So, good afternoon or good morning if you're over in the Perth time zone to everyone. Thank you for calling into our webinar today. We've got some handouts in today's webinar as well. We've got a guide to publishing and sharing sensitive data that is an ANN's resource and also an ANN's sensitive, it's called Sensitive Decision Tree. And that's a one-page summary of the information that's available in our guide. So I'd just like to introduce our two guests today. We've got Professor George Ulter, he's a research professor in the Institute for Social Research and Professor of History at the University of Michigan. His research integrates theory and methods from demography, economics and family history with historical sources to understand demographic behaviours in the past. From 2007 to 2016, he was the director of the Inter-University Consortium for Political and Social Research, ICPSR, the world's largest archive of social science data. He's been active in international efforts to promote research transparency, data sharing and secure access to confidential research data. He's currently engaged in projects to automate the capture of metadata from statistical analysis software and to compare fertility transitions in contemporary and historical populations and we're lucky to currently have him as a visiting professor at ANU. And Dr Steve McGeckin is the director of the Australian Data Archive at the Australian National University. He holds a PhD in industrial relations and a graduate diploma in management information systems and has research interests in data management and archiving, community and social attitude surveys, new data collection methods and reproducible research methods. Steve has been involved in various professional associations in survey research and data archiving over the last 10 years and is currently chair of the executive board of the Data Documentation Initiative. So, firstly, we're going to hand over to George, who's going to share the benefit of over 50 years of ICPSR managing sensitive social science data. Over to you, George. Thank you, Kate. It's a pleasure to talk to you today. ICPSR, as mentioned, has been in data archiving for more than 50 years and an increasing amount of our effort has gone into devising safe ways to share data that have sensitive and confidential information. At the heart of everything we do in terms of protecting confidential information is a part of the research process where when we ask people to provide information about themselves to us, we make a promise to them and we tell them that the benefits of the research that we're going to do are going to outweigh the risk to them and we say that we will protect the information that they give us. We have a lot of data that we receive at ICPSR and here at the ADA that include questions that are very sensitive. Often we're asking people about types of behavior that could cause them harm, that we might be specifically asking them about criminal activity, we might be asking them about medications that they take that could affect their jobs or other things, so we have to be careful about it. And we're afraid that if the information gets out, it could be used by various actors for specific purposes, it could be used in a divorce proceeding, sometimes we interview adolescents about drug use or sexual behavior and we promise them that their parents won't see it and so on. In the data archiving where we often talk about two kinds of identifiers. There are direct identifiers which are things like names, addresses, social security numbers that many of which are unnecessary, but some types of direct identifiers such as geographic locations or genetic characteristics may actually be part of the research project. And then the most difficult problem often is the indirect identifiers that is to say characteristics of an individual that when taken together can identify them. We refer to this as often as deductive disclosure, meaning that it's not obvious directly but if you know enough information about a person in a data set, then you can match them to something else. We were concerned that someone who knows that another person is in the survey could use that information to find them or that there is some other external database where you could match information from the survey and re-identify a subject. Deductive disclosure is often dependent on contextual data. So if you know that a person is in a small geographic area or you know that they're in a certain kind of institution like a hospital or a school, it makes it easier to narrow down the field over which you have to search to identify them. And unfortunately in the social sciences, contextual data has become more and more important. Whereas people now are very interested in things like the effect of neighborhood on behavior and political attitudes or the effect of available health services on morbidity and mortality. And there are a number of different kinds of contextual data that can affect deductive disclosure. So we're in a world right now where social science researchers are increasingly using data collections that include items of information that make the subjects more identifiable. So for example, people studying the effectiveness of teaching often have data sets that have characteristics of students, teachers, schools, school districts, and once you put all those things together, it becomes very identifiable. So we at ICPSR and I think the social science data community in general have taken up a framework for protecting confidential data that was originally developed by Felix Ritchie in the UK that talks about ways to make data safe. So I'm going to go through these points, what Ritchie talks about, safe data, safe projects, safe settings, safe people and safe outputs. And the idea of this is not that any one approach solves the problem, but that you can create an overall system that draws from all of these different approaches and uses them to reinforce each other. So safe data means taking measures that make the data less identifiable. Ideally, that starts when the data are collected. So there are things that data producers can do to make their data less identifiable. One of the simplest things is to do something that masks the geography. If you're doing interviews, it's best to do the interviews in multiple locations that adds to the anonymization of your interviewees. Or if you're doing them in only one location, you should keep that information about the location as secret as possible. Once the data have been collected, research projects have been using a lot of different techniques for many years to mask the identity of individuals. So one of the most common one is what's called top coding, where if you ask your subjects about their incomes, the people with the highest incomes are going to stand out in most cases. And so usually you group them into something that says people above $100,000 in income or something like that, so that there's not just one person at the very top, but a group of people which makes them more anonymous. And this list of things that I've given here, which goes from aggregation approaches to actually affecting the values, is listed in terms of the amount of intervention that's involved. Some of the more recently developed techniques actually involve adding noise or random numbers to the data itself, which tends to make it less identifiable, but it also has an impact on the research that you can do with the data. If projects means that the projects themselves are reviewed before access is approved, at most data repositories when the data need to be restricted because of sensitivity, we ask the people who apply for the data to give us a research plan. That research plan can be reviewed in several different ways. The first two things that we do regularly at ICPSR, we ask, first of all, do you really need the confidential information to do this research project? And if you do need it, would this research plan identify individual subjects? We're not in the business of helping marketers identify people for target marketing, so we would not accept the research plan that did that. There are also projects that actually look at the scientific merit of a research plan. To do that, though, you need to have experts in the field who can help you to do that. Safe settings means putting the data in places that reduce the risk that it will get out. And I'm going to talk here about three approaches. The first one is, or four approaches actually, the first one is data protection plans. So for data that need to be protected but the level of risk is reasonably low, we often send those data to a researcher under a data protection plan and a data user agreement which we'll come to in a couple of minutes. And the data protection plan specifies how they're going to protect the data. And here's a list of things that we worry about that one of my colleagues at ICPSR made up. One of the things we tell people is, you know, what happens if your computer is stolen? How will the confidential data be protected? And there are a number of things that people can do like encrypting their hard disk, locking their computers in a closet where they're not being used that can address these things. And I think that data protection plans need to move to just a general consideration of what it is that we're trying to protect against and allow the users to propose alternative approaches rather than saying, oh, you have to use this particular software or this or that. We have to be clear about what we're worried about. A couple of notes about data security plans. Data security plans are often difficult, partly because of the approach that has been taken in the past and also because, you know, researchers are not computer technicians and we're often giving them confusing information. One of the ways that I think in the future, in the U.S. at least, universities are going to move beyond this is I'm seeing universities developing their own protocols where they use different levels of security for different types of problems. And at each level, they specify the kinds of measures that researchers need to take to protect data that is at that level of sensitivity. And from my point of view as a repository director, I think that any time that the institutions provide guidance, it's a big help to us. The other way to make the data safe by putting it in a safe setting is actually to control access. There are three main ways that repositories control access. One kind of system is what I've called your remote submission and execution system where the researcher doesn't actually get access to the data directly. They submit a program code or a script for a statistical package to the data repository. The repository runs the script on the data and then sends back the results. That's a very restrictive approach but it's very effective. Recently, however, a number of repositories and statistical agencies have been moving to virtual data enclaves. These enclaves, which I'll illustrate briefly in a minute, use technologies that isolate the data and provide access remotely but restrict what the user can do. The most restrictive approach is actually a physical enclave. At ICPSR, we have a room in our basement that has computers that are isolated from the internet. We have certain data sets that are highly sensitive. If you want to do research with them, you can. But on the way into the enclave, we're going to go through your pockets to make sure you're not trying to bring anything in or the way out. We're going to go through your pockets again and you'll be locked in there while you're working because we want to make sure that nothing that is uncontrolled is removed from the enclave. The disadvantage of a physical enclave is that you actually have to travel to Ann Arbor, Michigan to use those data, which could be expensive. That's the reason that a number of repositories are turning to virtual data enclaves. This is sort of a sketch of what the technology looks like. What happens is that you as a researcher look over the internet, log on to a site that connects you to a virtual computer and then that virtual computer is in contact as access to the data, but your desktop machine does not. You only can access the data through the virtual machine. At ICPSR, we actually use this system internally for our data processing to provide an additional level of security. We talk about the virtual data enclave, which is the service we provide to researchers and the secure data environment, which is where our staff works when they're working on sensitive data. It's a little bit of a let down, but this is what it actually looks like. What I've done here is the window that's open there with the blue