integrity
by iThinking
Integrity is a data science solution that provides insights into scholarly publishing, funding, and research activity using industry standard datasets. Colourful, visual, and interactive search provide multiple points of entry for further discovery.
Using machine learning and artificial intelligence, Integrity provides transparent, real-time results, helping individuals and institutions discover patterns and relationships within scholarly data.
about iThinking
Board level CIO / CTO experienced in the alignment of business strategy with IT reality, and building or scaling up of departments in established and entrepreneurial businesses.
Roger Gordon
assess
Pragmatic assessment of the technology choices for the business as it is and most importantly, as it aspires to be.
advise
Specialist advice and hands on help for startups and technology businesses in their early and growth phases.
consult
Analysis of the brittleness, scalability & robustness of current business solutions, addressing single points of reference.
visualise
Data provenance, transformation, and visualisation using tools such as Neo4j graph databases, python and R.
lead
Interim or contract CTO / CIO for technical leadership, mentoring, reorganisation.
integrity
Integrity is a data science solution that provides insights into scholarly publishing, funding, and research activity using industry standard datasets and APIs from Crossref, ORCID, GRID, and DOAJ, and eventually data from ROR and Transpose.
The colourful, visual, and interactive search results provide multiple points of entry for further discovery.
The Integrity team uses Python for document ingestion (XML, PDF); Neo4j Bloom for hypothesis generation; and Neo4j Graph Databases for tracking data relationships.
Integrity clients and partners use a custom-built or white-label portal interface (built using vis.js or R) that provides graphical presentation of the Neo4j data and options for data download (eg CSV). The paid version of Integrity allows specific focus on particular data segments of relevance and custom taxonomies and datasets.