In this video, I'm trading this PC for a pair of shoes. The reason I'm doing that is because I want to see if I can make more money trading the PC first rather than just selling one straight up. My brother-in-law hit me up and sent me this ad of someone looking to trade their shoes for an upper level gaming PC. When we asked what that meant, he responded with something with at least a 3060. Now, I'm not a sneakerhead or someone that cares about flexing a nice pair of kicks. Right now it's just impossible to sell a PC on the local market. So, I figured since selling a PC is so hard, I would try selling a pair of shoes instead. Now, just a heads up, this video is sponsored by Gigabyte, but I wanted to put my money on the line here, so every part value listed here will be donated to a Ukrainian Relief Fund. So, for our motherboard, we have a Gigabyte Z690 Aero G, which goes for $275. For our processor, we have a 12700K, which goes for $385. For the RAM, we have Oris RGB memory. This is a total of 16GB clocked at 4400MHz for $150. For our storage, we have an Oris Gen4 7000's M.2 SSD. It has 2TB and costs $300. Cooling our processor is a Master Liquid ML240L. We didn't use that. Well, this costs us $68. Powering the system is a PowerSpec 650W bronze rated power supply going for $55. For the graphics card, obviously we have an RTX 3060, but this one is specifically from Gigabyte. It is their vision variant and it goes for $700. And lastly for the case, we have a Master Cooler MB311L. We didn't use that either. Which goes for $75, resulting in a grand total of $2008 for the whole system. This video is sponsored by Gigabyte. Now, they sent this Z690 Aero G motherboard, which is not only beautiful, but has a lot of features that make for a great product. First of all, it supports Intel 12th generation processors, which can go up to 16 cores and 24 threads. With the potential of using something like a 12900K inside this motherboard, you need to make sure that power is delivered correctly. With the direct VRM design that this motherboard has, your voltage regulator modules will be able to deliver power safely, as well as efficiently to where it needs to go. Now with all the power processing going on, you want to make sure that you have a good thermal design, which this motherboard has. Because when your system gets too hot, that's when the part degradation starts to occur, and your performance starts to suffer. The Oris innovative heat dissipation technology will keep your system and its components running cooler by 30% over previous generations. This means your rig can perform at its best. What else is cool about this motherboard is that it actually supports PCIe5. That is two times the bandwidth of the previous generation. And then lastly, for you Wi-Fi users out there, this motherboard boasts Wi-Fi 6e. 6e is on its own separate dedicated spectrum, which means less congestion and more internet. Thank you again, Gigabyte, for sponsoring this video. Now let's get back to the rest of it. Now if I wanted to do the absolute bare minimum of what this guy was asking for, I could bring the overall price down to about $1,600, so we'll keep that in mind for flipping sake. But anyways, now that we have all that out of the way, let's get this thing built. Nice! A few more things. We're going to meet up with this guy really quickly, so we just have to... That's normal. That's normal. We're going to go install Windows, troubleshoot it, do all that, and then we're going to meet up with this guy and do the trade. All right, let's go. So as far as these shoes go, it's not like I was just told, hey, these are really good. You got to pick them up. I did a little bit of research, and they're called Nike Air Foamposites, I think. And I saw some really popular basketball players wearing them. I think there was like an image of Kobe Bryant and Rondo having like a little one-view, one of the all-stars, and they were wearing them. So I think they're pretty cool shoes. They kind of look like the Fortnite Samsung Galaxy skin, but they range anywhere from being sold online as little as $750, and they were sold more commonly in the $2,000 range. And then at times, they peak for as high as $4,000. Think is this... Okay, let's do this. Hello! Caleb, yeah? What's up, man? How you doing? How's it going? I'm good. Oh, okay. I'm excited. So let me show it to you. Do you want to... Are we allowed to go inside? Can we show it to you? I'll turn it on and everything. I am very scared of reptiles. All right. Where can we plug this thing in?  After you. Yeah, everything works out great. If you have any issues with it, obviously just hit me up. I'm just a DM away. All right. So I am not a sneakerhead. So I'm down to learn. Is there like a history to these? Is there? Because I know almost nothing. Yeah. So basically what these are, every year basketball, you know, they have All-Star weekend, right? Right. So what they do for the All-Star weekend is usually they do galaxy themed shoes. And every year they do different shoes for the galaxy theme. Okay. So these are the penny hardaway air foam posit ones. So these are like one of the most sought after, just shoe in general, like the shape, the whole silhouette, everything. So the fact that it's the galaxy version, they only did a limited number of pairs. They were a quick strike release. So they sold out like the day after that they released and the retail was like 220, 240. So the day after they released, they were already up to like $2,000 plus. They got sole protectors.  They're like never even worn. Why don't we do this? We'll let you do the peel. It signifies you own it. Yeah. Yeah, it's yours now. Can I pet them? Yeah, definitely. Go ahead. Bye. That's Goldilocks, the way he frosty the snowman. Queenie sit down. He's just giving them love. Everything in about you. Have a good night. What do you think? Did we get scammed? You don't think we got scammed? All right. So it's been a couple of weeks and no one is biting on these shoes. I've posted it on Facebook marketplace, offer up Craigslist and I'm definitely getting a lot of views but I'm not getting anyone to even throw me a nibble. I've gotten no offers. No one is interested in these shoes because they're just too expensive. They're definitely a unique piece and I know that these things at least from what I have seen online go for around 2000 to more, especially for size 12 shoes, which is what we have. But I understand that people probably don't want to just meet up with some guy and drop a couple grand on a pair of shoes. So since I can't get a buyer to come to me, I figured we need to go to a buyer. The next solution I think is to go to some shoe appraisal places and then hopefully they would be willing to offer me something for these shoes. Hey, what's up, man? Hey, Josh, can I get some help? Yeah, what's up? So... Amazing great condition. Well, he still has this. Yeah.  So without the box, right, it does kind of hurt the value.  Ideally they want a pristine box and dead stock, which means like no one's ever worn them. Right. But when you have shoes like this that are very rare and they're still worth like a couple thousand even if they've only been worn like a couple times, like it'll be fine. Okay. Shoes more valuable than like everything else. Okay. Well then do you want to take it over to this grail dude or person or place and then see what they say? Yeah. I'm Chris nice to meet you.  What are you guys getting into? Just looking around. I traded a computer for shoes and so I don't know what to do with them now. So I need help. Please help me. Let's check them out. I'll be over your shoulder. I'm learning. Okay. It's an old pair. That's a used pair. So how much was a computer worth? When it was all finished, it was around 2000. If I wanted to really cheap out on them, I could have done you're giving me a look and I'm scared already. I mean I know the brand new with the original box and all that they're worth the pretty penny that the galaxy phone posits. I'd have to double check make sure they're real. So these could be fake. Oh yeah, there's a lot of fakes out there. You gotta be really careful. Okay. Okay. Here's my guy Kyle. He's the pro legit checker here We're looking at him at Goat and we saw three different size tags so far. We did see this one and we did see that on Goat but we saw three different ones. The other two are like with the white, you know glue like straight onto the fabric and then we saw different stitching on the back. Yeah. And but this one was on there. Yeah, it's pretty solid. Yeah, make sure the double stitches. Where do these have double stitching at? I would see it on the front. Right here under the under the penny. Yeah. Right here. Yeah, we see that. Right there. I could see a widow's beak at the top. Yeah. They look good. Cool. All right. Well, they're legit. That's a good sign. As far as value, you've seen what they're worth. So well, then without the box, it depreciates it a little bit more as well. He says, no, he says they're real. But at first I went through like emotional roller coaster figure if they were even real. I saw that. The problem is like that. He sees worth more than the shoe. No, no, no, no, no. We definitely made a great trade. I mean, okay. I may have lost more than half. What do we do with these shoes? What about this guy over here? Hey, my man. Hey. Yo. Sir, do you mind meeting us up over here? Is that okay? Can we help you over there? Yeah, yeah. What's up, man? What are you? Can I ask? What are you reading? The Bible? Hell yeah. Random thing. Do you need a pair of shoes, bro? What size are you? Two. Ten. Ten's nine. Okay. This is a little bit bigger, but would you be cool with these? Yeah. Sorry, darling. These are, these are 12s. Yeah. You mind trying them on? These are all you. Here, I'll hold your spot. Because I can't use these, but I, I figure like, you know, maybe you'd be a good, better with these. They fit all right? Yeah. Yeah? Yeah. Try this one too. See if they're all right. They should be a little bit comfy. They okay? Yeah. Hell yeah. All right.  So like, it was a couple hundred bucks, you know? Because I, I, I saw, I was like, I was appreciating the, the hustle on the reading. Because a lot of people don't read anymore, you know? Yeah.  Here, I'll, I'll, I'll box these up for you. So that way you can use both of them. Here. Yeah, no problem. For sure.  You know, I'm trying to figure it out. You know? Right. Thank you, brother. Yeah. Have a good one. Appreciate it, man. Have a good day. Hope it's not too hot. That's true. That's fair. You have a blessed day, man. Yeah, you have a blessed day too. It was funny. He said size 10s, but his shoes that he was wearing were size 12s. I was like, you know, do you need a new pair of shoes? He was like, I was like, oh, sweet.


