Alright, so let's take a look at a fundamental problem here, which is the problem of sampling. And so, in general, what I have, I want to introduce two new terms. So first off, let's consider a set of numerical values, for example, the age of every single person in the United States, or maybe I'll take a look at the scores of every single person who has ever taken a particular quiz, or maybe I'll take a look at the height of every single tree in a forest. And this complete set of numerical values is known as the population. And the primary goal of statistics is to determine information about the population. However, the problem is that it's in general very difficult to obtain this complete set of data. It's very difficult in general to find the complete population data. And so, what we have to do is we have to make do with a subset of the population. So for example, if I take a look at that population of ages of all persons in the United States, I may take a look at the ages of 100 persons, and that's going to be a sample of size n equals 100. Or maybe I'll take a look at the scores of 3 people, and that's a sample of size n equals 3, drawn from the set of everybody who's ever taken the quiz. Or maybe I'll find the heights of 73 trees, and that's a sample of size n equals 73, drawn from the heights of every single tree in the forest. And so we go to the fundamental problem of statistics. Given information about the sample, what can I determine about the population? So for example, to get a handle on this, let's take a look at some samples. So there's my population of quiz scores, and let's try to find 3 samples of size n equals 3. And so we're forming samples of size n equals 3, so I want to pick 3 of the quiz scores to be the values in my sample. So for example, I might pick the first 3 quiz scores, 8, 5, and 3. So there's one sample. Maybe I'll take the last 3 quiz scores, 9, 8, and 7. Maybe I'll take a couple of random scores in the middle, 10, 7, 9. Or maybe I'll take a different set, 5, 10, 7, and so on. So the idea is that I may take, I may form these different samples of size n equals 3 by taking n equals 3 of the quiz scores. Now, what can we do with that? Well, again, the idea is we'd like to find information about the population based on our sample. And so one thing we might start off with is our population has a mean, a population mean, and we'll use this Greek letter mu. And we can compute that population mean, turns out to be 8. On the other hand, let's take a look at those samples. So my sample of the first 3 quiz scores had sample mean 5.33 about. My second sample that I took, the last 3 scores, had a sample mean of 8. And my other samples had different sample means. In practice, we only ever see one of these samples. So maybe I'm collecting these quizzes nice, peel off the first 3 quiz scores, and I get my sample mean. Or maybe I wait until everybody's done, and I take a look at the last 3 quiz scores, and I get my sample mean of 8. Or maybe I take a few scores from the middle, and so on. So in practice, we only ever see one of these samples. And so the problem is given one observation, one mean, what can we say about the population? So maybe I see this, or maybe I see this one, or maybe I see this one, and so on. Well, we can approach the problem as follows. The 4 different samples of size n give us 4 different values of a sample mean. And what I can do is I can think about those 4 different samples as itself being a sample of size n equals 4, drawn from the population of sample means. This population mean is a set of values, and so the population of sample means will itself have a population mean. And I'm going to designate that mu of x-bar, that's the population mean of our set of sample means. And an important result, the fundamental result in statistics is known as the central limit theorem, and it guarantees that under a broad range of circumstances this population mean of the set of sample means is going to be the same as the population mean itself. And what that tells us is that our sample mean is going to be a good estimate for the population mean. We can use the sample mean to estimate the population mean. What about those square deviations that we looked at? Well, if our sample mean was exactly equal to the population mean, then the mean square deviation of the sample would actually be a good estimator for the mean square deviation of the population. But in general, the sample mean will not be equal to the population mean, which means that our mean square deviation of the sample is in general going to be a little too small of an estimate for the mean square deviation of the population. And so what we have to do is we have to remedy this by computing what's known as the standard deviation. And this is based on the concept of the variance. And so the variance is going to be formed as follows. If what I have in front of me are the data values for the entire population, which has size n, then the population variance is the sum of the square deviations divided by the number of values. That's just our mean square deviation. On the other hand, if I only have the data values for a sample of size n, then the sample variance is going to be the sum of the square deviations, same as before. But this time I'm going to divide by n minus 1. This is not the mean deviation. This is going to be a little bit larger than the mean deviation, because I'm dividing by a somewhat smaller number. In either case, the standard deviation is going to be the square root of the corresponding variance. For example, let's say I have data that consists of the values 5, 8, and 2. And first, suppose this is my entire population of data values. This is everything that I'm going to have. And so I can find the standard deviation when I view this as my entire population. On the other hand, if this is a sample of size n equals 3, drawn from a much larger set, then I can also find the sample standard deviation of this set. So if I view this as my entire set, our mean is just going to be our population mean, 25, and my standard deviation is going to be the mean of the square deviations. So that's going to be, first of all, I'll compute my variance, data value minus mean squared, data value minus mean squared, data minus mean squared, divided by n equals 3, because I'm viewing this as an entire population. The entire population, this data set, so I have three values. My variance is going to be 6, and my population standard deviation is going to be the square root of 6, about 2.45. On the other hand, if I view this set of three values as being a sample, then I'm going to compute the mean, first of all, in exactly the same way. So my mean is going to be 5, and note this is the sample mean, because I'm now treating this, I'm now thinking about this as a sample. And I can compute the sum of the square deviations, but to get the sample variance, I'm going to divide by n minus 1. So again, that variance, value minus mean squared, value minus mean squared, value minus mean squared, this time, because I'm viewing this as a sample of size n equals 3 drawn from a larger population, I'm going to divide by n minus 1. So that's n3 minus 1, and after all the dust settles, my sample variance is 9, so my sample standard deviation is going to be the square root of 9, about 3. Now the question is, how do you know if you're dealing with a sample or the population? The answer is, the problem will say, this is the population, this is a sample.


Okay, hi everyone. Welcome to this talk on data sharing. My name is Sarah Hart and I'm at the First State University in the Department of Psychology as well as the Forest Center for Reading Research. There's some contact information if you would like to reach out to me, you know I'm on Twitter probably more than I should be my email in my lab website, as well as you might be interested to check out if you're interested in topics related to meta science and development and the methods of developmental science you might be interested to check out the podcast I have with my colleague and friend Jessica Logan called the fit in between. Okay, so a little bit about what today is so that if you popped into this session and this doesn't I'm going to give you a second to hear what I'm planning to do so that you can leave if it does not fit into what you're are interested in because there's some other parallel sessions happening. This is an introduction to data sharing so talking about some of the topics within data sharing, you know defining some concepts, giving a few resources and tips of how to do some data sharing, but it really is meant to be fairly introductory so if you're familiar with data sharing if you've done it before, you might find some of the other more in depth sessions, kind of where you want to go and engage that way, or else you can hang out with me, I don't have very many slides, because the plan really is hopefully a lot of question and answer and talking about data sharing hesitations if you have them, or other experiences you had with data sharing. So a lot of my talk today is based on a paper that I published with my colleagues Jessica Logan and Chris Schott-Sneider, and you can check that out in the ERA Open, as well as some experience that I've gained in building a data repository for our field it's called LDBase, and LDBase has been built just to store and share data from educational scientists like you all on the phone. Okay, so a little bit about what data sharing is. So data sharing, if we take maybe a formal definition of what data sharing is, it's the process of taking any type of research data and making it available for other researchers to examine or use. And I added on this last bit, you can share your data anywhere really, and you could share it on your private internet or really anywhere to the open internet, but typically data sharing is thought to occur through an established data repository so that it's easier to find for other users. So something that I like to point out when I talk about data sharing is that if you've never you know some of you on the call may think well I've never done data sharing before, but if you've published you have been doing a form of data sharing. Simple statistics in your publications like mean scores or correlation tables are a form of data. And you have been sharing your data through your publications and the way you can kind of think about that is, you know meta analysts go collect data in the in the literature, by collecting the information that you have shared the data you have shared in your tables of your publications so pretty much everybody in education has been doing a form of data sharing with at the very least kind of descriptives and simple statistics in your publications. Generally though we tend to think when we're thinking about data sharing, we think of it as a sharing and a participant or variable level data so a little bit more detail than those simple statistics. But why I mentioned that simple statistics is, in the end if you're not able to share your full participant or variable level data sets, you know sharing variants covariance matrix is still useful for the field so usable for other users, and still can be counted as data sharing. Okay, a little bit. There's a really interesting example from developmental science about data share that has participated in data sharing for years I think since the early 80s I think they used to send around how to floppy drives of the data called. It's now more often I think called talk bank, but you may also know it as child or child as I actually don't know which way it's pronounced. So, this is a data repository of language corpora. And so it's like text from different language narrative conversation or conversations or other sort of language samples that are shared in this data repository. And it has hundreds of these language samples and those language samples have been used to in thousands of peer reviewed papers. And at any given time I think I saw a statistic that something like at least 100 users are on the data repository on a given day. And they have over 4000 have active enrolled users who are signed up to use this data repository. So this you know that the child's data repository has been incredibly useful for the field of language development and understanding and linguistics and I think serves as a great example of just the power and the benefit of data sharing. And what I'm motivated to do is to kind of expand you know what was, you know, this example from child's, the child's data repository and expand that, you know, and think about all the data that's around there in our field and how we can share it and expand that power of those data. Okay, so I started to let on kind of this next slide of what is the benefit of data sharing. There's so many benefits. The benefit is generating new ideas. So when you share your data, the idea behind sharing data is to allow others to use your data.   