# Should we train scientific generalists?

Gopal P. Sarma
<EMAIL_ADDRESS>
###### Abstract

I examine the topic of training scientific generalists. To focus the
discussion, I propose the creation of a new graduate program, analogous in
structure to existing MD/PhD programs, aimed at training a critical mass of
scientific researchers with substantial intellectual breadth. In addition to
completing the normal requirements for a PhD, students would undergo an
intense, several year training period designed to expose them to the core
vocabulary of multiple subjects at the graduate level. After providing some
historical and philosophical context for this proposal, I outline how such a
program could be implemented with little institutional overhead by existing
research universities. Finally, I discuss alternative possibilities for
training generalists by taking advantage of contemporary developments in
online learning and open science.

In the age of highly specialized science, the generalist is a long forgotten
job description. We have come to assume that the role played by those
intellectual titans of earlier eras, such as Da Vinci, Aristotle, or Gauss, to
name just a few, is an impossibility given the massive explosion of scientific
knowledge of recent decades and centuries.

There is a factual reality to this sentiment that is uncontroversial.
Certainly, as a percentage of existing knowledge, one could not conceivably
attain the breadth of understanding that one might have in previous centuries.
However, it does seem worth considering if a more modest goal could be
achieved which would serve an important stabilizing role for modern science
and engineering. That goal would be to train a critical mass of scientific
generalists, researchers, who in addition to the specialized training of an
ordinary graduate program, would also have broad exposure to multiple subjects
at the graduate level.

While the need for specialization might have been something of an
inevitability, it is also worth considering that there may be negative
ramifications to this kind of stratification of knowledge. With so much to
know, how can we be confident that we are allocating our intellectual capital
efficiently? How can we be confident in our collective understanding of global
trends in science?

There is no doubt that in the coming years, data analytics of the scientific
corpus will play a significant role in contributing to the creation of
precisely such a global view of the scientific enterprise. The digitization of
journals, the availability of open API’s for accessing scientific meta-data,
and the integration of reference management with social networking are all
poised to transform our understanding of the scientific process at a high-
level. However, it seems naive to imagine that data mining techniques alone
will allow us to conceive of and test the most important hypotheses about the
global structure and dynamics of science without some amount of guiding
intuition. To complement and maximally take advantage of the availability of
massive data sets about science, as well as the computational tools to analyze
those data sets, we need a critical mass of scientific generalists whose
training has been designed to encourage hypothesis generation about the
scientific process itself.

Furthermore, another major trend in contemporary science is the move towards
ambitious scientific agendas of substantially larger scope and project size
[1]. Whereas the pioneering theories of earlier eras were often crafted by
solitary thinkers working in isolation, today’s breakthroughs frequently come
about from large international collaborations involving hundreds or thousands
of people and research budgets in the billion dollar range. In this context,
the question of how to ideally train an individual scientist might be re-
conceptualized as the question of how to train a scientific team member.
Scientific generalists could be pivotal members of such large collaborations
and play critical organizational and leadership roles.

There are certainly scientific generalists today, although they are perhaps
not thought of in this way. I would broadly (and informally) categorize them
into thee types:

  * •

The organic academic generalist
This is someone who has led a traditional academic career on the tenure track,
and whose research has naturally led to developing significant breadth in
multiple topics. Certainly many fields have researchers in this category.

  * •

The academic-industrial wanderer
This is someone who has left academia, or possibly had extended post-doctoral
or research scientist appointments in subjects different from their PhD, and
ultimately came back to academia, or led significant efforts at major
industrial research laboratories. For example, the growth of computational
biology and theoretical neuroscience has been driven by many theoretical
physicists who have gone on to do post-doctoral training in the biological
sciences, or for example, physicists from the world of quantitative finance,
who have returned to academia armed with a new set of skills quite different
from their PhD training.

  * •

The autodidact
The widespread availability of advanced scientific materials via the Internet
has resulted in an organic trend towards the creation of generalists simply by
lowering the barrier to accessing knowledge from a wide variety of fields,
scientific or otherwise. Certainly, there are many brilliant scientists in
industry and elsewhere who do not have PhD’s and it is not uncommon these days
to encounter truly first class thinkers on a variety of topics who are largely
self-taught.

The question that motivates this essay is the following: should there be
another category of generalist who has been trained from outset to play a
different role in the modern scientific enterprise than researchers who set
out to be specialists?

As a means to stimulate discussion, but as an idea unto its own as well, I
propose the following: the creation of a new graduate program, roughly
analogous in structure to an MD/PhD, where in addition to the normal research
requirements for completing a PhD, students complete 5 or more qualifying
examinations in subjects of their choosing. For adequately prepared students,
I believe that after completion of the requirements for their home department
in their first or second year, students would be able pass 4 additional
qualifying examinations over the course of 2-3 years, after which they would
resume their PhD research and complete their degree.111Over 60 years ago, in
the essay “The Education of a Scientific Generalist,” Hendrik Bode, Frederick
Mosteller, John Tukey, and Charles Winsor argued for a program of similar
breadth, but at the undergraduate level [2]. One wonders what their reaction
would be to the current proposal given the enormous growth of science in the
intervening decades.