Hi, good morning and welcome to today's products and focus so looking at the US there to there you can see that we've come well off the session those it looks to be that the U.S. stabilized that the kind of the target price that the Chinese government have been looking at is a little bit higher than what some analysts have been thinking the midpoint range as we've been a little bit of a stabilization there and that's caused a little bit of a heart to be taken on most global equity markets I'll be at the US market has jumped the most this candle very very strong hammer formation followed up by positive move this morning other technicals relatively neutral and that gives an idea about where we stand today so in the middle of two ranges 175 61 potential resistance 17361 potential support and there should be seen as quite a strong technical message but if we do see some big moves on the UN again I wouldn't be surprised to see another little slide because this is beginning of a currency war which affects more than just China so looking at the UK with 100 again up well off the session though still negative territory we actually have a move a lot lower first thing this morning actually kind of a strange move right there and certainly we are in the positive territory right now about 65 89 is next potential support looking at from a daily perspective the seems to be struggling to keep head a little bit higher already so I'll say that pans out later on so looking at Japan 225 again bounced off potential support 20,087 higher this morning dollar yen has increased to 124.54 so it's made back about half the loss that made yesterday where it was firmly above 125.20 and Japan 225 moving back up towards 2868 level just now so quite an interesting potential resistance level has not been able to stay in close above there for any particular length of time maybe we might get there at some point in the future so having a look then at dollar yen you know very strong bearish reverse bearish engulfing pattern yesterday we've had a bounce this morning top of the range above 124.42 which also bounce off to 21 period SMA other technicals not yet over but so this still could edge a little bit higher but I just get a feeling that the the Americans is aren't going to raise rates now as likely as they were beforehand like when we're looking at the US 30 we've seen a big sell-off and treasury bonds as well which calls the US dollar to sell off there was like maybe a 50 60% chance off a September rate hike and now it's dropped down to 40% so you know what happens with the dollar next is a good question because you've seen sterling and year sterling dollar and euro dollar both be a lot higher the euro's coming up a little bit at the moment but we'll look at the effects in a second so then looking at west texas crude looks to be also in 43 30 we've got a bullish a bullish crossover on the rsi 30% level we're obviously massively oversold this little stochastic but we have been to some time the same with rsi to be fair maybe we are approaching an area where there are some interested buyers you still have a ram to think about the rain oil global demand from china blah blah blah so then looking at gold to gold still going from strength to strength I think this is this is really the telling one that you're seeing right here there's so much buying of gold I think some some traders are really taking the view now that interest rates in the US are just not going to happen when you've got this kind of backdrop of global uncertainty and there is a safe haven aspect to go but it's been widely ignored versus the japanese yen over the last couple of weeks so it's very interesting now that gold's going great guns and it's getting quite close to 1137 so I think this is the one to watch for a barometer for us rates at the moment and it certainly is a very strong technical bracket so then finishing up with euro dollar and gbpusd so euro dollar and another great day yesterday's people were unwinding their euro um chinese remember trades and that's caused a big spike advancement in the euro now we've had a bit of reversal already today kind of a tweezer top almost right here quite an ugly sign to have to be positive and then push back down again but one spot 11 is a potential support level and we've all been surprised by how high that has actually gotten but it is towards the bottom of this range already for today and if we finish up with gbpusd against oscillating around one spot 56 look at these candles here nothing should be changed my fact cable is pretty boring at the moment they're completely honest so not much to talk about so in regards to today you do have retail sales that's going to be obviously a big one for you know any interest rate bills out there but it's going to have to be something pretty amazing before it's going to have a big impact and we fast forward on to friday you've got a germany gdp a eurozone cpi and then you've got a us ppi and that'll be another decent one for for interest rate bills sort of safety makers out there again that has to be something pretty special before it will have a dent on people's new more negative outlook on rates in the us so as ever going to keep you on the chart for making sense part of your layout going forward and join me again tomorrow to find out what happened next


I'm super optimistic about the future just because we are seeing some results that we have not seen before with immunotherapy and the interplay where people with stage four disease and some situations such as melanoma or lung cancer, if they have the right series of mutations, we're seeing a long-term survivor, so that's been kind of a game changer recently. What's better? If we have a single lung tumor, should we treat it with stereotactic radiation or should we remove it surgically? And my argument is if we treat it with radiation, the cells as they go through the process of apoptosis actually kind of lice open up, the interior contents create antigens that are recognized by the immune system and so that it can make the immune system kind of primed and then immunotherapy after radiosurgery may be able to affect the development of metastatic disease. Super, super exciting.


Hello! I'm Octavio Perez-Kempner and I will be presenting the paper Improved Constructions of Anonymous Credentials from Structure-Preserving Signatures on Equivalent Classes. This is a joint work with Ashlyn Connolly and Pascal LaFourquête. I will start by introducing the concept of attribute-based credentials and the main motivation of our work. Right after, I will give an overview of our results and comment on the main building blocks, which are Structure-Preserving Signatures on Equivalent Classes and the recent credential framework from Fush Bayer, Hanser and Eslamaling from 2019. Before concluding, I will dive a bit into the notion of signer hiding, which is one of our main contributions. So, attribute-based credentials, or AVC, for short, allow a user to get a certified set of attributes from an issuer. The main idea is that users can later unauthenticate themselves to a verifier by showing some of the attributes or by proving relations between them. Anonymous AVCs allow users' authentication without leaking any other information besides the shown attributes. Some system can be multi-shown, which means that they allow an arbitrary number of unlinkable showings. In other words, once issued credentials can be reused. Finally, multi-authorities AVCs allow users to obtain a single credential for attributes not necessarily issued by a single authority. In this work, we consider multi-show AVCs in the single authority setting. The main differences between recent constructions include the expressiveness, for example, if they allow predicates over the attributes or not, and to which extent, the efficiency in terms of computational cost and bandwidth, the communication in terms of whether or not they provide non-interactive features, the security model, whether they use the generic group model, random oracle, etc., and on how and if they manage revocation features. Broadly speaking, one can identify different lines of work with respect to the underlying signature scheme that is used to build the credential system. Initial constructions were based on Kaminish-Lisienskaya signatures. More recent work includes aggregatable signatures, sanitizable signatures, reductal signatures, and last but not least, structure-preserving signatures on equivalent classes. One thing that none of the previous work addressed was the problem of leaking the issuer identity. In many scenarios, the identity of the issuer can reveal user information like the country or company where it belongs to. To address this issue, we proposed an ocean called Siner-Hiding, and it refers to the ability of hiding the identity of the issuer during showings. This concept was recently developed on concurrent and independent work under a similar name of issuer hiding and presented at Cannes 2021. To motivate the application of having Siner-Hiding features, let's consider the following scenario. We have a seller offering discounts for European non-Spanish residents. A user could claim its fringe, but that would give away too much information to the verifier. Furthermore, if credentials are issued by home countries, no matter what attribute users decide to disclose with the verifier, their home country will be leaked. What we want in scenarios like this one is to avoid such things and find a way in which users can convince the verifier without giving away information from their own country of origin. A way to convince the verifier about being European but not Spanish is to hide the issuer's identity in a set of valid countries. In order to see how one can build a credential system allowing this functionality, I will now introduce the concept of Structure Preserving Signatures on Equivalent Classes, which will be at the core of our Siner-Hiding feature. The main idea is that they allowed a control form of variability, where one can update a signature to a new message that is related to the original one. This control form of variability is possible if the message space can be partitioned into equivalent classes. If we consider a message space of vectors of length L, the following relation partitions the message space into equivalent classes. And for stability holds with respect to classes. No adversary should be able to update a signature and obtain a new one for a message that is not in the same class. In other words, switching representatives in a class is no longer seen as an attack, but rather as an advantage of the scheme, and the only allowed form of variability. Besides the notion of affordability, these types of signatures should also be unlinkable. If the equivalent relation provides indistinguishability on the message space, like in the previous example, and updated signatures are distributed like fresh signatures, message signature pairs falling into the same class are said to be unlinkable. Another important aspect is that the notion of equivalent classes has been recently extended to consider equivalent classes on the key space as well. When the signature acts on both, the message and key spaces, it is said to be a mercurial signature. We will be using this later on when discussing the notion of signer hiding, but now let's move on to the ABC framework used on this work. So, in a brief, a credential is a signature on a randomizable accumulator representing a set of attributes. A credential showing involves the joint randomization of a message signature pair. The accumulator uses batch