Recently I visited Tsinghua University in China for a seminar. It is quite amazing how fast Tsinghua has grown in their biomedical research, the buildings, the faculty, the students, and the research outcome! Meeting with faculty I also saw many faculty collaborating with each other, which is a very healthy trend. This really underlines the importance of having a critical mass. Many young scientists are now unwilling to start their faculty career in the best universities in China, because these universities don’t necessarily offer as attractive startup package, often have worse life quality (air, housing, traffic), but have very high pressure for publication and funding. While these are good considerations, there are also some advantages to being in the best universities. I have worked on transcription factor binding motif before, and know how small differences in every nucleotide could influence the overall transcription factor binding. For example, at a good institution with a critical mass, the faculty might get students that are 10% better, receive 10% more research funding, hear 10% better seminars, have 10% better equipment platforms, find a collaborator 10% more capable, get their samples sequenced 10% faster. Even though each advantage might not look big, the PI only needs to work 10% harder himself, to receive almost twice (1.1^7) the research outcome! I guess that’s why Harvard is so exciting too!
Ever curious whether paper and manuscript mean the same thing? Recently, I found this discussion which gave the perfect explanation: Difference between paper and manuscript. Simply put, a manuscript becomes a paper when it is published.
I sometimes see Chinese students / postdoc use the word “majorly” when they want to use the meaning of “major” / “majority” as an adverb. Instead of meaning “major” or “mostly”, “majorly” actually means “very” or “extremely”. So next time you want to say “majorly”, ask yourself “do I really mean extremely?”, “perhaps largely is more appropriate?” In fact, “majorly” is not a formal / academic word, so I would just suggest cutting this completely out of your vocabulary.
Recently I read a book called SCRUM, which is a strategy used in rugby games. Originally designed for software development, SCRUM can also be used for learning, managing other projects, including research projects. The basic idea for SCRUM is that when you have a team working towards a common goal, they keep good communication and reprioritize tasks as needed. The team first figure out the tasks involved and prioritize the importance and the time needed for each task. Then the team picks the most important few tasks and allocate resources and people to finish them first. During the project, there is usually a project backlog board, an ongoing task board, as well as a finished task board. Sometimes a bigger task is divided into smaller chunks and a person or a team will work to finish individual chunks. Very often a small miscommunication, waiting for other people, as well as getting stuck on a small task waste a lot of time. Therefore, team members spend 10 to 15 minutes going over the progress of tasks daily, moved the finished tasks over to the finished board, then pick the most important task from the backlog board to the ongoing board to be worked on next. Everyone knows what everyone else in the team is working on and they know the overall objectives of the whole team. In a particular team member gets stuck on one task, other members in the team try to help in order to reach the objective for the whole team. The backlog board is actively updated to reflect the priorities of the team. As the project progresses, sometime an initial task is no longer needed or important, so can be moved off from the backlog board.
Our research team tried to use the SCRUM strategy to manage our research projects over the last six months, and were surprised by its effectiveness. There is a free website called Trello which enables teams to use the SCRUM strategy to manage projects. We manage each project with one separate board, and within each board there are often multiple lists: a “Backlog” list, a “Doing” list, and one “Done” list for each week before. Within each list, each “doing” task is assigned to a team member, with deadlines and checklists. Say a project team has a postdoc, a graduate student, a programmer and me (in fact even two-member teams work too). The project owner decides how to prioritize tasks and makes the ultimate call on the direction and finished milestone (e.g. paper), which is me or a senior and independent postdoc. The scrum master is often the postdoc (or potential first author of the paper) who coordinates the daily meetings and updates, maintains the Trello board, helps removing troubles or roadblocks. During daily updates, each team member goes over his card, reports progress or challenges, moves the done card to the Done list, and gets assigned new tasks from the Backlog. The progress could be written as comments in the card, uploaded attachment files, or links to files in directories where the team shares. We found that 15-minute updates everyday works much better than two-hour meetings once a week or two weeks, and the team can move forward very quickly. If someone gets stuck on a challenge which takes longer to trouble shoot, the team members might just use the daily update time to schedule a separate longer meeting to discuss afterwards. Periodically the whole team meets to visit all the milestones, reprioritize the tasks, and discuss ideas on how to make the process more efficient.
Ever since we started using the SCRUM methodology, the productivity of our team has drastically increased and we were able to finish four papers lately. I have also introduced SCRUM to other colleagues and at least some of them told me it is working very well for them too. So I would highly recommend the SCRUM techniques to my other colleagues. What we are using is probably SCRUM in its simplest form, and every time I read the book and wiki page again, I pick up more ideas which SCRUM recommends implementing.
Recently a colleague suggested an interesting approach to calculate the impact of a specific paper by dividing the citation of the paper by the impact factor of the journal the paper is published. E.g. if paper A is published in Nature (IF ~36) and received 108 citations, while paper B is published in NAR (IF ~9) and received 54 citations, potentially paper B actually has better impact (54/9 > 108/36), because paper A has an unfair advantage of being read more by being published in Nature. Maybe the citation or the IF need to be further scaled (e.g. taking log or square root, or adding pseudo counts to journals with extremely low IF, etc), but this is certainly an interesting thought!