And, you know, sometimes like that's a really great idea I just never would have thought about that you know every person in our field has a different approach, a different background, a different way of thinking about research questions when they approach their science. And that includes when they would approach a data set. And so when you open a data set for others to use, you know, often others use it to come up with research questions and to explore scientific questions that are, you know, completely never would have thought of as somebody who generated that data set. So sharing data is a great way to generate generate new scientific ideas. And, yeah, and support our field related to generating new ideas. Data sharing is also great kind of more broadly for advancing the field, you know, we can then push the scientific boundaries of our field by kind of allowing other brains to you know, come up with research questions within data, to, you know, to, to really kind of maximize the potential from our data that's been collected because as you know in our field, our data is extremely time intensive and typically expensive to collect. And so by, you know, allowing others to use your data and in advancing kind of the science of our field by using and reusing data to generate with through generating new ideas. And really, you know, push push the boundaries of where our science is as a field. I'm kind of, you know, taking a slight turn away from those two the first two benefits of sharing data sharing data also can increase the transparency of the research process. So if you share your data and then hopefully share it with you know accompanying data documentation as well as you know maybe a code that you've generated to use that data to, let's say write up a paper using those data. You know, you allow others including potentially like trainees, you know, and in classes who are learning a new statistical procedure and that use your code and your data to understand how you do that statistical analysis, all the way to allowing people to check the results of your published work or any of your your scientific products. And, and, you know, make sure that, you know, they're even knows it's pretty easy to make coding mistakes you know you can have something kind of come behind you and make sure that there haven't been any mistakes in your, in your analysis, or kind of otherwise So, you know, sharing your data openly kind of, you know, in general increases are our feelings and our, and our thoughts about transparency of the research of our field. Sharing data also can increase collaboration I'm definitely experiencing this experiencing this firsthand right now. When you put out a data set that you've collected in a way that people can find you know those people who are finding it reach out to you. And either you know they might just simply ask for questions but they might actually ask you know do you want to collaborate on this paper. I would love to use your data to answer this research question you know they're generating new ideas. And, and so, you know, they might want to again, a co author, a publication using your data. And if they choose data sharing does not require that but it just naturally lends itself to this increasing collaboration, as well as you know hey, I saw your data, you know you're the expert in this area of these data, you know, is there a way we can put in a grant as colleagues to, you know, further maximize the potential of that data through a new research idea through a grant. So, sharing your data tends to resulted increasing collaboration. Your area I'm really compelled in with sharing data is promoting equity and research. You know, not every investigator in our fields can, you know, has an ideas grants, or has large, you know, research one university startup resources to be able to go and collect the pilot data that they might want for a grant. By sharing your data, you can allow other users to use that data, not only to write publications that might help support their career, you know, for their dissertation or for their tenure and promotion process or whatever it is because they don't have the resources to be able to collect that sort of, you know, the data that you were able to collect, but also, you know, not only publications but, you know, data that shared in repositories can used for pilot data for grant proposals. And just this morning I woke up to an email from somebody who reached out to me to say that they, you know, had used the data in the LD base of data repository, that I mentioned earlier to write to use it as pilot data to write a grant and they used the grant and now they're off doing their own research, but they didn't have access or the resources to be able to collect that data to, to, to serve as a preliminary data in their grant proposal so, you know, we can democratize access to high quality data through data sharing. And then finally, a little bit less hopeful of a reason to share your data is often funders require it these days, especially federal funders, I'm most familiar with the US funding ecosystem. But it's definitely the case I know in European funders and other areas where, you know, if, if the public taxpayers are paying to collect those data, then those data are part of the public and should be shared back to the public. So there are more funding mandates are coming out to require at least a plan a data management plan for how you're going to share your data, as well as sometimes also having my kind of teeth behind those requirements and checking to make sure you have openly shared your data. Okay, so those are some reasons why we may want to share data. I see a couple chats I'm going to take a second. And see if there's any questions. I think everyone thank you for your comments if we're starting a conversations in the chat. I love it. So please continue to do that. And we're going to have lots of time at the end of my talk to you to do that. Okay, so I gave you definition of data sharing I give you some reasons why you should share your data. And now we'll, we'll talk a little bit about how you might do that. So data sharing does not equal being a data dump. And you may have heard before what are either called, you know, thinking about data sharing within the, like a fair way that's not way for getting the word but and they're fair principles that's the word is for sharing your data with fair principles with fair standing for findable, accessible, interoperable and reusable. And I'm going to talk about each one of those, not in depth, but a little bit to the end with examples of what that might look like to our field when we're sharing data. So first is findable. So findable. I mean you can kind of gather what what I mean when I say that your, your data should be findable, you know, other users should be able to locate and find the data that you're sharing. And that's why like sharing data on a more private server is not doesn't quite fit the fair principles but sharing your data to a public data repository that's, you know, searchable, you know, via Google, or is is is findable from open is kind of the best example of how to share your data. Also port of findable is not only is a findable but that it's that sorry to help make your data findable, you need metadata. And one reason why search engines like Google work well is that they, you know, they only do I think they do like text mining now but they mine websites or other places or mine data repositories for metadata. So for information, describing the data that's held within that data repository. And so you as a user if you're up, you know, sharing your data in a data repository, you want to fill out to provide good metadata, good general information about your data so that people can find your data can not only find it on the internet so like go to a data repository like LD base and they're like okay I think your data is probably stored here but then within LD base they can search for let's say I'm interested in data related to math development and childhood. And the only way that that works well as if there's good metadata. So information about the project that your data was part of information about say the principal investigators of the data, the study design participant info variable info code books. This is all kind of under this umbrella of metadata and you know as important as data sharing is so like literally taking your data set and sharing it on a data in a data repository. So more important is actually the metadata that you supply around that data set so that people can find your data and use it in for it for what they need. Okay, let's see. So somebody. Yeah, okay we're going to come back to the funding requirements for the money in a little bit I think. And then open data sharing. So data sharing right is again you know providing participant or variable level data, and at best or even just you know summary data statistics in a way that others can find it openly from the internet. Yeah, so a user can, you know, can get access to that data without having to send you an email, necessarily to ask for, you know, can you share that data set with me and then they email it to you kind of the old way of doing you know contact the authors for access to that data. Okay, so that's findable remember talking about fair principles right now. Let's go on to a. Oh, I'm sorry. I like here's some more information about the code book and then and more metadata. Again, talking about kind of how important the documentation or the metadata is around your data sets. Here's some example of good, you know, a way that you can create a code book. You know that represents the data that you're sharing. So, you know, a good code book will include names of the variables in the data set the labels for those variables. So either the specific question or item that that variable represents and the coding system, the values and then the labels for each of those values for each variable. This is a level of detail that you can that you should create your metadata to support the findability of your data. Okay, now onto the accessible. So accessible means accessible from the internet. So at the very least, if your data and your data sharing follows under the fair principles, your data should be accessible from the internet. Now, I say that, and I also have the point that your data itself does not have to be totally open to the data to the internet there may be lots of reasons why you don't want to share your data you might be concerned with about your availability within your data or there's other, you know, concerns and your data sets. That means that you don't want the actual data set to be openly available from me for any user from the internet. But to fall under the fair principles, the metadata for that data. So, potentially the code books or at least project descriptors should be openly available from the internet. So the easy way to do that is to use a data repository. So many data repositories give you the option you could have, you know, your data set itself. And all of the company metadata can be openly available to the internet, or you can have just the metadata openly available and accessible. So the data set itself is only available through request or if you have, you know, you have to show that you have IRB permission to access that data or whatever it is. So this is the accessible principle of the fair principles. So examples of data