membership proofs to allow constant size showings. Observe that by randomizing the accumulator and the corresponding membership witness, no information from the user is leaked, but the verification still succeeds. The same applies for the updated signature. In this work, we will be referring to the credential accumulator as a set commitment scheme, which was the original name given in the previous work. Although one can have constant size showings because of the batch membership proofs, the main drawback of this primitive is that expressiveness is limited. Only a selective disclosure of attributes is supported. For users to prove that they do not have a particular attribute, they would need to encode the negation of it as an attribute. While doing so would work, it is far from being efficient and other alternatives are preferred. Taking the previously mentioned points into account, we focus on improving the following aspects when compared to the previous work from FHS 19. Expressiveness extending the accumulator, efficiency leveraging user and verifier computational costs, security model moving away from construction space on the genetic group model to one in the standard model requiring a common reference stream. Let's now have an overview of the results. We obtain a new structure preserving signature and equivalent classes which acts on both the message and the key spaces. Our departure point was a recent scheme proving secure under standard assumptions, which we modified to obtain a mercurial signature and to use a different non-interactive zero knowledge argument for our proof that improve its efficiency. We extended the accumulator to support batch non-membership proof. We call this extension set commitment scheme supporting this joint set. We see users can now prove that they do not have a given set of attributes in their credentials by showing a single witness for the whole set. Another improvement was the addition of a proof of exponentiation to outsource computational costs from the verifier to the user. This can be useful in some scenarios. It can be seen as an optional functionality and it basically allows the user to pre-compute some of the exponentiation that a verifier would normally have to do in order to verify a witness. For this to work, the user is required to provide a proof of correctness for those exponentiation. As one would expect, checking the correctness of the proof of exponentiation is cheaper than doing the exponentiation from scratch. We also propose a one out of n or proof so that users can hide the issuer identity during a showing. Finally, we extended the security model from FHS-19 to take into account the previous modifications. As can be seen on this slide, the modifications introduced on this work are marking gray. If we look at top down, we first replace the or proof on the original signature scheme. Then we built a set commitment scheme supporting these two sets based on the original set commitment scheme plus the use of batch non-membership proofs and the proof of exponentiation. Having a new instantiation for the ABC framework, we added the designer hiding part to it. Now that we went through the main building blocks, it's time to discuss more in detail our approach to achieve designer hiding property. How can we use the fact that signatures can be updated to verify under a different but equivalent public key? Well, the main idea goes as follows. We first randomize the credential and the issuer's public key consistently. Now the updated signature on it will verify under the randomized public key. Here it is important to stress that because of the unlinkability property, randomized public keys look like fresh public keys, which means that without knowing the randomizer, it's hard to tell whether or not two given public keys belong to the same equivalent class. The next step is to present the updated signature and public key to the verifier alongside a proof of correct randomization of the issuer's public key. This ensures that the user did not sample a public key and self-signed his own credential. For the proof of correctness, we can use a one-out-of-n or proof, which in our case is a fully adaptive NISIC argument. We see users can select arbitrary long sets of public keys to compute a proof with linear cost. The corresponding formalization reflects this. As can be seen, the idea is that an adversary should not be able to distinguish which was the organization that issued a credential when interacting with the environment under the role of a malicious verifier. Conclusions and future work. Our results explore multiple paths to extend the ABC framework from FHS-19, and although we did not mention it much on this talk, we also discussed revocation features. All in all, we obtained a more flexible framework leveraging different trade-offs. The proposed signer hiding notion enables more use cases, in particular for federated scenarios in where multiple authorities coexist. As future work, we think that exploring the use of aggregatable signatures with structure-preserving signatures on equivalent classes in the multi-authority setting could enable even more use cases. Finally, devising other ways to define equivalent classes could lead to new and more efficient constructions, and till now, all the now constructions rely on the difficult man-assumptions to achieve the desired properties. Thank you very much for your time.


All right, I think we've slowed down enough.  Shall we go ahead and get started? Thank you everyone for joining us today for this online workshop on how to document quantitative and qualitative data. My name is Maureen Haker, and I'm going to be presenting on the qualitative data bit. I've worked with the UK Data Service for over 10 years now doing everything from ingesting collections to reuse projects. And I also lecture at University of Suffolk. And Anka, did you want to introduce yourself? Sure, thank you. Hello everyone, my name is Anka, and I'll be doing the quantitative part of today's session. I've also worked in ingest for almost nine years now at the UK Data Service. And I also work at Cancer Research UK where I help manage their trusted research environment or TREs, SDEs, as you've heard them referred before. But yeah, that's me, back to you Maureen. So what we're gonna do today, we've got just a little bit of an overview here. We'll start with the basics around what is documentation, why it's important. And we'll move into a bit of a distinction between documentation for qualitative projects and documentation for quantitative projects. And we're also going to talk through what metadata is and why that's important. And then at the very end, we'll have some further resources, some signposting and also time for questions. But going back to basics now, what is documentation? Documentation is all the stuff that you collect that kind of sits alongside your data. It helps to explain and make sense of the data. So another way of thinking about it is really all of the work that you do kind of behind the scenes, the necessary paperwork and the paper trail that helps you complete your project. But at the same time, this may not necessarily feature in all of your outputs. So we'll go through some more specific examples of documentation, but just to start out, there's two broader categories of documentation. And one of these is project level documentation. And this is all the stuff that you collect that tells you about your research project as a whole. So this would be what the methodology is, who the research team were, what keywords might be used to describe your research or where your research took place. But there's also data level documentation, which gives more information about specific data parts of data, which don't necessarily reflect the whole collection, but just a specific element of it. So this might be metadata about an individual participant. So whether whatever their gender, their occupation, their age, or any other kind of demographic details are, and it would be compiled in a way that's quite easy to access. It could also be about specific variables that are collected. So you probably don't need to think so much about whether your documentation is project level or data level, but it is just to point out that there are different layers to a research project. And all of those layers of contacts are important to document and consider when assessing and evaluating your data and the overall research findings. So hopefully you're thinking about examples of what documentation, broadly speaking, is across all levels. So this might be your interview guide, your variable list, your blank consent forms, information sheets. Some of you, if you do qualitative, might take some memos like of your analysis, or if you're doing quantitative, it could be syntax. So anything basically that relates to the paperwork that you create as part of your project, that's not exactly data, is probably going to be documentation. And just to make matters a little more nuanced, all of this material from your research is called different things by different policies. So archives like the UK data service, we'll call it documentation. And under documentation, we have specific kinds of documentation. So there's user guides, data lists, there's data dictionaries, all of which we'll talk about in the workshop. There's also readme files, which you might have been asked to write for us if you've ever tried depositing data with us. But some policies like the ESRC's data policy refer to research materials, which starts to get a little bit messy as to what counts as a research material and what doesn't. It also refers to data assets, which is the system that's used to hold data. And there's also metadata, which I've been heard described as data about data and the specific kind of brand name types like DDI would also be metadata. So while the concept of documentation is simple, the deeper you go, the deeper those ideas run, it gets a little bit more nuanced. If you're interested in exploring some more around some of this terminology, Codeata has a working group that's dedicated to terminology and guidance on terminology, just as a side note, hopefully some of the practical examples we have of different types of documentation will give you enough of a flavor to really dive into any area that you might want to know a little bit more about. So now you know what documentation is, the different levels, and hopefully you're starting to see how embedded these are in doing and disseminating research. But why do we do it? Documentation from the point of view of the archive maximizes the reuse value and is also essential for us to review and publish the data. So you can't really understand data without documentation. So for example, if you just dropped an interview transcript in the middle of the street and someone asked you to pick it up, they can't really understand the data without understanding the context in which it was gathered. This also adds to the historical value of the data as it builds up a kind of provenance for it. Documentation also allows you to expand on the methods and the processes that might not normally get covered in a publication. So there's less space for documentation if you're trying to publish, but if you feel that it's useful for understanding the data, you can make that available as well. And doing so will also help you enhance your research outputs. So documentation in and of itself becomes an output and the documentation can be reused and we'll talk about that a little bit later too. It also adds a level of transparency to your research. So