Grenoble INP – ENSIMAG

Engineer assistant internship report
Okayama University - Human behavior understanding laboratory

Reciprocity in encounter between individual
pedestrians and dyads

MOIROUD William
2nd year – IF cursus
May 25, 2023 - August 25, 2023

Okayama University
3-chome-1-1 Tsushimanaka
Kita Ward
Okayama, 700-0082

Internship supervisor
Zeynep Yucel
School tutor
Derbal Moufida

2

CONTENTS

Contents
1 Introduction

3

2 Context

3

3 Internship topic
3.1 Problem to solve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Existing work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Goal to reach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Technical aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 DataBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.2 ”Baseline” deviation . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.3 Other metrics : Curvature, sinuosity, tortuosity . . . . . . . . . . .
3.5 Statistical relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.1 Anova test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.2 Effect Size test . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 My work
4.1 Architecture of my solution . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Working tools, existing architecture . . . . . . . . . . . . . . . . . .
4.1.2 What I added . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Algorithm test . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.2 Statistical test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Obtained results

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6 Conclusion

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1

Introduction

For this internship, my main objective was to discover the working world, particularly thefield of research. I have always been interested in research because I find its approach to
be very noble. Aiming for discoveries using scientific rigor is intellectually appealing, but
unfortunately, it is not well valued in today’s society. That’s why I absolutely wanted anexperience in the research domain to envision my future career.
Moreover, doing this in Japan adds a highly attractive cultural dimension to this activity. It is very interesting to combine science with English and to work with international
professors and colleagues. You discover new words to describe ideas/concepts, as well
as different working methods. This makes the experience more enriching and allows for
personal growth in addition to professional development.
I have also lot of personal interest in this country because of its culture mostly. I always
wanted to come one time in my life for that reason. That is why I was really happy to beable to come for 3 months because that means I could use my time outside my workdays
in order to visit the country.
The field of human behavior suits me perfectly; it provides a great playground to apply
my knowledge in data science and learn new things. Working in this domain gives a senseof dealing with something tangible, which is more enjoyable. Overall, the framework andcontent of the internship offer an excellent first immersion into the working environment.

2

Context

The internship takes place at Okayama University, located in Japan, in the Human Behavior Understanding Laboratory under the supervision of Professor Zeynep Yucel , who is also
my internship supervisor. The laboratory consists of two doctoral students (one French and
one Thai), four interns (three French and one Thai), and around eight Japanese students
pursuing their Master’s degree in Computer Science. The Japanese students are expected
to work on research projects in parallel with their Master’s studies at the university. Working in this lab allowed me to do the experiments of other students who needed data for
their research.
The Human Behavior Understanding Laboratory focuses on investigating various aspects of human behavior, employing scientific methodologies to understand and analyze
human actions, reactions, and decision-making processes. As an intern, my mission was
to actively participate in ongoing research projects and contribute to the team’s efforts in
exploring new avenues in the field of human behavior studies.
Throughout the internship, I had regular meetings with Professor Yucel Zeynep, myinternship supervisor, as well as another researcher in the domain, Francesco Zanlungo.

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3 INTERNSHIP TOPIC

These meetings occurred several times a week and served as opportunities to discuss the
progress of my work, analyze the results I obtained, and collaboratively strategize on the
future direction of the research. These interactions not only helped me gain valuable feedback and guidance but also allowed me to be actively involved in the decision-making
process, giving me a sense of ownership and responsibility for the project’s outcomes.
I worked from Monday to Friday, generally from 9:00 AM to 12:00 PM and from 1:30
PM to 5:45 PM. In reality, the hours were quite flexible, and my main priority was to makeprogress in my research for my meetings, which sometimes occurred on a daily basis.

3

Internship topic

3.1

Problem to solve

My internship project focuses on studying the trajectories of pedestrians based on theirsocial aspects. Specifically, I needed to investigate the deviation of pedestrians and pedestrian groups in various situations, such as encounters or undisturbed. This is a specificcase study within a broader research domain centered on human behavior, which serves as
the guiding theme of the laboratory.
Using public databases, which I will describe in more detail later, I had to identify
biases or patterns in the behavior of pedestrians on a straight street to simplify the task.
Specifically, the focus was on groups of 2 pedestrians, which are the most common groupin the street. The question was how a group of 2 pedestrians behaves when walking alone(undisturbed case) and when encountering other pedestrians and having to avoid them(encounter case).

