inGRADients have moved to wordpress.ingradients.com

We had a good time here, things were nice and simple. You built our confidence dear blogger, and made us realize that in fact we are worth listening to. Alas our time together must grow short. It is time for both of us to move on. Do not worry there will be someone else who will need you support. There will be someone else who will enjoy your friendly templates, not to mention the strength of the Google beast that slumbers behind your gentle eyes. Yes, there is someone else, someone who is a snappier dresser and a bit more exciting. We may be foolish to leave you, but friends we shall remain dear blogger. Maybe we can get together later, for a late night rant, or a short poll. Let just keep our options open.

keep not being evil dear blogger

XoXo inGRADients

-------------------------------------------
###########################################
-------------------------------------------

inGRADients have moved to wordpress.ingradients.com

-------------------------------------------
###########################################
-------------------------------------------

The Great List

The previous post was all about looking for big names in your field. Now lets talk about the next step, creating what I like to call " the state of the field" document, because you need to identify knowledge gaps and most effective ways to fill them.

Open up your favorite text editing software and write down all of the statements you can make about your field of study.

Right: X is Y when Z, or X is observed in nature often, or X and Y are observed together.

Wrong: wording your statements as a hypothesis. Your purpose here is to state what is known, not propose testable questions ( don't despair, that comes later).

Your list should be part observation and part established facts. After you do this it is time to pull together papers that substantiate each of the statements you just wrote down. Each statement should have a bunch of citations under it. Each citation should have two to three sentences that describe the major result. Here I also like to add a touch of visual organization. Simple tabs will do, just like the example bellow.

X is observed in nature.
       J.D. Haldane and P.F.Chang 1943
             - First to describe mechanism for X using infectious bacteria. Propose
constrains on X.
        S.T.Strauss et al. 2000
             - Models X as a function of Y. Equations that describe X through time are
               proposed. Documents X in ocean and terrestrial ecosystems.


Above are just a few examples, but I am trying to highlight some important points here. As you see, I chose a paper that first describes a phenomenon, the earlier the better, and a paper that shows that it is widespread in nature ( means it's important). Key is to populate this document with examples of your favorite scientific " thing" and it's description. During this process you may find that papers that you originally chose to support statement A, do a better job supporting statement B. This is great. This is the purpose of the document, to organize your thinking about the field.

Along the way you will also find that some of the statement will need rewriting. A very important thing here is honesty. You must, must, must be honest with yourself. If there is statement that you really like but can not find papers that support it, do not fall into the trap of saying "well this paper kind of supports it". "Kind of" is your worst enemy. Also do not get upset that you were wrong. If you can not find support for something, then it may be a good hypothesis to investigate. This is the second useful part of the document, it makes you see the holes.

Initially this will be the only document you need. However as your knowledge of the literature expands, and you dive further into the murky depth of scientific inquiry, this document will naturally split. Some of the original statements ( now likely refined) will form one of your projects, the rest will be better for another. Eventually you will end up with one of these documents for each project you do, and every time they will help you focus your ideas. Plus these documents make a great repository of citations for your papers.

Citation stalking -- also some stats

Fact: there is a lot of information in the world. That’s great. It is also great that my lightning fast broadband connection allows me access to it at any time of the day or night. Searching this glorious heap of facts is a different matter entirely. Google helps a great deal, even when it comes to scholarly work. However most of the time it’s just a starting point. There is something mildly uncomfortable about looking up papers that will eventually form the backbone of a thesis via Google. A solid, hard-core relational database just feels so much better.

Efficient ways of dealing with databases is something all graduate students learn eventually. There are many great databases, some of the biggest are NCBI, PubMed, EBSCO host, ISI, and many others. I am not really interested in discussing database use right now. Instead I want to discuss one very (incredibly, amazingly, fantastically, awesomely) useful feature of the ISI Web of KNowledge. Forward citation will make you happy.

Before rock-star scientists revolutionize a field, they need to know about it. Biology (or natural philosophy) has had a long and largely successful run, so “the field” is huge; navigating it can be a daunting task. Even recently invented biological pursuits like genomics, proteomics, and all of the other -omics have amassed large volumes of info. How do you find the key players, both contemporary and otherwise? To deal with this issue I like to engage in what is known (by me and my cats so far, but may become a common place name) as citation stalking.

Step one - get to a database that includes citation score with each entry. I like to use ISI Web of Knowledge because it also lets me sort by the number of citations for each publication.

Step two - pull up some papers. The more the better. This step can be a bit time consuming, ten to fifteen minutes even. You could probably watch three YouTube videos in the same amount of time, but I urge you to hold steady. Sort results by number of citations and look for a highly cited review. You want to start with a review because it will likely cite relevant articles. Remember you are not here to actually read the info, but to figure out what people to pay attention to. Every review usually covers the decade, or at least five years of info prior to its publication.