as part of the peer review process, reviewers can better understand the work and the data and re-users can also more accurately and efficiently reuse that data. And finally, it aids in the creation of fair research. So I'll talk a little bit more about fair data in just a moment, but I do want to try and do a quick exercise which hopefully might highlight some of these points for you. So if I could have Emma, please drop in worksheet number one into the chat box. So you should get a link in just a moment. There it is. And what I'd like you to do, let me just see if I can put this on my screen as well. Give me just a moment and I'll put it up, but I'm gonna give you about five minutes to read through this worksheet. And you should see that it is a interview excerpt. And what I'd like you to do is... Here we go. Resume, share. Lovely. I'd like you to read through it and think about what are some of your initial thoughts on this particular interviewee. So where do you think she lives? How old do you think she is? What might some of her hobbies be? Who might be some of her friends? And consider if you need any other kind of information in order to analyze the data. So I'll give you about... It's not very long. It's about a page long, but I'll give you about five minutes or so to give this a good read through and see what you can kind of extrapolate from the data that's there. All of you should be able to access this worksheet. So if you just click on the link that's in the chat, you should be able to see that and bring it up on your screen. We've already got... I'll give you another minute. We've already got a couple of comments about the difficulty in reading it, which I think even for native English speakers, this can be really difficult to read because it's written phonetically. I think that's all I'll say about it for now, but we will address that point as well in just a moment. All right, if anyone feels like they'd like to just kind of pop in the chat, if there's anything that you feel like, oh, yes, so we've already got some people typing some things in. So from Scotland, grandmother, excellent. Somebody has guessed maybe in their 80s. Ooh, even guessing the particular region as well of Scotland. So some of you with a little bit more local context are actually able to guess the area. So we've got some people kind of saying, what is it, Ashington versus others who are saying maybe Glasgow, excellent, great work, yeah. And we've got a few people now who are guessing, she's in her 80s, definitely grandmother, elderly, excellent, or Aberdeen, okay. And part of the age, someone has just written in because of some of the context that she's talking about corporal punishment at schools and that, excellent. Okay, so what I'd like to do now, Am I, if you could just pop in worksheet two, let me see if it'll let me if I just switch. Yes, it is letting me switch screens here without, we do have a second worksheet here, which is the context for this interview transcript. So what I'd like you to do is just take a, just take a couple of minutes to read through this context. And if you don't mind sharing again in the chat what you found most surprising about our interviewee. All right, so a number of you are kind of, some of you are focusing in on the age. So it probably is useful to know when the interview actually took place because some of you may have guessed the time period that she might have been born, but perhaps not her actual age. I think being a grandmother at 43 is probably a very different kind of norm would have been of years gone by. Yeah, so somebody's pointed out interesting that we guessed the age based on the present day rather than based on the time that the interview took place. Is there anything else that anyone else wanted to add? And I'll just give you a moment to type that in while I just switch my screens back to the slides. So just bear with me a moment. Okay, so I think some of you perhaps found some of the additional context surprising and there were perhaps details supplied by the additional context that you hadn't thought about before such as for example, when the interview took place which is actually quite important. So hopefully it gives you a clear demonstration of how context can matter and how it can change your perception of the data. But why share the documentation and data with other people? And this particular point is one of the key underpinning principles to fair data. So fair principles are relatively new guidelines or you might call them goals of research which aim to make research more transparent, more collaborative and constructive. Since the early 2000s, technology has had a massive impact on how research is done. We collect more data which is much more complex and we share it much quicker than we've ever been able to do before. However, despite collecting so much data all the time we still have challenges to processing that data. So just think about any organization where the data is not shared between departments and you have to constantly re-enter the same information again and again. So how do we solve this? Well, in 2016, Wilkinson and others published the fair principles which outlined what good data management looks like that would enable the sharing and the reuse of data. And the key here is to make the data machine readable to be able to use the technology that's a massively changed the way research is done. And these guidelines were so influential that an international collaboration was established. In the GoFair International Support and Coordination Office just a year later and these fair principles continued to influence policies. So you'll see fair references in data policies of the Research Data Alliance, the Association of European Research Libraries. For those of you based in the UK, UKRI and all of its research councils also refer to fair data. And if you receive any grants or taxpayer money to complete research, chances are if you're anywhere within Europe you will be asked at some point to share the data and the documentation at the completion of the project. Many publishers are also now requiring the sharing of data and any research materials before the publication of your work. And all of this is sort of done in the name of transparency and research rigor. The fair principles state that data should be findable, accessible, interoperable and reusable. So research is not just something that you complete in the solitude of your academic offices, but it's something which is completed in collaboration with others and then shared for future reuse. To make your data fair, however, also means documenting the data. To make data findable and accessible requires clear metadata. Equally to make data reusable means documenting the provenance of the data. I won't go further into the fair principles. They are listed here, but please do send any questions that you might have about the fair principles through and we should be able to get to it in the Q&A. So we're happy to give a little bit more detail if you're more interested in fair, but we just wanted to give you a bit of background into context into why we are documenting data and why that's important to share. So from here, I'm going to go a little bit further around documentation for qualitative data and then I'm going to pass off to Anka to talk about documentation for quantitative data and metadata. In talking about qualitative collections, I'll show you some good examples of documentation, but also talk about how you might utilize changing technology to make documenting your project a little bit easier. And then finally, I'll talk a little bit about reusing documentation. All right, so here are some examples of documentation for qualitative work. Basically, all the information you probably create along the way of doing research, but which never really sees the outside of your research team or maybe perhaps some of your participants. So this can include interview preparation. It can include instructions to interviewers, prompts, topic guides, blank consent forms, information sheets, or any other materials that the participant receives prior to taking part can also count as documentation. It could also be text that is written by you expanding on the methodology or sampling, including where it might be permissible under copyright, extracts of publications or draft work. What we don't often see, but can be really useful are things like research meeting minutes, research diaries, field notes, documentations from analysis. So that can include things like memos or code books or even initial analysis write-ups. We've got about 1500 or so qualitative collections and we don't often see things on analysis. It's really interesting. I'm not sure why it's not typically part of the documentation for qualitative collections because that's something that's pretty standard in quantitative collections, but there's a layer of kind of analytic transparency, if you will, which really helps to validate research findings and it can really help re-users better understand some of the methodological decisions that were made or perhaps not made in the process of cleaning up the data. And this kind of context is actually really, really important to qualitative work. This is part of the arguments of why you do things qualitatively. Some of that work can be embedded within some of the analytical documentation. So the better you understand that context, hopefully those materials then would be actively considering would be actively considered by the research team during the data collection and the analysis, collating them and making them available as documentation then really helps to achieve what that qualitative work is aiming to achieve. Within collections that are curated by the UK Data Archive, these materials would be collated into what we call a user guide. So the user guide is then bookmarked. So you can see in the image here in the upper right-hand corner of the screenshots, there's little bookmarks there which tell you which materials are available in that user guide and those tend to encompass the project level documentation. In addition to the user guide, the UK Data Archive also curates a data list which is basically an at-a-glance look at participants. So this would probably be considered more like data level documentation where you might have metadata like basic demographic details about your participants or even just like the file name where you can find the relevant data. The details on the data list are not necessarily standardized or all-encompassing. It's meant to be the details that were relevant to the project itself. So those can sometimes take a little bit time to assemble but they can be really useful organizational tools as well during the research. So it's generally a good practice to create one of those as you go along. While the collections curated by the UK Data Archive will have user guides, you may also likely see a folder of documents or just separate files on collections that are deposited by researchers themselves. So we do have part of the archive that's like a self-deposit system. And in the self-deposit system, you'll often find a folder kind of like this where it lists out different kinds of documentation. So in this collection on political dissatisfaction, you can see each file that was included with the data from an end of award report to the information that was given to participants. This particular study is a multi-method project. So all of the documentation definitely makes it easier for re-users to kind of piece together that project again and understand what was going