repositories for our field, there are, there are certain levels of data repositories the first level is called general data repositories so they are created to store pretty much anybody's data. They're not field specific. So they're these, you know, kind of domain general data repositories, an example is OSF. So fixed share is an example of a general data repository. The ICPSR, which is now normally more often how it's called but it actually stands for inter university consortium of political and social research. That is also a domain general repository they do take in and house data from educational scientists, as well as data verse. An example of data versus the link that I gave you here to Harvard but there are different instances of data verse kind of available and are, you know, accessible for for storing data from across domains. Different than a domain general repositories or discipline specific repositories. So these are repositories made for a specific community of people. And so they're, they are created with that community in mind and then therefore kind of optimize for that community. And so some examples are data Breary within our field so data Breary stores developmental video data so they are specialized in not only a developmental data but also in video data and so it was created purposely to be able to store videos. Similarly, there's the qualitative data repository. So this is a data repository to store not video data this time but qualitative data from the kind of the broad social sciences. Another example is LD base that's the data repository that I'm involved in and LD standing for not just learning disabilities but actually standing for learning and development data. And then kind of everybody on this calls data we we specialize in story and storing not in quantitative data from developmental and educational sciences. There's also grant repository, sorry, data repositories that may be specific to funders. So NIH is a prime example of this they have created data repositories to store data of their grantees dash is one example. This is the data repository created to store the data from an ICHG grantees which many of you may be familiar with that Institute NIH if you're educational scientists. As well as if you do autism research. There is the end our data repository so that's the story data from autism researchers. So there's many different places at different data repositories that are available to you that have been built to store your data from, you know, domain general repositories to more specific repositories that may be more optimized exactly for the type of data that you have and that you want to share, or data that you want to access as a data user. Okay. So what I know of the fair principles is interoperable. And this is a more of a computer term than a researcher term. This is this idea that your data, your metadata and then the company documentation of your data sets are readable to other computers. So that may sound like a really specific kind of like it term that you don't have to think much about but I'll give you a kind of a prime example of proprietary statistical software. So if you've ever tried to open, you know, say like an SPSS data sets in a different software, you have run into issues of proprietary software formats where it can be really difficult to open up data sets that are saved in proprietary formats and other softwares. You know, I've always joked that if you're an R user, well pretty much any actually any software that kind of the hardest part of any, any software is bringing in the initial bringing in of your data set right kind of getting those getting the format correct and making sure that your computer is reading that format correctly that it's reading the variable names correctly in the variable labels. So this is this idea of interoperable. That's, you know, your computer can open that data set recognize what the values are what the labels are what the variable names are, and properly and even missing data values and properly read that. So that's the interoperable principle of fair data sharing. And then the R is reusable. So, this idea is that that, you know, it's kind of like the idea of you know if you build it they will come if you put out your data set for sharing then you would hope other people will use it right so you want to have users be able to find your data set and actually reuse the data set. So once again we had metadata in the findable principle, but here we have it again in the reusable principle right. If you've ever gone somebody else's data. You will know how hard it is to reuse some data sets without good metadata, you know you need good data documentation and good project level information or kind of all the aspects of the data set that when if you are not the person who created that data, then you just don't know, you know, all of all of the you know those specific things that happen in a data set without good data metadata. So by having good metadata you make your data reusable by other users. And another aspect of reusable a little bit different than the metadata piece is provenance. So, you know you want other users to use your data but you also want to tell them how they can use your data. And so you can choose different licenses and a good data repository will have different license options for you to choose so that you can decide how you want your data to be reused. You know everything from, you know, let me know that you're reusing it to, I don't need to be involved that I'll go forth and write your publications in your grants with these data. Also part of that is do is for citability. So a data repository should hopefully give you a DOI for your data that makes your data set then citable, which you know as a key kind of currency to our field. You know, to to site products that we're using so that the creators of those products, including data sets can follow who's using their work and and also kind of track it maybe for, you know, their original grants tracking or whatever it is so part of this reusable function is assigning in the do is and then as a data user using those do is as part of a citation of using those data sets. So I've convinced you about why you should want to share your data and in some aspects about what makes data data sharing kind of fall within the fair principles. And now I'm moving you all into being ready to share your data. Some things to think about before you start sharing your data, you know, well, even sharing your data is pretty far along so let's take a step back in your in your research process and think about before your studies starts. So, inform consents and considering inform consents can be an important part of sharing your data.  And so you're writing and inform consent that your participants are going to fill out, you know, consider the language that you're including in your form consents. And you know how the research process works you know you don't necessarily rewrite an informed consent for every new project you copy over language from your previous informed consents. And maybe even your previous informed consents maybe you got those from, you know, back in the day in your advisors lab and those are the informed consents that were using your advisors lab and you know these informed consent language that have been recycled over and over again. And you know back in the day informed consents used to have to have pretty restrictive language around data sharing. And so what I would recommend to you is to just take a second and look at your informed consents language, and not use restrictive language in your informed consents. It's not needed by IRBs or ethic boards anymore. You know you can, you know, let your participants know that you plan to share your data you're going to share an ID identified way.  So think about the language or including your informed consents if you're collecting new data. You're collecting archival data so you are, you know, thinking about, you know, a project that you completed 10 years ago and now you're like hey I know data sharing support I know I can really increase the value of these data by sharing it so that others can use it because it's just sitting on my computer and nobody is using it right now. I encourage you then before you share your data to check your informed consents for maybe that old restrictive language. And don't fret if you do see an old informed consents of data you would like to share if you do see a language that is restrictive. But IRBs now know that there is a culture around data sharing that data that funders are, you know, more and more requiring sharing of data, even older data sets, and so they are open to what's called considering a waiver of consent. Whereas you write your IRB even the IRB you're at now and say I have this old data set and these old informed consents that had this old data, you know, I will not share data I'll destroy all data language. And I would like to wave that original consent I can't recontact participants, but all I would like to do is share this data in a de identified way for others to use these data sets and in my experience I've been very successful in receiving these waiver of consents, given kind of the change in our in our research culture around data sharing and understanding kind of the importance of data sharing, and then also understanding that the identified data is very low risk to participants. If you'd like to know a little bit more about what informed consent language you might consider and how you write protocols to IRBs for waivers of consent and otherwise. We have a resource page on LD based or where you can check out some resources where we have templates and other language to kind of help you navigate informed consents. Okay, also before your study starts you're going to think about your data entry. So if you're collecting new data, I would encourage you to take the time to have your data entry process enter item level data and not just, you know, if you let's say, and in my, my sub area of education, you know, we love the Woodcock Johnson test so you know you study reading and math ability and kids using the Woodcock Johnson battery, but you may not, you know take the time to enter in how did that child perform on item one and item two and item three of the letter word ID test but you just enter in the total score and move forward. A lot of really great data reuse ability comes from storing from using item level data or having access to item level data. That is, you know, one of the most common requests we get on LD base is looking for data sets that have item level data. So I encourage you to consider entering item level data. I also encourage you to use consistent variable naming approaches, you know, within a project and even across projects in your lab. So that it's, you know, variable names are kind of part of the metadata of your data set and it makes data reuse easier. So you have a consistent variable name approach, and then also please use double data entry that results in good high quality data that you then can give to others to use. And there's a citation of some work that's been done that's looked at data entry errors across different data entry procedures, and really kind of shows that the value of spending the time to do double full double data entry. So if you're interested in data management more broadly there's a lightning talk I recommend that you go see later. And I've also listed some resources towards data management and good data management practices that you might be interested in. Okay, so after your data is collected you're going to clean your data. And make sure that the missingness that is in your data is expected that it wasn't a data entry mistake but instead it's an expected missingness in your data you're going to check for out of range values. And you're going to check for inconsistency of values so things like you know a date stored as a character value rather than a numeric value in the light. So cleaning your data again kind of thinking about okay what type of data is a data user what I want and I would want you know high quality data that. And so, if I'm collecting new data and I, you know, I want to make sure