Live from San Francisco, it's theCUBE, covering Pure Accelerate 2017, brought to you by Pure Storage. Welcome to Pure 70 in San Francisco, everybody. I'm Dave Vellante with my co-host Stu Miniman, and this is Pure Accelerate 2017. Pure Storage in 2009 started a big wave of flash migrations, and the company's strategy was to specifically go after the large EMC install base of older symmetric mainframe class storage, and even to a certain extent, VNX and Clarion, if anybody remembers those terms, the install base. Pure's ascendancy was really a function of shifting from spinning disk to flash. Fast forward seven, eight, nine years later, and Pure is talking about big data and AI and machine learning and IoT, and is really trying to completely transform not only the storage industry, but itself as a leading player. The last time an independent storage company hit a billion dollars, about 20 years ago, a company called NetApp. Pure is trying to be the next to be a billion dollar company, Stu Miniman, a lot of action going on here. It used to be back in the day, I bought EMC for block, NetApp for file. Pure is trying to change that. Yeah, and Dave, storage, we've talked about it, when Dell bought EMC, what did that mean to the whole storage industry? I wrote an article when it happened and said, it's the end of the storage industry as we know it. When I came in here, it was like, oh, you know, we're going to be talking about storage, you know, you mentioned NetApp. I was at NetApp event last week and they said, storing is boring. It's really about, it's about the data, it's about the new applications. I really liked in the keynote, they were talking about new use cases, new applications, how do they fit into that multi-cloud world? You know, really interesting to hear, you know, Scott Deaton, who we've known since this company was in stealth, you know, laying out where the company is, they've got over 3,300 customers, a lot of SaaS applications, they're talking a lot about the machine learning and the AI pieces that are in here, but at the end of the day, I mean, Dave, this is their primary business, is a storage array, replaces, as you said, the traditional, you know, EMC boxes that used to be sold. So how much of this is still kind of an update on what the legacy is doing? How much are they ready for the future?  Well, it's one of the fastest growing companies in the storage business and the IT business, and the way that Pure has gotten there, is it, you know, in its early days, it never really talked much about so-called software defined, it just did it. One of the problems that Pure attacks is the problem of migration. David Floyer and Wikibon have written extensively about the cost of migration, the pain of migration. It was almost just assumed, well, if I'm buying storage, I'm going to have to migrate, and I'm going to spend 50, 100, maybe sometimes many hundreds of thousands of dollars migrating my workloads from older arrays to newer arrays. Pure Storage has this evergreen concept where through the use of software and software defined technologies, it's able to upgrade new customers, you know, quote-unquote seamlessly, there's that overused word again, but it's able to deliver essentially storage as a service, even though you're putting an appliance on their site. So it's a radically different model. They've announced some things today, for instance, like three site data replication, which is very, very complicated, trying to simplify that. So a lot of really novel ideas, again, they come back to their ascendancy, it was really based to, upon attacking the slow, expensive spinning disk, using its data reduction technology to create parity between the cost of disk, spinning disk, and the cost of flash. Something that David Floria predicted back in 2009 would happen by 2014 for the high spin speed. Now with FlashBlade, which is essentially the file-based system that Pure has, they're going after that same sort of mantra with higher capacity spinning disk, really going after the NetApp base. Yeah, Dave, you mentioned that Pure could be the most recent billion-dollar storage company. The company that might actually beat them to that is Nutanix. Now of course, Nutanix sells more than just storage, they're hyper-converged infrastructure, which means they're the compute that they're also selling that's being used there. So it's not quite apples to apples, but the last quarter, Nutanix had about $10 million more in revenue than Pure did. They also had IPO'd and that hyper-converged trend, one of the things that I saw early on on that, Dave, was attacking that migration cost. Hyper-converged, like what Pure does, a software layer, you create a pool of architectures, I can add in nodes, I can change configurations, I can update without the traditional way that we used to do it in storage, which was, you know, buy that box, take months to get it in there, load it up, transfer it over, retest it, all of those things that really kept your time to value on storage down, and that's something that Pure, and all the hyper-converged players, have been attacking that kind of legacy mindset that we had in storage for so long. Yeah, and of course Pure's approach to converged is in partnership with Cisco and presumably others, I'm not actually sure about that, but Cisco's the main partner there with FlashStack, that's their converge player, they kind of do a knock on hyper-converged, kind of depositioning it as sort of low end, kind of contained within small remote offices, whereas their positioning FlashStack is the scalable internet infrastructure. Pure does very well with SaaS companies, they do, you know, they're increasingly doing better with Fortune 500, they've still got a long way to go there, about 80% of their business is US, so there's a lot of upside internationally. We're talking about a company that'll be a billion dollars in their fiscal 2018, which is fundamentally the year we're in now, they've got about a $2.4 billion market cap, they've grown at about 30% a year, and very interestingly, they have mid 60% gross margins at one point last year, they had like 69.6% gross margin, which is unheard of, you know, we haven't really seen this since back in the hay days of NetApp and EMC, the question is, is that sustainable? And of course the big question that we have today, and we're going to talk to Scott Dietzen, nicknamed Dietz, a lot of nicknames here at Pure Storage, about is the concept of a large independent storage company. That concept is going away, it's like extinct, except for one company really, NetApp, is the only billion dollar storage company left, it's been 20 plus years, maybe even 25 years since that's occurred, what are your thoughts on that Stu? You know, we wrote a piece maybe eight years ago, can EMC remain independent? Recognizing that most of EMC's value was coming from VMware, and of course the EMC could not remain independent. Do you think a company like Pure can unseat the leaders of Dell EMC, HPE, IBM, and remain an independent storage company? Well, one of the things I always look at is, what is kind of, where are they going to hit their plateau? They're reaching towards a billion dollars and they do continue to grow, I think Pure still has plenty of headroom, but how long does it take them, Dave, to get to, you know, three or five billion dollars? The reason I throw out that number is, that's probably how much storage Amazon's doing today. You know, look at Amazon's a 15 billion dollar company, somewhere between 15 and 30% of Amazon's business, and nobody in the storage industry talks about that because it just ties to my application. So I want to follow the applications, follow the data. It's good to hear that Pure is getting in with a lot of SaaS providers, from Wikibon data, two thirds of the public cloud data, I'm sorry, of the public cloud revenue, is SaaS providers. So, absolutely, hear companies like Pure, SolidFire, saw it before when they were an independent company, sold to lots of service fighters, as well as SaaS providers, Kaminario, a Massachusetts-based flash company, sells to, I believe it's about half of their business, is selling to the SaaS providers, because these are companies that look at, okay, I need to own how I scale my environment, own those economics, and need to grow that, and just one more piece on that economics, Dave, look at that kind of multi- or hybrid cloud world. I bristle a little bit when I hear Scott Dietz and kind of almost say, well, public cloud, it's in the corner, about 20% of the use cases fit in that environment. Yeah, we'll do snaps to Amazon, and we'll do some other things, but you don't put the public cloud in a corner and just say, oh, 20% of the market's there, because that's today. And it is still growing 50, 75, 100%, depending on which public cloud you're talking about. We think that there's still plenty of upside, and when does that become a headwind that will slow the growth of what Pure's doing? You see a lot of the other software, storage companies out there, saying how do they become software? When we were at the Veeam Show, Dave, how did they really were, we're going to live in Azure. We're going to partner with AWS, and they don't really care. Pure very much, their growth, their revenue, and their margins today are all built that they're going to be selling gear with that. Yes, they have the Purity One software, and they have some cloud plays, but it very much seems to be saying that public cloud's not the direction. I'm sure Scott will probably give us a little bit more nuance there, but that legacy change to new distributed architectures has been a tailwind for Pure, and when will cloud be something that will push against their growth? Well, we're going to ask Scott Deetson about that, and you're right on. I mean, public cloud clearly is growing. It's growing like crazy, particularly the SaaS component of that. Now, of course, that can be a tailwind for Pure, because they do sell to SaaS companies. They even, Scott even had a slide up there today, showing Google, Uber, Facebook, AWS. Did you infer, like I did, that they were implying that they were selling to those companies? No, no, no. I saw because in the last quarterly report, they talked about basically the number four through a thousand. Four through a thousand, right. So they're not selling to the top three that they're clear on. So okay, so the top three would be Amazon, Google, and Microsoft, but then there's Facebook and Uber. Possibly they could sell to those companies. A Spotify is a SaaS company. So that SaaS part of the market is