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https:/.../5-6-19+Low+Q.mp4

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    One, because it's going to ruin our day,
    alright?
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    So, uh, if you know anything special
    about this paper, don't tell anybody.
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    What I really want you guys to do
    is to not use the internet.
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    We're going to take a close look at this
    paper, see what you guys think about it.
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    We're going to read it, I'm going to give
    you about 20 minutes
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    to read this paper and discuss it.
    Maybe half an hour.
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    Talk to your friends if you want to.
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    The idea is going to be to get through it
    as fast as you can, and let's talk about
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    if we like this paper or not, alright?
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    Cool.
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    (crosstalk)
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    >> Put them up here.
    >> Mia apparently doesn't staple papers
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    that she reads, so they're not stapled.
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    So it's just complete mayhem.
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    >> At least I got the job done.
    >> No, not really.
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    (crosstalk)
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    >> Did anybody not get one?
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    (crosstalk)
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    >> Alright. I'll be back.
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    (crosstalk)
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    >> Alright, so, maybe read for about
    five more minutes, and then maybe
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    discuss this for about five or
    ten minutes.
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    So read for five more minutes,
    then we'll discuss
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    for five or ten minutes, and then I'll
    lead the discussion on the paper.
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    The discussion is really going to
    center around what do you find convincing?
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    What do you like and dislike? Same kind of
    discussions we've had, but sort of
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    a short version, just to kind of see if
    maybe people will notice things
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    that other people didn't notice.
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    Does anyone know about this paper?
    Anyone know what I'm talking about here?
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    Has anyone read this paper before?
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    Alright, good, we're all
    on the same page.
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    Alright, read for a few more minutes, and
    then when you're ready, start discussing
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    with whoever your neighbors - or
    whoever you want to talk to about it.
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    Talk about this paper for
    ten or fifteen minutes.
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    See what you think about it, see if you
    can come up with a consensus.
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    (crosstalk)
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    >> Alright, okay, so uh, what do you
    guys think of this paper?
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    >> Cool.
    >> Yeah, super cool, huh?
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    Like really really good.
    Yeah, I think you guys got it.
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    Yeah, and what do you think?
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    >> We have a theory
    that it's all made up.
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    >> Ahh.
    (class laughing)
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    Anyone else think it's all made up?
    >> I was convinced.
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    >> Based purely on the fact that you
    told us that there was a retraction.
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    >> You can't find it, can you?
    >> You also gave us the author manuscript,
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    so, yeah.
    >> Yeah, yeah, no, no.
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    So, go do some Googling right now,
    everybody pull up your laptop or your
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    phone and see what you think here,
    because this is a really interesting
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    lesson in how to do this, right?
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    Yeah, yeah, yeah, no. But go see
    what you can find about this paper.
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    It's a useful exercise.
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    A couple of minutes of your own
    research by phone or laptop,
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    and then we'll have
    a little more conversation.
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    (crosstalk)
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    >> All right. That's pretty amazing,
    huh?
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    Okay, so not just the paper in "Cell,"
    but also the paper in PNAS, right?
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    So two different papers had to be
    completely retracted.
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    The retraction notice here in "Cell"
    is good reading.
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    Let's see - where's the retraction
    notice? Here's the retraction notice.
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    So, here we are.
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    Study reported the former family
    blah, blah, blah.
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    Shortly after publication, a lab
    with whom we had shared reagents
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    noticed that cell lines that were
    supposed to be stably expressing
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    GFE-4 were not, we subsequently
    found a western blot of the paper
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    had been inappropriately manipulated.
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    And that multiple cell lines were not
    as reported.
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    When we constructed and validated
    new cell lines and reagents,
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    our attempts to produce critical data
    in the paper were unsuccessful.
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    This led to basically office -ORI,
    is the Office of Research Integrity,
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    at the NIH, did a study, and
    actually found that there were
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    fabrications in figures
    two, three, five, six, seven,
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    and the majority of the supplement.
    Alright?
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    Now, what's even more stunning -
    what? Yeah, I'm getting there.
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    (crosstalk)
    >> From the NIH? This is ridiculous.
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    >> Yeah, well, so, okay, right.
