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Data in Brief 47 (2023) 10902 7
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Data in Brief
journal homepage: www.elsevier.com/locate/dib
Data Article
A dataset on the physiological state and
behavior of drivers in conditionally automated
driving
Quentin Meteier
a , ∗, Marine Capallera
a
, Emmanuel de Salis
b
,
Leonardo Angelini
a , c
, Stefano Carrino
b
, Marino Widmer
d
,
Omar Abou Khaled
a
, Elena Mugellini
a
, Andreas Sonderegger
e
a
HumanTech Institute, University of Applied Sciences and Arts of Western Switzerland, HES-SO, Boulevard de
Pérolles 80, Fribourg, 170 0 , Switzerland
b
Haute-Ecole Arc Ingénierie, University of Applied Sciences and Arts of West ern Switzerland, HES-SO, Rue de la
Serre 7, Saint-Imier, 2610, Switzerland
c
School of Management Fribourg, University of Applied Sciences and Arts of West ern Switzerland, HES-SO, Chemin
du Musée 4, Fribourg, 170 0 , Switzerland
d
University of Fribourg, Department of Informatics, Boulevard de Pérolles 90, Fribourg, 170 0 , Switzerland
e
Bern University of Applied Sciences, Business School, Institute for New Wor k, Brückenstrasse 73, Bern, 3005,
Switzerland
a r t i c l e i n f o
Article history:
Received 14 November 2022
Revised 20 February 2023
Accepted 22 February 2023
Available online 3 March 2023
Dataset link: A dataset on the physiological
state and behavior of drivers in
conditionally au tomated driving (Original
data)
a b s t r a c t
This dataset contains data of 346 drivers collected during
six experiments conducted in a fixed-base driving simula-
tor. Five studies simulated conditionally automated driving
(L3-SAE), and the other one simulated manual driving (L0-
SAE). The dataset includes physiological data (electrocardio-
gram (ECG), electrodermal activity (EDA), and respiration
(RESP)), driving and behavioral data (reaction time, steer-
ing wheel angle, …), performance data of non-driving-related
tasks, and questionnaire responses. Among them, mea-
sures from standardized questionnaires were collected, ei-
ther to control the experimental manipulation of the driver’s
state, or to measure constructs related to human factors
and driving safety (drowsiness, mental workload, affective
state, situation awareness, situational trust, user experience).
∗Corresponding author.
E-mail address:
quentin.meteier@hes-so.ch (Q. Meteier) .
Social media: @qmeteier (Q. Meteier), @AndreasSonde re1 (A. Sonderegger)
https://doi.org/10.1016/j.dib.2023.109027
2352-3409/© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
(
http://creativecommons.org/licenses/by/4.0/ )
2 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
Keywo rds:
Conditionally automated driving
Driver state
Physiology
Electrocardiogram (ECG)
Electrodermal activity (EDA)
Respiration
Situation awareness (SA)
Take ove r quality
In the provided dataset, some raw data have been processed,
notably physiological data from which physiological indica-
tors (or features) have been calculated. The latter can be
used as input for machine learning models to predict various
states (sleep deprivation, high mental workload, ...) that may
be critical for driver safety. Subjective self-reported measures
can also be used as ground truth to apply regression tech-
niques. Besides that, statistical analyses can be performed us-
ing the dataset, in particular to analyze the situational aware -
ness or the takeover quality of drivers, in different states and
different driving scenarios.
Overall, this dataset contributes to better understanding and
consideration of the driver’s state and behavior in condition-
ally automated driving. In addition, this dataset stimulates
and inspires research in the fields of physiological/affective
computing and human factors in transportation, and allows
companies from the automotive industry to better design
adapted human-vehicle interfaces for safe use of automated
vehicles on the roads.
©2023 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY license
( http://creativecommons.org/licenses/by/4.0/ )
Specifications Tabl e
Subject List of DIB categories not available (link not found)
Specific subject area Physiology
Work psychology and cognitive ergonomics
Computer science
Type of data Physiological data
Driving data
Socio-demographic data
Subjective measures
Performance measures
How the data were acquired Fixed-base driving simulator with one or two seats, a pedal set, a Logitech G27
or G29 steering wheel and the driving scenario displayed whether on a large
screen with a projector or a television screen (65”).
Physiological data:
- Biopac MP36 with lead sets and electrodes, collected with Biopac Studen t
lab 3. 7. 7.
- In the final experiment, collected with Biosignalsplux hardware.
Driving data acquired from open source driving simulation softwares: OpenDS
and GENIVI.
Performance measures: Samsung Galaxy Ta b A and GENIVI software.
Demographic and questionnaire data: Unipark
1
The experimental procedure, design, material and instruments are detailed in a
README file in each folder of the data repository.
Data format Raw
Aggregated
Filtered and processed
Description of data collection It is a dataset gathering data from 346 drivers, collected in 6 fixed-base driving
simulator experiments. Five of them simulated conditionally automated driving
(L3-SAE) and one simulated manual driving (L0-SAE). Each folder contains raw
and preprocessed data collected in each experiment. It contains three
physiological signals (electrocardiogram (ECG), electrodermal activity (EDA),
( continued on next page )
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 3
and respiration (RESP)), driving data, socio-demographic data, and
self-reported ra tings on standardized scales and questionnaires .
Data source location ·Institutions:
(1) University of Fribou rg
(2) University of Applied Sciences and Arts of West ern Switzerland (HES-SO)
·City: Fr ibourg
·Country: Switzerland
·Latitude and longitude (and GPS coordinates, if possible) for collected
samples/data:
(1) 46.79661602839843, 7.15654 8324 9698305
(2) 46.793461030370345, 7.159055598178482
Data accessibility Repository name: Zenodo
Data identification number: 10.5281/zenodo.7214953
Direct URL to data: https://doi.org/10.5281/zenodo.7214953
1
https://www.unipark.com/
.
Value of the Data
• This dataset [1] gathers heterogeneous data (driving, physiological, behavioral, perfor-
mance, questionnaire responses) collected from a large number (N = 346) of individual
drivers in different psychophysiological states (fatigue, mental workload, affective state),
specifically in the context of conditionally automated driving (L3-SAE). To date, such a
dataset does not exist.
