Abstract: Adoption of automated milking systems （AMS） and other precision livestock farming （PLF） systems offers access to large,multidimensional data that allow exploration of resilience traits and sustainable farming strategies in real-world scenarios. Since their inception in the Netherlands in the 1990s,AMS have seen increased adoption Adoption of automated milking systems （AMS） and other precision livestock farming （PLF） systems offers access to large,multidimensional data that allow exploration of resilience traits and sustainable farming strategies in real-world scenarios.Since their inception in the Netherlands in the 1990s,AMS have seen increased adoption in the Nordic countries,with around a third of the total milk production collected by robots. The major brands of farm management systems（FMS）in the Nordic region are only configured to report data as a current overview,discarding older information that is vital to studies of the herd’s genetics,behaviour,and environment.In this work,we present the infrastructure for dairy cattle data at the Swedish University of Agricultural Sciences,Gigacow（SLU Gigacow）that collects data from a set of Swedish dairy farms. Each farm’s FMS sends nightly reports to SLU Gigacow,where records are harmonised and collected in a central database. Collected records include milking statistics,health events,traffic data,and SNP genotypes for thousands of cows,and are made accessible to researchers through SQL or R queries. SLU Gigacow also integrates data from the Swedish national cow registry,including pedigrees and herd transfers for cows resident at participating farms.SLU Gigacow’s longitudinal observations （first data collected in 2020） link genotype to phenotype and animal welfare with the goal of accelerating pilot studies in dairy science,as well as providing a big dataset from cows in active,commercial settings. The data collection software written in Python 3 （Beaverton,USA） has modules that enable collection from several versions of DeLaval DelPro （Tumba,Sweden）, and can be extended to any FMS with a graphical user interface running on most consumer operating systems. After harmonisation to resolve differences in language and FMS versions,data are stored in a database maintained at SLU with SQL Server Integration Services （SSIS） （Microsoft, Redmond,USA）. By agreement with Växa Sverige AB （Uppsala, Sweden）,participating farmers also get a large number of animals genotyped using the 45k EuroG MD beadchip （Amsterdam, The Netherlands）. Currently,the database includes information on over 17 000 cattle,over 3 000 000 milkings,and 2 969 SNP genotypes. The cross-referenced data can be mined for various purposes,including stress responses and resilience traits.While SLU Gigacow is intended to collect from Swedish farms and support Swedish researchers,it serves as a proof-of-concept that data from diverse sources and systems at dairy farms can be automatically gathered and collated in a researcherfriendly format. We believe that this shows the great utility of farm-to-table statistics and increased FMS interoperability. SLU Gigacow was constructed essentially without standardised interfaces for dairy data communication. Establishment of such data standards is ongoing within the industry but the development and adoption of standards take time and rely on active participation of multiple actors. The researchdriven approach of SLU Gigacow enables more rapidand integrated measurements of many facets of the dairy farm environment,creates new niches for PLF equipment,and opens great new vistas of information to explore for adaptation to changing climates.

Key words: dairy science, big data, resilience