Wow. These reviews are extremely incriminating; reviewers obviously spotted some of the key problems with this paper. “This paper claims that cells from any somatic tissue can be reprogrammed to a fully pluripotent state by treatment for a few days with weak acid.
This is such an extraordinary claim that a very high level of proof is required to sustain it and I do not think this level has been reached”
Retraction Watch readers are of course familiar with the STAP stem cell saga, which was punctuated by tragedy last month when one of the authors of the two now-retracted papers in Nature committed suicide.
In June, Science‘s news section reported:
Sources in the scientific community confirm that early versions of the STAP work were rejected by Science, Cell, and Nature.
Parts of those reviews reviews have surfaced, notably in a RIKEN report. Science‘s news section reported:
For the Cell submission, there were concerns about methodology and the lack of supporting evidence for the extraordinary claims, says [stem cell scientist Hans] Schöler, who reviewed the paper and, as is standard practice at Cell, saw the comments of other reviewers for the journal. At Science, according to the 8 May RIKEN investigative committee’s report, one reviewer spotted the problem with lanes being improperly…
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I adapted this code and produced my first plot with error bars in R! Thanks Martin!
Ah, the barplot. Loved by some, hated by some, the first graph you’re likely to make in your favourite office spreadsheet software, but a rather tricky one to pull off in R. Or, that depends. If you just need a barplot that displays the value of each data point as a bar — which is one situation where I like a good barplot — the barplot( ) function does just that:
Done? Not really. The barplot (I know some people might not use the word plot for this type of diagram, but I will) one typically sees from a spreadsheet program has some gilding: it’s easy to get several variables (“series”) of data in the same plot, and often you’d like to see error bars. All this is very possible in R, either with base graphics, lattice or ggplot2, but it requires a little more work. As usual when it…
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MOOCs (Massive Open Online Courses) are an interesting development in higher education. At their most impressive, they are a way for brilliant educators to reach thousands of students at a time, all across the world. MOOCs have the potential to remove educational barriers like learning disabilities, economic constraints, geographic realities, or busy life schedules. My alma mater, Cornell, launched their first wave of MOOCs last semester. Cornell prides itself on catering to “any person, any study” and its not hard to see how MOOCs can drive this mission forward.
Of course, MOOCs are entering the scene at a time when higher education is reconsidering its educational tenets. A traditional classroom brings to mind stuffy tiered lecture halls with esteemed professors reciting knowledge to enraptured students (Tom Wolfe’s I Am Charlotte Simmons comes to mind). Educators have long suspected that these teaching methods are not ideal, but recent high profile publications have provided clear evidence that there are better ways to engage students. Active learning is a broad term, but it encompasses classrooms where students participate in activities or discussions. Instead of tuning out a professor (intentionally or not), a student must engage the material.
MOOCs certainly don’t have to embody one form of education or another. They have the capacity to be very active educational formats. Discussion boards, problem sets, live text or video chats, course projects, and peer review all require students to work alone or together to master material. On the other end of the spectrum, a MOOC can be a string of youtube videos or reading assignments, with multiple choice quizzes at the end of each section. The value of such courses is questionable. When a MOOC is offered for free, it’s not a big problem. But as institutions offer “online certificates” for participation, the issue becomes an important one.
I’ve been participating in the Data Science course track on Coursera, and am rounding the bend on the second module. This sequence of courses is put together by Brian Caffo, Jeff Leek, and Roger Peng at John Hopkins University. I think that theres a lot of positive examples in this course. The community message board is quite active, with attentive TAs who field questions. Much of the coursework is active- each week I’ve been asked to write my own code to accomplish certain tasks. The course itself is crowdsourced for grading, a sort of peer-review lite. The swirl() modules deserve a special commendation- these educational R packages teach you how to perform tasks in R right within the R environment.
On the other hand, the weekly video lectures don’t offer very much beyond some light structure for the course. Skipping them and relying on a search engine for the quizzes is more economical and less frustrating (nothing is worse than listening to a lecture series and taking extensive notes, only to be quizzed on what I view as minutiae).
Overall I’m very grateful to the professors for putting the course together, and even more grateful that they offer it for free online (coursera offers an optional paid certificate). Where the course succeeds, it exemplifies the potential for active learning in MOOCs. At the end of the day, we have to remember that this is the internet. There are seemingly limitless resources for education available. What we demand from formal instruction is mentorship, guideposts, motivation, and accountability. We can watch a video lecture any day- coursework should ignite our curiosity to work with the material ourselves (or at least hold a deadline over us to demand we do!). Ultimately, we won’t learn unless we’re motivated and take the time to do so.
If you’re interested in learning more about active learning practices, I recommend using the resources that “Centers for Teaching Excellence” at various universities (including Cornell) have put together.
Handy coding reference guide.
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Is doing work for free and without recognition the best way to ensure rigorous, honest, and timely peer review?