0em individual_<participantID>_<timestamp>.csv
(semicolon delimited text file, 1st row is a header)
This CSV file is the Baseline Individual Affective Task Data/Statistics for each Participant. The Participant ID is in the of the file name. There will be three of these files in the directory. One for each Participant, Lion, Tiger, and Leopard. A summary of data contained in this file is: Record Timestamp (in Global, Monotonic, and Human formats), Name of Image being shown to the Participant, Subject ID (Participant ID), The Participant’s Arousal Score, The Participant’s Valence Score, and the Event Type (start_affective_task, show_blank_screen, show_cross_screen, show_image, show_rating_screen, intermediate_selection,
final_submission).

Baseline Individual Affective Task Fields:

• time - [##########.######] (in seconds)
  Unix Time https://www.unixtimestamp.com/.

• monotonic_time - [#######.#########] (in seconds)
  How long since the computer that hosts the task was booted up.

• human_readable_time - [yyyy-mm-ddThh:nn:ss.######Z] (hh in 24 hour)
  UTC-0 time in human-readable format.

• image_path -
  Name of image being shown to the Participant. You can see these images in the code of baseline task.

• subject_id - [#####]
  Participant ID. (If an experimenter sat-in, the ID will be entered as 99999 for that Participant)

• arousal_score - [-2 to +2]
  Arousal measure of emotion (calm vs. intense).

• valence_score - [-2 to +2]
  Valence measure of emotion (unpleasant vs. pleasant).

• event_type -
  What event and when. (start_affective_task, show_blank_screen, show_cross_screen, show_image, show_rating_screen, intermediate_selection, final_submission).

0em individual_<participantID>_<timestamp>_metadata.json
(JSON data format)
Baseline Individual Affective Task Participant configuration information. This is the sequence that the computer shows for each image: blank screen, cross screen, blank screen, image, rating screen. The timing for each screen is specified in this JSON file as shown below.

Participant Configuration Information JSON File:

                {
                    "participant_ids":
                        ["#####"] ("99999" for subbing-in experimenter),
                    "blank_screen_milliseconds": [####],
                    "cross_screen_milliseconds": [####],
                    "individual_image_timer": [##.#] (in seconds),
                    "individual_rating_timer": [##.#] (in seconds),
                    "team_image_timer": [##.#] (in seconds),
                    "team_discussion_timer": [##.#] (in seconds),
                    "team_rating_timer": [##.#] (in seconds)
                }

0em team_<timestamp>.csv
(semicolon delimited text file, 1st row is a header)
This CSV file is the Baseline Team Affective Task Data/Statistics. A summary of data contained in this file is: Record Timestamps (in Global, Monotonic, and Human formats), Name of Image being shown to the Participants, Subject ID (Participant ID), The Participant’s Arousal Score (blank if this participant was not selected to score this image), The Participant’s Valence Score (blank if this participant was not selected to score this image), and the Event Type (start_affective_task, show_blank_screen, show_cross_screen, show_image, show_rating_screen, intermediate_selection, final_submission).

Baseline Team Affective Task Fields:

• time - [##########.######] (in seconds)
  Unix Time https://www.unixtimestamp.com/.

• monotonic_time - [#######.#########] (in seconds)
  How long since the computer that hosts the task was booted up.

• human_readable_time - [yyyy-mm-ddThh:nn:ss.######Z] (hh in 24 hour)
  UTC-0 time in human-readable format.

• image_path - [Team###.jpg]
  Name of image being shown to the participants. You can see these images in the code of baseline task.

• subject_id - [#####]
  Participant ID. (If an experimenter sat-in, the ID will be entered as 99999 for that Participant)

• arousal_score - [-2 to +2]
  Arousal measure of emotion (calm vs. intense, will be blank if this participant was not selected to score this image).

• valence_score - [-2 to +2]
  Valence measure of emotion (unpleasant vs. pleasant, will be blank if this participant was not selected to score this image).

• event_type -
  What event and when. (start_affective_task, show_blank_screen, show_cross_screen, show_image, show_rating_screen, intermediate_selection, final_submission).

0em team_<timestamp>_metadata.json
(JSON data format)
Baseline Team Affective Task Participant configuration information. This is the sequence that the computer shows for each image: blank screen, cross screen, blank screen, image, rating screen. The timing for each screen is specified in this JSON file as shown below.

Team Configuration Information JSON File:

                {
                    "participants_ids": [
                        ("#####","#####","#####"; "99999" for experimenter)
                        "<lion_participant_id>",
                        "<tiger_participant_id>",
                        "<leopard_participant_id>"
                    ],
                    "blank_screen_milliseconds": [####],
                    "cross_screen_milliseconds": [####],
                    "individual_image_timer": [##.#] (in seconds),
                    "individual_rating_timer": [##.#] (in seconds),
                    "team_image_timer": [##.#] (in seconds),
                    "team_discussion_timer": [##.#] (in seconds),
                    "team_rating_timer": [##.#] (in seconds)
                }

0em <timestamp>.csv
(semicolon delimited text file, 1st row is a header)
This CSV file is the Baseline Finger Tapping Task Data/Statistics. A summary of data contained in this file is: Record Timestamp (Unix Time, Monotonic, and Human-readable formats), Row Data Event (team, individual), Countdown Timer (integer - 10 to 0), Was a Tap on Keyboard recorded for each participant (0 = no-tap, 1 = tap). The last three column (Fields) names for the Tap Data are the IDs of the Participants (<lion_participant_id>, <tiger_participant_id>, <leopard_participant_id>, If an experimenter sat-in, the column name will be "99999" for that Participant).

Baseline Individual Affective Task Fields:

• time - [##########.######] (in seconds)
  Unix Time https://www.unixtimestamp.com/.

• monotonic_time - [#######.#########] (in seconds)
  How long since the computer that hosts the task was booted up.

• human_readable_time - [yyyy-mm-ddThh:nn:ss.######Z] (hh in 24 hour)
  UTC-0 time in human-readable format.

• event_type -
  What event and when. (team, individual).

• countdown_timer - [##] (intiger - 10 to 0)
  Countdown Timer.

• <lion_participant_id> - [0 or 1]
  Tap on keyboard from Lion (0 = no-tap, 1 = tap).

• <tiger_participant_id> - [0 or 1]
  Tap on keyboard from Tiger (0 = no-tap, 1 = tap).

• <leopard_participant_id> - [0 or 1]
  Tap on keyboard from Leopard (0 = no-tap, 1 = tap).

0em <timestamp>_metadata.json
(JSON data format)
Baseline Finger Tapping Task configuration information. The configuration information in this file: participants_ids session seconds_per_session seconds_count_down square_width and count_down_message.

