Designing an institutional research data management infrastructure for the life sciences
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1 Designing an institutional research data management infrastructure for the life sciences Paul van Schayck PhD student, data steward Maastricht University Medical Center + p.vanschayck@maastrichtuniversity.nl Peter Debyelaan 15, 6229 HX Maastricht P.O. Box 616, 6200 MD Maastricht The Netherlands 1
2 providing Research Data Management services for Life Sciences Faculty Academic Hospital Independent research groups Heterogeneous (meta)data Right incentives Patient privacy Electronic Health Records Bridging organisations 2
3 Life science background Life science depends more and more on the collection and analysis of comprehensive datasets. Small Science. Life science is performed in small temporary project groups. Open Science. There is an urgent call for more open, transparent and reproducible science. 3 3
4 DataHub characteristics FAIR-inspired from start. Open-source where possible. (Meta)data structuring + ontology enrichment. Project data structuring; Hierarchical organisation in projects and datasets. Faceted search, Lucene & ontology-powered, authorization controlled High volume; The infrastructure has been designed and tested with petabyte scale and high throughput in mind. 4
5 Healthcare Research data governance Scientist EHR Ontology Service CrossRef epic Persistent Identifier s Master Person Index ELK HL7CDA export DataHub core services Frontend HL7v3 XML Extract Transform Load Data Warehouse API Facetted search UI Life science Web portal Transform Data drop zone metadata XML Hitachi NAS storage Rule based object store Replication storage REST API Browser External Repository WebDAV DataHub
6 Authentication (federated) Web Portal IdP login SAML based SSO irods-php via proxy user Generate irods temporary password Browser WebDAV temporary password Providing federated authentication in two methods: proxy-user and temporary password Outstanding issue: Automated handling of user provisioning/expiration 6
7 Ingesting high volume data SMB/CIFS Network Share msiphypathreg irods Mounted Collection irods Collection data cifs-utils msicollrsync Web Portal irods-php SMB/CIFS network share connected as irods mounted collection is ingested into irods using msicollrsync Advantage: No extra (client) software for users SMB/CIFS performs very well Disadvantage: Not compatible with federated authentication msicollrsync not performing (yet) 7
8 Project collection structure /nlmumc/projects/ P C Dataset: any number of files and directories P C P C Providing a generic project collection hierarchy with no assumptions Unidentifiable collection names Virtual collections? Title AVUs on Project and Collections 8
9 Project authorization Project: P contributor Can create Project collection: C own manager Can assign inherited Open phase write contributor read only Closed phase read reader Keeping data authorization in irods using the rule engine to enforce policies Disadvantages: Only on project level Too simplistic? Note: irods groups are organizational units (departments) 9
10 Metadata modeling: being FAIR Ontology Service CrossRef epic Persistent Identifiers Islandora XML forms metadata.xml ETL C Validation Helping users early with annotating data FAIR Project -> Investigation -> Sample -> Assay (PISA) Inspired by ISA tools, compatible with HCLS Implemented Project and Investigation level Descriptive metadata stored in file (!), AVUs for system metadata 10
11 Metadata indexing metadata. xml Ontology Service CrossRef DWH frontend API REST API Transform Data Warehouse Providing a user friendly facetted search interface for data findability Indexed in SOLR: All metadata Semantics (OLS) References (CrossRef) Authorization on data (irods) Rebuild on demand 11
12 Metadata: making use of semantics Autocomplete for ontology terms Ontology derived facetted search 12
13 DTAP: deployment for development External Web portal WebDAV REST API DWH frontend CrossRef Browser Ontology Service Challenge Interactions with external services (AD, NAS storage) Highlights 16 interacting containers for full environment Runnable from laptop 13
14 DTAP: deployment for acceptation/production External WebDAV REST API DWH frontend CrossRef Browser Ontology Service Web portal Challenge Differences in deployments and some environments 14
15 Todays challenge in the data life cycle Active data Preserved data Phases Create Process Analyse Highly specific RDM solutions BRIDGE THE GAP! Phases: Archive Access Re-use Generic repositories Domain specific repositories 15
16 Lessons learned 1. Dual position of staff. Decentralize data stewards 2. Micro Service approach 3. Remote Procedure Calls for rules 4. Funding for long term storage is hard 5. Open Source re-useable parts 16
17 Questions? Pascal Suppers Managing Director DataHub Maastricht P. Debyelaan 15 L. van Kleeftoren 6229 HX Maastricht 2nd floor (route 11) The Netherlands T E p.suppers@maastrichtuniversity.nl Paul van Schayck PhD student, data steward Maastricht University Medical Center + p.vanschayck@maastrichtuniversity.nl Peter Debyelaan 15, 6229 HX Maastricht P.O. Box 616, 6200 MD Maastricht The Netherlands 17
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