Step three - pull up citations for that review. This makes you work backwards, which is fine. You also want to work forwards as well. The greatest thing about ISI Web of Knowledge is that in addition to showing everything that a paper cites, it shows all of the papers that cite it.

Step four - rinse and repeat. Another good idea, that is after you make a list of important people in your field, is to look up their earlier work. Find out what is their claim to fame. After only a few hours of work you will be impressing your PI with mad knowledge of literature in your field, its major players and their contributions.

Since I mentioned ISI Web of Knowledge a few times, I want to leave you with the following. I typed in a few words that scientists may care about and saw what happened. I also pulled up authors of the most cited papers for each of the search-topics. Note: 10,000 is the total number of hits that the ISI Web of Knowledge will display at one time.

Drosophila 10,000 hits
BRAND AH, PERRIMON N - 4,088 citations

HIV 10,000 hits
Heil F, Hemmi H, Hochrein H, et al.- 1,169 citations

Evolution 10,000 hits
Bartel DP - 564 citations

Cancer 10,000 hits
Thiery JP, Acloque H, Huang RYJ, et al. - 131 citations

Ecology 8,930 hits
EMLEN ST, ORING LW - 2,782 citations

Population genetics 2,366 hits
Kumar S, Tamura K, Nei M - 7,437 citations

E.coli 1,484 hits
Datsenko KA, Wanner BL - 3,079 citations

C.elegans 1,125 hits
Bartel DP - 3,744 citations

Darwin 771 hits
BECKER PJ, COPPENS P - 1,243 citations

bears; number one threat to time management in America

I have a sneaky suspicion that the week is not going to be productive. Right now it is just a shadow of a thought, sneaking around the fringes of possibility. On the other hand it's Monday evening and already I am defeated by my to do list. Meetings are the problem. In addition to two lectures I have to teach for two hours, I must attend two lab meetings, a TA meeting and an exam coaching session. All of this equals zero time to do actual work. Naturally the solution is clear:

1. Deal with as much stuff as possible via e-mail.

2. Read e-mail twice a day. The delay will coach everyone around you to ask themselves "is it worth the wait, can I do this by myself".

3. Have a clear outline of what you want to accomplish during each meeting.

4. Most importantly, have a time limit. Stick to your time limit.

Also food for thought: meeting is about to take a turn for the worse if –

1. Someone starts with "this one time ...."

2. "The central limit theorem is like shoelace" - or similar clumsy metaphor.

3. "I did not include this on the agenda but", or " you know this already but".

4. "I am going to read every word on a slide .... "

Make no mistake, in order to accomplish real work you have to make time for it. In reality, making-time is one of the most important skills you will learn during you grad-school years. I cannot say that I mastered the art, or even came close, as evident by the intro to this post. If you value your time, show it, it is the best way to make others value it too. Time is your most precious resource. Also bears.

Three week-ish mark

Approaching the mark, three weeks since the beginning of this year. Three weeks is the time it takes to figure out which ones of the New Years resolutions stick. I try to remove the pressure by not calling them resolutions, but lets face it thats what they are. It is a common experience that most of them do not work out. I want to share some with you.

Scientists love data, seriously like LOVE IT, so I decided to keep a detailed log of daily activities with a goal of plotting my daily time use. I called it “Two Week Schedule Challenge”. This challenge failed, mostly because it is incredibly tedious. Good news, I learned a few things. I spend most of my time pipetting. Not generally doing bench work, but specifically pipetting. Second comes reading papers that fall in my field. Then comes computational work and writing. I spend surprisingly little time reading recent literature. I feel that its an important piece of information. Hopefully as I become more aquatinted with the history of my field I will have more time to read recent research.

Getting to work early was a win. During the past few weeks I increased my average work day by two hours or so. I started to have breakfast at work. Now instead of not having breakfast every day, I have a delicious cup of oats. Also some fruit. Sometimes there are berries. Raspberries are surprisingly furry.

Cutting down meeting time did not work, but combining lunch and a weekly co-worker-supper-awesome-science-update-time stuck. Outsourcing writing of perl scripts failed miserably, this led to discovering ruby. Creating more time for writing worked, increasing writing output during that time did not go so well.

I think the word success does not adequately describes my ... my win. Next week my schedule is going from school-lane to monster-truck (sometimes I try to coin expression, feel free to let me know how that goes). Deadlines and time-crunch tend to crystalize priorities; will see what goes away. Also I hope to play with a new robot, maybe this one will have lasers.

Remember “less” do “more”.

There are many tasks out there and every day more and more bid for your attention. You will fail if you try to do them all, and you will fail if you don’t do them. Bellow are a few tricks I learned that allow maximum productivity and minimum pain. Caution: painless is impossible (you are in grad-school to work smart and hard).
Before you read on, there is one thing that you must accept, otherwise all of this is useless. Your goal is not to do less stuff. Your goal is to spend time doing what you want.
Good news is: laziness can be a great tool.
Bad news is: prioritizing is difficult due to shear volume and variety of tasks.

