1. Climate change is putting the fate of ectothermic animals at stake because their body temperature closely tracks environmental temperatures. The ability to adjust thermal limits and preference through acclimation (i.e., acclimation capacity) may compensate for temperature changes. However, although necessary for forecasting the future of ectotherms in a changing climate, knowledge on the factors modulating these plastic responses is fragmentary. For instance, the influence of an animal’s sex in driving acclimation capacity has been underappreciated.
2. Here, we present the first systematic review and meta-analysis on sex differences in thermal acclimation capacity. Using 239 effect sizes from 37 studies and 44 species, we revealed that males and females did not differ significantly in their overall capacity to acclimate their thermal limits and preference. However, in some instances, females expressed significantly greater plastic responses than males.
3. In wild animals, females had a greater heat tolerance plasticity than males. In addition, females had a greater cold tolerance plasticity in terrestrial habitats, but the strength and direction of this sexual dimorphism was associated with the duration of acclimation. We also found a negative correlation between body mass and plasticity. Finally, we demonstrated that the capacity for each sex to adjust their thermal tolerance and preference was remarkably limited.
4. It is important to acknowledge that the above effects were weak and heterogeneous. Hence, in the species we investigated, minor differences in acclimation capacity may not translate into major ecological mismatch between sexes with climate change.
5. Our systematic review also revealed that over 75% of the studies we identified either did not report or confounded the sex of the animals. This under-reporting may cause to overlook ecologically relevant sex differences in plasticity in ectothermic taxa. We stress the need for further research on sex-based responses to temperatures.
6. Our synthesis provides additional evidence that the capacity for ectotherms to acclimate to temperatures is limited, and likely insufficient to compensate for the impacts of climate change.

The methods for collecting and analysing the data are presented in the manuscript and supplementary methods. 

Here, we share the data and code from Pottier et al. (2021). Sexual (in)equality? A meta-analysis of sex differences in thermal acclimation capacity across ectotherms. Functional Ecology. 

We recommend creating different folders to facilitate the re-use of the materials. First, create a folder that contains the "Data_and_code.Rproj" and the README file. Then, create three sub-folders: `data`, `output` and `R` containing the files as outlined below: 

# `data` 

This folder contains the different data sets used in our analyses. 

     * data_plot_fm.csv : data used to assess and plot the mean ARR of each sex (see Figure 3). This data is akin to "data_processed.csv", except that the data was converted to a long format. 

     * data_processed.csv : processed data used for the analyses. This data differs from "Raw_data.csv" by the addition of the calculated effect sizes and the removal of one outlier study. 

     * metadata.docx : full description of the extracted and calculated data. This table can also be found in Table S3 in the online version of the manuscript.

     * Raw_data.csv : raw data without effect sizes. This data was used for the data exploration. 

# `output` 

This folder would host the figure outputs. Note that few cosmetic adjustments were made on these figures in Powerpoint prior to their inclusion in the manuscript. 

# `R` 

This folder contains the R file we used for the data exploration, processing and all analyses. Note that an easy-to-read html version of this code is also provided. 

     * Code.html : Code for the exploration, processing and analyses in a user-friendly, self-contained knitted format. We recommend the reader to navigate this document if interested in the code. 

     * Code.Rmd : R markdown file containing the exploration processing and analyses.

Missing values are indicated as "NA" in all datasets.