Finger Tapping Configuration Information JSON File:

                {
                    "participants_ids": [
                        ("#####","#####","#####"; "99999" for experimenter)
                        "<lion_participant_id>",
                        "<tiger_participant_id>",
                        "<leopard_participant_id>"
                    ],
                    "session": [ (typical: "0,1,0,1")
                        (0 or 1),
                        (0 or 1),
                        (0 or 1),
                        (0 or 1)
                    ],
                    "seconds_per_session": [ (typical: "10.0" for all)
                        ##.#,
                        ##.#,
                        ##.#,
                        ##.#
                    ],
                    "seconds_count_down": [##.#] (typical: "10.0"),
                    "square_width": [###] (typical: "200")
                    "count_down_message": ["string"]
                    (example: "Practice session:
                     Press SPACEBAR and observe the squares")
                }

0em competitive_<team>_<timestamp>.csv
(semicolon delimited text file, 1st row is a header)
This CSV file is for the Baseline Competitive Ping-Pong Task Data/Statistics. The <team> in the file name is "0" for Lion vs Tiger and "1" for Leopard vs Cheetah. (If an experimenter sat-in, the column name will be "99999" for that Participant). (For <team> = "1" in the file name, the participant2 ID will always be "exp"). A summary of data contained in this file is: Record Timestamp (Unix Time, Monotonic, and Human-readable formats), Score on Left, Score on Right (For <team> = "1" in the file name, right score will be for experimenter on "Cheetah"), Game Started (False = countdown for game to start, True = game has started), Ball’s X Coordinates, Ball’s Y Coordinates, Participant 1 Paddle X Coordinates, Participant 1 Paddle Y Coordinates, Participant 2 Paddle X Coordinates , Participant 2 Paddle Y Coordinates, Seconds Timer on Screen (If game has not started, started = False, the seconds will count down from 10 to 0. If game has started, started = True, the seconds will count down from 120 to 0.)

Baseline Competitive Ping-Pong Task Fields:

• time - [##########.######] (in seconds)
  Unix Time https://www.unixtimestamp.com/.

• monotonic_time - [#######.#########] (in seconds)
  How long since the computer that hosts the task was booted up.

• human_readable_time - [yyyy-mm-ddThh:nn:ss.######Z] (hh in 24h)
  UTC-0 time in human-readable format.

• score_left - [##]
  Current score for left team participant.

• score_right - [##]
  Current score for right team participant (For <team> = "1" in the file name, right score will be for experimenter on "Cheetah").

• started - [False or True]
  Has the Ping-Pong game started.

• ball_x - [####] -
  The ball’s X coordinate on the screen.

• ball_y - [####] -
  The ball’s Y coordinate on the screen.

• <participant1_id>_x - [####] -
  Participant1’s (left team) paddle’s X coordinate on the screen.

• <participant1_id>_y - [####] -
  Participant1’s (left team) paddle’s Y coordinate on the screen.

• <participant2_id>_x - [####] -
  Participant2’s (right team) paddle’s X coordinate on the screen (For <team> = "1" in the file name, the participant2 ID will always be "exp").

• <participant2_id>_y - [####] -
  Participant2’s (right team) paddle’s Y coordinate on the screen (For <team> = "1" in the file name, the participant2 ID will always be "exp").

• seconds - [###]
  Seconds left in game (120 counts down to 0).

0em competitive_<team>_<timestamp>_metadata.json
(JSON data format)
Baseline Competitive Ping-Pong Configuration Information. (For <team> = "1" in the file name, the participant2 ID will always be "exp"). The configuration information in this file: left_team participant ID, right_team participant ID, client_window_height, client_window_width, session_time_seconds, seconds_count_down, count_down_message,
paddle_width, paddle_height, ai_paddle_max_speed, paddle_speed_scaling,
paddle_max_speed, ball_x_speed, ball_bounce_on_paddle_scale.

Baseline Competitive Ping-Pong Configuration Information JSON File:

                {
                    "left_team": [
                        "#####" (left team participant ID, "99999" for experimenter)
                    ],
                    "right_team": [
                        "#####" (right team participant ID, "99999" for experimenter,
                        "exp" for <team> = "1" in file name.)
                    ],
                    "client_window_height": [####] (typical: 1440),
                    "client_window_width": [####] (typical: 2560),
                    "session_time_seconds": [###.#] (typical: 120.0),
                    "seconds_count_down": [##.#] (typical: 10.0),
                    "count_down_message": ["string"]
                    (typical:"Move the mouse to move the blue paddle"),
                    "paddle_width": [##] (typical: 20),
                    "paddle_height": [###] (typical: 120),
                    "ai_paddle_max_speed": [##] (typical: 13),
                    "paddle_speed_scaling": [#.#] (typical: 0.6),
                    "paddle_max_speed": [###.#] (typical: null),
                    "ball_x_speed": [##] (typical: 9),
                    "ball_bounce_on_paddle_scale": [#.##] (typical: 0.25)
                }

0em cooperative_0_<timestamp>.csv
(semicolon delimited text file, 1st row is a header)
This CSV file is for the Baseline Cooperative Ping-Pong Task Data/Statistics. For the Cooperative Ping-Pong Task, the participants on Lion, Tiger and Leopard play together as a team and play against the AI machine. (If an experimenter sat-in, the column name will be "99999" for that Participant). A summary of data contained in this file is: Record Timestamp (Unix Time, Monotonic, and Human-readable formats), Score on Left, Score on Right, Game Started (False = countdown for game to start, True = game has started), Ball’s X Coordinates, Ball’s Y Coordinates, Participant 1 Paddle X Coordinates, Participant 1 Paddle Y Coordinates, Participant 2 Paddle X Coordinates, Participant 2 Paddle Y Coordinates, Seconds Timer on Screen (If game has not started, started = False, the seconds will count down from 10 to 0. If game has started, started = True, the seconds will count down from 120 to 0.)

Baseline Cooperative Ping-Pong Task Fields:

• time - [##########.######] (in seconds)
  Unix Time https://www.unixtimestamp.com/.

• monotonic_time - [#######.#########] (in seconds)
  How long since the computer that hosts the task was booted up.

• human_readable_time - [yyyy-mm-ddThh:nn:ss.######Z] (hh in 24h)
  UTC-0 time in human-readable format.

• score_left - [##]
  Current score for left team.

• score_right - [##]
  Current score for "AI" team.

• started - [False or True]
  Has the Ping-Pong game started.

• ball_x - [####]
  The ball’s X coordinate on the screen.

• ball_y - [####] -
  The ball’s Y coordinate on the screen.

• <left_team_participant1_id>_x - [####] -
  Participant1’s (left team) paddle’s X coordinate on the screen.

• <left_team_participant1_id>_y - [####] -
  Participant1’s (left team) paddle’s Y coordinate on the screen.

• <left_team_participant2_id>_x - [####] -
  Participant2’s (left team) paddle’s X coordinate on the screen.

• <left_team_participant2_id>_y - [####] -
  Participant2’s (left team) paddle’s Y coordinate on the screen.

• <left_team_participant3_id>_x - [####] -
  Participant3’s (left team) paddle’s X coordinate on the screen.

• <left_team_participant3_id>_y - [####] -
  Participant3’s (left team) paddle’s Y coordinate on the screen.

• ai_x - [####] -
  AI’s (right team) paddle’s X coordinate on the screen.

• ai_y - [####] -
  AI’s (right team) paddle’s Y coordinate on the screen.

• seconds - [###]
  Seconds left in game (120 counts down to 0).

0em cooperative_0_<timestamp>_metadata.json
(JSON data format)
Baseline Cooperative Ping-Pong Configuration Information. The configuration information in this file: left_team (participant IDs for Lion, Tiger, and Leopard), right_team ("ai"), client_window_height, client_window_width, session_time_seconds, seconds_count_down, count_down_message, paddle_width, paddle_height, ai_paddle_max_speed, paddle_speed_scaling, paddle_max_speed, ball_x_speed, ball_bounce_on_paddle_scale.

