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Recent decades have seen an exponential rise in the application of machine learning in geoscience. Fundamental differences distinguish geoscience data from most other data types. Geoscience datasets are typically multi-dimensional, and contain 1-D (drillholes), 2-D (maps or cross-sections), and 3-D volumetric and point data (models/voxels). Geoscience data quality is a product of its resolution and the precision of the methods used to acquire it. The dimensionality, resolution, and precision of each layer within a geoscience dataset translates to limitations in spatiality, scale and uncertainty of resulting interpretations. Historically, geoscience datasets were overlaid cartographically, to incorporate subjective, experience-driven knowledge, and variances in scale, and resolution. The nuances and limitations that underpin the reliability of automated interpretation are well understood by geoscientists, but are rarely appropriately transferred to data science. However, for true integration of geoscience data, such issues cannot be overlooked without consequence. To apply data analytics to complex geoscience data (e.g., hydrothermal mineral systems) effectively, methodologies must be used that characterise the system quantitatively, using collocated analyses, at a common scale. This paper provides research and exploration insights from an innovative district-wide, scale-integrated, geoscience data project, which analysed 1,590 samples from 23 mineral deposits and prospects across the Cloncurry District, Queensland, Australia. Ten different analytical techniques, including density, magnetic susceptibility, remanent magnetisation, anisotropy of magnetic susceptibility, radiometrics, conductivity, scanning electron microscopy (SEM)-based automated mineralogy, geochemistry, and short-wave infrared (SWIR) hyperspectral data with 561 columns of scale-integrated data (+2151 columns of SWIR). All data were collected on 2 cm x 2.5 cm sample cylinders; a scale at which the confidence in coupling of data from techniques can be high. These data are integrated by design, to eliminate the need to downscale coarser measurements via assumptions, inferences, inversions, and interpolations. This scale-consistent approach is critical to the quantitative characterisation of mineral systems and has numerous applications to mineral exploration, such as linking alteration paragenesis with structural controls and petrophysical zonation. [ABSTRACT FROM AUTHOR]

The literature on knowledge spillovers provides evidence that higher education institutions (HEIs) positively affect regional firm location (i.e., start-ups or firms located in a region). However, less is known about how HEIs in different fields of study impact regional firm location in different industries. To investigate this question, we exploit the establishment of universities of applied sciences (UASs)—bachelor's degree-granting three-year HEIs in Switzerland. We find that the effects of UASs are heterogeneous across fields of study and industries. UASs specializing in "chemistry and the life sciences" and "business, management, and services" are the only UASs that positively affect regional firm location across several industries. Positive effects emerge in service industries characterized by radical service, incremental product, or process innovations. Thus, UASs are not a one-size-fits-all solution for increasing regional firm location. Instead, only UASs specializing in particular fields of study positively influence firm location in certain industries. Plain English Summary: Universities of applied sciences (UASs) foster regional firm location but not for all fields of study or industries. Most gains stem from UASs in Chemistry & Life Sciences or Business, Management & Services and emerge mainly in service industries. UASs as knowledge suppliers provide regional firms as knowledge consumers with results of applied research and with human capital. However, positive effects on firm location only emerge if knowledge outputs of UASs as suppliers match knowledge needs of firms as consumers. Our study shows that good matches occured for UASs in Chemistry & Life Sciences or Business, Management & Services with service industries, which are typically characterized by radical service innovations and incremental product or process innovations. For policymakers who want to push regional innovation ecosystems, our findings imply that establishing UASs which promote these ecosystems requires careful consideration of regional firms' knowledge needs. If UASs provide matching knowledge, UASs stimulate the regional innovation ecosystem and thereby firm location. [ABSTRACT FROM AUTHOR]

This study investigates the impact of a tertiary education expansion on regional firm development, as measured by average profits per firm. We exploit the quasi-random establishment of universities of applied sciences (UASs) – bachelor's degree-granting three-year colleges teaching and conducting applied research – to construct treatment and control groups and to apply both a difference-in-differences model and an event study design. We find that after the establishment of new UASs in Switzerland, average profits per firm in the treated municipalities increase by 19.6% more than in the control group. This increase corresponds roughly to an additional annual growth in average profits per firm in the treatment group of 0.7%. The effects start shortly after the establishment of UASs but also persist over a period of up to 10 years. [ABSTRACT FROM AUTHOR]

The literature on the economics of science and technology shows that academic universities—institutions focusing on basic research—positively affect innovation activities in regional economies. Less is known about the innovation effects of universities of applied sciences (UASs)—bachelor-granting three-year colleges teaching and conducting applied research. Furthermore, the evidence for positive innovation effects is predominantly based on average effects, while heterogeneity in innovation effects due to the economic environment is far less considered. By exploiting a public policy development in Switzerland that led to the quasi-random establishment of UASs, we investigate the regional heterogeneity in innovation effects of these UASs. We rely on patent and business census data and analyze the influence and importance of three economic preconditions—labor market size, labor market density and high tech intensity—on innovation effects of UASs. Our results show that only regions with a large or a dense enough labor market or with an above average high tech intensity experience significant innovation effects of UASs. Comparing the relative importance of the three economic preconditions, we find that labor market size is the most important factor that drives heterogeneity in innovation effects of UASs. [ABSTRACT FROM AUTHOR]