Underground coal miners work in an environment that is safer and healthier than ever before. This is partly due to the many advances that have been made over the years in personal protective equipment. Miners may not think much about this equipment during their shift. However, it becomes very important when an emergency occurs. Being on a mine rescue team, we had to go into a mine that was on fire. And the people that came out of the mine before we had arrived, it saved their lives. If you learn how to use the unit, properly dawned and properly maintained, it will save your life in a mine emergency. This is an SR-100 self-contained self-rescuer. If you ever need to escape from your mine through unbreathable air, the SR-100 will supply about 100 liters of oxygen. It is rated for one hour of use. It can be a lifesaver, but it must be cared for and maintained daily. The inspection, care and use of the CSC SR-100 self-contained self-rescuer. First, we will emphasize some of the SR-100 features. We will show you step by step how to thoroughly inspect the unit before you take it into your workplace. We will show you examples of units showing signs of normal wear and tear compared to those with evidence of excessive wear and tear. This will help you make thorough daily inspections, which are vitally important to ensure that your SCSR will work properly when needed. Finally, we will review proper donning techniques with you and show you what it is like to wear the SCSR in an evacuation exercise. Your SCSR cannot be expected to help you in an emergency if it isn't cared for and inspected properly or if you don't know how to get it on and understand wearing and breathing through such an apparatus. So you'll want to watch closely while we show you training aids in the care and use of this life-saving apparatus. It may be one of the most important lessons in your life. For your own protection, it is important to familiarize yourself with the SR-100. It is equally important to properly inspect your unit before each and every shift. Proper daily inspection means performing a visual inspection of your SCSR. This is the device you may need to wear to save your life. While you are using the SR-100 in a life-saving scenario, the oxygen you need is supplied by a chemical that reacts with the moisture in your breath. You consume the oxygen and exhale carbon dioxide. The chemical that reacts with the water vapor then reacts with the carbon dioxide and removes it from your breathing air. If water vapor in the surrounding atmosphere enters the SR-100 while you carry it on your belt during your normal workday activities, the chemical will react with it. This means some of the chemical will become spent and will not be available when you need it. Color indicators on both the top and bottom covers are designed to alert you if your unit is leaking, allowing the chemical to become partially consumed, thus depriving you of all the oxygen you may need for an escape. First, remove the SCSR from the carrying case. It should come out smoothly without much effort. Now take a look at the SCSR from all sides, taking special note of the seals. You should expect to find minor denting in the stainless steel covers. This is the result of normal wear and tear. Similarly, the corners on the plastic case may be abraded. These wear points are the result of abrasion that does not adversely affect the performance of the SR-100, that is, if it passes the remainder of the inspection. Your SR-100 should never be used as a tool to apply or absorb force. Such use may decrease its effectiveness. Hold it! Hold it! Let's get a hammer and do this. Any unit that has been misused should be removed from service immediately. Any suspect unit should be sent to the manufacturer for examination. Extreme temperatures will affect the operation of your SR-100. Avoid exposing the unit to temperatures above 130 degrees Fahrenheit. If the SR-100 is exposed to temperatures above 130 degrees Fahrenheit, it must be removed from service. Avoid storing the SR-100 high in bathhouses or near heaters. Do not put your SCSR on power centers or in the trunk of your car during extreme hot or cold weather. If the SR-100 is exposed to temperatures below 32 degrees Fahrenheit, it must be warmed up to at least 32 degrees Fahrenheit before it can be used. Next, check the security bands. Are they in place and secure? If at any time the security bands are out of place, the seals are no longer in their proper position. This usually results from forceful impacts. If this is the case, the unit must be removed from service. After searching the unit for breaks in the seal, check the moisture indicators in the top and bottom covers. The color indicators are readily visible when they are not covered by dirt. Make certain that you keep the indicator window clear for easy viewing. Normally the color indicator is blue. Should it turn any color such as pink or white, moisture has gotten inside and the unit must be removed from service because there is a leak somewhere in the unit. This moisture will react with the chemical and the unit protection time will be diminished. This is true even if the color returns to blue. Also, it is important to understand that leaky seals allow the outside atmosphere to penetrate the unit. The important thing to remember is if the color indicator changes color, remove the unit from service. Blue is the color to watch for. Anything else and performance will be compromised. In fact, you can confirm the readiness of the SR100 visually at any time by checking the moisture indicators in the top and bottom covers. Let us review. In a proper visual inspection, the case itself is first examined for damage. If the orange plastic case is punctured, burnt or crushed, it must be taken out of service. Next, the stainless steel end covers. Are they in position? Are they severely dented or damaged, especially around the seal areas? Now take a look at the security bands. Make sure they are not slack, unattached or unfastened. Next, inspect the seals. Are they cut, split or displaced? Finally, check