background is our virtual data enclave and I've opened a window for state inside there. The black background is my desktop computer. If you look closely, you'll see in the corner of the blue box that you see the usual windows icons. That's because when you're operating remotely in the virtual enclave, you're using windows. It looks just like windows and acts just like windows, except that you can't get to anything on the internet. You can only get to things that we provide for a level of security. On top of that, the software that's used and we use VMware software, but there are other brands that do the same thing, essentially turns off your access to your printer, turns off your access to your hard drive or the USB drive. You cannot copy data from the virtual machine to your local machine. You can take a picture of what you see there, but because you have that capability, we also restrict people with data use agreement. That's my next topic. How do you make people safer? The main way that we make people safer is by making them sign data use agreements or by providing training. The data use agreements used at ICPSR are frankly rather complicated. They consist of a research plan, as I mentioned before. We require people to get IRB approval for what they're doing, a data protection plan, which I mentioned, and then there are these additional things of behavioral rules and security pledges and an institutional signature, which I'll mention now. The process, if you look at the overall process of doing research, there are a number of legal agreements that get passed back and forth. It actually starts with an agreement made between the data collectors and the subject in which they provide the subjects with informed consent about what the research is about and what they're going to be asked. It's only after that that the data go from the subject to the data producers. Then the data archive, such as ICPSR or ADA, actually reaches an agreement with the data producers in which we become their delegates for distributing the data. That's another legal agreement. Then when the data are sensitive, we actually have to get an agreement from the researcher. These are pieces of information we get from the researcher. In the United States, our system is that the agreement is actually not with the researcher, but with the researcher's institution. At ICPSR, we're located at the University of Michigan, and all of our data use agreements are between the University of Michigan and some other university. In most cases, there are some exceptions. It's only after we get all of these legal agreements in place that the researcher gets the data. One of the things in our agreements at ICPSR is a list of the types of things that we don't want people to do with the data. For example, we don't want someone to publish a table across tabulation tables where there's one cell that has one person in it because that makes that person more identifiable. There's a list of these things. I think often we have like 10 or 12 of them that are really standard rules of thumb that statisticians have developed for controlling re-identification. The ICPSR agreements are also, as I said, agreements between institutions. One of the things that we require is that the institution takes responsibility for enforcing them, and that if we at ICPSR believe that something has gone wrong, the institution agrees that they will investigate this based on their own policies about scientific integrity and protecting research subjects. DUAs are not ideal. There's a lot of friction in the system. Currently in most cases, a PI needs a different data use agreement for every dataset, and they don't like that. We can, I think in the future, reduce the costs of data use agreements by making institution-wide agreements where the institution designates a steward who will work with researchers at that institution. There's already an example of this, the DataBury project, which is a project in developmental psychology that shares videos, has done very good work on legal agreements. My colleague, the current director at ICPSR, Margaret Levenstein, has been working on a model where a researcher who gets data use agreement from one dataset can use that to get a data use agreement for another dataset so that individuals can be certified and include that certification in other places. One of the things that I think we need to do more about is training a number of places like ADA, train people who get confidential data. We've actually done some work on developing an online tutorial about disclosure risk, which we haven't yet released, but is, I think, something that should be done. Finally, there's safe outputs. One of the last stage in the process is that the repository can review what was done with the data and remove things that are a risk to subjects. This only works if you retain control, so it doesn't work if you send the data to the researcher, but it does work if you're using one of these remote systems like remote submission or a virtual data enclave. Often, this kind of checking is costly. There are some ways to automate part of it, but a manual review is almost always necessary in the end. So, a last thing about the costs and benefits. Obviously, data protection has costs, modifying data affects the analysis. If you restrict access, you're imposing burdens on researchers. Our view is that you need to weigh the costs with the risks that are involved. There are two dimensions of risk. One dimension is, in this particular data set, what's the likelihood that an individual could be re-identified if someone tried to do it? Secondly, if that person was re-identified, what harm would result? So, we think about this as a matrix where you can see in this figure, as you move up, you're getting more harm. As you move to the right, you're increasing the probability of disclosure. So, if the data set is low on both of these things, for example, if it's a national survey where a thousand people from all over the United States were interviewed and we don't know where they're from, and we ask them what their favorite brand of refrigerator is, that kind of data we're happy to send out directly over the web without a data use agreement with a simple terms of use. But as we get more complex data with more questions, more sensitive questions, we often will add some requirements in the form of a data use agreement to assure that data are protected. And when we get to complex data where there is a strong possibility of re-identification and where some harm would result to the subjects, in that case, we often add a technology component like the virtual data enclave. And then there are the really seriously risky and sensitive things. My usual example of this is we have a data set at ICPSR that was compiled from interviews with convicts about sexual abuse and other kinds of abuse in prisons, and that data is very easy to identify and very sensitive, and we only provide that in our physical data center. So that's the end of my presentation, and I thank you for your attention, and we'll take questions later. Great. Thank you, George. So we'll pass over to Dr Steve McGeckin to give his presentation about managing sensitive data at the Australian Data Archive. Okay. So my aim today is to build off what George has talked about, particularly taking the five-saves model and looking at what the situation is in the Australian case. Now I'll talk about the Australian Data Archive and how we support sensitive data, but I wanted to put it in the context of the broader framework of how we access sensitive data in Australian social sciences generally. So I'm going to talk about some of the different options that are around sort of picking up on somewhat George's discussed in terms of some of the alternatives that are available and sort of demonstrate the different ways these are in use here in Australia. So I'm really focusing more on the five-saves model and its application in Australia than I am specifically on ADA. As I always say, we are one component of the broader framework for sensitive data access here. So just to say, what I really wanted to cover off here is thinking about sensitive data and the five-saves model. I'll look at the different frameworks for sensitive data access in Australia and where you might find them and then how we apply the five-saves model at ADA in particular and then depending on time I might say something briefly about the data life cycle and sensitive data as we go through. So I wanted to just pick up on the particularly the ANZ definition here of sensitive data and say, I'll frame this in the context of most of what we deal with at ADA at some point in its life cycle has been sensitive data. It's more often that it's information, it's collected from humans often with some degree of identifiability at least at the point of data collection, not necessarily at the point of distribution. So a lot of what we deal with and this is true for a lot of social science archives has been subject to would fall into the class of sensitive data. But there's a distinction there between what we get and what we distribute that we would probably draw a distinction. So in terms of our definition here, this is the handout I think that's in the handout section and it's available online. Data that can be used to identify an individual species, object, process or location that introduces a risk of discrimination or unwanted attention. Now we tend to think in terms of human risks more than anything else to say the risk to humans and individuals, but it does apply in other cases as well. So for example, the identification of sites for Indigenous art might in and of itself lead more people to want to go and visit that location and in a sense destroy the thing that you're actually trying to protect. So the more visits that they actually get, the more degraded the art itself becomes. So it doesn't just hold for human research, but that's probably our emphasis at ADA. So just to reiterate the five states again, so we talked about five things, people, projects, settings, data and outputs. And the reference here is down at the bottom you can look at the document that Felix Richie and two of his colleagues developed, sort of framing out the five states model. What I would say about this is that it's been adopted directly with our UK data service. That's where it has origin. The basic principles are applied in a lot of the social science data archives and it's now actually been adopted by the Australian Bureau of Statistics as well. So their framework for thinking about output of different types of publications literally leverages this model. So what we think is quite a useful sort of framework for talking about. I'm going to take a slightly different approach to George in thinking about how we think about what we're worried about. And I'm going to take, as a depositor, you worry about the risk of disclosure. As a researcher, what's the flip side of that? Why do we need access to sensitive data? What does it provide? The National Science Foundation about four or five years ago put out a call around how could we improve access to micro data, particularly from government sources. And it sort of highlights the sorts of things. Why do we talk about the need for access? The sorts of research you can do. And this comes from a submission from David Card, Raj Shetty and several economists in the US and elsewhere. They were highlighting what's needed. Direct access is really the critical thing here. And direct access to micro data. And by micro data, we mean individual information about individuals line by line. Aggregate statistics, synthetic data, you can create fake people as it were, or submission of computer programs for someone else to run, really don't allow you to do the sorts of work you need to answer policy questions in particular. And a lot of the particular social policy research is focused in this way. So in order to do certain things, access to this data is necessary. And so how do we facilitate that, taking account of the sorts of concerns that have been raised? Alongside that is, well, how do people expect to access it? This was an interesting blog post from a researcher based at previously the University of Canterbury comparing how you access US census data versus the New Zealand census and similarly, we could say the Australian census as well. In the US, you can get a 1% sample of the census. You can just go and download a file directly. It's open as what's called a public use micro sample file. And those are directly available. In New Zealand, there's a whole series of instructions you have to go through. You might be subject to data use agreements. You might be subject to an application