• Further quantitative analyses (in addition of those made in the referenced publications)
can be conducted using the large range of measure collected in different situations of
conditionally automated driving. This can help to better understand the role of human
factors and driving situation in such context, helping to define guidelines for the design
of human-vehicle interfaces, to support drivers and increase safety on roads.
• In the field of affective and physiological computing, several research questions can be
investigated on the basis of this dataset, such as determining the most predictive physio-
logical indicators of certain psychophysiological states (stress, mental workload or fatigue),
along with the optimal time windows for assessing them. The consideration of a baseline
(i.e., the physiological state at rest) for assessing someone’s condition can also be investi-
gated.
• In the field of computer science, the physiological dataset can serve in developing innova-
tive artificial intelligence models to assess the driver’s state, including the consideration of
several psychophysiological states such as mental workload, fatigue, or the affective state.
Such models could provide driver’s biofeedback, and thus give the car the possibility (or
not) to give back control to the driver according to his/her state.
• Automotive industries can also use the data to understand the driver’s state and behav-
ior in a simulated environment (in a research context). This can be a basis for designing
human-vehicle interfaces implemented in future vehicles that will drive at this level of
automation (L3-SAE) on roads.
1. Objective
The idea behind the creation of this dataset is the design of an adaptive autonomous sys-
tem called AdVitam (for Advanced Driver-Vehicle Interaction to Make future driving safer). The
goal of this system is to maintain the driver’s situation awareness and takeover quality in con-
ditionally automated driving (L3-SAE). To fulfill that role, the idea is to adapt dynamically the
human-vehicle interaction, depending on the driver’s state and the driving situation.
4 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
In order to develop this system and particularly the module assessing the driver’s state, it was
necessary to collect physiological data (electrocardiogram (ECG), electrodermal activity (EDA),
and respiration (RESP)) from drivers in different states. Thus, several experiments were con-
ducted on a fixed-base simulator. The collected data were used to train various machine learning
models capable of predicting certain psychophysiological states (fatigue, mental workload, affec-
tive state) continuously. Both objective and subjective measures related to human factors linked
with driving safety were also collected (takeover quality, situation awareness, trust, task per-
formance, user experience). Based on statistical analyses, dynamically adaptable human-vehicle
interfaces for supervision (lights on the dashboard, a haptic seat and a mobile application) and
intervention (adaptive takeover modality) were designed. The overall AdVitam system was fi-
nally tested and evaluated in a preliminary user study with 35 drivers. The dataset contains all
data collected in the framework of the AdVitam project.
2. Data Description
2.1. Global folder structure of the dataset
The dataset [1] consists of data collected in 6 different experiments conducted on a fixed-
base driving simulator. Each experiment is identified by a code: Exp1, Exp2, Exp3, Exp4, ExpTOR,
ExpFinal. The purpose of each experiment is explained below:
- Exp1: Experimental manipulation of relaxation before driving and presence of passenger
while driving (manual driving, L0-SAE)
- Exp2: Experimental manipulation of cognitive workload at 2 levels using a verbal task
(backwards counting)
- Exp3: Experimental manipulation of cognitive workload at 3 levels using visual and audi-
tory tasks (N-back task)
- Exp4: Experimental manipulation of fatigue (sleep deprivation) and driving environment
(rural vs. urban scenario)
- ExpTOR: Multiple takeovers requested through different modalities (visual, auditory, hap-
tic), while performing different non-driving related tasks
- ExpFinal: Testing a contextual multimodal system for maintaining situation awareness and
takeover quality in conditionally automated driving
For all experiments, the folder structure follows the same pattern, as shown in Fig. 1 . Each
experiment folder contains two subfolders (Raw, Preprocessed) and a README file. The data
collected in each experiment are stored in the respective folder. Since in each experiment addi-
tional measures were collected in addition to the standard data, the folder structure varies from
one experiment to another. Thus, the structure of each experiment folder is shown on Figs. 2-7 .
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 5
Fig. 1. Global view of the folder structure
Fig. 2. Folder structure of the Exp1 folder
6 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
Fig. 3. Folder structure of the Exp2 folder
Fig. 4. Folder structure of the Exp3 folder
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 7
Fig. 5. Folder structure of the Exp4 folder
Fig. 6. Folder structure of the ExpTOR folder
8 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
Fig. 7. Fold er structure of the ExpFinal folder
2.2. Folder structure and metadata common to all experiments
2.2.1. README file
The README file of each experiment contains an abstract of the experiment, a summary of
the methods and material employed to conduct the study. It also contains information on the
structure of the files and their content. Metadata (variables and coding) are also documented so
that anyone can use every file contained in the folders. Relevant scientific references are also
included.
2.2.2. Raw data
The raw data are contained in the Raw folder. For all experiments, it contains driving data of
each participant in a .txt format, contained in the Driving folder. For experiments that consisted
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 9
of several scenarios/phases, there are several .txt files for one participant. Besides, all experiment
folders (except ExpFinal) contain physiological raw data of drivers contained in a Physio folder.
The data are available in .acq format (Biopac folder, raw files generated by the data collection
software) and .txt format (Txt folder). In the Txt folder, there are two files associated with each
participant, one file containing the raw data (ECG, EDA and RESP) and one file with timestamps
corresponding to the beginning and the end of each experimental phase. For some experiments,
there are other folders with other types of raw data: socio-demographic data and questionnaire
data extracted from the online platform Unipark, or data collected from a mobile application
developed specifically for the experiment and running on a handheld tablet.
• /Physio: contains two folders with physiological data collected during the experiment
◦/BioPac: contains the raw files (in .acq format) with physiological signals: ECG, EDA
and RESP. These files were generated by the BioPac Student Lab 3.7.7 software, using
the BioPac MP36 hardware for signal collection.
◦/Txt: contains two .txt files for each driver, identified with the code.
< code > .txt: contains the raw physiological data extracted from the BioPac Student
Lab. Each column contains the raw values collected with sensors for each signal
(ECG, EDA. RESP) at a sampling rate of 10 0 0Hz. The file contains metadata in the
first 11 rows. Columns are separated with tabs. The first column is the elapsed time
in minutes.
