Focus and Read Without Getting Distracted

Prologue [should have been an epilogue, as I wrote this after the post, but it fits better here]: While this was written with researchers in mind, it is equally applicable to others who feel distracted and find it difficult, challenging even, to sit through a cognitive task. The tricks mentioned are not fully my own; I have picked them from my various readings, but not one specific source. Mostly I enumerate what works for me. Internalize these or any other tricks that may work for you, and you can add to the comments for others to benefit from them.

You sit down to read a research article. The paper PDF is open. Your tea/coffee is hot. For thirty-seven seconds, there is peace. You hit the first citation and turn to the references section to check it.  Thereafter, an itch to check that author’s other citations takes you to another tab.  Then it starts: a buzz of social media notifications in your pocket. IMDB ratings for the current Hollywood/Bollywood blockbuster. A sudden, critical need to know the AQI in Delhi-NCR. Sound familiar? You are not alone !

After 15 minutes, you realize you are not reading. You are performing a ritual of attempted focus, haunted by your wavering attention. This appears like personal failure, but I think mostly it is systematic hijacking.

When we were kids, we were told to get disciplined and “Just focus.” Now it's not possible anymore in the digital age and is akin to surfing in high tides. Our brain has two powerful ingrained systems, to decide what to do:

1. Novelty Seeker: Drawn to novelty. A new tab, a new email, a new idea, a new reference. It promises a dopamine hit, a tiny reward for discovering something new.

2. The Sustainer: The quiet, deep-work discipline that wants to follow a single thread to its logical conclusion.

(Daniel Kahneman- Thinking fast and slow)

The modern digital world is a never-ending, constant stream of a never-stopping roller-coaster ride in your pocket, a browser with infinite tabs, which is a paradise built for the Seeker. But it is also responsible for starving out the Sustainer. Your research, your deep work, requires the Sustainer. Unfortunately, you are trying to meditate in a fireworks factory. As soon as you try to focus on something, the fear of missing out (FOMO) hits you hard, and like a drug addict, you open social media feed or a new chrome tab.

The Digital Grief (aka the FOMO) makes you lazy: This is not real laziness but digital grief. Your linearity (focus) drives you through complex tasks but is dead now. You had it before the unending feeds, alerts took you over.

You can’t read a paper because reading the paper is no longer just that task. It is resisting the urge to google the co-author’s biography mid-sentence. It’s fighting the impulse to dive into a ten-tab rabbit hole about a tangential methodology. The paper ceases to be just a document; it is a battlefield with a constant barrage of ever-pointy arrowheads, baying for your attention.

Your attention is fractured into a thousand tiny pieces, each reflecting a different, incomplete piece of the world. While it's easy to feel stupid, but you’re just overwhelmed.

How to Reset: Some Uncomfortable Truths

Fixing this cannot come from new apps. Let's just accept the three truths:

1. Willpower is a Non-Renewable Resource

Every novelty unattended (i.e., notifications you ignore, every itch to browse you resist) will drain the energy tank you have, no matter how big. Before you know it, you’re running on fumes. So, instead of fighting distractions as they happen, it is more effective if these are treated before they happen. In other words, declare war at the source. Before you sit down:

• Phone: Airplane mode or in another room.
• Browser: Use a brutal blocker for complete freedom. Block everything except the journal site for 90 minutes. This is not a suggestion. Use it as a law.
• Environment: Face a wall, not a window. The world is too interesting right now.

2. Re-learn Boredom

When our brains have forgotten how to be bored, they keep screaming for stimulus and novelty. Even in the two-second lull between sentences, it now screams for stimulus. Friends/dates are glued to their smartphones in between conversations and restaurant lunch/dinner. We must retrain it.

The 90-Minute Corpse: If you set a timer for 90 minutes, assume you are dead to the world. You may only read this PDF/paper or write in the document. The itch to do anything else will feel physical, a panic even. Sit with it. Let it scream. Know that it will pass. You are teaching your Seeker that this time is a barren, reward-less place. It will eventually stop shouting.

