Domain experts do not have the necessary tools for publishing research results in an open format for reuse.

Organizations struggle to remove existing barriers related to proprietary data formats and access standards and explore research work as structured, open, linked data.

Integration of public data sources such as AAT, GND, ULAN or Wikidata with own research data is cumbersome.

Researchers do not have access to detailed, fine-grained data rich with provenance information.

Public data is not exposed to the general public through abstracted and intuitive end-users interfaces.

Scientific discussions and supporting arguments that lead to the generation of new knowledge are not exposed to domain experts.

metaphactory’s intuitive authoring interfaces guide domain users through a structured data curation process, while integrated workflows ensure data quality.

Controlled vocabularies and standardized ontologies drive auto-suggestion in form interfaces, allowing domain users to capture their research results in an open, linked data format.

With metaphactory, datasets consisting of potentially conflicting information can be integrated, reconciled, and curated, mapped to own research data, and published in an open format for reuse in other domains or sub-domains.

With metaphactory, dataset metadata is managed in the knowledge graph alongside instance data, providing domain users with trustworthy information on lineage and provenance.

metaphactory's out-of-the-box search and discovery capabilities allow end users to immediately extract valuable knowledge from publicly available data.

metaphactory supports scientific discourse moderation allowing researchers with differing opinions to present their arguments, discuss and collaborate with peers, extract the provenance of conflicting arguments, track on-going discussions, and understand how decisions were reached.