sub:assertion {
  <https://osf.io/92snx> dct:issued "July 2025" ;
    dct:license <http://purl.org/np/RAQ__sGdY_Qc7l1O_zmn4nr-pMBOxKU04Ur9s998rS6Fc#CC-BY-4.0> ;
    <https://w3id.org/fdof/ontology#hasEncodingFormat> <https://www.iana.org/assignments/media-types/application/pdf> ;
    <https://w3id.org/fdof/ontology#materializes> sub:STAYAHEAD_DA1 ;
    <https://www.w3.org/ns/dcat#accessURL> <https://osf.io/download/92snx/> .
  sub:STAYAHEAD_DA1 dct:contributor orcid:0000-0001-7871-2073 , orcid:0000-0001-8888-635X ;
    dct:creator orcid:0009-0004-2188-0817 ;
    dct:hasVersion "1.0.0" ;
    dct:isPartOf <https://w3id.org/np/RAz72LNwv9hNpTiQICSUAbaMYnTXV0HmBsD31W1MtWoYY> ;
    dct:language <https://www.omg.org/spec/LCC/Languages/LaISO639-1-LanguageCodes/en> ;
    dct:publisher <https://ror.org/027bh9e22> ;
    dct:subject <http://aims.fao.org/aos/agrovoc/c_4318> ;
    a <https://w3id.org/fair/ff/terms/article> , <https://w3id.org/fdof/ontology#FAIRDigitalObject> ;
    rdfs:comment "This article presents an extended dataset description and methodology behind a large number of theoretical and empirical SARS-CoV-2 spike receptor-binding domain (RBD) variants, developed under the STAYAHEAD project for pandemic preparedness. It integrates large-scale in silico structure predictions with empirical biophysical measurements." ;
    rdfs:label "Structure-Based Prediction of SARS-CoV-2 Variant Properties Using Machine Learning on Mutational Neighborhoods" ;
    <https://schema.org/funder> <https://ror.org/056cwr036> ;
    <https://w3id.org/fdof/ontology#hasMetadata> this: ;
    <https://www.w3.org/ns/dcat#contactPoint> "e.a.schultes@lacdr.leidenuniv.nl" ;
    <https://www.w3.org/ns/dcat#endDate> "July 2025" ;
    <https://www.w3.org/ns/dcat#startDate> "March 2023" .
}