growing like crazy. Now the other point is, Wikibon released a study. We've been talking about it for the last couple of weeks on theCUBE around the true private cloud market forecast. True private cloud is an on-prem infrastructure that substantially mimics the public cloud at a much lower cost. And we came up with this notion of true private cloud because there was so much cloud washing going on, which really was virtualization. Now the true private cloud is growing actually faster than any other cloud segment. Now from a smaller base, granted, but we see about a $230 billion TAM over the next 10 years evolving. Now the most important part of this, and Scott Deetson touched upon this in the morning as they had, used some nicknames again, that companies are really focused on lowering their IT labor costs. And we see $150 billion approximately of IT labor moving out of non-differentiated heavy lifting into what we sometimes call vendor R&D in the form of cloud or on-prem products, appliances, and other software frameworks that can automate and eliminate this low value provisioning and patching and lung management. So Stu, you were very much involved in that true private cloud report. That market's exploding. I mean, to me, it's all about TAM expansion for Pure. They're a billion-dollar company, roughly. They're participating in a 30 or 40 billion-dollar market, so they have a long way to go. Yeah, absolutely. Because really, Dave, it's about the application. It is not a winner-takes-all environment. When you look at multi-cloud, it's what applications, and even we start teasing apart pieces of my applications and where they live. So I look at that, there was a nice logo slide that Pure put up, and you say, okay, Hulu is a customer. Well, is Pure helping with their CDN? I really doubt it. You look at Workday. Workday, up on stage at Amazon ReInvent, talking about how they partnered with Amazon. So what applications is Pure winning? Which ones are their customers using the public cloud for? And how does all of that sort out? Absolutely, true private cloud is really that reinvention of the data center. That flipping, if you will, of, I mean Dave, you probably know better than me, that the saying that IT spends 80 or 90% of their budget on keeping the lights on. How do we flip that so that we can spend money on innovating, driving the business forward, stop spending on one of our favorite terms, undifferentiated heavy lifting, and move to innovate and drive the business, and have IT serving those applications and serving the things that help me differentiate from the competition and move faster. Because absolutely, I'm sure something we'll hear at this show, is it's that agility and that speed is what companies need. And Pure, with their six nines of availability and that if you buy it today, your future proof, if you will, is going to help customers say that they can have a platform that they buy today and know is going to serve them well in the future. Well, Mark Benioff, I think was the first that I heard said it. Or it might have been Peter Burris, I can't remember. But basically there are going to be many more SaaS companies coming out of non-tech companies than tech companies. That to me, Stu, is a big, big tailwind for a company like Pure, who's software first, software defined, knows how to sell to SaaS companies. The other thing is Pure is the latest company. They didn't say this, but they certainly, one could infer it. The latest company to basically say, tape is dead. So it used to be sort of offsite back up to tape. Now they're talking flash to flash to cloud as the long-term retention. So a lot of really interesting things going on here. The venue is actually quite amazing. It's at Pier 70, this place is going to get torn down right after the show.  They used to make battleships here, about two battleships a year during World War II. The new warrior's facility is going to be here in Dogpatch, Stu. And I know everybody's super excited about that. Yeah, well, yeah, a lot of purple hats here. A lot of excited warriors fans. All right, we'll be back. We've got day to day, all day, wall to wall coverage of Pure Accelerate, half hashtag Pure Accelerate. This is theCUBE. I'm Dave Vellante with Stu Miniman. We'll be right back with Scott Deetson right after this short break.


In this video, we are going to explain how to translate a switch statement to ABR assembly code. And the first thing we need to know is both the meaning and the syntax of this statement. So this statement can be written with the keyword switch. Then it will be an expression surrounded by parentheses. And then with curly brace, we're going to delimit an area that is going to have the following structure. For every case we want to test, we need to write the keyword case. Then value, let's call it v1, column, and then some arbitrary code. And we can repeat this several times. Let's say, for example, v2, column, another arbitrary code. Let's call this code block v1. This one we call it block v2. And we also have the possibility to specify a default block. Let's call it generically block d, which is executed whenever this expression has neither of all the values that are specified before. Now one additional feature of the switch expression, or the switch statement, is that optionally we can terminate each of these blocks with the statement break. But this is not part of the statement itself, this is totally optional. The effect of this break statement is that if we include it, after evaluating block v1, this break will take us out of this statement and continue the execution in whatever follows in the code. Whereas if we do not insert this break statement after executing block v1, if the expression is evaluated to v1, the code will continue executing all the blocks. So this is basically an statement that is very useful whenever you want to execute different codes or different portions of the code depending on the value of this expression and you segregate them like this. Now what would be the generic structure in an assembly program? Well, let's suppose we had some previous instructions. We need to have some instructions to evaluate the value of the expression. In other words, we need to compute whatever has been specified here in parentheses. So we need to have an arbitrary number of instructions to compute the expression. This is what we know as the evaluation of this expression. Now let's assume for the sake of this discussion that the result of that expression has been stored and registered R18. What we need to do is to start comparing whatever the value is in R18 with the constants specified in the cases. So we can do that directly with the CPI compared with immediate instruction and we compare R18 with value v1 and value v1 is taken directly from here. Now what happens now is we need to execute a conditional jump and this conditional jump will be implemented as follows. If we detect that R18 and v1 are exactly the same value, then we are in the case in which we need to execute this code. So we're going to put precisely a conditional jump that will jump to the location v1. Let's put v1 block if these two operands are identical. If they are different, what we need to do following the interpretation of this statement is to move on and compare now with value v2 to see if we need to execute this block too. And this would be done exactly in a similar way. CPI R18 which is the result of my expression with v2. And we do something similar. And if these two are found to be equal, then I want my program to jump to another label. Let's call it v2 block which is going to be at the beginning of the instructions calculating block v2. Now I could have an arbitrary number of cases which would require each of them these pair of instructions, but eventually if all the comparisons fail and if I have a default block here in my construction, what I would do is basically an unconditional jump to the default block which will write farther down. Okay, now once we have written all the instructions to take care of the comparisons, we need to now insert the different blocks. So if we go back here, the first case we check was r18 with v1. If they are identical, then we need to jump to this v1 block which will be placed right next to the previous instruction, as many instructions as we need to implement whatever functionality we have in block v1. Now here comes the interesting part. If the block finishes with a break, the break will translate exactly into an unconditional jump that takes me outside of the statement, in other words to a label which I'm going to put farther down, which is done. So here we have a case in which one statement inside my switch statement translates directly into one instruction, machine instruction in the AVR assembly code. Okay, once we have done this, we place block v2 in an analogous way. All the instructions go here for block v2 and the unconditional jump to the label done because we have inserted the break statement here. Note that if we do not insert the break statement, then we do not need these two unconditional jumps and we would achieve what the semantics of this block is telling us, which is if the expression value is v1, then you execute block v1 and unless you get a break, then you keep executing all the blocks, which is exactly what it would happen here if we remove the break, which means removing the jump to the dump part. And the only thing remaining is insert here the default case, which it will have its own instructions. We call that block d over here. And finally, the label here, which will be the destination of this jump and this jump. So with this structure, we replicate exactly the operations required to evaluate this switch statement. Let's put up an example to illustrate this translation with an expression that is a bit more complicated. Let's suppose we have the expression x plus y plus z. And we have, for example, two cases, k0. We have code. Let's call it the block 0. We're going to insert a break a statement here. Then case one, we're going to have block one. And we're going to put immediately the default block. But we're going to assume that there is no break a statement at the end of case one. In other words, what it has to happen or the equivalent assembly code we're going to write to the right of this code. It's going to behave such that if the result of this expression is equal to one, then block one and block d need to be executed sequentially. How do we do that? Well, first, as we said here in our template, we need to write down the instructions needed to compute the