process, et cetera, et cetera. Now, he's criticising saying it must be much easier. It should be the US model that's appropriate here rather than the New Zealand model. But what we're really probably talking about here is, although they're both appropriate, depending upon the sorts of detail, the sorts of identifying information that are available, both might be valid models that just allow you to do different things. The first model really focuses on, in a sense, masking the data to some degree. In some of the safe data models that George talked about, the other uses other types of aspects of the safe model to address confidentiality concerns. And what you'll also find is researchers understand these, that there has to be some trade-offs. The recognition of the need for confidentiality is recognised and understood, and that there may well be, and there ought to be, trade-offs in return for that. So, for example, Carden's colleague suggested we'll use a set of criteria that you could put in place for enabling form of access to sensitive micro data. And it might, they reference access through local statistical officers, through some remote connections, such as the virtual enclave that George talked about, and monitoring of what people are doing. If you're going to have highly sensitive data available, the trade-off for that for access should be appropriate monitoring. So there is a recognition that these, I mean this is just one possible approach, but a recognition that access brings with responsibilities and appropriate checks and balances. So what I wanted to talk about is all how is that eventuated in Australia? What do we see? So the sorts of models that we see here in Australia, I've broken them out here broadly. I'd say I think we can look at some four broad areas. The one that people are probably most familiar with is the ABS, Australian Bureau of Statistics. They have a number of systems and access methods that suit different types of safe profiles. So these include what's called confidentialised unit record files or CURFs. They have the Remote Access Data Lab, which is one of their online execution systems. They have an on-site data lab. You can go to the bowels of the ABS buildings, certainly in Canberra and I believe in other states as well, and do on-site processing. Then they have other systems that probably the best note of these is what's called a table builder, which is an online data aggregation tool, which does safe data processing on the fly. Our emphasis at ADA is primarily on what these confidentialised unit record files are. We provide unit record access and some aggregated data access as well. Then we have the remote execution or the remote analysis environments. I put under this model the Australian Urban Research Infrastructure Network for geographic data access in particular. The secure unified research environment produced by the Population Health Research Network is an example of George's remote access environment as well. Even David Linkage Facilities, another part of the PHRN network, fit to some degrees under this type of secure access model. That's in a sense a more extreme version of that. Then we have other ad hoc arrangements as well. Things like the physical secure rooms. A number of institutions have a secure space. There are a number here at ANU, for example. Then you might have other departmental arrangements as well that exist. We can probably classify those in terms of the distinctions in the type of approaches that we have. What I've done here is a very simple assessment from not at all to a very strong yes, it fits within this sort of addressing this safe element from low to high. I have some question marks on some of the facilities, particularly Shor and the Data Linkage Facilities. Not because I don't think they can do it. It's like I don't have enough information to make an assessment there. But if you look at the different types, things like the ADS models have tended towards safe data, so the sorts of confidentialization and randomization routines, data output checking and secure access models. Tabulation systems are a secure access model as well. They've tended less towards safe people and safe projects. So checking of people and checking of projects, we tend to put more, a lot of cases, there's more trust in the technology than there is in the people using the technology, which I think is a little bit problematic. Given that they're, and I'm going to talk to this in a moment, there are some fairly good processes in Australia for actually assessing the quality of people in particular and to some extent to projects. So this is, we can kind of profile, the point here I'm making is that you have different alternatives to how you might make sensitive data available. There's not a one solution. It's what's the mix of things that I might do and I'll come back to that at the end. So in the Australian experience, I'd say we had a strong emphasis on safe data. We came up with the term in Australia of confidentialization. That's probably the term you'll see most regularly. In anywhere else in the world, you would hear the term anonymization. I'm not quite sure why this is the case, but I'd say in Australia, the term is that we tend to use the confidentialization. The Australian data archive used this model, the ADS, and the partner social services, things like the household income labor dynamics in Australia, use anonymization techniques as sort of the starting point. So you can make data safe before you release it. It has its limitations and a good example of that is some of the data that's released into the data.gov environment used anonymization. Safe data is sort of the priority, the potential for it to be reverse engineered. If you haven't done your anonymization properly, then it could be reversed and you get a safe data risk. So it has its flaws. And this is why we tended towards looking at sort of a combination of techniques. But as George pointed out, I'd say if the risk of actually being identified is low and particularly the harm that comes from that is low, then there may be the case that this is sufficient. Certainly a lot of the content that we have at ADA most are emphasis action, safe data, more than it else. Safe settings. We do have, as I say, examples here. Tabulation systems, you know, things where you can do cross tabs online are fundamentally a safe settings model. People don't get access to the unit record data, they just get access to the systems, produce outputs. Remote access systems, the research data lab, PHRNs, Shure system, and a new system that the ABS are bringing on at their remote data lab. They're making their data labs available in a virtual environment. They're in pilot stages that we're working with them on at the moment, increasingly being used as well. They're also secure environments, as I mentioned, the data lab in the secure rooms. Safe outputs. A number of the safe settings environments, because they tend to use highly sensitive data, have safe output models as well. The real problem has been with these in scaling them. It requires manual checking, more often than not. So reviewing the output of these sorts of systems, that requires people, that requires time. It's hard to automate as well. The ABS have invested a lot of money into automating output checking. Point of fact, the table bill system is one of the best that's around. But the new remote lab still has manual checking of outputs. So it depends on what you're trying to do and the sorts of outputs you're checking as to the extent to which you're producing, as to whether you can actually automate the checking as well. The other side of this that I think will become increasingly relevant to is the replication and reproducibility elements of things that come out of systems like this. How are we going to facilitate replication models within those environments? I'm not sure that question's been addressed yet. Safe researchers and safe projects in Australia, to be frank, they are considered in most models, but they're not really closely monitored. And that's because they're difficult to monitor. How do you follow the extent to which people are followed, the things that they've signed up to? Anyone who's involved in reporting and research outputs for ERA or anything will know that getting people to actually fill out the forms, putting in place or by produce would be hard. Filling out forms to say I've been compliant with a daily use agreement is even harder. That said, we do have some checks and balances that are there, certainly in terms of the ethics models and the codes of conduct and research do provide some degree of vetting insurance for those that go through that sort of system. That's it. We have some checks and balances in place for particular university researchers to address the sorts of concerns. So I think an emphasis increasingly on safe researchers and projects might be one that we can leverage a bit more carefully. As I say, because of the frameworks we have in place, Australia Code of Conduct and increasingly professional association and journal requirements as well for data sharing, I go to put a degree of assessment on the sorts of practices we use as well. The American Economic Association, the DART agenda in the political science plus ones required for data sharing, these are actually a mechanism also for addressing partly the extent to which, why by sharing, assessing the sharing of data and also assessing the extent to which you're potentially becoming dyslexic as well. That's something to be considering in the future. I'll quickly turn to the ADA model and then wrap up. The ADA model, I say our emphasis is primarily on safe data. Data is anonymized, either it tends to be through the agencies that provide or the researchers that provide data to us in their dance. We will also do some review on content as well. We'll provide recommendations back to our depositors as to these are the sorts of things you probably want to think about in terms of have you included things like post codes or occupational information. If I know someone's post code, their occupation and their age is a fair chance that I could identify them in many cases in remote locations in Australia in particular. There's some basic checks you can do. Certainly safe people and safe settings, our data access is almost all mediated. You must be identified, you must provide contact information and supervise the details. So we do some checking on safe people and we provide information on project descriptions, what do you intend to do with the data as well, particularly for where we have more sensitive content. Often that's a requirement for our depositors. We don't apply, frankly, safe settings and safe outputs. That's not the space that we work in. We work with other agencies such as the ABS where there's access to certain sensitive content will point people off to the relevant locations and where you've got highly sensitive content that you want to make available. I'd say something like the remote data lab. Whereas it's focused, they focus less on safe data. So they're a virtual enclave. They don't prohibit the use of safe data practices so they do limit where you have highly sensitive data. There's a more dedicated assessment process on the projects and the outcomes. Highly safe settings. Sitting at the ABS, the problem is that the cost they have is in establishing the system itself. They vet all of the outputs. It has costs associated with it. They have safe people. They have training for researchers prior to accessing the system. There is some challenging assessing the backgrounds of people, for example. This is where the need for domain experts. If you're going to fully assess people in projects and you're going to assess their domain expertise, you need domain experts to be able to make that sort of evaluation. So the emphasis might be on are you using appropriate techniques? Are you maintaining secure facilities? And are you potentially, what's the research plan itself look like? It's more the emphasis than the quality of the science. That's a much harder thing to evaluate. Safe projects. That has been used in some places at the ABS. Sometimes it's required for legislative reasons. The extent of data release is dependent itself upon, meaning a public good statement, for example. One of the questions for future for some organisations is should this matter, ASIC research itself might generate useful insights that you didn't expect? So, as I say, in some cases, again, you're going to be probably moving the levers focusing on different types of aspects of the safe data environment. I guess the message we want to put through here is certainly there are sweeter options that