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    So an interesting thing to do
    in these cases when you see retractions
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    is to read the ORI report, uh,
    and the ORI report goes on for
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    pages and pages and pages of
    all the things that were altered,
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    but the real meat of it is here.
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    This is for the "Cell" paper.
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    Results which did not originate from
    experimental observations,
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    which did not originate from experimental
    observation using selected regions
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    of the same original to represent the
    control and the rescue.
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    Lying about whether they were 2.5
    or 3.5 microliter channels,
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    not originating from experimental.
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    And then, what's particularly crazy
    about this, is that there's a lot of
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    falsification data here, but almost
    every single figure in here has the
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    end numbers exaggerated.
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    The error bar is mislabeled, etc.,
    etc., right?
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    And so this is a relevant thing to
    think about.
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    So this was a very talented scientist.
    I saw her talk at
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    a Gordon research conference and
    immediately asked her to apply to UT.
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    I said "You should apply for a faculty job
    at UT," completely sold on the story.
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    So was UT Southwestern, who actually
    offered her a job
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    as an assistant professor.
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    So was the Cancer Prevention and
    Research Institute of Texas,
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    which offered her a two million dollar
    recruitment grant.
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    All of which was of course rescinded
    when this was discovered.
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    Turns out she had fabricated all the data
    in a second paper in PNAS as well.
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    And so I think it's worth considering
    these things and sort of paying attention
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    to what happens here, right?
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    So Claire Waterman, the senior author
    on these papers is a very famous
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    cell biologist, who's been in the
    business for 25 years,
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    at least as long as I have.
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    And is very well regarded, very
    respected by all of her colleagues,
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    Obviously this puts her in
    quite a bind, right?
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    And it's worth thinking about.
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    We've had faculty in this department
    where this has happened, right?
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    This appears to be a case where
    a very intelligent young scientist
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    somehow felt something about the
    pressure of the job, needed to
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    make up all the data in two entire papers.
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    Then so one of the questions you ask is,
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    that you get asked a lot from
    non-scientists is
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    "Well, why doesn't peer review find it?"
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    Right? How does this get through
    peer review?
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    Did anyone see anything wrong
    with this paper? If I had just given
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    you this paper, you'd have read
    it and you'd have loved it, right?
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    Because if you're a smart person,
    and you're a dedicated fraud,
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    it's very hard to get caught, right?
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    If you just make up numbers and put
    them on a graph, and it looks good,
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    very hard to see that in peer review.
    Right?
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    If you pick the part of the image that
    shows what you want to show...
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    it's very easy to get away
    with this actually.
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    >> The only thing I was suspicious about
    in the whole paper was that
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    it all came together so perfectly,
    and that's just like not how it should
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    ever happen with cancer cells.
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    >> Yeah, but it does sometimes, right?
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    Yeah, right, it does, and when you sit
    there and you watch the data,
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    and it's from a good lab-- right?
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    It's very hard to say,
    "Ah, it's too perfect."
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    You know, you wouldn't have said
    "Oh, clearly you've made all of this up."
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    Right? Yeah, yeah, yeah. Right?
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    >> I don't - I was only suspicious
    because I had thought already
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    that that's what happened with
    this paper.
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    >> Yeah, yeah, yeah. No.
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    It's hard to do the exercise
    any other way.
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    August had a question and
    then Jifa.
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    >> No, just because we were quite -
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    you know, they made a little bit
    far-fetched of a claim, but
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    at the same time we can see that,
    yeah, the phenotype's right.
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    So I think...
    >> Yeah, Jifa?
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    >> So, were the other scientists
    here in on it, or was this like -
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    did she orchestrate it?
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    >> No, the Office of Research Integrity
    puts it all on the one person.
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    Basically just a dedicated fraud
    all the way through.
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    And again, it wouldn't be that
    hard to do, right?
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    So probably the other authors
    involved are the authors who
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    generated other reagents,
    also possible that many of them
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    just generated reagents and gave
    them to this person, to do work on,
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    and then she did experiments on
    them and made up the results.
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    But actually in the entire
    investigation, only one person
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    was found to be the root of all of this.
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    And so...
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    >> (inaudible).
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    >> Huh?
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    >> I said, it sucks for the other
    authors of the paper.