expression. Let's assume that both all x, y and z are 8-bit integers and they are in memory. And they're in memory in locations labeled with exactly those names. So the instructions that would comprise this block would be first bring to register r18, for example, variable x from memory. Next, we will bring variable y to another register. Let's say, for example, r19. Now we perform the addition of x and y. So we say r at r18, r19, which means that r18 will contain now the addition between x and y. We'll bring the third variable, LDS, again to r19. We can reuse r19 because we don't need it anymore. y doesn't need to be there anymore. And we store the value of z. And we accumulate these to the addition we already have in r18. That's it. So at this point, r18, register18 contains the value of the evaluation of this expression. What do we do next? We start comparing, as we said here, the different values of the different cases. So the first case is k0. So we CPI r180. And we branch if this value is equal to, let's call it block0, a label that will write a little bit further down. If this is not the case, if this comparison fails and therefore we don't execute this branch, then we have to compare for case one. So we just do CPI r181. I still can use r18 because if this branch has not been taken, r18 still holds the value of this expression. And we repeat the same instruction. If these two are equal, then we've detected the case in which we need to branch to block1. If this fails, then we are in the case in which we unconditionally jump to the default. Okay, so we covered this part of the implementation.  So the first case, we decided to call it block0. So we put the label block0 here. And we will insert all the instructions that implement block0. Now, careful here because after block0, we did write the statement break.  After we've done that and following this template, we are going now to insert the code for case one, which we call it block1 to the label. So we put block1, we put the code here for block1, whatever that is. And now, since we haven't written here the break statement, we are not going to write the jump done instruction. Immediately, what we're going to do is place the default block, blockD, and then the label done. So as we can see, this switch is telling us that if the value of this addition is one, we jump to this block and block1 and blockD need to be executed. And this is precisely what is going to happen in this assembly code. We load expression, we compare its result, this condition is false, therefore it's not going to execute. This condition is true, the jump is going to be taken. We jump over here, we execute block1 and then immediately blockD. And we terminate the execution of the switch statement, which was the correct meaning that we wanted.


This is my mosaic cross that I did. I did it because I liked it, I liked how it turned out. The most challenging part about it was probably scraping all of the grout off of the stones.


I think that's what you need to do. I think that's what you need to do. Yeah. You see, the rest of the book is here for you. What do you think? This is about a chance to get to the end of the book. And I'm going to just do it first time around. So if you can see, so it's going to be a great book. That's going to be for you. So this is going to be for you because of your vision. So I'm going to just have to get to the end of the book. So this is going to have to be because I have the ability to write books. So just have to get to the end of the book. And it can be really fun. So at the end of the book, have to get to the end of the book. And so you can get to the end of the book. And you can either read a book or go over the end of the book. So you've got to get to the end of the book. So if you can see, you can now read a book. Please be sure to follow some of your experience behind-the-scenes. So finally, I'm going to mention that you will find a good opportunity to be a part of this event. OK. And I think for this program, I would like to give you a brief introduction to the program that I'm going to be using. What else do you want me to do with this event? That's it. You will find a good one. That's the reason I'm asking you this question. You can use your own language experience, which is only about grammar to your parents, and people make the forms of those which help you to get the motivation, and the workplace, and the environment. And I think that might be a good way for you to start. Because time is a place for you and often you're in a state of emergency. You work there. You're always working in a good place with all the questions and genres and to have this kind of as an outcome in the future. You can do the programming with them on a big platform. You want to do the following. The solution is to go out to another government and it's you, not the people in the community, and say, you, Jim, you have to make it out of the community. And then you decide, who is going to do it? What is going to be the public element that you're going to try to do for you? And that's all you need. Only the public element managed to defend themselves from the community. And I think it's really important. All the time you're working in the community and you're working through that kind of business, I think it's really important to analyze and maybe enter the one to the right house. And of course, if you're trying to understand that it's really important to analyze and understand there's nothing there at the bottom of the middle, there's no question.  Even though, guys, if you're trying to start with doing it, but you've got the power of it, like we do, a little bit of the necessary, look at that, there's the rest. So for that, it's just, so I think it's just a little bit of a problem. But for us, we should have been working and trying to do it, but, if you think that the rest of the public might be going to the community, what about it? Well, I think it's really important. We should just really start inside and finish. And if we're not, if you're going to do the full one, because it would take about three years to go to bed, and it wouldn't actually work. So it was a long, nice display, but not the time for falling. It was just going to be stopped only for some. It turned into a different culture. It was clear, it was clear to us we would like others to come up. So we're going to give it to the public. So for the person out there, it's just going to be a really long, long, short, long time. Not going to go that long. So we'll reach you into the public. And then we're going to start to move forward. We're going to move forward. Of course, we can just move from the top. But that's not going to work. I think that's crazy. I think we'll have to make sure it's going to work. But it will work. But, so that's how we're going to move forward. And then we're going to have to go through again. I think we're going to need to start to advance to work on our own needs. So it's going to be amazing, not to jump in, it's going to be amazing. But I think that we're also going to have to move forward. So it's going to work. So I'm going to start on the top. So it's going to be a lot of work. So we're going to move forward. I think it's the best thing that we can do now. And really, because we need to plan, it's going to be for now. She's not going to move. She wants to do it because there's a lot not doing for her. I think we should try to advocate and remember, now, that we're going to move forward. I think that we're going to have a lot of work to do with the political decisions. So we've got, we must say, it's going to be amazing. That is going to happen. Then we have a kind of a deal that is going to work. And then, of course, it's going to be pretty good for this class. They work and help families from a little to much. But it's going to work. And then the thing is not. And it's going to make the money we need to start from the end, start working to do it. Or doing something. So we're going to have to do it. So what is happening is we are trying to see the time limit to our own. But what's this looking for? Look at what's ahead. We're going to have to do something that we need to put the work back up to form a class. And we're going to do it. We're going to have to do this class. But we must respond with a bit more the same way we're going to do it. And so what's going to happen is it's going to come back up to the end of the class. And really, what's going to happen is we're going to start working from the end of the class. It's going to be the end of the class. And I want to remind you that we have a lot of trades that are going to move into the next class. And it's going to bring the best results in class. Remember, when you think of school days, can you come out with me? You can go to school to see what's going on with you. Just look at me with your hands.  You can have me. What do you think of me? Do you think of me? Do you think of me? That's all. It's going to be great, too. And I can tell you, if I'm glad that I'm going to talk to you, I'm going to be able to talk to all of you, but if I'm going to talk to you, I might notice things from different people that we are in a couple of years ago. Because, really, no matter what, I'm happy to keep going for the time that we want to travel, to get people to think about you. With me, hook me up.  I'm going to do it. This is going great with me. I'm going to turn to my mother so I can listen to her when I'm feeling better. And then we look at things like, well, who's here? And then we look at them. One way is having a missing place on the other side of the road with you. On the other side of the road. That's where we're coming in. On the other side of the road. So we go here. We're here, and we're there. If we can go through, we can use that. But of course, if we're going to end this, we're going to have to go through that. But I do think that there's a difference. So we're going to have to go through that. So we're going to end this. Because we're looking at, who can make like the difference on the other side. They're doing the same things on the other side of the road. So one of the things that we're going to do is we'll do the same thing. One of the things we do is to do a difference on the other side of the road. That means that there's very good accessible people to reach. And some of the numbers are not that great, So think of things about a good building. The dark ways of coming to the point is to move, and you must not be the right person to do that. And you are the right person as a person. So it doesn't matter what you do. For me, it's best to move. For me, it's best to move. To move, I mean, the right person, and that's very, very good. And it's a lot to think of. It's a very, very good work. And it's a very, very good work. It's a very good work. Because this is kind of fun.  