The choice of the qualifying examination as the focal point for this program
is that it encapsulates the basic vocabulary of a field, the core knowledge
required to conduct in depth research. The aim of this program is emphatically
_not_ to train researchers who have in depth, specialized knowledge of 5
different subjects– that would be an unreasonable, if not outright impossible
goal. Rather, the aim is to train students who understand the culture, the
basic tools, and broad perspectives of multiple subjects, so that they can
contribute to strengthening the very foundations of the scientific
establishment.

Certainly, universities who undertake the process of creating such a program
might choose to begin with a fewer number of qualifying examinations. I chose
this number because it would allow for individual students to engage with
multiple, quite different subjects over the course of their graduate
education, and because 6-8 month blocks per subject would create a program
roughly on par with the length of an MD/PhD. Part of the value in creating
such a program would be the message and the vision it would send to younger
students who are aspiring to life-long careers as scientists. Just as
undergraduates who aspire to careers as physician-scientists must adequately
prepare themselves with appropriate exposure to both research and clinical
work, aspiring scientific generalists would need to prepare themselves with
advanced coursework of sufficient breadth to tackle the challenging initial
years of this graduate program.

For an ambitious program such as this one to maximally benefit both the
student and the scientific establishment at large, there would need to be a
strong culture to support those students who choose to undergo such a rigorous
and extended training. In particular, in order for the knowledge gained by
these students to develop into something much more rich and robust than a
massive list of facts and problem solving techniques from 5 different
subjects, they would need to be part of a mentoring program in which the
process of learning each of these different subjects was accompanied by
historical and philosophical discussion. During each qualifying examination
block, students would ideally also attend regular seminars in the department,
and perhaps nominally be affiliated with a research group and attend group
meetings. There would need to be a culture among the students and faculty
mentors that supported reflection about problem solving strategies, about the
structural differences between the vocabulary and subject matter across
different fields. Ultimately, these observations and insights, whether in raw
or more developed form would need to be communicated more broadly.

One possibility might be to accompany the qualifying examination process with
a historical essay exploring some topic of interest to the student in
consultation with a faculty mentor. For instance, a student whose PhD was in
theoretical condensed matter physics and who passed examinations in physics,
mathematics, chemistry, biology, and computer science might write an in depth
essay on the emergence of quantitative methods in the study of natural
selection. A mathematics PhD student specializing in stochastic analysis and
who passed qualifying examinations in mathematics, physics, statistics,
computer science, and economics, might write about the contributions that
mathematical finance pioneer Fisher Black made to macroeconomics.

While this program may seem daunting, I would like to emphasize that
individuals who pursue MD/PhD degrees and ultimately become board certified in
a medical specialty need to pass a similar array of hurdles– in addition to
PhD requirements for their research training they have to pass multiple level
of board examinations to become licensed physicians.

It is also important to keep in mind that the training program described here
is a graduate level training program, and consequently, should be thought of
as being the first step in a career-long trajectory. A person who completed
this program is no more a mature scientific generalist than a person who
completes an ordinary PhD program is a mature specialist. In order for the
subsequent phases of growth and development to take place, there would need to
be a supporting infrastructure overseeing the post-doctoral period of the
students’ training. Furthermore, it is certainly possible, and would be
expected even, that a subset of students who successfully complete this
training would simply choose to pursue tenure track jobs in their area of
specialty. Again, the MD/PhD is something of a guide– certainly many dual-
degree graduates become purely clinical practitioners or pure researchers and
do not actively build careers bridging the two. Students who pursue the more
traditional routes will not be at a disadvantage and one would hope that the
unique and rigorous educational experience they went through would inform the
remainder of their scientific careers both as researchers and as teachers. But
for those that wish to mature into the novel role of scientific generalists
that I am proposing, there would need to be special post-doctoral programs
providing generous several year funding that would give them the freedom to
develop their vision. For the initial batch of students, there would
inevitably be some amount of trial and error while both students and faculty
developed an understanding of the strengths and weaknesses of the program.

While one can only speculate about the contributions graduates of this program
would ultimately come to make, I close by suggesting a few possibilities. We
might imagine tenured professorships for generalists who have smaller research
groups than they would otherwise have, but who are active members of several
different groups led by other faculty. In addition to playing a critical
organizational role, these faculty members would bring their considerable
technical expertise and scientific breadth to each group in the capacity of
something along the lines of a scientific consultant.

Venture capital might be another place where scientific generalists could have
a significant impact, playing the role of bridge builders between academia and
industry, and perhaps actively managing their own portfolios and overseeing
scientific startup incubators.

