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Abstract Bacillus Calmette-Guérin (BCG), an attenuated strain of Mycobacterium bovis (M. bovis), is the lead candidate vaccine for control of bovine tuberculosis (TB) in cattle. However, BCG vaccination sensitises cattle to bovine tuberculin, thus compromising the use of the current bovine TB surveillance tests. To address this, we have developed a diagnostic skin test that is not compromised by BCG vaccination and is able to detect BCG vaccinated animals that subsequently develop bovine TB following exposure to M. bovis. Building on previous work using ‘in house’ formulated protein cocktail reagents, we herein present test performance data for a single fusion protein (DST-F) containing the mycobacterial antigens ESAT-6, CFP-10 and Rv3615c formulated as a ‘ready to use’ reagent by a commercial manufacturer. Our results demonstrate that, unlike tuberculin reagents, a diagnostic skin test using DST-F maintained high specificity in BCG vaccinated animals. Furthermore, the DST-F skin test demonstrated a high relative sensitivity in identifying M. bovis infected animals, including those where BCG vaccination failed to prevent bovine TB pathology following experimental exposure to M. bovis. The DST-F is currently undergoing field trials in Great Britain to support its licensure and commercialisation.

Abstract An ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research. It used a CAM6 configuration of the Community Earth System Model (CESM), several million observations per day, and the Data Assimilation Research Testbed (DART). The data saved from this global, $$\sim 1^\circ $$ ∼ 1 ∘ resolution, 80 member ensemble span 2011–2019. They include ensembles of: sub-daily, real world, atmospheric forcing for use by all of the nonatmospheric models of CESM; weekly, CAM6, restart file sets; 6 hourly, prior hindcast estimates of the assimilated observations; 6 hourly, land model, plant growth variables, and 6 hourly, ensemble mean, gridded, atmospheric analyses. This data can be used for hindcast studies and data assimilation using component models of CESM; CAM6, CLM5, CICE5, POP2. MOM6, MOSART, and CISM; and non-CESM Earth system models. This large dataset (~ 120 Tb) has a unique combination of a large ensemble, high frequency, and multiyear time span, which provides opportunities for robust statistical analysis and use as a machine learning training dataset.