on. Other types of documentation can include observations that were written by researchers in the moment of data gathering. This is a little bit interesting because there is perhaps some types of documentation which are a bit of a gray area between whether it's documentation or data. So this is probably a good example of one of those. Some people might use something like interviewer comments or field notes as data itself. But it can also be considered documentation. Some types of methods will actually dictate taking time for self-reflection as part of the method. So this may be taken as standard and again, probably would be used as data. Other types of methods would just recommend this as a good practice. So these particular comments, they're just a few sentences that were written after every interview by the interviewers of the affluent worker which is the research which eventually was used to create our ONS categories of class. So they help to contextualize the relationship between the researcher and the participants and they're actually really helpful data level documentation. Again, this is a little bit unusual. I'm not sure if it's that the methods don't often dictate this kind of quick reflection after the interview or at least to actually write it down or perhaps it's just that this is something that's not often shared. But again, this really helps to rebuild what the power dynamics were of the interview. A step further, perhaps, then field notes are the draft work of analysis. So this is taken from Dennis Marsden's mother's alone collection and he's got this piece on felt poverty. And it was written but never actually made it to publication and it was included in the documentation for the collection. And it's a really interesting collection. So this was led by white educated males who were interviewing single mothers who were living on welfare. And the decade that this took place in the seventies, some really interesting gender dynamics going on as well as class dynamics. And this piece I think really helps to provide context to show the kind of sympathy that the research team felt toward their participants even despite the kind of time period and what kind of attitudes you would think would normally persist even within research. Annette Lawson's study, which is called adultery, an analysis of love and betrayal was a project that was conducted in the 1980s aiming to explore the extremely taboo topic at least at the time of adultery. As such, it was really, really hard for her to recruit participants. So Lawson chose to put out a call for participants in a newspaper but it created an arguably biased sample. So most of her participants were white, mostly middle class women and they were the ones that sort of self-selected to return her call for participants. So Lawson was a little bit preoccupied with her sample. So she wrote a 54 page defense of this sample. So she started here, as you can see a couple pages with a discussion on some of the ethical conundrums that arose from her sampling strategy which included, for example, jealous partners sending in the information of their married partners to participate or another man who seemingly called in from a psychiatric ward. She also had extensive comparisons between her sample and the national population. So she explored what she thought was, you know, what was said to be a significant difference from the national population and kind of expanded a little bit more on this, saying, well, the areas that it is differing from the national population do those actually matter very much and really sets out in great detail what the differences were. Until she finally comes to some interesting conclusions here about sampling strategies more broadly, including the point that sampling needs to match the context of the study and that exploratory studies are benefited from a greater focus on the ability to talk about the topic in detail rather than focusing on the participants. Earlier I had an example of interviewer notes but field notes are another great, you know, example of very detailed documentation. Field notes are a little bit different in that, again, they're occupying this kind of gray space between being data and being documentation at different times. So it's worth pointing out that documentation is something that's normally openly available within an archive. So things like field notes and other reflections, depending on the level of detail, may need to be put under similar access restrictions as data. There are probably only about a half a dozen collections with examples of field notes like this and almost all of them are from ethnographic studies. But, you know, who knows, maybe we'll see a little bit more of that kind of documentation in detail about our collections in future. And there's also new possibilities with changing technology. So for example, EnVivo and other computer-assisted qualitative analysis software allows you to download your code books, your memos and your mind maps that you made along the way of your analysis. And here you can see a list of what EnVivo calls nodes and their descriptions and this is all very easily downloadable. When we received, for example, the Edwardians collection which is our founding collection deposited in the 90s contains 453 80 plus page interviews with British residents who were born during the Edwardian period. And it was accompanied with all of the documentation of the work that was done by hand. So there were 16 shelves that were dedicated to holding the coding of those transcripts into key themes. But of course, now we can receive the single project file if you are using something like EnVivo or Atlas TI or Mac stuff. And all of that can be downloaded at any point after coding and turned into a PDF. Research teams can also use their blogs and their websites to keep in touch with participants. They might keep a research blog which updates others on the progress of the projects. It might post information for participants or it might even send out calls for participants. And then once the project is done, that site again can sort of sit alongside the project as a related resource providing, again, additional documentation about the process as a whole for going through that research. We also see more creative documentation such as this photo story. Again, this is the sort of thing that can be classed as either documentation or potentially data. And because it contains images of people, you would want to make sure that this is something that there is appropriate consent in place to use but there is scope to be able to use video and audio files to accompany data. We have also kind of interviewed PIs about their projects for them to expand on the methods and the sort of things that came up during the research. Finally, changing technology is not just for how research is done, but also how we archive. So the UK Data Service has created Qualibank which is an online tool for searching, browsing and citing qualitative data. And as part of that tool, you can search and view qualitative data online but you can also view the linked documentation. So this documentation can relate to a specific piece of data or it can relate to the collection as a whole but it allows the distinction between project level and data level documentation and would kind of seed it right next to the data within our user interface. And finally, I just want to make one final point about reusing documentation. So often we think about the reuse value of the data specifically, but less about how documentation has its own value. So documentation can serve as an inspiration for good practices. So adapting consent forms, for example, or information sheets rather than writing them up from scratch. So we have hundreds of consent forms documented within our collections, including snippets of the one that you see here which explains what data sharing means. So feel free to explore those, learn from the good practices of others and reuse it in your research as well. And you can also examine how to do research, for example, with children or vulnerable groups or other really challenging features of research. Things like writing up information letters and consent forms for children can be especially difficult. So we have some really good collections which provide good examples of how this has been done in the past. And you can also do the same with data collection. So one collection, the Foot and Mouth Disease in North Cumbria, deposited their interview guides which were then reused by medical students to better understand that doctor patient dynamics. I also refer dissertation students to find similar interview guides on their topics before setting out to make one themselves, just to help them see what's important to ask, perhaps what they might not want to ask and basically how those decisions are made. All right, I think we are over to Anka now. So give us a moment while we just switch screens. Thank you, Maureen. Just share my screen now. I'm just gonna go through the chat as well and see if there's any questions that are coming up. We've got a few in the Q&A but if we miss any from the chat, feel free to pop it in the Q&A box and we can get to those either at the end for the Q&A or I can try to type in some answers as well. Okay, so we're going to talk in this second half of the presentation about quantitative data, documenting quantitative data as well as metadata. So in this section on documentation for quantitative data we're going to look at exactly what documentation should accompany quantitative data. We're gonna have a quick look at embedded documentation as well as what good documentation looks like and of course we're going to have quite a few examples to look at and also quite a few links as well for you to come back at a later point of course when you'll have the slides. Okay, so just a quick summary for in terms of documentation for quantitative data. So we have on the screen a few examples as Maureen said you've probably already heard these mentioned around if not at the point of deposit then when producing them in your own research when using them as research instruments. So we have questionnaires, code books. This would either be an embedded code book or a separate file to the data file. We have data dictionaries, user guides, experiment protocols and read me files. We are going to have a look at each individual one in detail in a second but I just wanted to give a summary of documentation that is specific for quantitative data and really what we're gonna touch on in this second part of the presentation. Okay, so the diving into it. In terms of documentation for quant data I think the first sentence there also applies to quality data, applies to documentation in general. So in terms of what we would need to include when we share our data in an archive or repository really we need to ask ourselves what would someone need with of course no prior knowledge of the project or the data perhaps someone who wants to I don't know use it in say five years from now what would they need to be able to understand and use the data that we are sharing correctly in their own research project. Okay, so for quantitative data so this is to be a bit more clear as we're referring to structured tabular data documentation can be as I said they can be embedded in the data file. So you can have a variable and code descriptions in the database itself or you can have it separate in a separate file that you then upload alongside the data file. As I'm sure you're aware most data analysis software packages will have this option to add data notation