One of the students that I supervised last year got a prestigious award last night at ScotSoft. You can read about it in the School of Computer Science blog!
A research career is a complex career. It involves many skills and knowledge that are not necessarily related to the specific topic that you choose to investigate.
In my experience, students just before of at the beginning of their PhD research (at the beginning of their research careers) are often quite disconnected from the actual skills and background that will make them successful. This is why I try to supply my students with some of the knowledge that, sooner or later, they will need to apply. To help with this, I have selected four books that all my students get at the beginning of their PhDs (to read in their free time). Here is the selection, and why I selected each book. Note: I’d love you to share other books you think are valuable at this stage (use the comments below).
Science/Engineering students often think that writing is the boring part of the job. Most of them realise that they have to do it, and some might even know that they have to do it well to be successful. However, telling a student that they have to get better at writing is not the best approach. In the best case, they already know that they have to do it, and in the worse, they might start hating it.
Instead, I like to consider writing (of academic papers and reports) as thinking on paper. It is often not until I have written the last bit of a paper (e.g., the discussion section) that I fully understand the research that I have done, the implications, and the value of it. Of course, the research is mostly already in your mind (and in your code, data, etc), but putting it on paper takes you to the next step: you can communicate it to the world and, perhaps most importantly, to yourself.
And this is what this book is about: setting up questions, understanding the problem, structuring a solution, all mediated through writing. A particular favorite of mine is the bit about making arguments; being able to make a claim and support it with evidence in a convincing way is one of those things that students think they know, but only learn after supervision, much experience and, perhaps, reading this book.
Once you know why you are writing, you need to know how. Although some authors think that this book might not be as good as everybody else thinks it is, it takes many students out of some of the worst habits in writing, namely:
- Writing to look smart.
- Writing without thinking of the reader (e.g., long sentences).
- Writing to fill in the space (lack of brevity).
Although the grammar advice might be somewhat antiquated and not always completely correct, the rest of the book, in particular the parts about style, helped me significantly improve my writing (although I certainly don’t claim mastery!). I think this book is particularly useful for students who are not native speakers of English and who come from traditions where clarity and brevity is not as central as in the English speaking scientific community (I’m from Spain, and I’m in shock most of the time I have to read or review a thesis in Spanish).
The most important point of the book might be summarised in a quote attributed to Blaise Pascal: If I had more time, I would have written a shorter letter. Well, a student should have the time, so the text should be shorter while keeping the crucial information. It takes time and effort, but readers (and markers) will be happier, the world will waste less paper, and the paper/dissertation will be more likely to be read and used by others.
Very often students lack context. They might know that they want to do research, they might even know that they like research. But what else is involved? Why would a PhD be useful? What does it get you? Most importantly, what does it NOT get you?
This book might be a bit harsh to start on (sometimes reality is a bit hard), but it provides a nice glimpse on the world of research and highlights much of what really becomes the focus of what you do as a researcher and academic. The bad news is that there is a lot more of politics, strategy, and marketing in this job than what we all expect when we start. The good news is that you can be prepared for it, and might even get to enjoy some of those bits. In any case, and from my personal opinion, being in research is awesome, but it is better to be ready for what it requires from you.
Note: there are other similar/related books about research and academia that are worth mentioning and reading (e.g., this, this, and this), but perhaps not strictly necessary at the beginning of a PhD).
So, what is really required from a PhD? Effective work and perseverance. Most people in academia know that you don’t have to be a genius to get a PhD. Gosh, you don’t even have to get the best or most novel ideas. But your ability to work hard, avoid procrastination, and persevere will determine the chances of being successful in your PhD and of being able to take your career further.
Although there is a lot of crap in the self-help and productivity literature, this does not mean that it is better to ignore it all. This book describes my favorite system, and although it is not perfect and I still work really really long hours, it has helped me enormously. This might not be the best system for productivity that there is, or be the best system for everyone, but at least is honest, well explained, and feasible. I’m a fan.
The reality of a PhD is that, if students think they are busy during their undergrads or MSc, the demands on time will only keep increasing. This is certainly true after you have become a doctor. If you don’t like GTD, you better find something else!
Have you come across other books that you think are useful? I’d love to compile a list with your suggestions, and I might even add a book or two to my list!
The School of Computer Science at the University of St Andrews is offering a number of 7th century scholarships. If you are interested in working with me in any of my topics of interest (mostly within HCI and Information Visualization), send me a line. Here is more information about the offered projects and how to apply:
The current deadline is March 31st, 2014.
You have worked hard on your project. You searched the literature, learned about the methods, painstakingly designed an experiment, and have almost finished implementing the software, but… what about the logging?
Most students think that logging is easy. Just write some lines on a text file. A couple of hours on the software should do, right? I don’t think so.
Experience has shown me that logging is extremely error-prone, and that paying little attention to it results in incredible loss of valuable information and time, and that most students do not realize how important doing good logging is.
Before I go on let me qualify a little bit what kind of logs I’m talking about. I’m referring mostly to the kind of experiment that you often see in HCI or Experimental Psychology, were there are many participants, and each participant performs many trials, possibly in multiple conditions. This is usually information that is suitably recorded in a simple format like comma separated values (CSV).
