A Tale of Two Visions

Exploring the Dichotomy of Interest between Academia and Industry in

Visualisation

Richard Roberts

, Robert Laramee

, Paul Brookes

, Gary A. Smith

, Tony D’Cruze

and Matt J. Roach

Computer Science Department, Swansea University, Wales, U.K.

QPC Ltd, Mold, North Wales, U.K.

Keywords:

Visualisation, Industry, Collaboration, Software.

Abstract:

The pairing of a commercial organisation with an academic institution is a typical example of a symbiotic

relationship. Commercial organisations dedicate money, time, and often data into a university project with the

ultimate goal of a ﬁnancial return on their prudent investments. Academic institutions welcome these relation-

ships as it supports their research in a ﬁeld where obtaining funding is extremely competitive. Speciﬁcally in

the ﬁeld of visualisation, the culture, visions, and goals of both academia and industry differ in unique ways.

In this position paper we explore the dichotomy of interests between the two groups, based on ﬁrst hand ex-

perience and interviews, deriving recommendations for any organisation considering entering into a working

relationship with either party.

1 INTRODUCTION

Imagine two organisations. Each has an input, and

each has an output. Both require ﬁnancial capital as

an input, but their outputs are not the same. The ﬁrst

organisation outputs a product or service; neatly pol-

ished and saleable. The second organisation outputs

knowledge; packaged in the form of a written docu-

ment or an educated person. The nature of knowledge

is such that it isn’t ever really complete - just further

developed, whereas saleable products should be ﬁ-

nal and complete. The juxtaposition between the two

organisations is so evident that it seems difﬁcult to

imagine a symbiotic relationship developing between

the two.

Despite the duality between the university and in-

dustry operational processes, relationships are often

formed between the groups. To maximise the po-

tential of these relationships, each organisation must

consider how the relationship can be of mutual bene-

ﬁt to for both parties by considering some questions;

What do I expect to receive from this relationship?

What do I expect to provide in this relationship? What

are the deliverables of my project? What are the de-

liverables of their project?

Depending on the subject ﬁeld, the two groups

have varying degrees of shared interests. For example

the ﬁeld of oncology is almost entirely research based

(Johns et al., 2003), where the difference between

the industrial component and academic component is

minimal, relationship challenges still remain (Stossel,

2005). However, as we stray away from charitable or-

ganisations and medical groups and move towards the

business-oriented industries we see a shift in motiva-

tion. No longer are the scientiﬁc breakthroughs mutu-

ally beneﬁcial to both parties, but instead a dichotomy

appears whereby a business intends to utilise the part-

nership for ﬁnancial reward, and the academic part-

ner desires to produce publishable research. This isn’t

necessarily incompatible, but the scope and expected

output of each party should be thoroughly discussed

before entering into the partnership.

We have worked with our industry partner since

2013 and have mutually learned a lot from one an

other. Whilst we would describe our working rela-

tionship as positive, we have deﬁnitely found that ex-

pectations of each project rarely line up perfectly.

In this paper we explore how the knowledge trans-

fer between the two parties beneﬁts each side. We

identify where businesses can learn from data visuali-

sation practices as well as describe the value of work-

ing with industry has provided us. We examine the

nature of this relationship in the ﬁeld of data visual-

isation and perform an interview study with our in-

Roberts, R., Laramee, R., Brookes, P., Smith, G., D’Cruze, T. and Roach, M.

A Tale of Two Visions - Exploring the Dichotomy of Interest between Academia and Industry in Visualisation.

DOI: 10.5220/0006635803190326

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 3: IVAPP, pages

319-326

ISBN: 978-989-758-289-9

319

dustry parter and others to explore both sides of the

scenario. The result is a set of recommendations both

parties should consider when initiating an academic-

industry relationship.

In the next section we cover related work in this

topic, followed by our analysis of the dichotomy of in-

terest within university/industry collaboration. Then

we present some informal interview studies from both

our industry partners and small business owners who

are at varying stages of university collaboration. We

conclude with an evaluation and recommendations for

any party considering a collaboration.