I clean my data before I share that data in a data repository so these are some things you can think about when cleaning your data. So as part of that data cleaning process you're going to de identify your data, you know, a specific data birth can be identifiable, but you know, age is important and develop and developmental or educational work. And so you can turn that data birth into an age which is less identifiable, you know, removing names and other direct identifiers then like zip codes or addresses, you know remove those from your data. You need to think further than just direct identifiers though sometimes you need to think about indirect identifiers, especially with often called across tabs identification so let's say, you know, everybody knows, you know I'm at Florida State, maybe my data collection is probably from the surrounding Tallahassee area or, you know, Florida Panhandle. And, you know, in that set of schools you know it's a fairly limited set of schools I have in my data set, let's say, you know, a male teacher who identifies as African American. And, you know, that once you do that cross tabs across three variables, you know, or two variables, gender and race, it actually can make a participant identifiable within that data set whereas just gender or just race would not be identifiable so this is this idea of looking across these indirect identifiers and combination of variables to see if you can get to a cell that has only one participant in it and that participant might be identifiable. We've created some resources to help you check your identification, including an app, a shiny app that will that will go through your data and identify any areas where you may have identifiable data in your data set so I encourage you to check out some resources that we have an LD base to kind of help you with that data identification. Okay, also throughout your study you're going to want to document your study we've talked I've talked about how metadata is so important. And that metadata, it can be pretty frustrating and time consuming if you create all the metadata at the very end of your study so I do recommend that you take the time throughout your study to just take a moment and write down the information that you're going to need for your metadata later so you know summary information about your study, sample information, protocols, the numbers that you use in your study, missing this and how you're coding missing this in your data, as well as a data dictionary are all a kind of key aspects of documenting your study that will then create the metadata when you share your data and a data repository that can be incredibly frustrating to you have to create at the end of a project or if you're like me and can barely remember what happened last week, never mind what happened on the, you know, a protocol decision you made three years ago, during active data collection, it will be impossible to recreate at the end of your study so throughout your study document your study with an eye towards what metadata you'll need in your sharing of your data. And then, at the end, you know you're going to pick a data repository, I suggest you will pick ldbase.org but I gave you a list of all kinds of different data repositories you may consider they all have different pros and cons, and different kind of ways that they can optimize to be the right place for you to share your data, and you upload your clean de identified data. Okay, this is a question that we get I saw earlier in the chat that this question already came up this idea of resources sharing data is not. It is not cheap in regards to money or in time. And it is a common that I hear about you know, it just does take resources to properly share your data. So some places that you may consider to find resources, you might check your institution to see if they have small grants with better within the institution or other kind of pot somebody to help you to share your data or to do the data management that you need for your project. And even maybe something like, you know, a funded graduate research line so that a graduate student can help kind of oversee, you know, good data management practices in your lab, or, you know, to create the data documentation so that you can share it. So consider if you're a grant writer investigator, at least here in the US, writing in data management and data sharing budget items are allowable expenses. And I encourage you to put those expenses onto your budgets, so that you have the funds that to to get the expertise to do to do everything that you need to do to share your, your final data. And they're also an amazing resource at universities. You know, I have, we have a librarian as a co investigator on our grant for LD base, and they're part of our tech staff and creating our data repository, you know, librarians specialize in storing information. And if you data is just another type of information and so librarians when they go to library school or trained in data management, and can be really great resources within your university to help support your data sharing. You know, questions or potentially resources as well. And there are some funding sources out there for a US based investigator that NIH has a grant mechanism that you can submit. It's $100,000 in direct costs for I think up to two years to support your data sharing the grant is entirely just to give you the money needed to get your data sets where they need to be to share them in a data repository so you can check out that grant mechanism or there are probably others that I'm not familiar with that is specifically to support investigators to get archival data sets shared. And then you also might look like I said to your libraries to see if they have mechanisms for funding. Okay, that's the end of my formal talk. I see there's been lots of chatting and questions and I'm going to stop sharing my screen so I can see those better. I would love to hear questions I would love to hear about hesitations, or other concerns that you have I keep a list of those. And so that I can, you know better inform future presentations like these and to think about how we can support our community towards data sharing what however that looks for our community. So, there's my contact information. And yes, that's the end of my talk so I'm going to turn off turn this off and turn into questions and chat. Okay, well I actually just see the video of myself. But I see there's question and answer. So I'll go to the chat. See here, navigating. It's hard not to people to see or talk to anybody. So I answered the question of what is open data sharing I believe. Can you make a clear difference between open data and sharing data. I, you know, I don't actually think that I like firmly have a clear difference I think I just when I'm speaking and so this might have been being a little unclear, I kind of use those terms interchangeably. I prefer data sharing because I think that more directly refers to what what it is you know sharing your data however, you know, however you share your data, as even if it's just the metadata. But I would at least say open data is kind of similar but some may define open data as that data that's not only shared but like shared freely to the internet so there is no request process to use that data. I don't really remark the two terms the same way but others may cite me on those the differences in the terms of open data to data sharing. Yeah, so is there a requirement procedure for how or whether to write data sharing into an IRB a consent. You know, full full consent full knowledge is ideal and your IRB would love you to explicitly say in your informed consent that you know you plan to share the data and that would include to, you know researchers outside of your team. I'm not going to give you an example language that like that on the resource section of IR of LD base but in reality my experience of working with least American based IRB is is you can share your data, even if you don't explicitly say that you just run into problems if you've explicitly said you will share your data. So not saying that you're not going to share that you're not saying you're going to share the data does not preclude you from being able to share your data. Although open and free consent, you know, kind of ethical guidelines would say you should tell your participants that's your plan. I think that's a requirement to actually share your data in my experience. The issue is, if you've explicitly said I will not be sharing that nobody common language or thing or words like only the research team will access these data, or data will be destroyed after seven years or five years is the common one I've seen. I can get a little confusing because if you I think what we used to think when we said that was that the paper data will be destroyed, but in reality, now our data is electronic and so that would suggest that all of your access to your data should be destroyed after seven years so don't write anything like that into your into your informed consents. Yeah crystal asked if I could see the chat I can there's just been a lot and it's hard to scroll so I'm sticking to the question and answer here for now, but I can maybe move over to the chat after I go through this last one so I've heard concerns from researchers who put the tremendous time and resources. Yes, this is definitely a concern that I hear often about getting scooped on mining their own data sets for publication. So I have a couple answers to that. First, you know, I, I, I like to show the positives of data sharing and into support researchers and any way towards data sharing, and that includes very hesitant or skeptical researchers I like to support everybody into sharing and so I support people from where they are. So if a researcher is, you know, very hesitant and they're worried about being scooped. I talk to people about the idea of, you know, maybe your data sharing is the data that that's underlying a paper so you've already once you've published a paper from a specific data set then, then you share the data to that publication so you've already published the research question that you intended to from that data. So when I'm writing new grants with people and I'm working on creating data management plans with them. I also can suggest ways to that you might. You know in your data management plan, you might say that you will share your data after you know the three main papers of your three specific aims or whatever it is are published. So once the major specific research questions for that grant are published, then all the data sets the data from that grant will be fully public fully made open so that you get the chance to write the key. Let's say if you're doing like an RCT, you know, the original investigator is going to want to publish the, you know, the impacts paper from that RCT and so you write into you can write into your data management plan this is this is still data sharing that you will share your data, but after the impacts papers written and published. So that's my way of trying to, you know, again, you're still getting to the final goal of sharing the data with our community in the end, but the timeline does not have to be as soon as the data that piece of data is collected it shared, it can be on, you know, a more elongated timeline to get you to publish. So if you're writing into a project so you know, scooping might be a biggest concern for, you know, early career researchers. And so I say I've said things in data management plans like we will, you know, until you know, three early career researchers have written papers or whatever on these data, then we will share the data to give the early career researchers on the team that chance to publish first before then sharing the data. And, and then also generally what I usually talk about with people when we're talking about concerns about scooping is at least my experience in our field and this may be you know specific to my sub area of our field. It's just, it is so rare for somebody to come up with the same research questions that I came up that I come up with. And that I had planned for a data set, you know, I, at least in my again in