are available for you for accessing sensitive data. Different models exist and they have different ranges of the five safes. You can certainly incorporate safe people models. Curious, a lot of models focus on the expectation that we have an intruder. Hackers are coming in to access that system. Actually, what tends to be the case more often than not is the silly mistakes. I made a mistake by leaving my laptop on the train or leaving my USB in the computer lab. That's far more common. We tend to try and profile two default options in terms of our mix of safe settings, but I say there are options available to you and what you have to think about is what's appropriate for the form of data that you're trying to work with. Fundamentally, the argument is that principles should enable the right mix of safes for a given data source. Thank you very much, Steve. That was a really great overview about the different ways that the five elements of the safes can be mixed and used in different cells. I thought it was really interesting that both of you mentioned that a safe location was in a basement. I've just got these images of people locked up in basements. I also wanted to note that George mentioned data masking and using de-identification methods. Steve also mentioned confidentialization and anonymization. They're similar sorts of words for similar processes and has a de-identification guide available on our website now. If you're interested in that, it's more detail on that information. We have that guide there that you can have a look at. I was also wondering about, George, you were talking about with the data protection plan and the data use agreement that the onus is on the institution that if someone breaks it, that they need to put them through some sort of research integrity investigation or something like that. If that doesn't happen, is there any potential recourse for the university like could ICPSR turn around and say, well, you didn't follow this process. You're not going to be testing any of our data anymore? Sure. Actually, on our website, we actually list the levels of escalation that we're willing to go to. We can certainly cut off the institution from access to ICPSR data. What really gets people's attention is that the National Institutes of Health in the U.S. has an Office of Human Research Protections. If we thought that someone was breaching one of our agreements and endangering the confidentiality of research subjects, I would report them to that office. That office has a lot of power. They regularly publish the names of bad actors. What's more, they can cut off all NIH funding to universities and they have done that in the past when they thought that protections weren't worked in place. I always think of that as the nuclear option. I know for a fact that university administrations and their trustees and regents are terrified that NIH will do something like that.  Excellent. Steve, I was wondering with your the Australian Data Archive with the use agreement that people are signing with that, is that with the individual user or with the institution as it is with ICPSR? Primarily it's with the individual. We have a small number of organisational agreements, but not many. There is, I would say there's more efficient, yeah, a keen focus on an agreement between the individual and the organisation rather than the organisational. Some organisations do ask for them, but frankly it's more actually for pragmatic reasons than it is for compliance reasons, is that they will want to host content and manage access by requesting access to a particular dataset for all members of their research team, for example. It just makes that easier as it were. There are other models. The AGS model is actually the agreement is with the institution and then the individual sign up to the institutional agreement. The Department of Social Services model is the same as well. It will be interesting to see the extent to which we move in one direction or another. I think the compliance argument hasn't been one that's been all that common here in Australia. It's actually been, except in the case of where you have government data, I would say, it's probably the situation. But for academic reduced data, that hasn't tended to be a need for this. And with George's agreement with institutions where the course is that the institution should then have some integrity investigation, what level of recourse do you have with the agreement is with the individual? We would probably report back to the institution for which they belong and that's a safe way. We do have the question about supervisory arrangements. We would probably also follow some of the questions under the Code of Combat for research. So that's why I sort of make reference back to there is an overarching set of obligations on those within Australian academic institutions and we would pursue something in that way. One of the challenges for us and I'm going to guess for George as well is just finding where you get breaches of compliance. One of the hardest things to do is actually point out what happened in the first place. We've had one case that I'm aware of in certainly my predecessor's lifetime, which is going back to the late 90s. So it's not common occurrence that we're aware of, but yeah. Okay, excellent. So George mentioned standardized study agreements between US institutions. Has that been formalized across a number of institutions as part of a consortium arrangement or is it more of an informal and gaining momentum? Well, the example I gave is the data buried project and they're the only ones I know that have done this in a formal way where they get institutions to sign on as an institution and then that covers all of the researchers at that institution. It took them a while to negotiate that and get the bugs out, but I think it's paying off for them. And this is something that I think other groups like ICBSR should move to, but right now it's a big problem that about one in six of our data use agreements at ICBSR involved a negotiation between lawyers at the University of Michigan and lawyers at the other institutions. So it's a major cost. I think it's one of the ways to go. I would say that, I mean in Australia we have a pretty strong example, which is the University of Australia ABS agreement. I mean that model facilitates a whole lot of things. So it's sort of enabled access to the broad collection of ABS curve data under a single agreement. The other is the University slide up to a cost that comes with that as well. So they're paying it for that, but I'd say it covers the full spectrum of what they can do. The challenge in some cases is what forcing you've got for dissemination of the content. As I say, if I went to the next department, I've had this discussion with the various departments, could we establish a consistent data access agreement? And it's because the departments themselves are set up under different models and different legislation, sorry, the impact of that is they can't necessarily have the same set of conditions. But certainly there is some, you know, capacity to try and optimize some of that. And I'd be interested to see the extent to which the Practical Commission report that's coming out of data access might address some of those sort of questions as well. Okay, great. So just quickly, there's a question about, are there any checklist or guidelines for new researchers to assess their research surveys for the level of confidentiality? So I think that they're talking about like privacy risk assessments. This is, okay, we have an internal checklist. This is something we've talked about in terms of thinking about whether you, what you need to do in terms, but it really depends on publication. We talked before about the fact that in order to do certain research, you need to have actually some things that might be identifying. So it depends on which point in the data life cycle you're actually talking about here. When we're thinking about data release, then you say we would basically apply some basic principles for these are the sorts of things that we look for. And actually we've talked about making that checklist available in terms of these are the sorts of things you have to be concerned about. There is advice around that we could probably bring together. But I'd say it's this usability versus confidentiality question again. So one of the things we sometimes do is we split off those things that have a high confidentiality risk and we actually release separate different sets of data. So if you need that additional information, you can actually make that available under a separate additional set of requirements, possibly under a different technological setting. So I think it depends a little bit on when in the life cycle you are talking about here. It often is useful to have as much, have information, particularly for example if you're running a longitudinal study, you must have identifying information going forward. You can't be able to contact someone the next time around. It depends on what you're trying to achieve. But there are some basic advice that you can put out. There's a literature that's been used by statistical agencies about what they can release. But that whole area is right now somewhat contentious because the statistical agencies develop that literature largely in the age when data were released in the form of published tables. When the data are available online and you can do repetitive, iterative operations on them, you're in a new world and there's a there's a separate literature that's developed in the computer science world. Anyway, it is a problem. There is guidance out there in really complex areas like in some healthcare areas. Doing a full assessment of a data set can be very complicated and difficult. So I think my recommendation is that people start at the basics and think about how would you identify this person and if this information got out, what harm would it cause? Often the researchers themselves have a good sense of that from the research they're doing. Just one last question. Are the five safes applicable in all research disciplines or are they specifically limited to social sciences? I think they're already applicable. It's interesting we're having a discussion here about social sciences, but for example we work a lot with health sciences and environmental sciences and the like. I don't see any reason why they shouldn't be applied elsewhere. That's part of the question actually is it's more present about what do you have to think about in terms of the privacy and conflict and gently risks far more so than what's the topic. The topic helps you make some sort of judgment about the harm in George's terms, but yeah it's the confidentiality questions. The framework is still applicable. Thank you very much to George and Steve for coming along to our webinar today and thank you everyone for calling in.


Tech the halls with Appalachian Wireless offering you up to $850 off select devices extended through Christmas Eve with no activation fee up to $850 off the latest and greatest devices. We are you. We are Appalachian Wireless. Garrett Area Volunteer Fire Department is giving back this holiday season and will be hosting their annual Santa Run December 8th and 9th from 6 to 10 p.m. Okay basically it started years ago with this department that we would take Santa and put him on a fire truck and take him out through the district and at the time that it started we actually was using an old Dodge pickup that we had. We would put him on the truck and go out and hand out fruit and some candy. This year it's going to be a little bit different. Our Santa Run is going to actually be at night. This is the first time that we're ever doing a night Santa Run. We thought that it would be cool to do to be able to light the trucks up and give a better shell for the kids and kind of break it up over two days to where we're not as rushed as what we've been trying to do it in one day years before. This year's Santa Run will not only feature Santa and his sleigh, but Mrs. Claus will also be in attendance. The main reason that we do this is that we've got a lot of children in our area that this may be the only thing they get for Christmas. So we can't do a lot, but we try to do what we can and we try to make sure that every child has at least a small toy in the bags that they get as well as an apple, orange, banana and some candy as well because Christmas has got to be fun too. So we try to give them a little bit because this may be the only thing that they get. For more information and to view the Santa Tracker, go to Garrett Area Volunteer Fire Department on Facebook. Reporting for Mountain Top News, I'm Brianna Robinson.