Baseline Cooperative Ping-Pong Configuration Information JSON File:

                {
                    "left_team": [
                        "#####", (left team member 1 ID, "99999" for experimenter)
                        "#####", (left team member 2 ID, "99999" for experimenter)
                        "#####"  (left team member 3 ID, "99999" for experimenter)
                    ],
                    "right_team": [
                        "ai"
                    ],
                    "client_window_height": [####] (typical: 1440),
                    "client_window_width": [####] (typical: 2560),
                    "session_time_seconds": [###.#] (typical: 120.0),
                    "seconds_count_down": [##.#] (typical: 10.0),
                    "count_down_message": ["string"]
                    (typical:"Move the mouse to move the blue paddle"),
                    "paddle_width": [##] (typical: 20),
                    "paddle_height": [###] (typical: 90),
                    "ai_paddle_max_speed": [##] (typical: 20),
                    "paddle_speed_scaling": [#.#] (typical: 0.6),
                    "paddle_max_speed": [###.#] (typical: null),
                    "ball_x_speed": [##] (typical: 12),
                    "ball_bounce_on_paddle_scale": [#.##] (typical: 0.4)
                }

0em <timestamp>.csv
(semicolon delimited text file, 1st row is a header)
This CSV file records the Start Time and End Time for the Baseline Rest State. A summary of data contained in this file is: Record Timestamp (Unix Time, Monotonic, and Human-readable formats), and Event Type ("start_rest_state" or "end_rest_state").

Baseline Rest State Timestamp CSV Fields:

• time - [##########.######] (in seconds)
  Unix Time https://www.unixtimestamp.com/.

• monotonic_time - [#######.#########] (in seconds)
  How long since the computer that hosts the task was booted up.

• human_readable_time - [yyyy-mm-ddThh:nn:ss.######Z] (hh in 24h)
  UTC-0 time in human-readable format.

• event_type - [string]
  Start or End of Rest State ("start_rest_state" or "end_rest_state").

0em <cat>_eeg_fnirs_pupil.xdf
(Extensible Data Format XDF, binary file format)
The <cat>_eeg_fnirs_pupil.xdf contains <cat> data files and data streams for the Experiment. You must use a XDF viewer program to view or extract the data contained in this file. Some common software packages used to view or extract data from this XDF file are: MNE-Python, Matplotlib, and Qtgraph. A summary of data recorded in this XDF file is: fNIRS LSL Streams, EEG LSL Streams, and Pupil Data.

Data files and streams contained in the <cat>_eeg_fnirs_pupil.xdf:

• fNIRS LSL Streams -
  fNIRS LSL Streams being transmitted from the "NIRx - Aurora" software programs running on the "fNIRS Server Computer" during the Experiment for participants on Lion, Tiger, and Leopard.

• EEG LSL Streams -
  EEG LSL Streams being transmitted from the "Brain Vision - LSL-actiChamp" software programs running on the "EEG Server Computer" during the Experiment for participants on Lion, Tiger, and Leopard.

• Pupil Data -
  Pupil Data files recorded from the "Pupil Labs - Pupil Capture" software programs running on the participant’s iMacs, Lion, Tiger, and Leopard during the Experiment.

0em Trial-<trial_id>_Team-<team_num>_Member-<player_num>.wav
(WAV - Waveform Audio File Format, RIFF "little-endian" data, mono 48000 Hz)
This file is the audio recording from the participant’s microphone during the Minecraft Trials (Missions). Example of file name:
Trial-0720f53b-df85-42a8-ba44-0508094653b4_Team-4_Member-Player877.wav

0em Trial-<trial_id>_Team-<team_num>_Member-<participant_id>.wav
(WAV - Waveform Audio File Format, RIFF "little-endian" data, mono 48000 Hz)
This file is the audio recording from the participant’s microphone during the Minecraft Trials (Missions). Example of file name:
Trial-f4f65fe1-e105-4e67-8682-f9b3dc4eedb1_Team-34_Member-00131.wav

0em Face Image Files <yyyy-mm-dd>T<hh_mm_ss.sssssssss>Z.png
(PNG - Portable Network Graphic, raster image file)
Participant Face Image files recorded from the built-in "web camera" on the Lion, Tiger, and Leopard iMacs. The files are recorded at a frequency of 10Hz and have a resolution of 1280 x 720, 8-bit/color RGB, non-interlaced. Typically, there will be over 50,000 files per <cat> for the experiment.

0em Screenshot Image Files <yyyy-mm-dd>T<hh_mm_ss.sssssssss>Z.png
(PNG - Portable Network Graphic, raster image file)
Screenshot Image files recorded from the participant’s iMac (Lion, Tiger, or Leopard). The files are recorded at a frequency of 10Hz and have a resolution of 1280 x 720, 8-bit/color RGB, non-interlaced. Typically, there will be over 50,000 files per <cat> for the experiment.

0em Face Image Files (block_1):
<seq ######>__<hh_mm_ss.sss.AM/PM> <ms last image ###>.png
(PNG - Portable Network Graphic, raster image file)
Participant Face Image files (block_1) recorded from the built-in "web camera" on the Lion, Tiger, and Leopard iMacs during the Baseline Tasks portion of the Experiment. The files are recorded at a frequency of 10Hz and have a resolution of 1280 x 720, 8-bit/color RGB, non-interlaced. Typically, there will be over 15,000 files in this directory per <cat> for the experiment.

0em Face Image Files (block_2):
<seq ######>__<hh_mm_ss.sss.AM/PM> <ms last image ###>.png
(PNG - Portable Network Graphic, raster image file)
Participant Face Image files (block_2) recorded from the built-in "web camera" on the Lion, Tiger, and Leopard iMacs during the Minecraft portion of the Experiment. The files are recorded at a frequency of 10Hz and have a resolution of 1280 x 720, 8-bit/color RGB, non-interlaced. Typically, there will be over 30,000 files in this directory per <cat> for the experiment.

0em Screenshot Image Files (block_1):
<seq ######>__<hh_mm_ss.sss.AM/PM> <ms last image ###>.png
(PNG - Portable Network Graphic, raster image file)
Screenshot Image files (block_1) recorded from the participant’s iMac (Lion, Tiger, or Leopard) during the Baseline Tasks portion of the Experiment. The files are recorded at a frequency of 5Hz and have a resolution of 1280 x 720, 8-bit/color RGB, non-interlaced. Typically, there will be over 8,000 files in this directory per <cat> for the experiment.

0em Screenshot Image Files (block_2):
<seq ######>__<hh_mm_ss.sss.AM/PM> <ms last image ###>.png
(PNG - Portable Network Graphic, raster image file)
Screenshot Image files (block_2) recorded from the participant’s iMac (Lion, Tiger, or Leopard) during the Minecraft Tasks portion of the Experiment. The files are recorded at a frequency of 5Hz and have a resolution of 1280 x 720, 8-bit/color RGB, non-interlaced. Typically, there will be over 14,000 files in this directory per <cat> for the experiment.

