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Law Center, Central Islip ​you I want to introduce you to two researchers her names are Carmen Reinhart and Kenneth Rogoff and in recent history these two have been considered to have written the seminal paper on economic austerity policy the idea behind the paper is that for countries with high debt those countries governments should spend less money during an economic recession to recover better from that recession and this idea was widely adopted around the world a little bit in the United States more predominantly in countries across Europe but the problem is that this paper is wrong and it came out that it's wrong because of this gentleman he's named Thomas Herndon and he was an economics grad student so he had a classroom assignment to go out and reproduce a published article he picked the Reinhart and Rogoff article and he took the article he gathered a bunch of publicly available data and said about reproducing the results but he couldn't make their results work out and he got more and more frustrated and finally he just contacted the authors and said help me Yahoo's were very nice and they sent Herndon their data and that's where the real problem started because Herndon opened up that spreadsheet and he found errors he found data points that haven't thrown out that shouldn't have been he found mistakes and calculations and when he corrected those errors he found that austerity was not actually proven by the data so here we have a policy in a paper that's been hugely influential around the world in the United States it was estimated in 2012 from the Congressional Budget Office that if we had enacted austerity instead of stimulus that is less spending instead of more spending we would have had 1% higher unemployment and instead of a projected 1.7 percent economic growth we would had an economic decline of 0.5 percent and you only have to look at Greece to see how damaging these policies can be Greece has enacted wide austerity measures received multiple expensive bailouts has had a lot of political unrest and their unemployment rate is about 25 percent but this talk is not about economic austerity this talk is about something more important this talk is about the unusual thing that Reinhart and Rogoff did and that's that they shared their data and I want to tell you a little bit about research today and why that's so important that people should share their and should start doing that more and to understand how research works a researcher gets an idea and they go out and get funding for a project they do the project they come up with something interesting and they write it up as an article and the article gets published but the data that go along the article has data to tell an important story that gets pushed to the side that gets forgotten that gets trashed and that's a problem and this problem is best summed up by two researchers named buck height and Don Ho and they say that a scientific publication is not the scholarship itself it is merely advertising of the scholarship we're only getting a polished version of the story we're not getting the data we're not getting the full version of the story and that's a problem to put this in context we spend a hundred and thirty billion dollars every year in the United States to publicly fund research and there's research is critical this leads to really important innovations and really important technologies so for example if you go to your doctor's office and your doctor says we need to get a picture of your head to diagnose your condition you can go get an MRI because an MRI was partially developed with public funding and we need to keep funding that research but we need to do that in a more transparent way so there's a saying in pop culture that goes pictures or didn't happen so if I go out and I'm having a night in a town and oh my gosh it's Bill Murray that's amazing I'm at bull Murray my friends are not gonna believe it until I have some sort of photographic proof so I'm saying that we should adapt and adopt this expression for research and say data or it didn't happen so if you're telling me that you found the cure for cancer I kind of want to see your data to know how strong that claim is and we need to get in a system where we say data or didn't happen because an article and advertising alone is not telling us enough and it's leading to several problems the first is a problem of reproducibility that idea that you can take an article going to do exactly what that researcher did and get the same or similar results and a study of cancer researchers found that over half of them 55% of them have had trouble at one point in time reproducing the results from a published article to put this in context of money and preclinical research it's estimated we're wasting thirty billion dollars a year on irreproducible research that's research that's supposed to go into your doctor's office and into the hospital and it doesn't stand up to repeat a testing that's a problem the second problem with this system is it lets fraud creep in and one of the best examples of this or the worst examples comes from a guy named Dietrich Stu pal and here he is profiled in the New York Times Magazine mr. Paul was a very prominent researcher in psychology and he studied the idea of priming and that was that you could unconsciously introduce a topic to somebody and have it affect they leave it Debbi that they behave so he was widely respected until it came out that he had been making up most all of his data and this really shook the whole field of

psychology to its roots and they realized if one of our prominent researchers can publish so widely I'm totally madeup data maybe we have a problem maybe we're letting other kinds of errors slip in to our articles step L by the way has the distinction of being number four on the retraction watch leaderboard list if you like scholarly publications we're all interested this blog is amazing because they go through and they track whenever it articles retracted and basically that means we no longer have faith in the results of that study and there's all sorts of crazy reasons why articles get retracted but one of the other things they do is they keep a leaderboard list of the number of articles that certain people have retracted by count so Dietrich's Dipel is that at fifty four articles retracted he's not at the top actually at the top is Yoshi taki Fuji II 183 and he can't tell me that we don't have a problem with this system when someone can publish a hundred and eighty-three papers on suspect data and no one caught them step L by the way lost his job and voluntarily gave up his PhD to avoid criminal charges a third problem with the system where we're only publishing the advertising and only posting the article is that we don't value data and so we lose it and studying biology found that we lose data at a rate of 17 percent a year 17 percent so if I'm climate researcher and I'm looking at a particular species and I want to look at that species now versus ten versus twenty years ago the likelihood that I can find that older data is really really small and that affects the questions that I can ask that affects the kind of science that I can do and that affects the my ability to help that species survive so we need to get over the system we're only publishing the advertising we're only publishing the article and the alternative is to publish article and the data and the code and by code I mean computer code I mean the code that you throw the raw data into and it doesn't analysis and spits out a number that we can use and we can understand I mean the code that actually creates the data