3. You Are Recovering/Curing an Addiction

This is the hardest truth pill to swallow. The constant switch between tasks, the quick hits of new information is a well known (but never acknowledged) addiction. The recovery path is daily, humble, and non-negotiable.

• The First Page is a Mountain: Don’t commit to reading the full paper. Commit to reading the abstract. Then the first paragraph. Trick your brain into starting. Build momentum since momentum is the only cure for the initial resistance.
• Print It Out. Without thinking twice, do this. The tactile nature of paper, the inability to click away creates the much needed cage for your focus.

The fight is no longer to be knowledgeable, but to be able to build a cell of quiet in a screaming world. Your value is no longer in how much you can process, but in how deeply you can ignore.

By doing this, you are not simply trying to focus but trying to become a human being again. Close this tab. Open a PDF. And stare at it until your mind, kicking and screaming, finally comes home.

Data parasites vs idea parasites

In their editorial, Longo and Drazen (yeah, the-now-infamous NEJM editorial, Data Sharing, 21st Jan, 2016) raise an interesting, though misdirected, concern about data sharing. While they articulated the merits of data sharing correctly when they raise the moral issue of honoring the collective sacrifice of the patients who put themselves at risk to generate the data, they have conveniently ignored the public funding part when they incorrectly raise the abominable research/data parasites issue. Due to this oversight, the piece seems to be focused on the assumed unrequited authorship rights of the clinicians who generated the data in every subsequent study, instead of honoring public funding which allowed it to happen or the so-called moral imperative to honor the patients. While their concern is founded in reality when they observe that, an independent researcher may not fully understand the nuances of a study design or parameters; but it takes a wrong turn when they advocate continued reaping of the rewards of a publicly funded project where the patients put themselves at risk. The assumed importance of data generation is overtly inflated and takes centre stage so as to claim full ownership.

While the data generated may be of extreme importance and collaborations arising out of newer hypothesis are well-intentioned and thoughtfully done in the proposed manner, it in no way represent the only mechanism to do good or meaningful research. In the field of mass spectrometry based proteomics, there have been several groups generating data for free public use to facilitate better algorithm developments without asking for anything in return. These groups were funded by public money, and they contributed back to science and society in general. A privately funded project, however, may be out of purview of my counterclaims to the authors' views and may as well hold true there. Yet again, the funding body then becomes the claimant of data ownership, if they so decide.

Authors also make an incorrect audacious suggestion about having totally new hypothesis arguing that one should not build upon the work of others. It bore out from the malicious (of course in their opinion only, not  mine) practice of data scientists to use their data to disprove their hypothesis or claims. Prime example of anti-science. Oh, did it arise out of frustration that many NEJM retractions could be because of enthusiastic data parasites who happened to recheck their claims? I don't know. Science has always progressed by building up on work of predecessors, lest we keep reinventing the wheel. Using the suggested modus operandi would cripple clinical research, and even discovery of biomarkers would become near impossible, let alone development of a drug. The suggested recourse to the assumed data parasitism problem is another lame pitch for authorship. Authors probably forgot/never heard this remark by Carl Sagan -  "If you wish to make an apple pie from scratch, you must first invent the universe."

In the lines that follow, I revoke their four point agenda in this new light - 

One, starting with a novel idea that isn't an obvious extension to previous work, is somewhat of a fallacy in itself. If it was obvious enough, the main group generating the data could/should have done it. Everything in science seems obvious once the idea is shared. On pretext of data, the generators are trying to be idea parasites. 

Second, identifying potential collaborators isn't always necessary but researchers do collaborate when they require. Adding the data generating person as a collaborator is not wrong unless one starts to force it. It's obvious enough, they aren't happy with citations. Only authorship counts in their opinion.
Third, working together to solve a problem is not a novel idea and always has been promoted by funding bodies and scientific administration, but is correct only if required. Just because someone generated data, does not automatically give him insights into how another researcher sees a scientific problem that can be addressed using that data. It is noted however, that he may be of great help and the question poser is the best person to recognize this. Some researchers may actually be using data incompetently by not involving those who generated it, but such researchers are down-valuing their own research. Is the data parasitism concept even truly valid in this scenario? 