the moisture indicators on both the top and bottom end caps. Are they blue, indicating no moisture has entered the unit? With proper inspection and proper use, the SR100 can be counted on to provide dependable and reliable protection. Take a look now as we show conditions you should watch for. The following instances indicate potential danger and are not acceptable. In this example, the end cover and security bands are out of position. The seal is exposed. In this example, the seal is protruding from under the stainless steel cover. In this example, the seal is exposed and the indicator is blue. Even though the indicator is blue, the exposed seal puts the unit in jeopardy and it must be removed from service. Although the SR100 is very rugged, your SR100 should be inspected every 90 days for external and internal damage. As part of the testing of the SR100, it is necessary to perform an acoustic test with an ASMD tester. To determine if there is excess movement of solid particles within the SR100 chemical bed. The occurrence of excess movement may affect the operating performance of the unit. This test should be performed every 90 days by the person assigned to maintain the SCSRs. Generally, this may be a lampman or some other outside person. Now that you are familiar with the SR100 and proper testing of the unit, you will want to spend the next few minutes viewing how to put it on and use it correctly. This part of the tape is part of a training program designed to show you an efficient way to don the SR100. After you finish viewing this tape, you will want to practice donning the SR100 as if you were in an actual emergency. Knowing how to put on the SR100 and use it properly can save your life in a toxic or oxygen deficient atmosphere. The guy that I was working with with my body, he and I had just gone through the SCSR retraining class. That shift before he went into mine. So it was still fresh in our mind how to put it on. So we proceeded to put ours on and as you looked around you could see other people were struggling with theirs. So I do fully realize the importance of the SCSR. But you have to know the daily maintenance of your SCSR, know how to wear it, how to use it and know when to use it. It can save your life. You will watch as a worker demonstrates how to don the SCSR. Take special note as you watch him kneel on the floor, remove the unit from his belt and place it on the floor in front of him. He then puts his cap on the floor with the light shining on the SCSR. He next opens the unit by lifting the latch located on the top and removing both covers, top and bottom. In the event either the top and or bottom covers cannot easily be removed or for some reason you cannot tightly grip the covers and pull loose, then use a tool to disengage the cover. An easy way to remove a stuck bottom cover is to place one side of the attached security band under your foot or knee and lift the unit upward. Note that once he loops the neck strap over his head, the worker will follow a special sequence doing the most critical things first. It is very important to not remove the mouthpiece plug from the mouthpiece until you are ready to insert it into your mouth. Unfolding the breathing bag will help the bag to easily fill with oxygen. Pull the large fluorescent orange oxygen actuator tag down to activate the oxygen. Pull the mouthpiece assembly away from the tethered mouthpiece plug. After checking to make sure the hose is not twisted, insert the mouthpiece and exhale into the bag. These first two steps are very important because they are often reversed, so you should practice it until it becomes automatic. Then, he pulls the nose pads apart and puts the nosepiece on so that both nostrils are completely closed. Once his lungs are isolated, he has time to take care of the remaining three steps. One, he puts on the safety goggles which are located in the bottom cap of the case. Two, he adjusts the height of the unit so the neck strap doesn't pull on the mouthpiece when he raises his head. Then, he fastens and adjusts the waist strap. Three, the worker replaces his cap and moves out of harm's way. After you have donned your SR100, discard any non-essential equipment and or gear to minimize your carrying weight. To review, first the worker got on his knees, put his cap on the floor so the light illuminated the SR100 unit. Once he opened the apparatus and looped the neck strap over his head, he is ready to perform three critical steps. Activate the oxygen, remove the mouthpiece plug, insert mouthpiece and exhale into breathing bag and put on the nose clips. The worker was then ready to complete the three secondary steps. He put on his goggles, he adjusted the neck and waist straps and he replaced his cap and moved out of danger. In the unlikely event that the small oxygen bottle inside the SCSR fails to deliver starter oxygen to the breathing bag, the SR100 can be started manually. Simply exhale three to six breaths into the apparatus in order to fill the breathing bag. Then continue through the process of donning the SR100. Reminders while wearing. One, never remove your mouthpiece. Two, walk at a comfortable pace. Three, if warm air or resistance becomes uncomfortable, reduce your speed or work rate. Four, the oxygen cylinder and breathing bag holds 10 liters of oxygen for easy start. In the event no oxygen comes from the cylinder, inflate with three to six breaths. If the breathing bag decreases in volume, slow down your pace to match production, but continue to move. Remember that breathing with the SR100 differs from normal breathing. The temperature of the inhaled air will be slightly higher and there will be some breathing resistance. This is in no way harmful and never warrants removal of the mouthpiece until you are out of danger. Because of the differences, it is best to attempt to breathe normally and move out slowly for the first five minutes. Once acclimated to breathing with the device, you can then speed up to a comfortable pace. Your SCSR cannot be expected to help you in an emergency if it hasn't been cared for