This dataset comes from a pseudo-F1 progeny of 186 two year-old genotypes obtained as the first generation from a reciprocal cross between the grapevine (Vitis vinifera L.) cultivars Syrah and Grenache (Adam-Blondon et al., 2004; Fournier-Level et al., 2009). In 2012 and 2013, six and five clones, respectively, of each genotype (all offsprings and the two parents) were phenotyped as replicates in randomized blocks into the PHENOARCH high-throughput phenotyping platform (https://www6.montpellier.inrae.fr/lepse_eng/M3P). Two watering scenarios were imposed by maintaining soil water content in pots at target values using watering stations coupled to weighing terminals. Daily water loss by transpiration was recorded for each potted plant using variation in weigh after correction for soil evaporation determined on empty pots. Individual plant dry weight was calculated daily using image taken in the platform and processed for conversion into biomass. Total water loss by transpiration and increase in dry biomass per plant were calculated over a period of 10–15 days when soil water content had stabilized. Whole plant transpiration efficiency was determined as the ratio between biomass increase and the total amount of transpired water over the same period. Nighttime transpiration, daytime transpiration and midday leaf water potential were measured on a specific day when the plants were taken off the platform and placed into a chamber to ensure stable and repeatable climatic conditions. Soil-to-leaf hydraulic conductance was then deduced from daytime transpiration and leaf water potential using the conventional, evaporative flux method for the whole path from soil to transpiring leaves. This work was supported by funding from the project Long Term Impacts and Adaptations to Climate Change in Viticulture and Oenology (LACCAVE) of the French National Institute for Agricultural Research (INRA) and by Grant ANR-09-GENM-024-002 from the French National Research Agency. Aude Coupel-Ledru received a PhD grant from the French government. Along with genotypic data (Huang et al., 2012) for QTL detection, this dataset was used to explore: (i) The genetic and physiological origins of iso vs anisohydric behaviours (Coupel-Ledru et al., 2014 J Exp Bot), (ii) The role of abscisic acid in the control of isohydry through the regulation of hydraulic conductance (Coupel-Ledru et al., 2017 Plant Phy) (iii) The genetic architecture of nighttime transpiration and its impact on water-use efficiency (Coupel-Ledru et al., 2016 PNAS) This dataset was also re-analysed using univariate and multivariate methods for genomic prediction and QTL detection with denser genotyping data (Brault et al., 2021).