and descriptions, variable attributes, data types and so on. So and then that can easily be exported and uploaded alongside the data file and of course it can be left in the data file as well. We're going to see later on why having it embedded in the data file might be an issue but I won't touch on that yet. And then as I said alternatively that information if it's not embedded in the data file it can be exported or recorded separately in a document such as a code book or a data dictionary. Okay, so this is an example where we have embedded documentation, SPSS for those of you that have used it are very familiar with this view, screen shot. So in the variable view section we have all the information about the variables and this should really be if you're planning on sharing an SPSS file this is really in terms of documentation this is really where we find quite a few issues when we have this type of file deposited especially in the value section. So not all the values will be declared for example and just to be more specific for categorical variables it wouldn't be clear what zero is where one is, where two is there wouldn't be any value labels. The labels as well they should be quite intuitive and easily understandable and they should ideally correspond to or it should be clear to what question and the questionnaire data referred to and the missing values should also be declared but we're gonna have a look at that at a later stage but this is an example of embedded documentation in the data file. Under data view of course we have the data itself and it will be deposited as one file in the archive. Okay, I mentioned user guides so what should a user guide contain? So this will differ across projects there isn't necessarily a golden standard of how these should look like, they vary but in terms of what content they include this will be data collection methods, field work, information, consent procedures, interview schedule, et cetera. So of course as I said this will depend on the project and I've added here three examples of data collections that you can have a look at their user guides they're very good and very comprehensive just to have as an example and you can of course adapt them to your own project. Code books and data dictionaries. So as I said a code book can be, this is the information that could be embedded in data files and I said earlier that that could be an issue potentially because of access restrictions. So if we deposit a data file that has embedded documentation that data file will potentially be under some sort of access restriction unless it's published openly of course but if it's under access restriction then also the embedded documentation will be under access restriction whereas that the data service and in general for all repositories, excuse me, and data archives that their documentation should be openly available. So this is why it's preferable if the documentation is embedded for that to be exported from the data files that it can also be uploaded separately as a documentation file or that a code book is also created separately if the data file you are uploading doesn't have the facility to have embedded documentation then you would just need to create that separate documentation file to a company. I'm sorry, my voice is not the best today. So this should, so in terms of what a code book or data dictionary should include. So this is, as I said, detailed and sufficient information about all the data items. Any variables of course that are new that have been produced or derived ideally including as well the frequencies, the command files that we use to create them just so that whoever uses in the future is aware of that information and how they were produced based on what variables, et cetera. In terms of code book creation tools, so there are a couple mentioned on the screen. So we have DD Editor and Nestor Publisher. Again, these are aimed for curation purposes. So they are used in general by data archives not as much by individual researchers but I just wanted to point it out, put them up for you in case this is something that interests you and you'd like to look at a more automated way to produce documentation files and metadata. Okay, a couple more examples on code book and data dictionaries. As I said, I wanted to give plenty of examples so that you can go to the data collection and have a look exactly at what we mean by good code books and good data dictionaries. So I was just going to leave you to have a look at this in your own time, obviously when you have the slides. So moving on to data level documentation. Marina's made the distinction between study level and data level documentation for quantitative data. Data level documentation includes adequate variable names as well as variable and value labels. I've already touched on this previously on the SPSS slide but in terms of variable names, these should question, they should mention the question number in the questionnaire so they should relate to that so it's clearly what question they correspond to. They should have a numerical order system. There should be meaningful abbreviations or combinations of abbreviations and they should be of course consistent in naming conventions across the entire project. So if there's more than one dataset produced by perhaps collaborators at different locations then make sure that you're using the same naming conventions throughout. And of course for interoperability, I always have issues saying that across platforms, these should not be longer than eight characters and please avoid using any spaces. So instead of space use underscore. That's why I usually do it. Data level documentation. So moving, continuing with that. So similar principles we have for valuable variable labels. So these should be as I mentioned earlier in the SPSS file as brief and concise as possible. Intuitive, make sure you keep it short though. Make sure to include the unit measurement where that is appropriate. Make sure that you're aware of any coding or classification schemes that are used. And of course as I said, reference the question number or the question number of a survey or the questionnaire that was used. And there's an example there of a variable name and label. So I won't go through that but that is exactly how we got to that variable name. Okay. For value labels, some of the same. So ensure there are no out of bounds values for categorical variables. I mentioned this in the SPSS slide already. So make sure that all the values are described and it's clear what category corresponds to what number. Avoid having blanks as well. System missings are zeros. Make sure you declare those. Yeah, and make sure that that is clear to make that distinction between different types of missing. So whether it's not recorded or skipped or, yeah, et cetera. This is an example of variable information for different variables. As we see, we have the label, missing values, the measurement level. So all this is very important and it can be easy to be produced. This is an SPSS output, but you would be able to obtain the equivalent in other software as well. Variable values as well. So as we can see, this is clearly labeled in this case for three different variables. Again, this is also an export that we can obtain as well as the next. We have two different variables here and we have the label. We have whether it's numeric or string or, yeah, and then we have the different values as well as the labels. So it's very clear what is what. Okay, moving on to metadata. So we are going to firstly discuss very quickly what it is, although Maureen has already touched on that and what qualifies as good metadata. We're also going to have a quick look at metadata standards and how that will be produced and how we go about producing metadata. Okay, so what it is, it's basically a different type of data documentation. It's a subset of core data documentation, but it's a type of documentation that provides standardized and structured information. And as opposed to a file, a documentation file that we would produce, it's intended for machine reading. So it is, because it is indexed, that is the word that we use, it is indexed, it's available to, it's possible for that to be read by machines. It allows data to be found in data catalogs. So that's why it is so important that it is machine readable. So it's important for the purposes of cataloging, citing, discovering data collections in a data catalog. So if we don't have good quality metadata, of course, we will have data collections that are not just impossible to be found in a data catalog, but also they would have limited value because users, first of all, wouldn't be able to find them, but also wouldn't be able to use them correctly or decide what collection is suitable for their project or not. And of course, at the end, they wouldn't be able to cite that data collection once they've used it. Some examples of metadata, I listed here on the screen. So we have abstract keywords, topics. Yeah, I won't go through all of them. Metadata in general refers to data about data, but when it's usually study level documentation. So we don't necessarily refer to metadata as a variable label. Metadata mostly applies to study level documentation. So collections that would appear in the data catalog and the information that you land on a page and you see that information. And that is exactly what we have on the screen here. I think there was a question about keeping metadata. How do we keep that from revealing information that we cannot reveal about research participants? And I think a key aspect there is that this is study level documentation. So it is general information about the whole study, not about individuals in the dataset. Also, good metadata. So Maureen already touched on the fair principles and as we can see on the screen, although some of the rectangles are not exactly where they should be, but we can see how many of them relate to metadata. So all of them except for three. And I'm not going to go through each individual one because you can find this and indeed it in your own time. But this is how important metadata is when trying to comply with the fair principles. And if there are any questions in more detail about this or maybe it's not exactly clear what is required in terms of metadata from that particular principle, do add a question or if you're happy to contact us, we're always happy to talk about this. Okay, producing good metadata. So how do we go about producing this? So archives or repositories will collect this information. At the point of deposit, you probably have to fill in a data deposit form or data offer form depends. It's different repositories and archives use different terminology, but they all are intended for the same purpose, collecting that information so that then the archive can use metadata standards to index that information so that they are able to be discovered and discoverable in their data catalog. They also promote interverability across platforms and of course for data to be reusable once found. So when you submit your data set to a trusted data repository or an archive, these standards are automatically applied. So this is not something that you need to worry about producing good metadata. This is something that as long as you pick an accredited fair complying repository or archive, they would do this themselves. At the UK Data Service, we use the DI to support, to structure our catalog records and that stands for the Data Documentation Initiative. And again, we don't have the time to go into this, but we included a link here and