Fourth, fighting for a co-authorship makes the irony come full circle because authors now want to become idea/authorship parasites on the so-called data parasites. Once an idea parasite generates a valuable dataset from public funding and patients' sacrifice, he/she can rake in on it continuously by piggyback riding the idea, hard work, funding and sacrifices of others.

Conflict of interest: I am a data parasite by their definition. I thrive on public data.

(PS: I will update the post with proper references and links. Recheck in few days if you are interested)

Why Biologists Should Program and How I survived it

In my opinion, Biologists are subjective, programmers are objective (I have been both). Why should a biologist learn to program? Given enough funding, they¹ can always hire a programmer, right? Yes, but they will never understand the wizardry a bioinformatician (a pet one in this case) has put in to achieve the result, and whether it (contribution, also sometimes read as authorship ) can be measured in terms of time devoted or the novelty of method devised or applied.

Programming is the Zenith of learning and adventure where knowledge, skill and fun meet. Yes, I am being geeky here. With my own little experience I have with Perl (which is not really considered a great programming language by many folks), I have learnt to think about science in a more systematic and less obfuscated way. Programming clears your logical thinking and helps you design better research studies² and test them in an objective way as far as possible (only if you are honest enough to admit these shortcomings) . When you combine the descriptive biological thinking with the objective and techniques of programming, you gain a new understanding of the scientific world. Most people think it is too difficult to learn to program. I would say it is a lot easier but also a lot harder than you think. Contradictory? Yes, deliberate. It is easier to start if you take baby steps and brave through the period where you do seemingly easy but not practically useful exercises. This period can give you a sense of being totally lost and a you will keep asking- (i) why? and (ii) Will I be able to make a real program? ...and usually the negative self answers- not my cup of tea/ I know other tools on web that can do it/ have a bioinformatician friend. The art of programming is slow at the beginning takes some time (and a lot of dedication and honesty) to perfect where you start making small usable scripts like parsing files, finding patterns (TF binding sites, restriction sites in sequence etc).

Once you get over this period by constant practice, it feels like fun. That's where your creativity starts to flow and you design programs in your head and can't wait to do the next cool thing. On the practical side, it is suggested that the biologists must learn to program as the next big boom in science is expected to come from dry lab methods due to the data avalanche.

I too had a tough time starting to program (in 2007) but after some 7 odd years (2014), I can write software worth thousands of lines of code³ (ProteoStats) taking months of development time. I took up programming as part of my PhD coursework where my PhD adviser taught Perl. By the time I could understand command line, the course was over (it was a concise one with 3 classes). I sulked and almost believed I cant program...ever ! Then I tried with a book, learnt a little in few weeks but my interest again waned as I was not habitual of sitting at a computer for long hours (which can't be said for current generation, I think). So, in effect, although I did learn some syntax, I couldn't put it to any use. Problems I tried were either too easy & non-useful or too hard for me and often I remained clueless about what it means to program. I must also admit that my woes were also compounded because I was lost and didn't know when or why to program. Just like any other biologist, I too thought that I can do it (some task X) using a web based tool. Programming just seemed like a waste of time when others could do it for me.

Then one day, I was given a real problem which couldn't be solved by any tool. It was pretty complex and seemingly not cut for me. Now, I started learning again, quite earnestly, but again took some time to come to grips and then it dawned upon me- programming isn't about knowing all the syntax, or being able to do anything at once (rather one attempt). It can be built up doing small inane exercises just like maths. Your problem solving skills will eventually define whether you can program instead of your familiarity with complete syntax. All programmers, no matter how adept, Google for answers/syntax/tricks. The idea is to know enough which can be put to use in small problems and then keep adding one little tool, nuance, trick, coolness code slowly one at a time. Small successes make you want to program more and then my friend, gradually you become a programmer !