< code > -markers.txt: contains the timestamps for each period of the experiment.
Metadata corresponding to the timestamps in each experiment can be found in
the README of each experiment. Be careful, the timestamps are here in seconds
while they are in minutes in the raw data ( < code > .txt) .
• /Driving: contains raw driving data collected from the driving simulation software (either
OpenDS or GENIVI, see README of each experiment).
◦Metadata:
Time = Time elapsed since the software was launched (in seconds)
EngineSpeed = Engine speed (in rpm)
GearPosActual = Current gear
GearPosTarget = Next planned gear
AcceleratorBrakePedalPos = Position of gas/brake pedal. Gas pedal is pressed when
the value is between 0 and 1 (maximum acceleration), brake pedal is pressed when
the value is between 0 and -1 (maximum braking). 0 means no pedal is pressed.
SteeringWheelAngle = Steering wheel angle (in degrees)
VehicleSpeed = Vehicle speed (in km/h)
Position X = Vehicle position along the x-axis in the simulated driving environment
Position Y = Vehicle position along the y-axis in the simulated driving environment
Position Z = Vehicle position along the z-axis in the simulated driving environment
Autonomous Mode (T/F) = Autonomous pilot status. True = autonomous pilot acti-
vated, False = autonomous pilot deactivated (driver in control of the car)
2.2.3. Preprocessed data
Some of the raw collected data described above were processed and stored in the Prepro-
cessed folder of each experiment. All experiments contain at least a Physio folder with physio-
logical features (in a .csv file) of each participant during the different phases of the experiment
(baseline and driving scenarios). Features were calculated with and without baseline correction.
Also, a database containing socio-demographic information and answers to questionnaires dur-
ing the experiment is located in the Questionnaire folder. Data were processed and gathered in a
.csv file. A documentation file (in .xslx format) is associated to each database, containing abbre-
viations and item text, description, coding and range of each variable contained in the database.
Besides, most of the experiments also contain Driving folder with features (reaction time, maxi-
mum steering wheel angle, ..) calculated for each takeover situation and saved in a csv file.
10 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
• /Physio: contains physiological features processed with the Neurokit library
1 in Python
[2] . Each column corresponds to a physiological indicator. More details on the significance
of each indictor can be found in physiological_indicators.xlsx. Each indicator contains in
its name the signal with which it has been calculated. The HRV indicators are calculated
from the ECG, the RRV indicators are calculated from the RESP signal, and the RSA from
the combination of the ECG and RESP signals.
◦/periods: contains features calculated for each period of the experiment (e.g., Baseline
and Driving). The name of each file depends on the segmentation level (segm_1: fea-
tures calculated on the whole periods, segm_10: signals segmented in 10 equal win-
dows and features are calculated for each window). The baseline phase is not seg-
mented and features are always calculated once.
◦/windows: contains features calculated for the driving phase, with sliding time win-
dows with varying length and overlap. The size of time window used (60, 90 or 120
seconds) and the percentage of overlap with the previous window (0%, 25%, 50%) is
specified in each file name.
◦Metadata:
subject_id: ID of subject
period: corresponding period of the experiment
segment_id: id of segment
time_start: time marker corresponding to the beginning of the window
time_end: time marker corresponding to the end of the window
Code for indicators: _Bl = values during baseline; _Dr = values during current pe-
riod; _Dr-Bl = values during current period corrected with baseline (subtraction).
• /Questionnaire:
◦Exp X _Database.csv: contains the raw data collected in experiment X from the ques-
tionnaire, including socio-demographic information from participants.
◦Exp X _Documentation.xslx: contains a complete documentation for the data contained
in the database. It includes terms and abbreviations, the participants to exclude for a
statistical analysis (with the reason), and both the data and metadata variables (with
variable name, type, description, range and coding)
2.3. Specificities of files folders for each experiment
In this section, the additional files or folders that are specific to each experiment are de-
scribed below. Specific metadata (labels for timestamps markers, events in the driving data, ...)
to each experiment are also specified here, but can also be found in the README corresponding
to each experiment.
- Exp1
• /Raw
• /Physio: Metadata for labels of timestamps corresponding to experiment
phases: Anfang = Start; Ende = End; Fragebogen1 = Questionnaire be-
fore the experiment; Hörbuch/Entspannung = Relaxation/Audiobook; Frage-
bogen2 = Questionnaire after the relaxation/audiobook phase; Probe-
fahrt = Training phase; Fahrt = Main driving session.
• /Driving: There is one folder for each driver, containing one .txt file for each
lap (4 laps in total).
• /Preprocessed
• /Physio: Metadata for labels corresponding to the experimental manipulation of the
driver’s state:
1 https://neuropsychology.github.io/NeuroKit
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 11
label_relaxation: 0 = No relaxation (audiobook), 1 = relaxation
label_passenger: 0 = No passenger while driving, 1 = passenger while driving
- Exp2
order_obstacles.csv: Order of obstacles apparition for each participant. A = Deer, B = Traffic Cone,
C = Frog, D = Can, E = False Alarm1, F = False Alarm2.
• /Raw
• /Physio: Metadata for labels of timestamps corresponding to experiment phases:
Training1 = Baseline phase; Training2 = Practice phase in the driving simulator;
Driving = Main driving session in conditionally automated driving.
• /Driving:
There is one file for each driver, identified by the code of the participant.
/! \ Due to recording problem, the “AcceleratorPedalPos” and “Decelera-
torPedalPos” columns do not correspond to the gas and brake pedal po-
sition.
/Audio: audio recording of each participant in the experimental group (in
.wav format). Can be used to control for the engagement in the non-driving-
related task.
• /Preprocessed:
• /Physio: Metadata for labels corresponding to the experimental manipulation of the
driver’s state:
label_st: 0 = NST, not engaged in the cognitive non-driving-related task (only
monitoring the environment), 1 = ST, engaged in the cognitive non-driving-
related task (backward counting)
• /Physio and Driving:
timestamps_obstacles.csv: Time elapsed (in seconds) between the start of the
main driving session and the appearance of the obstacles (TrigObsX), the time
when the driver pressed the button to report having understood the situa-
tion (DetObsX), and the time when the driver actually took over control (Re-
pObsX). X corresponds to one of obstacle or the false alarm.