and inspected properly or if you don't know how to get it on. I found that once I put the apparatus on and started to breathe through it, it was very, very comfortable breathing. Although there was some initial breathing resistance simply by the design of the apparatus, it was still very easy to breathe through. After about 20 minutes, I could tell that the breathing resistance was beginning to increase slightly. The one thing that I did notice was the heat built up from the unit, which occurs naturally as the unit operates. I could feel the unit starting to get warm right here at the back of the unit where it is up against my chest. But the heat that I was feeling was nothing that you could not tolerate. After 30 minutes of wearing the apparatus, however, I could now see a definite increase in the breathing resistance of the unit, but still nothing that one could not tolerate. And I found that about 30 minutes into wearing the apparatus, I had to begin slowing my walking pace down so that the unit could keep up with my oxygen demand. Once I reached the 50-minute mark, however, the breathing resistance had increased remarkably. In fact, as I was reaching down to touch the breathing hose, I could feel it beginning to collapse just slightly as I would inhale. Also, I noticed the breathing bag at the bottom of the unit also collapsing more as I was breathing. Right at about 60 minutes, however, I stopped and took the apparatus off. It was still working for me, however, in terms of drawing from the unit, the draw was so great that the breathing hose was starting to collapse. And I also felt myself becoming just slightly lightheaded, but still the apparatus was working. One point I want to make, however, a lot of miners think that when they put this apparatus on and start to breathe through it and feel that breathing resistance, they think the unit is not working. It is working for you. As long as you can get oxygen from the air and you can breathe from the apparatus, you know it's working for you. So don't, at any time, take the unit off thinking it's not working. When you feel that it's becoming harder to breathe, slow your pace down. And I know that it's hard to do in a real mind emergency situation, but still if you slow your pace down, that unit will be able to keep up with your needs. Always remember to, at no time, never ever take the mouthpiece out to breathe air in the mind because you never know how much carbon monoxide there may be in the air. And if there's enough carbon monoxide in the air, one breath of the outside air with the mouthpiece out could possibly cause you to go down. So as long as you can breathe from the unit, it's giving you oxygen. Keep the unit on, keep the mouthpiece in and continue your escape. The SR100 is rugged and durable and it will work if cared for properly. But don't take chances. Preach your SR100 with respect. It could help save your life.


Welcome everyone. This is a webinar around the management of data and information in research guide and the related code for the responsible conduct of research. I'm Adrian Burton. We'll also have Justin Withers from the ARC. Before we begin, I'd like to acknowledge and celebrate the first Australians on whose traditional lands we meet and pay our respect to the elders past and present. Over the last couple of years, the authors of the co-authors of the guide for the responsible conduct of research, that's Universities Australia, NHMRC and ARC, have been updating the code and bringing out a series of guides related to the code in our fairly mostly unregulated or self-regulated, the self-regulated system that we have for research in Australia. This policy document is the sort of peak statement about research integrity and the updating of the code and the related guides is a very important development in the research sector. So the ARDC is facilitating a number of webinars around this change and particularly we are focusing in on the guide to the management of data and information. So today we have a series of events. Today's event is really to familiarise ourselves with the changes in both the code and the related data guide, which was the abbreviation I'll use. So it's really to get an idea of what the changes are and have a clear idea of what the responsibilities of researchers and research organisations are in this area. So that will be today's event and we'll adjust into a briefing about that. Tomorrow and next week we have two workshops, which are having established what data management, what the responsibilities and roles of different actors are. We'll have two workshops to exchange information about what good data management practices are and what experience the institutions have had in implementing those. So there's one workshop tomorrow targeted at universities and there's one in exactly one week's time for the MRIs. Today we've got Mr Justin Withers who's the Director of Access International and Integrity at the Australian Research Council who will be giving us a background to the changes and the new content of the code and the related data guide. Justin. Thanks Adrian. What I'll do now is I'll share my screen and we'll get rolling. So hopefully you can all see my screen. So let's get moving. All right. Okay, so today I'm going to talk about primarily funder's expectations in relation to data management. That will be focusing on the data management guide in a lot of detail. But I'd also like to take the opportunity to just set the scene about the research funding landscape in Australia. Talk briefly about the development of the new version of the Australian Code for the Responsible Connective Research which was released in 2018. Also I've got a slide on the national statement on ethical conduct in human research which is part of the suite of documents and guides that are in place to support and underpin the ethical conduct of research in Australia. And then following on from the data guide where I'll talk about responsibilities of institutions and also of researchers. I'll also touch on ARC and NHMRC expectations regarding the management