@prefix this: <https://w3id.org/np/RA3Wt6jY7rpChWyGpQaqC0u1t44i4Bhd3PDIsyyA9uyOM> .
@prefix sub: <https://w3id.org/np/RA3Wt6jY7rpChWyGpQaqC0u1t44i4Bhd3PDIsyyA9uyOM/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix nt: <https://w3id.org/np/o/ntemplate/> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix orcid: <https://orcid.org/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .

<https://w3id.org/np/RA3Wt6jY7rpChWyGpQaqC0u1t44i4Bhd3PDIsyyA9uyOM> a np:Nanopublication;
  np:hasAssertion sub:assertion;
  np:hasProvenance sub:provenance;
  np:hasPublicationInfo sub:pubinfo;
  dct:created "2026-01-25T09:24:13.269Z"^^xsd:dateTime;
  dct:creator orcid:0000-0002-1784-2920;
  dct:license <https://creativecommons.org/licenses/by/4.0/>;
  npx:wasCreatedAt <https://nanodash.knowledgepixels.com/>;
  rdfs:label "PICO Research Question: DGGS as an AI-Ready Framework for Multi-Source Earth Observation Data Integration";
  nt:wasCreatedFromProvenanceTemplate <https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU>;
  nt:wasCreatedFromPubinfoTemplate <https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw>,
    <https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI>;
  nt:wasCreatedFromTemplate <https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ> .

<https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ/research-question>
  dct:audience "Multi-source EO datasets requiring integration for AI/ML applications";
  dct:description "Can DGGS provide an AI-ready spatial framework that eliminates the need for costly harmonization?";
  dct:relation "Traditional harmonization workflows (reprojection, resampling, vector-raster conversion)";
  dct:subject "DGGS-based spatial indexing as a harmonization framework";
  dct:title "DGGS as an AI-Ready Framework for Multi-Source Earth Observation Data Integration";
  dct:type <https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ/effectiveness>;
  <http://schema.org/expectedResult> "Preprocessing time/cost, data alignment accuracy, AI model performance, reproducibility across research groups";
  rdfs:comment "Multi-source Earth observation data cannot be directly fed to AI algorithms without costly spatial harmonization — including reprojection, resampling, and vector-raster conversion. This preprocessing bottleneck limits the scalability and reproducibility of machine learning workflows in EO. DGGS offers a potential solution by providing a standardized spatial index where heterogeneous datasets become directly associable via zone IDs, potentially eliminating traditional harmonization steps. However, no systematic synthesis exists evaluating DGGS effectiveness specifically for AI-ready data preparation. This review will assess whether DGGS can serve as a scalable, interoperable framework that enables direct ingestion of multi-source EO data into AI pipelines." .

sub:assertion prov:wasAttributedTo orcid:0000-0002-1784-2920 .

orcid:0000-0002-1784-2920 foaf:name "Anne Fouilloux" .

sub:sig npx:hasAlgorithm "RSA";
  npx:hasPublicKey "MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDWv2pJnmDsBOq8OlT1aSvYXSuWT34WOp4FYqEzdnn2F0kqzcFevBqWGZDxJWC0lqCrDEuNfp2QFyPe/+nES9dlHGYIhqPi68fwK6ZiNUotRFxXou+rjFznVvUxtCL8Ede79EBHwWN61QtwSIcU12bLoZsNPFlqQASQ93BJuKlihwIDAQAB";
  npx:hasSignature "tkFXjfc0VSB8gfvQ7a30qSMaHyAvJU5d/CloirG8k4gfDCvDPxQC7Tw4iTp6oB6ZRcp2X9y6hKD1HSzb9FyEdRnJgDIbzNxRKfjQNtd3XocEkAZE4fkgXttfNqa1xKtRFoJUhaBUPBUSAvmR/tLQGVvw/KrOQYpMY0LnDoxLOcQ=";
  npx:hasSignatureTarget <https://w3id.org/np/RA3Wt6jY7rpChWyGpQaqC0u1t44i4Bhd3PDIsyyA9uyOM>;
  npx:signedBy orcid:0000-0002-1784-2920 .