/takeover_epochs: features calculated for time windows shorter than 10 sec-
onds..
• features_tor_1s_8s_with_driving_features.csv: physiological and driving
features, calculated from the signals collected from 8 seconds before to
1 second after each takeover situation.
• features_tor_1s_8s_with_driving_features_processed.csv: Same than
above but in this file, features corresponding to one driver are on the
same row.
/takeover_interval: features calculated for time windows larger than 10 sec-
onds.
• features_tor_120s_0s.csv: physiological features calculated from the
signals collected 120 seconds before each takeover situation
• features_tor_120s_0s_processed.csv: Same than above but in this file,
features corresponding to one driver are on the same row.
- Exp3
order_obstacles.csv: Order of obstacles apparition for each participant. A = Deer, B = Traffic Cone,
C = Frog, D = Can, E = False Alarm1, F = False Alarm2. See the experimental design for further details.
• /Raw
• /Physio: Metadata for labels of timestamps corresponding to experiment phases:
Baseline = Baseline phase; Training = Practice phase in the driving simulator;
BlockX = One block of the main driving session in conditionally automated driv-
ing (1 to 5). ST = Secondary task, beginning or end of a task sequence.
12 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
• /Driving: There are three files for each driver, identified by the code of the par-
ticipant: one for the baseline ( < code > _Baseline.txt), one for the first two blocks
( < code > _Part1.txt), and one for the last three blocks ( < code > _Part2.txt).
• /Tablet: contains raw data recorded by the tablet
• raw_data_pvt.csv: data of task performance
• raw_data_sart.csv: data of situation awareness (SART [3] ratings and identification
rate of the cause of takeover) collected after each takeover situation.
• /Preprocessed
• /Physio
/st: contains features calculated based on signals collected during task se-
quences.
/periods: contains features calculated based on signals collected during each
period of the experiment (Block 1 to 5).
Metadata for labels and measures corresponding to the experimental manip-
ulation of the driver’s state:
• label_instructions: 0 = No instructions about limitations of automated
vehicles before the experiment (NL), 1 = instructions received (L)
• label_app: 0 = No context-related information through mobile applica-
tion during the drive (NA), 1 = received information through app (A)
• task_id: id of task sequence (0 to 14)
• label_difficulty_st: 0 = No task (low), 1 = 1-back task (medium),
2 = 3-back task (high) (possibility to remove the ’No Task’ condition
to classifiy with two tasks)
• label_modality_st: 0 = No task (low), 1 = visual task, 2 = auditory
task (high) (possibility to remove the ’No Task’ condition to classifiy
with two modalities)
• task_perf: aggregated score of task performance for this se-
quence, according to this formula: TaskScore = (TotalAnswers −
WrongAnswers −MissedTargets)/TotalAnswers
• nasa_score: subjective ratings of mental workload made after the task
sequence (Mental Demand item of the NASA-TLX [4] questionnaire, on
a 0-20 scale)
• /Driving: contains takeover quality metrics computed during each takeover situation
of the experiment for each participant
takeover_features.csv: contains the raw data
Exp3_Documentation_Takeover_Features.csv: contains the documentation of
the takeover features database
- Exp4
• /Raw
• /Physio: Metadata for the experiment phases: Baseline = Baseline phase; Train-
ing = Practice phase in the driving simulator; BlockX = One block of the main
driving session in conditionally automated driving (1 to 2).
• /Driving: There are three files for each driver, identified by the code of the par-
ticipant: one for the baseline and training phase ( < code > _Training.txt), and one
each driving scenario ( < code > _City/Country.txt). City = Urban area, Country = Ru-
ral area.
• /Questionnaire: contains raw exports of the participants’ answers to questionnaires,
with one file for each language (German and French) in CSV format.
• /Sleep: contains the file used by experimenter to report the information collected
by the sleep tracker. They were retrieved from the desktop Fitbit application (Win-
dows) after synchronizing the watch.
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 13
• /Preprocessed
• /Physio
For the /periods and /windows folders, the physiological signals considered
for the calculation of features are those collected during each scenario (both
Rural and Urban environments), before the take-over request occurred.
/takeover_interval: features calculated for time windows larger than 10 sec-
onds. Each file is identified by the time considered before and after the
takeover request (e.g., features_tor_ < time_before > _ < time_after > .csv)
Metadata for labels corresponding to the experimental manipulation of the
driver’s state:
• label_sleep: 0 = Not sleep deprived (A = Alert), 1 = sleep deprived
(D = Drowsy)
• label_first_scenario: Countryside (C; rural area) or Urban (U; urban
area)
• label_time_exp: 10 = 10:00am, 16 = 4:00pm
• /Driving: contains takeover metrics for the takeover situation in each scenario.
The features calculated for time windows larger than 10 seconds. Each file is
identified by the time considered before and after the takeover request (e.g.,
features_tor_ < time_before > _ < time_after > .csv)
• /PVT: contains CSV files with the participants’ reaction time to targets on the psy-
chomotor vigilance task (PVT). Participants had to press a steering wheel button
when a red circle was displayed on the screen (every 5 minutes).
data_PVT_exp4_scenario_type.csv: raw values of reaction time extracted from
driving data (Events column), for both environments and for each participant.
data_PVT_no_outliers_mean_sd.csv: processed values of reaction time where
outliers were removed according to the mean and standard deviation of the
data distribution (Threshold = Mean + /- 2
∗SD) [5] .
data_PVT_no_outliers_quantile.csv: processed values of reaction time where
outliers were removed according to the 0.05 sample quantile (Lower thresh-
old = q0.05, higher threshold = q0.95) [5] .
- ExpTOR
• /Raw
• /Physio: Metadata for labels of timestamps corresponding to experiment phases:
Baseline = Baseline phase; Training = Practice phase in the driving simula-
tor; LapX = One lap of the main driving session in conditionally automated
driving (1 to 3).
• /Driving: There are two files for each driver, identified by the code of the partic-
ipant: one for the baseline and training phase ( < code > -B.txt), and one the main
driving session ( < code > .txt).
• /Preprocessed
• /Physio: contains features calculated from the last 90 seconds before each takeover
request (TOR), for each participant and each situation.