0em participant_<participant_ID>.wav
(Symbolic Link to a file in the "presession" directory, WAV - Waveform Audio File Format, RIFF "little-endian" data, WAVE audio, mono 48000 Hz)
This is a Symbolic Link to file "participant_<participant_ID>.wav" in the presession’s directory that has the presession files for this <cat>’s participant.
The "participant_<participant_ID>.wav" was created in the participant’s presession and is the audio recording of the participant speaking the first task:
The first task is where the participant sees a map with two locations, start and end, marked. The participant is given written instructions to explain to their "friend" how to get from start to end with as much detail as possible. Once the participant understand the instructions, their voice for this task will be recoded.
Example of the Symbolic Link mapping:

        participant_<participant_ID>.wav ->
            <presession directory>/participant_<participant_ID>.wav

0em participant_<participant_ID>Task2.wav
(Symbolic Link to a file in the "presession" directory, WAV - Waveform Audio File Format, RIFF "little-endian" data, WAVE audio, mono 48000 Hz)
This is a Symbolic Link to file "participant_<participant_ID>Task2.wav" in the presession’s directory that has the presession files for this <cat>’s participant.
The "participant_<participant_ID>Task2.wav" was created in the participant’s presession and is the audio recording of the participant speaking the second task:
The second task is where the participant is asked to read aloud a passage that contains words we would expect them to use during the experiment. An example of the passage is "Minecraft is a multiplayer online game where players take roles such as medic, engineer, and transporter...".
Example of the Symbolic Link mapping:

        participant_<participant_ID>Task2.wav ->
            <presession directory>/participant_<participant_ID>Task2.wav

0em blinks.pldata
(proprietary binary file used by the Pupil Player.)
This file contains a sequence of independently msgpack-encoded messages for recorded Eye Blinks. Pupil Core’s Blink Detector leverages the fact that 2D pupil confidence drops rapidly in both eyes during a blink as the pupil becomes obscured by the eyelid, followed by a rapid rise in confidence as the pupil becomes visible again. The Blink Detector processes 2D pupil confidence values by convolving them with a filter. The filter response – called ’activity’ – spikes the sharper the confidence drop is and vice versa for confidence increases. Blinks are subsequently detected based on onset and offset confidence thresholds and a filter length in seconds. The "Pupil Lab - Pupil Player" application uses this file to play back Eye Blink data during playback. More information about the data contained in this file can be found at:
Pupil Labs - Basic Concepts - Blinks
https://docs.pupil-labs.com/neon/basic-concepts/data-streams/#blinks

0em blinks_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
Eye Blink timestamps for the Pupil Capture glasses and system. Blinks are subsequently detected based on onset and offset confidence thresholds and a filter length in seconds. The "Pupil Lab - Pupil Player" application uses this file to play back Eye Blink data during playback.

0em eye0.intrinsics
(proprietary binary file used by the Pupil Player.)
This file stores camera intrinsics persistencies for the right eye.
More information about the data contained in this file can be found at:
Pupil Labs - User Guide - Camera Intrinsics Persistency
https://docs.pupil-labs.com/core/software/pupil-capture/

0em eye0.mp4
(MP4 file: formally ISO/IEC 14496-14:2003
is a digital multimedia container format most commonly used to store video and audio.)
This file contains video of the participant’s right eye and pupil recorded from the pupil capture sensor mounted on the right side of the "Pupil Capture Glasses". The sensors, right and left eyes, record IR video at 200 Hz with a resolution of 192x192px. The two sensors are synced in hardware, such that they record images at the exact same time. The resulting images a concatenated in a single video stream of 384x192px resolution.

0em eye0_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
This file contains timestamps that relate to the video captured in "eye0.mp4" (right eye).

0em eye1.intrinsics
(proprietary binary file used by the Pupil Player.)
This file stores camera intrinsics persistencies for the left eye.
More information about the data contained in this file can be found at:
Pupil Labs - User Guide - Camera Intrinsics Persistency
https://docs.pupil-labs.com/core/software/pupil-capture/

0em eye1.mp4
(MP4 file formally ISO/IEC 14496-14:2003
is a digital multimedia container format most commonly used to store video and audio.)
This file contains video of the participant’s left eye and pupil recorded from the pupil capture sensor mounted on the left side of the "Pupil Capture Glasses". The sensors, right and left eyes, record IR video at 200 Hz with a resolution of 192x192px. The two sensors are synced in hardware, such that they record images at the exact same time. The resulting images a concatenated in a single video stream of 384x192px resolution.

0em eye1_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
This file contains timestamps that relate to the video captured in "eye1.mp4" (left eye).

0em fixations.pldata
(proprietary binary file used by the Pupil Player.)
This file stores fixation data to be used by the Pupil Player. The two primary types of eye movements exhibited by the visual system are fixations and saccades. During fixations, the eyes are directed at a specific point in the environment. A saccade is a very quick movement where the eyes jump from one fixation to the next. Properties like the fixation duration are of significant importance for studying gaze behaviour.
More information about the data contained in this file can be found at:
Pupil Labs - Basic Concepts - Fixations
https://docs.pupil-labs.com/neon/basic-concepts/data-streams/#fixations

0em fixations_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
This file contains timestamps that relate to the fixations captured in "fixations.pldata".

0em gaze.pldata
(proprietary binary file used by the Pupil Player.)
This file stores gaze data to be used by the Pupil Player. The Neon Companion app can provide gaze data in real-time. When using a OnePlus 8 Companion device, the available framerate is +120 Hz (the achieved framerate varies from  200Hz in the first minute of a recording to  120Hz for longer recordings). More information about the data contained in this file can be found at:
Pupil Labs - Basic Concepts - Gaze
https://docs.pupil-labs.com/neon/basic-concepts/data-streams/#gaze

0em gaze_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
This file contains timestamps that relate to the gaze data captured in "gaze.pldata".

0em info.player.json
(JSON data format)
Pupil Capture application’s metadata about this participant.
Information contained in this file:

Pupil Capture Application Participant Information JSON File:

            {
                "duration_s": [####.########, (Example: 2107.33979455)],
                "meta_version": [string, (Example:"2.3")],
                "min_player_version": [string, (Example: "2.0")],
                "recording_name": [string, (Example: "2023_05_01")],
                "recording_software_name": [string, (Example: "Pupil Capture")],
                "recording_software_version": [string, (Example: "3.5.7")],
                "recording_uuid":
                    [string, (Example: "7078d724-c0f9-478f-bdb8-d81495bbc9f7")],
                "start_time_synced_s":
                    [####.############, (Example: 5604.700757881999)],
                "start_time_system_s":
                    [####.############, (Example: 1682975254.133834)],
                "system_info":
                    [string, (Example: "User: LabWorker, Platform: Darwin,
                     Machine: tiger, Release: 20.6.0,
                     Version: Darwin Kernel Version 20.6.0:
                     Wed Jan 12 22:22:42 PST 2022;
                     root:xnu-7195.141.19~2/RELEASE_X86_64")]
            }

0em notify.pldata
(proprietary binary file used by the Pupil Player.)
This file stores "Notification Messages" to be used by the Pupil Player. Pupil uses special messages called notifications to coordinate all activities. Notifications are key-value mappings with the required field subject. Subjects are grouped by categories category.command_or_statement. Example: recording.should_stop
More information about the data contained in this file can be found at:
Pupil Labs - IPC Backbone Message Format
https://docs.pupil-labs.com/developer/core/network-api/#notification-message

0em notify_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
This file contains timestamps that relate to the "Notification Messages" data captured in "notify.pldata".

0em pupil.pldata
(proprietary binary file used by the Pupil Player.)
This file stores "Pupil Capture" data to be used by the Pupil Player. More information about the data contained in this file can be found at:
Pupil Labs - Developer - Core - Recording Format - pldata Files
https://docs.pupil-labs.com/developer/core/recording-format/#recording-format

0em pupil_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
This file contains timestamps that relate to the "Pupil Capture" data captured in "pupil.pldata".

