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> Publishing raw data itself is definitely a good start but there also needs to be a push towards a standardized way of sharing data along with it's lineage (dependent sources, experimental design/generation process, metadata, graph relationship of other uses, etc.).

Linked Data based on URIs is reusable. ( https://5stardata.info )

The Schema.org Health and Life Sciences extension is ahead of the game here, IMHO. MedicalObservationalStudy and MedicalTrial are subclasses of https://schema.org/MedicalStudy . {DoubleBlindedTrial, InternationalTrial, MultiCenterTrial, OpenTrial, PlaceboControlledTrial, RandomizedTrial, SingleBlindedTrial, SingleCenterTrial, and TripleBlindedTrial} are subclasses of schema.org/MedicalTrial.

A schema.org/MedicalScholarlyArticle (a subclass of https://schema.org/ScholarlyArticle ) can have a https://schema.org/Dataset. https://schema.org/hasPart is the inverse of https://schema.org/isPartOf .

More structured predicates which indicate the degree to which evidence supports/confirms or disproves current and other hypotheses (according to a particular Person or Persons on a given date and time; given a level of scrutiny of the given information) are needed.

In regards to epistemology, there was some work on Fact Checking ( e.g. https://schema.org/ClaimReview ) in recent times. To quote myself here, from https://news.ycombinator.com/item?id=15528824 :

> In terms of verifying (or validating) subjective opinions, correlational observations, and inferences of causal relations; #LinkedMetaAnalyses of documents (notebooks) containing structured links to their data as premises would be ideal. Unfortunately, PDF is not very helpful in accomplishing that objective (in addition to being a terrible format for review with screen reader and mobile devices): I think HTML with RDFa (and/or CSVW JSONLD) is our best hope of making at least partially automated verification of meta analyses a reality.

"#LinkedReproducibility"; "#LinkedMetaAnalyses", "#StudyGraph"

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