3.2

Existing work

This topic had already been studied by Prof. Zeynep Yucel , who has published research
papers on it [5] . This allowed me to quickly immerse myself in the subject and understandits challenges and issues. Additionally, my work environment was in Python, and some
progress had already been made by Adrien Gregorj, one of the doctoral students in thelaboratory. He had previously worked on this subject and had already made progress onorganizing data related to databases. Additionally, he was able to share some of the results
he had obtained. In summary, he efficiently trained me in the domain, which was facilitatedby the fact that he was French, and he provided me with the Python files he had created.

3.3

Goal to reach

My objective was to adopt a rigorous methodology to measure human behavior. For thispurpose, several metrics were proposed to me, which I will detail in the following paragraph.

3.4 Technical aspect

5

My goal was to explore the avenues of these metrics with the aim of publishing a research
paper on the subject. My results had to be comprehensible, scientifically accurate, andrigorous. The comprehensible aspect led me to create graphs that I could present andexplain, and each of these graphs had to be supported by statistical validation.

3.4

Technical aspect

3.4.1

DataBase

The databases used in this project is DIAMOR (public databases [6] [10] [3] [9] [4]),which contain the successive positions of numerous pedestrians in delimited rural spaces
with a sampling time of 0.1 sec. In this database, pedestrian groups have been classified
based on their level of social interaction with each other (from 0 to 3) 0 represents nointeraction in the group, 1 a weak interaction, 2 a mid interaction and 3 represents a big
interaction. My role was to find a way to measure the deviation of these pedestrians basedon their level of social interaction, in other words, to find a metric capable of providing
consistent results to measure their deviation.
3.4.2”Baseline” deviation

To calculate the deviation of pedestrians, my main approach was to utilize the concept of”baseline” deviation, which I will now explain. In this method, the primary objective is tocreate a line that represents the initial trajectory the pedestrian appears to follow in theirinitial steps, and then measure how much they deviate from this trajectory. To achievethis, I implemented the following process :• For Undisturbed trajectories, I divided the pedestrian trajectories into sets of 5 consecutive points and then used the first two velocity vectors of each sub-trajectory to
determine a ”baseline”.
• For encounter situation, I just compute the baseline for both of encounter using thetwo velocity vectors.
This baseline essentially forms a straight line that represents the path the pedestrian would
have taken had they continued along their initial course. Subsequently, I identified the pointthat deviated the farthest from this baseline. When referring to the distance between the
point and the baseline, I actually calculated the length of the segment perpendicular tothe baseline passing through the trajectory point as shown in Figure 1, the baseline for an
encounter situation between one group and one alone pedestrians (7 trajectory points)
The explanation I provided constitutes the final solution I refined over the course of myinternship. For instance, the idea of using trajectories of 5 points was discovered throughempirical observations and the aim of it is to be able to compare undisturbed case toencounter case. In reality, in order to compare the deviation of two trajectories, these
trajectories must have approximately the same length. Encounter situations are detectedusing a vicinity threshold, and since the pedestrians’ speed is lower in these situations,6

3 INTERNSHIP TOPIC

that’s why, for an equal distance in an encounter situation and an undisturbed situation,
we have more trajectory points in the encounter situations. Considering that the actual
solution is more intricate, I have omitted several other assumptions in my explanation toenhance clarity.
3.4.3

Other metrics : Curvature, sinuosity, tortuosity

I will briefly explain the other measures that were attempted to measure pedestrians’
deviation :• Curvature is a measure of the amount of bending or curvature in a trajectory ata specific point. It represents the rate at which the direction of the path changes.
Higher curvature values suggest more significant deviations from a smooth, straightpath.
• Sinuosity : This measure was introduced in [2] as a way to quantify the randomnessof an animal’s path (especially in the case of foraging patterns, for instance whenstudying how ants locate and collect food). It is formally defined as Si = 1.18 √σqq
where q is the step size of the trajectory, i.e. the distance between two consecutive
positions and σq is the standard deviation of the turning angles Θ.
• Tortuosity [1] Tortuosity is defined as the ratio of the net displacement to the gross
It is clear that τ takes values between 0 and 1. In particular,
displacement : τ = D
L
for an infinitely tortuous trajectory, τ assumes the value of 0, whereas for a perfectly
straight trajectory (i.e. D = L), it leads to the value of 1.
Unfortunately, these last three measures did not yield good results, so I will not present
them in the results section. Only the ”baseline” deviation provided convincing results fromboth a statistical perspective and in terms of graphical interpretation. In reality, thesemeasures are not bad or difficult to calculate, but the issue is that they do not correspondto our specific situation. While they provide different information about our trajectory,
ultimately, they do not contribute significantly to the understanding of the pedestrian’s
movement.