0em user_info.csv
(semicolon delimited text file, 1st row is a header)
The Pupil Capture optionally stores information about the User in this file. In the ToMCAT Experiment, this option is not enabled. Therefor, the file only contains the header. user_info.csv Fields:

• key - [text]

• value - [text]

• name - [text]

• additional_field - [text]

• change_me - [text]

0em world.intrinsics
(proprietary binary file used by the Pupil Player.)
This file stores camera intrinsics persistencies for the World camera.
More information about the data contained in this file can be found at:
Pupil Labs - Core - Terminology - World
https://docs.pupil-labs.com/core/terminology/#world
And at:
Pupil Labs - Core - Terminology - Camera Intrinsics
https://docs.pupil-labs.com/core/terminology/#camera-intrinsics

0em world.mp4
(MP4 file formally ISO/IEC 14496-14:2003
is a digital multimedia container format most commonly used to store video and audio.)
This file contains video of the participant’s physical scene field of view. The World Camera is mounted on top center of the "Pupil Capture Glasses". More information about the video contained in this file can be found at:
Pupil Labs - Core - Terminology - World
https://docs.pupil-labs.com/core/terminology/#world

0em world_timestamps.npy
(NPY binary format:
A simple format for saving numpy arrays to disk with the full information about them.
The .npy format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. You can use numpy.load() to access the timestamps in Python.)
This file contains timestamps that relate to the "World Video" captured in "world.mp4".

0em <cat>_self_report_data.csv
(semicolon delimited text file, 1st row is a header)
This file has information and answers for the following categories: "Subject Information Sheet", "Informed Consent Form - Multiple Subject-Two Sessions", "COVID-19 Screening", "Demographics Survey", "Big Five Inventory - 2 Short Form (BFI-2-SF)", "Attachment Style Questionnaire", "Pre-Session Notes For Research Team ONLY", "Session 1 Notes For Research Team ONLY", and "Informed Consent Form - Multiple Subject-Four Sessions".

<cat>_self_report_data.csv Fields:

• record_id - [text]
  Record ID created by REDCap system.

• redcap_event_name - [text]("Pre-Session")
  Event when this information was collected.

• redcap_survey_identifier - [text](can be blank)
  Survey Identifier.

• subject_information_sheet_timestamp - [text](can be blank)
  Sheet Timestamp.

• subject_id - [#####](Required)
  Subject ID assigned to participant.

• head_size - [text]
  Participant’s Head Size (cm).

• presession_date - [text](yyyy-mm-dd hh:mm:ss)
  Pre-Session Date.

• presession_exp_initials - [text]
  Pre-Session Experimenter(s) (Initials only).

• session1_date - [text](yyyy-mm-dd hh:mm:ss)
  Session 1 Date.

• team_id - [text]
  Team ID that has been assigned to this participant.

• session1_exp_initials - [text]
  Session 1 Experimenter(s) (Initials only).

• session2_date - [text](yyyy-mm-dd hh:mm:ss)
  Session 2 Date.

• session2_exp_initials - [text]
  Session 2 Experimenter(s) (Initials only).

• subject_information_sheet_complete - [0=Incomplete, 1=Unverified, 2=Complete]
  Subject Information Sheet Complete.

• informed_consent_form_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Informed Consent Form Timestamp.

• consent_date - [text](yyyy-mm-dd)
  Consent Form date.

• informed_consent_form_complete - [0 = Incomplete; 1 = Unverified; 2 = Complete]
  Informed Consent Form Complete.

• informed_consent_form_multiple_subjecttwo_sessions_timestamp -
[text](yyyy-mm-dd hh:mm:ss)
  Informed Consent Form Multiple Subject Two Sessions Timestamp.

• consent_date_2 - [text](yyyy-mm-dd)
  Consent Form 2 date.

• informed_consent_form_multiple_subjecttwo_sessions_complete -
[0 = Incomplete; 1 = Unverified; 2 = Complete]
  Informed Consent Form Multiple Subject Two Sessions Complete.
  

• covid19_screening_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  COVID-19 Screening Timestamp.

• covid_symptoms___1 - [checkbox]
  Fever or chills.

• covid_symptoms___2 - [checkbox]
  Cough.

• covid_symptoms___3 - [checkbox]
  Shortness of breath or difficulty breathing.

• covid_symptoms___4 - [checkbox]
  Fatigue.

• covid_symptoms___5 - [checkbox]
  Muscle or body aches.

• covid_symptoms___6 - [checkbox]
  Headache.

• covid_symptoms___7 - [checkbox]
  New loss of taste or smell.

• covid_symptoms___8 - [checkbox]
  Sore throat.

• covid_symptoms___9 - [checkbox]
  Congestion or runny nose.

• covid_symptoms___10 - [checkbox]
  Nausea or vomiting.

• covid_symptoms___11 - [checkbox]
  Diarrhea.

• covid_symptoms___12 - [checkbox]
  No Symptoms now or in the past 72 hours.

• covid_symptoms___13 - [checkbox]
  Not applicable, I recovered from COVID-19 in the last 90 days.

• covid_symptoms___14 - [checkbox]
  Yes, I have symptoms or was diagnosed with COVID-19 in the past 10 days.

• covid_close_contact - [1 = No; 2 = Yes I was in contact]
  During the past 14 days, have you been in close contact (within 6 feet for 15 minutes or more) with a confirmed case or someone with symptoms of COVID-19?.

• covid19_screening_complete - [0 = Incomplete; 1 = Unverified; 2=Complete]
  COVID-19 Screening Complete.
  

• demographics_survey_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Demographics Survey Timestamp.

• age - (Required)[text]
  Your age in years.

• sex - (Required)[1 = Male; 2 = Female; 3 = Other; 4 = Prefer not to say]
  What is your sex?

• hisp - (Required)[0 = No; 1 = Yes]
  Are you of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture of origin (Hispanic)?.

• race - (Required)
[0 = European American; 1 = African American; 2 = Asian American;
3 = Native Hawaiian or Pacific Islander; 4 = Non-Hispanic White; 5 = Other]
  Which best describes your racial background?.

• income - (Required)
[0 = $0 - $25,000; 1 = $25,000 - $ 50,000; 2 = $50,000 - $75,000;
3 = $75,000 - $ 100,000; 4 = $100,000 - $150,000; 5 = Greater than $150,000]
  What is your typical yearly household income before taxes?

• edu - (Required)
[0 = Less than high-school; 1 = High-school; 2 = Professional program;
3 = Some college; 4 = Undergraduate degree; 5 = Graduate degree]
  What is the highest level of education you have completed?

• exp - (Required)
[0 = Never played them; 1 = Have played them occasionally;
2 = Have played them fairly often; 3 = Have played them regularly for years]
  How much experience do you have playing video games?

• exp_mc - (Required)
[0 = Never played it; 1 = Have played it occasionally;
2 = Have played it fairly often; 3 = Have played it regularly for years]
  How much experience do you have playing Minecraft?

• handedness - (Required)
[0 = Right-handed; 1 = Left-handed; 2 = Ambidextrous]
  Which is your dominant hand?

• trackpad_preference - (Required)
[0 = Trackpad; 1 = Mouse; 2 = Doesn't matter]
  Do you prefer using a trackpad or mouse when working on the computer?

• sph_label - ("SPEECH/HEARING & LANGUAGE")[text]
  Label for the speech/hearing or language impairments.

• shl_impairements - (Required)[0 = No; 1 = Yes]
  Do you have any speech/hearing or language impairments?

• shl_impairment_specify - [text]
  Please specify the speech/hearing or language impairment?

• shl_impairment_agediagnosis - [text]
  When was the first time you got diagnosed with the speech/hearing or language impairment?

• shl_impairment_therapy - [text]
  Do you currently see a speech therapist or other healthcare professional for the impairment?

• first_language - [text]
  What would you say is your first language(s)?

• languages_spoken - [text]
  What languages do you speak on a daily/weekly/monthly basis?

• language_age_learned - [text]
  At what age did you learn the language? (For example, if you learned Spanish when you were 5 years-old, then enter the name of the language and in parentheses the age; so Spanish (5-years-old).).

• countries_live_one_year - [text]
  What countries did you live in for more than one year?

