This dataset contains behavioral and metabolic measurements examining how altered gravity exposure affects locomotion, activity, and energy storage in Drosophila melanogaster. Adult flies were exposed to hypergravity either acutely, developmentally, or across multiple generations using a centrifuge-based simulator. Following exposure, behavioral assays and metabolic measurements were performed at Earth’s gravity (1g). The data underlie analyses of gravity-, exposure-, and time-dependent effects on geotaxis, activity, and energy homeostasis.

Description of the data and file structure

All data files are provided in CSV format and organized by assay type and gravity level (1g, 4g, 7g, 10g, and 13g). File names indicate gravity condition, exposure paradigm, and sampling time point where applicable. All data can be accessed from AllData.zip.

Behavioral datasets include measurements from startle-induced negative geotaxis, negative geotaxis without startle, and longitudinal locomotor activity assays. Variables include climbing score, distance traveled, velocity, activity counts, directional movement, and trajectory-based metrics such as tortuosity. Longitudinal activity files span multiple post-exposure days.

Metabolic datasets contain whole-body triacylglyceride (TAG) measurements collected at defined post-exposure time points.

Missing values are indicated as blank cells or 'NA'. Common abbreviations include g (gravity level), SING (startle-induced negative geotaxis), DAM (locomotor activity monitoring), and TAG (triacylglyceride).

Variables and Units

Behavioral variables:

• Climbing score (unitless)
• Distance (cm)
• Velocity (cm/s)
• Activity (counts/min)
• Zone transitions (counts)
• Tortuosity (unitless)

Metabolic variables:

• TAG concentration (mg/dL)
• Replicates (count)

General variables:

• Gravity (g)
• Date
• Time

Data Organization by Folders

Folder Name: Figure1&2

Subfolder: SING

This folder contains climbing score data for all gravity levels across all days of measurement for the SING assay.

Metrics: Gravity (1g, 4g, 7g, 10g, 13g); Climbing score; Fly Identity; Day of measurement

Figure 1C

1. Each “g” level has a folder with data from Day 0 to Day 7 - - Run Plottingforclimbingscore.py on each gravity-specific (“g”) folder to generate climbing score plots.
2. It also has a master_file for each “g” level which is used for statistical analysis - Run statsforclimbingscore.py on the corresponding master file for each gravity level to perform statistical analysis.

Subfolder: Vertical

This folder contains distance, velocity, zone transition, and trajectory data of all g levels on all days of measurement for the negative geotaxis assay without startle.

Metrics: Distance (cm); Velocity (cm/s); Zone Transitions (top to bottom, bottom to top)

Figures 1D–1F, 2

1. Verplot has a subfolder for each “g” level - Run verticaldynamicSING.py on each “g” folder to plot the distance and velocity data.
2. Stats has a subfolder for each “g” level – Run VerticalstatSING.py on each “g” folder to analyze distance and velocity data.
3. Trajectories has a subfolder for each “g” level - Run Tortuosity.py, Tortuosityplot.py, and Tortuositystats.py on each “g” folder for trajectory tortuosity plotting and analysis. The ‘control’ and ‘experimental’ folder within the trajectories directory contains PNG files representing individual fly movement trajectories.

• Each PNG file corresponds to a single trial under a specific experimental condition (e.g., gravity level and day of measurement). The trajectories were generated from EthoVision tracking data and represent the 2D movement paths of individual flies during the assay period.
• These images are provided for visualization and qualitative assessment of locomotor behavior, such as tortuosity, and complement the quantitative metrics reported in the corresponding data files.

1. Proportion has Excel and csv files for both control and experimental data for each “g” level.

• Run proportionplot-up.py on the control and experimental csv file for each “g” level to calculate the proportion of flies moving from bottom to top.\
• Run proportionplot-down.py on the control and experimental csv file for each “g” level to calculate the proportion of flies moving from top to bottom.
• Run statszoneswithFDR.py on the control and experimental csv file for each “g” level to perform statistical analysis of directional movement.

Folder Name: Figure3

Figure 3

This folder contains daily activity data recorded from flies across their lifespan.

Metrics: Date, Time, Gravity, Mean Activity (count/min), Fly Identity

Subfolders: A folder for each “g” level

1. Run Combinecurvewithstats.py on Daily("g").csv files in each “g” folder to generate longitudinal activity curves with statistics.
2. Run peak4G.py on processed_locomotor_activity("4g").csv files to generate bar plots of morning and evening activity peaks.
   • Use peakhigherG.py for 7g, 10g, and 13g datasets.
   • Run peakwithanalysis.py on processed_locomotor_activity.csv files to perform statistical analysis of activity peaks.
3. Run CombineBoxwithstats.py on individual daily locomotor activity data ("g").csv files to generate daily activity box plots.
4. Run CombineDayandNightwithstats.py on processed_activity_data("g").csv files to generate day and night activity bar plots with statistical analysis.

Folder Name: Figure4

This folder contains triacylglyceride concentration measurements for different g levels on different days of measurement.

