Dataset for paper "Machine learning reveals key drivers of at-vessel mortality in demersal sharks"

The present database was used to fit the boosted regression trees models presented in this scientific article. ### This database contains information on: #### (1) The relative identification of each individual studied, including: Database code Full name Units scientificName Scientific name of the study species Scyliorhinus canicula, Galeus melastomus spCode Code given to name each species Scyliorhinus canicula == Scanicula, Galeus melastomus == Gmelastomus organismID identification number given to each specimen ranging from 1 to 3079 towN identification number given to each tow analysed ranging from 1 to 66 vessel identification number given to each trawler collaborating in the study ranging from 1 to 8 date date when the tow occurred ranging from 02-12-2020 to 15-06-2022 Vessel name and fishing location was omitted as observation campaigns were conducted on board commercial trawlers and such information is confidential. #### (2) Survival stage of the specimen at the time when sharks were released back to sea: Database code Full name Units mortality Mortality stage of the specimen 0 == alive, 1 == dead #### (3) Biological, environmental and fishing operation predictors considered into the modelling approach. Database code Full name Units TL Body size centimeters MAT Maturity 0 == immature , 1 == mature SEX Sex 0 == male, 1 == female DEPTH Tow depth meters DUR Effective towing duration hours SPEED Towing speed knots TOWMASS Total catch biomass in the tow cod-end kilograms DECKTIME Time exposed on deck minutes CLOUD Cloud coverage % SEASTATE Sea state Douglas scale (0 to 9) WIND Wind force Beaufort scale (0 to 12) ATEMP Atmospheric temperature ºC DTEMP Change from atmospheric to sea bottom temperature ºC