and access of data that's produced as a result of our funding. So Australian government investment in R&D the last one each year was $9.6 billion across a variety of portfolios. It's quite a unique funding system here in Australia where the delivery of R&D funds is quite diverse through a number of portfolios including industry, education, health, defence and other agencies such as environment and agriculture. Of the funding that's delivered they're $9.6 billion. That's directly related to the Australian Code. There is about $800 million that comes from the ARC and an equivalent amount of money that is delivered through NHMRC. And part of the requirements for receiving that funding is an explicit adherence to the code and it's supporting documentation. So that is included in all of the ARC and NHMRC guidelines and funding rules. So for those of you who are not familiar I'll just touch on the purpose and key activities of the ARC and then likewise the same for the NHMRC. So ARC has been around in one shape or form for quite some time but in its current form there was some legislation introduced in 2001 the ARC Act which basically sets out what the responsibilities of the ARC are and they're delivered through three key activities. Key activity one is funding the highest quality research. Activity two is assessing the quality and engagement impact of research delivered through train universities and also we provide advice to the government on science and research matters. We're part of the education portfolio and as a result we report directly to the Minister for Education. The NHMRC, I like to refer them to the MZR sister agency. We work very closely and hand in glove with each other. The ARC funds all disciplines including the very basic side of biology and medical research but all clinical research is delivered through the NHMRC whose budget is slightly larger than ours and their three key missions and themes are investing in high quality health and medical research to build research capability within Australia, supporting the translation of health and medical research in a clinical practice and also maintaining a strong integrity framework for research and guideline development to underpin rigorous research practices in Australia. So whilst the ARC and NHMRC and UA are co-authors of the code and a number of other accompanying documents the secretariat support and the driver for that development of that information is generally res with the NHMRC. At the moment they're also delivering a number of programs under the Medical Research Future Fund which was established in 2015. That's a $20 billion perpetual fund where the interest earned on that investment is drawn down and and directed towards strategic health related research and a lot of those schemes are delivered through NHMRC whilst they're managed primarily through the Department of Health. A couple of years ago NHMRC also went through a bit of a revisit and review of the way they delivered their funding and came up with a revised funding stream of four main streams, investor data grants, synergy grants, ideas grants and strategic and leveraging grants. So they cover off various aspects of medical research through those grants. So the next slide looks at the framework for the Responsible Connective Research. I'll get rid of all these bubbles. Someone who did this for me, didn't tell me they were flying in like that. But anyway, so essential to the framework is the code. The Australian code for the Responsible Connective Research and it's essential to our research integrity framework. It's been in place known as the code since 2007. There was a previous document before that pending 96 by the Australian Vice-Chancellors Committee but it took on its name of the Australian code in 2007 and it was revised again in 2000 or released in 2018 after a couple of years of revision. So I'd like to emphasise here that research integrity in Australia is a shared responsibility. So the ARC and NHMRC as the two largest government funders of competitive grants have worked with UA to develop the code and supporting documents. Research institutions themselves have primary responsibility for maintaining research integrity and ensuring the concerns about potential breaches of the code are appropriately addressed. So the code is supported by other codes and statements including the National Statement which covers research with human subjects. There's also supporting documentation regarding research with Aboriginal and Torres Strait Islander peoples and communities and also AACSIS has just released a revised code on research involving Aboriginal and Torres Strait Islanders peoples and communities and that is part of the system of research integrity underpinning the responsible research in Australia. ARC and NHMRC funding guidelines and funding agreements require compliance with the code and the other codes and statements as a condition of funding. So the research that we do fund are legally obliged to adhere to the principles in these documents. As well as these as the codes and documents, NHMRC and ARC also have policies in place, research integrity policies that require institutions to report relevant breaches of the code to us and also provide mechanisms for our organisations to apply actions and consequential actions in response of breaches to the code. So it basically opens up a line of communication for us to be across research integrity issues at universities. It makes the system transparent particularly to us given that we are providing a fair amount of support for that research. Now given that research integrity in Australia is primarily a self-managed system where institutions themselves are responsible for I guess investigating themselves, there's an avenue of appeal to make sure that those processes are all above board and that is the Australian Research Integrity Committee. So they're designed to assure integrity through its review functions. So if someone has concerns about the procedure fairness and the way in which institutions have investigated issues, they can put