also at the end of the presentation for you to read more if you'd like. But what it basically is is a detailed metadata standard. It was originally designed for describing social and economic science data and it is still used by most social science data archives in the world. And of course it contains both mandatory and optional metadata elements. When you fill in that data offer form I mentioned earlier at the point of deposit, there will probably be some fields that are marked with an asterisk, meaning they are mandatory, so they absolutely are needed for us to build that catalog record in our data catalog. And there are of course other metadata standards as well and this will vary usually by disciplines and I included a link there for you if you'd like to read more about this. We also have controlled vocabularies. A controlled vocabulary is a consistent and organized way of describing data, essentially to again make it more findable and shareable research communities. So examples here are subject headings, disarray, ontologies, and taxonomies. At the UK Data Archive we use HACET which is the Humanities and Social Science Electronic Dessaurus, and we also are curating its multilingual sister which is available across, I think now it's 16 different languages, so yeah called ELST or European Language Social Science Dessaurus. So in terms of reviewing a data set, you can find this online and I included the link at the bottom of the screen. This is for reshare which is our self-deposit repository. So you can find this available in terms of what checks are done to the data once you submit it for review. So when you want to publish your data set it would come to us, we will check it and then we will probably come back to you if there are any further edits needed but if you want to speed up that process and you want to deposit a great project that wouldn't necessarily need any edits then you can have a look at the type of checks that we do and already work on that to make sure that the data set you are submitting for review passes the first time around and can be published. And of course there are project level checks, file level checks and I'm not going to go into detail but of course we check all the files that are uploaded we check the data in terms of disclosure, a risk. So someone asked about an organisation and how we make sure that what we publish is in line with what the respondents have agreed to so that is where when we look potentially at the blank consent forms and we make sure that they align with the access level that we choose on the data and the level of detail that we choose on the data and if you're interested more in an organisation we also have a workshop on that which we ran a couple weeks ago but the recording should be available and the slides as well on our website. Okay so we're reaching the final slides here so in terms of a data sharing checklist with documentation in mind so our advice would be to check with the archival to repository where you're planning to share your data in terms of their guidelines and creating the type of documentation that they will be needing perhaps they have templates that they recommend using that you can just download and adapt to your project and we have those as well in terms of the data list and the readme file and then of course you'll have to fill in the data for the data deposit form as I mentioned earlier but please make sure that you do this in as much detail as possible because that will allow the archival to repository to create that valuable machine readable metadata and the richer the better for all the purposes that we saw earlier and of course ensure that your data files also contain data level documentation so all that important information that we already saw it's important from a data quality perspective to be able to have that in present for others to reuse it Finally in terms of accessing data so I mentioned that documentation files are usually made available under open access but these are other access options that we have at the UK data archive so we have open safeguarded and controlled open it's available without any registration safeguarded the user would need to register and sign our end user license there's also special agreements there in terms of asking permission from the depositor to use it or the option to place that under embargo for a fixed time period and then we have the controlled access this is for our secure lab or our version of trusted research environment and this would be data that is quite sensitive and quite detailed so there will be some extra steps there in terms of training the users etc and testing them as well there's an assessment there as well so yeah these are the different access options and of course they usually apply to data documentation as I said is made available openly except for I know there has been situations where a data list for example has been placed under safeguarded access because there was some individual level information there but that is just decided on a case by case and I know there are some questions about this earlier in the chat but usually in terms of data dictionaries, code books especially for quantitative data that is usually made openly available and as I mentioned we included some links and for the resources at the end of the presentation here for you so we have a book as well that we I think last year we produced a second edition and of course we have quite a few training events and I included there a link to our training page on our website in practice I think this slide should have come right after the access the different access levels somehow it skipped one so in practice we have of course collections that we have file names under different access levels so we don't apply access restrictions at the at study level so one data collection as you access it in the catalog could have a few data files and those data files would be under different access levels an example here we have the food and mouth disease in North Cumbria where we have interviews written diaries we also have the transcripts and those are available for registered users but we also have well had they were embargoed until 2015 and then audio files as well were only available for with permission only so yeah this is an example just to flag that the possibility is there to apply different access levels to different data files it doesn't have to be the same access level especially if you have a mixed method collection for example yeah the option is to to have different access levels some tools and templates as I said other archives and repositories might have these as well we also have them to make it easier for researchers and depositors you can just download these and adapt them to your project some further resources as well for you to have a look of course get connected let us know if you have any questions or yeah there's recordings of past webinars and workshops on our YouTube channel as well I mentioned the anonymization webinar we ran a couple weeks ago that should be there now and of course we run recurring workshops on different topics data management topics if that's something that interests you and I linked the events page where you can register and we added our contact information here again if you have questions that we're not able to answer now at the end of this session or there was something that wasn't clear please do get in touch with us okay I think we're almost on time so thank you everyone hopefully we answered all the questions well I know I haven't answered a couple I'm happy to do it yeah if we missed anything and yeah thank you very much I hope you have a nice rest of the day and hopefully to see you in another workshop sometime soon bye everyone


While he may not have a full house-sized family in real life, John Stamos a.k.a. Uncle Jesse upgraded to a spacious estate in the coveted Hidden Hills in 2019. He, his wife, Caitlin McHugh, and their son Billy currently still live here. Prior to this, John owned a gorgeous bachelor pad-turned-family villa in the Beverly Hills Post Office, which last time I checked, he was still trying to sell. Today, we'll check out the spots Uncle Jesse has called home in real life, both past and present. In these videos, we don't reveal any addresses and even though I've done a house tour of my own place, please do not show up at any private residences because it's not safe for anyone. John Stamos is an actor, producer, musician, comedian, and singer who is best known for his work in TV, especially his starring role as Jesse Ketsopoulos or Uncle Jesse on the ABC sitcom Full House. I mean, who didn't love Uncle Jesse? John first gained recognition for his role as Blackie Parrish on General Hospital, for which he was nominated for a Detame Emmy Award, and then he moved on to Full House. After Full House concluded in 1995, Stamos has appeared in a handful of TV series and films. He had a starring role on the NBC medical drama from 2005 to 2009, where he played Dr. Tony Gates and then he played the role of Albert in the Broadway revival of Bye Bye Birdie with another Broadway role to follow. Of course, these aren't the only acting jobs that John has scored. I almost called him Jesse, by the way. Sometimes I forget that's not his real name. He's been an executive producer on the Netflix series Fuller House, and then he also reprised the role of Uncle Jesse for this. Recently, we've seen him in another popular Netflix series too, when he served as Dr. Nicky in the psychological thriller show You. At the time of this recording, John has an estimated net worth of about 25 million dollars, and he has used some of that to purchase a gorgeous family nest in the Hidden Hills to call home. Hey guys, it's Kara the Vampire Slayer, and today we're bringing you another house tour here in Famous Entertainment.  We post a new video daily. Today we're checking out where Uncle Jesse, I mean, John Stamos calls home, including his current Hidden Hills estate and former Villa-style house. We've done a house tour on Mary Kate and Ashley Olson in the past, which we'll link after, but if you'd like to see more of the Full House cast and their real life homes, be sure to let me know. Don't forget to follow me on Instagram and now let's get into this video. In 2020, it was reported that John was still trying to sell his home in the Los Angeles Beverly Hills Post Office neighborhood that he had on the market for a while. While I said earlier it could still be up for sale, it seems he finally sold it for 4.24 million dollars after over a year on the market. He even previously gave Architectural Digest a tour of the home. The beautiful villa was in the Ritzy Mountains between Beverly Hills and Studio City situated on a private nearly one-acre parcel of land in the gated community. There was a 3,500 square foot main house and a guest house, which John converted into a padded music room. He bought the home for 3.6 million dollars over 15 years ago, just after his divorce from his first wife, Rebecca Rahman. So basically this served as a stamos bachelor pad for many years until his new wife and son came along and he made it into a family home. John's former home was entered through gigantic iron gates between towering pillars, while a stone path takes you to the sun-drenched courtyard garden and carved wood front doors. The vine-covered Tuscan-inspired villa offers four beds and 3.5 baths throughout. And I've gotta say the home is super charming. In his interview with Architectural Digest, John said the house has transformed with him over the decade and it was more him