sbd-data-book

Titanium Oxide

Because the eNanoMapper ontology [1] is loaded, we can take advantage of the hierarchy of the ontology. For example, we can list all titania’s (npo:NPO_1486) with this SPARQL:

SPARQL sparql/allTitanias.rq (run)

PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX obo: <http://purl.obolibrary.org/obo/>
SELECT DISTINCT ?nm (SAMPLE(?nmLabel_) AS ?nmLabel)
WHERE {
  VALUES ?superclass { npo:NPO_1486 obo:CHEBI_51050 }
  ?nm rdfs:subClassOf* ?superclass ; rdfs:label ?nmLabel_ .
} GROUP BY ?nm

This gives us this list of nanomaterials:

nm
R-TiO2
coated titanium dioxide nanoparticle
JRCNM01000a
JRCNM01001a
JRCNM01002a
JRCNM01005a
JRCNM01003a
JRCNM01004a
JRCNM10201a
JRCNM62001a
JRCNM62002a
JRCNM10200a
JRCNM10202a
NM-105
NM-100
NM-101
NM-102
NM-103
NM-104
titanium dioxide nanoparticle
ERM00000062
ERM00000064
ERM00000093
ERM00000587
ERM00000595
ERM00000599
ERM00000600
ERM00000604
ERM00000605
ERM00000606

Resources

Using the hierarchy, we can find resources about a specific nanomaterial (here JRCNM01005a) or about any of its superclasses:

SPARQL sparql/allTitaniaResources.rq (run)

PREFIX sbd:  <https://www.sbd4nano.eu/rdf/#>
PREFIX sbdbel:  <https://www.sbd4nano.eu/bel/#>
PREFIX dcterms: <http://purl.org/dc/terms/>
PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX obo: <http://purl.obolibrary.org/obo/>
PREFIX enm: <http://purl.enanomapper.org/onto/>
PREFIX sio: <http://semanticscience.org/resource/SIO_>
PREFIX sbdbel2:  <https://h2020-sbd4nano.github.io/sbdbel/>
SELECT DISTINCT ?superclasses (SAMPLE(?superclassesLabel_) AS ?superclassesLabel) ?type (COUNT(DISTINCT ?resource) AS ?count)
WHERE {
  VALUES ?superclasses { npo:NPO_1541 npo:NPO_1486 enm:ENM_9000077 } # metal oxide, TiO2, JRCNM01005a
  ?nm rdfs:subClassOf* ?superclasses .
  ?superclasses rdfs:label ?superclassesLabel_ .
  OPTIONAL {
    ?resource a ?type ;
              sbdbel:NP | sio:000332 | sbdbel2:NP ?nm .
    ?type rdfs:label ?typeLabel .
  }
} GROUP BY ?superclasses ?superclassesLabel ?type
  ORDER BY DESC(?count)

This returns:

superclasses type count
Metal Oxide https://www.sbd4nano.eu/rdf/#Model 12
Metal Oxide https://www.sbd4nano.eu/rdf/#Database 7
Metal Oxide https://www.sbd4nano.eu/rdf/#Dataset 7
Metal Oxide https://h2020-sbd4nano.github.io/sbdbel/CausalAssertion 6
R-TiO2 https://www.sbd4nano.eu/rdf/#Dataset 1
R-TiO2 0
Metal Oxide 0
JRCNM01005a 0

Causal relationships

This approach can be used to find datasets, models, causal relationships, etc applicable to a certain nanomaterial or nanomaterial class. For example, we can list all relationships for JRCNM01005a [2,3]:

SPARQL sparql/allJRCNM01005aRelationships.rq (run)