in an ARIC request for review. And another element of this quality framework is TEXA, the Tertiary Education Quality and Standards Agency. It's an independent national quality assurance and regulatory agency of higher education. And all institutions that are given the status of universities have to comply with TEXA's requirements. You may have noticed recently that looking to expand their remit a little bit and have included an integrity unit to look at a high level research integrity compliance at institutions as well. And the NHMRC and UA and ARC are working closely with TEXA to make sure that there's no overlap between the responsibilities that we deliver for research integrity and what TEXA is planning. So as I've alluded to before, the code underpins the framework for responsible kind of research in Australia, essential to that. And in 2018, the latest version of this code was released. It's been co-authored again by ARC, NHMRC and UA. And essentially the version we have now builds on the 2007 version. It's a title or compact document and it's presented in a more simpler way, designed to support high quality research, enhance credibility of research and promote community trust in research. By no means do we think that the code has been diluted by its short and condensed version into a principle based document. Because as I'm about to speak to shortly, there are a whole lot of series of guides with detail that was in the previous 2007 version that can provide assistance to institutions and researchers in meeting their requirements of the code. So the code is now a principle based document. There are eight principles of research integrity and there's 29 responsibilities associated with those that institutions and researchers need to adhere to and be responsible for. So I won't go into too much detail regarding the actual responsibilities, apart from those related to data management, which I'll cover later. But the codes eight principles are honesty in the development, undertaking and reporting of research, rigor in the development, undertaking and reporting of research, transparency in declaring interests and reporting research methodology, data and findings, fairness in the treatment of others, respect for research participants, the wider community, animals and the environment, recognition of the right of Aboriginal and Torres Strait Islander peoples to be engaged in research that affects or is of particular significance to them, accountability for the development, undertaking and reporting of research and the promotion of responsible research practices. So these eight principles inform the 29 responsibilities that apply for institutions of which there's 13 and 16 responsibilities for individual researchers. So some of these responsibilities relate to only one of the eight principles, others relate to multiple. So as I read through those principles, you probably would have picked up, there's a few that could either directly or literally relate to data management practice and integrity. So as I said, the 2018 version of the code hasn't been watered down at all. It's been tightened. And if you look at it in its entirety with the supporting guides that have been put in place to underpin those, the principal base code, there is a lot more detail and detail that's presented in a guide, a way to guide institutions and researchers to meet those principles. So those guides include, of course, the investigation guide, which was released at the same time as the code, so that outlines how institutions should address allegations of breaches of the code and they should go about investigating those and being procedurally fair about doing that. And then the old version of the code in 2007 had a series of chapters at the front, a lot of those related to various elements of research undertaking. And now these are represented in these guides. So I'll be speaking in some detail about the data guide, but there's also a guide on authorship, one on supervision, peer review, disclosure of interests, collaborations, publication and dissemination of research, and also, as I've mentioned already, the investigation guide. Now these guides are really prescriptive. This is keeping the character as guidance documents, but also recognize that under Australia's self-regulated system is institutions that must ultimately be responsible for their policies and procedures for ensuring that they are consistent with the code. So they're not written in blood, so to speak. Institutions need to apply these guidance material to their own policies and requirements they put in place for their researchers. So as I said earlier on, there's a number of the principles that sort of relate directly to data management, and I've drawn those out here in this slide. Principle two, rigor in the development undertaking and reporting of research, which requires that research be characterised by attention to detail and robust methodology, and that researchers avoid or acknowledge biases. Principle three, transparency and declaring interests and reporting research methodology data and findings, which requires researchers to share and communicate research methodology data and findings openly, responsibly and accurately. And principle seven, accountability for the development undertaken and reporting of research so as to comply with relevant legislation, policies, guidelines and ensure good stewardship of public resources used to conduct research. Now I mentioned earlier on before I go into the exact details of researchers and institutions. The national statement on ethical conduct and human research, most recently updated in 2018, does have some specific requirements regarding data. So section three of the national statement provides guidance on ethical considerations in the design, development, review and conduct of research. It provides specific and detailed guidance on the collection, use and management of data and information for human based research and it addresses particularly ethical issues related to generation, collection, access, use, analysis, storage and