my sub domain and education and developmental science. There, there's just not a lot of overlap of the type of research questions that everybody is kind of hunting around that exact framing of that research question. I, I'm a little bit less concerned about scooping that exact research question because what I have most more often have seen is that people from different backgrounds and different trainings and different approaches come at a data set from a completely different, you know, or at least a different enough approach that still make, you know, publication possible for everybody using those data. There are some ways that I think about concerns and talk with people when they have concerns about scooping on using data sets. I would love to hear if other people have other ideas again I'm always, I have a pen and paper with me I would love to write down thoughts and how we can communicate and support people where they are and show them that there are many different ways to get to data sharing that does not have to be your entire collected data set out as soon as it's complete as soon as you've collected it and there can be kind of other other other ways to share data, even for those who are hesitant around aspects like data scooping the larger change in a sense of structures of the field. My research area is definitely not like examining incentive structures. I'm toying around with the idea of doing trying out different, like doing an experiment to see what incentives we can provide investigators to share their data. One of the ways that I got into data sharing actually, years ago was I entered in the Center for Open Science had a competition where you would get cash if you did a pre registration. And I did that I did that competition and I published my, my first open science related paper using that and incentive of cash. And I've considered doing something like that through LD base and trying out different experimentally trying out different incentives that might get people to share their data. But in the meanwhile before that I've, where I've seen big changes from top down incentives so the funders, mostly kind of forcing change that really worked well for open access publications you know if you're NIH. I you know I get NIH funding and you know before every progress report every year of my NIH grant when I say we've written these papers on this grant. Those papers have to be openly available or else I can't get my next year's worth of funding, you know kind of that top down linking your next year's of funding or or future grants to creating open access publications has worked in open access publications I think. I hope that the kind of the new data sharing requirements that are coming down the pipe from NIH and I yes, I yes has had it for a while but you know that there will have a little bit more kind of teeth to them, and requiring what investigators are saying about their data management plans. And the next question is there are so many great data sets out there that become accessible to investigators yeah the struggle is finding. Yeah. Yeah, so that's why I've been compelled this idea that question. Everybody can see the questions I hope. But you know I think that the hope is that I like domain specific repositories like LD base and why I'm encouraging everybody to use LD base is that that it is easier for the community to know where to go to find data, you know if data sets are existing some on some personal websites. Some are out on say ICPSR somewhere out on, you know, COS somewhere out you know on all these different places, and the community doesn't know where to go to find data. It's nice about a domain specific repository instead we're like, if I need, you know if I'm looking for behavioral data in education and developmental science, I will go to LD base, because that's where the investigators who work in that field are storing their data, and that's where I can find it. And in addition, you know that finding data is such an important part right it's the F of fair principles that, you know, we created, for example, I was talking about LD base because that's where I have my most you know specific knowledge but we created you know the meta data fields in LD base to be completely optimized to our field so that when you're searching for data when a new user searching for data, they will use the type of terms that we have already created the search terms around. You know that's, you know, so that it, it, you know that searching and finding data is easy, because it is specifically made for you as a user to find that same type of data. And so it makes it easier to find the data so I would suggest, you know, being thoughtful about what data repository you put your data in, because where are users going to go to try to find your data. And that will kind of make it easier if we start to maybe call us around kind of communities around data. Yes, do I think it's important to teach students about data sharing and open data, I do. I actually taught a graduate level. I was in the development class last semester, and by four weeks of that class was actually not traditional data, you know, professional development topics but instead was about open access principle, sorry, open science principles, as well as good data management practices, because good data management kind of lends itself then to sharing data. And so I do think it's really important. I don't think there has been, there's not a lot of explicit training in that, in that the lightning talk I told you all to go see later this afternoon at three. They will be discussing some recent research of survey data that look to see exactly like where have people learned about data sharing and good data management practices, and it has not really been explicit formal training and graduate school or the like. And we're hoping Jess Logan and I are hoping to get a grant funded we've been putting in grants to create training workshops around data sharing and data management practices to help support our community, because otherwise it really isn't typically trained at the graduate school level. Okay, those are the formal questions so I'll just glance through the chat here. Everybody has been putting the questions in the chat. Thank you. So everybody about not I just see now that you were able to see the questions that I see that they've been posting the questions over there so you can see the full text to read them fully out loud because I thought you all could read them. Oh someone put a great resource in there for a cost infographic for data sharing. That's a great resource. Oh and for budgeting data management. Oh good some resources from the qualitative data repository yeah I'm, I'm a quant researcher so I mostly think about quantitative data but it looks like somebody's been sharing some, some qualitative data, data sharing resources so thank you for that. Somebody asked about libraries that have a formal data sharing funds. I've said that in a hopeful way but I actually don't know of any explicit examples I know at FSU, the librarians are available for expertise in that area, and actually will spend some of their time helping investigators do data management and data sharing so not a fun that you can apply for necessarily but what I've seen is that they are big that the resources that they give us their time and their work on helping you do that. That's something that happens at FSU and that that may be more how the librarians could support you with their time and their expertise in helping you set up your data management and data sharing. But yeah if anybody has any example of actual funds available internally that would be great and maybe we suggest there may be some librarians here today I bet, you know, I've seen funds on supporting open access publishing, and also open textbooks and it would be great for funds to be available through libraries for investigators to share data sets that they have, or prepare to share data sets. Somebody asked if LDBase includes a direct observational data. Yes anything that is a, you know, a quantitative data point can be shared in LDBase and may be available there so I encourage you to go and you can search for it. And off the top of my head of the projects and I'm super familiar with LDBase, I'm not sure if we have any data sets that have direct observation like research or observation assuming with self reports, but there are a lot of really big RCTs, especially in reading interventions that are in that data set and it would not shock me if there were some maybe classroom observations or teacher observations in those data sets. I'm going to scroll back down to the new ones because we have like a minute left. Oh great yeah somebody's talking about some funds for open access textbooks. Librarians are amazing y'all and you should work with your librarians at your university. There's a whole breed of research librarians that are here to support your research. And that includes your data sharing. Yes librarians for the win. Well I think we're just about to run out of time and so I kind of wrap it up but I, this may stay open for a little bit so I'm happy to just watch the chat. Just in case anybody wants to continue to chat with me or move into maybe an open data so I can actually see you and hear from you and not be talking to the world. So thank you everyone for coming to the talk today and again I'm always happy to talk about data sharing talk about if you know if you have data management plans that you're writing for grants if you're writing IES grants next summer I'm happy to talk to you about how the LD base might support you in your data sharing goals. Oh great lots of great resources thank you everyone. Thank you so much Sarah I think in general the consensus is this was a really informative interesting wonderful talk and. Thank you. Yeah, thank you everyone.


The South House Solidarity Group, SSSG, has alleged that some contestants for the 10th Senate leadership have bought a whole housing estate in Abuja to bribe senators-elect to vote for them. Mr. Peter Iban Ilefa Kenvena of the SSSG raised the alarm on Saturday at a media briefing in Abuja. Ilefa said the group discovered a plan being orchestrated by some contestants to bribe senators-elect with houses. He called on the Economic and Financial Commission, EFCC, and the Nigeria Police Force and the Directorate of State Security to swing into in-depth investigations. The discovery of a plan being orchestrated by some contestant who allegedly bought a whole housing estate in Abuja, ostensibly to bribe senators-elect to vote for them. Mr. Iban Ilefa Kenvena of the SSSG raised the alarm on Saturday at a media briefing in Abuja. He called on the Economic and Financial Commission, EFCC, and the Directorate of State Security to swing into in-depth investigations. Mr. Iban Ilefa Kenvena of the SSSG raised the alarm on Saturday at a media briefing in Abuja. The Nigeria Police Force and the Department of State Security, GSS, to launch an in-depth investigation into this food stretch against the sanctity of our national assembly by a handful number of persons. When found wanting, they should be immediately reeling in and taken before the law for their inundated ambition to compromise the integrity of the Hanochaba. Hello, hope you enjoyed the news, please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Would you say your spelling program is organized? Well, this is just one way that I keep organized in my spelling program. I have this tub that's a file folder system and inside of it. I have hanging files with all of my options for spelling. I have pre-copied all of them. I have labeled all of them and they are ready to go. So all I need to do is pull this tub out and each week my students know that they have two separate papers they need to complete for spelling or word work among other things. So all I need to do is pull out put them in here and I'm done. I don't need to go copy things. I don't need to look through a binder or folders. It's all right here in these files. So this is just something that's helped me in my classroom and how we stay organized in our spelling program. You might try something like this in your classroom.


