That actually wasn’t the case -- I’ve been with Elsevier for a long time -- 10 years ago, 15 years ago. We really ramped up our quality. That’s something I think we can be proud of.

...other large companies. Springer Nature is one of them and Wiley is one of them, Taylor Francis probably. You see that we do pretty well in the top 10 percent on that field-weighted citation impact. We do very well on the 10 to 25 percent. We do also pretty good ...

It’s far more difficult to get citations in mathematics, so you have to compensate for that. That’s why the field-weighted citation impact. It’s a proxy for impact, for quality. Here you see the orange is the Elsevier journals, and then...

Every discipline has a different way of working. A typical article in the life science will have 50 references, and therefore many more citations. In mathematics, you can write an article from first principles, nothing wrong with that. You can have one reference.

Exactly. We are still a publisher, what we call a quality information provider. We still very much focus on getting the right content in. This diagram, this is where we see the different segments based on what is called the field-weighted citation impact.

Yes.

Very good.

You see on the next slide, very digital. 81 percent is digital today. Also strong on technology -- lots of machine learning, search, data visualization. One of the big databases we have is called Opus, which is all the digital metric data. Are you familiar with this?

The approach from Elsevier is that we have the big data solutions, we have the rich content, and we have all these networks. If you combine that, it’s quite powerful.

Because you’re younger.

If we talk about Elsevier, more the science publisher, today’s researcher has many, many difficult tasks to survive. Get grants, get published, do the teaching, attract the right people to work with, start collaborations, keep up-to-date with today’s research. I’m sure there are other things here, but the form size ...

What do we do with the technology? These are the typical big data solutions. We have all these datasets. How can you make sense out of that? How can you focus on the quality? How can you do entity resolution, the link analysis, the clustering analysis, etc.?

Yeah, exactly. We’re actually building up on in Amsterdam, where I’m based, which is kind of funny because it’s our headquarters. From a technology perspective, we’re a little bit behind. They always had the big hubs in the US and in the UK.

When I was there, I did...

No, it’s more than hundreds and thousands.

Yeah, something like it.

That’s a good question. We can double-check.

We do lots of analytics. We have got an example we’ll get into a little bit later specific for Taiwan. Today, we hired lots of technologist, so to say, invest a lot in technology as well. We have technology hubs. One is actually here in Singapore.

Today we have 15 million articles on our platforms, every single one peer reviewed. When you’re a computer scientist, you can trust them, just as I can trust, I guess the ACM or IEEE. We also have database for drugs, for chemicals, etc.

What kind of data sets do we have? We get 1.5 million article submissions every year. Of those, a third survive. Two thirds are rejected. The ones that survive, they’re usually rewritten two or three times. It’s quality selection and quality enhancement.

Exactly. We’ll just look at for Elsevier. We focus on scientists. We focus on the health professionals. We have amazing networks, 17,000 journal editors, almost a million reviewers. We have 10 million monthly unique visitors that come to our platform. It’s a great example of economy of scale there.

Exactly. That’s what you would expect, actually. The core drive for our industry is first a deep understanding of our customers, very high-quality content and data sets, good analytics, and also very powerful underlying technology. To go a little bit deeper, this is for the four businesses.

This is a bit how our digital transformation took place -- very happy about that because it’s been pretty fast for 2000. Digital was still a small percentage. Now it’s the vast majority. The orange is face-to-face. People still come to exhibitions in person.

A quick overview. We’re a large company or medium sized. €8 billion in revenues, 33 billion in capitalization. We’re 74 percent digital right now in revenue. More active in North America and then Asia and Europe. Lots of income through subscription model.

Very good. We didn’t check your publishing record. Then there’s legal risk business or information for insurance companies. We’re one of the largest exhibitors. Without people knowing when they go to big exhibitions, often they’re done by our colleagues from Reed Exhibitions, like Comic-Con, for instance, if you have ever ...

The RELX Group, we help scientists do better science, very important lawyers win their cases. We help doctors treat their patients better and work for the insurance companies as well. There are four big areas. The one we work for is Elsevier. You might have heard of this. It’s a ...

OK. [laughs] I quickly talk and then see what resonates with you and go a little bit deeper. As a quick agenda, maybe the introduction of the group we work for, how digital we are, what we do here in Taiwan, maybe an interesting example from my own country -- ...

She’s into it.

Yes. I’ll ask Weiwei.

Yeah. You could share it.

We didn’t know what you know about the organization we work in, so we have a quick introduction through the slides and then maybe something where we could explore about...

You don’t get hungry here.