Hi everyone, today we will learn about condition variables. So I have written the code for the producer-consumer problem that you have seen in the lectures. So let's open this code in using code. So here I have just included some libraries. So let's first see what all is there in this program. We have a buffer which will store pending requests that are to be consumed by the consumer. So we have written this consume request function that will take out requests from the buffer and consume them one by one. Also this produce request function will add requests to the buffer when the buffer is empty. So we have two threads. First thread is the producer thread which runs this produce request function and another is consumer thread which runs this consume request function. So let's see what all variables are there. So first is the count variable. This variable stores the number of pending requests that are there in the buffer currently. This denotes the total number of requests that will be added by produce request function to the buffer and then we have maximum buffer size. So our buffer is bounded which means that we cannot have more than max buffer size number of requests in the buffer. So if let's say max buffer size is 5. That means that our buffer can only store 5 pending requests at a time and if produce request want to add more requests to the buffer it needs to wait for the consumer to consume some request and only then it can add more requests. So let's go through the code. I'll first go through the main function. So we initialize count with 0 and here we just take two arguments. Number one is the total number of items that we want to produce and second is the maximum buffer size. Then we use the malloc function to allocate an integer array of size max buffer size. Then we have two threads producer and consumer thread as discussed earlier and we call the pthread join function on both the threads so that the main thread waits for both the threads to finish and finally we free the buffer that was allocated and return 0. So let's have a look at produce request and consume request function. So this is the produce request function. Here we iterate total item number of times and in every iteration we will produce one item and put it in the buffer queue. Before putting in the buffer queue we need to check if the buffer is already full and if it is so then we need to wait for the consumer. We have two condition variables here one is space available and another is items available. So let's say consume request function is executed first. Then what will happen here it will first lock the mutex as discussed in the lectures it is necessary to hold a lock whenever you are checking for condition because we do not want the computer to context switch after checking the condition otherwise there will be a miss signal. So it will acquire the lock then it will check if count is 0. Now count is equal to 0 means that there is no item in the buffer. So consume request cannot consume request because there are none in the buffer. So it needs to wait for the items available condition variable and it will also pass the mutex. So this function will release the mutex and this will cause this thread to sleeve. Now the produce request function will have to execute and here it will also acquire the mutex and it will check if count is equal to maximum buffer size which is not the case because initially count is 0. It will just add one element in the buffer array that will set buffer count equal to i plus 1 and then it will print that it has produced this particular request and then it will increase the count by 1. So now we have one request produced in the buffer queue. So now we call pthread condition signal so we signal this condition variable that items are available now and we unlock the mutex. So what will happen here because this thread was slipping for the items available condition variable so once it gets a signal that items are available this function will return it will acquire the lock again and return and here this will decrease the count by 1 and it will consume the item and print that consume this item. Now after consuming the item it will signal that space is available and it will unlock the mutex. So why do we need this space available condition variable? So here in produce request let's say it is producing request but consumer is not consuming it. So buffer will get full at some point of time so it needs to check this condition that while count is equal to maximum buffer size which means that it cannot produce more request it needs to wait for the space available condition variable again we pass the same mutex to this function as well. So these two condition variables space available and items available result in coordination of these two functions. So produce request will keep producing request and if buffer is full then it will wait for space available and consume request will signal space available after consuming an item and in both these functions we add a random sleeve time so that the next request is produced and consumed after some time. So that is the complete code and let's compile and run this code. So I will first do cd desktop and now gcc producer consumer.c with library pthread. So this will produce the idota executable let's run that executable and if we directly run it we say that we need to mention total items and maximum buffer size so let's write a dot out total items let's say hundred and maximum buffer size may be four so here you see that every item will be produced first and only then it will be consumed so here we see it produced item ever one then it produce item number two then we consume item number two and there is no consume for some item before it is produced so that is the coordination that we get using the condition variables so it will keep producing items and adding it in the buffer and consumer will consume them one by one so that was it for this video thanks and have a nice day


Countdown for blast off x-minus 5 minus 4 minus 3 minus 2 x-minus 1 fire From the far horizons of the unknown come transcribed tales of new dimensions in time and space These are stories of the future Adventures in which you'll live in a million could be years on a thousand maybe worlds the national broadcasting company presents x-minus 1 tonight's story Mars is heaven When the first space rocket lands on Mars, what will we find? Only the ruins of a dead and deserted planet or will there be life? intelligent life in some strange form that we can only imagine Will we be welcomed with open arms? Or will the Martians treat us as invaders? Only one thing is certain some day a Giant metal ship will take off from earth to travel through the black velocities the silent drops of space To descend at last into the darkness of the upper Martian atmospheres And on that day Man will finally know the answers The day we first land on Mars Now I hear this now here this Approaching critical deceleration fast and gravity suits stand by the land There it is. We've intersected the course vector sir. All right, mr. Lustig over to manual control. I said masters sound general quarters What do you make of the terrain there seems to be a heavy ground miss captain We won't be able to use the infrared lights and we'll have to come in on radar There's not a little risky sir landing in the dark I'd rather run the danger of a blind landing lieutenant and come in without the cover of darkness Remember, we don't know what kind of reception is waiting for us down there airspeed 500 altitude now four thousand Bridge to engine room stand by for deceleration fire forward tubes one and three Skits down Altitude 500 For three upper point now. Let's set her down By battle stations I said all secured Well gentlemen Gentlemen, we're now on Mars April 20th 1987 for 33 Greenwich time enter that in the log masters. I said Well gentlemen, it's less than two hours till dawn As soon as it's light we'll send out a landing party Masters get me an all-over hookup I'll hear this All right, man. The smoking lamp is lit Well We're on Mars The first man shipped from earth to land here We don't know what we're going to find or what dangers that we may face We're 17 man on an alien world And it's up to us whether we ever get home again The next few hours should tell the story And I want instant obedience to all commands I'll court Marshall the first man who doesn't jump to when he's ordered And one other thing We may be on Mars, but this is still the United States Naval vessel Officers will conduct a personal and weapons inspection in one hour. That's all Inspection kept now Mr. Lustig We've got an hour and a half to sweat out Before we find out what's outside that airlock And rather have a man worried about his stripes About what's waiting outside I'm Mars Now I hear this Landing party report the forward airlock Captain black lieutenant Hinkston lieutenant Lustig and dr. Horst report immediately the forward airlock is now landing time minus five And they're paging us You ready dr. Horst? Yes, mr. Lustig as ready as I will ever be come on. Let's get in the lock Hinkston Lustig and Horst reporting in the airlock very well, so the captain will join you Four minutes to go At least the captain would get here. What difference does it make? I just want to get it over with that's all Anybody got a cigarette? Yeah, I think you're smoking too much lieutenant Lustig Are you nervous? I offer your horse wondering what's hidden outside underneath that ground mist I've been giving it some thought It'll be very interesting to find out a very unusual planet Mars Why it has an atmosphere a wonderful thing in atmosphere where you find one you find life In Martians What do you think they'll look like who knows? Intelligent life can take many forms. You mean they may have green skins and eyes on stalks or something The comic book conception is possible, of course Or they may have developed are beyond us Perhaps they have a science that can produce weapons far more dangerous than our atomic missiles You think we may have to fight our way up after all We are invaders minus two. All right. All right. We heard this Well, I'd like to find outside that airlock Good old Illinois Ever been there Lustig? Only Chicago.  