A Laboratory-Based System for Managing and Distributing Publically Funded Geochemical Data in a Collaborative Environment

Publically funded laboratories have a responsibility to generate, archive and disseminate analytical data to the research community. Laboratory managers know however, that a long tail of analytical effort never escapes researchers' thumb drives once they leave the lab. This work reports on a research data management project (Digital Mineralogy Library) where integrated hardware and software systems automatically archive and deliver analytical data and metadata to institutional and community data portals. The scientific objective of the DML project was to quantify the modal abundance of heavy minerals extracted from key lithological units in Western Australia. The selected analytical platform was a TESCAN Integrated Mineral Analyser (TIMA) that uses EDS-based mineral classification software to image and quantify mineral abundance and grain size at micron scale resolution. The analytical workflow used a bespoke laboratory information management system (LIMS) to orchestrate: (1) the preparation of grain mounts with embedded QR codes that serve as enduring links between physical samples and analytical data, (2) the assignment of an International Geo Sample Number (IGSN) and Digital Object Identifier (DOI) to each grain mount via the System for Earth Sample Registry (SESAR), (3) the assignment of a DOI to instrument metadata via Research Data Australia, (4) the delivery of TIMA analytical outputs, including spatially registered mineralogy images and mineral abundance data, to an institutionally-based data management server, and (5) the downstream delivery of a final data product via a Google Maps interface such as the AuScope Discovery Portal. The modular design of the system permits the networking of multiple instruments within a single site or multiple collaborating research institutions. Although sharing analytical data does provide new opportunities for the geochemistry community, the creation of an open data network requires: (1) adopting open data reporting standards and conventions, (2) requiring instrument manufacturers and software developers to deliver and process data in formats compatible with open standards, and (3) public funding agencies to incentivise researchers, laboratories and institutions to make their data open and accessible to consumers.