Metrics: Gravity, Day of measurement, Triacylglyceride concentration (mg/dL), Replicates

Figure 4

Run TAGstats.py on the Day1&Day7.xlsx to generate plots and perform statistical analysis.

Folder Name: Figure5

This folder contains negative geotaxis assay without startle data of flies recorded across their entire life span.

Figure 5

Subfolders: Day4, Day21, Day38, Day55

Metrics: Distance (cm); Velocity (cm/s)

1. Run verticaldynamic.py on each gravity-specific (“g”).xlsx file in each subfolder (Day4, Day21, Day38, Day55) to plot the data.
2. Run Verticalstat.py for statistical analysis on each gravity-specific (“g”).xlsx file in each subfolder (Day4, Day21, Day38, Day55) to analyze the data.
3. Run SurvivalcurveNG.py for the survival curve plotting and analysis. The script contains the data required for analysis.

Folder Name: Figure6

This folder contains daily activity data of flies recorded across their entire life span.

Figure 6

Subfolders: Days5-7, Days22-24, Days39-41, Days56-58

Metrics: Date, Time, Gravity, Mean Activity (count/min), Fly Identity

1. Each time point has 4g, 7g as subfolders.

• Run Combinecurvewithstats.py on Daily("g").csv files in each “g” folder to generate longitudinal activity curves with statistics.
• Run CombineBoxwithstats.py on individual daily locomotor activity data ("g").csv files to generate daily activity box plots.
• Run SurvivalcurveDAM.py for the survival curve plotting and analysis. The script contains the data required for analysis.

Folder Name: Figure7&8

This folder contains SING and daily activity data from 1st and 10th generation flies exposed to 4g and 7g conditions.

Subfolders: SING, DAM

SING:

Figure 7

Metrics: Gravity (1g, 4g, 7g); Climbing score; Fly Identity; Day of measurement

1. 1st gen and 10th generation data in separate subfolders. Within each generation, data for each “g” level are stored in separate subfolders.

• Each “g” level has a folder with data from Day 0 to Day 7 - Run Plottingforclimbingscore.py on each gravity-specific (“g”) folder to generate climbing score plots.
• It also has a master_file for each “g” level which is used for statistical analysis - Run statsforclimbingscore.py on the corresponding master file for each gravity level to perform statistical analysis.

DAM:

Figure 8

Metrics: Date, Time, Gravity, Mean Activity (count/min), Fly Identity

1. 1st gen and 10th generation data in separate subfolders. Within each generation, data for each “g” level are stored in separate subfolders.
2. Run Combinecurvewithstats.py on Daily("g").csv files in each “g” folder to generate longitudinal activity curves with statistics.
3. Run peak4G.py on processed_locomotor_activity("4g").csv files to generate bar plots of morning and evening activity peaks.\
   • Use peakhigherG.py for 7g
5. Run CombineBoxwithstats.py on individual daily locomotor activity data ("g").csv files to generate daily activity box plots.
6. Run CombineDayandNightwithstats.py on processed_activity_data("g").csv files to generate day and night activity bar plots with statistical analysis.

Folder Name: SupplementaryData

Subfolders: FigureS1, FigureS2, Figure S3

FigureS1

This folder contains SING data of 13g flies measured in groups.

Metrics: Gravity (1g, 13g); Climbing score; Day of measurement

• Run GroupSINGPlot.py and GroupSINGPlotwithstats.py on Day013G and Day113G excel files to plot and analyze the data.

FigureS2

This folder contains distance, velocity data of all g levels on all days of measurement for the SING assay.

Metrics: Distance (cm); Velocity (cm/s)

• Run verticaldynamicSING.py and VerticalstatSING.py on the excel files in each “g” folder to plot and analyze distance and velocity data (Fig. S2A,B).\
• Run IndivvariationSING.py on the excel files in each “g” folder to plot to visualize within-subject variability (Fig. S2C,D).\
• Run DeltastatsSING.py on the excel files in each “g” folder to perform statistical analysis of within-subject variability.

FigureS3

Metrics: Distance (cm); Velocity (cm/s)

• Run IndivvariationNG.py on the excel files in each “g” folder (of VerPlot subfolder from Figure2) to plot to visualize within-subject variability (Fig. S3A,B).
• Run DeltastatsNG.py on the excel files in each “g” folder (of VerPlot subfolder from Figure2) to perform statistical analysis of within-subject variability.

Sharing / Access information

Links to other publicly accessible locations of the data:

• Not applicable

Data were derived from experiments described in the associated Journal of Experimental Biology manuscript “Hypergravity exposure leads to persistent effects on geotaxis and activity in Drosophila melanogaster.”

Code / Software

Behavioral trajectories were extracted using EthoVision (v18.0, Noldus). Locomotor activity data were collected using standard activity monitoring systems. Data processing, statistical analysis, and figure generation were performed using custom Python scripts with common scientific libraries, including pandas, numpy, matplotlib, seaborn, and statsmodels. Script-level workflows are described above.