• major_schooling_country - [text]
  Where did you complete the majority of your schooling?

• health_label - ("HEALTH")[text]
  Label for the health questions.

• health_concussion - (Required)[0 = No; 1 = Yes]
  Have you ever been diagnosed with or experienced concussions?

• health_seizure - (Required)[0 = No; 1 = Yes]
  Have you ever been diagnosed with or experienced seizures?

• health_trauma - (Required)[0 = No; 1 = Yes]
  Have you ever been diagnosed with or experienced other neurological trauma (e.g., traumatic brain injury)?

• health_other_trauma_specify - [text]
  Other neurological trauma.

• health_medications - (Required)[0 = No; 1 = Yes]
  Are you currently taking any psychoactive medication (e.g., anti-depressants, ADHD medication, etc.)?

• health_vision - (Required)[0 = No; 1 = Yes]
  Do you have any visual impairments other than wearing glasses/contacts, such as partial or full colorblindness?

• health_vision_specify - [text]
  Visual impairments.

• demographics_survey_complete - [0 = Incomplete; 1 = Unverified; 2=Complete]
  Demographics survey complete.
  

• big_five_inventory_2_short_form_bfi2s_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Big five inventory 2 short form timestamp.

• bfi2_q1 - I am someone who: Tends to be quiet.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q2 - I am someone who: Is compassionate, has a soft heart.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q3 - I am someone who: Tends to be disorganized.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q4 - I am someone who: Worries a lot.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q5 - I am someone who: Is fascinated by art, music, or literature.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q6 - I am someone who: Is dominant, acts as a leader.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q7 - I am someone who: Is sometimes rude to others.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q8 - I am someone who: Has difficulty getting started on tasks.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q9 - I am someone who: Tends to feel depressed, blue.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q10 - I am someone who: Has little interest in abstract ideas.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q11 - I am someone who: Is full of energy.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q12 - I am someone who: Assumes the best about people.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q13 - I am someone who: Is reliable, can always be counted on.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q14 - I am someone who: Is emotionally stable, not easily upset.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q15 - I am someone who: Is original, comes up with new ideas.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q16 - I am someone who: Is outgoing, sociable.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q17 - I am someone who: Can be cold and uncaring.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q18 - I am someone who: Keeps things neat and tidy.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q19 - I am someone who: Is relaxed, handles stress well.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q20 - I am someone who: Has few artistic interests.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q21 - I am someone who: Prefers to have others take charge.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q22 - I am someone who: Is respectful, treats others with respect.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q23 - I am someone who: Is persistent, works until the task is finished.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q24 - I am someone who: Feels secure, comfortable with self.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q25 - I am someone who: Is complex, a deep thinker.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q26 - I am someone who: Is less active than other people.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q27 - I am someone who: Tends to find fault with others.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q28 - I am someone who: Can be somewhat careless.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q29 - I am someone who: Is temperamental, gets emotional easily.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• bfi2_q30 - I am someone who: Has little creativity.
  [1=Disagree strongly; 2=Disagree a little; 3=Neutral; 4=Agree a little; 5=Agree strongly]

• big_five_inventory_2_short_form_bfi2s_complete -
  Big five inventory 2 short form complete.
  [0 = Incomplete; 1 = Unverified; 2=Complete]
  

• attachment_style_questionnaire_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Attachment style questionnaire timestamp.

• attach_q1 - I find it relatively easy to get close to other people.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q2 - I feel confident that other people will be there for me when I need them.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q3 - I feel confident about relating to others.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q4 - I am confident that other people will like and respect me.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q5 - I find that others are reluctant to get as close as I would like.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q6 - I worry that others won’t care about me as much as I care about them.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q7 - I worry a lot about my relationships.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q8 - I often feel left out or alone.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q9 - I prefer to keep to myself.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q10 - I find it hard to trust other people.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q11 - I have mixed feelings about being close to others.
  [1 (totally disagree) to 6 (totally agree)]

• attach_q12 - While I want to get close to others, I feel uneasy about it.
  [1 (totally disagree) to 6 (totally agree)]

• attachment_style_questionnaire_complete -
  Attachment style questionnaire complete.
  [0 = Incomplete; 1 = Unverified; 2=Complete]
  

• presession_notes_for_research_team_only_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Presession notes for research team only timestamp.

• notes_presession_date - [text](yyyy-mm-dd hh:mm:ss)
  Notes Presession Date.

• notes_presession_exp___1 - [checkbox]
  Paloma Bernardo.

• notes_presession_exp___2 - [checkbox]
  Savannah Boyd.

• notes_presession_exp___3 - [checkbox]
  Valeria Pfeifer.

• notes_presession_exp___4 - [checkbox]
  Eric Andrews.

• notes_presession_exp___5 - [checkbox]
  Diheng Zhang.

• notes_presession_exp___6 - [checkbox]
  Ashley Minks.

• notes_presession_exp___7 - [checkbox]
  Daria Letson.

• notes_presession_consentedby___1 - [checkbox]
  Paloma Bernardo.

• notes_presession_consentedby___2 - [checkbox]
  Savannah Boyd.

• notes_presession_consentedby___3 - [checkbox]
  Valeria Pfeifer.

• notes_presession_consentedby___4 - [checkbox]
  Payal Khosla.

• notes_presession_consentedby___5 - [checkbox]
  Eric Andrews.

• notes_presession_consentedby___6 - [checkbox]
  Diheng Zhang.

• notes_credit_type___1 - [checkbox]
  SONA.

• notes_credit_type___2 - [checkbox]
  Amazon gift card.

• notes_credit_granted___1 - [checkbox]
  Yes.

• notes_credit_granted___2 - [checkbox]
  In process.

• notes_speech_baseline - [text]
  Speech Baseline Notes.

• notes_other - [text]
  Other Notes.

• presession_notes_for_research_team_only_complete -
  Presession notes for research team only complete.
  [0 = Incomplete; 1 = Unverified; 2=Complete]
  

• session_1_notes_for_research_team_only_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Session 1 notes for research team only timestamp.

• session1notes_session_date - [text](yyyy-mm-dd hh:mm:ss)
  Session 1 Notes Session Date.

• notes_session_exp_v2___1 - [checkbox]
  Paloma Bernardo.

• notes_session_exp_v2___2 - [checkbox]
  Savannah Boyd.

• notes_session_exp_v2___3 - [checkbox]
  Valeria Pfeifer.

• notes_credit_type_v2___1 - [checkbox]
  SONA.

• notes_credit_type_v2___2 - [checkbox]
  Amazon gift card.

• notes_credit_granted_v2___1 - [checkbox]
  Yes.

• notes_credit_granted_v2___2 - [checkbox]
  In process.

• notes_other_v2 - [text]
  Other Notes.

• note_session_observations_v2 - [text]
  Testing Session #1 Observations (upload the hardcopy).

• session_1_notes_for_research_team_only_complete -
  Session 1 notes for research team only complete.
  [0 = Incomplete; 1 = Unverified; 2=Complete]

0em <cat>_post_game_survey_data.csv
(semicolon delimited text file, 1st row is a header)
This file is the participant’s answers to the REDCap’s "Post Game Survey". The survey is presented to the participant at the end of the experiment.
<cat>_post_game_survey_data.csv Fields:

• record_id - [text]
  Record ID created by REDCap system.

• redcap_survey_identifier - [text]
  Survey Identifier.

• subject_information_sheet_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Sheet Timestamp.

• subject_id - [#####](Required)
  Subject ID assigned to participant.