Recently I finished another round of GCAT review and thought of lessons learned during the meeting. Some of it is similar to another blog I wrote going to GCAT for the first time, but good to refresh those ideas…
1. Significance plays an important role. Many methods and techniques can be conceptually novel or even fancy, but if the reviewers don’t believe this will work, or answer important biomedical questions, or be used by many people in the community, they will not be the excited supporters of the proposal.
2. Track record of the team is very important. If the PI is at the forefront of the technology, publishes good work in reputable journal, or has previous developed methods that are widely used, has earlier access to unique and large datasets, it gives reviewer confidence that the proposed work will likely have an impact.
3. In the biosketch “contribution to science” section, when discussing each contribution, highlight the main idea in a phrase first before describing it in a more detailed paragraph. In fact, anywhere in the main proposal using bold, italic, color, or underline to help highlight the important points will help reviewers read and get the ideas.
4. In the specific aims page, use the first two paragraphs to establish the motivation and significance, then in each aim try to explain in as much detail as possible the research strategy, what experiments, the idea behind the algorithm, the unique clever ideas, etc. Most reviewers only have this page to refer to during the review, so make the aims description meaty for reviewers to have concrete ideas what you are going to do.
5. In the Innovation section of the proposal, educate the reviewers on the specific technological or methodological innovation, unique system or clever idea in the proposal.
6. To develop a computational proposal, the PI should know the state-of-the-art methods, demonstrate clearly the gap in existing methods, the motivation of the proposed method. Sometimes a computer- or statistics-oriented proposal could completely loose the biology reviewers. Explain the intuition or the unique cleverness behind the method, the input / output of the method, so at least reviewers roughly understand what the method is trying to do. Finally, discuss the exact deliverables and metrics to measuring success (e.g. how to evaluate whether a new parameter or new model will make the results better, systematic experimental validation, etc).
7. Some computational grants include an experimental collaborator to do validation, but sometimes the validation is just some experimental descriptions to generate more data without a clear idea how they validate or help refine the computational method. Some experimental grants will generate a lot of data, and data analysis is only added as an afterthought. Reviewers appreciate solid and coherent combination of the computational and experimental plans.
8. Large scale ($) comprehensive data generation projects without clear motivation, good biological problem, and deeper mechanistic and functional follow up plans don’t usual fair very well. Also, it is probably wise for new investigators to stick to modular budget.
9. Positively and fully address the previous reviewers’ comments in a revision. Reviewers give credit to good efforts in addressing previous reviews, especially more preliminary results and strengthened aims, which demonstrate the group is devoted to the project instead of “we will work on this crazy idea if we get $”.
10. Pay attention to grantsmanship: have logical and coherent aims, discuss work around for potential pitfalls, include support letters to demonstrate significance or expertise or cover weakness, use clear and friendly ext format, check figure and equation visibility.
Last summer, Haowei decided to quit his job and started Waylens with our savings. Over the last year, Waylens formed an awesome team, attracted investments, built a beautiful prototype, and got a lot of help from friends. Today, it is launching the first product, which includes a high quality camera, a wireless adaptor that connects to a car’s computer, and a stylish steering wheel remote. This beautifully crafted camera system empowers drivers to easily capture, edit, and share interesting moments right from the road.
We would like to share this exciting news with our friends and colleagues. We would appreciate you to support Waylens by either making a pre-order on kickstarter or help spread the words to your friends who love cars, enjoy driving, and like to share their fun experiences.
This year my older son was demoted from the A team to the B team in our town soccer team. He is a healthy and energetic boy, and loves many sports (swiming, lacrosse, soccer, skiing, etc). I was initially a little disappointed about this, but a few things gradually changed my mind and also gave me a new prospective about exercise.
First, looking back at when I was little, I loved sports, participated in school teams and sports games, was good but never great at anything. My kids are probably among the most sporty of Asian kids in our town, which should be good enough whether or not they can excel.
Second, my mom told me, to really excel in sports is to ruin your health, instead exercise is about staying active and healthy. Looking at competitive and professional athletes, I realized she was right. I remember my colleague Jon Aster said sports like biking and rowing are all about pain management, ouch!
Third, when discussing exercise with colleagues lately, Hao Wu told me when running for exercise, the right speed is not about running at the fastest you can keep up with, but at a speed you can chat with another colleague. Only at a leisurely pace can we enjoy running, establish the craving and habit of running. Indeed, that’s why I liked Yoga Studio (nice and enjoyable) better than 7 Minute Workout (too intense and painful).
This might only be relevant to junior scientist, since senior people probably all know it. I learned it in the 2002 BCATS conference at Stanford. Many scientific meetings have session chairs for their morning / afternoon / evening talk sessions. Session chairs have 3 responsibilities: introduce each speaker, make sure the speaker stay within the allotted time, and facilitate discussions after each talk. It is very embarrassing to a speaker if no one asks a question after his / her talk. So if there is silence after a talk, the session chair should step in and ask a couple of insightful or informative questions, and this often will encourage other questions from the audience. Also, if one audience is dominating the Q&A session by going back and forth with the speaker about some questions, the facilitator should wrap up the conversation and let other audiences have a share asking questions. Finally if aspeaker goes over time, the facilitator limit the QA to just one short question.
Happened to find an HHMI training material site, which might be very useful for early career scientists.