Active evaporative cooling by ultrasonic humidification, refrigeration and traditional storage of potato (Solanum tuberosum L., cv. Spunta) in a hot Mediterranean climate: a reproducible hierarchy across four campaigns (2022–2025)
Description
This dataset supports the article "Active evaporative cooling by ultrasonic humidification, refrigeration and traditional storage of potato (Solanum tuberosum L., cv. Spunta) in a hot Mediterranean climate: a reproducible hierarchy across four campaigns (2022-2025)" (CTPTA, Manouba-Essaida, Tunisia), submitted to the Journal of Agriculture and Food Research (Elsevier). It contains three components: 1) 17 raw IoT sensor workbooks (2024-2025), recording temperature, relative humidity, tuber core temperature and CO2 inside the 7 degrees C cold room, the ultrasonic humidification chamber, the traditional greenhouse storage, and an outdoor reference node. 2) iot_processing.py - the Python/pandas script that cleans these raw records , derives the vapour pressure deficit and wet-bulb temperature, and aggregates them into the values reported in the manuscript (Section 3.12, Table 14, Figure 11 and the supplementary tables). 3) Figures_Data_Fig3_a_Fig11.xlsx - the data underlying Figures 3 to 11 of the manuscript, one sheet per figure, with the exact values plotted, their source, and a verification note against the published manuscript values. Storage mode codes used throughout: 7C = refrigeration at 7 degrees C; H = ultrasonic humidification; Trad = traditional (greenhouse) storage. Running iot_processing.py on the 17 workbooks in this deposit fully reproduces the sensor-derived figures and tables of the manuscript. Keywords: potato, Solanum tuberosum, post-harvest storage, evaporative cooling, ultrasonic humidification, vapour pressure deficit, IoT monitoring, weight loss.
Files
Steps to reproduce
This deposit supports the reproduction of the results reported across the manuscript's figures (Figures 3 to 11). For Figures 3 to 10 (weight-loss kinetics, reproducibility of the Mode effect, reducing sugars, dry matter, firmness/titratable acidity, quality radar, principal component analysis and correlations - campaigns 2022-2025): open Figures_Data_Fig3_a_Fig11.xlsx. Each sheet corresponds to one figure and lists the exact values plotted, the source file or table it was verified against, and a note stating whether the value was independently recalculated or reconstructed from a published aggregate. The README sheet of this workbook indexes all tabs and their sources. For Figure 11 (vapour pressure deficit and thermal load in the three storage modes): run iot_processing.py on the 17 raw IoT sensor workbooks provided in this deposit: python3 iot_processing.py --input <folder containing the 17 files> --output <output folder> The script merges the raw temperature, relative humidity, tuber-core-temperature and CO2 records by timestamp, derives the vapour pressure deficit and the wet-bulb temperature, and reproduces the exact per-mode, per-window and per-node statistics underlying Figure 11 and the associated supplementary tables. A pre-merged version of these 17 files, one row per timestamp and sensor node, is also provided as IoT_Data_Consolidee_17noeuds.xlsx for direct inspection without running the script. Statistical tests reported in the manuscript (ANOVA, GLM, Tukey/Welch post-hoc comparisons, principal component analysis) were carried out in SPSS on the individual-replicate measurements; the values used as inputs to these tests are given, figure by figure, in Figures_Data_Fig3_a_Fig11.xlsx.