One of the most important roles generalists could play would be to aid in the
development of younger scientific institutions, particularly in the developing
world. The specific aim of this program is to train scientists who have
significant exposure to the cultural elements of advanced science in multiple
disciplines, whose training allowed them to be both scientists as well as
participatory anthropologists of the scientific process. For both younger
universities in the developed world, as well as new institutions in the
developing world, scientific generalists could be critical leaders and agenda
setters, and perhaps, will be in a position to identify important research
trajectories, or important cultural elements for executing those trajectories,
that existing institutions have overlooked.

It would be incomplete and short-sighted to discuss novel educational
initiatives without considering important contemporary developments in online
education and open science. Furthermore, given that another major contemporary
theme in graduate education is the over production of PhD’s relative to the
availability of faculty positions, it would be understandable if a lengthy and
extremely demanding variant of the PhD program is difficult to mobilize. One
possibility to balance these different factors would be to create an open
system for crediting a student for having passed a qualifying examination.
Just as universities (and private companies) now offer certificates for
coursework completed in a non-degree granting context, an open certification
for anyone who is able to pass a qualifying examination would be a valuable
credential that an individual could earn to demonstrate competency at the
beginning graduate level. For this certification to be available across all
disciplines would be one step towards many different forms of educational
innovation in the research world, including the training of scientific
generalists.

Before closing, let me re-examine the choice of the qualifying examination as
the focal point for this particular proposal and consider alternatives.
Although the qualifying examination is an important rite of passage in
graduate education, many will correctly point out that it is hardly something
that contributes to depth of research maturity. This is certainly a valid
point, and in response, I would argue that the purpose of the program is not
to train individuals who have achieved the maturity of the best specialists in
multiple subjects, but rather individuals who can appreciate and communicate
the knowledge of specialists, and who therefore would make strong
collaborators, bridge builders, program managers, and journal editors etc. The
purpose of organizing a program for training generalists around the qualifying
examination is in a large part analogous to why we have such examinations in
the first place- they do contribute to some amount of intellectual and
technical maturity and are an important experience to have early in one’s
education. Furthermore, it is a simply stated idea that would require little
institutional overhead, and would circumvent the inevitably controversial
process of otherwise designing a curriculum.222It is worth mentioning that
even within the same subject, there are many different types of qualifying
examinations. In the context of this essay, perhaps Caltech’s Computation and
Neural Systems program (CNS) provides a possible template. The model they
employ is to give students a list of 100 questions that they use as
preparatory material in the year leading up to an oral qualifying examination
with 5 faculty members [3]. In a sense, the program is aimed at training
“generalists” within the computational and neurobiological sciences. It seems
natural to ask if this model could be extended to incorporate other subjects
as well. That is, what if a list of 500 questions were to be assembled
spanning multiple subjects and a set of oral qualifying examinations were
conducted by faculty spanning a number of different departments? Or a few
thousand questions from which a student selected some subset to prepare?

It is not difficult to imagine alternatives, however. One possibility would be
a several year post-doctoral program where fellows rotated through several
different laboratories and research groups in succession. Or, in the spirit of
the newly emerging trend of “hacker schools” and data-science boot camps, we
could imagine creating analogously structured mini-courses designed by experts
in the field targeted at advanced graduates whose training was in another
field entirely. Indeed, many academic research areas have highly topic
specific summer schools and winter schools and one could imagine a several
year post-doctoral program built around a handful of different sessions spread
across multiple subjects. Perhaps there should be a component both at the
beginning of the PhD, like the hybrid qualifying examination system I outline
above, as well as a post-doctoral component. Ultimately, it is difficult to
imagine that some trial and error would not be required in the design of such
a program. In addition, one thing is certain- successfully executing a program
like this would require an organization to support students’ growth for many
years, and given the fundamentally experimental nature of such an effort,
several years longer than we are accustomed to supporting graduate students.
Perhaps then, the ideal path forward would be to set into motion multiple
efforts aimed at the common goal of training scientific generalists, so that
over time, we can learn from our successes and mistakes. To do so, of course,
would require long-term institutional efforts to scientifically investigate
the efficacy and impact of different training programs.

## Acknowledgements

I would like to thank Aaswath Raman, Doug Bemis, Venkatesh Narayanamurti, and
Rob Spekkens for insightful discussions.