PREFIX sbd:  <https://www.sbd4nano.eu/rdf/#>
PREFIX sbdbel:  <https://www.sbd4nano.eu/bel/#>
PREFIX enm:     <http://purl.enanomapper.org/onto/>
PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX sio: <http://semanticscience.org/resource/SIO_>
PREFIX sbdbel2:  <https://h2020-sbd4nano.github.io/sbdbel/>
SELECT DISTINCT
  ?nm (SAMPLE(?nmLabel_) AS ?nmLabel)
  (COUNT(DISTINCT ?relation) AS ?relations)
WHERE {
  VALUES ?ca { sbdbel:CausalAssertion sbd:CausalAssertion sbdbel2:CausalAssertion }
  VALUES ?nm { enm:ENM_9000077 }
  ?nm rdfs:label ?nmLabel_ .
  OPTIONAL { ?relation a ?ca ; sbdbel:NP | sio:000332 | sbdbel2:NP ?nm . }
} GROUP BY ?superclass ?nm

But for a specific material, such relationships may not exist:

nm relations
JRCNM01005a 0

In that case, we can better look for relationships for the class of nanoforms this material is part of.

All tatanium dioxides

For example, we can list all relationships for all metal oxides:

SPARQL sparql/allTitaniumOxideRelationships.rq (run)

PREFIX sbd:  <https://www.sbd4nano.eu/rdf/#>
PREFIX sbdbel:  <https://www.sbd4nano.eu/bel/#>
PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX sio: <http://semanticscience.org/resource/SIO_>
PREFIX sbdbel2:  <https://h2020-sbd4nano.github.io/sbdbel/>
SELECT DISTINCT
  ?superclass (SAMPLE(?superclassLabel_) AS ?superclassLabel)
  ?nm (SAMPLE(?nmLabel_) AS ?nmLabel)
  (COUNT(DISTINCT ?relation) AS ?relations)
WHERE {
  VALUES ?ca { sbdbel:CausalAssertion sbd:CausalAssertion sbdbel2:CausalAssertion }
  VALUES ?superclass { npo:NPO_1486 }
  ?nm rdfs:subClassOf* ?superclass ; rdfs:label ?nmLabel_ .
  ?superclass rdfs:label ?superclassLabel_ .
  ?relation a ?ca ; sbdbel:NP | sio:000332 | sbdbel2:NP ?nm .
} GROUP BY ?superclass ?nm

But this still returns no relationships:

All metal oxides

For example, we can list all relationships for all metal oxides:

SPARQL sparql/allMetalOxideRelationships.rq (run)

PREFIX sbd:  <https://www.sbd4nano.eu/rdf/#>
PREFIX sbdbel:  <https://www.sbd4nano.eu/bel/#>
PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX sio: <http://semanticscience.org/resource/SIO_>
PREFIX sbdbel2:  <https://h2020-sbd4nano.github.io/sbdbel/>
SELECT DISTINCT
  ?superclass (SAMPLE(?superclassLabel_) AS ?superclassLabel)
  ?nm (SAMPLE(?nmLabel_) AS ?nmLabel)
  (COUNT(DISTINCT ?relation) AS ?relations)
WHERE {
  VALUES ?ca { sbdbel:CausalAssertion sbd:CausalAssertion sbdbel2:CausalAssertion }
  VALUES ?superclass { npo:NPO_1541 }
  ?nm rdfs:subClassOf* ?superclass ; rdfs:label ?nmLabel_ .
  ?superclass rdfs:label ?superclassLabel_ .
  ?relation a ?ca ; sbdbel:NP | sio:000332 | sbdbel2:NP ?nm .
} GROUP BY ?superclass ?nm

We find here that basically all relationships are defined at a metal oxide level:

superclass nm relations
Metal Oxide Metal Oxide 6

TiO2 datasets

Toxicity of titanium dioxide has been extensively studied. Noting that for many resources we do not have detailed annotation of the nanoforms described by those sources, some do:

SPARQL sparql/allTitaniaData.rq (run)

PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX obo: <http://purl.obolibrary.org/obo/>
PREFIX sbd:     <https://www.sbd4nano.eu/rdf/#>
PREFIX sbdbel:  <https://www.sbd4nano.eu/bel/#>
PREFIX foaf:   <http://xmlns.com/foaf/0.1/> 
PREFIX rdfs:   <http://www.w3.org/2000/01/rdf-schema#> 
PREFIX erm:    <https://nanocommons.github.io/identifiers/registry#>
PREFIX enm:    <http://purl.enanomapper.org/onto/>
SELECT ?materialIRI (SAMPLE(?material_) AS ?material) ?dataset ?datasetLabel
WHERE {
  VALUES ?superclass { npo:NPO_1486 obo:CHEBI_51050 }
  VALUES ?type { sbd:Dataset sbd:Database }
  ?dataset sbdbel:NP ?materialIRI ; a ?type ; rdfs:label ?datasetLabel .
  ?materialIRI rdfs:subClassOf* ?superclass ; rdfs:label ?material_ .
} GROUP BY ?materialIRI ?dataset ?datasetLabel
  ORDER BY ?dataset

This gives:

materialIRI material dataset
obo:CHEBI_51050 titanium dioxide nanoparticle calibrate - eNanoMapper database
obo:CHEBI_51050 titanium dioxide nanoparticle harmless - eNanoMapper database
obo:CHEBI_51050 titanium dioxide nanoparticle nanoinformatix - eNanoMapper database
obo:CHEBI_51050 titanium dioxide nanoparticle nanoreg - eNanoMapper database
obo:CHEBI_51050 titanium dioxide nanoparticle nanoreg2 - eNanoMapper database
obo:CHEBI_51050 titanium dioxide nanoparticle riskgone - eNanoMapper database
erm:ERM00000062 ERM00000062 riskgone - eNanoMapper database
erm:ERM00000064 ERM00000064 riskgone - eNanoMapper database
obo:CHEBI_51050 titanium dioxide nanoparticle sbd4nano - eNanoMapper database
erm:ERM00000093 ERM00000093 sbd4nano - eNanoMapper database
erm:ERM00000587 ERM00000587 sbd4nano - eNanoMapper database
erm:ERM00000595 ERM00000595 sbd4nano - eNanoMapper database
erm:ERM00000599 ERM00000599 sbd4nano - eNanoMapper database
erm:ERM00000600 ERM00000600 sbd4nano - eNanoMapper database
erm:ERM00000604 ERM00000604 sbd4nano - eNanoMapper database
erm:ERM00000605 ERM00000605 sbd4nano - eNanoMapper database
erm:ERM00000606 ERM00000606 sbd4nano - eNanoMapper database
enm:ENM_9000204 NM-105 Transcriptomic study of NR8383 rat macrophages cells following exposure to TiO2 (NM105, p25 anatase/rutile)
obo:CHEBI_51050 titanium dioxide nanoparticle Transcriptomic study of NR8383 rat macrophages cells following exposure to TiO2 (NRCWE001)
obo:CHEBI_51050 titanium dioxide nanoparticle TiO2-induced gene expression profiles in rat lung: a subacute inhalation study

TiO2 Models

We can list all models for titanium dioxide with the following query:

SPARQL sparql/allTitaniaModels.rq (run)

PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX obo: <http://purl.obolibrary.org/obo/>
PREFIX rdfs:    <http://www.w3.org/2000/01/rdf-schema#>
PREFIX sbd:     <https://www.sbd4nano.eu/rdf/#>
PREFIX sbdbel:  <https://www.sbd4nano.eu/bel/#>
PREFIX dc:      <http://purl.org/dc/elements/1.1/>
PREFIX dct:     <http://purl.org/dc/terms/>
PREFIX skos:    <http://www.w3.org/2004/02/skos/core#>
SELECT ?model ?modelLabel ?provider
       (group_concat(distinct ?material_;separator=", ") AS ?material)
WHERE {
  VALUES ?superclass { npo:NPO_1486 obo:CHEBI_51050 }
  ?materialIRI rdfs:subClassOf* ?superclass ; rdfs:label ?material_ .
  ?model a sbd:Model ;
     dc:source ?providerRes.
  { ?model sbdbel:NP ?materialIRI . } UNION { ?model dct:subject / skos:narrower ?materialIRI . }
  OPTIONAL { ?model rdfs:label ?rdfsLabel }
  BIND(COALESCE(?rdfsLabel, str(?model)) AS ?modelLabel)
  ?providerRes dct:title | dc:title ?provider .
} GROUP BY ?model ?modelLabel ?provider
  ORDER BY ?model