Young Appalachian singer-songwriter Adeline Raimi released a new single. The song is called East Kentucky Morning and I thought that every artist has a song about home. East Kentucky Mornings is a captivating composition that showcases Raimi's rock talent and heartfelt storytelling. So it's about a man who is from East Kentucky but he moved to the city and he really misses home and so it's basically about how he is coming home to see those East Kentucky mornings. Cancer, it's a very scary word but an outstanding treatment facility was here at home. I was led to the Lawson Cancer Center. As a nurse practitioner at the Lawson Cancer Center we are champions for our patients. I am so happy for Robert. The treatment worked. The talented young artist has already achieved remarkable success charting at an impressive number 26 on the Bluegrass Jamboree Top 100. What I do, I play out stories in my head and then I think of the characters and what they're feeling and then I write it down and then I ask my dad for help with the melody. You have to publish it and then copyright it. No, copyright then publish and then there's your song. This incredible achievement marks Rami's fourth self-written and released song. To show your support be sure to listen to East Kentucky mornings on all major streaming platforms and keep an eye out for her upcoming performances. So I'm super excited for the Country Music Highway tribute show on July 29th and I'm going to be at the Brakes Gospel Sing and in November I'll be at Renfrew Valley at the Kentucky Music Hall of Fame. Adeline Ramey has proven that age is just a number when it comes to talent and determination. Reporting from Elm Top News, I'm Ethan Miller.


I haven't seen a movie this good about Stockholm syndrome since Beauty and the Beast. Hi to our distributor, actually it is, I'm Corbin. Idiasha, are you okay? Idiasha? You Idiasha? Uh, and please follow us on Instagram and Twitter!  It's so cheesy. It is. Um, and today we are doing a movie review. Movie review time! We did, uh, highway. Lash has been requested for an awful long time, starring our favorite, Ali Abad. We are the official Ali Abad fanboys. And if you are asking, why aren't we doing another Ali Abad film? I took a poll on Twitter. If you want to say... I put it right here for you, because I took a screenshot. It's not just us folks. I didn't decide it, it's not just us. I looked it up to you. Yeah, and look what you did. Thank you though. Uh, but yes, we, uh, we watched Highway, which obviously, of course, stars Ali Abad, and I, uh, I didn't get the other guy's name. I would tell you right now though. It starts with a Z. No, it doesn't. Randip. Randip Huda. Huda. Uh, and it was directed by... There's the Z. I knew there was a Z in there somewhere. Emtiaz Ali. Emtiaz Ali? Emtiaz Ali. Okay. She did, uh, Jabli Met... He did. Sorry, he did Jabli Met and Rockstar as well, which we have not seen in the trailer. Correct. Correct. But, um, yes. And so you want to read the synopsis real quick? Right before her wedding. A young woman finds herself abducted and held her ransom. As the initial days pass, she begins to develop a strange bond with her kidnapper. That's a pretty good synopsis. Thank you. So, uh, please, what did you think? Start it off. Um, you might be surprised. You might be surprised. Didn't like it. Why? Uh, there's a lot of things about it I didn't like. Okay. What I do like, I'll start with, because I always like to start with the things that I liked about it. Okay. I liked the premise. I think the premise is extremely strong. Okay. As far as the flow of what the story could be. Gotcha. Um, I also, um, I like... Uh, I, obviously, we love Alia, but as much as I like moments that she had, this was the first time I've seen her work, and it makes sense because this was one of her first films. Mm-hmm. And we've seen some of her later work, which is several years later, and her maturity and her growth as an actor has improved dramatically for me. Okay. Um, so what did you think? What did you not like about the film, though? Uh, there's a lot I didn't like. Okay. Um, I, I, I, first of all, they left out two of the most important elements of Stockholm Syndrome. Mm-hmm. Um, and you mentioned the film, didn't you just mention Beauty and the Beast? Yeah. Yeah, I did. That's a great Stockholm Syndrome film, as is King Kong. Yeah. Um, I think there's, there's several that I can think of that are really, really good. And what it missed, there's basically, there's several elements, but the primary elements that were missed for me are the element of the kidnapped person finding something in the other person that they can relate to that was completely unexpected to them when it began, and they begin to develop a compassion for that person early in the stages of their connection with them, and they see a justification for what they're doing as a kidnapper. Um, I, I saw that happen later and it felt very forced. It didn't feel like that happened naturally, which is another thing for me was script. I had some challenges with the script and the directing in terms of kind of her, her Stockholm Syndrome was more internally revelatory when she's talking out loud about, I don't know why I'm enjoying this and I don't know why I'm talking so much. And versus it being connected to the person of her kidnapper. Okay. So from a psychological aspect, I didn't see a genuine depiction of Stockholm Syndrome. I think you're completely not really wrong and I think you completely misunderstood the film itself. I may have. Because it's not completely a Stockholm Syndrome film. And I know that. Because it's not just, that's literally not what it's about. The reason, oh, this is a great idea I'm having. Um, the reason that she fell in love with, well not, I don't even think she fell in love with him. The reason that she found a liking to him is because he was the first guy in her life really that even though he abducted her, he wasn't trying to have sex with her. He wasn't trying to make her something like confine her. Right. He wasn't trying to do any of that.   Her uncle is clearly a terrible person. Yeah, her uncle is obviously a terrible human being. But her father really, I don't think was around. He's some big shot, rich guy, good brands of companies. I'm guessing he wasn't around. Right, right, right.  Right, instead of saving her, he's like, I told you, this is what was going to happen. Right, right. You're getting kidnapped. Told you so. But that's the reason she started to fall in love. And it was this movie, I think, was actually more about the girl finding herself. And it was about Stockholm Syndrome. So I think you're all your stuff. One, I think this is my favorite Alibak performance.  You're kidding me. No, because everything that you hated, I thought was brilliant. So let me take you to a spot where I am the most, where I saw her work the most. When she was talking to herself? No, no, no. Where I saw her working the most was the last scene. Oh, by the way, we didn't say this. All of our reviews have spoilers. So if you haven't seen Highway...  Yeah, so. Watching.  I saw her fighting to find where she was emotionally and pushing places rather than just being. I saw a young actor trying to do the scene as best she could and her working is very good. But it did not... I don't agree at all. I think she was trying to... The reason it looked like she was finding is because she was feeling so much in that moment. She had just lost a guy that she had started feeling for. She was just kidnapped and so that's the whole thing.  So she's never talked to her family or anybody like this. Absolutely. Which is all the monologues she had. I'm always a very polite person. Right. She's doing all this stuff because it's an inner monologue. She's never actually had... One, she didn't have anybody to talk to which is why she was talking to herself. Right. And then she was talking everything out because one, she was also going a little crazy. But she was talking it out because she didn't actually know what she was doing. She's never done any of this before. She's never gone out. And then she was starting to like it.  I don't understand it. And so everything that you think is bad I think is actually a brilliant performance, a move, a decision by the director. I thought this was an absolute gem of a film. And I'm... I think you were judging it way too hard. I think you were looking for stuff. No, I really wasn't. Yeah, I think you are. No, no, no, I really wasn't looking for stuff. I think you're looking for stuff to not like in this film. No, why would I do that? I don't know why you're doing it. Why would I look for something to go wrong? I'm rooting for her all the time. I don't know why you're doing it, but you're doing it. No, it's my genuine observation of the fact that what I saw was a young actress doing her best with material that wasn't strong, a director that didn't guide her. And she was forcing some things at times, not all the time. But there were some times where she was forcing herself to portray something rather than being in the moment with it. I didn't believe when she was confronting her uncle that she was going through the multifaceted layers of that kind of a traumatic moment in front of family as well as the loss of a loved one. Why does it need to be this big thing? There's nothing big. She literally went through it all with the guy.  That's what it is. That's how I would do it. So no, you can't just say no, that's because that's not how you would do it. You would start crying. Not everybody is like that. No, I wasn't saying that she needs to be crying. I wasn't saying that she needs to do it like I would do it. I'm saying I didn't believe her. I would completely do it the exact same way because not everybody feels in the exact same way you do. No, absolutely, they don't have to. And she doesn't care about these people anymore. So she's not going to go like this big, do this big ordeal about the guy when she's already kind of gone through it. She's already decided. They've trained her. They've done all this stuff.  