I have no family when I was a child They were gassed to death in the Daha concentration camp tough. No, it has its advantages I have no ties on earth nothing to lose now. I imagine I'm the only one on board It was free to enjoy our present peculiar position. All right masters. You can button it up now Well gentlemen check your sidearms In one minute, we'll be the first men to set foot on Mars Quite an honor, right as long as the metals are not rewarded posthumously still uneasy dr. Horst captain black I've been uneasy ever since I can remember on earth and on Mars Well 30 seconds Give me the intercom phone listening. Yes, sir Masters ice battle stations are to be manned till we return if we're not back in two hours I want no rescue party sent out last off and say the ship you understand Five seconds four three two one Lustig open the outer airlock. Hi, sir. It's fresh air Let's go Take it easy It's too dark to move fast Quiet isn't it? Not even a wind can't see anything from this ground this time. We don't know what's out here All right, come on that sounds like a rooster. I don't hear it anymore very homely but unlikely sound a Rooster crowing on Mars Higston. I said set that machine gun 25 yards to the flank We'll stay here to the ground mist lifts. I said What do you make of the ground horse grass plain grass? You can see some large foliage there with a mist stemmed out. What's up? I could see the tracers, but it's still standing come on horse Dr.. Where are you careful horse wait for us? All right, captain Alon ornament well that that's impossible Interesting isn't it a whitewashed Victorian iron deer sitting on a lawn in the middle of Mars I don't understand look around the mists lifting captain with their house a regular old-fashioned house on Mars Good Lord.  I want to take a look through that front window Well There's an upright piano some sheet music on it plastic. It's It's beautiful Ohio We can't be sir horse Horse do you think that civilization of two planets could be identical? I don't know that specific variety of geraniums Only 50 years old or not. Is it logical that they should develop in Mars? How about that port swing of the piano and and beautiful Ohio? That's impossible captain black This looks like the town. I was born in well it It looks like my hometown too. I thought of something sir. It's the only solution Maybe maybe we're not the first ship to reach Mars from earth.  Let's suppose some scientists got together They invented some spaceship and planted a colony here. That's the only answer. That's impossible lestig been space travel It couldn't be secret you have any idea what ships cost what industrial power is needed No, there's got to be some logical reason I think perhaps we might find out captain the light just went on in that house Kingston cover that door of the machine gun I say all right. Come on horse We're gonna ring that doorbell There's got to be a scientific answer to all this There's something moving in there stand back horse give me a clear shot. Are you sure a bullet can stop a Martian steady now? Can I help you? I Will we if you're selling anything it's much to earn it. No no no wait just a minute. What? What town is this? What do you mean? Are you census takers? No, no? We're strangers here We want to know how this town got here. Is this a game? No, no, it's not a game. We're from earth from where? From earth do you mean out of the ground are you sure you're feeling well madam? We came in a flying ship across space. We're from the third planet earth. This is Mars Now do you understand Mars you go away now you here? I'll call my husband from upstairs and he'll chase you go on But this is Mars Isn't it? This is green light Wisconsin in the United States of America Bounded on the east by the Atlantic and on the west by the Pacific now go away. Goodbye Horst you suppose it's really possible. I've got to find out more about this You've got to tell me one thing first What year is this year 1928 of course for goodness sake you hear that Horst and we know it's 1987 and we know this is Mars Of course is it possible that we got fouled up made made some tremendous blunder circled around and landed back on earth in 1928 Well, maybe some switch in time or dimension could we have shifted somehow gone gone backward in time? Oh This won't hold water. It's it's not logical. We've we checked every mile. We went past the moon out into space with We're on Mars. Let's dig out at point Hengston in the rear keep that gun at half load.  Oh, it's not what is you Plastic what is going on here? Oh, but it's been so many years. How you've grown Good to see Captain Grandma, I want you to meet my friends. This is captain black captain. I want you to meet my Grandfather any friend of our books is a friend of ours How long have you been here grandma? Oh a good many years ever since we died ever since you what? Oh, yes They've been dead 30 years. What?  It's all right.  Oh nonsense. No All we know is here. We're alive again And who are we to question God's infinite ways? Well, I let's think we're going back to the ship Captain I I want to talk to my grandpa turn it lustig I don't like any part of this you'll come back with us if I have to club you and carry you I See now, let's go heaven only knows what they've run up against back of the ship Use force if you have to It's you Edward It can't be of course it is Johnny Johnny Dr. Horst This is my Brother Edward. How do you do hello, sir? It's wonderful to see you Edward Look, I've I've got to get back to my ship Johnny wait I almost forgot Mom's waiting at home mom.  Well, of course, don't I feel real? We've got lunch for you Danny Mom's making corn fritters. Dr. Horst. I haven't you found anybody. Oh, no captain. I have nobody Well, then you come on home with me right Ed. Why? Sure. Horst.  You too, Dr. Horst. Well, Johnny, you're stealing the Navy, Ed. That's right dad I'm in command of the ship. We're an old Navy family, Dr. Horst All three of our boys in the service. Ed was the best pilot in the Pacific too What did happen Ed? What's the difference? I'm here now. Yeah, but you know, it's almost perfect All we're missing is your brother will then the whole family could be together Well, it won't be long mom wills in charge of the XR 54 next rocket coming out to Mars. Well, little will When does he leave Johnny? Well, they take our schedule for September, but it depends on what we report Oh, yeah, there's no question about that now, huh? No Christmas together again, that'll be something to you. Well, yes, sir Well, this calls for a celebration how about a little of the old dandelion wine a journey now father, don't you go giving Johnny too much wine?  Well, sir, isn't everything just fine Well, Dr. Horst, what are you doing sitting over here alone? What do you think of my little family? Very nice You know, I can't understand why you didn't find any folks here, Dr. Horst Just a shame everybody else is so happy I never remembered my family Mrs. Black all I know is they were gassed at Dachau during the second world war When I was liberated. I was in delirium three months. I cannot remember anything before them a Psychiatric phenomena, that's terrible. Isn't there anything anybody can do? I don't want to remember I Have not had a pleasant life. I prefer to be free of emotional entanglements. They interfere with a scientific approach I'm sorry, Dr. Horst. Oh, I'll get it. That's our ring along in three shorts. I remember that Well, maybe we'd better call it a night. You must be getting tired Johnny. I'd better be going back to the ship No, I'm saying you stay the night there we insist. I just couldn't rest thinking of you all alone on that ship I'll be all right. Well good night. Oh wait a minute. Dr. Horst that phone message was for you Me yes, that's right a message from Anna No, I don't well there. She must be an old friend. Isn't that nice? I don't You should was for me. I don't remember any Anna. Well, she asked if you were better Perhaps she's someone who knew you at Dachau. I know she said she's coming over here first thing in the morning So you'll have to stay over. Yes, but that was it then you stay here Horst You can bunk with me in my old room if a Johnny we thought you'd like to be with Edward So you could talk the way you're used to we can't put dr. Horst on the day But I think we better share the room tonight be plenty of time for talking Ed Yes, I guess so I suppose I better drop back to the ship. You know Ed security check But why do you have to do that here? I don't know mom. There's no good reason. I guess Suppose we skip it tonight. Oh Good night everybody. Oh, it's good to have you home Johnny. It's good to be home mom You sleep no, no, I've I've been thinking about what we were expecting Green skinned Martians all the time. There was only mom and dad and and Edward waiting That's funny what tricks your imagination can play on you Well, I guess Mars is heaven horse, you know, I've been thinking about Martians, too Captain Just suppose Suppose there were Martians and they saw us land and suppose they thought of us as invaders What would be the best weapon they could use against our atom bombs? Well, I don't see what you're getting at They would want to disarm us first To wipe out all suspicion to make us feel at home Suppose this house isn't real Suppose the people are just images stolen from our own memories buying Martians created for us by telepathy Hypnotism. Oh, that's that's a craziest theory I ever heard Maybe that's why there was no one for me because in all my life. There is no happy memory. No real love person Not even my mother. I don't remember her only the piles of rotting corpses of Dahal There was no happy emotion for these people to Recreate how about that phone call Anna? Yes Anna. I didn't remember who she was but I do now I just remember When I was freed from Dahal sick delirious I raved about a wonderful kind nurse named Anna that took care of her. You are it's logical She's coming to see you tomorrow, but there was no Anna. I've been nursed by a man What Anna was only a dream And there's only one way they could have learned about her by reading my subconscious mind. That's impossible horse. Why a Whole crew was thinking of home Suppose the Martians read our minds. Yes, but if there are Martians if there are They have a separated each man in a different house sleeping trusting No one at the guns I left my pistol downstairs. Do you think there's something to this horse? It's a perfect trap Who would suspect his own mother? His grandparents How easy just a knife in the heart of each sleeping man That's impossible Horst But we've we've got to get back to the shift listen The crickets have stopped Come on. We don't know when they change back to Whatever they really are Where are you going John? We We wanted to drink of water that's That's all Ed. You're not thirsty John You don't want a drink. Look out You don't want it It's changing. He's a monster run horse run This this is captain John black the XR 53 calling for Mars I've locked myself in the ship, but they've crippled it I can't take off or fire the guns and they're coming for me now the Martians I'm all alone here. All the rest are dead Hinkston. Lustig dr. Horst poor horse. He didn't even reach the door Edward and mom and dad all the folks but changing now They're melting and changing back into their Martian. Can you understand they make you think that Mars was heaven and we fell to the trap Can't trap him to X minus one has brought you the science fiction classic Mars is heaven written by Ray Bradbury and adapted for radio by Ernest Kenoy featured in the cast were Wendell Holmes as captain black and Peter Cappell as dr Horst with Bill Zuckert as masters Bill Lipton as Hinkston Margaret Berlin as the old lady Bill Griffiths as Edward Ken Williams as Lustig Ethel Everett as mom and Edwin Jerome as dad Your announcer Fred Collins X minus one was directed by Fred Wayne as a transcribed NBC radio network production


