The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

The dataset named “Dataset: Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction” consists of Raw time-averaged images, which are generated by sequence of 100 frames extracted from experimental videos captured at various voltages (5V, 10V, 15V, 20V, and 50V), and saved as .tif files. These images were analysed to produce the data used in figure 2 and 3 of the article. The dataset also includes two Excel files named as “Figure 2_Experimental data.xlsx” and “Figure 3_Experimental data.xlsx”. These excel files contain the data used to create the experimental plots shown in Figure 2C, and Figure 3 of the research article respectively.

In the “Figure 2C_Experimental Data.xlsx” excel file, each sheet corresponds to a different voltage value shown in the figure, and contains three columns: A, B, and C. which represents the X-location, Y-location, and orientation angle (in degrees) of the experimental plot (red rods in the figure) respectively. This plot is overlaid on the model data (black rods in the figure) and displayed in Figure 2C given in the article.

The “Figure 3_Experimental data.xlsx” file contains three sheets for each voltage (5V, 10V, 15V, 20V, and 50V) and each of these three sheets provide data at three different X-locations (X=579, X= 1079, and X= 1779) as a function of Y-location as shown in the Figure 3 of the article. Each sheet has five columns: A, B, C, D, and E. These columns represent the X-location, Y-location, Orientation angle (in degrees), Coherency, and Error in the orientation angle (in degrees), respectively. These data points are used to create the experimental scatter plot shown in Figure 3 of the article.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

In "Sample Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described (CrP Sample Dataset, Glycolytic Dataset, Oxidative Dataset). Additionally, there are three sheets with sample graphs created using one of the three datasets (CrP Sample Graph, Glycolytic Graph, Oxidative Graph). Each dataset and graph pairs are from different subjects. · CrP Sample Dataset and CrP Sample Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Glycolytic Dataset and Glycolytic Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Oxidative Dataset and Oxidative Graph: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a sustained, light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.