I don't want to be with y'all anymore. No, and it's a whole thing. I'm not even saying, I'm not saying she needed to, that her emotional palette needed to be any different. I'm not talking about the emotional plane she was in. I'm talking about her capacity to tap into the emotions in a real place and present it to me as an actual happening by a being in the moment. I saw Alia working. No. Yeah, I would love to know if she felt that way as well. I felt like she was into material that was one of her greatest performances by everybody. I don't know, not everybody loved her performances. I've read reviews of it. No, literally, this is everybody says this is probably one of her best performances. This is one of my, this is my least favorite performance of hers. I think her work in Uda Punjab and Razi and Gully Boy are, I think you can see a trajectory in her work because we've now seen quite a bit of her stuff and this is the earliest thing we've seen. This is like her fourth film. And I saw someone who was attempting to portray some things that she herself didn't have an emotional connectivity to and rather than actually be in that moment with something like substitution or any other thing she could use as an artist, she was showing us what she could do with what she had. It reminds me of when I did a play and I had to be, this is when I was in high school and I had to be in love with somebody. And the director of the play was saying to me, let's do this, let's do that. And then the director just came over to me and said, have you ever been in love before? And I said, no. Said, okay. And she was trying to help me wrap my head around how to portray what it's like to be in love and I just wasn't able to get there. That's all I'm saying. I saw a actor working with the best that she had, which her working is better than a lot of people's best. But for me and for my standards of what I think she can do, I think incredibly way too critical. And I think you were looking for stuff because this was an amazing performance by Alia. I loved everything she did from the monologues that you seem to hate it. I loved. I thought that was a hatred. I think it was an amazing, because one, it was very different. Yeah, I don't see people doing that. No, I thought she did a, I actually think she did a really good job with monologues that I didn't like the writing of. What part of the writing didn't you like? I didn't particularly like, and I think you would agree with me, I didn't particularly like in those self-discovery monologues, the saying of a self-discovery monologue, so it's like in between a soliloquy and a monologue, just let me watch her develop into what she's going through. I don't need to hear her saying what she's realizing and she's realizing. I'd rather see her realizing it. That was literally the whole point of those. To tell us. No, no, to tell her. And tell us. No, she's talking to herself like a soliloquy. Yeah, so like it's literally for her. It was also for us. She was literally talking to herself because trying to figure out what she's going through in her mind. It is, you completely misunderstood this film. No, no, no. Yes, you did. Okay. You completely misunderstood this film because literally everything you're complaining about is what the intention was and it was a brilliant idea. The only thing I would agree with you about this film is that actually the other guy I saw working. Not her. No, I saw him working as well and they didn't have any chemistry. I didn't believe that they were inclined toward one another in a way that I cared about. Well, I don't think you're supposed to. Oh, really? No. You don't think that when he shot you should feel like Faye Ray watching King Kong die in her heartbroken that the guy who kidnapped her, you've seen a change and you have compassion for the guy. You don't think that was the intent? No, because it's still, he's one, he's still an awful person. He's a murderer. But the guy has literally Stockholm syndrome. And so you're supposed to feel bad that she feels so attached to this guy. No, no, no, no. In real Stockholm syndrome, you're supposed to with her have an emotional attachment to the kid now. Another thing that I think you misunderstood about this film, it's not all about the Stockholm syndrome. No, I know that. It's about her finding herself and she just happened to be there when she was finding herself. Yeah, but you can't deny the fact that part of the storyline was the fact that the two of them became interlinked because they had the Stockholm syndrome but you complained right at the beginning about how this is not exactly Stockholm syndrome. This is, which is why I don't think you actually understood the film. Well, no, if the film ends with her crying out his name, clearly she's, she felt for him. I'm not saying she didn't. In a way that's supposed to be indicative of Stockholm syndrome. I told you it's partly about Stockholm syndrome. It's not all about Stockholm syndrome. I didn't say it was all about Stockholm syndrome. I said the Stockholm syndrome aspect of it was hollow because they missed the key elements. Well, I think that's all you were looking for. No, because I was also looking for good acting. And you, it was in there. No, she was good most of the time. She was never great and he was hardly good for me most of the time which shocked me because I understand he's done theater and I don't know if it's because of the script or the directing or what took place but there were even moments like something as simple as this. There were moments where when she had her monologue with him and she's sharing the fact that her uncle had raped her repeatedly, right? I really was hoping that the director was going to just stay tight on her and leave us in that intimacy of moment with her so that we could just sit on it. But for whatever reason, the director decided to cut away several times into a separate angle that wasn't a two-camera shot because you could tell from continuity mistakes that it was separate takes for something that poignant and powerful emotionally, especially for a young actress, leave the camera on them so we get one singular performance and we don't lose the dynamics of what she's going through where you go to one side and the tear is gone or she's in a completely different emotional landscape because it's her second take. Those kinds of things were very distracting for me. Something's wrong with you. You're no longer allowed to be in there while you've got fan boy club. Well, I am because as you know, you're not allowed. There is no actor or artist who's higher than the art form. So there's no artist I love, including Daniel Day-Lewis. You know who's at the highest? Apparently, Rick. No, no. Even Daniel Day-Lewis? Even Daniel Day-Lewis when he does something that is a subpar for him and for the art form, there's no one above the art form. Nobody. And no one does it perfectly. Except Rick. He knows all. Apparently. Because I have an opinion different than yours. Performers.


Now, let's look at some key creations that you need to remember when it comes to understanding and analysing the character of Lenny. Now, of course, always remember that Lenny, whilst he's really powerful, quite large, he has a lot of brute force, he doesn't necessarily know how to wield this. Now, this is illustrated, especially his power, but also his lack of awareness on how to wield this power. This is illustrated in this first quotation that describes him in the opening chapters. Lenny had large, pale eyes, ellipsis, wide, sloping shoulders. Now, the word-level analysis you want to do here is firstly when it comes to the adjective's large pale to describe his eyes, so they're really large, they're really pale, but also the sibilant sloping shoulders. Now, the image that perhaps comes to mind when we're reading this description is almost of this Neanderthal type of figure, the kind of image that we see in science books that show the evolution of man, this huge Darwinian figure. So he kind of looks like the pre-evolution figure, this Neanderthal character. And what this shows is that whilst he's really big, he's quite brutish, he also isn't very evolved, okay? Now, the second quotation to remember for Lenny's character, again, tied to his appearance, is when Steinbeck describes him as a bear that drags his paws. So the quotation is, a bear drags his paws. And again, this is a metaphor describing him as a bear, again, illustrating just how large and how almost fearsome Lenny looks, but actually he's so harmless. And he's harmless because he doesn't know how to wield this power. He's also intellectually impaired, which makes him unable to survive in what is a very brutal, competitive Darwinian environment. The third quotation to remember when it comes to Lenny's character is when he states really hopefully, and especially when George is articulating the dream of them owning a farm with rabbits and with all these creatures, he says, we could live off the fat of the land, okay? And the main thing that you need to remember in terms of word level analysis here is the alliteration of L, so live and land, and of course the metaphor fat of the land, because of course the land doesn't actually have fat, it's more the vegetables and things like that. Now, what this quotation illustrates is just his almost childlike fascination with this dream. He doesn't quite understand the complexities that go into actually building a farm, managing it, running it, making it profitable, being able to farm and protect yourself against the winters. What this quotation illustrates is almost his childlike naivety in this American dream, which makes him both endearing to us as readers because we quite love Lenny, but also we realize just how incapacitated he is mentally speaking to really survive and to understand the complexities of life and the complexities of running a farm. The other quotation to remember when it comes to Lenny's character is when he states to George and they remind each other of the loyalty to each other, he states, I've got you to look after me and you've got me to look after you. And here what you want to focus on is the repetition of got, OK, and of course also you, OK, so this idea, this repetition illustrates kind of in contrast to the other migrant workers, the other migrant laborers who live very solitary, very lonely lives, these lives almost which seem very pointless, nihilistic. George and Lenny have created this very unique brotherly bond and this brotherly bond is what really sustains them through these periods of hardship. And this illustrates kind of that love and that paternal almost feeling that George has towards Lenny, but also Lenny kind of looks to George almost as a child and they look after each other. The other quotation to remember for Lenny's character is when he actually goes to Crooks's place on the ranch once all the men have gone to the brothel and Crooks is by himself. Now Lenny is very simple minded, so he doesn't quite understand the Jim Crow era. He doesn't understand racial segregation and racial separation. Crooks is really taking it back because the reason why he lives separate to the other men is because he's black. He is living in a very racist society that sees him as different and thus separate. Lenny doesn't quite understand this and he asks this simple question, why and you wanted and of course in terms of word level analysis here, you want to focus on the idea that's a question and interrogative sentence, but most specifically also you can think about alliteration of W, why and wanted. And now here actually, and this character is really interesting because it's actually his simplicity. He's to some degree his foolish question because of course to outsiders they will say, well of course we live in a segregated society, but it's actually his simplicity that allows him to ask a very simple question and Steinbeck uses Lenny's character to actually question and to reveal the stupidity of segregation and racial separation based on someone's skin color rather than the quality of the character and rather than anything else.  And then he doesn't quite understand this. So this is, whilst this question is really simple, what Steinbeck is doing is he's asking a very profound thing. He's asking why should we see people who are African Americans as different to us? Just because they are a different skin shade to us. So Lenny is used almost as that kind of mouthpiece through his very simple question. The final quotation remember for Lenny's character is when George finds him, he's run off, he's killed Curly's wife, George finds him. He doesn't realize of course that George is planning to kill him.  Now here you want to focus on the pronoun you versus me, okay? Now here there's a separation and pronoun rather than him and George always being together. Now this is the first indication of that separation and the first indication that there will be permanently separated. George is no longer able to look after Lenny and protect him from the outside world. And ultimately of course he asks this question before he is killed. So that's it when it comes to understanding key quotations for Lenny's character and the word level analysis that you need to do. And I will suggest, but simply basically memorizing it, downloading the revision document that's part of this lesson. And of course just memorizing these key quotations and considering the word level analysis. Thanks so much for listening.


