<p>Assessing the fitness of data for reuse may require knowledge of how that data was produced. If knowledge of how data is produced can be represented using a standard data model, automated assessments of data fitness may take place, based on aspects of its production. In addition to knowledge of data's production, knowledge of how it has or hasn't been used can also be used to assess its fitness for further reuse. <p>Since 2014 we have had an international data model for representing data's production, namely the W3C's provenance data model, PROV-DM. It can also be used to represent how data has been used which is known as 'forward provenance'. <p>Here we present several types of provenance queries one may pose in order to assess data's fitness for reuse. These include discovering the methods used in data production; determining the reputation of ancestor data; determining the reputation of agents (human or machine) involved in data production; and assessing the social acceptance of data via its reported use which we believe to be the best form of social endorsement for data's utility.

Geoscience Australia (GA) is a government agency that delivers much scientific data via web services for government and research use. As a science agency, the expectation is that GA will allow users of its data to be able to cite it as one would cite academic papers allowing authors of derived works to accurately represent their sources. We present a methodology for assisting with the citation of web service requests via provenance information recording and delivery. We decompose the representation of a web service request into endurant and occurrent components, attempting to source as much information as possible about the endurant parts as organisations find these easiest to manage. We then collect references to those parts in an endurant 'bundle', which we make available for citation. Our methodology is demonstrated in action within the context of an operational government science agency, GA, that publishes many thousands of datasets with persistent identifiers and many hundreds of web services but has not, until now, provided citable identifiers for web service-generated dynamic data

<div>Environmental DNA (eDNA), elemental and mineralogical analyses of soil have been shown to be specific to their source material, prompting consideration of the use of dust for forensic provenancing. Dust is ubiquitous in the environment and is easily transferred to items belonging to a person of interest, making dust analysis an ideal tool in forensic casework. The advent of Next Generation Sequencing technologies means that metabarcoding of eDNA can uncover microbial, fungal, and even plant genetic fingerprints in dust particles. Combining this with elemental and mineralogical compositions offers multiple, complementary lines of evidence for tracing the origin of an unknown dust sample. This is particularly pertinent when recovering dust from a person of interest to ascertain where they may have travelled. Prior to proposing dust as a forensic trace material, however, the optimum sampling protocols and detection limits need to be established to place parameters around its utility in this context. We tested several approaches to collecting dust from different materials and determined the lowest quantity of dust that could be analysed for eDNA, geochemistry and mineralogy, whilst still yielding results capable of distinguishing between sites. We found that fungal eDNA profiles could be obtained from multiple sample types and that tape lifts were the optimum collection method for discriminating between sites. We successfully recovered both fungal and bacterial eDNA profiles down to 3&nbsp;mg of dust (the lowest tested quantity) and recovered elemental and mineralogical compositions for all tested sample quantities. We show that dust can be reliably recovered from different sample types, using different sampling techniques, and that fungal, bacterial, and elemental and mineralogical profiles, can be generated from small sample quantities, highlighting the utility of dust as a forensic provenance material.</div> <b>Citation:</b> Nicole R. Foster, Belinda Martin, Jurian Hoogewerff, Michael G. Aberle, Patrice de Caritat, Paul Roffey, Robert Edwards, Arif Malik, Priscilla Thwaites, Michelle Waycott, Jennifer Young, The utility of dust for forensic intelligence: Exploring collection methods and detection limits for environmental DNA, elemental and mineralogical analyses of dust samples, <i>Forensic Science International </i>, Volume 344, 2023, 111599, ISSN 0379-0738, https://doi.org/10.1016/j.forsciint.2023.111599. ISSN 0379-0738,

This article is to be published in the industry journal "Food Australia". The article describes the motivation for a collaborative project between CSIRO, GA, ANSTO and NMI to create a federated data platform to share and deliver environment isotopic data, to support verification of where Australian agricultural and food products come from and how they were grown. The project is funded by the Australian Research Data Commons (ARDC) as part of its Food Security Data Challenges program. <b>Citation:</b> Welti, N., Fraser, G., Gerber, C., Kethers, S., & Flick, L. (2024). Backing food product claims with evidence. Food Australia, 76(2), 14–17. https://search.informit.org/doi/10.3316/informit.T2024051600004100587971500

<div>The ubiquitous nature of dust, along with localised chemical and biological signatures, makes it an ideal medium for provenance determination in a forensic context. Metabarcoding of dust can yield biological communities unique to the site of interest, similarly, geochemical and mineralogical analyses can uncover elements and minerals within dust than can be matched to a geographic location. Combining these analyses presents multiple lines of evidence as to the origin of collected dust samples. In this work, we investigated whether the time an item spent at a site collecting dust influenced the ability to assign provenance. We then integrated dust metabarcoding of bacterial and fungal communities into a framework amenable to forensic casework, (i.e., using calibrated log-likelihood ratios to predict the origin of dust samples) and assessed whether current soil metabarcoding databases could be utilised to predict dust origin. Furthermore, we tested whether both metabarcoding and geochemical/mineralogical analyses could be conducted on a single sample for situations where sampling is limited. We found both analyses could generate results capable of separating sites from a single swabbed sample and that the duration of time to accumulate dust did not impact site separation. We did find significant variation within sites at different sampling time periods, showing that bacterial and fungal community profiles vary over time and space – but not to the extent that they are non-discriminatory. We successfully modelled soil and dust samples for both bacterial and fungal diversity, developing calibrated log-likelihood ratio plots and used these to predict provenance for dust samples. We found that the temporal variation in community composition influenced our ability to predict dust provenance and recommend reference samples be collected as close to the sampling time as possible. Thus, our framework showed soil metabarcoding databases are capable of being used to predict dust provenance but the temporal variation in metabarcoded communities will need to be addressed to improve provenance estimates.&nbsp;</div> <b>Citation:</b> Nicole R. Foster, Duncan Taylor, Jurian Hoogewerff, Michael G. Aberle, Patrice de Caritat, Paul Roffey, Robert Edwards, Arif Malik, Michelle Waycott and Jennifer M. Young, The secret hidden in dust: Uncovering the potential to use biological and chemical properties of the airborne soil fraction to assign provenance and integrating this into forensic casework, <i>Forensic Science International: Genetics,</i> (2023) doi:https://doi.org/10.1016/j.fsigen.2023.102931