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    >> Oh, it sucks for everybody,
    absolutely.
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    >> I'm sorry, I just want to know,
    how about the (inaudible)
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    >> I think we don't know. I think
    someone will have to go back
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    and redo all those experiments, right?
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    So these are the findings of what
    people said, but even those findings
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    in the first few figures, clearly the
    numbers are inflated, right?
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    And so I think someone will have
    to go back and start over.
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    >> I didn't read the summary at
    the beginning because (inaudible),
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    so I just read the introduction, but I
    feel like the titles are very attractive,
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    and then I read-- briefly go the the
    introduction and read each figure.
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    I think they're basically true then you
    gave us a little time so I went back
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    to read the summary.
    >> Yup.
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    >> And I felt like some of the summaries
    they were giving for the whole picture
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    of this paper, I felt like how could
    it be so perfect, you know in
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    animal models, and the (inaudible)
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    >> Yeah, but every once in a while
    you get lucky, right?
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    And so that's the thing with fraud, right?
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    If it's really consistent and you
    stick to your guns, it's really hard
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    to catch. Now, the funny thing is
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    that surely she must have thought
    someone would try to repeat -
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    I mean it's a cell paper. She was
    on the job market, she gave
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    talks all over the country.
    She must have known that
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    somebody was going to do
    these experiments.
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    And so I just don't know.
    August?
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    >> I'm pretty sure that Skau's
    reputation is completely shattered,
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    but how is the reputation of
    Clare Waterman?
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    >> I mean that's always a danger, right?
    And time will tell, right?
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    It's the only time she's ever
    been associated with it.
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    The independent investigation
    found that it was all this one person.
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    So, time will tell.
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    But right now I would say
    it's still very good.
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    >> Oh, okay.
    >>Right? Yeah.
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    >> This makes me wonder like, what
    is so terribly wrong with the system
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    that somebody (inaudible)
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    >> Sure. Right? I mean I think
    that's one of the big questions,
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    and what's kind of related to
    that that I think is a really
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    subtle point - I'll get to
    your questions in a minute,
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    but I do want to make this point,
    because it comes straight to this.
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    Right?
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    It's very easy to start believing
    your own bullshit. Right?
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    And there's a big difference between
    really making up entire papers
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    and not being honest with yourself,
    but they all are part of the
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    same spectrum, and something you
    have to guard against is
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    "Am I really doing this the right way?"
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    "Am I really seeing what
    I think I'm seeing?"
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    "Am I really being completely honest?"
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    Because especially once you get halfway
    into a paper, halfway into your PhD,
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    it gets, you know, there's
    a lot of pressure.
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    And it's something you have to
    fight against, right?
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    Especially when a project's
    going south, right?
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    You've invested a lot in it,
    and everything's starting to
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    fall apart, and so you've
    got to be vigilant.
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    And again, I'm not saying that
    you're likely to decide to just
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    make up all the data in the
    last three figures of your paper.
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    But the system is stressful.
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    It's a very competitive system.
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    It's a very competitive world.
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    Is fraud any worse now than it used to be,
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    or can we just detect it better, right?
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    I don't actually know. My guess is that
    we can detect it a lot better, right?
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    There's a lot better tools for it.
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    But there's also a lot more money
    in science than there ever was before
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    at all levels, even in academia,
    which puts a big incentive in
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    for people to be unethical.
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    But I mean these are serious concerns,
    I think there's things that as
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    graduate students, you guys should
    be thinking about and considering.
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    There were some hands up.
    I can't remember who they all were.
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    Will?
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    >> I was just going to say that it's
    crazy that it seems like the
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    way that she started to get caught
    was that she sent
  • 59:07 - 59:11
    stably-transfected cell line, and it
    wasn't actually stably-transfected.
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    >> Yup.
    >> It seems so basic.
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    >> Yeah, I mean but once you're there,
    right, they ask for the line,
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    what are you going to do?
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    >> That's also easy to hand away,
    to be like "Oh, it might have..."
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    You know, you build these lines
    and they fall apart, so I mean...
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    and if you're this confident in
    falsifying data that ends up
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    in the "Cell" paper, you're probably
    willing to think that you can
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    argue your way out of
    something like that, right?