3.5

Statistical relevance

3.5.1

Anova test

To demonstrate the coherence of my results, I need to focus on the statistical aspect ofthe data I am handling. First let me describe what ANOVA is and then explain why
it is relevant in my case. ANOVA (Analysis of Variance) is a statistical test used tocompare means of three or more groups to determine if there are significant differencesamong them. It assesses the variability within and between groups, allowing researchers to
identify whether the observed differences in means are due to random chance or meaningful
factors.

3.5 Statistical relevance7

Figure 1: Baseline for an encounter situation of one group and one alone pedestrian (nongroup)ANOVA is relevant in my case because I am analyzing the deviation of pedestrians indifferent social interaction scenarios. By using ANOVA, I can compare the mean deviationsof pedestrians in various situations, such as encountering other pedestrians or walking
alone. This statistical test will help me to determine if there are significant differences indeviations between the different social interaction levels, providing insights into how social
factors may influence pedestrian behavior on the street.
3.5.2

Effect Size test

As I analyze the data and interpret the results of my study, it is crucial to consider the effectsize test and Cohen’s coefficient [7] [8] (p.599). These statistical measures are essential in
my case to assess the practical significance and strength of the observed differences between
groups.
Effect size provides a standardized measure of the magnitude of the effect or relationshipunder investigation. In my research on pedestrians’ deviation, it allows me to quantify how
much the social interaction levels impact their behavior. By calculating Cohen’s coefficient,
I can express the size of the effect in standard deviation units, making it easier to compare

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4 MY WORK

and interpret the results across different studies.
Cohen’s coefficient d is calculated as the difference between the means of two groups2
divided by the pooled standard deviation d = x̄1 −x̄
. The pooled standard deviation issq22(n1 −1)s1 +(n2 −1)s2defined as follow s =where the variance for one of the groups is defined
n1 +n2 −2
P
n1
1
2
2
as s1 = n1 −1 i=1 (x1,i − x̄1 ) . It can be interpreted as the number of standard deviations
that separates the means of the two groups. A small effect size corresponds to a d value
around 0.2, indicating a relatively small difference between the groups. A moderate effectsize ranges from 0.5 to 0.8, while a large effect size is typically considered to be above 0.8,
indicating a substantial difference between the groups.
By including Cohen’s coefficient in my analysis, I can go beyond simply determining
if there is a statistically significant difference between groups and gain insights into thepractical significance of the observed differences. This information is invaluable in understanding the real-world implications of my research findings and their relevance in thecontext of pedestrian behavior.
Using these statistical tools is relevant in my case because they complement the traditional significance tests like ANOVA. While ANOVA helps determine if there are significant
differences among groups, effect size (Cohen’s coefficient) gives a clearer picture of the practical significance of those differences. By incorporating effect size measures, I can better
understand the real-world implications of social interactions on pedestrians’ deviation andprovide more meaningful insights in my research.
In brief, the essence and challenge of this project lie in the fact that we do not knowwhat results we are supposed to find (which is often the case in research). Therefore, it is
crucial to maintain rigor and precision in all the assumptions made about the data to beable to interpret the obtained results from our own perspective. Unlike a typical classification project, where metrics like F1-score or accuracy can provide immediate ”measured”
feedback on progress and direction, in my internship project, such tools do not exist. Of
course ANOVA can tell you if your result is statistically relevant but not tell you if itmeans something. Instead, we must rely on our intuition and the rigor of our approach tonavigate through the uncertainties and complexities inherent in the research domain.