Hey, this is Brett and this 2022 Ram 2500 Crew Cab Short Box Laramie Level A Night Edition is stock number 13575Z. I am here at Summit Automotive in Fond du Lac, Wisconsin, you're new and used heavy duty truck and Ram headquarters this 2022 Ram 2500 has the 6.7 liter Cummins diesel engine. It's paired up with the 6 speed 68 RFE automatic transmission. This one can pull up to 16,660 pounds and has a max payload of 2010 pounds. I found those numbers on Ram's tow guide website, I'll link those in the description below. This truck has been fully safety and inspected by our service shop has a fresh oil and filter change. All the fluids have been checked and topped off and this truck is 100% ready to go. I'm going to go all the way around in this video inside underneath, start it up and take a look under the hood to give you the most accurate representation of the vehicle possible. Granite crystal metallic is the color and I shoot all my videos in 4K so if you have HD capabilities turn them on now because it is your best way to check out the quality condition options and cleanliness of the truck before seeing it in person.  I didn't see any dents or dings. The doors and cab are all in fantastic condition as well and the front fender is in great shape too. If you like the video you can subscribe to my YouTube channel which is youtube.com slash Summit Auto. Click the bell notifications and you'll get updates on the videos I do each and every day here at Summit. Very nice down that side. This one comes with the 20 inch painted black alloy wheels and it has Firestone Trans Force AT tires these are LT28560R20s and they have just about all the tread left on those tires. Frame and underbody is in excellent shape. This is a one owner clean title history, clean car facts out of Illinois. Get the LED headlamps and running lights as well as the LED fog lamps. The night edition gives you the painted front and rear bumpers.  The hood is absolutely perfect. Didn't see any dents or dings and you do get the cab lights up top. Passengers side front fender very nice and clean and the passengers side front wheel excellent condition as well. Granite crystal one of my favorite colors on the Rams has a lot of metal flake in it. Looks really good and as you go down this side of this 2022 Ram 2500 take note of how clean that body is, how reflective and mirror like that paint is. I take these HD videos so if you are far away or even if you're close by and you just cannot make the trip down but you're still interested in purchasing the truck. You can see the truck, hear the truck and have confidence in the vehicle that you're looking at before you even get here. So when you do get here there's absolutely no surprises and you can make a smart and informed buying decision from wherever you're at. If this video helps you make that decision let your salesman know it just saw the video that it was helpful and that Brett sent you.  This one does have the auto leveling air suspension I can show you better on the other side but the frame and underbody is an excellent shape it's like that all the way underneath has all the remaining factory exhaust so it hasn't been altered and you get the nice factory black step bars. You also get the fender flares on there part of the Laramie package you also have the LED tail lamps rear bumper parking sensors full towing package which includes the receiver hitch 4 pin and 7 pin wiring the tailgate is in excellent condition you get all the blacked out 4x4 ram and Laramie logos as well and this one does have the power drop down tailgate you can do that from the key. Spray and bed liner 5th wheel and gooseneck prep package you also get 4 pin and 7 pin wiring back here and 115 volt 400 watt plug-in that spray and bed liner is in excellent condition and that goes on to the tailgate as well. Also has a back flip hard fold up tonneau cover that is in excellent condition and for full disclosure there's back wheel just as nice as the rest so you can see that rear airbag suspension right there there's a lever right here so you get a heavy load it pushes that lever up tells it to inflate those airbags so that your back of your truck stays nice and level and once again down the rest of the side doors all look really good has the heated fold up tow mirrors they fold up like so and they fold in like that pretty sure the power fold in you also get LED side lights on there inside the Laramie level A package gives you the black leather and suede interior you get the 40 20 40 split bench seating in the front here you also get gray piping on the seats no rips no tears on any of those seats they look really good both of the seats are heated and cooled you get the Berber floor mats auto headlamps and power pedals power windows locks and mirrors you also get those power folding mirrors like I said I was like showing both sides so you know that both sides are working properly you also get the memory driver seat suede insert on there as well back seats are just as clean as the front seats no rips or tears back here you do get the latch child safety system for any child car seats you may have power sliding rear window with the built-in rear defrost these seats fold up and you get a load floor right here which is nice if you got muddy boots or tools put them on there not gonna get your seats dirty or if you got pets put both sides down they got a nice flat place delay and they're not gonna get your seats dirty storage underneath there storage in here with removable bin for easy cleaning you can put ice and drinks in there it's like you have too many coolers wherever you go or if you're not from Wisconsin just extra storage space two USB's two USB C's and a hundred fifteen volt four hundred watt plug-in side curtain airbags and child safety locks on the back doors bottoms and inside of the doors all look brand new we'll hop inside check out the miles radio and everything that this truck has to offer on the interior you can see that this one has 6,333 miles you get the seven inch LCD display with the digital speedometer compass display you can put mileage on there miles to empty and all that good stuff has the heated leather wrap steering wheel Bluetooth and gear select I'm sorry cruise controls and gear selector on the right Bluetooth and information center controls on the left and audio controls on the back of the steering wheel there's your shifter for the six speed 68 RFE automatic transmission this one has the Uconnect 8.4 inch 5 radio so this is Uconnect 5 it's run on Android supposed to be five times faster than the old radios but you get AM FM and Sirius XM radio capabilities on here you also have your different apps including Android Auto and backup cameras check that up those backup cameras are crystal clear they made those HD and the heavy-duty trucks now and you can zoom in over the receiver hitch to get hooked up to your boat camper trailer the first time every time this one also has the Alpine premium sound system and you get your climate controls and heated and cooled seat buttons there push button four-wheel drive factory brake controller six auxiliary outfits which is you get the alternate trailer height that's part of that rear airbag suspension so you can program different trailers in here for different weights tow-haul mode factory exhaust brake and your front and rear sensors you can turn those off and on down here you get two USB's two USB C's and an Augstrak get your keyless entry with the drop-down bed and remote start buttons on there passenger side format seat are an excellent condition you get the speakers in the ceiling part of that Alpine sound and the headliner is absolutely perfect the system SOS buttons map lights and your power siding rear window button is right there as well and homelink buttons for your garage door security systems and lighting systems I'm gonna show you the tire and loading information sticker real quick as well as the VIN sticker and then we'll start it up starts right up no check engine lights or anything like that except it's telling me my doors open and my hoods open but let's go take a look also check out the LED lights on the side here super bright also the LED lights in the front and I would personally like to thank you for checking out the video today and hopefully from this HD video you've been able to verify the quality condition options and cleanliness of this truck all the way around inside and out all the lights are working great under the hood we have the 6.7 liter Cummins diesel engine and today's very clean runs very smooth once again this truck has been fully safe and inspected by our service shop has a fresh oil and filter change all the fluids have been checked and topped off this truck is 100% ready to go there's the emission sticker and I would highly recommend this truck from a quality and condition standpoint I'd send this one to California Florida New York Texas wherever whoever's gonna get this trucks gonna love it it's pretty much absolutely perfect and to see more pictures of this truck or one of our other 550 new and use cars trucks SUVs minivans wranglers half tons three quarter tons one tons you name it we got to go to that website right there summit auto comm full pictures and descriptions of every single vehicle two locations all at summit auto comm if you'd like to check out more HD videos you can go to youtube.com slash summit auto remember to like subscribe and share on this video and all the videos that you see there click the bell notifications to get updates on the videos I do each and every day here at summit auto in fact in a second you'll see a link to subscribe to my YouTube channel in the upper left link to more ram 2500 truck videos like this one the upper right link to this vehicle on our website in the lower left and a link to one of our latest YouTube videos in the lower right click those check us out and we're super excited to help you with this ultra clean 2022 ram 2500 crew cab short box Laramie level a night edition and granite crystal metallic thanks again for checking out the video remember to like subscribe and share on the YouTube channel I really appreciate it thanks again


Welcome to English in a minute. A battle is a fight between people or groups. But what does it mean to pick a battle? Let's find out. Pick your battles. Jonathan, what is the best relationship advice you have ever been given? Hmm. I'd say pick your battles. That's a good one. Fighting over every single little thing would be so tiring. Exactly. Save your energy to fight over the important stuff. Fighting takes energy and time. To pick your battles means to fight over the really major things and let the rest go. Jonathan is very wise. I know. And that's English in a minute.