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    Do you see what I'm saying,
    like the mental-- yeah, yeah.
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    >> Well but also at this level, you know
    it's slid over into pathology, right?
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    I mean literally every single thing
    in this paper has something
  • 59:54 - 59:55
    fraudulent about it.
  • 59:56 - 59:59
    Right, which is really evidence to me
    that someone has gone
  • 59:59 - 60:04
    way past "Oh crap, I've got to just
    save this one paper" into
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    "I'm invincible".
  • 60:06 - 60:07
    Right?
    >> Yeah.
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    >> Yeah yeah yeah, I mean
    I don't know, but yeah.
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    >> Yeah, so apart from the reputation
    and embarrassment, what is the
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    punishment for such (inaudible)?
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    >> Right, so the punishment for her
    is-- I mean, the punishment
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    is her entire scientific career
    is completely ruined.
  • 60:24 - 60:26
    She'll never work in the
    field again, right?
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    >> What about the taxpayer's money?
    >> Right, right, right.
  • 60:28 - 60:31
    Taxpayer's money, there's nothing--
    I mean, nothing done.
  • 60:32 - 60:35
    They have these voluntary settlements
    here that you can read about it.
  • 60:36 - 60:42
    And these things are really built
    around the assumption of
  • 60:42 - 60:44
    small amounts of fraud, you know?
  • 60:44 - 60:50
    You made a mistake, you're a savable
    person as a scientist, then they
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    put a supervisory plan in place
    where someone's really
  • 60:52 - 60:55
    looking a lot more carefully at your work,
    and then for three years,
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    you're not allowed to work for the NIH
    and these kinds of things.
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    But at this level, her reputation
    is just destroyed, so there's--
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    we won't see her anywhere
    in academic science again.
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    In terms of monetary, these kinds of
    things, I don't know of any,
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    and there's much more-- there was
    a big "New York Times" piece
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    about a cancer biologist at
    Penn State who's apparently
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    been under investigation for fraud
    like every other year for ten years,
  • 61:19 - 61:23
    and he still has millions of dollars
    of research money and it just sort of
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    never comes home to roost, so.
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    You guys should educate yourself
    about the fraud situation,
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    and what's there.
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    Did someone else have a hand up?
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    >> Can I same something quick?
    >> Yeah.
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    >> I saw in the news, UT Southwestern
    removed a million dollar grant from her.
  • 61:38 - 61:40
    >> Yeah yeah, that's the grant.
    Yeah-- oh yeah, she--
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    the job was rescinded, the grant
    was rescinded, so she's not
  • 61:44 - 61:46
    getting the grant, she didn't
    get the job, she will absolutely
  • 61:46 - 61:48
    have to leave science.
  • 61:48 - 61:51
    As I understand it, she's like a
    program officer at a foundation now
  • 61:51 - 61:52
    or something. Yeah.
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    >> But not just the monetary loss,
    what about killing so many mice
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    and falsifying data? (inaudible).
    (laughter)
  • 62:01 - 62:04
    >> If you go down the road that
    killing life is a problem,
  • 62:04 - 62:06
    then you're going to have
    a real problem.
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    >> If you're doing science, doing
    something productive,
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    it makes sense but if
    you're falsifying data.
  • 62:11 - 62:12
    >> Yeah.
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    >> I was--
    >> Hang on, you had a hand up.
  • 62:16 - 62:19
    >> How often-- obviously this one
    seems like it was very intentional,
  • 62:19 - 62:21
    and so that's really-- but how
    often do people get
  • 62:21 - 62:23
    caught up in this just because
    they weren't careful enough
  • 62:23 - 62:27
    and they didn't force themselves
    to be very thorough?
  • 62:27 - 62:32
    >> I mean mistakes get made in papers
    all the time, right, and some papers
  • 62:32 - 62:36
    get retracted because we went back
    and resequenced the mouse allele,
  • 62:36 - 62:39
    and it wasn't what we thought
    it was, right?
  • 62:39 - 62:42
    But generally what happens is there's
    a correction. Generally when that happens,
  • 62:42 - 62:45
    it's not like the entire paper's ruined,
    "Oh, surprise, we mutated
  • 62:45 - 62:47
    totally the wrong gene".