4

My work

4.1

Architecture of my solution

4.1.1

Working tools, existing architecture

I implemented everything using Python, which was well-suited for the task due to its
various libraries like Matplotlib, Pickle, NumPy, and SciPy. Python’s object-oriented
programming was crucial for organizing the data. The data preprocessing to convert the
DIAMOR database into Python dictionaries was already done and has been write mostly
in object oriented code. To detect encounter situations, I used a vicinity window during a4.2 Methodology

9pedestrian’s trajectory and checked for other pedestrians with opposite velocity vectors at
each time step. For undisturbed trajectories, I considered all the data, excluding encounter
situations.
To provide some more detail about my architecture, there was an object structure
implemented by Adrien GREGORJ that simplify the organisation of data. Here is a listof most usefull object that exist :• Pedestrians (containing information like their trajectories)• Groups which include the number of pedestrian in the group, the social binding andother information
By utilizing iterative loops, it is possible to perform almost any desired operationon the data. This ranges from simple filtering, for instance, to find all groups of threepeople considered as friends, to calculating deviation using each trajectory point from allpedestrians in the dataset.
Given that the dataset is quite substantial, operations that involve extensive calculations (e.g., filtering trajectories to identify encounter situations) or computing metrics
based on various assumptions could easily take several hours without proper code optimization.
Hence, it became essential to devise a method of data storage to minimize computation
time. This is where the use of Python’s Pickle library proved highly beneficial. It allows
the storage of dictionaries, enabling, for example, the differentiation of undisturbed trajectories from encounter situations using specific thresholds for vicinity and sampling time.
These trajectories are then saved in a dictionary via Pickle, and subsequently, the desiredalgorithm is applied to each of the saved trajectories.
4.1.2

What I added

Since data preprocessing was already done, I focused on implementing algorithms to calculate metrics from the trajectories. I created an evaluation grid for hyperparameters suchas vicinity window, sampling time for trajectories, and the minimum number of points
required per trajectory to find the best values. For each hyperparameter I wanted totest, I used iterative loops to test the desired hyperparameter values. Then, for each hyperparameter, I plotted the associated graphs and performed the desired statistical tests.
These results were saved in folders, allowing me to study and analyze the results wheneverI wanted. The most frequently used graphs in my case were boxplots and scatter plots
generated using the matplotlib library. The statistical tests were also automated to be
performed at each iteration.

4.2

Methodology

Throughout the internship, I regularly engaged in discussions with my professor (internshipsupervisor), the researcher, and the doctoral student to interpret the results I obtained and10

4 MY WORK

exchange ideas to progress in resolving the problem.
In summary, the final solution I achieved in this project was the result of a long anditerative process. The calculation of pedestrian deviation involved making numerous assumptions, and the previous paragraph only provided a partial list. To arrive at the selected
hypotheses, I went through multiple phases of research and reflection.
In hindsight, the approach I used resembles a scrum-like method. My work was organized as follows:• The professor provided me with a metric to explore for calculating pedestrian deviation. It could also be, ”I want you to compute that ... in that case ...” (for instanceif the speed have an influence in deviation)• I conducted a research phase, where I contemplated how to implement these measurements to resolve the problem.
• I implemented my solution with specific assumptions and hyperparameters.
• I performed tests to identify the best hypotheses and hyperparameters, discussed theresults with the professor and researcher, and then returned to the previous steps asneeded.

Communication played a vital role in my work. With almost weekly feedback fromexperts in the field, I was able to progress efficiently and propose a robust solution that
addressed the problem effectively. The continuous feedback loop and open discussions were
instrumental in refining the approach and achieving a solid solution.
Overall, the project was an intensive and collaborative effort, involving critical thinking,experimentation, and adjustments. Through this process, I gained valuable experience
in research methodologies and problem-solving, further enhancing my understanding ofpedestrian behavior and its intricate dynamics in different social scenarios.