I've been thinking about it, and people will come up to you. And it's a lot to think of. So I'm going to start with this topic from the end of the year. So how many of you have been able to go to the World Bank? I will speak to you. So I think many of you have been able to go to the World Bank specifically. We can talk a little bit more about what you think is true. And for the new World Bank website, if you're planning to go to the World Bank website, you can ask me to do this. And I'm going to talk a little bit more about what you think is the best way to go. I think we're going to put it on that. And the big thing is we have a program that we can think of as being the best way to go. So let's look at five. So the real thing is, this was me, but it was only open. And I think it's really good to be able to sort of make things very well. You can get all kinds of things that you're managing and working on your own. It costs a lot of time to try to make it very quickly. You can't really be waiting for the right person to come and start with this program. So I think in the next few months, in the next few months, I'm going to be able to be as happy as I can at two months. And I really want to be happy just with the work that I'm doing with this program. And then, you know, I think, for you as well, I think that the best way to go in is to be able to come into the World Bank website. I think we will be able to be like the best in the World Bank project. And that's going to be in the World Bank I think he's the only person who takes the people to do things. I think he's the only artist I know who knows what. So the hardest part is that he knows what. I think he's active too. I think his talent is just doing that. So as you can imagine, he's up upstairs. I think he's thinking about what he's going to do. He takes his man, he's going to open, and he takes his way on the work that he does. So like you can't do it by yourself, if you're going to have a future. And if you're old, or you're going to do something that's not right for you, it's just not part of it. It's just that I don't want to do anything else. I don't want to do anything else. I want to do everything with care and care.  And I think that he's the only person who's been there.  But the point is that you're a good person. And you're going to get paid for that. You're going to get paid for your own work. You're going to find a lot of money. You've got lots of money to go.  So, I think that our world is very, very much well-deserved. And what you're going to do is going to explore the world. And if you think it's right on the market, it's really hard to go and explore. It won't outlast the nature of everything I do and I'm going to talk about in the next video. So, if you see the skies, there is a very, very good place for a very good queue. And there's one that is filled with respect to me. When you're telling me, you give me, well, you have to do that for others. And you have to keep a close eye on me.  Can you feel, when you are opening the doors of the community first and first in a week? I think that's a pretty good thing. I think that the people who are doing that, maybe they're not sure what's up, but they are trying to do the most in the long, really quick, perfect, long, just really, really, really, really good. I look at the world, I look at the world, I look at the world, I look at what's best for me, I look at what's best for me, I look at what's best for me, I look at what's best for me. But what's a community just like when you're in a leisure world, doing the things you love and sticking with those in the middle of the world? And you have a pretty good thought. Because the majority of people have loved, and people are not in a community just like that, people don't love. But there's a real space like this that's not living through now. So it's just a lot of people who are pleasing here, and it's a bit of a new period here, because people are always looking for people who are able to do things that people can do and love them. So I'm glad to have a wonderful group of people, who that were good to me, It was kind of early, early, early, early, early. But it was, now, the strange, like we need to see something, that's going to be up to the need and the intention. So, I'm going to take it, but in and out, I think that's what we're going to do, in the future, because I think this is the start of the future. And I think that's where we've started up. And I think that's the way to do it. I'm going to tell you, As you said, I think this is a critical moment because of everything. If you try this, if you have some good stuff in your life, I'll just use the good ones. Well, if you look at some of these, I'm not going to block the time with them. If I'm in a field where you understand that you really want to get to know each other so much, then. I'm going to continue to encourage really, really, lots of people in this field.


Hi everyone. Welcome to the fourth and final module of the Fair Data 101 course. My name is Liz Stokes. I'm from the Australian Research Data Commons and I would like to acknowledge the traditional owners of the land on which we are meeting today. For me, based in Sydney, that is the Gadigal people of the Eora Nation. I'd like to pay my respects to elders past and present and acknowledge that this land has not been ceded. I would also like to extend a warm welcome to any First Nations people who are joining us today for this final module, reusable. Okay, so today let's get into this. We are going to, so just front matter business first. Please use the question or chat component for any questions or if you've got any tech issues at all today or if suddenly you can't hear me. The NBN technician visited on the weekend. I'm pleased to announce and repaired not one but three broken cables between us and the node. So hopefully today works. I do encourage you to use the channels in the Slack also for after this webinar if you have further questions coming along. And you can also tweet out using Fair 101 or ARDC training on Twitter. And there is also a link to our code of conduct that we have for this course to ensure that it remains a friendly and accessible opportunity to get into the fair data principles. So today I'm going to start with an extended permacultural metaphor. Excellent things growing compost like mushrooms and other fungi. So for my first visual metaphor, let's ground reusability in a permacultural lens, which concentrates on the health of the compost and soil for excellent things to grow. Okay, so this reusability is going to concentrate on practical applications. Okay, the ultimate goal of the fair data principles is to optimize reusability. So the umbrella principle that data and metadata are richly described with a plurality of accurate and relevant attributes is further defined by three sub-principles or we could call them vice principles if you wanted, which highlight the importance of clear and accessible usage rights, data provenance, and domain-relevant community standards in supporting reusability. So how are we going to get into these? Here are some concepts I'd like to step through in the next 40 minutes or so and then we can have questions and answers. I will probably keep these things on a fairly high level and I know that's probably a little bit of an oxymoron to go high level and practical, but I don't know, that's probably the tension that we live every day. However, Matthias on Wednesday is going to expand on some of these into detail at a greater depth, shall we say, and look at fair beyond data into the associated outputs in the glorious ecosystem that is research. So in practical terms, how do we talk about reusability and what aids data reuse? In one sense, it's all about the metadata, keeping our eyes on the prize and looking at what the metadata is exposing and facilitating. So data that is available for reuse is accessible and these are just sharing with you some thoughts that occur to me off the top of my head when I think about what that might mean. So I translate that into, at the click of a button, I don't have to go deep into scrolling or any convoluted processes to actually access the data. The data is also well described. It does what it says on the tin, for example, which makes it easier for searching and finding and retrieval. The data is also familiar. When I'm thinking about familiarity, I'm thinking about things like formats that are in current usage. I'm thinking about the way that data is expressed or encoded appears in ways that are familiar to its users, that it is also easy to cite. So it's relatively painless for me to tell you where it came from and also that it is licensed so that the providers, the creators of that data are very explicit in how you or I are allowed to use that data. So let's start pulling these fair data principles apart. So at one level, rich description that metadata are richly described with a plurality of accurate and relevant attributes is an encouragement for the metadata author, whether they are humans, machines or data librarians to be generous with their information, generous with volume and specific with regards to the structure of that data. For me, this brings to mind two things. Firstly, a rich or thick description. I'd like to get into a little ethnographic story. And secondly, a certain enthusiasm for machine readable metadata schemers or rather documentation of metadata schemers that is machine readable and fair. So damn it, I probably should have put a reference into the slides. I will add it afterwards. I'm looking at me in my notes right away. So rich description brings to mind Clifford Geertz Maxim for anthropologists to provide a thick description in their field notes, that is to go beyond factual or literal descriptions. And he provides an example of reporting a wink. So instead of describing an eyelid stretching over an eyeball, he encourages ethnographers and anthropologists to consider talking about providing the context in which that wink might have occurred. So looking at the social and cultural things that are going on as well as the as well as a literal description of what is happening. I bring I bring this up because it's a I'm I'm talking now in this extended metaphor about anthropological research practices. Okay. And these