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Soil data form the basis of soil information systems across the globe. Soil information needs, and the questions posed by users, are likely to evolve in response to advances in technology in this era of Big Data. This poses a challenge to the pedological community which is already experiencing a decline in soil knowledge and expertise. With a decrease in soil data collection by governments, it is timely to reconsider howand what soil information should be provided to future users. A public-private partnership is advocated to deliver timely and accessible soil information to users. Two public-private provisioning programs are presented, and advantages and considerations for sharing soil data and information amongst industry, government, research organizations, service providers and land managers for these are discussed.Interoperable, open-source and agreed soil community standards are used to present soil data and information through spatial web portals with tools for interpretation of soil data for public and private beneficiaries. K E Y W O R D S accessible, data interoperability, interactive, interpretable, public-private partnership, soil data standards, soil information systems | 95 ROBINSON et al.

Systematic toxicological approaches that employ both ideology changes and improvements in instrumentation and sample extraction allow for improved toxicology testing efficiency through lower sensitivities, higher specificity and minimized resource use. Historically, the San Francisco Office of the Chief Medical Examiner relied heavily on a gas chromatography mass spectrometry (GC–MS) testing regime, comprised of individual drug-class confirmation and quantitation assays. Traditional methods utilizing GC–MS typically require iterations of testing, exhausting sample volume, and hindering productivity and turnaround times, particularly for polypharmacy cases frequently seen in modern postmortem toxicology. The method described here consolidated the scope of seven legacy methods into a single liquid chromatography tandem mass spectrometry (LC–MS/MS) method for better sensitivity, higher throughput, minimal sample consumption for the quantitation of drugs of abuse and improved quality assurance with the incorporation of smart, automated processing. About 100 μL of blood or urine were rapidly extracted using a simple acetonitrile protein crash and subsequent in-vial filtration and injected on to an LC–MS-MS system. The developed method was fully validated to SWGTOX and international guidelines and incorporated 55 analytes along with a customized query that facilitates rapid and consistent application of acceptability criteria for data processing and review. Applicability was demonstrated with the analysis of 1,389 samples (858 blood and 531 urine) where at least 41% of positive results may have been missed due to their decreased sensitivity and 11% of results were not within the scope of the previous analytical methods estimated. On average, cases in this study would have previously required three distinct GC–MS assays, 3 mL of blood, and upwards of 30 h of active staff time. The described LC–MS-MS analytical approach has mitigated the need to perform multiple assays, utilized only 0.1 mL of sample, significantly reduced analyst work time, incorporated 10 additional analytes and allowed for a more comprehensive testing regime to better inform cause of death determinations.

The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community.We present our insights developing FAIR thresholds in a domain specific context for self-governance by a community (agricultural research). 'Minimum thresholds' for FAIR data are required to align expectations for data delivered from providers' distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed). Data providers were supported to make data holdings more FAIR. There was a range of different FAIR starting points, organisational goals, and end user needs, solutions, and capabilities. This informed the distilling of a set of FAIR criteria ranging from 'Minimum thresholds' to 'Stretch targets'. These were operationalised through consensus into a framework for governance and implementation by the agricultural research domain community. 2Wong et al.