  • 62:47 - 62:51
    Usually it's like "Oh we thought it was
    exon 3, but in a frameshift,
  • 62:51 - 62:53
    but in fact it was something else."
    Right?
  • 62:53 - 62:54
    >> Right.
  • 62:54 - 62:59
    >> And so you get a lot of corrections
    in papers, and some retractions,
  • 62:59 - 63:03
    but generally, you know, you've
    got to-- the whole paper
  • 63:03 - 63:06
    really has to be systematically
    wrong to retract the entire paper.
  • 63:06 - 63:09
    >> So it's the dishonesty that
    gets prosecuted.
  • 63:09 - 63:13
    >> Yeah, or I mean, it-- yeah, so the
    dishonestly immediately you want to
  • 63:13 - 63:16
    retract the paper, because that's
    also what the PI is going to
  • 63:16 - 63:17
    want to do.
    >> Right.
  • 63:17 - 63:21
    >> Right? But if you just get it wrong,
    you know, you get it wrong,
  • 63:21 - 63:22
    we're human, right?
  • 63:22 - 63:26
    If there was no-- and there have been
    investigations where they find
  • 63:26 - 63:29
    "Oh, no, they weren't unethical,
    they were just dumb."
  • 63:29 - 63:30
    (laughter)
  • 63:30 - 63:32
    No, seriously, right? And that can
    happen, right?
  • 63:32 - 63:34
    And that's not a crime.
  • 63:35 - 63:37
    So, yeah.
  • 63:37 - 63:41
    >> Who does these investigations?
    Is there like a secret service
  • 63:41 - 63:42
    for scientists, like--
    >> Yeah.
  • 63:42 - 63:44
    The Office of Research Integrity
    at the NIH, right?
  • 63:44 - 63:45
    >> At the NIH?
  • 63:45 - 63:49
    >> Yup, and then all universities will
    have an office for research integrity,
  • 63:49 - 63:53
    so I'm sure that-- so if it happens at UT,
    there's a UT office that will investigate,
  • 63:53 - 63:56
    but the NIH will also investigate
    if it's NIH-funded research.
  • 63:57 - 63:59
    Yeah, so several differnet bodies.
  • 63:59 - 64:01
    And there's a...
  • 64:02 - 64:03
    Yeah.
  • 64:05 - 64:07
    >> Has anyone here read
    the book "Bad Blood?"
  • 64:07 - 64:09
    >> "Bad Blood" what is that?
  • 64:09 - 64:11
    >> It's about the Theranos controversy
    and (inaudible).
  • 64:11 - 64:14
    >> Oh, wow. No, is it good?
    >> It's amazing.
  • 64:14 - 64:16
    >> Really? "Bad Blood."
    >> Everyone should read it.
  • 64:16 - 64:18
    >> Okay.
    >> It's really good.
  • 64:18 - 64:19
    >> The documentary on
    HBO's pretty good.
  • 64:20 - 64:22
    >> Same title? Okay.
  • 64:22 - 64:23
    >> I think so.
    >> Okay.
  • 64:24 - 64:26
    >> So lots of these
    kinds of things existing.
  • 64:26 - 64:27
    >> Yeah (laughs).
  • 64:27 - 64:28
    >> Just like the...
  • 64:30 - 64:33
    I don't know how to
    say that vitamin C is
  • 64:33 - 64:38
    for everything, and so now,
    the (inaudible) for everything
  • 64:38 - 64:40
    and the (inaudible) can do everything,
  • 64:40 - 64:42
    and they just make money from that.
  • 64:42 - 64:43
    (laughter)
  • 64:43 - 64:45
    >> As long as they don't kill anyone,
  • 64:45 - 64:48
    they just sell the ideas
    to the (inaudible).
  • 64:48 - 64:51
    >> Yeah, I mean that's the entire
    supplement industry, right?
  • 64:53 - 64:56
    All right. Any other questions?
  • 64:56 - 64:58
    Thoughts, comments?
  • 64:58 - 65:01
    >> When is the next assignment due?
  • 65:02 - 65:05
    >> Next assignment is due
    a week from Wednesday.
  • 65:08 - 65:10
    No, so nine days from now.