4.3

Test

4.3.1

Algorithm test

Testing my code was a crucial and integral part of my work, as data manipulation could
sometimes be challenging. For instance, I had to develop graphical methods to visuallyinspect the trajectories I was filtering. Having a graphical representation of the data underanalysis also helped me in making informed decisions during the hyperparameter tuning
process. Additionally, a significant portion of my work involved implementing mathematical formulas, such as those related to curvature calculations. Therefore, I had to rigorously
test my algorithms on small datasets to ensure they produced the desired results before

4.3 Test

11

applying them to the larger dataset.
By testing my code on smaller subsets of data, I could verify if the algorithms yielded
accurate and reliable outcomes. It also allowed me to gain confidence in the correctnessof my implementation. Whenever necessary, I made adjustments and improvements toenhance the code’s performance and accuracy.
Having a visual understanding of the data and observing the algorithm’s behavior withsmaller datasets were instrumental in refining and perfecting my implementation. As aresult, I could ensure that my code was robust and capable of handling the larger dataset,leading to meaningful and trustworthy results in my analysis.
Overall, the testing process played a vital role in guaranteeing the quality and accuracyof my work, enabling me to confidently draw conclusions from the data and achieve reliable
results in my research.
4.3.2

Statistical test

The organization of ANOVA tests was conducted on various data arrangements, and here
are a few examples. As a reminder, the social binding of the pedestrian groups is represented as 0, 1, 2, 3, where 0 indicates the weakest bond and 3 the strongest. ”Alone”
corresponds to individual pedestrians. I performed ANOVA tests for both encounter cases
and undisturbed cases. ANOVA test has been realized between the following categories :• 0 / 1 / 2 / 3 / alone• Group (which is all 0 to 3 social binding together) / Alone• 0/1/2/3• 0 and 1 / 2 and 3
Next, in order to verify if there was a significant difference in various situations, I realised
ANOVA test in the following situation :• 0 in encounter case / 0 in undisturbed case
• 1 in encounter case / 1 in undisturbed case
• 2 in encounter case / 2 in undisturbed case
• 3 in encounter case / 3 in undisturbed case
• Alone in encounter case / Alone in undisturbed case
Of course, the verification of effect size was also conducted in each of these situations,and the results were presented in tabular form. By calculating Cohen’s coefficient for eachcomparison, I was able to quantitatively assess the magnitude of the observed differences

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5 OBTAINED RESULTS

between the various groups. This approach allowed me to determine the practical significance of the effects and understand the strength of the relationships between social binding
levels and pedestrians’ deviation behavior. By presenting the effect sizes in tabular format, I could easily compare the results across different situations and identify patterns or
trends in the data. This enabled me to draw more robust conclusions about the influenceof social binding levels on pedestrians’ deviation behavior in various encounter scenarios
and undisturbed situations.

5

Obtained results

In both boxplot graphs Figures 2 and 3, the first box represents deviation for social
binding 0, followed by 1, 2, 3, and finally, the last box represents individuals pedestrians.
So in the y axis we can see the deviation and in the x axis the social binding. Also next tothe social binding, the number of data used for the boxplot is written. The x and y-axis
scales are the same for both graphs. In the boxes, the black line represents the median,and the two points represent the mean. Quartiles, minimum, and maximum values foreach situation can also be read. The left graph corresponds to an undisturbed situation,
while the right graph corresponds to an encounter situation.
Firstly, it can be observed that for each social category, the deviation values are higher
in an encounter situation than in an undisturbed situation. This is because during an encounter situation, the group needs to avoid the approaching people and deviate from their
original trajectory. Secondly, it can be noticed that regardless of the situation, groups witha strong bond tend to deviate less than others. This is because stronger interaction within
the group leads to less attention being paid to the surroundings, resulting in less deviationfrom the baseline trajectory to avoid obstacles. However, this deviation becomes almost
similar between groups with social binding 0 and individuals walking alone. The latter are
the most likely to alter their trajectory to avoid confrontation.
These two graphs are supported by increased statistical significance as the p-value associated with the ANOVA test is well below 0.05. Although the deviation difference between
the groups is more pronounced in the encounter situation, the ANOVA test produces aless favorable result. This is simply due to having significantly fewer data points for theencounter situation in our database. However, the quantity of data remains sufficient toobtain a meaningful result, as shown by the p-value.
Here are also presented the tables listing the data related to the size effect 1 2,
including the Cohen’s d values for each data pair. The first row of the table represents thesocial binding of the group (including ”alone”) along with the corresponding number ofdeviation values. This number of data points is different from the number of pedestrians
for the ”undisturbed” case because the pedestrians’ trajectories have been subdivided into
trajectories of 5 consecutive points. As a result, there is actually a deviation value foreach sub-trajectory of the pedestrian. Therefore, for each given pedestrian, there can be13