2 RELATED WORK

The study of this unique relationship spans almost all

of the academic disciplines. Due to the nature of these

studies, the viewpoint is often from the perspective

of the university and not the industry partner. Each

study focuses on a slightly different facet of the rela-

tionship, ranging from motivations and beneﬁts to the

introduction of a third party – the government.

Ankrah and AL-Tabbaa present a systematic re-

view of the literature surrounding this ﬁeld (Ankrah

and Omar, 2015). Showing the top three publication

venues to be ‘Research Policy’, ‘Technovation’, and

‘R&D Management’. The survey contained 109 ar-

ticles from all areas of academic collaboration with

industry.

Appropriately, a sizeable amount of the university

and industry research focuses on the beneﬁts of their

collaboration. De Fuentes and Dutr

enit examine the

long term beneﬁts of these relationships (De Fuentes

and Dutrenit, 2012). The research addressed three pri-

mary questions, why do universities collaborate with

industry? What are the main types of knowledge

transferred? And what are the beneﬁts of the collab-

oration? In this example the industry viewpoint is fo-

cused on, addressing the beneﬁts to companies work-

ing with a university. Chan and Anderson explore

the beneﬁts to academic students when collaborat-

ing together in what they describe as ‘Action Learn-

ing’ (Chan and Anderson, 1994), the paper concludes

by stating that the union of the two groups provide

students with an excellent opportunity to experience

‘real world’ business interactions ﬁrst hand. The mo-

tivation behind the relationship has also been studied

(Deste and Perkmann, 2011).

Fujisue explores the transfer of knowledge from

academia to industry within a collaborative relation-

ship (Fujisue, 1998). This transfer of knowledge is

important as it contributes to the businesses inputs

to be converted into proﬁt (Friedman and Schwartz,

1975).

Whilst these research papers broadly explore the

nature of university-industry collaboration, this pa-

per focuses speciﬁcally on visualisation research in

academia along with the strengths and weaknesses of

such a relationship. It also examines a very speciﬁc

relationship - that of Swansea University with QPC

Ltd.

In Ben Shneiderman’s book ‘The new ABCs of

Research’ he encourages the pursuit of real solutions

to real world problems (Shneiderman, 2016). He

highlights the double value that a real world solution

brings to both academia and industry or civic organ-

isation. Shneiderman highlights that the selection of

a project with actionable problems triggers great re-

search.

A panel discussion addressing the themes of in-

dustry and academic partnerships took place at an

event called ”Uncertainty Management & Uncer-

tainty Quantiﬁcation for Industry” in 2017. At the

panel a range of barriers were discussed that pre-

vent academic contributions from eventually impact-

ing real-world applications. Some of the factors in-

clude, cost, knowledge of customer demand, engi-

neering knowledge, time, computing resources, data

validation and uncertainty, results and interpretation.

Also presented was a phenomenon we call, ”The Soft-

ware Development Death Cycle” which describes the

fate of 99.9% of research prototype software at the

end of a PhD degree (Robert S Laramee, 2017).

3 A TALE OF TWO VISIONS

Consider again those two organisations. Identifying

the standardised processes of inputs and outputs in

these organisations is necessary to create the model

for collaboration. First we will look at each organi-

sation individually and then attempt to merge the two

processes and create a symbiotic model where each

organisation contributes and beneﬁts from the rela-

tionship.

First we look at the industry organisation. Sim-

pliﬁed, businesses operate in a state of production,

whereby proﬁt is generated from the creation of a

product or service. Most of this proﬁt is then rein-

vested into the business to grow and expand the busi-

ness. See Figure 1.