A nuclear research reactor is a device that generates neutrons. These tiny particles are used to check the composition of materials and to produce radioactive substances for medical and scientific uses. Research reactors are smaller than power reactors, operated at lower temperatures and need far less fuel. They are usually found in universities and research centres, including countries where no nuclear power reactors are operated. With the support of the IAEA, Jamaica operates the Caribbean's only nuclear research reactor, which scientists use to support agriculture and trade, for example, by helping verify the authenticity of coffee. Coffee samples are put into the reactor to be irradiated, which quickly reveals their exact composition and origin. We can use nuclear analytical techniques to differentiate Jamaica and coffee from coffee's pronouns. Blue Mountain Coffee is one of the most expensive coffees in the world, so it's very important for us to be able to protect that brand. This method is also helping experts to check the components in the soil. We can see concentrations from things like calcium to cadmium to iron, so if you're a farmer this might be particularly useful because you know the soil in itself helps sometimes to contribute to what's grown in your plant. More than 55 countries worldwide are using nuclear research reactors to help improve the lives of millions of people.


So people I am back with another Out of the Ring video today guys I'm going to show you a sweet little exploit which sees you get into pretty decent items for more or less no work at all. Now the items you will get for this are the Banished Knights Greatsword which is a beast if you like that kind of thing but you also get the Curve Sword Talisman which enhances guarding counters. How's it going guys my name is DPJ and if you do enjoy the video leaving a like really helps out and if you like what you see and want to see more be sure to subscribe. Okay so to do this firstly you will have to be an Marge already the Foul Almond which you've done him guys just follow the path up to Stormvale Castle you'll come to the main gate to grace there come back around the corner you'll come to where I'm at here the cliffside grace now guys it's pretty straightforward so let me just reset it so the ads are here for you guys I've just been up there and killed a couple of the ads but this is what you need to do so I mean you can run past these guys but I'm just going to kill them anyway because these also drop exile armor which is pretty cool but these ain't who we're going to be doing the exploit on don't worry about these let me just kill them get them out of the way please don't fall off you know how many times I fell off the map there it's unbelievable I fell off the map there so many times okay so here guys you just want to run past as you know I'm going to put my torch on because it's a bit dark in there and it's the pain you have to lighten up the video where's my torch where's my torch there she is there she is hello okay so run past all these don't worry about these guys now you want to dodge you want to keep doing the rolls doing the rolls doing the rolls now I'm a bit heavy because of that torch doing the rolls doing the rolls doing the rolls so this door you'll have to open if you haven't done this already and you want to climb up here so up the ladder we go guys up the ladder we go now you can actually go a different route with not using this ladder I just think it's a lot easier and plus the enemies down there um you'll escape them so yeah some of them don't climb up the ladder which is cool so you want to go this way then you jump across here guys jump across here and it's right below us right there right here so in here guys you'll have to open this door and there's a knight in here and there's also a chest from the knight you get a this sword and from the chest you get the talisman now this is how you exploit it so you're running you'll see me right there in the corner then grab your attention the door will be open and you just want to run you stand here if you stand here guys you can hit him through the wall through the wall what is this what is this and then once you kill him you can simply grab his loot come over here there's a chest in here I can't figure out I can't remember where it is but it's in here anyway there's also that guy's got something uh yeah it's in that corner right there and there we have it guys now I did this ages ago ages ago uh is obviously a lot easier for me to kill now especially with my beast of a weapon but um if you're struggling I want to get in here you can use this exploit get this talisman get this uh get this talisman get this talisman get this sword and you are good to go guys and it really is that simple and I hope the video helps you out if it does leave a like really helps me out if you're new around here I want to see more be sure to subscribe and hopefully people I will see you on that next one


I speak with people often who are dealing with issues from the past, residue of something catastrophic or something major that happened in their life, something negative. It could have been something that was done to them or something that they did. And what ends up happening, especially if it's something that you did, it ends up becoming a regrettable situation. Should Christians have regrets? Well, if you buy that, does that mean that should Christians, or this question is, should Christians regret things they've done in the past? Well, yeah, if you're sorry about it, but does that mean that you should live a regretting or regretful life? Meaning that you carry the baggage of that, that you carry the weight of that. And the word for you all who are wondering that, the actual answer to that is no. Paul says this. He says, Brother, I do not regard myself as having laid the hold of it yet, but one thing I do for getting what lies behind me, reaching forward to what lies ahead, I press on toward the goal for the prize of the upper call of God in Christ. Listen, you have done some things. You have messed up. You have screwed up royally. I can attest to that I have. I would sit there in prison and think about all the things that I've messed up, the lives that I may have heard and I don't know if I destroyed because I don't know. But I would look at it like that. I would feel that way. I would look at what happened with the people that were associated with me in business, the people at church, the name of Christ as well, but also my family who I would see all the time struggling and that would bother me. So what would I do? Should I focus on that? Well, if you focus on that too much, you'll stay right there in those same thoughts. You'll keep thinking about how you've messed up and it's fine. I know that you messed up, but the Bible says that we have become a new creature. Even if after becoming a new creature, we mess up, we are still being renewed. We are still growing in him. His work is not finished. As a matter of fact, Paul says this that I'm confident is one thing that he, that's God who began a good work in us. He is the one that's going to complete it. Now does that mean that from the time that he starts halfway through, he's completed halfway through? We're there. Now he's speaking in terms of salvation, but as we're moving, obviously there are going to be some hiccups. There are going to be some things that we're going to do. Maybe what you did in the past was before you were a Christian or even after you were a Christian. How could you do such a thing? How could a believer mess up so bad? Well, because he's a human being. Paul says the bad things I don't want to do. I keep doing. I wish I could do the good things, but he says the good things I want to do, I don't do. Does that mean that he didn't do any good things? No, but he keeps messing up and bothers him. Sin bothers you. And if sin is bothering you, that's a good thing. That's an indication of something on the inside that doesn't sit well with you. But if you can mess up and sit well with you or not bother you, well, now we have a problem. And so if your past mistakes, your failures, your sins, be they intentional or unintentional, it doesn't really matter how ever gross or however major it is, however bad it's affected people. If that doesn't bother you, well, that's a problem also. But is that the end? No, it is not. We are overcomers, even overcomers of our own sin. Paul, I mean, not Paul. Jesus says that no man having put his hand to the plow and looking back is fit for the kingdom. That means looking back towards the world and the sin and even the sin that you committed. All of those things are behind you. You do not have to and do not let the enemy keep you bound by past mistakes, past sins. As he says to the woman, Go and sin no more. Where are your accusers? Is there anyone here that is accusing you, that's condemning you? Now, Joseph of Jesus makes this statement to those people who come and accuse this woman in adultery. And yes, there's an issue as to whether that particular part of the Bible belongs there or not. But his point is still well taken. The pastor is still well taken. He says, neither do I. So go and sin no more. Just move forward. Just move forward, forgetting about the regrets, because I promise you, if you carry that burden of regrets, it will be pretty hard for you to even reach your potential in Christ. So do not let the enemy remind you of yours. Matter of fact, if you're open with it, folks know that, hey, I was a rotten person. I was no good. Amen. Now, what's next? That was 1990. That was 1995. That was 2001. That was 2010. That was 2022. That was last week. Now, I am trying to move forward. And if anyone stops you or wants to stop you from moving forward, that person dismissed that person and looked to Christ. Who guess what he is? As the Bible says, he is the author and the finisher, the completer, the one who completes our faith. That's what we look forward to. Amen.