Metadata:
• label_environment: 0 = Adverse weather (Rainy), 1 = Nice weather
(Sunny)
• tor_modality: modality of takeover request (TOR). Ta = visual-auditory,
Th = visual-haptic, Tall = visual-auditory-haptic
• lap: in which lap the takeover was performed (out of 3)
• reaction_time: Time elapsed in seconds between the takeover request
(TOR ZONE in the raw driving data) and actual take over by the driver
(Autonomous Mode (T/F) to False in the raw driving data)
• max_swa: Maximum steering wheel angle between the takeover re-
quest and the reactivation of the autopilot
14 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
- ExpFinal
• /Raw
◦/Driving: There are two files for each driver, one for each driving scenario
( < code > _RURAL/URBAN.txt) identified by the code of the participant.
◦/Questionnaire: contains raw export of the participants’ answers to question-
naires in CSV format.
◦/Limitations: contains the raw file (.xslx) with the experimenters’ notes about
participants’ French verbal statements when a limitation was identified. It
also comments about potential problems during the experiment. The file was
also converted in CSV format.
◦/NDRT: contains raw data recorded by the tablet regarding the performance
on the non-driving-related task (NDRT)
• /Preprocessed: contains preprocessed data.
• /AdaptiveModel: contains all the data collected by the model and logs of predic-
tions/choices made by each module. Each subfolder contains data and logs for one
module.
/driver_state: contains data collected and predictions made by the Driver
State module. There are two folders for each driver, one for each driving sce-
nario ( < code > _RURAL/URBAN).
• baseline.csv: physiological features processed in real-time during the
first 90 seconds of the driving scenario, with the Neurokit library in
Python [2] . They are considered as the baseline features and used for
the prediction of the Driver State module.
• features.csv: physiological features processed in real-time during the
experiment with the Neurokit library in Python [2] . This was done ev-
ery time new raw physiological values were collected by the sensors.
Each column corresponds to a physiological indicator.
• features_live_dr.csv: the last physiological features calculated, based on
raw values of the last 90 seconds
• features_live_all.csv: the last physiological features calculated, based on
raw values of the last 90 seconds, with additional features (correction
with baseline)
• values.pkl: raw physiological values (ECG, EDA, and RESP) in the last
90 seconds of the participant
• fusion.csv: continuous predictions made by the Driver State module ev-
ery second
• last_driver_state.pkl: array with last predicted of driver’s mental work-
load (m2) and global driver’s state (global_scale)
/supervision: contains data collected and choices made by the Supervi-
sion module for conveying information to the driver via in-vehicle inter-
faces. There are two files for each driver, one for each driving scenario
( < code > _RURAL/URBAN.log). The lines to check in the log is the one with
the "Supervision model result".
/intervention: contains predictions made by the Intervention module.
There are two folders for each driver, one for each driving scenario
( < code > _RURAL/URBAN).
• tor_modality_log.csv: contains the timestamp and the prediction made
by the Intervention module for the modality of take over request
(TOR). 0 = visual-auditory, 1 = visual-haptic, 2 = visual-auditory-
haptic.
• last_modality.pkl: the last TOR modality predicted by the module. The
value is read when the severity in the environment equals 3 (high
severity), and the according modality is triggered for the TOR.
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 15
• /Driving: contains takeover quality features for the takeover situations of the exper-
iment. Each column corresponds to a takeover quality metric in one of the scenario
(RURAL or URBAN)
• /NDRT: contains processed data on task performance in each scenario (Rural or Ur-
ban), based on raw collected data with the tablet.
• /Limitations: contains processed data from participants’ statements about potential
limitations (i.e., factors that may limit the proper functioning of the vehicle). The
type, severity, and location of each limitation verbally announced by the partici-
pants were coded by two experimenters, based on the raw responses during the
experiment. A documentation of the variables’ name is available in this folder.
3. Experimental Design, Materials and Methods
The experimental design, materials, and methods used is described for each experiment sep-
arately. This information can also be found in the related published scientific papers, and in the
README of each experiment.
For the driving simulation, 2 different driving simulators and 2 different open source driv-
ing simulation software were used. They are described below and referred in each experiment
(Simulator and Software 1 or 2). Also, 2 different hardware were used for the collection of phys-
iological signals. They are described below and referred in each experiment (Hardware 1 or 2).
3.1. Driving simulators
1. Simulator 1: Fixed-base simulator with two adjacent car seats, a steering wheel (Logitech
G27), and pedals (gas and brake), as shown in Fig. 8 . The driving simulation was back-
projected using a projector (Epson EH-TW3200). Two speakers located behind the seats
played the sound of the driving simulation to immerse drivers in the driving environment.
2. Simulator 2: Fixed-base simulator with one car seat, a steering wheel (Logitech G29), and
pedals (gas and brake), as shown in Fig. 9 . The driving simulation was displayed on a
television screen (65").
Fig. 8. The driving simulator 1.
16 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
Fig. 9. The driving simulator 2.
3.2. Software used for driving simulation
1. Software 1: Free version of OpenDS.
2. Software 2: GENIVI vehicle simulator
3
. The driving scenes (Yosemite, rural area; San Fran-
cisco, urban area) were modified for each experiment to match the experimental design
(takeover requests and limitations in specific locations).
3.3. Hardware for collection of physiological signals
1. Hardware 1: BioPac Student Lab 3.7.7 software and the BioPac MP36 hardware at a sam-
ple rate of 10 0 0 Hz. Lead sets (SS57LA and SS2LB, Biopac) with disposable Ag/AgCl pre-
gelled electrodes (EL507 and EL503, Biopac) were, respectively, used to record the EDA
and ECG of participants. Electrodes recording the EDA signal were placed on the distal
phalanges of the middle and ring fingers of the non-dominant hand of participants. The
SS5LB respiratory effort transducer (Biopac) recorded the respiration via chest expansion
and contraction.
2. Hardware 2: Biosignalsplux hardware at a sample rate of 10 0 0 Hz while running the
model in Python. Lead sets with disposable Ag/AgCl pre-gelled electrodes were used to
record the EDA and ECG of participants. Electrodes recording the EDA signal were placed
on the distal phalanges of the middle fingers of the left hand of participants. A respiratory
effort transducer recorded the respiration via chest expansion and contraction. This hard-
ware allowed to get raw physiological values in real-time through Bluetooth collection,
for processing the signals and perform the driver’s state prediction continuously while
driving.