retention of the data of this information that in many cases can be very sensitive. It also requires the development of a data management plan as a specific requirement in the national statement. And I'll talk a bit more about ARC expectations regarding data management plans later on. So now the data guide. This guide was released in June 2019. In essence this was about the same time that the 2018 version of the code came into force. Whilst it was released in 2018, we gave institutions years grace to be able to put in place the necessary policies and supporting information for their researchers and before it became in place in June, July 2019. So guides and assists institutions and researchers to adhere to the relevant principles of the code. It was one of the first guides released, recognizing the central role of the subject matter to research endeavor. So critically, best practice data and information management facilitates the justification and verification of the outcomes of research that maximizes the potential for future research and minimizes waste of resources of the researchers and the wider community. The guidance and the data guide corresponds directly to multiple responsibilities of the code. It specifically references the following institution responsibilities. Responsibility four. Institutions are required to provide ongoing training and education. Responsibility five. Ensure supervisors of research trainees have the appropriate skills, qualifications and resources and responsibility eight. Provide access to facilities for the safe and secure storage and management of research data, records and prime materials. And this is important given the current appetite worldwide and within the Australian government to allow access as openly as possible. As well as the specific following researcher responsibility, responsibility 22 is to retain clear, accurate, secure and complete records of all research, including research data and prime materials. And as I alluded to earlier on, we're possible and appropriate allow access and reference to these by interested parties. As I mentioned earlier, the guides are rarely prescriptive. The data guide is no exception. Instead, providing institutions with what might more accurately be described as a checklist of relevant considerations for inclusion in institutional policies, along with some limited instances of some very specific guidance. For example, of the specific guidance under the category of storage, retention and disposal, the data guide alerts institutions to the fact that research data should be consistent with any copyright or licensing arrangements, being accord with research discipline, specific practices and standards, comply with relevant privacy, ethical and publication requirements and comply with other relevant laws. Now, the specificity of the guide also includes prescribed data retention periods. So I'm bringing those to your attention. For short-term research projects, there are assessment purposes only such as research projects conducted by students, retaining research data for 12 months after the completion of the research project may be sufficient. For most clinical trials, however, retaining research data for 15 years or more may be necessary. For areas such as gene therapy, research data must be retained permanently. And if the work has community cultural historical value, research data should be kept permanently, preferably within a national collection. So the responsibility institutions to develop and implement policies and provide facilities and processes. The policies should include guidance for managing research data and primary materials that address the following ownership, stewardship and control, storage, retention and disposal, safety, security and confidentiality, access or interested parties. Policy should apply to all research conducted under the auspices of the institution and may be influenced by the funding arrangements for the project in particular. So some other responsibilities of institutions include training. Training and education may assist researchers and others in relevant roles to follow not only the institution's data management policies, but also other relevant disciplinary specific policies. In relation to ownership, stewardship and control of research data and primary materials, disemphasizing with respect to ownership of data and information used or generated by Aboriginal and Torres Strait Islander research and communities, institutions or researchers may hold data. However, they should not make decisions about the access or reuse of this data without proper consultation with the Indigenous owners. Institution of policies shouldn't be designed to unnecessarily impede the normal use of research data and primary materials by researchers. So basically they should be in place to facilitate access and use rather than impede access and use. Storage, retention and disposal, retaining the research data is important because it may be that all remains of the research work at the end of the project. So the storage, retention and disposal research data should be consistent with any copyright or licensing arrangements being accord with discipline specific practices comply with relevant policy and comply with relevant laws, regulations and guidelines. One of the key concerns about researchers is data safety and access and ownership. So the policies should also assist researchers in informing them of relevant confidentiality agreements and restrictions on the use of research data, ensure that computing systems are secure, ensure that IT personnel understand their responsibilities for network security access control and ensure that those holding primary material including electronic material understand their responsibilities. So there's a big role in training those with access to research data as well as those that generate it to ensure the safety and security of that data. As I said earlier on with the open science, open research, agenda access by interested parties is also extremely important. So the policy should describe how to