highly descriptive entries and monographs of anthropological research are all part of doing that kind of research. So for other researchers to glean insight requires deep and sustained reading. And even if this does become tedious for the human, it actually becomes impossible for the computer, which is unable to filter strings by itself, unless someone has manually marked up that text or provided explicit structure to the data or digital information there. So I'm just going to park that tension here for a bit and then move on to unpacking attributes, which is that aforementioned enthusiasm I have for machine readable documentation metadata schemas. And another nice visual metaphor. Okay. So remembering that the core value of metadata is that it is structured data about data. I'll remind you of those variety of those concepts of data models that we were talking about in our previous module. So metadata assumes that the research data we are concerned with is always already structured. And that this principle goes for the metadata which describes or structures the research data, as well as the data itself. So for the fair sharing of research data, this accurate and relevant attributes point towards what gives us basic information about it. Information about how to find it, the act, what it's about, and the permitted usage. Content descriptions should cover both the content. Hi, everyone. We're sorry about the technical difficulties. If you will please bear with us, I will attempt to just take over from where Liz left off. Thanks, Matias. Am I back? Oh, sorry. I do not need to take over because Liz is back. All right, Liz. Where did I get up to? You have just gotten onto this slide with the rice paddies. All right. Okay. Excellent. Great. Okay. On with the show. Awesome. Thank you. Sorry, everyone. I appreciate your patience. Clearly, I did not touch wood when I extolled the virtues of the NBN. Moving right along. Okay. So let's have a look at this metaphor that I have thrown up for you. So as I was saying, talking about the content and the context and giving a rich description of metadata and this research data. Sometimes, so those practices in research practices that are familiar with us are not instantly translatable sometimes to computational processes. So when we do want to do something like that, we have to take a few steps of structuring our data in ways that can make it possible to harness the power of technology. So, for example, when we're making decisions about weighing up the purpose of what we're describing, we're doing that against the utility of describing it. We might not need to describe everything in a rich semantic ontology. Maybe it's only a few components. It really depends on weighing up costs and time and effort that are available to us. So in this balance between describing everything and what is fit for purpose for our users, whether they are researchers or data librarians, stewards, et cetera. Sometimes it might mean that not all of the detail goes into one long notes field, for example, which even though it's tedious or could be delightful for some humans, it is generally impossible for the computer. So here we have fields which are arranged according to the context in the shape of a landscape here. So now I'm going to really go into this metaphor. Obviously, they are also impacting the shape of the landscape and how that landscape is exploited for agriculture. In this image, not all the landscape is structured terrace rice fields. The farmers have made a decision about how to optimise the land for farming. As you can see, it's not all one uniform or level field, literally. And in some cases, you can see it's not structured at all potentially over around the borders of these structured rice paddies. We can see maybe some banana palms or other palms around in obviously very rich ecosystems themselves. So now I'd like to move into an example around Darwin core. So I've mentioned this, I think a couple of times, Darwin core being a metadata schema for describing biological things using standardised ways of what terms are and the elements that they reuse from other standard vocabularies. So what Darwin core does is they use the conventions of schema documentations, which are themselves specific standards to aid machine passability and human readability. Once you know what you're looking for. Okay, so what I'm going to do is navigate to this navigate to this website here. Okay, I trust that you can see the Darwin core basic vocabulary documentation here. So what this documentation says is lists, we can see some versions of the vocabulary. But what I want to draw your attention to firstly is that under this section for the term lists that are part of this vocabulary. And you can see we have some fairly standard just information about about different terms that are incorporated into Darwin core. Okay, so Darwin core actually borrow terms from the Dublin core legacy namespace and also from their terms namespace so from their terms and their elements. There are also some other lists here. And then here you can see under this IRI, which is like a URI resource identifier. Okay, a persistent thing. You can see that you can see that Darwin core define their own terms for the purpose of biological description. But they also reuse some from Dublin core. So I'm going to click over to the DC terms, Dublin core terms and show you here again that they are providing us some information about what they what they've created and how and under this section for again, terms that are members of this list. And here they are starting to provide us with more information about about what terms they are using and what has been borrowed. For example, there is a location term, which is called location. They provide a definition, a spatial regional named place, and that it actually replaces a previous term that they had specified. Okay, the modification was in 2008. The same thing here actually incidentally has happened with access rights. So they're using the Dublin core terms for access rights to provide information about who can access the resource or give an indication of its security status. But this documentation here is showing the term that it replaced, which was previously specified in the Darwin core term list. But now they have decided to reuse a term from Dublin core. Okay, so you can see that, you know, you don't have to make all the right decisions at the start. Maybe and I'm sure they had some very good reasons for choosing to have metadata about access constraints rather than access rights. And in fact, if I even follow this link, it would take us to the Darwin core quick reference guide. So this is where the machine readability documentation also is complemented by a much more human readable documentation. Because here in this quick reference guide, we get to see when we look at some of these attributes here, information that's going to be much more relevant to a human interpreter of the Darwin core terms than a machine. So humans need to know we like to have a bit of a comment and a definition and examples. Examples are really good for humans. So we know how to apply it and what to expect. And we can see that here in this, for example, modified term, it relates to date time when a resource has been changed, and also that it's conforming to a specific ISO standard about how to present that time. Okay. And that's the Y Y Y Y MMDD time and then the time little section after it. But anyway, so when we so we've got we've got standard ways of talking about metadata that's useful for humans to interpret and use and apply. And we also have if I went if I went backwards or not sure how I could go backwards in that right now, to the kinds of terms that the computer is going to want to know. So they want to know a bit more about the structure and the whether something's a class or a property and the semantic level on how the terms relate to each other. Okay, because knowing what values can be aligned or how often a field or element can be repeated. And whether it's mandatory or not. So the usage of that metadata description, then has implications on the things that we might want to do managing that research data at scale. Okay, so decisions that a, for example, data repository might want to make when they're considering bulk ingest of records or transforming records so that they can apply some preservation long term preservation work. This is where it matters what the rules are for managing that data. So we really like the metadata to aid that reusability to be very clear and very accurate when it comes to identifying attributes of that data. This is getting really circular references, referencing, isn't it? Okay, so let's move on. Great Darwin call. Okay, now it's time to talk about licensing. Oh, better speed up. Okay, so first I'm going to address licensing and then data citation. Okay, when the aim is actual reuse of research data, this principle encourages us to be clear about how people can do that. So when the data citation tells you where you got data from and can aid provenance in that way, a license sets out your expectations for others to follow. I just got to have to acknowledge that this is probably the most meta of slides I could ever give you right now. And I'll try not to get us lost. So many licenses actually feature attribution as an expectation of usage. And as this slide says, Quill West actually, the purpose of attribution is to give credit to the original creator of something you are using. It relates to the thing and is a legal requirement of using openly licensed works. Okay, because this speaks to the fact that a licensed work is a license is a legal instrument. So although licensing research data tends to come up at publication points, research data could be licensed during any part of the research life cycle, during planning or negotiating with potential collaborators, for example. I'm going to concentrate on creative commons licenses. After I make a few notes about Australian copyright law, drawing from the ARDC research data management, research data rights management guide in the next one. But as you can see, I am reusing this slide, this particular slide, which is from citations versus attributions by Quill West. They have licensed it through the creative commons attributions license, CCBY4, which is an international license. And you can see even on this slide, there is a picture of a LOL cat, which has been attributed and under a license for creative commons attributions and share alike 2.0. And they even provide acknowledge that this was a derivation