This simpliﬁed model shows the generated prof-

its reinvested into the company. Some of the capital

will contribute towards research and development or

investment in new infrastructure, and some may be

used to pay the shareholders dividends to encourage

retention of their investors. A signiﬁcant proportion

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

320

Figure 1: This ﬁgure shows the simpliﬁed process of busi-

ness. Internally money is invested on many things such as

R&D or marketing, but ultimately proﬁts should be made

on investments through the sale of products. Proﬁts are ei-

ther saved for liquidity, re-invested in the company, or paid

to shareholders (In the case of a public limited company).

of it, however in some way contributes as input back

into the business.

The academic model looks quite different. Specif-

ically in the ﬁeld of visualisation, the inputs required

are both capital to fund research and access to inter-

esting data. The generated outputs for this model cen-

tre around knowledge, either as an education student

or as a published research paper. See Figure 2.

Figure 2: This ﬁgure shows the academic process model for

data visualisation. Unlike industry, it cannot utilise its out-

puts as inputs. The larger organisation makes money from

education, however academic research typically requires

external funding to operate. The outputs of educated peo-

ple and publishable research cannot be directly reinvested

in the organisation.

The standard method of merging these models in

collaboration is for the industry partner to provide

funding and/or data to the university generate research

around a topic. In return, the university should pro-

vide something of value to the industry partner. See

Figure 3. However it is rare for industry funding alone

to be sufﬁcient to cover all costs of collaboration. A

third party funding body is usually required to provide

a large proportion of the capital.

Figure 3: This ﬁgure demonstrates the union of both organ-

isations in a symbiotic relationship. Third party funding is

typically necessary to encourage such collaboration.

The method in Figure 3 utilises the outputs of the

industrial partner as well as in the inputs of the aca-

demic partner. Third party funding is often neces-

sary to create the collaboration as industry partners

are sometimes reluctant to invest large amounts of

money in something considered experimental or risky.

Notice the outputs of the academia model are incom-

patible with the inputs of the industry model. A big

question mark remains over what contributions can be

made to industry. How is this gap in the utility work-

ﬂow ﬁlled?

3.1 A Case for Collaboration in

Visualisation

In this section, we address how exactly visualisa-

tion research is capable of providing unique value to

a business. The gap in the utility workﬂow is best

solved by addressing data challenges that the industry

partner faces.

If we look at the strengths and weaknesses of both

the software development industry and academia in

visualisation, the former is structured to create soft-

ware that is saleable. Their staff posses generalised

development skills, with the intention to create a well-

built and durable piece of software. Visualisation re-

search operates with more experimentation in mind,

often utilising quicker development lifecycles that en-

able visualisation ideas to come to fruition quickly.

Moreover the academic researcher has the knowledge

specialisation to know what visualisation methods are

available and which suit the industry partner’s data.

The value of visualisation centres around the idea

of visual data interpretation (Van Wijk, 2005). It’s not

possible for a human to read and understand a large

dataset. Even reducing the data down into statistics

A Tale of Two Visions - Exploring the Dichotomy of Interest between Academia and Industry in Visualisation

321

and reports limits the audience ability to interpret re-

sults. Visualising the data in the right way opens up

interpretation to a broader audience as well as sim-

pliﬁes the important aspects so that analysis can ex-

plore and better understand the data. There is an in-

herent value locked up within real-world datasets, but

the knowledge required to unlock that value is rarely

held. This is where academic research can help indus-

try parters in their data challenges. Both through the

transfer of knowledge from academia to industry, but

also in the creation of visual designs tailored speciﬁ-

cally to the datasets provided by industry. This skill is

not widely taught and so industry staff members are

generally limited in their ability to attempt visualising

complex data without the university support.

Whilst we’ve established that the academic ﬁeld

of visualisation offers unique value through knowl-

edge and ability to unlock value in large datasets, it

is still unclear what deliverables are beneﬁcial to the

industry partner. Whilst the transfer of information

to industry is useful, it may not fully compensate the

cost of funding a research position. Typically visuali-

sation research involves the creation of new software

that facilitates the analysis of data. If designed for the

use case of industry analysis, then the software could

be handed over to the industry partner as part of the

deliverable. However, research software is unlikely to

be saleable. A company would ﬁnd it difﬁcult to proﬁt

directly from the software itself, but instead take ad-

vantage of the insights from using the software.