## References

  * [1] Michael Nielsen, _Reinventing Discovery: The New Era of Networked Science_ , Princeton 2011.
  * [2] Hendrik Bode, Frederick Mosteller, John Tukey, and Charles Winsor, “The Education of a Scientific Generalist,” _Science_ June 3, Vol. 109, 1949.
  * [3] Computation & Neural Systems Program. “100 Questions.” California Institute of technology. Accessed October 10, 2014. http://www.cns.caltech.edu/academics/100questions.html


# DVM-CAR: A Large-Scale Automotive Dataset for Visual Marketing Research and
Applications

Jingmin Huang  School of Computing Science
University of Glasgow
Glasgow, UK
<EMAIL_ADDRESS>Bowei Chen  Adam Smith Business School
University of Glasgow
Glasgow, UK
<EMAIL_ADDRESS>Lan Luo  Shigang Yue  Marshall School of Business
University of Southern California
Los Angeles, USA
<EMAIL_ADDRESS>School of Computer Science
University of Lincoln
Lincoln, UK
<EMAIL_ADDRESS>Iadh Ounis  School of Computing Science
University of Glasgow
Glasgow, UK
<EMAIL_ADDRESS>
###### Abstract

There is a growing interest in product aesthetics analytics and design.
However, the lack of available large-scale data that covers various variables
and information is one of the biggest challenges faced by analysts and
researchers. In this paper, we present our multidisciplinary initiative of
developing a comprehensive automotive dataset from different online sources
and formats. Specifically, the created dataset contains 1.4 million images
from 899 car models and their corresponding model specifications and sales
information over more than ten years in the UK market. Our work makes
significant contributions to: (i) research and applications in the automotive
industry; (ii) big data creation and sharing; (iii) database design; and (iv)
data fusion. Apart from our motivation, technical details and data structure,
we further present three simple examples to demonstrate how our data can be
used in business research and applications.

###### Index Terms:

Automotive analytics, product exterior design, visual marketing, big data,
machine learning

††publicationid: pubid: 978-1-6654-8045-1/22/$31.00 ©2022 IEEE

##  I Introduction

The automotive industry is a major industrial and economic force worldwide.
There is a rising interest in applying data mining, machine learning and
artificial intelligence to automotive research and applications. Marketing
researchers team up with automotive designers and computer scientists to solve
the research questions and challenges in automotive exterior design, consumer
analytics and sales forecasting. These are the key areas of marketing, and
solving these research questions and challenges can have positive social
consequences for automotive manufacturers and business researchers. On the
other hand, data mining, machine learning and artificial intelligence are
built upon data, in many cases, massive data. However, business details such
as product sales are consistently reserved by companies and are rarely shared
with researchers. Besides, data preparation can often be a lengthy, difficult
and expensive process for many researchers, particularly, social science
researchers’ primary focuses are not on programming and database management.
Sharing reusable data with the academic community can be an effective and
alternative way that makes this process more approachable.

We present our multidisciplinary initiative of creating a publicly available
automotive dataset, which can be used to facilitate business research and
market forecasting applications in the automotive market. Our initiative is
motivated by the growing interest among the business school in big data and
computer scientists are becoming interested in product aesthetics analytics
and design but publicly available datasets that cover a wide range of product
information, such as product appearance, model specifications and sales data,
are extensively lacking. Therefore, we propose a data approach that integrates
different online product information to obtain a comprehensive car dataset and
hopes our work could boost the emergence of more product datasets for research
purpose. In addition, three research samples are provided, illustrating the
resulting dataset can comprehensively support the data needs in business
analysis, product design and market forecasting studies. It is hoped the
shared dataset can be used by researchers and practitioners from different
disciplines to conduct economic or business related automotive research and
applications.

This study makes four major contributions. First, in the context of domain
application, our dataset meets the growing need for a comprehensive automotive
dataset for economic and business research. For example, our dataset can be
used for automotive exterior design and consumer analytics, which will benefit
automotive manufacturers, help them better predict their targeted consumer
segment preferences, and then use the obtained insights to direct the design
of new exterior stylings. Our dataset can also be used for car sales
forecasting which benefits all the participants in the automotive ecosystem
including car dealers, consumers and marketers. Second, from a perspective of
big data, to the best of our knowledge, our developed dataset is the very
first large-scale automotive dataset. It contains 1.4 million images from 899
different car models and corresponding specification and sales information
from over ten years in the UK market. The four characteristics of big data
(i.e., volume, variety, velocity, and veracity) are satisfied, which makes the
dataset can be used for different types of analytics and forecasting tasks.
The dataset can be used by multidisciplinary researchers to solve different
tasks. Many interesting insights, data-driven models and forecasting
applications can be derived. Third, in terms of database design, we conduct a
survey study with the researchers working in either business or computer
science field. The data challenges these researchers meet can be categorized
into three major issues: coverage, accessibility and quality. We design the
proposed dataset by addressing these issues with the hope that our data can be
researcher-friendly and thus can have a large impact in research and practice.
We also demonstrate a good practice of developing a dataset which can
alleviate the common data issues faced by researchers. Last but not least, our
work contributes to the multi-source data fusion as it includes different data
formats from different sources. Car images, model specification and sales
information are collected from different online sources while they are merged
and stored in a flexible and hierarchical structure that allows it to be
easily expanded with new data and used by researchers.