3. GENVI Vehicle simulator. https://github.com/GENIVI/genivi- vehicle- simulator
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 17
3.4. Description of experimental design, material and methods used in each experiment
- Exp 1
• Description of experiment: The main manipulation was to induce (social) stress by the
presence of a passenger unknown to the participant. To reduce the potential negative ef-
fect of such stressor, half of drivers listened to a guided mindfulness meditation podcast
for 10 minutes, while the other half (the control group) listened to an audio book (Sher-
lock Holmes - The Three Students). Before that, all participants listened to the audiobook
for 5 minutes, as a baseline phase. Then, they had to drive for 10 minutes in the simulator.
The scenario consisted of a 2 ×2 lane highway without traffic, with repeatedly occurring
construction zones on the right lane. The experiment was conducted in German. More de-
tails on the experimental design and procedure, and material and instruments used can
be found in [6] .
• Experimental design: 2 Independent Variables (2 ×2 between-subjects design):
◦Between-subjects factor(s):
Presence of passenger while driving for half of participants (label_passenger)
Practice of pre-driving relaxation (listening to a guided mindfulness medita-
tion) by half of participants (label_relaxation)
◦Within-subjects factor(s): None
• Experimental procedure:
◦1st questionnaire > label = Fragebogen 1
◦Baseline (5 minutes): listening to an audiobook > label = Baseline
◦Audiobook/Relaxation (10 minutes): keep listening to the audiobook or listen to a
guided mindfulness-meditation podcast > label = Hörbuch/Entspannung
◦2nd questionnaire > label = Fragebogen 2
◦Training session for driving > label = Probefahrt
◦Driving (4 laps, 10 minutes): Manual driving on a highway without traffic > la-
bel = Fahren
• Material and instruments:
◦Physiological signals: Hardware 1
◦Driving simulation: Simulator 1 and software 1
◦Questionnaire: German version of the Positive and Negative Affect Sched-
ule (PANAS) [7] ( https://zis.gesis.org/skala/Breyer- Bluemke- Deutsche- Version- der-
Positive- and- Negative- Affect- Schedule- PANAS- (GESIS- Panel) )
- Exp2
• Description of experiment: The main manipulation was to induce cognitive workload to
half of the participants through a verbal cognitive workload (backward counting from
3645 by steps of 2) while driving in conditional automation for 20 minutes. The other
half of the participants only had to monitor the driving environment. During the driv-
ing phase, all participants had to react to 6 takeover situations, randomly triggered by
the experimenter (between 1min30s and 4min after the previous one). 4 were caused by
obstacles on the road (deer and frog crossing the road, traffic cone and can standing in
the middle of the road) and 2 were false alarms (no obstacle on the road). The appari-
tion order of obstacles was controlled between participants using a Latin Square design
[8] . After each takeover request, participants were asked to press a button on the steering
wheel once they saw and understood the situation. Then, they could choose to take over
control or not, according to their evaluation of the situation being dangerous or not. They
could take over control by braking, turning the steering wheel, or pressing a button on the
steering wheel. Once they estimated that the situation was safe again, they were asked to
reactivate the autopilot. The experiment was carried out in French, German and Italian.
More details on the experimental design and procedure, and material and instruments
used can be found in [ 9 , 10 ].
• Experimental design: 3 Independent Variables (2 ×3 ×2 mixed design):
18 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
Between-subjects factor(s):
• Performance of verbal cognitive non-driving-related task (backwards count-
ing) for half of participants: label_st
Within-subjects factor(s):
• Movement of obstacle causing the takeover request: moving vs. static vs.
none
• Danger/Hazard of obstacle (i.e., potential for causing damages to the driver
and the car) causing the takeover request: dangerous vs. non-dangerous vs.
none
• Experimental procedure:
Baseline (5 minutes): Conditionally automated driving, driver monitors the environ-
ment > label = Baseline
Practice session (5 minutes): 3 fake takeover requests (audio-visual TOR; no obsta-
cle on the road) + manual driving until the end of the 5 minutes > label = Training
Driving session (20 minutes): Conditionally automated driving in a rural environ-
ment without traffic > label = Driving. 6 takeover request due to obstacles: Deer,
Traffic cone, Frog, Can, 2 false alarms.
• Material and instruments:
Physiological signals: Hardware 1
Driving simulation: Simulator 1 and software 2
• Questionnaires:
• NASA Task Load Index (NASA-TLX) [4] to control for mental workload inducement
• Situation Awareness Rating Technique (SART) [3] measured for the takeover situa-
tions (4 obstacles and once for both false alarms)
• Changes to questionnaires:
SART only with 9 items (Information quality item is missing)
Inversion of scale for 1 item of NASA-TLX for the first 29 participants. And modifi-
cation of scale from 20 to 10 to make sure participants could see the whole scale
without scrolling.
Questionnaires translated in French and German
- Exp3
• Description of experiment: Half of participants first took knowledge of limitations of au-
tomated vehicles through printed material. Then, all the participants had to perform the
different N-back task sequences while the car was driving in conditional automation. The
main driving session was divided in 5 blocks of 12 minutes. Each participant had to per-
form 3 task sequences in each block (15 task sequences in total), lasting 90 seconds each,
followed by 60 seconds of rest. Participants had to rate their level of mental workload
after each task sequence. In each block, a takeover occurred because of a factor limiting
the operation of the automated vehicle. The N-back task type was randomized, except
before the takeover request for which it was controlled with a Latin Square design [8] .
After each takeover situation, participants had to rate their situation awareness and find
the origin of the takeover request sent by the vehicle. Half of the participants could use
an additional mobile application conveying information on the driving environment while
performing the task on the tablet. These participants had to rate their user experience
with this mobile application at the end of the experiment. Besides, all participants also
rated their trust towards automated vehicles, both before and after the driving session.
They also rated their user experience in the simulator. More details on the experimental
design and procedure, and material and instruments used can be found in [ 11 , 12 ].