from the original work, which was pretty much the picture of the cat without oh hi, I open source this for you. Okay, back to Australian copyright law. So look, it's complicated. But it's a fun time. Okay, so the conventions of academia to comply with copyright have developed citation and attribution practices. Okay. And while it is true that creative commons licenses can only protect material in which copyright or similar rights exist, there are two important considerations at play. Firstly, strict determination of whether copyright subsists in a data set can be complicated. And some data sets will definitely attract copyright. Secondly, for those data publishers and researchers who wish to broadly share their data, protection is not the primary objective in their selection of a particular license or right statement. Rather, in that case, the dual objectives in the selection of life of a license are or should be to unambiguously declare to everyone that the data can be reused and to indicate that the license or would like to be attributed when someone does so. So the bottom line is regardless of whether copyright exists or not, you can still apply a license to instruct how people might use the data you are making available. So as you can see on this slide, Australian law doesn't recognise copyright in machine-generated data, but it does recognise the impact of human authorship, which is demonstrated creativity in selection and arrangement of data. So if you have some raw data and you have analysed it and corrected, reformed, made modelling choices, this may actually influence whether or not copyright subsists in that data set, but it is always a case by case basis in Australian law. And the final important point is to know that rights in data usually rest with the creator of that data. So this is why we advocate for the use of licenses to make something usable when we want to be sure that we have a method of giving people a license to reuse the data. So we don't want to be resting on the conventions of citation alone. Creative Commons suite of licences have varying levels of usage, which you can bolt on to your data assets and it's important to acknowledge that they don't waive or replace copyright. And this image here we have is the attribution licence, which is quite popular because it's relatively easy to do and maps well to standard academic citation practices. Behind the CC attribution licence is a legal instrument that works internationally. That's the version four there. To apply the licence, you display this image and the words below, which link to a human readable version, a machine passable version and the legal instrument, which you are welcome to read. Okay, so let's have a look at what some of these licences look like in the wild. I'm using an example here from the Australian Ocean Data Network Portal and this is a data set about Australian phytoplankton. As you can see there, they're using the attribution licence CCBY and the portal actually licences all data within their repository as CCBY in their data use acknowledgement statement. Over here in the metadata record and I'm providing a screenshot of that, are some additional constraints to attribution depending on the parts of the data set that you might be using and how to provide attribution. Another example is from the Atlas of Living Australia and this is for the little magpie record, the Australian magpie I should clarify now that I know the difference roughly, Jim Narayna Tibbersen. So the Atlas of Living Australia actually has gets data from a lot of different data providers and they all have are welcome to use different licences. This particular image here, which is attached to the record, to the magpie occurrence record is licensed under a CC attribution non-commercial, that's what NC stands for, by a contributor called Wingspanner and that is just for the use of the image. That is not the whole record, there are other components there. So if I do that special screen sharing thing again, okay, let's try not to make this terrible. Here we are. Okay, so looking at the actual record here, let's scroll down so you can see on this page, the provenance of the who is providing data to this record is shown by these little provided by and the links to their links to the data sets which are being supplied contributed from various data partners. Actually if I scroll up, you can look over here and under the data partners tab, I'll just click that now, we can see which data partners are providing what data sets and under which terms and you can see a whole range of CCBYs, Creative Commons licences, sometimes they have non-commercial limitations on them, sometimes they only want attribution, only specify attribution. Okay, cool. So this is a nice slide into provenance and I'm probably going to finish up with this looking at provenance and then we can, I'll let Matias take you deeper into the domain relevant community standards. So what does provenance mean? Okay, well ultimately I think it's about asking what is useful for the users, the researchers to know about how the data was created. Okay, and often it's not until somebody goes to actually reuse someone else's data that they realise what is actually practically useful in terms of how the data was created or generated and what processes had been applied to that data. So provenance is something that allows people to trust data so that they know where it comes from, how it was created and can be aware of limitations. For example, if we're thinking about a temperature sensor, it might actually only do measurements in whole degrees. Now we all know that temperature changes over, like I suppose we could say degrees of degrees. So if you had a data set that only reported temperature at particular times in whole degree terms, then you would need to be careful about visualising that data and the implications for further analysis when that data had been normalised in that way. Here is a tale of two sensors and I would like to acknowledge this is actually from Matthias if you are wondering about the provenance of this example. When we were talking about how do we talk about reusability and what is helpful information to know about sensors that may have been used in the collection of data. So these sensors they collect data on humidity readings and temperature. DH-1 and DH-2, double one is the blue one actually, double two the slightly larger one, have different ranges, ranges of humidity readings. They are optimised differently for temperature, different temperature ranges and they also perform differently. So they have different rates of accuracy and sampling rates and there is a slight cost difference. So when the sensor or instruments, any kind of research instruments have similar names, the provenance is important because different capabilities will produce different results. So if we were using this particular sensor in a data collection activity it would be very helpful to be able to link out to record the exact sensor name and then link out to the properties or attributes of that sensor because then we would know what degree of accuracy we can infer from the results of the data that that sensor collects. So our decisions in terms of the fair principle of reusability is about being clear and accurate when it counts. I'm going to skip this one I think in the interest of having a chat about and answering any questions you might have but you can have a look through, I'll share the slides after this and you can have a little look through here. This is a nice example of how changes in the actual processing analysis pipeline mean that research teams can get very different results from the same data set. So probably time for me to wrap up right now. There are some links and it's a good read. Hey Matias. Hey Liz thank you very much for that and thanks for a graceful recovery that saved me from having to deliver your presentation. We do have some questions in but there are time for more questions than the number of questions we have so please do type your questions into the question module as Liz and I address these first ones. Okay so back at about the 12 minute mark you mentioned you're talking about the relevance of metadata attributes and someone in our audience says that sounds highly subjective and asks whether we have any guidance for how broadly they should think with respect to assessing relevance. Ah I'm going to be very candid and it kind of goes to is nope okay so my candid answer is what can you be bothered with right perhaps the more prudent response would be what what is fit for purpose so where do you need to you don't have to collect all of the metadata but what what is the metadata that really counts for for example for a a repository to provide a reasonable finding aid to the contents in their data repository so how much how much aboutness do they need to know about the research data in order to in order to make it easy for people to find the stuff that's in their repository and also how this is a this is a pun it's going to happen how fair is it on researchers and data stewards data curators people contributing data to repositories to ask them to provide extensive descriptive metadata about what they're producing so you've got to balance it up and often I think you want to be looking at what metadata can you automatically pull from other organizations or other enterprise systems first and then you know it's that last it's it's the last resort that you want to ask the contributors to put in extra data themselves and also I guess it kind of depends on the you know like the community standards what people find acceptable to provide okay great thank you okay next we have a drive-in busting question so it's possibly a little confusing in that list from the ala how there are all those different kinds of cc licenses so this particular question asks is cc by a different license to cc by 4.0 yes I will expand there are different versions of licenses so as the practice of openly licensing outputs and things develops there are different there are different engagements which work according to different legal jurisdictions so for example the creative commons attribution license version 3 or 3.0 that works in an Australian context okay version 4 is the international version of that license and it also happens to be the latest license so people who've come to a position where they've gone well you know what let's just apply the international license because then that will just work everywhere and we don't have to worry about gatekeeping and geographic borders that is