##  II Data Collection and Cleaning

Nowadays researchers often collect online data and process them as new
datasets for their research. Datasets like ImageNet [1] and OpenImages [2] are
characterized by their enormous size and have achieved massive success in
computer science research and applications. The two existing car image
datasets, Stanford-Car
111https://ai.stanford.edu/~jkrause/cars/car_dataset.html[3] and CompCars
222http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html[4], are also
based on the web scraped contents. Motivated by these existing works, we
develop the dataset by collecting and integrating data from different online
sources. First, car images are collected from the popular automotive
classified advertising platforms. These platforms are popular online
marketplaces in the UK for buying and selling used cars, which host millions
of car images and their selling prices for almost all car models from
different automotive manufacturers. Second, car sales data is collected from
the Driver and Vehicle Licensing Agency (DVLA), which is part of the
Department for Transport, holding over 49 million driver records and over 40
million vehicle records in the UK. The DVLA publishes the statistics of newly
registered vehicles in its seasonal reports. We extract and add the sales
appropriately for various car models. Third, new car prices are collected from
car review websites, which cover the selling prices of various car trims sold
in past years.

In the preparation of data sharing, a number of data cleaning and fusion steps
are performed. First, to comply with the General Data Protection Regulation
(GDPR) [5], we use machine learning techniques to detect and remove privacy or
unnecessary information. Mask R-CNN [6] (trained with COCO [7]) is used to
detect the appearing objects in car images. It helps to locate the positions
of car bodies and detect whether other objects such as human body appear in
raw images. Images with unexpected contents are dropped, and remaining images
are cropped according to detected car positions. These image pre-processing
steps aim to ensure resulting images are primarily occupied by car bodies
without other unnecessary information. Moreover, we apply the algorithm from
[8] to detect registration plates and then blur corresponding regions. Second,
machine learning models are used to filter out non-exterior-viewing images. An
ImageNet pre-trained convolutional neural network (CNN) [9] is fine-tuned with
manually prepared samples where pictures are labelled according to their
qualities and observation viewpoints. The trained CNN classifies images
according to observation viewpoints. As illustrated in Fig. 1, only images
taken from the eight targeting viewpoints are kept for later usage. This
reduces the size of the raw image set from over six million to less than 1.5
million. Third, all the non-visual contents are processed into attributes and
stored as data tables. The ambiguous values of observations are unified or
corrected. All car models are assigned unique identifiers (IDs) used for
information integration. Information such as annual sales is aggregated into
more abstract levels. Images are listed in the table with the observation
viewpoints and source advert IDs.

##  III Towards a Good Research Dataset

During the data preparation, we deploy a survey using Qualtrics to explore the
most common data issues that people face in their research and practice. We
collect responses from 54 researchers, including 26 participants with a
computer science background and 28 with a business studies background,
including economics, marketing and management. The reported issues from our
survey study can be broadly divided into three categories: coverage,
accessibility, and quality.

Coverage refers to the issue of a dataset not containing the needed
information. This happens in two situations. First, the dataset is not
comprehensive enough. It does not contain the attributes or variables that a
researcher is interested in for a specific study. Second, the dataset does not
cover enough records or samples. Our dataset is designed to have excellent
coverage of both variables and observed samples. For the former, as presented
in Table I, we design the dataset to contain many important car specifications
and sales variables used in the related marketing studies. In addition to
these variables, as also demonstrated in Fig. 1, we provide corresponding
images for various car models in various angles over a long-term period. The
existing public car image datasets, such as the Stanford-car and CompCars
datasets, do not include the mentioned car specification and sales variables
as they are mainly designed for computer vision tasks. In terms of volume, the
dataset covers millions of registered cars in the UK market in the past
decades.

TABLE I: Summary of car specification and sales variables in related studies and our dataset. Variables/features |  Our dataset | Related work
---|---|---
[10] |  [11] |  [12] |  [13]
Sales/market share | ✓ |  | ✓ | ✓ | ✓
Price | ✓ | ✓ | ✓ | ✓ | ✓
Brand | ✓ | ✓ | ✓ |  | ✓
Exterior features | ✓ | ✓ | ✓ | ✓ | ✓
Fuel economy | ✓ | ✓ |  | ✓ |
Horsepower | ✓ | ✓ |  | ✓ |
Engine | ✓ | ✓ |  | ✓ |
Transmission | ✓ | ✓ |  | ✓ |
Equipment features | ✓ | ✓ |  |  | ✓
Life-cycle/model year | ✓ |  | ✓ | ✓ | ✓
Advertising |  |  | ✓ | ✓ |
Reliability |  | ✓ | ✓ |  |
Safety |  | ✓ | ✓ |  |
Driving/handling |  | ✓ |  | ✓ |
Ergonomics/rooming |  | ✓ |  | ✓ |
Interior |  | ✓ |  |  |