• Experimental design: 4 Independent Variables (2 ×2 ×3 ×3 mixed design)
Between-subjects factor(s):
• Presentation of automated vehicles limitations: label_instructions
• Use of an additional mobile application to receive context-related information
on the driving environment: label_app
Within-subjects factor(s):
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 19
• Task difficulty (no task vs. 1-back vs. 3-back): label_difficulty_st
• Task modality (no task vs. visual vs. auditory): label_modality_st
• Experimental procedure:
Baseline (5 minutes): Conditionally automated driving, driver monitors the environ-
ment > label = Baseline
Training session (5 minutes): 3 fake takeover requests (audio-visual TOR; no obsta-
cle on the road) + manual driving until the end of the 5 minutes > label = Training
Driving session (around 1 hour): Conditionally automated driving in a rural envi-
ronment without traffic
• 5 blocks and 1 takeover request per block > label = Block1, Block2 ... Block5
• 3 sequences of non-driving-related task per block > label = task_id (0 to 14)
• IDs of task sequences in which a takeover occurred: 2 (Slope), 4 (Lanes), 7
(Rock), 9 (Rain), 13 (Deer)
• Material and instruments:
Physiological signals: Hardware 1
Driving simulation: Simulator 1 and software 2
• Questionnaires:
Mental Demand item of the NASA Ta sk Load Index (NASA-TLX) [4] to control for
mental workload inducement
Situation Awareness Rating Technique (SART) [3] (collected after each takeover sit-
uation)
Scale of Trust in Automated Systems [13]
Official French and German versions of the User Experience Questionnaire Short
version (UEQ-S) [14] , used to measure user experience in the driving simulator and
with the mobile application (half of participants)
• Changes to questionnaires:
Trust in automated system questionnaire was changed to "trust in automated driv-
ing systems"
Trust questionnaire, SART and NASA-TLX were translated to French, German and
Italian due to no official validated translation
Using only the
∗Mental Demand
∗item from the NASA-TLX questionnaire to control
for mental workload inducement
- Exp4
• Description of experiment: All participants were asked to come the day before the exper-
iment to collect a sleep tracker (smart watch) and to be given instructions about their
sleep (sleep deprived or not). The time of the experiment (10am or 4pm) was controlled
to ensure that it did not impact alertness levels. On the day of the experiment, partici-
pants first rated their level of fatigue and affective state (valence and arousal). Then they
observed the car driving autonomously for 5 minutes, which was considered the baseline
phase for physiological measures. Afterwards, the participants were instructed to test the
simulator and learn about the principle of takeover request (TOR). Then, the main driving
session consisted of two 30-minute scenarios in each of the two environments (urban or
rural). The order of the scenarios was controlled: half of the participants started with the
rural, and the other half with the urban. Drivers were required to observe the environ-
ment, so that the task was monotonous and an increase in drowsiness could be observed.
They also had to press a button on the steering wheel when a target (red circle) appeared
on the screen every 5 minutes (vigilance task). After each scenario, participants rated their
fatigue (before the takeover and after answering all the questions), their emotional state,
their situational awareness at the time of the TOR, and their confidence in the car. Fi-
nally, they rated their user experience in the simulator. The experiment was conducted in
French and German.
• Experimental design: 4 Independent Variables (2 ×2 ×2 ×2 mixed design):
◦Between-subjects factor(s):
20 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
Sleep deprivation: less than six hours of sleep the night before the experi-
ment vs. more than seven hours: label_sleep
Scenario order: driving in rural area first vs. driving in urban area first: la-
bel_first_scenario
Time of experiment: 10:00am vs. 4:00pm: label_time_exp
◦Within-subjects factor(s):
Driving environment: Rural area vs. urban area: period
• Experimental procedure:
◦Baseline (5 minutes): Conditionally automated driving, driver monitors the environ-
ment > label = Baseline
◦Training session (5 minutes): 3 fake takeover requests (audio-visual TOR; no obsta-
cle on the road) + manual driving until the end of the 5 minutes > label = Training
◦Driving session (around 1 hour): Conditionally automated driving in 2 scenarios: A
rural environment and an urban one > label = Block1, Block2
• Material and instruments:
◦Physiological signals: Hardware 1
◦Driving simulation: Simulator 2 and software 2
• Questionnaires:
◦Karolinska Sleepiness Scale (KSS) [15] to measure self-reported fatigue
◦Animated Self Assessment Manikin (AniSAM) [16] to assess the drivers’ affective
state (valence and arousal)
◦Situation Awareness Rating Technique (SART) [3] to measure the drivers’ situation
awareness in both takeover situations
◦The Situational Trust Scale for Automated Driving (STS-AD) [17] , to measure trust
in the vehicle in both environments
◦Official French and German versions of the User Experience Questionnaire Short
version (UEQ-S) [14] , to measure user experience in the driving simulator
• Changes to questionnaires:
◦Questionnaires were translated in French and German when no official translation
could be found
- ExpTOR
• Description of experiment: Participants started the experiment by sitting in the simulator
and monitoring the car’s environment while it was driving autonomously for 5 minutes.
This was used as the baseline measure for physiological data. Afterwards, the participants
were instructed to test the simulator and learn about the principle of takeover request
(TOR). Then, the main driving session consisted of three laps lasting 12 minutes each in a
rural environment without traffic. In each lap, drivers were required to engage in a differ-
ent NDRT (visual 2-back task vs. auditory 2-back task vs. no task) and take over control
of the car accordingly when requested. They performed the task on a handheld device.
Besides, they had to take over control three times in each lap, with each takeover request
through a different modality: icon on the dashboard and audio chime (audio-visual), icon
on the dashboard and vibrations in the seat (audio-haptic), or a combination of all three
(audio-visual-haptic). In total, the participants encountered 9 takeover situations each,
caused by a fixed obstacle appearing on a road with a time-to-collision of around 7 sec-
onds. For half of participants, the weather was always sunny, whereas it was rainy for the
other half. The experiment was conducted in French. More details on the experimental
design and procedure, and material and instruments used can be found in [18] .