Some resources:-
Look at scratch⁴, a visual programming language developed at MIT which is good for beginners and children.

References:
1. No gender bias here. So avoided the use of he/she.
2. both theoretical and experimental studies
3. http://sourceforge.net/projects/mssuite/files/ProteoStats/
4. http://scratch.mit.edu/

Stages of a PhD progression

This is a fun take on how PhD progression occurs, mainly in Indian context (more specifically my grad institute), but I presume similarities may occur worldwide. This is in context of giving your ideas or discussing/arguing/contesting what the mentor suggests. (Take it with a pinch of salt).

Generally, it is 5 year duration here- mentors won't allow 4 years easily nor would you be able to complete the work in that time. Sometimes, even 6 or 7 years can pass by without you noticing.

So here are the Stages of the PhD progression-

1st year - You are naive and don't know what is good or not (scientific project wise). You work on all ideas of mentor even if they are trash. You don't know when to speak up.

2nd year- You have started to feel you should speak up against some of the (silly) ideas but don't know when. And whenever you do, you get trashed.

3rd year- Having had enough of side tracks, you get bold and start speaking at (against?) all (almost) ideas the boss suggests. Not knowing when to say what, you always argue (not good for getting a PhD in the long run).

4th year- You develop tact and know when to say what but its too late, the damage has been done last year. Now that you want a PhD, you try to play ball using the new found wisdom to just get it done. Adviser also realizes this and never fails to mention you need to do XYZ before you can graduate.

5th year- Both of you understand each other like husband and wife. No pretenses work. Conversations are less animated and most talking is done by body language only. Both are in "you don't kill me I won't kill you" mode. Mentor is sharp enough to know you can't take that risk anyway. Tries to get done a few things you were always avoiding but now can't refuse.

6th and 7th year- You are still here? Either you haven't seen it coming or(bad planning/tough project) or your mentor is plain greedy. Why hire a post doc when you can get the work done through a miserable student who will do anything to get a PhD. You think I don't have an idea what to do next, let's get anything done to graduate. You do almost everything you are asked of, and arguments are as much as in 1st year again.

Networked Thoughts

It's been incredibly difficult to get started but finally here I am, writing my first blog post to get things rolling. I am a computational biologist who works at the interface of biology, chemistry, computer science, statistics and mathematics. This is an exhilarating experience but sometimes scary as well because I don't know what to do and how. Thankfully, I have experts all around me (physically and/or virtually) who help me get through. This help comes from fellow researchers, people I meet at conferences or otherwise, scientists on social media etc.

It's been very long since I wanted to write a blog. Somehow, couldn't get this started. I think it was writer's block, with a tinge of laziness. Hopefully, I'll get out of this slump and write more and better blog posts and subsequently papers, even if people don't read them. Of late, few excellent blogs I read inspired me to start mine. One of them is the blog by Claus Wilke, who writes excellent posts and I strongly recommend it if you are in academia and research. Most useful information, and even interesting literature I would have otherwise missed (I follow RSS feeds from hundreds of journals. Needless to say, I can never keep up) comes from twitter feed where excellent researchers share their knowledge with me and others for free. Yasset Perez-Riverol, a fellow proteomics researcher, also recently blogged about most active proteomics tweeple (people on twitter).

So, this is an example of how networked our life is and thus the thoughts we have are immediately affected by our networks. I see networks everywhere and influenced by them. Their importance has been well realized by many and rightfully network science is a field in its own right which affects and interests people from biology, medicine, computer science, mathematics, social sciences, economy... the list goes on.

On this blog, I will write about my experiences and share my thoughts on science, career, sports, chess, cricket, philosophy of success and failure, coding , mathematics, bioinformatics, health science, mentoring, being a mentee, self development, learning, happiness and enjoying life. Hoping to build an audience that engages here, shares its views and helps me grow into a better writer, a better scientist and a better human being.