make research data available to interested parties and also provide a license so people can at a glance understand how that data can be used and reused. Where sharing of research data has been requested and access has been refused the reasons for not sharing the data should be transparent and justifiable. So if you've got a research project involving a private investment for a company or industry and that data produced from that research project is owned by the company rather than the researcher there's a justify reason why that shouldn't be made openly accessible. Institutions also should look to provide facilities. So research data controlled by the institution and its researchers should be stored in facilities provided by or approved by the institution. So there's a big role there for institutions to put in place systems and databases where researchers can store use access and manage their data. So I know there's a fair few universities out there that have been doing a lot of work recently in providing that sort of data service for their researchers including a couple of spring to mind university Queensland Tasmania and UTS and Sydney are through that I'm more familiar with but I understand that there's a lot of universities there are taking that responsibility very seriously and providing facilities and support for the the safe storage and access of data. So moving on the responsibilities of individual researchers researchers should adhere to the institutional policies okay the policies are there in place to support and guide the researchers and those policies of course cover off on as I've said the management of data the relevant laws regulations and guidelines and research discipline discipline specific practices standards that apply to those in individual researchers. A data management plan should also be developed early in the research process for researchers. Duable records derived from them such as assays test results transcripts and laboratory and field notes must be retained and accessible as well. So the data guide responsibilities of research relates specifically what's on the slide here retention and publication so individual researchers of course have primary responsibility for deciding which research data and primary materials is for long-term retention and wider accessibility. So that's a judgment called by individual researchers themselves. Researchers should also manage confidential and other sensitive material appropriately and exercise care and handling confidential or other sensitive information used or in or arising from a research project. Institutions are required to provide the training but responsible researchers are responsible for ensuring that they engage with this relevant training and another responsibility for researchers is to acknowledge the use of others data in their research and they should do that by appropriately referencing and citing the work of others in the presentation publication or sharing of research. So in essence the responsibility rests with institutions to put in some systems and policies to assist their researchers as well as storage facilities etc but a lot of the onus on appropriately managing that research data about how it should be accessed and reused and cited risks with individual researchers themselves but institutions should also be able to provide support for those decision-making processes. So the ARC has long been cognizant of the value of research data and whilst we don't have a specific policy in place we do have a data statement and also have some provisions in our funding rules and grant agreements that specify our expectations regarding the management of data. So these requirements are designed to encourage researchers to consider the ways in which they can best manage stored disseminate reused their data. Data management planning from the beginning of a project helps to guide how data will be collected formatted described stored etc etc so it's very important that researchers from the very beginning of their research project or even the research concept are thinking about what data is going to be generated and how they'll manage that data. So as a co-author of the code we're committed to effective data management and again underpinned by ensuring open access to publicly funded research data whenever possible. Some institutions may have infrastructure processes in place of storing managing and sharing data and these are valuable resources that should be utilized. So since February 2014 we have required researchers to consider how they plan, manage and research data arising from ARC funded research and originally we asked in applications that researchers give a brief synopsis of how they'll manage their research data that recently evolved to rather than requiring that at the application stage we require all funded research projects to have in place a data management plan prior to the commencement of the project and this forms this is part of requirement in the funding agreement now. So this slide here explains the the expectations so around data management plans it's at the moment from ARC funding agreements from 2020 it's in section A to 2.5. Institution researchers and participating organisations have an obligation to collect and maintain research data in accordance with the 2018 code. It must be developed prior to the commencement of the project it should be consistent with the requirements contained in the 2018 code and the data guide and the data management plan should be compatible with disciplinary standards and how participants will manage the long-term preservation of data arising from the funded projects including but not limited to storage access and reuse arrangements and we also strongly encourage that data arising from the project is deposited in appropriately publicly accessible discipline or institutional repository and you must maintain you must retain your data management plan and make it available to the ARC if it's requested so we're not asking to see every one of these data management plans but we do have undertake institutional