These new insights into industry datasets hold a

unique value. Companies often employ teams of anal-

ysis to discover features in datasets or even validate

theories they may have about the data. Visualisation

offers the ability to discover and explore very large

datasets whereby the output can be interpreted by staff

members beyond just analysts. Therefore we propose

that the most signiﬁcant contribution to industry is the

outsourcing of information exploration.

3.2 Dichotomy of Interest in

Visualisation

Whilst there is a signiﬁcant case for collaboration

among academia and industry, there are a number of

cases where the interest of the two organisations di-

verges. We do not consider these to be ‘deal break-

ers’, but we do recommend that they are considered

and discussed before entering into a collaboration ef-

fort.

PhD Research is Niche. If the university funding is

used to employ a PhD candidate in the research, the

goals of that student are mostly ﬁxed around the pro-

posed topic. The researcher will spend long periods

of time working on a number of similar projects in or-

der to complete their studies. This can result in less

interest from industry partners.

Visualisation Research Has to be Novel. The

development of unique visualisation methods may

sound appealing to industry partners. However some

data challenges may best be addressed using already

existing visualisation methods or off-the-shelf tools.

Whilst there is scope for research visualising new

data with old methods, it is not the most desired

form of output from the perspective of the univer-

sity. Developing new visualisation techniques and in-

teractions can improve the output of analysis, but the

time/reward ratio for industry may be unfavourable.

Research Has Necessary Outputs. An absolute re-

quirement of academic research is the publication of

research. The writing of a publishable research paper

is a long process of uncertain duration. Rejected pa-

pers may take years to be published which can be off

putting to potential industry partners who gain very

little from being associated with a high quality pub-

lication. A serious challenge also arises if visualisa-

tions of the industry data cannot be published for data

protection reasons.

Pressure of Proﬁts. Businesses operate with the ab-

solute requirement to make a proﬁt. Academia does

not operate under the same conditions and so the work

requirements place less emphasis on reducing project

overheads. Less creative freedom is allowed within

business software development to streamline the cre-

ation process.

In the next section we discuss a range of informed

opinions from individuals within industry who seek

collaboration with academia and also the views of

academics seeking partnerships with industry to fur-

ther their research.

4 INTERVIEW STUDY

In order to fully evaluate the collaboration sentiments,

we performed a number of interviews with both aca-

demics and industry. During a business technology

conference (dig, 2017) we talked to small business

owners about collaboration opportunities and col-

lected their thoughts and feelings on working along-

side a university for their research. We also inter-

viewed our industry partners to evaluate their expe-

rience of collaborating with us.

Additionally, we discuss industry collaboration

with other academics who have engaged in relation-

ships of this nature so that we can explore both the

visions of both organisations.

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

322

4.1 Industry

Digital Conference. Over the duration of the Digi-

tal Festival we talked with many people from differ-

ent backgrounds about the relationships between busi-

nesses and computer science academia.

We note that so much of the initial relationship be-

tween academics and industry is focused around writ-

ing grant bids. It appears that universities have more

experience in writing these bids and so they poten-

tially have more bargaining power in the negotiations

of the relationship. Third party funding is essential

for relationships with SMEs (Small-Medium Enter-

prise) as these projects are seen as unexplored and a

potential risk.

One technology business owner discussed with us

the reasons he has previously engaged in a university

collaboration - “We realised that universities have in-

vested a lot of money is R&D which has become more

advanced than what’s available in industry. They

have this deep well of knowledge that we can access,

and we’re looking to convert that into saleable prod-

ucts”.

Throughout the conference we demonstrated the

utility of data visualisation at our exhibition stand.

A business employee was interested in the concept

and spoke about how they needed their software to

be more comprehensive in its analsysis, “We want to

be able to visualise our data so that we can monetise

its output. our software is really good but it lacks that

visual component”. Showing the demand for visuali-

sation but the lack of availability for third party fund-

ing may be preventing a number of these relationships

from forming.

