We Should Not Accept Scientific Results That Have Not Been Repeated
So, we should have a structured way to represent that one study reproduces another? (e.g. that, with similar controls, the relation between the independent and dependent variables was sufficiently similar)<p>- RDF is the best way to do this. RDF can be represented as RDFa (RDF in HTML) and as JSON-LD (JSON LinkedData).<p>... " #LinkedReproducibility "<p><a href="https://twitter.com/search?q=%23LinkedReproducibility" rel="nofollow">https://twitter.com/search?q=%23LinkedReproducibility</a><p>It isn't/wouldn't be sufficient to, with one triple, say (example.org/studyX, 'reproduces', example.org/studyY);
there is a reified relation (an EdgeClass) containing metadata like <i>who</i> asserts that studyX reproduces studyY, <i>when</i> they assert that, and <i>why</i> (similar controls, similar outcome).<p>Today, we have to compare PDFs of studies and dig through them for links to the actual datasets from which the summary statistics were derived; so specifying <i>who</i> is asserting that studyX reproduces studyY is very relevant.<p>Ideally, it should be possible to publish a study with structured premises which lead to a conclusion (probably with formats like RDFa and JSON-LD, and a comprehensive schema for logical argumentation which does not yet exist).
("#StructuredPremises")<p>Most simply, we should be able to say "the study control type URIs match", "the tabular column URIs match", "the samples were representative", and the identified relations were sufficiently within tolerances to say that studyX reproduces studyY.<p>Doing so in prosaic, parenthetical two-column PDFs is wasteful and shortsighted.<p>An individual researcher then, builds a set of beliefs about relations between factors in the world from a graph of studies ("#StudyGraph") with various quantitative and qualitative metadata attributes.<p>As fields, we would then expect our aggregate #StudyGraphs to indicate which relations between dependent and independent variables are relevant to prediction and actionable decision making (e.g. policy, research funding).