AI-Assisted Profile Curation transforms how research information is captured and maintained in Symplectic Elements.
Using AI-powered document-to-profile workflows, users can convert unstructured content, such as CVs* or free text, into structured, high-quality records in seconds.
Reduce manual effort, improve metadata quality, and accelerate profile completion at scale.
Available to Digital Science-hosted Elements customers. AI Credits required.
*CV import available end of May.
The challenge
Maintaining accurate and complete faculty profiles is time-consuming. Much of the information institutions rely on exists primarily in CVs and other unstructured documents, making it difficult to capture consistently and at scale.
The solution
With AI-Assisted Profile Curation you can:
- Accelerate onboarding of new faculty
- Complete incomplete profiles ahead of assessment or review cycles
- Reduce manual data entry across disciplines
- Improve data quality through structured metadata and intelligent matching
- Maintain full control with user validation before any data is saved
How it works:
- Upload or paste
Paste plain text or upload a document, including full CVs, covering publications, grants, teaching, and professional activities. - AI structures the data
AI extracts and organises content into structured Elements records, mapped to your institution’s metadata schema, including custom fields and item types where applicable. - Review and confirm
All extracted items are presented for review before being saved. Users retain full control—nothing is added without approval—ensuring transparency and data integrity.
Enhanced matching and deduplication
Recent improvements further strengthen data quality by identifying potential duplicates earlier in the process.
For publications, matching now includes:
- DOI, Dimensions ID, PMCID, arXiv ID, and Scopus EID
For grants:
- Matching against funder name and grant reference (via Dimensions)
Additionally, extracted metadata is cross-checked against existing Elements records to prevent duplication.
Granular access control
Administrators can control access to AI features at group level, enabling phased rollouts and ensuring institutional oversight.
Proven impact
‘University of Oregon’s experience using the AI-assisted entry tool has helped us implement the system quickly at the university, has particularly supported the data collection for faculty in our professional schools, and has encouraged faculty and administrator buy-in. Our faculty provide their activity information in the system quickly and in a more standardized and readable manner, which means we are then able to use it for all of our major faculty reviews.’
Coming soon: CV import
An upcoming release (end of May) introduces a complete CV import workflow designed to accelerate onboarding and large-scale profile completion.
Users will be able to upload a CV in PDF format or paste full-text content. AI will extract publications, grants, teaching, and professional activities and map them into structured records.
Key capabilities:
- Intelligent extraction
Automatically captures and structures data from full CVs - Duplicate prevention
Advanced matching prioritises existing records to avoid duplication - Flexible review workflow
Users can:- Import records in bulk
- Review and edit items individually
- Save progress and return at any time
- Controlled data ingestion
Draft items remain separate until approved, ensuring transparency and accuracy
CVs to structured profiles — faster and with less effort
AI-Assisted Profile Curation enables institutions to move from fragmented, manual data collection to scalable, intelligent profile management, unlocking more complete, accurate, and actionable research information.
AI-Assisted Profile Curation
To learn more about AI-Assisted Profile Curation, speak to a member of our team.