than any other home he's previously owned. He explained, I even got married on the property so it just represents a lot of changes throughout my life. It started out as a party house and then I straightened my act out and it became a healing house. And then all of a sudden Caitlyn came into my life and that was joyous and it took a turn there. Inside the feel was classic yet rustic, decorated with earthy tones and reclaimed materials. There were dark hardwood floors and beamed and vaulted exposed ceilings in the spacious living room, while a stacked stone fireplace set at one end of the room and sets of French doors open to the patio. The living room joins a long dining room which accommodates two separate eating areas and had a farmhouse-style table ideal for entertaining. John is a romantic so his home was intimate and there were pictures and other personal items all over like frame letters from his late parents and more. On the other side of the main living room there was a piano from the 1940s that John bought in the 80s when he got his first home and the side table here had a typewriter gifted to him from Tom Hanks. The kitchen while it wasn't massive was arranged ideally and decked out with expensive appliances. The reclaimed wood table here was lit with a skylight overhead and there were black cabinets and a farmhouse sink as well. Looking at John's former home you can see that the custom design combined both classic details and modern amenities. Moving on to John's former master suite it was exotic and masculine with a custom Portuguese-influenced bed. There was also a sitting area with fireplace and updated vintage style bathroom with marble line shower and classic claw foot tub. His bedroom previously had a big thick shag rug but that didn't fly with his current wife. Photos showed the one-room detached guest house was used as John's music room and exercise space. It was basically his man cave and boasted blood-red wallpaper and beige shag carpeting. He also trimmed this space with Disney memorabilia which he loves and said he used to have some pretty wild parties in here. Out back there was a large pool which John added a jacuzzi and water feature to when he moved in as well as a large covered terrace. There was an outdoor kitchen with built-in grill and pizza oven which was also part of the couple's first date. While he's left this property behind I'm positive John brought his many memories from the home with him to the equestrian estate he now lives in Hidden Hills. Let's take a look. In 2019 John and his wife found their current family home and according to property records they shelled out 5.75 million dollars for the place. The sellers were Swedish music producer Adam Anders and his singer-wife Nikki Anders who started off with the unrealistic asking price of nearly 7 million dollars. The family size estate is located in the guarded gates of the celeb-packed Hidden Hills community on 1.5 acres of land. Some other residents in this exclusive neighborhood include Jessica Simpson, Jeffree Star, Drake and some of the Kardashian Jenner's just to name a few. John and Katelyn have plenty of room to Roman raise their young son in their current house as it spans a 5,750 square feet inside. The Cape Cod inspired residents also offers a total of six beds and 5.5 baths between the main house and the guest house. The guest house was decked out as a state-of-the-art recording studio by the previous owner so I wonder if this serves as John's new music room slash man cave. A circular drive leads up to the mostly single-level estate and walk-in in the red front door. You're greeted with the combo living and dining space with oak floorboards and a row of skylights along the vaulted and bean ceiling. This whole space is open concept and double height making the room feel even larger. Nearby the Gourmet kitchen is also topped with skylights and boasts a marble island with bar style seating and seems to offer more space to cook than John's previous kitchen. The kitchen opens to a casual dining area and cozy TV nook with two-way fireplace. In fact listing materials see the home has multiple fireplaces and custom built-ins throughout. John and Caitlin's master suite is spacious also including an indoor and outdoor seating area fireplace and walk-in closet. The master bathroom is spa like with both wood and marble details. Elsewhere in the home there are other guest rooms and options for children's bedrooms too even an office children's learning room or playroom and a room that looks like a small family lounge space. Outside a giant terrace offers an outdoor fireplace and dining table with optional canvas sunshade. This overlooks the simple pool and spa and the treeline grounds also boast a basketball court small barn children's playground and garden. I think all of that will make John's son Billy happy to grow up in this home. So now we've taken a look at the homes of John Stamos also known as Uncle Jesse and I think we can wrap up this house tour here. We got an in-depth look at his former bachelor pad turned family home in Beverly Hills that he sold and his current estate in the famous Hidden Hills neighborhood. Between these two properties which was your favorite what would you rate them? Honestly I find it hard to choose because while the current house is bigger John's former house was just so charming inside and out. If he and his wife chose to decorate their current home like the last one then maybe I would pick that. All together I rate both of John's houses a solid nine. They have it on. Be sure to tell me your thoughts down in the comments. I'm also trying to get to 1,000 subs on my personal channel and I'm almost there so if you haven't subscribed please go do so. I'll link you to my latest vlog. Leave me a comment there and we can be friends. Thanks for tuning in and I'm still reading all of your comments and amazing suggestions so keep them coming. Don't forget to follow me on Instagram and tell me whose house tours you guys want to see. I'll see you all in the next video. Bye!


बिस्मा लायय रह्खबान रहीं आप पक ठूत बश्ची यहगे समन हैं दाड़्द थी वाद जो बाग्द इसचिछे अया मेंझी घिना पक यहगे सवळस्ती हैं. कर रिस्क थे हैं हैं कर रिस्सी का एंपैक्त मग़ी पूड्पोलिो में क्या एं? अद्टाग ठाँ खीज करना भी चाही हे, अन खीच रहते है। तो क्या हुता हुता हुता हुता और लिट्गिकली एक बी जाएगा, अथकम्स क्या आईँ वो हम आज दिसकष गरेंगें। अपनी गॉ़रे लिक्विती श्वेत, वो बहुने � volte तु्रईज़् हेज्ँ देशाच करन्तु सीज जे तुर्छक्ते वो हैजिंक्त में आईईगा, वो वो बहुनेगे पूगच्रग ज़ थी, वो फbagsस द्र हैजिंक। उत्हाँस तुर्छकचक करन्त्ते, थुर्छक। वो 0% । । आप यरो 4% । अवल या बद़ा आप या ईंपक्त है लगज्ट की ही भात् रही है लगरन्सी लिए सूँस नका है क्या और लीज तॉर जोलर औलगे seven percent so even with the hedging you can see variation and the impacts are different leaving investment un hedged can lead to wide discrepancies in absolute returns but also in risk adjusted returns if you don't have any impact then its variation discrepancies are very high and the risk adjusted returns have an impact because you are taking an extraordinary risk so maybe your absolute return is more but the risk adjusted return will not be so much because you have kept the risk levels more hedged returns tend to provide better risk adjusted returns what is the benefit of hedging your risk levels are less then with the given level of risk which you have earned returns is better when you are hedged as over long term currencies increase volatility but not return because currencies have to get more returns so much impact so when you are hedging them then you are reducing the risk factor so your risk adjusted return will get lost and we have already said that absolute return doesn't keep as much as risk adjusted return because take a lot of risk and earn more returns this is not an efficient structure efficient is that you earn more returns at a given risk level that's where the key role is that the finance professional is that you earn good returns at a low risk so when there is a comparison then the risk adjusted base is more meaningful also most global indices are heavily skewed towards us asset this we know that the major indexes are more inclined towards the US dollar impact more as a result of difference between hedge returns and hedge returns are limited so the impact of hedging can also come to a certain level because how much are the currencies in your currency relevant how much are the other currencies so it will also impact that your hedging is so effective how fruitful it can be for other countries the return difference can differ widely depending on timing that at which level you are entering at which level you are falling or the movement of the currency is coming at which level so this is a big impact on the returns of our entire portfolio the entire dynamic is being impacted so we have to see how many proportions there are other currencies how much proportion is our domestic and what time is entering because as we have already talked about 2016 there was an extreme case when in currencies in the pound or dollar there was a huge change which the portfolio has greatly impacted the fixed income assets are generally considered less volatile fixed income is that you are in bonds or anything which is having a fixed stream of interest rates which is well known so more than the risk level compared to stocks compared to equity already lower side and therefore make sense institutionally to hedge any foreign currency so in this the same way our stream cash flow is defined we know when we have to come how much maturity how much co-ponds how much so here hedging role is more good trigger but it is same for equities no equities dynamics difference equities structures we don't know when the company has to give up dividend not to give profits what movement will come in the stock price how much change will be there that is quite a volatility so the stock's own risks of individual capacity are very high commonly accepted thought is that equities embed a degree of currency hedging let me come again that what we are saying is that the stocks they already have some currencies hedging is it so yes for example you bought a textile mill share textile mill more work is exported now when it is exported from outside its money means it already has an exposure of currency so the company itself is managing the currency risk very well so this means if the investor buys his share then directly if he does not do the hedge then the company's hedging will get benefit to him because the company who has to take foreign payments foreign work then he is already doing his own hedge so you are getting a kind of hedge stock most of the case is not necessarily that all the companies are doing very well but every company is trying to hedge out their portfolio while this may be true it is difficult to understand the extent to which this hedging occurs but we don't know how many percent hedging can happen that we are the bearish on the currency so that can be the difference of views now we will keep it here then we will discuss it further


The spontaneous symmetry breaking reported in a turbulent swirling flow at v equals 40,000 by quartet at all, 2010 P-H-Y-S Rev. Let. 105214501 can be described through a continuous one-parameter family transformation, amounting to a phase shift of steady states, and the associated susceptibility diverges at the transition in a way similar to what is observed in the experimental results, meaning that the zero-mode mechanism is a good candidate to explain the experimental symmetry breaking. This article was offered by B. Sainte-Michel, F. Davio, and B. Jubrule.