Let me state then my five fundamental laws of logging:
1. Log everything you can
Disk space is cheap, your time is expensive. Probably the most common mistake here is not to record enough information in each record because you think it is redundant and a waste of space. For example, why record today’s date in each record of each trial, if they are all the same? I tend to record the same information in each trial anyway, because it is always easy to discard info, but it takes a long time to recover data from different sources (including the name of the file, the creation date that the OS stores etc.). Don’t assume that you will remember where you are storing all that information when the time for analysis comes. Things that I tend to save in each trial record: userId, all condition values for all factors, the number of the trial with respect to the condition, the number of the trial with respect to the cell, the absolute number of the trial within the experiment (and the phase) and, of course, all the dependent variables.
Perhaps the only caveat to this is that all this recording should not negatively affect the performance of your program or the accuracy of the time measurements. If performance and timing accuracy is important, good strategy is to write first to memory, and only save to disk in between trials (or when timing is not an issue).
2. Make your logs self-contained
Name your variables wisely, and always include names on top of the file. This should be quite explanatory, and most analysis programs (e.g., SPSS) will allow you to name the variables automatically from the file. Handy and convenient. A good complementary practice is to have some comments (or a separate file) that provides an explanation of how each variable is recorded, but this requires discipline to maintain, because the logger program tends to evolve. Best to keep your measures simple.
3. Debug, debug, debug
Never assume that your code is recording properly. Simple visual inspection won’t cut it. I have experienced many problems that only became visible after all the experiments were recorded. The best way to avoid problems here is not only to debug, but also to use your pilots to gather realistic data, and analyze it in the same way that you will analyze the overall results from the finished study. This is not only good for your logs, it is also helpful to avoid possible flaws in your statistics (e.g., I do not believe in a posteriori power analysis).
4. Backup, backup, backup
Don’t trust your hard-drive, don’t trust your experimental software. Within your program, save the data to drive as soon as you can (but take into account the comments in point 1). This will allow you to recover from failures in your software. It is actually kind of nice to code your experimental software so that you can restart it again at any given trial within the session. When the experiment is finished, the first experimenter action should be to verify that the data is in the right place, and perhaps making a copy (or send yourself the data to your gmail account – if your data is properly anonymized, of course).
5. Protect yourself against confusion
If something can go wrong during the experiment, it probably will. It is good practice to save the date hour and second of experimental recording in the name of the file that your program saves. This will help you prevent accidental overwrites. Similarly, try to leave as little as possible human intervention for the actual session. For example, I never trust the experimenter -often myself- to select the right name for multiple files depending on the condition. Have the software do something reasonable for you. The only thing that I often make configurable is the participant identifier, so that I can separate real trials from debugging logs.
Hopefully these might be useful to you some time. Write a message below if you agree/disagree or want to add some more advice!
Last week, Uta and I had the chance to take a tour of three impressive labs in Germany and Switzerland. The German and Swiss hospitality cannot be overstated, but most impressive was the range of research.
In Munich we visited the Media Informatics and Human-Computer Interaction Group, invited by Andreas Butz. It was really nice to see finally the curve (in the picture), among lots of other excellent research, including work by Alice Thudt, a current collaborator of Uta.
In Konstanz, we visited the Human-Computer Interaction group led by Prof. Harald Reiterer. The range of research and development is very broad. A particular favorite of mine is the work on zoomable multi-display environments (the ZOIL API), and a number of other interesting experiments related to large displays.
Finally, we had the chance to visit Dr. Elaine Huang and her ZPAC laboratory; we have strong links with this lab (including Helen, another iLab graduate), but there were many other strong research reasons to visit ZPAC; most related to me is the work by Gunnar Harboe, but it was great to learn too about projects on sustainability, cultural communication, and domestic ubicomp.
Naturally, I cannot make justice to everything that all these researchers do in a few lines… maybe you should just visit them too :). We really would like to thank all our hosts for wonderful and insightful visits (special thanks to Fabian, Hans-Christian, Christian, Alice and Helen for bearing with us for so long). We are looking forward to your visits!
Yesterday was a busy day. We have been working with the University of St Andrews Library and the Museum of the University of St Andrews (MUSA) to put together some seminars (first, second) on the use of interactive technology in libraries and museums, and to get some exciting collaborations started. We were lucky to have very interesting speakers and, as a treat, we got to take a tour of the Special Collections. Daryl Green and Maia Sheridan showed us some amazing artifacts that we are hoping we can help make available to more people through interactive technologies in new and exciting ways. The photos show some of the amazing books (although they have all sorts of interesting artefacts). I’ll leave it to you to go to the library and found out which ones these are, but some of the stuff we saw has existed for more than 900 years!!
This is one of the perks of working in an institution with 600 years of history. If you like this stuff, you should not miss the blog of the Special Collections.
Great moment of pride and satisfaction for a job well done… Now it is time for Umer to leave the nest and show the world his best researcher skills. Good luck in Lincoln!