• session1_date - [text](yyyy-mm-dd hh:mm:ss)
  Session 1 Date.

• session1_exp_initials - [text]
  Session 1 Experimenter(s) (Initials only).

• session1_comp_name - Computer Name.
  [1=Cheetah; 2=Lion; 3=Tiger; 4=Leopard]

• session1_player_name - [text]
  Minecraft Player Name.

• session1_game_crash - Did the game crash between missions?
  [1=Yes; 0=No]

• session1_updated_player_name - [text]
  Updated Player Name.

• subject_information_sheet_complete -
  Subject information sheet complete.
  [0 = Incomplete; 1 = Unverified; 2=Complete]

• postgame_survey_timestamp - [text](yyyy-mm-dd hh:mm:ss)
  Postgame survey timestamp.

• post_game_survey_subject_id - [#####](Required)
  Postgame survey Subject ID assigned to participant.

• survey_date - [text](yyyy-mm-dd hh:mm:ss)
  Survey Date.
  

• agent_calm - calm or relaxed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_anxious - anxious or stressed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_excited - excited or energized.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_sad - sad or depresse.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_guilty - guilty or ashamed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_angry - frustrated or angry.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_happy - happy or content.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_lonely - lonely or ignored.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_proud - confident or proud.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_friendly - friendly or amused.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_calm - calm or relaxed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_anxious - anxious or stressed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_excited - excited or energized.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_sad - sad or depressed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_guilty - guilty or ashamed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_angry - frustrated or angry.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_happy - happy or content.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_lonely - lonely or ignored.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_proud - confident or proud.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• game_friendly - friendly or amused.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• agent_intell - (Slider) "Intelligent" (0) <-> (100) "Unintelligent".

• agent_care - (Slider) "Cares about me" (0) <-> (100) "Doesn’t care about me".

• agent_honest - (Slider) "Honest" (0) <-> (100) "Dishonest".

• agent_expert - (Slider) "Inexpert" (0) <-> (100) "Expert".

• agent_concern -(Slider)"Concerned about me"(0) <-> (100)"Unconcerned about me".

• agent_trust - (Slider) "Untrustworthy" (0) <-> (100) "Trustworthy".

• agent_comp - (Slider) "Incompetent" (0) <-> (100) "Competent".

• agent_insens - (Slider) "Insensitive" (0) <-> (100) "Sensitive".

• agent_honor - (Slider) "Honorable" (0) <-> (100) "Dishonorable".

• agent_bright - (Slider) "Bright" (0) <-> (100) "Stupid".

• agent_understand - (Slider) "Not understanding" (0) <-> (100) "Understanding".

• agent_phoney - (Slider) "Phoney" (0) <-> (100) "Genuine".

• agent_well - I got along with the agent pretty well.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_smooth - The interaction with the agent was smooth.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_acc - I felt accepted and respected by the agent.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_like - I think the agent is likeable.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_enjoy - I enjoyed the interaction.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_awk - The interaction with the agent was forced, awkward and strained.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_place - The agent said things that were out of place.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_play - If I were to play the video game again, I would want to have the agent there.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_perform - I think having the agent there helped me to perform better in the game.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_calm - calm or relaxed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_anxious - anxious or stressed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_excited - excited or energized.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_sad - sad or depressed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_guilt - guilty or ashamed.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_angry - frustrated or angry.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_happy - happy or content.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_lonely - lonely or ignored.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_proud - confident or proud.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_friendly - friendly or amused.
  [0=not at all; 1=small amount; 2=moderate amount; 3=large amount; 4=very large amount]

• team_intel - (Slider) "Intelligent" (0) <-> (100) "Unintelligent".

• team_care - (Slider) "Cares about me," (0) <-> (100) "Doesn’t care about me".

• team_honest - (Slider) "Honest" (0) <-> (100) "Dishonest".

• team_expert - (Slider) "Inexpert" (0) <-> (100) "Expert".

• team_concern - (Slider)"Concerned about me"(0) <-> (100)"Unconcerned about me".

• team_trust - (Slider) "Untrustworthy" (0) <-> (100) "Trustworthy".

• team_comp - (Slider) "Incompetent" (0) <-> (100) "Competent".

• team_insens - (Slider) "Insensitive" (0) <-> (100) "Sensitive".

• team_honor - (Slider) "Honorable" (0) <-> (100) "Dishonorable".

• team_bright - (Slider) "Bright" (0) <-> (100) "Stupid".

• team_understand - (Slider) "Not understanding" (0) <-> (100) "Understanding".

• team_phoney - (Slider) "Phoney" (0) <-> (100) "Genuine".

• team_wrong - It seemed like my emotional reaction was wrong or incorrect because of my team member’s responses.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_forget - I felt like I should forget about my feelings and move on because of my team member’s responses.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_minimize - It seemed like my feelings were minimized because of my team member’s responses.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_insult - I felt insulted when I shared my feelings.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_irrat - I felt like my feelings were irrational because of my team member’s responses.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_crit - I felt my team members were being critical of my feelings.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_fault - I felt like my feelings were my fault because of my team member’s response.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_ignore - I felt ignored when I shared my feelings.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_impt - I felt like my feelings were unimportant because of my team member’s response.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_weak - I felt weak because of my team member’s response to my emotional reactions.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• team_along - I got along with my team members pretty well.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_smooth - The interaction with my team members was smooth.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_accept - I felt accepted and respected by my team members.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_like - I think my team members are likable.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_enjoy - I enjoyed the interaction.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_awk - The interaction with my team members was forced, awkward and strained.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_place - My team members said things that were out of place.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_likeme - I believe other group members liked me.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_genuine - I felt that I was a genuine member of the group.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_part - During the game, I got to participate whenever I wanted to.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_listen - Other members of the group really listened to what I had to say.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_ilike - I liked the group I was in.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_interact - I enjoyed interacting with this group very much.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_itrust - I trusted group members.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_fit - The group was composed of people who fit together.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_cohesion - There was a feeling of unity and cohesion in the group.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_work - Compared to other groups I have been a part of in life, this group worked well together.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_knit - We were a closely knit group.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_like_mem - I like the members of the group.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_work_well - Our group worked well together.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_decisision - This group used effective decision making techniques.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_express - This group provided for comfortable expression for members.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_organize - I believe we approached the game in an organized manner.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_accomplish - The group accomplished what it set out to do.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_approp - I believe our group’s decisions were appropriate.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_alt - I believe we selected the right alternatives.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_influ - I believe I had a lot of influence on group decisions.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• team_contrib - I contributed important information during the decision process.
  [-2=Strongly Disagree; -1=Disagree Slightly; 1=Agree Slightly; 2=Agree Strongly]

• agent_emot1 - It seemed like my emotional reaction was wrong or incorrect because of the agent’s response.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot2 - I felt like I should forget about my feelings and move on because of the agent’s response.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot3 - It seemed like my feelings were minimized because of the agent’s reaction.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot4 - I felt insulted when I shared my feelings.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot5 - I felt like my feelings were irrational because of the agent’s response.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot6 - I felt the agent was being critical of my feelings.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot7 - I felt like my feelings were my fault because of the agent’s response.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot8 - I felt ignored when I shared my feelings.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot9 - I felt like my feelings were unimportant because of the agent’s response.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• agent_emot10 - I felt weak because of the agent’s response to my emotional reactions.
  [5=Strongly Agree; 4=Agree; 3=Neither Agree nor Disagree; 2=Disagree; 1=Strongly Disagree]

• know_team_members - Did you know any of the other team members?
  [1 = Yes; 0 = No]

• know_person_at_cheetah - [text]
  Did you know the team member on Computer 1: Cheetah? If so, then to what extent have you known this team member?