Accessibility refers to the difficulties regarding data usage caused by closed
or proprietary datasets or the underlying complex data structure. Our
participants in the survey highlighted that many datasets require license
purchase, and in many cases, these datasets are sold at remarkably high
prices. Researchers find that it is difficult to secure funding to purchase
their needed datasets. On the other hand, the free shared datasets are often
presented in a researcher-unfriendly way. The developers of those datasets may
either put too much trivial information or leave data unprocessed. Even worse,
many datasets are shared online without providing basic descriptions. A lot of
efforts have been made to improve the accessibility of our dataset. First, we
create an easily accessible data webpage on GitHub where researchers can
download our dataset and find the needed description and usage instructions.
Second, there are also no specific restrictions on our data usage. Moreover,
we hope our data can be easily used by researchers from different backgrounds,
so it is presented in a researcher-friendly manner. For example, all non-
visual contents are processed into tabular attributes and organized into
separate tables according to their categories. All the car models have been
assigned a unique identifier (ID), which can be used for information
integration and future data fusion. In addition, we remove the background of
collected images, which can simplify the potential applications for
researchers interested in automotive exterior design.

Quality refers to missing data and error values. If problems exist in the
original data, they can hardly be resolved by researchers using the data.
Therefore, a series of data cleaning procedures are adopted to ensure the
resulting data quality. First, the ambiguous or inconsistent values are
unified or corrected. For example, a car’s brand or name containing “Benz” is
corrected into “Mercedes”. Second, data values at different granularities are
aggregated according to certain groups. For instance, annual sales regarding
various car trims, a trim level representing the equipment levels in a
specific car model, are aggregated to the model levels. Third, uncertain
contents are largely abandoned. The raw collected data has more than six
million images, but most of them are abandoned in later processes for the
purpose of quality control.

Besides the aspects above, ethics issues are thoughtfully reviewed throughout
the data preparation. We carefully pre-process and reproduce image contents to
strictly comply with the GDPR (e.g., removing image background and covering
plate numbers). It is worth noting that the UK government has special laws to
encourage research studies related to creating and using web content based
datasets333https://www.gov.uk/guidance/exceptions-to-copyright. Therefore, we
do not have copyright concerns of our collected data if people use it for non-
commercial purposes. As our dataset only contains car-related information
(e.g., images, car model specification, sales), it seems unlikely it will
generate negative societal impact.

##  IV The DVM-CAR Dataset

(a)

(b)

(c)

Figure 1: Illustration of car images in our dataset: (a) angles of car image
labeling; (b) example of background removal for Audi A2 from angle 270 degree;
(c) example of background removal for Audi A2 from angle 45 degree.

(a)

(b)

Figure 2: Summary of image data in the DVM-CAR dataset: (a) the number of car
models across years and the average images for each model; (b) the annual
percentage composition of images by body type.

As our primary motivation to facilitate the visual marketing research and
forecasting applications, we call the proposed dataset Deep Visual Marketing
Car (in short DVM-CAR), which is publicly available under the CC BY-NC license
at:

https://deepvisualmarketing.github.io.

The DVM-CAR dataset consists of two parts: image data and table data. The part
of image data contains 1,451,784 car images (in JPEG format with resolution
300$\times$300) which are compressed in a ZIP file (13.6 GB file size). These
images have been selected from eight observation viewpoints and stored under
the categorization “brand-model-year-color”. This structure allows researchers
to locate target images easily. The backgrounds of all car images are removed.
A table is prepared for image indexing, so researchers can select the images
via this table. In total, our image data covers 899 car models that sold in
the UK market over the last 20 years. As Fig. 2 (a) shows, the automotive
classified advertising platforms have more data for newly-launched models than
older car models. In spite of this, it contains 138 car models sold in the
year 2000, each with an average of 28 images. The wide longitude of the
dataset makes the observation of long-term trends easy, thus, is particularly
useful for economic and marketing analytics and forecasting. For instance,
through Fig. 2 (b), it is shown that SUVs are becoming more popular and taking
higher market shares over the last two decades. The part of table data (i.e.,
non-visual part) comprises six tables in the CSV format (156 MB file size),
which are named the basic, sales, price, trim, ad and image tables. Together,
these tables cover various variables and form a relational database [14], as
each two of them can be joined via the primary or secondary keys.More details
of these tables can be found in Table II.

TABLE II: Description of table data in the DVM-CAR dataset.

