When conducting experiments (especially those that are time-consuming or difficult to repeat), it’s important to capture as much useful data as possible. Video recordings are one of the most effective ways to do this.
However, recording a lot of video can introduce two problems:
- Tracking which video corresponds to which experiment
- Synchronizing multiple camera angles
Luckily, similar problems have been solved by filmmakers 100 years ago, with a simple but powerful tool: the clapperboard.
A clapperboard (also known as a slate) is a device traditionally used in filmmaking to provide information about a shot and to synchronize video with audio, which are often recorded separately. The standard process involves writing details like the scene number and take on the board, holding it in front of the camera at the beginning of the shot, and clapping the stick shut. Later, in post-processing, editors sync the video and audio by aligning the clap sound spike with the moment the clapper closes on screen.
We can adopt this same method for engineering experiments. The only real differences are:
- We’re syncing multiple video feeds rather than audio and video.
- The metadata on the clapperboard is experiment-specific.
A Clapperboard Design for Experiments
To make this process easier, I designed a simple clapperboard for experiments. It includes four labeled sections:
- Project Title
- Experiment Title
- Trial Number
- Date
The board is made from acrylic, and the clapper sticks are 3D-printed from PLA.
How I Use It
- Hold the clapperboard in front of the first camera and start recording, but don’t clap yet.
- Repeat the process with all additional cameras.
- Once all cameras are rolling, clap the board once, and then begin the experiment.
This method ensures that each video contains clear identifying information at the beginning, and all footage can be easily synchronized by matching the visual clap with the audio spike.
By borrowing this simple tool from filmmaking, we can make experimental data collection more organized, scalable, and easier to analyze—especially when dealing with multiple cameras or trials.