just one step too far great thank you okay another question here would you say there is a preference when choosing a creative commons license for data sets especially when we want data to be open does it depend on the researchers preference or choice yes yes um what I what I haven't talked about at all are are institutional policies around intellectual property okay and whether that how that plays into who is providing the data and for what purposes so this is like this is a familiar attention to many of you publishing student hdr student theses okay and and managing different rights there but go back to the question because I think I was coming to the point but I've forgotten it yep so my understanding is there a preference for choosing a creative commons license over perhaps any other kinds of license when it comes to data sets yeah so I think that the creative commons licenses are I would recommend them because they are straightforward and they are they work work well for the purposes of sharing data sometime like your data could be really really old okay or your your resources could be really really old so in fact copyright may not even come into it and you may have be able to use something like a public domain mark which is another thing that the creative commons licenses include which is putting things in the public domain and um but hey there are weeds there okay I can see Matias starting to feel anxious yep okay so we've got one last question uh and I might handle this one if it's okay Liz uh so um Liz you asserted that rights and data usually rest with the creator uh can an institution assert their right to IP for data generated by academics in their employee also could a funding body assert the same as a part of the employment or funding contract now the reason why I wanted to answer this one is because I have been through this process um so as an employee an academic as an employer at the university anything that they generate any IP they generate during the course of their work would naturally fall to their employer unless there's been a contract signed saying otherwise so for example many institutions will allow their academics to hold the IP of their research outputs their publications sometimes even their teaching materials um but these agreements don't generally cover data so um and in fact in the past I have signed uh an extra contract I had my employment contract but on top of that when I worked on a particular project I was asked to sign an extra piece of paper that explicitly stated that the outputs of this project belong to the institution now strictly speaking that second bit of paper wasn't necessary but it was certainly an instrument that the institution wanted to use to protect its own IP uh and the same goes for funding um contracts as well um so for example the ARC um does specify that uh publications should be released under a particular or should be made openly available um and other funding bodies do the same now they don't necessarily go as far as saying that they own the research outputs um but they certainly do stipulate a particular kind of licensing or or access that should be used there uh did you have anything to add to that Liz nope I think you handled that wonderfully okay uh all right that's actually all the questions we have and I am sorry we ran a little bit over time but I will hand over to you Liz to wrap up oh that's it everyone um Matthias will follow up on Wednesday with a bit more detail in community standards and looking at um reusability from um uh with reference to reproducible workflows uh so stay tuned for that and um we will have quiz activities and um quizzes and activities ready for you for um for Wednesday I hope okay see you later bye


Hi everyone Joe for jazby's case breaks calm coming at you with a 14 team Number block randomizer for pick your team number 23, which is sold out. We'll be in a separate video We also did another number block. That's also in a separate video as well So that's how those number blocks work There's one two three four five six seven eight nine There you go. They're the all the 14 teams right there big thing big. Thanks, too Grab the list big. Thanks to these folks right here for getting into it and Thanks, too These numbers right here Let's roll it. Let's randomize it two and a five seven times for each list one two three four five six and Seven after seven times Derek Melanson down to Nancy two and a five seven times for the numbers And seven eight down to six After seven All right, so Derek with eight Oliver with four Jim with five and one Aaron with two and three Dan with zero So you get any and all redemption for those 14 teams on that previous list including one of one Redemptions Jim you'll still get live one of ones. You also have seven Tom. It's with nine and Nancy with six All right, so there you go ladies and gentlemen break coming up next jazby's case breaks calm


Welcome to the module on working with tags in Zotero. It's one of the five modules in our unit on managing Zotero collections. In this module we're going to cover how Zotero works with tags. We'll cover where tags appear in Zotero, adding tags manually, using the tag selector to manage tags, using colors to manage your favorite tags, renaming and deleting tags, where Zotero's automatic tags are, and what they are, working with Zotero automatic tags, and tips, general tips, for working with tags in Zotero. To follow along in this module you need to have Zotero installed on your computer. You can get the software at Zotero.org. And for more information on the basics of using Zotero, see our online guide, or check out the Mastering Zotero guide on GitHub. This module also uses some examples from a sample Zotero library that I'll be working with. You can use your own Zotero library to try out the features that I'm covering in general, but I've also made the sample items that I'm working with for this course available for you to import into your own Zotero library so that you can see exactly what I see, and click along. To learn how to get and import those sample items, see the course setup module and come back to this one when you're ready. Now tags are a great way to supply keyword descriptions to your Zotero items. These keywords can be used to organize your library. You can use them to describe the contents of your library in nice clean ways to make it easier to find items later or to keep track of your work. Tags appear in a couple of places in Zotero. One is down in the tag browser, which is at the bottom left area of the Zotero window. Another place you'll see tags is on individual items in the item pane. Finally, you should know that in addition to putting tags on items, you can actually individually tag attachments to items. So if you have a PDF version of a journal article, if you have a web snapshot of a page, if you put images in your library, notes, for most of these you'll see that the tags are listed at the top of the item panel when you select them. For notes, however, you'll see them at the bottom. Regardless of where you see these tags listed though, they all work the same way. Overall, there are two ways that tags get added to Zotero. The first is tags that you add manually. You can add tags to an item in Zotero with the tags button in the item pane. Just click the add button and type in the name of a tag. So I'm going to add privacy to this article on resetting the privacy discussion. I'm going to hit enter and there you see the tag. Note that there's a little blue tag icon next to it. Now that blue icon means that it's a manually added tag. So this is a great method for adding tags one at a time. If you want to add more than one tag, say you're brainstorming a list, what you can do is hold down the shift key when you press your enter key after the first tag and then that little single tag field will expand into a text field. And then in that text field, you can enter multiple tags, one per line, and then just click outside the entry field or use the add button again to turn these individual lines into separate tags. So this is a really great way to do it if you have a list of tag in another document that you just want to copy and paste into an item or if you're just brainstorming and trying to come up with a list of tags that you might clean up later. Now once you've started to build a set of tags, you'll see that Zotero will try to auto-complete your tag based on existing tags that you've already got in your library. So here I want to add culture as a tag to this item and you see that as soon as I start typing, Zotero offers suggestions based on tags I already have in my library. This is actually a really good way to make sure that you're staying consistent with your tagging and reusing existing tags wherever possible. Now as you build a list of tags for your library, they're going to start to appear in the tag selector on the bottom left. I can resize this area if I want by dragging the divider between it and the collections area above it. Now these two areas are connected. When I have my library selected, Zotero shows me all of the tags in the tag selector across my entire library. When I select a specific collection item in Zotero, the tag selector shows all of the tags on items within that collection. So here I choose this collection related to Canadian author Miriam Taves. So if I go to Related Authors and Works, which is a sub-collection, I'm actually only seeing items from this small collection. And then of course, when I select an item, I can see the tags for that item on the right in the item information. But you'll see that the tag selector still shows me all of the tags in the currently chosen collection. It's an important difference, so make sure you keep that in mind so you don't get confused. Now there are some really cool things you can do with the tag selector. First, let's say I have my whole library listed, I have my library to see all the tags in my whole library, and I select a tag in the selector. For example, I'm going to select History. Now, all of the items with that tag will be displayed in the items view. So what I've essentially done is filtered the contents of my library to display only items that have been tagged with History. I can just click the History tag again to unselect it, and then the view returns to showing everything in my library. If I select more than one tag, I will see items that contain both tags. So if I select History again, and then I select Quebec, I see that I have item in my library tagged with both of those keywords. And of course I can deselect these