Name Table description Attribute name Attribute description Basic It is mainly
for indexing other tables. Genmodel Generic model name Genmodel ID Generic
model ID Automaker Automaker name Automaker ID Automaker ID Ad It contains
more than 0.27 million used car advertisements information posted on the
automotive classified advertising platforms, including variables like
advertisement’s creation time, used car registration year, cumulative mileage,
selling price, etc. Genmodel Generic model name Genmodel ID Generic model ID
Maker Automaker name Adv ID Advertisement ID Adv year Advertisement’s creation
year Adv month Advertisement’s creation month Color This car’s color Reg year
This car’s first registration/selling year Bodytype This car’s body type
Runned Miles This car’s runned mileage Engin size This car’s engin size
Gearbox This car’s gearbox Fuel type This car’s fuel type Price This car’s
selling price Seat num This car’s seats number Door num This car’s doors
number Image It contains image data related information like predicted
viewpoint and quality check result. Genmodel ID Generic model ID Image ID
Image ID Image name Image name Predicted viewpoint This image’s predicted
viewpoint Quality check Manually check result Price It contains the entry-
level new car prices. It is designed for people who only need the basic price
of car models. Genmodel Generic model name Genmodel ID Generic model ID Maker
Automaker name Year Generic model’s selling year Entry price Generic model’s
entry-level price Sales It contains car sales data of the UK market (based on
the released statics from the DVLA). Genmodel Generic model name Genmodel ID
Generic model ID Maker Automaker name Year 2001 to 2020 Generic model’s annual
sales Trim It includes 0.33 million trim level information such as sell -ing
sellingprice, fuel type and engine size. It is designed for people who are
interested in the price of car model at a specific trim level. Genmodel
Generic model name Genmodel ID Generic model ID Trim Trim name Maker Automaker
name Year Trim’s selling year Price Trim’s price at selling year Gas emission
Trim’s CO2 emission Fuel type Trim’s fuel type Engine size Trim’s engine size

##  V Application Examples

This section briefly illustrates three application examples for demonstrating
how the DVM-CAR dataset could be applied to business research and
applications.

###  V-A Understanding Automotive Exterior Aesthetics Design

Product aesthetics design is a determinant of consumer acceptance and product
success [15, 16, 17, 12]. Marketing scholars have discussed aesthetics from
various aspects, including the influence of aesthetics on product
differentiation and new product development, and specific determinants of
consumer responses to aesthetics. For example, morphing techniques were used
to quantify and incorporate aesthetics design into empirical car sales models
[13, 18]. This is an important step in modeling the effect of aesthetics
design on sales. However, the used method is still limited in its ability to
process image data. Car visual attributes are only extracted by pre-defined
feature extractors, which are coarse-grained, and their respective
implications for sales analysis and car appearance design are limited.

The recent advancement of machine learning technologies has provided marketing
researchers with new tools to investigate product aesthetics. Several studies
applied deep neural networks such CNNs to interpret the perceived design
features [19, 20]. Compared with the traditional quantitative methods used in
marketing research, deep learning algorithms can automatically learn high-
level representations of visual features from car image data. Thus, the deep
models can be used end-to-end, which solely requires raw images and tagging
data as labels. Such advantages of deep models make them widely applied for
visual based predictive studies [21].

The DVM-CAR dataset provides an excellent base for researchers to apply deep
learning to extract visual attributes from car images. For instance, Fig. 3
(b) shows the sample using car images to infer the modernity score (i.e.,
labels computed from car registration years). By fine-tuning the ImageNet pre-
trained VGG’16 [22], the results show deep learning models are capable of
predicting the design fashion for family cars. Besides, we can further
recognize the design language or patterns from the inside gradients. Fig. 3
(a) displays the predicted scores for Land Rovers. Although all these models
are from the same automaker and were sold in the same year, the trained deep
model rates them with entirely different modernity scores. The second row of
Fig. 3 (a) presents, with the help of visualization methods such as guided
back-propagation [23], researchers can identify specific designs (i.e.,
highlighted in blue color) that make the appearance outdated.

Based on the car models’ modernity predictions, we can further investigate the
associations between cars’ modernity of appearance and their market
performance by using the sales records in the DVM-CAR dataset. As Table III
illustrates, we find the models’ future withdrawn chances are correlated to
their modernity scores (Note: the numbers in brackets of Table III are the
sample sizes of the groups). The car group with low modernity scores have a
higher chance of being withdrawn from the market, while the high modernity
group tend to survive longer. As essential marketing variables such as new car
prices and sales are all covered by the dataset, users can investigate the
association between cars’ exterior styling and market performance from diverse
perspectives.

Figure 3: Illustration of application examples: (a) modernity scores of Land
Rover models registered in 2015; (b) distribution of the predicted modernity
scores of car models from 2000 to 2015; (c) new BMW front designs generated by
CycleGAN according to given facial shapes; (d) automobile design sketches
generated by CycleGAN.

###  V-B AI-Powered Automotive Exterior Design

Anthropomorphism [24, 25, 26] refers to the attribution of human or animal
characteristics to non-living objectives. As a common phenomenon in
consumption environments, anthropomorphism has drawn wide attention from both
social psychology and marketing researchers. Psychological studies [27] reveal
that our brain is highly specified for face perception, which is so evolved
that we often perceive faces from non-living objects. A group of marketing
studies extensively investigate how products with human-like or animal
characteristics lead to face perception. Typically, in car related studies,
existing investigations [28, 29, 24] show consumers have a strong tendency to
anthropomorphize the car front. However, in these studies, human or animal
characteristics are predefined and only used in user surveys. They cannot be
further deployed for facial feature recognition for unseen images. Thus,
providing limited insights on car front appearance design for car
manufacturers.