  • 65:10 - 65:11
    >> Nine days?
  • 65:11 - 65:12
    >> Nine days.
  • 65:13 - 65:15
    Whatever the date is here,
    actually I have a calendar
  • 65:15 - 65:18
    in front of me.
    >> It is the 6th so the 15th.
  • 65:18 - 65:21
    >> The 15th.
    >> Ooh, I'm good at simple math.
  • 65:21 - 65:24
    >> It is as my calendar says,
    'cause I have an 11 year old
  • 65:24 - 65:27
    girl at home, and "Riverdale"
    season three starts.
  • 65:27 - 65:28
    (laughter)
  • 65:28 - 65:30
    But also your assignment is due,
    so get it done before River--
  • 65:30 - 65:31
    >> "Riverdale" season three
    already happened.
  • 65:31 - 65:34
    >> What?
    (laughter)
  • 65:34 - 65:36
    Oh, no, no, it comes out
    free on Netflix. Yeah.
  • 65:36 - 65:38
    (laughter)
  • 65:39 - 65:41
    >> Judge me all you want.
  • 65:41 - 65:42
    >> I have a shared--
    >> I know all of you
  • 65:42 - 65:44
    watch "Game of Thrones,"
    and I don't, so.
  • 65:44 - 65:46
    >> No, no I don't either.
  • 65:46 - 65:47
    (crosstalk)
  • 65:47 - 65:48
    >> Jifa?
  • 65:48 - 65:49
    >> What's going on Wednesday?
  • 65:50 - 65:52
    >> Haven't decided yet. It's going to
    be so much fun, though.
  • 65:52 - 65:54
    (laughter)
  • 65:56 - 65:58
    All right, cool. We're done.
  • 65:58 - 66:01
    (crosstalk)
  • 66:08 - 66:12
    >> I mean I sent in my paper
    just last Friday afternoon.
  • 66:12 - 66:14
    >> Okay.
    >> Was that too late, 'cause I--
  • 66:14 - 66:16
    >> It was too late, 'cause we
    told you again and again and again--
  • 66:16 - 66:19
    >> Oh my--
    >> All right, so anyway
  • 66:19 - 66:21
    >> Sorry about that.
    >> Did you get your paper in today?
  • 66:21 - 66:22
    >> Yes. Yes, yes.
    >> Okay. Great.
  • 66:22 - 66:25
    We will survive it.
    >> Thank you, thank you.
  • 66:25 - 66:27
    >> All right.
  • 66:27 - 66:28
    (crosstalk continues)
  • 66:28 - 66:30
    >> Oh sorry, I wasn't (inaudible).
  • 66:32 - 66:36
    And on Wednesday-- I mean, this is
    not about the class,
  • 66:36 - 66:40
    it's slightly about the class,
    because (inaudible)--
  • 66:40 - 66:43
    invitation, having lunch--
    >> With Roy Parker?
  • 66:43 - 66:45
    Yeah, you leave early
    and go to that.
  • 66:45 - 66:48
    >> No no, not for that, but on
    Wednesday afternoon,
  • 66:48 - 66:52
    I'm flying to (inaudible) to walk
    in the commencement.
  • 66:52 - 66:54
    >> Oh okay. So you'll
    miss this talk?
  • 66:54 - 66:58
    >> Yeah, I'll miss this talk likely,
    and I'll be away for two weeks
  • 66:58 - 67:01
    because my parents are coming.
    >> No problem.
  • 67:01 - 67:04
    So send your paper in before that.
    >> So I was going to submit it
  • 67:04 - 67:05
    on Wednesday.
    >> Great, no problem.
  • 67:06 - 67:08
    Okay, cool, good, it works.
    >> Thank you.
  • 67:08 - 67:09
    >> You bet.
  • 67:09 - 67:09
    >> Mina?
    >> Yeah?
  • 67:09 - 67:12
    >> So are you going to be in
    your office right now, or?
  • 67:12 - 67:15
    >> Yeah yeah, I'll be here all day
    doing experiments,
  • 67:15 - 67:17
    so if I'm not there, just leave it
    on my desk.
  • 67:17 - 67:18
    >> Okay.
  • 67:18 - 67:21
    (crosstalk)
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