Ontology type: schema:ScholarlyArticle Open Access: True
2019-12
AUTHORSAlessandro Crimi, Luca Giancardo, Fabio Sambataro, Alessandro Gozzi, Vittorio Murino, Diego Sona
ABSTRACTThe analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets. More... »
PAGES65
http://scigraph.springernature.com/pub.10.1038/s41598-018-37300-4
DOIhttp://dx.doi.org/10.1038/s41598-018-37300-4
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1111223634
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30635604
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1109",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Neurosciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "University Hospital of Zurich",
"id": "https://www.grid.ac/institutes/grid.412004.3",
"name": [
"Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy",
"Institute of Neuropathology, University Hospital of Z\u00fcrich, Z\u00fcrich, Switzerland"
],
"type": "Organization"
},
"familyName": "Crimi",
"givenName": "Alessandro",
"id": "sg:person.0757303371.13",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0757303371.13"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "The University of Texas Health Science Center at Houston",
"id": "https://www.grid.ac/institutes/grid.267308.8",
"name": [
"Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy",
"Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA"
],
"type": "Organization"
},
"familyName": "Giancardo",
"givenName": "Luca",
"id": "sg:person.0623701123.71",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0623701123.71"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Udine",
"id": "https://www.grid.ac/institutes/grid.5390.f",
"name": [
"Department of Experimental and Clinical Medical Sciences, University of Udine, Udine, Italy"
],
"type": "Organization"
},
"familyName": "Sambataro",
"givenName": "Fabio",
"id": "sg:person.01301505757.63",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01301505757.63"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Italian Institute of Technology",
"id": "https://www.grid.ac/institutes/grid.25786.3e",
"name": [
"Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy"
],
"type": "Organization"
},
"familyName": "Gozzi",
"givenName": "Alessandro",
"id": "sg:person.01367245201.42",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01367245201.42"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Verona",
"id": "https://www.grid.ac/institutes/grid.5611.3",
"name": [
"Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy",
"Department of Computer Science, University of Verona, Verona, Italy"
],
"type": "Organization"
},
"familyName": "Murino",
"givenName": "Vittorio",
"id": "sg:person.01147163731.38",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01147163731.38"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Fondazione Bruno Kessler",
"id": "https://www.grid.ac/institutes/grid.11469.3b",
"name": [
"Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy",
"Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, Italy"
],
"type": "Organization"
},
"familyName": "Sona",
"givenName": "Diego",
"id": "sg:person.01320216161.42",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320216161.42"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1111/j.1467-9868.2010.00740.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000696823"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1467-9868.2010.00740.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000696823"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biopsych.2009.08.024",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002268818"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1161/strokeaha.113.004137",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002466874"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1161/strokeaha.113.004137",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002466874"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2010.06.041",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002516416"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pcbi.1000100",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003321082"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2015.03.069",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003517578"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1523/jneurosci.2787-07.2007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004496076"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrn2575",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004953014",
"https://doi.org/10.1038/nrn2575"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/nyas.12360",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007685416"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/hbm.22278",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008180623"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-319-24574-4_72",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010806412",
"https://doi.org/10.1007/978-3-319-24574-4_72"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuron.2009.03.024",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011850909"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biopsych.2007.06.025",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011888284"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2010.05.081",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012067626"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/1961189.1961199",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013637525"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/hbm.21333",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014532716"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0197-4580(93)90015-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014717127"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0197-4580(93)90015-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014717127"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1148/radiol.10091701",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015080097"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/tp.2014.69",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015471926",
"https://doi.org/10.1038/tp.2014.69"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2013.03.066",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015889618"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/brain/awp089",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016722055"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1515/1544-6115.1792",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017320119"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0019071",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019157013"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biopsych.2011.02.019",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020953647"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3389/fnsys.2012.00059",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021123783"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/hbm.10102",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021974779"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2009.10.003",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022342218"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biopsych.2010.08.022",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025983222"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.schres.2005.11.020",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026737394"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/hbm.23007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027897550"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2015.05.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028357410"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2009.12.120",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028581904"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s12859-015-0575-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028801735",
"https://doi.org/10.1186/s12859-015-0575-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s12859-015-0575-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028801735",
"https://doi.org/10.1186/s12859-015-0575-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2013.04.007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030801178"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2013.09.050",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033556624"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2016.06.034",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036180331"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2010.02.040",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036370969"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/brain/awn262",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036438207"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.brainres.2009.02.070",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036649298"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2013.04.056",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037316334"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2008.05.050",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037798802"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1167/iovs.06-1029",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038720140"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3389/fpsyt.2011.00077",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041482830"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3389/fncom.2013.00171",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043867737"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1467-9868.2005.00503.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043971564"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0076655",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045293543"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1749-6632.2010.05888.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045416901"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biopsych.2009.07.022",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045970745"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/1531-8249(199902)45:2<265::aid-ana21>3.0.co;2-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047436296"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s13064-015-0033-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047478270",
"https://doi.org/10.1186/s13064-015-0033-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s13064-015-0033-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047478270",
"https://doi.org/10.1186/s13064-015-0033-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2377-12-46",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047921675",
"https://doi.org/10.1186/1471-2377-12-46"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0006-8993(03)02354-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047938481"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1467-9868.2011.00783.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050208266"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2014.07.031",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050265488"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.nicl.2014.05.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051151338"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1523/jneurosci.3539-11.2011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051512745"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/1362361310386506",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051752066"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/1362361310386506",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051752066"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1001/archneur.64.10.1482",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052907745"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tmi.2010.2059709",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061695602"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tmi.2011.2140380",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061695736"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tmi.2013.2276916",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061696161"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tmi.2013.2281398",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061696174"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tmi.2015.2463723",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061696579"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1136/jnnp.64.1.138",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062818912"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1136/jnnp.64.1.138",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062818912"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1198/tech.2011.08118",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064199782"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1089/brain.2016.0474",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090908049"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/isbi.2017.7950677",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093707950"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/prni.2013.14",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094341426"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.nicl.2018.01.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100770680"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1110628402",
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1110629995",
"type": "CreativeWork"
}
],
"datePublished": "2019-12",
"datePublishedReg": "2019-12-01",
"description": "The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets.",
"genre": "research_article",
"id": "sg:pub.10.1038/s41598-018-37300-4",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.2687006",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.7132465",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1045337",
"issn": [
"2045-2322"
],
"name": "Scientific Reports",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "9"
}
],
"name": "MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis",
"pagination": "65",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"781ec8b39612ff46e31701567139f230351ed84a64623997386e877c0c64369a"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"30635604"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"101563288"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/s41598-018-37300-4"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1111223634"
]
}
],
"sameAs": [
"https://doi.org/10.1038/s41598-018-37300-4",
"https://app.dimensions.ai/details/publication/pub.1111223634"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T08:40",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000319_0000000319/records_11196_00000000.jsonl",
"type": "ScholarlyArticle",
"url": "https://www.nature.com/articles/s41598-018-37300-4"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37300-4'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37300-4'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37300-4'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37300-4'
This table displays all metadata directly associated to this object as RDF triples.
343 TRIPLES
21 PREDICATES
100 URIs
21 LITERALS
9 BLANK NODES