Whilst businesses were very keen to instigate a

collaboration, their interest was either in the knowl-

edge and insight a university can offer, or simply

cheap manual labour of software development. The

latter being incompatible with the output of academia.

The former is certainly possible, but the university

would still have academic obligations such as paper

publication. The company would need to provide an

outlet for that, e.g. supply interesting data.

Alternatively, the output of academia is a contri-

bution towards the product. In most cases in the vi-

sualisation world, this would be in the form of a vi-

sualisation design or even the visualisation research

software handed over as a deliverable. This can be

the perfect outcome for both organisations, but intel-

lectual property rights need to be discussed ﬁrst. Dur-

ing our conversations at the conference, a number of

people raised IP as a potential issue with starting a

collaboration.

Industry Partner Interview. We’ve been collabo-

rating with QPC Ltd. since 2014. During this time

we have engaged in a two-way knowledge transfer,

spending time learning about the operations of the

other party and then developing visualisation software

that utilises their data. We instigated our relationship

through an Innovate UK funding opportunity and con-

tinued our relationship using the KESSII PhD funding

scheme.

We interviewed the key collaborators in QPC Ltd.

to explore their experience in partnering with us.

The central theme was the funding opportunity that

brought us together - “So any commercial business

has to have one eye on its cost, and then how do you

invest that money wisely? What grants and funding

opportunities are available that would create oppor-

tunities we wouldn’t normally have?” We asked them

about the beneﬁts of funding. “What funding with this

type of relationship offers is the ability to ﬁnance and

create something new that we would normally have

the opportunity to do - the best example is where we

are today.”.

We ask what academic collaboration offers to in-

dustry - “Our focus is 100% on things that we can

build to sell. We can’t really focus on anything out-

side of that. With you guys, you have the ability to

step back and experiment to create something differ-

ent which is actually where the real growth comes

from in any business.” Reinforcing our ideas about

academic creative freedom. They go on with; “So

in R&D, it’s the research part of the development we

just don’t have the time to do because of the numerous

deadlines imposed upon us.”.

Another beneﬁt this collaboration offers is a fresh

perspective on insights - “It was also good to have

you guys look our data with a fresh set of eyes. We

didn’t want to guide you too much in the early stages

because we wanted to see what insights you would

make without our biases of what we already know.”.

They continued; “The visualisations you make always

explore different avenues to the ones we make.”

We then ask speciﬁcally what we have been able

to contribute over the project duration. “When we ﬁrst

set up with you guys, we found it to be very compet-

itive. We wanted to keep pace with what you were

teaching us. Knowledge transfer was huge! There

was so much we didn’t know and so we tried to just

absorb as much information as we could... It was

useful to have you guys to provide validation for our

visualisation work to show that we’ve actually done

something unique, and then we could provide valida-

tion for your visualisation work to show that it has a

real world application.” .

Whilst our collaboration has been successful, we

A Tale of Two Visions - Exploring the Dichotomy of Interest between Academia and Industry in Visualisation

323

ask about the challenges they have experienced during

the relationship - “I think there’s a bit learning curve

for both parties in this. I think it took longer than

we both expected to complete that knowledge trans-

fer. I felt that by the time it was complete, the In-

novate UK project was almost over. Which is why the

KESSII funding was so great for us so we could start a

new project without the learning curve we previously

had.” reiterating the value of third party funding.

When asked what advice they would give to com-

panies starting a collaborative relationship with a uni-

versity they claimed - “The values and objectives of

both entities are very different, they do conﬂict in a

way. And that’s something that needs to be discussed

and brought into alignment by compromise on both

sides.”

One of the interviewees summarised the collabo-

rative experience by stating “It offered us as a small

company a way of complimenting our current skillset

with resources we probably wouldn’t have been able

to afford at any other time.”