in the first place that says that you say I want to figure out what happens if I simulate X versus simulate y and in many research projects that code is just as are more important than the data and that needs to be made available to the cool thing is if we get in a system where we can share the article and the data and the code we can do a lot of really interesting things the first is that we can build off of previously published results and this is just a fundamental way that research happens this happens all the time so for example if I see Sue Smith study and I think it's really interesting and I could use it in my work the first thing I'm gonna go out and do is take her published article and do it exactly as she did it to make sure I understand it to make sure it's working right to make sure everything's good and then I'm gonna adapt it and adopt it for my research but if I'm trying to do that just from her article alone just from the advertisement that's inefficient that's frustrating and that process could be a certainly a lot smoother if I had access to the data and to the code the second thing that this publication system is gonna allow us to do is catch those errors before they become big problems Thomas Herndon he found the errors in austerity because he had access to the data and you can certainly imagine that Dietrich's Dipel wouldn't have published 54 articles that were retracted if we had asked for his data early on the third thing the system's gonna allow us to do and this is where it gets really exciting is that we're gonna have access to data we never had access to before we can ask new questions we can do new research and one of the best examples of this comes from a project called Galaxy Zoo and Galaxy Zoo took an open data set called the Sloan Digital Sky Survey and that's a bunch of pictures of the universe and to astronomers realize that those pictures had a lot of galaxies in them and if we knew a little bit about the galaxies we could answer some really important research questions but to classify all those galaxies from the images is something that it would take a computer a really long time to do so these astronomers decided to take the Sloan Digital Sky Survey data to take what they know about galaxies and put that information out to the public and they say help us classify galaxies and pretty soon the public had helped them classify 40 million galaxies so that's a data set that didn't exist before that's new questions they can ask about galaxies but that's not the best part during this process they discovered new galaxies and the little green galaxies I'm showing you here is called Hanny's Voorwerp it was discovered by a high school physics teacher in the Netherlands and named after her and I just think that's so cool that we can do all these interesting things because we had access to data because we put it in the hands of people who didn't have it before and imagine if we can do that for all data the fourth thing that a system where we published article and data and code is going to allow us to do is make better decisions and this is something that affects everybody in the room because it means your economic policymakers are going to make better decisions for you it means your doctors are gonna make better decisions for you because they have all the information and this isn't just theoretical so another member of the retraction watch leaderboard list he's actually just above Dietrich's Dipel is Yocum bolt and Yocum bolts claim to fame is that he studied a drug called hydroxy FL starch hydroxy alkyl starch is used in surgery to control blood flow and it was adopted and used in millions of surgeries around the world partly because the open boat was such a big proponent on it because he published so widely and I'm sure you know where this is going that Yocum bolt also made up a little bit of data and a drug Seattle starch is actually not better than alternative treatments in fact when they look at the family to which hydroxy ethyl starch belongs called colloidosome were more likely to die using colloids as

compared to alternative treatments four out of a hundred so think of all the people in your family who have had surgery your siblings your parents your aunts and uncles your grandparents maybe they didn't get prescribed hydroxy ethyl starch maybe they did maybe they got lucky and nothing happened but it's a terribly tragic waste of human life to have put this drug into so many hospitals because we didn't ask you a combo for his data so we need to get to into regime where we say data or it didn't happen this is unacceptable anymore and to do that we need to do three things the first thing is we're paying for a lot of research in this country and that's really important we need to keep paying for this research but we can also say you know that's our tax money you should do this better you should be more transparent we want more accountability for where our money is going and the good news is that some of this pressure is paying off so some of our biggest funding agencies the National Institutes of Health the National Science Foundation are starting to put data policy into place that say researchers have to take care of their data better they have to share it but the problem is these policies aren't strong enough they don't have enough teeth and so we need to keep that pressure up to get to a point where this is standard the second thing we have to do is just change the culture of research researchers are used to publishing articles they're not used to sharing the area you're very used to saying the data are mine I'm gonna hoard them and that's been okay but that's not okay anymore we need access to the data that's gonna better our decision-making that's going to better our research the final thing we need to do is build a better infrastructure the reasons my articles have been so successful in research is because we have systems to publish them and distribute them and find them and share them in the infrastructure it doesn't really exist for data or where it does it's not very strong so again as taxpayers we can say this is important we want to put money into this we want transparency and accountability and we want to build that infrastructure so an average citizen can go out and say I want to look at this data and I can I want to end by giving you an example of how successful this system can be if we publish articles and data together that example comes from the Human Genome Project if you're familiar with the project it started just before 1990 and ended just after 2000 and the goal was to map the human genome and what you might know not know about this project is that one of the principles of the project was that the data were be were supposed to be made available as soon as anyone discovered a piece of the human genome so by the end of the project all that genome data that was available for anybody to use so a researcher went back and study that data and what they found they compared it to similar data those own bike a company and patented and proprietary they found for every article published on the patented proprietary data - articles were published on human genome data twice as much research twice as much innovation twice as likely that we're gonna have some breakthrough that leads to saving human lives in a doctor's office imagine if we can take this and extend it to all research imagine the new questions we can ask imagine the new kind of answers we can find and the new discoveries you can make if we say data or didn't happen and because I believe in this so much I'm sharing with you my data thank you you New York State School of Industrial and Labor Relations.