Povolotsky, Arseny V</au><au>Muller, Eilif</au><au>Courcol, Jean-Denis</au><au>Arkhipov, Anton</au><au>Marinazzo, Daniele</au><aucorp>Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The SONATA data format for efficient description of large-scale network models</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2020-02-24</date><risdate>2020</risdate><volume>16</volume><issue>2</issue><spage>e1007696</spage><epage>e1007696</epage><pages>e1007696-e1007696</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32092054</pmid><doi>10.1371/journal.pcbi.1007696</doi><orcidid>https://orcid.org/0000-0002-2147-5895</orcidid><orcidid>https://orcid.org/0000-0003-4309-8266</orcidid><orcidid>https://orcid.org/0000-0002-8221-7988</orcidid><orcidid>https://orcid.org/0000-0001-8911-2321</orcidid><orcidid>https://orcid.org/0000-0001-5200-4992</orcidid><orcidid>https://orcid.org/0000-0003-0906-8389</orcidid><orcidid>https://orcid.org/0000-0002-2915-720X</orcidid><orcidid>https://orcid.org/0000-0002-8562-8213</orcidid><orcidid>https://orcid.org/0000-0001-5963-8576</orcidid><orcidid>https://orcid.org/0000-0002-9351-1461</orcidid><orcidid>https://orcid.org/0000-0002-7547-3297</orcidid><orcidid>https://orcid.org/0000-0002-4793-7541</orcidid><orcidid>https://orcid.org/0000-0003-1106-8310</orcidid><orcidid>https://orcid.org/0000-0003-0071-3265</orcidid><orcidid>https://orcid.org/0000-0001-5725-6910</orcidid><orcidid>https://orcid.org/000000022915720X</orcidid><orcidid>https://orcid.org/0000000157256910</orcidid><orcidid>https://orcid.org/0000000282217988</orcidid><orcidid>https://orcid.org/0000000293511461</orcidid><orcidid>https://orcid.org/0000000152004992</orcidid><orcidid>https://orcid.org/0000000275473297</orcidid><orcidid>https://orcid.org/0000000285628213</orcidid><orcidid>https://orcid.org/0000000159638576</orcidid><orcidid>https://orcid.org/0000000343098266</orcidid><orcidid>https://orcid.org/0000000311068310</orcidid><orcidid>https://orcid.org/0000000300713265</orcidid><orcidid>https://orcid.org/0000000221475895</orcidid><orcidid>https://orcid.org/0000000189112321</orcidid><orcidid>https://orcid.org/0000000309068389</orcidid><orcidid>https://orcid.org/0000000247937541</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1553-7358
ispartof PLoS computational biology, 2020-02, Vol.16 (2), p.e1007696-e1007696
issn 1553-7358
1553-734X
1553-7358
language eng
recordid cdi_plos_journals_2377705416
source MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central
subjects Algorithms
Applications programming
BASIC BIOLOGICAL SCIENCES
Biological computing
Biology and Life Sciences
Brain
Brain - physiology
Brain Mapping
Cognitive Sciences
Computational Biology
Computational Biology - methods
Computational neuroscience
Computer and Information Sciences
Computer Simulation
Construction
Databases, Factual
Format
Humans
Interfaces
Life Sciences
Medicine and Health Sciences
Models, Neurological
Nervous system
Neurobiology
Neurons
Neurons - physiology
Neurons and Cognition
Neurophysiology
Neurosciences
Physical Sciences
Programming Languages
Psychology and behavior
Reproducibility of Results
Research and Analysis Methods
Scale models
Simulation
Software
Supervision
Time series
title The SONATA data format for efficient description of large-scale network models
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T17%3A45%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20SONATA%20data%20format%20for%20efficient%20description%20of%20large-scale%20network%20models&rft.jtitle=PLoS%20computational%20biology&rft.au=Dai,%20Kael&rft.aucorp=Lawrence%20Berkeley%20National%20Laboratory%20(LBNL),%20Berkeley,%20CA%20(United%20States)&rft.date=2020-02-24&rft.volume=16&rft.issue=2&rft.spage=e1007696&rft.epage=e1007696&rft.pages=e1007696-e1007696&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1007696&rft_dat=%3Cgale_plos_%3EA615716315%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2377705416&rft_id=info:pmid/32092054&rft_galeid=A615716315&rft_doaj_id=oai_doaj_org_article_96cd62f444274b59b61eee3ed7ac4d6c&rfr_iscdi=true