This gives us:

model provider material
Nanocompound: LDH(TiO2) SbD4nano Nanocompound titanium dioxide nanoparticle
Nanocompound: LDH(TiO2+ZnO) SbD4nano Nanocompound titanium dioxide nanoparticle
Nanocompound: LDH(TiO2): Serratosa2022 SbD4nano Nanocompound titanium dioxide nanoparticle
Nanocompound: LDH(TiO2+ZnO): Serratosa2022 SbD4nano Nanocompound titanium dioxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_10 Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_12 Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_14 Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_16 Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_20 Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_21 Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_23a Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_23b Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_25e Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_29a Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_29b Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_2a Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_31a Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_31b Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_34a Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_34b Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38a Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38b Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38d Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
This table is truncated. See the full table at sparql/allTitaniaModels.rq

But more general metal oxide models may be useful too:

SPARQL sparql/allMetalOxideModels.rq (run)

PREFIX npo: <http://purl.bioontology.org/ontology/npo#>
PREFIX obo: <http://purl.obolibrary.org/obo/>
PREFIX rdfs:    <http://www.w3.org/2000/01/rdf-schema#>
PREFIX sbd:     <https://www.sbd4nano.eu/rdf/#>
PREFIX sbdbel:  <https://www.sbd4nano.eu/bel/#>
PREFIX dc:      <http://purl.org/dc/elements/1.1/>
PREFIX dct:     <http://purl.org/dc/terms/>
PREFIX skos:    <http://www.w3.org/2004/02/skos/core#>
SELECT ?model ?modelLabel ?provider
       (group_concat(distinct ?material_;separator=", ") AS ?material)
WHERE {
  VALUES ?superclass { npo:NPO_1541 }
  ?materialIRI rdfs:subClassOf* ?superclass ; rdfs:label ?material_ .
  ?model a sbd:Model ;
     dc:source ?providerRes.
  { ?model sbdbel:NP ?materialIRI . } UNION { ?model dct:subject / skos:narrower ?materialIRI . }
  OPTIONAL { ?model rdfs:label ?rdfsLabel }
  BIND(COALESCE(?rdfsLabel, str(?model)) AS ?modelLabel)
  ?providerRes dct:title | dc:title ?provider .
} GROUP BY ?model ?modelLabel ?provider
  ORDER BY ?model

This gives us:

model provider material
PhysChem: Zeta potential NanoXtract model NanoSolveIT Tools Metal Oxide, metal oxide nanoparticle
Nanocompound: Toxicity Metal-Oxide: Anantha 2021 SbD4nano Nanocompound Metal Oxide, metal oxide nanoparticle
Nanocompound: Toxicity Metal-Oxide: Gajewicz 2015 SbD4nano Nanocompound Metal Oxide, metal oxide nanoparticle
Nanocompound: LDH(TiO2) SbD4nano Nanocompound titanium dioxide nanoparticle
Nanocompound: LDH(TiO2+ZnO) SbD4nano Nanocompound ZnO, zinc oxide nanoparticle, titanium dioxide nanoparticle
Nanocompound: LDH(TiO2+ZnO) SbD4nano Nanocompound ZnO, zinc oxide nanoparticle
Nanocompound: Toxicity Metal-Oxide: Puzyn 2011 SbD4nano Nanocompound Metal Oxide, metal oxide nanoparticle
Nanocompound: Toxicity Metal-Oxide: Serratosa2022 SbD4nano Nanocompound Metal Oxide, metal oxide nanoparticle
Nanocompound: LDH(TiO2): Serratosa2022 SbD4nano Nanocompound titanium dioxide nanoparticle
Nanocompound: LDH(TiO2+ZnO): Serratosa2022 SbD4nano Nanocompound ZnO, zinc oxide nanoparticle, titanium dioxide nanoparticle
Nanocompound: LDH(ZnO): Serratosa2022 SbD4nano Nanocompound ZnO, zinc oxide nanoparticle
NanoSolveIT Cytotoxicity (Cell Viability) Prediction for Metal Oxide NPs NanoSolveIT Tools Metal Oxide, metal oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_10 Computational models for the assessment of manufactured nanomaterials Fe2O3, iron (III) oxide nanoparticle, ZnO, zinc oxide nanoparticle, R-TiO2, TiO2, titanium oxide nanoparticle, Cobalt (II) oxide nanoparticle, cobalt oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_11 Computational models for the assessment of manufactured nanomaterials (Fe2O3)n(Fe3O4)m, iron oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_12 Computational models for the assessment of manufactured nanomaterials Fe2O3, iron (III) oxide nanoparticle, ZnO, zinc oxide nanoparticle, Fe3O4, iron (II,III) oxide nanoparticle, R-TiO2, TiO2, titanium oxide nanoparticle, Cobalt (II) oxide nanoparticle, cobalt oxide nanoparticle, Co3O4 nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_14 Computational models for the assessment of manufactured nanomaterials Fe2O3, iron (III) oxide nanoparticle, ZnO, zinc oxide nanoparticle, Fe3O4, iron (II,III) oxide nanoparticle, R-TiO2, TiO2, titanium oxide nanoparticle, Cobalt (II) oxide nanoparticle, cobalt oxide nanoparticle, Co3O4 nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_15 Computational models for the assessment of manufactured nanomaterials Fe2O3, iron (III) oxide nanoparticle, Fe3O4, iron (II,III) oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_16 Computational models for the assessment of manufactured nanomaterials R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_18 Computational models for the assessment of manufactured nanomaterials (Fe2O3)n(Fe3O4)m, iron oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_1a Computational models for the assessment of manufactured nanomaterials (Fe2O3)n(Fe3O4)m, iron oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_1b Computational models for the assessment of manufactured nanomaterials (Fe2O3)n(Fe3O4)m, iron oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_20 Computational models for the assessment of manufactured nanomaterials Fe2O3, iron (III) oxide nanoparticle, ZnO, zinc oxide nanoparticle, Fe3O4, iron (II,III) oxide nanoparticle, R-TiO2, TiO2, titanium oxide nanoparticle
https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_21 Computational models for the assessment of manufactured nanomaterials Fe2O3, iron (III) oxide nanoparticle, ZnO, zinc oxide nanoparticle, Fe3O4, iron (II,III) oxide nanoparticle, R-TiO2, TiO2, titanium oxide nanoparticle
This table is truncated. See the full table at sparql/allMetalOxideModels.rq

References

  1. Hastings J, Jeliazkova N, Owen G, Tsiliki G, Munteanu CR, Steinbeck C, et al. eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment. J Biomed Semantics. 2015;6(1):10. doi:10.1186/S13326-015-0005-5 (Scholia)
  2. Totaro S, Cotogno G, Rasmussen K, Pianella F, Roncaglia M, Olsson H, et al. The JRC Nanomaterials Repository: A unique facility providing representative test materials for nanoEHS research. Regul Toxicol Pharmacol. 2016 Nov;81:334–40. doi:10.1016/J.YRTPH.2016.08.008 (Scholia)
  3. Ontology IRIs for the JRC representative industrial nanomaterials [Internet]. 2023 Feb. Available from: http://nanocommons.github.io/specifications/2023/WD-jrc-20230226/