one by one, but if I have a bunch of tags selected, I can also use this little pop-up menu in the bottom right corner and choose Deselect All to clear all of my selections. Note that the tag selector also dynamically updates as I choose tags, which constrains the list. So when I click on History, you'll see that the list of tags decreases. That's because Zotero's tag selector is only showing me tags for the items that are in my library, and at least at minimum have the History tag. So from here then, for example, if I add Montreal to my selection, the tag selector list updates again to show me the limited number of tags that appear on items that both have History and Montreal. In my items view, I only see two items, and in the tag selector I only see a list of the tags that are used together with History and Montreal. So let's clear all of my selections again using the Deselect All option in the bottom menu. This is going to reset my tag list back to the default. If I have a bunch of tags in my library, and I do, I can use the search bar underneath the tag selector to narrow down the list. It will update dynamically as I type. So as I slowly type the word Indigenous, you can see that my list slowly narrows to tags containing those characters in that order. You can click the little X at the end of the search bar field to clear your text and remove the filter. If the tag selector's way of dynamically updating this tags as you filter and type is confusing, you can use that menu at the bottom to select display all tags in this library. And when you have this turned on, you will actually always see all of the tags in your library. They will all be listed. And then when you have selected a collection or a tag on the left, any tags in the tag selectors that do not appear in that collection or alongside that tag will just be grayed out. So here with the Miriam Taves collection selected, any tags that do not appear in items in that collection turn gray. And then if I select fiction, the item pane shows me all of my Miriam Taves items that are tagged as fiction and all the tags that are not used in conjunction with that get grayed out as well. I have a lot of tags in my library and I find the grayed out thing a little bit messy. So to keep things more clean, I'm going to use the bottom menu again to deselect all to reset my selection and I'm going to turn off that show all tags option. But you can make your own choice. So this leads me to the next tip. There's another way to add tags to an item using the tag selector. Just drag an item onto a tag and the tag will be added. So I've just noticed that I forgot to tag Miriam Taves book women talking as fiction so I can add the tag manually of course or I can just drag the item onto the fiction tag to add it. There we go and see here it appears now in the list of tags on the book item. There's one more feature of tags that can be very helpful for focusing your work. You can assign a color to as many as nine tags. Now here's why this is cool. Let's say I am doing research on confederation as one of the many topics I'm looking at in my history course. I can right click on the tag confederation and then assign one of the nine colors. How about purple? And then I set an order or rank from one to nine. Now two things happen when I do this. So the first thing is that that tag turns into the color that I picked so confederation is now my purple tag and that tags with colors are always shown at the top of my tags list. So up to nine of my most important tags can be set so that they're always visible and that order or rank number actually dictates the order that they're showing in from one to nine. The second thing that this does and I like this a lot is that it places a little colored box on items in your library that have that tag. So with my full library selected now I see that I have two items listed with a small purple box and I can very quickly and easily see that I have two items now that have the confederation tag. So like I said I can assign up to nine colors I can change the order of them I can remove colors in any way that I see fit and I can dynamically change it anytime my whims change it's just another great way that you can organize your library and keep track of your work. Of course I can also rename tags or delete tags in Zotero. I can do this by right clicking on a tag in the tag selector and choosing the appropriate option. Now this will rename or delete the tag everywhere it appears in your library so this is actually a really nice way to manage and clean up the tags in your library. So for example here you'll see that I've used two similar tags in my library colonization and colonialism to describe items. Now I know that these words have slightly different meanings in different contexts but for the project that I'm working on I'm just making a subjective decision that I only want to use colonialism to keep things simple. So if I right click on colonization and rename that tag to colonialism what I've done is I've effectively merged those two tags together so this is a really nice and easy way to clean up and organize your tags to simplify them. Now the second kind of tags you'll see in Zotero are called automatic tags. These work mostly the same way as regular tags but the way that they get into Zotero and how they're displayed are a little bit different. Now for some database platforms like EBSCO or PubMed or even if you're looking in the library catalog using this Zotero connector in your web browser to add an item to your library can also bring in a set of tags as part of the information that it imports for an item. So here's an example using an article from the MLA International Bibliography Database which is on the EBSCO platform. So when I access this article and use the Zotero connector to import it into my Zotero library I'm just going to put it in the my library folder I get the citation information in my library. Now I haven't added it to a collection yet so that means that this item that I just added is going to be easy to find if I click on my unfiled items collection. Now you can see that the information tab here has the author, the title and the journal information which is great and it looks fine. Now if I go into the tags tab you'll see that I actually also have a set of tags that have been automatically imported. Now automatic tags are always shown with a small orange icon you see here. Now remember that your own tags are marked in blue. Now the tags that you get here usually relate to the keyword subject headings that have been supplied for the item but sometimes you'll also get author supplied keywords or other things as well it depends on the database that you're using for your import. So I can delete any tags I don't want by using the minus sign on the right hand side. So let's say that the 2000 to 2099 classification isn't helpful for me in my work so let's just delete that with the minus sign button. There are some things about automatic tags that you should know. First if I use the tag selector to rename a tag I'm basically changing it from an automatic tag to a manual one. So for example if I rename this gender studies courses tag to just gender studies it changes it to blue. It's not an automatic tag anymore because I've modified it. Now I have mixed feelings about automatic tags sometimes they help me organize my library because I might see keywords imported that I might actually realize are cool and I want to use but sometimes they just create clutter. If you do not want Zutero to import these tags at all you can go to preferences and then on the general tab which should be the first one you can deselect automatically tag items with keywords and subject headings and then Zutero will not import these automatic tags anymore. The second thing to know is that if you're okay with having the tags in your database but you don't want to see them all the time you can uncheck show automatic in the tag selector. If I do that now you'll see that the only tag listed for the unfiled items collection now is the gender studies tag which is the only blue tag on that article I imported. This does not hide them from the individual item view. You'll always be able to see the orange and the blue tags there. Finally if you have a bunch of old automatic tags in your library and you just want to get rid of them all you can use the menu in the tag selector to select delete automatic tags in the library. You'll be asked for confirmation because you cannot undo this once you've done it so I'm going to cancel this for now because I want to do that. I'm going to finish up with just a couple of general tips for working with tags in Zotero. Keeping consistent is going to help you. There are many ways to design a scheme for tags but you should choose principles that work for you and then most importantly stick with them. So if you're working with others on a Zotero library for example in a shared collection it might be good for everybody to agree on a common approach. Here are just a couple of examples of questions that you might have to answer for yourself. Unless the tag is for a proper noun and needs capitalizing you can generally use lower case text. Tags in Zotero are case sensitive so racism racism and racism all sound the same but they're actually treated differently in Zotero so sticking with lower case just keeps it from getting confusing. Now in cases where a noun has a singular and a plural form I like to choose one over the other. I prefer plural but it's really up to you so I might use case studies instead of case study and I'll try to be consistent about it so that it will end up with both tags in the library. I can think of a couple of examples where this doesn't work so the rule is not absolute. So for example in my library I have a tag called theater and a tag called theaters they're not the same right theater is a type of performing art and theaters are the places where performing arts are shown. Remember that the goal is consistency there aren't really any hard and fast rules for how to use tags just go with what works for you and then try to be consistent about it so there you are those are the basics of working with tags in Zotero in this module we covered where tags are located and used in Zotero adding manual and automatic tags using tags and collections to filter your library color coding your favorite tags and working with automatic tags all of this builds on the previous module about working with collections now in the next module I'm going to talk briefly about the related items feature in Zotero see you there