Live from Orlando, Florida, it's theCUBE. Covering Microsoft Ignite, brought to you by Cohesity. Welcome back everyone to theCUBE's live coverage of Microsoft Ignite here in the Sunshine State. I'm your host, Rebecca Knight, co-hosting alongside of Stu Miniman. We are joined by Theresa Miller. She is the principal technologist technology advocacy group at Cohesity. Thank you so much for coming on theCUBE. Thank you for having me, it's a pleasure.  You're here to talk about some of the integrated solutions with Microsoft at Cohesity, but this conference is really all about community. And I'd love to have you riff a little bit on what it means to be part of this community, to be such a key player and to be partnered with Microsoft and how you all come together here. Right, so outside of Cohesity, being part of the MVP program is really a pretty spectacular thing. It does take a lot of work and commitment to it. I always tell people to pace themselves if they're going to try to work toward a Microsoft MVP, but it really is about helping others, teaching others and being involved. Now from the Cohesity side, I do see a lot of benefits to both. One kind of supports the other and vice versa. Cohesity is doing a lot of really spectacular things in the Microsoft space as a partner. Specifically, it really is a nice relationship. We have a lot of products that support Azure staff. We just announced this week, for example, we're in the Office 365 space protecting data there and traditional Azure backups as well. Yeah, so Teresa, I'd love if you could bring us inside the community a bit more. So this show has been around for decades. It wasn't called, night back in the day, it was TechEd. But one of the things that was highlighted in the keynote is community and the future of jobs and the citizen developer and moving people from they might've before been configuring their server to run email in their office. And today, Office 365 takes care of a lot of that. So bring us through as to how the community is helping introducing new people as well as the people that have been here for a while, helping them learn new skills and be ready for the cloud AI world that we live in today. Yeah, the community around Office 365 is pretty amazing. I get to network and be involved with some pretty spectacular people in that space. There are some podcasts that people can definitely check out. There's always new writing that happens that covers the various angles of what people need to learn to make sure they're successful. And it really is pretty tight knit group to be a part of. There've been so many new product announcements and new developments that they're being announced here. What are you most interested, what are you most excited about in terms of getting it in front of your customers? So from the Cohesity side, there's two different angles that I'm really interested in getting in front of the customers. One being the Office 365 piece, Exchange Online and OneDrive specifically, and how data protection can get them to that next level. I'm also very excited to talk to companies about a tool we have called Cloud Spin that will help them take advantage of 2008 servers end of life. They can, Microsoft has a nice program that will allow customers to stick with their 2008 servers if they go to the cloud and we can help them get there as well as protecting their Azure workloads. Yeah, Teresa, bring us inside the customers today. We all talk about cloud journeys and what they're doing, what's going well? What are some of the challenges that they have day to day and the type of activities that they do at a show like here to help further their businesses? Yeah, enterprises, I think the longterm is going to be hybrid. Everybody's going to be maintaining some level of on-prem as well as cloud. And so with that in mind, you really need, I'm going to put my Cohesity hat back on here. You really do need a solution that can support you in both areas from a single location. If you try to piece it together, you're not going to have a single solution to manage everything. You're going to have kind of a confusing mess at the end of the day. So having a single solution to help support that hybrid journey is a big deal. Yeah, no, I think you bring up a great point. Customers, when they move to SaaS, when they move to public cloud, data protection, security are even more important than they were when I had it in the four walls of my data center. So having a solution that can span those environments and companies like Cohesity are doing that. We're actually, in our analysis this morning, we talked about Microsoft in many ways in the announcements that they're making this week is putting out their vision for how they think it should be and while Microsoft has positions in all of the environments that we know that there's work that we all need to do to make that hybrid environment of today and tomorrow even better. One other thing I'd like to add to that is I think a lot of enterprises assume when they go to the cloud that their data is just protected. And so I like to really call out the differentiation between what a cloud provider like Microsoft offers and your corporate data.  If there's not a backup of some form or a copy of that data, then you may not ever be able to recover it. And so cloud companies like Microsoft are really great at making sure you have disaster recovery and high availability. Companies like Cohesity help with the backup, the point in time backup that protects your data for the long run. I mean, what you just described with companies not even realizing, just assuming that once they're in the cloud everything is safe and secure, is would you describe companies as naive to what the cloud can offer? I mean, how would you, how are companies thinking about this and approaching this? Particularly when it comes to those disasters. Right, I certainly don't think companies are naive. I think we don't know what we don't know, right? And I think sometimes too, like I've talked to even enterprise peers, people that I've worked with in the past that you think you have the most secure system ever and stuff just happens, things happen. And so backup and recovery in a way becomes an insurance policy even. You may not ever need it, but if you do you'll be glad you had it. No. Teresa, one of the things that Satya Nadella spent a lot of time this morning talking about was trust. To talk a little bit about the relationship customers have with Microsoft, Microsoft is in a great resurgence under Satya Nadella, went from a company that most of us didn't spend much time talking to, to now Microsoft is once again front and center. For the people that have been in the trenches working with it, do they have a different relationship with Microsoft today? Does the word trust resonate with you and your peers when they think about Microsoft? It absolutely does resonate. Things have changed a lot since Satya has stepped in. I absolutely do trust what they're doing and I think a lot of companies do. Were there growing pains as part of that process? Absolutely. Did it take time? Absolutely. But yes, I do see a lot of enterprise customers trusting what Microsoft is doing. And what does that mean? I mean, so how does that manifest itself at a time where there is deep distrust and skepticism toward a lot of big technology companies? How do you think that that trust is enabling Microsoft to go from strength to strength? I find them to be a very transparent company. I think when there have been issues in the past they've always been very forthcoming about it and transparency does go a long way for the customer. Plus, I've seen a lot of stability out of the products and a lot of enhancements and improvements across the board that also create that trust that companies want. Theresa, you're going to be doing some speaking this week for people that don't have the chance to see it in person. Gives a little taste of what you'll be sharing with everyone at the show here. Yeah, absolutely. So I have three theater sessions this week, one of which will cover IT burnout. I brought in a panel of some Microsoft MVPs from around the world. I wanted that global perspective and we're going to really hammer through what people are facing but also how they can avoid burnout. So that's one of them. The second one that I'm doing is a Cohesity Sponsored session but I actually take on a subject matter expert approach in that session where I talk about backup and recovery from an Office 365 and Azure perspective, what Microsoft offers and some of the challenges that I see companies needing to consider if they move forward with those native options. And then I'll talk a little bit about Cohesity in that session where I'll cover where we may be able to help if some of those challenges are concerning to customers. And then the third session that I'm doing is my MVP session on Exchange Online Security where I'll dive into Exchange Online Protection and Advanced Threat Protection. So I'd love to talk about the first session because it just sounds so interesting. I mean, this is a time and it's an extremely tight job market. Everyone's being asked to do more with less. There's a lot of industries that face burnout and stress. What are the special challenges that IT industry workers face, would you say? So I'm going to step back a few years in my career. I did go through burnout. And I think that for me, and I think this will correlate and translate to probably what others feeling burnout are going through is one, I didn't even realize I was burned out when I was burned out. That's one issue. But stepping back when I finally realized it because I knew I needed a change. I took the leap, I made a change. Looking back, I didn't take lunch breaks. I didn't take long enough vacations and how I start my day makes a big difference. I work out every morning, even if it's just a light jog. Taking time for me every day has really gone a long way to how I feel, which not only benefits me and my quality of life, it benefits my employer as well. That's kind of my nutshell version of what I think some of will talk about. One of the tools that Microsoft is introducing or reintroducing is Microsoft Teams. And it has a lot of those time management attributes and characteristics to it. Some of the email reminders, and there's one element that will not, if you want to write an email to a colleague, it will say, wait, let's make sure this person's actually not working right now. It's 10 o'clock at night. Why don't we send it the next morning at 7.30? So it's waiting for them when they get in. How much do you think Microsoft is thinking about these things, tech burnout, stress, employee satisfaction, in terms of the way they're bringing this humanity to the tools that they're putting out there? So I think the tools say a lot for that already, but you bring up a good point. And actually, one of the things I've noticed from some of the Microsoft employees, and I haven't quite done it yet, but I'm very tempted to, you'll see in a lot of their email signatures that even though they have sent that email, maybe they didn't wait till the person's working at 7.30 in the morning, but they understand that everybody has different working hours. It's okay to wait, because you're right, that can be a stressor too. If you don't know when to disconnect and you're checking email all night long, an email could drag you right into work when you're not expecting. So it's a great point. Great. Well, Teresa, thank you so much for coming on theCUBE. It's a pleasure to have you on. Thank you so much for having me. I'm Rebecca Knight for Stu Miniman. Stay tuned for more of theCUBE's live coverage of Microsoft Ignite.