drop - data recording open-source platform

drop is an open-source platform for projects that aim to a present a task on computer screen and collect time-locked data from sensors during the task. drop was born from the need to collect data from multiple sensors synchronously during psychology research and to share experiments between testing sites and colleagues. drop is designed to be open source and modular platform to easily add new modules such as recording devices. It has been programmed on python.

• Open source, programmed in python and expandable by anyone with programming skills
• JSON-scripting schema to present stimuli on a screen (movies, images, sounds)
• Modular interface that allows user to attach sensors that are being synchronized and record their own data streams receiving tags from the presentation module
• Interface that allows user to monitor the recording devices during the task

Drop is developed in python and currently works at least on following platforms (compatibility may vary with different os-versions):

• Linux (primary platform, tested on Linux Mint 17) (Vagrant config and installer script included for Ubuntu 16.04 LTS (Xenial). Simply install Vagrant and run 'vagrant up' on cloned git repository directory to test. User ubuntu's password is set to 'drop' in virtual machine config.)
• MacOS High Sierra

drop uses MIT licence.

The project was started by researchers from Infant Cognition Lab, University of Tampere, Finland. Please cite us if you publish using drop.

Home page ICL Tampere

• Experiment file = a JSON-file that formally describes the structure of an experiment. See later for more info of the file format.
• Section = part of the experiment that repeats the similar sequence of stimuli with specified variation. Experiment is a series of sections.
• Trial = one round of presented stimuli. Section consists of trials.
• Phase = one set of stimuli played fixed or dynamic time (data condition). Trial consists of phases.
• AOI = Area Of Interest, a square-shape area that is defined by coordinates x1,y1,x2,y2.
• Sensor = a sensor element that can be used to record data during experiment on drop. Each sensor needs a plug-in that matches with drop sensor-API.
• Data condition = A condition in the experiment when phase does
• Working directory = A directory that contains the experiments, media folders and possible recordings by sensors. The working directory at the moment is ~/Documents/drop_data which contains folders experiments, media and recordings. The working directory is the source for experiments and media displayed by drop.

If drop has been installed using pip it can be started by typing drop on terminal. A window with program controls should appear. Window acts as a control interface during experiment. On the left the user must select an experiment, connect necessary sensors and input user-id. On the top-right the user is presented with a black window which displays information from the experiment and possibly from the sensors during experiment-session. Drop is configured to work in a computer with primary and secondary (non-mirroring) displays where the experiment is shown on the secondary display and the leader of the experiment controls the flow of the experiment in the primary display. Insert your experiments and media on the working directory (see above).

• Play: starts the selected experiment if all start conditions are met.
• Stop: ends the current section (might proceed to next section or prompt a cross-section dialog).
• Continue: forcefully continues to the next phase on the experiment if the experiment is halted by a data condition or by fixed duration break.
• (Debug/windowed): when enabled, the experiment will be shown in a window instead of full screen on the secondary display.
• Experiment info: displays a brief overview of the experiment structure and checks that the JSON-syntax is correct (does not check logical errors or missing items etc.). Also checks if the files are present on the working directory. Files that are missing are labeled on red.
• Participant id: A field for the participant identification code - needs to be filled for the experiment to run.
• Log: the log collects messages sent from the experiment instance and inserts a new line each time a new trial is completed displaying little information of the trials variables etc.

Experiment files presented by drop are stored under the work folder tasks/. Experiments are automatically discovered by drop and presented on the user interface. Experiments need to follow JSON-syntax and have a suffix .json in order to work. An experiment is created by defining list of “sections”. A section is an instance, which is created by defining a python-dictionary with certain keywords an values. Sections are run on the order of appearance.

The possible keywords are:

• name: identifier of the node [required]
• mediafolder: string representing media folder [required]
• trialcount: integer [required]
• options: list of strings [required]
• trial: defined later [required]
• images: list of strings, needed if images are presented on the node
• orders: dict of items, integers, strings etc. that are indexed by the round number
• aois: list of lists of four elements [x1, x2, y1, y2]
• permutations: list of lists of order keys that are permuted with the same permutation

A simple example of an experiment that displays a sequence of wallpaper and wallpaper + image "cat.png" for 10 times. The image is first displayed 5 times on the right edge of the screen and 5 times on the left edge of the screen. The example experiment should work if it's inserted on the experiment directory and the working-directory's media folder includes a folder "my_media" which includes files "wallpaper.png" and "cat.png". If a supported eyetracker is used the cat disappears when the gaze arrives at the correct aoi. Otherwise the cat stays for 5 seconds (or as long as the leader presses "continue").

[
    {
        "name": "example_section",
        "mediafolder": "my_media",
        "images": ["wallpaper.png", "cat.png"],
        "orders": {"aoi_order":[1,1,1,1,1,2,2,2,2,2]},
        "trialcount": 10,
        "aois" : [
            [0.0, 1.0, 0.0, 1.0],
            [0.85, 0.95,0.45, 0.55],
            [0.05, 0.15,0.45, 0.55]
        ],
        "options": ["collect_data"],
        "trial": [
            {
                "duration":2000,
                "tag":"wait_phase",
                "extratags":{
                    "stim": 0,
                    "aoi" : 0
                },
                "stimuli":[
                    {"type":"image", "id":0, "aoi":0}
                ]
            },
            {
                "duration":5000,
                "gc_aois":"aoi_order",
                "tag":"cat_phase",
                "extratags":{
                    "stim": 1,
                    "aoi" : "aoi_order"
                },
                "stimuli":[
                    {"type":"image", "id":0, "aoi":0}, {"type":"image", "id":1, "aoi":"aoi_order"}
                ]
            }
        ]
    }
]

Program code is documented with docstrings on functions. Sensor API is most easily understood by examining the sensor superclass from which the sensor-plugins inherit. Some features are not completed and may miss functionality or disfunction.