• know_person_at_lion - [text]
  Did you know the team member on Computer 2: Lion?If so, then to what extent have you known this team member?

• know_person_at_tiger - [text]
  Did you know the team member on Computer 3: Tiger?If so, then to what extent have you known this team member?

• know_person_at_leopard - [text]
  Did you know the team member on Computer 4: Leopard?If so, then to what extent have you known this team member?

• postgame_survey_complete -
  Postgame survey complete.
  [0 = Incomplete; 1 = Unverified; 2=Complete]

0em Format: (Text File)

0em ac_aptima_ta_measures.log
This file contains log entries for the "Analytic Component - Aptima - TA3 - Measures".

0em AC_CMUFMS_TA2_Cognitive.log
This file contains log entries for the "Analytic Component - Carnegie Mellon University - Functional Modeling Systems - TA2 - Cognitive". This agent, written in Python, computes the current cognitive load for the overall team, as well as arelated probability of forgetting by the overall team. These are computed solely with respect tointeractions with victims, not marker blocks.

It publishes on only a coarse grained schedule, publishing a message to the bus only when therehas been an interaction that can be expected to make a substantial change to the cognitive load,and does not continuously reflect the fine-grained changes reflecting decay of memories over shortperiods of time.

0em ac_cmu_ta1_pygl_fov_agent.log
This file contains log entries for the "Analytic Component - Carnegie Mellon University - TA1 - PYGL FOV Agent".

0em ac_cmu_ta2_beard.log
This file contains log entries for the "Analytic Component - Carnegie Mellon University - TA2 - Beard".

0em ac_cmu_ta2_ted.log
This file contains log entries for the "Analytic Component - Carnegie Mellon University - TA2 - Ted".

0em AC_CORNELL_TA2_TEAMTRUST.log
This file contains log entries for the "Analytic Component - Cornell University - TA2 - TEAMTRUST".

0em ac_gallup_ta2_gelp.log
This file contains log entries for the "Analytic Component - University of New Mexico-Gallup Campus - TA2 - GELP".

0em ac_gallup_ta2_gold.log
This file contains log entries for the "Analytic Component - University of New Mexico-Gallup Campus - TA2 - GOLD".

0em ac_ihmc_ta2_dyad-reporting.log
This file contains log entries for the "Analytic Component - Institute for Human & Machine Cognition - TA2 DYAD-REPORTING".

0em ac_ihmc_ta2_joint-activity-interdependence.log
This file contains log entries for the "Analytic Component - Institute for Human & Machine Cognition - TA2 joint-activity-interdependence".

0em ac_ihmc_ta2_location-monitor.log
This file contains log entries for the "Analytic Component - Institute for Human & Machine Cognition - TA2 location-monitor".

0em ac_ihmc_ta2_player-proximity.log
This file contains log entries for the "Analytic Component - Institute for Human & Machine Cognition - TA2 player-proximity".

0em AC_UAZ_TA1_ASR_Agent-heartbeat.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 ASR Agent-heartbeat".

0em AC_UAZ_TA1_ASR_Agent.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 ASR Agent".

0em AC_UAZ_TA1_ASR_Agent-Mosquitto.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 ASR Agent-Mosquitto".

0em ac_uaz_ta1_speechanalyzer_adminer_1.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 Speechanalyzer Adminer 1".

0em AC_UAZ_TA1_SpeechAnalyzer-db.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 SpeechAnalyzer-db".

0em AC_UAZ_TA1_SpeechAnalyzer-heartbeat.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 SpeechAnalyzer-heartbeat".

0em AC_UAZ_TA1_SpeechAnalyzer.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 SpeechAnalyzer".

0em AC_UAZ_TA1_SpeechAnalyzer-mmc.log
This file contains log entries for the "Analytic Component - University of Arizona - TA1 SpeechAnalyzer-mmc".

0em ac_ucf_ta2_playerprofiler_container.log
This file contains log entries for the "Analytic Component - UCF TA2 Playerprofiler Container".

0em asistdataingester.log
This file contains log entries for the "Asist Data Ingester".

0em clientmap.log
This file contains log entries for the "Client Map".

0em cmuta2-ted-ac.log
This file contains log entries for the "CMUTA2-TED-AC".

0em cra_psicoach_agent.log
This file contains log entries for the "CRA PSICOACH Agent".

0em crazy_ritchie.log
This file contains log entries for the "Docker Container System - Crazy Ritchie".

0em dozzle.log
This file contains log entries for the "Dozzle". Dozzle is a simple and responsive application that provides you with a web based interface to monitor your Docker container logs live. It doesn’t store log information, it is for live monitoring of your container logs only.

0em elasticsearch.log
This file contains log entries for the "Elasticsearch Application". The Elasticsearch’s application is used to log and monitor your cluster and diagnose issues. If you run Elasticsearch as a service, the default location of the logs varies based on your platform and installation.

0em filebeat.log
This file contains log entries for the "Filebeat". Filebeat is a lightweight shipper for forwarding and centralizing log data. Installed as an agent on your servers, Filebeat monitors the log files or locations that you specify, collects log events, and forwards them either to Elasticsearch or Logstash for indexing.

0em heartbeat-speech_analyzer_agent.log
This file contains log entries for the "Heartbeat-Speech Analyzer Agent".

0em heartbeat-uaz_tmm_agent.log
This file contains log entries for the "Heartbeat-UAZ TMM Agent".

0em kibana.log
This file contains log entries for the "Kibana Tool". Kibana is a data visualization and exploration tool used for log and time-series analytics, application monitoring, and operational intelligence use cases. It offers powerful and easy-to-use features such as histograms, line graphs, pie charts, heat maps, and built-in geospatial support.

0em logstash.log
This file contains log entries for the "Logstash". Logstash is an open server-side data processing pipeline that ingests data from a multitude of sources, transforms it, and then sends it to your favorite "stash."

0em malmocontrol_Local.log
This file contains log entries for the "Malmo Control Local".

0em Measures_Agent_Container.log
This file contains log entries for the "Measures Agent Container".

0em metadata-docker_metadata-app_1.log
This file contains log entries for the "Metadata-Docker Metadata-App 1".

0em metadata-docker_pgadmin_1.log
This file contains log entries for the "Metadata-Docker Page Admin 1".

0em metadata-docker_postgres_1.log
This file contains log entries for the "Metadata-Docker Post Gres 1".

0em metadata-web_metadata-web_1.log
This file contains log entries for the "Metadata-Web 1".

0em minecraft-server0.log
This file contains log entries for the "Minecraft-Server0".

0em mmc.log
This file contains log entries for the "MMC".

0em mosquitto.log
This file contains log entries for the "Mosquitto".

0em mqttvalidationservice.log
This file contains log entries for the "Mosquitto Validation Service".

0em nginx.log
This file contains log entries for the "Nginx".

0em Rutgers_Agent_Container.log
This file contains log entries for the "Rutgers Agent Container".

0em speech_analyzer_agent.log
This file contains log entries for the "Speech Analyzer Agent".

0em speechanalyzer_db_1.log
This file contains log entries for the "Speech Analyzer DB 1".

0em uaz_dialog_agent.log
This file contains log entries for the "UAZ Dialog Agent".

0em uaz_tmm_agent.log
This file contains log entries for the "UAZ TCPdump utility - Traffic Management Microkernel (TMM) Agent" regarding the connections to "Mosquitto".

0em vosk.log
This file contains log entries for the "Vosk Speech Recognition Toolkit and API".