Your day will be a balancing act between task-completion and break-taking. The ideas is to maximize productivity during both of those activities. Simply put you should always be doing something. Web surfing can be a break from pipetting, getting a water drink can be a break from reading. Need some coffee? Turn it into a walking meeting, or just grab a few colleagues and talk bout science. This is my personal favorite because science moves so fast that it is impossible to keep up with all the published material. Let a few of your friends synthesize it for you over a coffee break. Just say: “I have been swamped with running gels all week, what’s been going on in (insert friend’s field of study)?” Just do it to a few buddies and you will be all caught up. Also this strategy insures that you maintain contact with a diverse group of scientists.

Though your goal is to extract benefit from every break, its not real work. Be careful not to turn your workday into a continuum of breaks disguised as tasks. A surest way to be productive and to feel good about yourself is a good ol’ to do list. I am not a huge lister, but I do recognize utility of being organized. Here is how to use a “to do list”, and prevent it from becoming a source of stress (making a list should not be one of your tasks).

step one: list everything that needs to be done in a given week. More is better, just let it all flow.

step two: Pick three things that are most important (this often means most scary). Make sure that one of the three tasks is time sensitive or organism dependent (like growing up culture, or turning over flies, or extracting DNA). This is our productivity insurance.

step three: On monday ask yourself: “If I can only accomplish one task out of the three, what would it be?” Plant to spend half a day doing that task, and half a day doing the time sensitive task. The point of the time sensitive task is to make sure that even if you are not done with the first task, you will move on. Most often though, you will be able to complete both tasks and still have room for more.

I like to repeat the above steps every evening, so I do not waste time the next day on planning. I have two more quick things. The morning ritual is necessary, no one gets out of bed blasting on all cylinders (at least not consistently). I find it best to take my breakfast and read morning mail/web at work. This way when I am finally ready to work, I do not have to deal with the commute and can jump right into it. The second thing is efficiency. There is immediate efficiency and overall efficiency. Sometimes you may have to complete a task that is time-consuming initially, but will greatly increase your efficiency in the future. Do it. I can explain it best with an example: taking an hour to read an article discussing effective ways of asking for help may seem like a waste of time. After all you are not spending that time working. In the future however, you will be able to ask for help quickly and efficiently and get rid of the most annoying thing ever; the email-back-and-forth.

The Internet is here and it wants to hug you

The Internet, the way it should be is here. Well, it is not actually here but there are signs, tremors and whispers. The future has been glimpsed and we want it.

I was going to spend today’s post talking about effective ways to partition graduate work-day, but late in the evening I came across a particularly interesting tweet by Mashable’s Pete Cashmore. He talked about five web technologies to watch in 2011 – and one of them, most exciting one, is real-time-click-stream sharing. In a nutshell: your social network sees what you are surfing while you are surfing them Internets. You can also define who can see what, when and how. The full article is available on Mashable/Tech (http://mashable.com/2011/01/05/web-technologies-2011/).

It is obvious how these types of services is a natural extension of the social-network boom that we have experienced since the likes of myspace and facebook joined the Internet playground. What I want to talk about here is an extension of the online conversation that such services afford, and the revolutionary way in which this conversation-extension can impact scientific communication.

Lets do this one step at a time. What exactly do I mean by conversation extension. Conversation can be fluid, these start, proceed and conclude in one session and conversation can be interuprtable. For example our emails are interuptable (message, time, reply). Originally emails contained loads of information precisely because of the response wait-period. Turns out people wanted to use email for short communication and conversations, these desires birthed instant messaging and texting (SMS). Instant messaging is more like a conversation. Success of instant messaging made us (the consumer) want to talk/share with more and more people at once. Naturally this paved the way for social networking, which is both continuous (since we never really stop existing as part of a network) and interuptable (post-by-post timeline approach).

This next part is key. Lets ask ourselves what do we talk about on social networking sites? We talk about things/media that we have recently ingested. Its not that we lack originality, we just channel it into opinion about information. Now we want to share the media that we are consuming among large groups of individuals as fast as we can. The fastest way to do it is while we are consuming it. This is a very interesting and dynamic way to converse. Now we can skip the description part and dive right into a discussion with our friends.

Lets bring this back to science. Fact, technological progress during the last few decades has created a richer brand of research science that can only be done by TEAMS of scientist. Internet (as an idea) has removed all geographical constrains on the make-up of such teams. Unfortunately routes of communication have remained poor, most successful of which is the wiki setup. The problem is that a wiki has to be maintained, updated and organized, but scientists want to do science instead of playing with code.

Imagine a world where your collaborators can see the articles you are pulling up from Genbank, PubMed and the like. These are searchable and catalogued, and can be sorted whichever way you wish. Some can be even starred, branding them according to importance. All of this without forwarding, copying and pasting, attaching and deleting. Your research group is a hub in your social network, and has a dedicated (limited by relevance) glimpse into your media consumption. Finally after a decade or so, the Internet is here and it wants to hug you.