4.2 Academia

In this section we discuss our own views on academia

industry collaboration in visualisation, as well as per-

form interviews with staff members responsible for

developing collaborative relationships with industry.

From the perspective of academia, collaboration

efforts are usually for the purpose of furthering re-

search. In our discussion with a university staff mem-

ber whose role is to encourage the development of

these relationships, they emphasise that philanthropic

motivations from either organisation are rare. An

academic is unlikely to engage in a collaboration

project that demands complete focus on the business

product without any opportunity for publishable re-

search. Engaging in a knowledge transfer program

from academia to industry also may not beneﬁt the

university. Conversely, a business who invests in col-

laboration where they are unable to utilise any of the

research is also not likely to beneﬁt.

When asked why they thought that university col-

laboration still appealed to businesses, they claimed

that it came down to creative freedom to explore po-

tential solutions to problems. In business, time is

considered money. Which means that any time spent

not working on a direct solution is often considered

wasted. They are not allowed the freedom to explore

new ideas as it seen as a risk whereby money will

ultimately be lost if the research is unfruitful. This

is where academic research becomes appealing, but

also explains the reluctance to invest large amounts

of their own money into the project as it might not

produce the output they desire.

“Industry is rife with rules and regulations, they

are very limited in their creative processes. However

they can outsource their thinking time, buying a cre-

ative research outlet.” states the university collabora-

tion staff. “The reason industry moves to academia

is that sometimes they want the credibility of their

knowledge. It comes with the validation of peer re-

view.”

Whilst talking to some of the staff members at the

university who are collaborating with industry we no-

ticed a trend in some of the stories. A number of these

relationships attempt to force an unbalanced of work-

load on one party. We were told stories of companies

requesting more time to be invested in generic soft-

ware development projects that cannot further aca-

demic research. Where companies see the collabo-

ration as a cheap source of labour. These stories were

told as a warning to clearly deﬁne the boundaries of

the project requirements before entering into collabo-

ration.

Primarily, visualisation research offers valuable

and unique insights into data. The approach used of-

ten looks at the data from a different angle to that of

a standard data analyst due to these creative freedoms

allowed in research. These insights are a valuable de-

liverable for a collaborative project.

5 EVALUATION & CONCLUSION

After spending time talking to individuals who are ex-

ploring potential for new relationships to either fur-

ther their business or to further academic research, we

see that the dichotomy of interests is not insurmount-

able. In fact, a collaborative relationship can be ex-

tremely beneﬁcial to both parties through knowledge

transfers, data insights, and novel research opportuni-

ties.

Below we summarise the primary arguments for

collaboration and outline a number of recommenda-

tions to potential collaborators based on our experi-

ences as well the experiences of our interviewees.

Visualisation Collaboration Evaluation. Creating

a symbiotic relationship between a business and aca-

demic visualisation research organisation can produce

great value for both parties. The academic institution

is provided with an opportunity to explore new and

interesting datasets that would typically be unavail-

able to them. Using these datasets, new visualisa-

tion methods can be devised to explore and unlock

the value held within the data and publish the results.

The collaboration opportunity provides the research

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

324

organisation a funding platform to conduct their re-

search as well as a real world dataset to apply their

skills to. Ben Schneiderman explains this “Choosing

actionable problems triggers great research. Work-

ing on real world problems with real data can lead to

real solutions and enable theory validation in living

laboratories.” (Shneiderman, 2016).

The business is provided with an opportunity to

develop visual data analysis tools without the cre-

ative restraint that corporate operations typically im-

pose. More risks can be taken in the research and

so the potential for unique and valuable output is in-

creased. The business will have access to the spe-

cialised knowledge that the university holds which

can be utilised in their own development processes.

Additionally the publishable research the university

creates should contain valuable insights into the busi-

nesses datasets.

Because the process is still seen as a risk, third

party funding is usually a requirement, especially for

SMEs who may not be able to fund external research

entirely by themselves. From the informal interviews

conducted with small business owners and staff mem-

bers, we established that governmental ﬁnancial sup-

port is a necessity for the development of small busi-

nesses. The future of the UK is uncertain, but we sin-

cerely hope that this vital funding remains available to

small businesses and universities in the United King-

dom as it leaves the European Union.