Figure 2: 0/1/2/3/Alone
Undisturbed case
ANOVA p-value ≈ 10− 24

Figure 3: 0/1/2/3/Alone
Encounter case
ANOVA p-value ≈ 10− 8

multiple deviation values. The number in the table corresponds to the number of deviations
calculated from the pedestrian sub-trajectories.
I would like to remind you that this division into sub-trajectories was done to have
trajectories with comparable distances to those in the encounter case, allowing for deviation calculation under similar experimental conditions. In the case of encounter situations,
there is only one data point per studied group (no sub-trajectories in this case).
The table below 3 contains descriptors for magnitudes of d = 0.01 to 2.0, as initiallysuggested by Cohen and expanded by Sawilowsky [7] . For the undisturbed case we mostly
have Cohen’s d coefficient between 0.01 and 0.2. That suggests that there is a small
difference between the two compared groups, but this difference is weak and not practicallysignificant. In other words, the effect is present, but it is minimal and cannot be consideredclinically or practically relevant.
In the other end, the ANOVA test shows a very low p-value, indicating that there is astatistically significant difference among the groups. However, it’s important to interpret
this result in conjunction with the small effect size observed with Cohen’s d. A low p-value
only indicates that the observed difference is unlikely to be due to random chance, but it
doesn’t provide information about the magnitude or practical significance of the effect.
Given the small effect size and the low p-value from the ANOVA test, it suggests that14

6 CONCLUSION

Table 1: Cohen’s d in undisturbed case for baseline deviation
Social Binding / Data points
0 / 190
1 / 552
2 / 3037
3 / 988
alone / 38784

0 / 190 1 / 552 2 / 3037 3 /988 alone / 38784
0.0
/
/
/
/
0.02932
0.0///
0.15599 0.18449
0.0//
0.24443 0.25570 0.07484
0.0/
0.02254 0.00750 0.15964 0.22274
0.0

Table 2: Cohen’s d in encounter case for baseline deviation
Social Binding / Data points
0 / 28
1 / 622 / 332
3 / 83alone / 495

0 / 28
1 / 62 2 / 332 3 / 83 alone / 495
0.0
/
/
/
/
0.09494
0.0///
0.46850 0.34023
0.0//
0.41261 0.29613 0.01972
0.0/
0.01747 0.11950 0.43774 0.39911
0.0

Table 3: Descriptor of magnitude for Cohen’s dEffect size
Very small
Small
MediumLarge
Very large
Huge

d
0.01
0.20
0.50
0.80
1.20
2.0

while there might be a statistically significant difference between the groups, this difference
may not have substantial real-world implications. The effect size of Cohen’s d between 0.01
and 0.2 implies that the practical significance of the observed differences may be negligible,
and any practical implications or applications based solely on statistical significance should
be considered with caution.
The same reasoning can be applied to the encounter situation, even though the results
may appear higher overall, they are not significantly higher.

6

Conclusion

It may sound strange, but the most challenging aspect for me was maintaining impeccablecode rigor. When writing 200 lines of code to generate a graph that needs to be interpreted,ensuring the precision and reliability of the code becomes crucial. Without rigorous code,
the results obtained lack fundamental value. However, this challenge is also an essential

REFERENCES

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part of the charm of research and the scientific process in general. I had not encountered
this level of rigor in my previous projects, especially those completed at the university.
Another significant aspect that this internship provided was the daily communication
in English for work purposes. It required a whole different vocabulary and necessitatedpreparation when presenting my results regularly. Having an external perspective on mywork regularly acted as a catalyst for my efficiency.
In conclusion, my internship was a highly enriching cultural experience. It allowed me
to explore the profession of a researcher in the university setting. Working here with myteam for three months was an enjoyable and enlightening experience. While I have always
been curious about this profession, I had never seriously considered pursuing it as a career.
As a student, the experience was rewarding and pleasant, given that most of us in thelaboratory were of similar age. However, my professor was the only one at her age, making
it a bit harder for me to envision myself in that role. I still think of pursuing a career inthe corporate world for my future.
Of course, the geographical context of the internship allowed me to become familiar
with the local culture, and I was able to enjoy the country during my weekends, which
made the experience even more enjoyable.

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