Figure 4: Predicting the changes of market share changes according to design
changes. The displayed two samples are the historical Audi A3 and Fiat 500 in
the last decade. The position of displayed car images indicates the year for
model redesign, the blue bar indicates the predicted market share change.

As a group of generative models with neural network structures, deep
generative models can learn to generate highly realistic representations from
data, thus making them a hotspot in machine learning and statistics in past
years [30]. These models have become extremely successful in the applications
such as image translation and fake data generation. Particularly, marketing
researchers apply these methods to generate novel product designs [31, 32,
33], including cars. Pan et al.[34] and Burnap et al.[35] used the deep
generative models in the combination of design evaluators (i.e., deep
convolution models trained for aesthetics evaluating) to generate appealing
car appearance designs. The 1.4 million car images of the DVM-CAR dataset make
it extremely useful for generative model training. Two examples are provided
in Fig. 3 (c) and (d). The former shows new BMW 3 Series front designs
generated by cycle-consistent adversarial networks (in short CycleGAN) [36]
based on the fed semantic views. The latter presents sketches generated from
real car images via the CycleGAN. These are from our working topics where we
try to obtain bio-inspired designs by morphing the cars’ layouts after cheetah
faces.

###  V-C Visual-based Car Sales Forecasting

Several existing studies [37, 38, 39, 40] investigate the product exterior
design as an optimization problem that searches for best-selling designs with
considering aesthetic attributes as controllable factors in manufacturing. For
example, Rajeev and Krishnamurti [38] simulated market sales using the share-
of-choice setting that viewed each product as a profile with a collection of
design attributes at different discrete levels. Due to their heterogeneous
needs, the consumers are willing to pay more for the product with matched
profile and thus more likely to buy. Differently, Kaul and Rao [39] considered
consumers’ heterogeneous needs as coordinates distributed in a high-
dimensional space of product attributes, where utilities of products are
indicated by their distances from the coordinates. Their study tried to infer
the best coordinates in the market that maximize the overall purchase
likelihood. However, as designs are represented by the profiles in these
existing studies, no actual designs are generated. The complexity of real
scenarios is thus too simplified to be applicable. For instance, the existing
studies didn’t clarify how to compute an aesthetic profile of a given design
and whether particular aesthetic profiles are not achievable (e.g., a super
sporty and cute design).

One of our working studies incorporates the deep learning-based design
evaluation and generation procedure into product design optimization.
Historical images and sales of the DVM-CAR are used to predict the changes in
sales when adopting a new design. Being trained with massive car images, the
GAN model is capable of generating various novel yet highly realistic car
designs. Meanwhile, after collecting aesthetic ratings from surveys, a CNN
model is trained with these aesthetic scores and car images to assess designs
in terms of aesthetics. Then the trained CNN is used to evaluate both real
designs from the dataset and the ones generated by the GAN model. For sales
forecasting, an RNN model is trained with these obtained aesthetic scores and
annual sales records. Thus, recognizing a car model’s prior sales and its
aesthetic changes, the trained RNN can forecast the posterior sales (or market
shares)., see Fig. 4 for a series of the predicted market share changes of
Audi A3 and Fiat 500.

TABLE III: Withdrawn chances within the next three years of car groups with
different modernity scores.

Year Modernity score [0,1) [1,2) [2,3) [3,4) [4,5] 2000 12.0%(25) 2.8% (36)
0.0% (10) -(2) -(0) 2003 23.1%(13) 10.7%(56) 0.0%(28) 0.0%(10) -(0) 2006 -(4)
13.9%(36) 8.3%(60) 7.4%(27) -(1) 2009 -(1) 33.3%(9) 16.1%(62) 3.3%(61)
15.8%(19) 2012 -(0) -(1) 17.9%(28) 9.1%(88) 5.4%(56) 2015 -(0) -(0) 18.2%(11)
9.0%(67) 7.3%(109)

##  VI Conclusion

This paper demonstrates the design and development of a large-scale dataset
for business research with online sources. As highlighted in Section I, our
work has significant contributions for information and knowledge management,
ranging from domain application to big data, to database design, and to data
fusion. The targeted users of our dataset are business researchers and
computer scientists who work in visual-related research and applications,
particularly on (but not limited to) the topics of automotive exterior design,
consumer analytics and sales prediction. Through this paper, we would like to
make an announcement that the DVM-CAR is now released publicly to research
communities. We hope it can have a large impact. More importantly, we would
like to maintain and keep updating the dataset by incorporating researcher’s
feedback over time.

## Acknowledgment

The first author acknowledges the Adam Smith Business School and the School of
Computing Science of University of Glasgow’s funding support for this
research. The second author would like to thank the funding support of the
Region Bourgogne Franche Comté Mobility Grant, Nvidia Accelerated Data Science
Grant and Google Cloud Academic Research Grant.

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