• Experimental design: 3 Independent Variables (2 ×3 ×3 mixed design):
◦Between-subjects factor(s):
Weather condition: sunny (S) vs. rainy (R)
◦Within-subjects factor(s):
Non-driving-related task (NDRT): visual 2-back task vs. auditory 2-back task
vs. no task
Takeover modality: visual-auditory vs. visual-haptic vs visual-auditory-haptic
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 21
• Experimental procedure:
◦Baseline (5 minutes): Conditionally automated driving, driver monitors the environ-
ment > label = Baseline
◦Training session (3 minutes): 3 fake takeover requests (TORs, 1 of each modal-
ity) + manual driving until the end of the 5 minutes > label = Training
◦Driving session (around 36 minutes): 3 laps of conditionally automated driving,
with 1 NDRT performed in each lap > label = Lap1, Lap2, Lap3
• Material and instruments:
◦Physiological signals: Hardware 1
◦Driving simulation: Simulator 2 and software 2
• Questionnaire:
◦Official French version of the User Experience Questionnaire Short version (UEQ-S)
[14] , to measure user experience in the driving simulator
- ExpFinal
• Description of experiment: On the day of the experiment, participants first rated their
level of fatigue and affective state (valence and arousal). The participants started the ex-
perience in the driving simulator with a training session to become familiar with the
driving controls and the takeover request (TOR) principle. Then, the main driving session
consisted of two 10-minute scenarios in two environments (first rural then urban area).
Each scenario started with a period of 90 seconds while participants only had to moni-
tor the vehicle’s environment and no takeover could be requested. This phase was used
to calculate the baseline physiological features of drivers, used afterwards by the model.
During each scenario, participants had to engage in a cognitive NDRT (visual 2-back task)
on a handheld device at certain moments. Otherwise, they were asked to monitor the ve-
hicle’s environment. Half of participants received additional context-related information
through in-car interfaces (Supervision module): ambient lights on the dashboard show-
ing global severity of the environment, vibration in the seat to warn about lane mark-
ings state and obstacles, and pop-up icons on the handheld device with the severity and
type of limitation. The other half did not receive any additional information. Besides, the
takeover request modality was smartly selected (visual-auditory, visual-haptic, or visual-
auditory-haptic) for half of participants (Intervention module), depending on their current
physiological state (last 90 seconds). The other half were required to take over with a
unique visual-auditory modality. All drivers had to take over control once in each sce-
nario. Besides, the Driver State module continuously predicted the driver’s state (every
second) using the physiological features of the last 90 seconds, according to four com-
ponents: fatigue, mental workload, affective state and situation awareness. At the end of
each scenario, drivers had to rate their situation awareness, mental workload, situational
trust towards the vehicle, affective state, and fatigue. at the end of the experiment, they
were asked to rate their user experience in the simulator, as well as giving feedbacks.
The experiment was conducted in French. More details on the experimental design and
procedure, and material and instruments used can be found in [ 19 ].
• Experimental design: 3 Independent Variables (2 ×2 ×2 mixed design)
◦Between-subjects factor(s):
Supervision: availability of the Supervision module vs. not
Intervention: availability of the Intervention module vs. no
◦Within-subjects factor(s):
Driving environment: Rural area vs. urban area
• Experimental procedure:
◦Training session (5 minutes): Explanation of Supervision/Intervention modules if
available + manual driving until the end of the 5 minutes
◦Driving session (around 20 minutes): Conditionally automated driving in 2 scenar-
ios. A rural environment and an urban one > label = Rural, Urban. Each scenario
started with a baseline of 90 seconds while the car was driving (getting baseline
physiological features)
22 Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027
Scenario 1: Rural area
Scenario 2: Urban area
• Material and instruments:
◦Physiological signals: Hardware 2
◦Driving simulation: Simulator 2 and software 2
• Questionnaires:
◦Mental Demand item of the NASA Ta sk Load Index (NASA-TLX) [4] to get self-
reported mental workload during each scenario
◦Karolinska Sleepiness Scale (KSS) [15] to measure self-reported fatigue
◦Animated Self Assessment Manikin (AniSAM) [16] to assess the drivers’ affective
state (valence and arousal)
◦Situation Awareness Rating Technique (SART) [3] to measure the drivers’ situation
awareness in both takeover situations
◦The Situational Trust Scale for Automated Driving (STS-AD) [17] , to measure trust
in the vehicle in both environments
◦User Experience Questionnaire Short version (UEQ-S) [14] , to measure user experi-
ence in the driving simulator
• Changes to questionnaires:
◦Translate in French when no official translation existed
Ethics statements
We confirm that relevant informed consent was obtained from all subjects in the six experi-
ments carried out. The research was carried out in accordance with the Declaration of Helsinki,
and approved by the Ethical committee of the department of Psychology (protocol number IRB-
445) at the University of Fribourg (Switzerland).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal rela-
tionships that could have appeared to influence the work reported in this paper.
Data Availability
A dataset on the physiological state and behavior of drivers in conditionally automated driving
(Original data) (Zenodo).
CRediT Author Statement
Quentin Meteier: Methodology, Software, Formal analysis, Investigation, Data curation, Writ-
ing – original draft, Visualization; Marine Capallera: Methodology, Software, Formal analy-
sis, Investigation; Emmanuel de Salis: Methodology, Software, Formal analysis, Investigation;
Leonardo Angelini: Conceptualization, Supervision, Validation, Funding acquisition; Stefano Car-
rino: Conceptualization, Supervision, Validation, Funding acquisition; Marino Widmer: Super-
vision, Validation; Omar Abou Khaled: Resources, Funding acquisition; Elena Mugellini: Re-
sources, Conceptualization, Supervision, Project administration, Funding acquisition; Andreas
Sonderegger: Conceptualization, Methodology, Supervision, Validation, Formal analysis, Inves-
tigation, Resources, Funding acquisition, Writing –review & editing.
Q. Meteier, M. Capallera and E. de Salis et al. / Data in Brief 47 (2023) 109027 23
Acknowledgments
This work has been supported and funded by the Hasler Foundation in the framework of
AdVitam project. The time spent for ensuring that the dataset follows the FAIR principles, as
well as the costs involved in publishing and hosting the dataset, are funded by the University
of Applied Sciences and Arts of Weste rn Switzerland (HES-SO), as part of the call for Open Data
projects.
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