The hurdles and hardships of science in China

In my last post on China I discussed why China is becoming a scientific juggernaut. I focussed on all the things that seem to be working in its favour (funding, high expectations on scientists, etc.). While I do think that science in China is good and getting better, it is also important to point out some of the hurdles and limitations that hold back some aspects of scientific advance here.

In my previous post I noted that the expectations placed on students and researchers (i.e., to produce a minimum number of papers in journals with high impact factors, IFs) provided motivation to do good science. This is undoubtedly true, however, these strict expectations also reinforce a strategy of ‘paper-chasing’ where students are encourage to figure out how to get a paper. This is because the reward structure is so quantitative. While this type of evaluation systems has pros and cons, it does create a different sense of urgency than I’ve experienced elsewhere.

Pragmatic factors

The Great Fire Wall of China from "Cracks appear in the Great Fire Wall of China" posted by the China Daily Mail, Sep. 25th 2013.

I have never yelled at my computer or cursed the internet as much as I have in China. In the west we often hear about the ‘Great Firewall of China’ and probably do not think much about what this actually means. It sucks. The internet barely functions for significant proportions of the working day. I thought that this might have to do with the number of people and lack of infrastructure, but I no longer believe this to be true. Other countries in the region have great internet, and China has very advanced infrastructure. I’m pretty sure that when there is high traffic, the national security protocols and activity monitoring servers are the bottleneck.

Because the government policy is to block certain websites, most of the scientific internet websites and data sharing portals are not accessible here, but this may change at any given time. For example: Google Drive, Dropbox, Facebook, Blog sites, Twitter, Google Maps, and Google Scholar are all services routinely used by scientists and which are blocked in China. The reason for these to be blocked, as far as I understand it, is that they do not share users’ activities and the government cannot monitor what individuals share and download (which reinforces the value of these services to me). I also suspect that they are blocked to give local companies a chance to succeed without competition from global corporations, or perhaps simply because of disagreements with the companies.

I have had immense trouble trying to share files with my lab back in Canada (and to post this blog entry –which is why I’m doing it from Cambodia!). I am not currently engaging in social media –something that I saw as a legitimate activity for communicating science. I am having a very hard time searching for articles without Google Scholar. I also have trouble with other websites that should not be blocked, but that use third party encryption. For example, I can’t log in to my University library in Toronto, and I couldn’t connect my Canadian grant application to the Canadian Common CV (which we are required to do in Canada) because the CCV web interface was blocked (I had to get my post doc in Canada to do it for me). I have tried to go to researchers’ websites to find that they are blocked because they use a blogging site (e.g., Wordpress). The amount of time I spend doing basic online professional activities has increased 3 to 4-fold.

This is important because Chinese scientists are at a disadvantage when it comes to international collaboration and participating in online initiatives. I would encourage scientists outside of China to consider these imposed limitations to ensure that information and collaboration is barrier-free. Here are some tips:

1. Don’t link to your Google scholar publications on the publications page of your website
2. Don’t use a blog site to host your website (e.g., Wordpress)
3. Don’t use Dropbox or Google drive to collaborate on papers
4. Don’t use gmail as your work e-mail, Air China, for example, won’t send e-mails to gmail.
5. Social media has emerged as a great way to communicate with broader communities, it is important to recognize that these dialogues exclude Chinese scientists.
6. Ironically, as I write in this blog, blogs are blocked and while blogs provide a great platform to discuss ideas and issues, they are not available to Chinese scientists. 

These last two are interesting as journals increasingly require or request tweets or blog posts to help maximize exposure, but these forms of communication are not on scientists’ radar here.

Chinese science has been increasing by leaps and bounds despite these limitations. This is a testament to the hard work and dedication by Chinese scientists. I have no doubts that basic scientific research in China will continue to increase its stature and impact.

Postscript

One thing that is interesting to me is that many of the graduate students here use VPNs (Virtual Private Networks) to mask their IP addresses. They are able to access blogs, Google Scholar, etc. In conversations with people, VPN use is extremely widespread and successful at circumventing government filters, most of the time (there seems to be an arms race between the government and VPNs). It really makes me wonder how much longer these governmental controls can be realistically maintained.

Fear of missing out

There are so many tools, techniques, communication platforms and methods out there for ecologists that it can be hard to keep track of them all. Whether it be LaTeX, Linux, or Twitter, someone has probably suggested that your research life will improve if you use their favourite tool.

Online science has allowed fantastic networking and information exchange. However, one side effect of the increased usage of social media can be the feeling that you can’t keep up. The phrase ‘Fear of Missing Out’ in social psychology was coined to describe the anxiety arising from looking at social media and fearing that you have chosen how to spend our time poorly. But this can extend to academic working life too – that nagging fear that your colleagues are doing cooler things (going to more conferences, using more cutting edge techniques, publishing more papers) than you. It can be disconcerting to see that there’s a cool R package you know nothing about, or to receive yet another invite to a work-based social media platform you’ve never heard of.

There are people who are the early adopters and the technological trendsetters; people who derive their success from their methodological/technological skill sets. These people are fantastic, since they provide the examples for others in the field to follow. But for most researchers, it is practical to recognize that knowing every new technique and tool doesn’t promise success.

Graduate students may suffer this fear of falling behind most intensely, given how closely it relates to imposter syndrome and the general skills gap that grad students have to overcome. But I’d argue for most students, that balance is key. Otherwise it can be a slippery slope: maybe you learn R, then find that people recommend knitR and R Markdown, then you learn Shiny to show your results beautifully, then someone suggests things would be faster in a lower level language – and maybe months have gone by. That’s not necessarily bad, intellectual freedom and self-teaching are some of the best parts of graduate school, and if you are going to learn a bunch of new techniques, that is the perfect time. But aficionados will make arguments for everything from Python, Linux, Emacs (apparently the one true operating system), Github, to LaTeX/BibTeX, etc, etc. And every one of these is a useful tool, but maybe not always a necessary tool.

Researchers can never learn everything, and some things will fall out of fashion as quickly as they arose. Sure, it is less than ideal to be a researcher who hasn’t learned a new approach since the advent of SAS. But we need to balance necessity (is it fundamental to my research?), the time investment, and the utility of these skills for future endeavours (e.g. if you are applying for a data analyst job after grad school, learning a few languages might be smart - but if you will rarely use it again, perhaps it is not so important). The danger for many students is that either the time investment in learning a less-than-fundamental tool is very high, or else they learn a number of tools but never master any of them. This isn't to say you can shirk on quantitative skills - on the contrary, having those skills is far more important than mastering any specific program or language. I can think of fantastic ecologists who don’t know what Twitter is, who still use SAS, who have never learned R, and who produce brilliant work. The questions, the ideas, and the knowledge matter too, after all.

(*I should note that I'm a quantitative/theoretical ecologist, and I think every ecologist should have a strong quantitative education! Just that you can do that using lots of different methods and doing it well should always be the first priority.)