Recommendations. Below we make a series of rec-

ommendations for entities planning to engage in aca-

demic - industry collaboration;

1. Discuss what contributions each party might bring

to the collaboration.

2. Discuss what personal requirements each party

has for the project. i.e. published papers or

saleable software.

3. Discuss what each party wishes to gain from their

collaboration partner. i.e. knowledge transfer, ac-

cess to data.

4. Firmly establish expected outputs for the collabo-

ration project as well as the deliverables from each

party, placing emphasis on the deliverable from

academia to industry.

Whilst simple, this process helps prevent either

organisation entering the collaboration with any mis-

conceptions as to the requirements of the project. A

common theme among the interviews was a propen-

sity to change requirements or modify deliverables of

the other party over the lifetime the project. This sys-

tem ensures that expectations are managed correctly.

In this paper we have attested the potential for col-

laboration between academia and industry. Although

the two organisations might not align perfectly all

the time, we believe that there is much potential for

valuable research and business development. We are

grateful for the opportunity to work alongside our in-

dustry partner QPC Ltd. and for their contributions

towards our continuing research.

These relationships are valuable because we be-

lieve it is important to implement the knowledge we

develop in the real world. Without it our visualisa-

tion research has less meaning, living only as words

on an unread document in some digital library. We

also beneﬁt from experiencing how industry operates

in the real world, learning about their differing re-

quirements and processes - which helps us align our

creative development to maximise utility in the real

world scenarios. The future of this collaborative re-

lationship is uncertain within the UK. As EU fund-

ing dries up, both industry and academia could suffer

from having this tie cut - especially the small, devel-

oping businesses who might one day become major

contributors to the UK economy.

REFERENCES

(2017). Digital Festival: for Innovation, Investment, and

Industry.

Ankrah, S. and Omar, A.-T. (2015). Universities–industry

collaboration: A systematic review. Scandinavian

Journal of Management, 31(3):387–408.

Chan, K. and Anderson, G. C. (1994). Academia–industry

fusion: Action learning for teaching enterprise. Indus-

trial and Commercial Training, 26(4):28–32.

De Fuentes, C. and Dutrenit, G. (2012). Best channels of

academia–industry interaction for long-term beneﬁt.

Research Policy, 41(9):1666–1682.

Deste, P. and Perkmann, M. (2011). Why do academics

engage with industry? the entrepreneurial university

and individual motivations. The Journal of Technol-

ogy Transfer, 36(3):316–339.

Friedman, M. and Schwartz, A. J. (1975). Money and

business cycles. In The state of monetary economics,

pages 32–78. NBER.

Fujisue, K. (1998). Promotion of academia-industry coop-

eration in japanestablishing the law of promoting tech-

nology transfer from university to industry in japan.

Technovation, 18(6):371–381.

Johns, M. M., Barnes, M., and Florencio, P. S. (2003).

Restoring balance to industry-academia relationships

in an era of institutional ﬁnancial conﬂicts of interest:

promoting research while maintaining trust. Jama,

289(6):741–746.

Robert S Laramee, Tony O’Hagen, A. D. (2017). Academic

- industry gaps. Uncertainty Quantiﬁcation and Man-

agement for Industry.

A Tale of Two Visions - Exploring the Dichotomy of Interest between Academia and Industry in Visualisation

325

Shneiderman, B. (2016). The new ABCs of research:

Achieving breakthrough collaborations. Oxford Uni-

versity Press.

Stossel, T. P. (2005). Regulating academic-industrial

research relationships-solving problems or stiﬂing

progress? The New England journal of medicine,

353(10):1060.

Van Wijk, J. J. (2005). The value of visualization. In Visu-

alization, 2005. VIS 05. IEEE, pages 79–86. IEEE.

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

326