reviews occasionally and often the questions are we're looking at compliance with our funding agreements and if a project was identified to have a place to look at we'd expect that it would have a data management plan in place. As I've said before we encourage researchers to deposit data arising from a project in a publicly accessible repository now this has been in place since 2007 and in fact it was a requirement in the 2007 version of the code where researchers can't meet this requirement reasons need to be provided in our final report and as I said earlier on we don't have a specific mandate or policy in relation to this and this is in reflection of the the challenges involved in addressing the rapid growth and changes appearing in relation to data and also the very large variances in the types of data that are generated from ARC funded research so it's not necessarily a one-size-fits-all. We also actively monitor the international landscape and what's occurring there and we are actively engaged with with the number of players within Australian system including ARDC the Australasian open access strategy group call and others and as an example I'm a participant on the fair access steering group that's findable accessible interoperable and reusable which is an ongoing committee established under the Deputy Vice Chancellor's Research Committee at UA so NHMRC's expectations aren't too different than the ARC's they also acknowledge the importance of making research data publicly accessible they strongly encourage researchers to consider the reuse value of their data and take responsibilities responsible steps to share research data and associated metadata. Now part of their expectations are that researchers should ensure that appropriate metadata accompanies the data sets so people can at a glance understand what the detail is in those data sets and they also must consider the appropriate level access rather than specifying a data management plan is in place in their funding agreements NHMRC funding agreements requires that all research metadata arising from the project must be listed in the final report for each project so that's about it from me happy to take some questions and I'll throw back to Adrian to facilitate those I did get a few questions earlier on that people had sent through so I might cover off on those now before I throw to Adrian I've had a question here different research fields have orders of magnitude differences in data volume will funder support storage of the later of the larger volumes in budgets not necessarily the ARC budget is a one-line budget and whilst the the decision on whether the research project is funded is primarily on the excellence of that project there is some provisions for this dissemination of research so individual researchers or research teams could consider the value in directing some of that funding towards data retention and access but primarily the responsibility for storing large-scale volume of data is addressed with institutions there's a question regarding the expectations around fair principles for genomic databases I can't really answer that question given I'm not an HMRC and we don't fund that that type of research necessarily here but I would point to the fact that there needs to be a consideration of the safety and security of the information and people's privacy so it's really a case by case situation and that's all part of the process of developing and considering a data management plan that undertake that is looks looks to look at access arrangements and reusability and how will we support questions on the data management guide on an ongoing basis well I think if you email the ARC or an HMRC directly we can look to provide some further advice regarding specific questions whilst there but although there are you know the owners is primarily on institutions we are happy to provide some advice and consistent advice where we can I think the ARC is also a very valuable resource to do that another question how should researchers interpret principle 22 to retain clear accurate secure and complete records it's particularly in disciplines where enormous data volumes make it impractical impossible to store or complete research data the key point there is where possible so it's if an institution or a researcher has and there's no real value in retaining the whole data set there needs to be a record of the the the data that supports the research output or the publication so I mean in cases where there's massive data sets that can't be accessed or stored easily there's no responsibility at this stage to make all those accessible if you're publishing you need to make sure that the data are underpinning that research is available for those to check so is there any obligation or responsibility regarding the management of data and information facilities such as interest that support enable researchers or does the guide only apply to host institutions basically at the moment the code and the guide apply to any research that's funded by the NHMRC and ARC I do know that some of the publicly funded research agencies such as CSIRO and ANSTA are also requiring adherence to the principles in the code and I know that they do manage some increased facilities so I think there is a loose expectation that the code and the guide itself can apply to broader research endeavors in Australia outside those which are funded by ARC and NHMRC but specifically at this stage it's only that research which is funded by us that is noted in funding agreements and the and the code itself so thank you for your attention I'll hand back to Adrian now and we can facilitate some more questions and discussion if needed thank you very much great thank you very much Justin it's a really great overview of very key documents there all right well I'll go through some of these questions remembering that we have two more workshops to address kind of the implementation questions of data management at institutions and I also note that within the question the chat part of our our objective here was to set up forums in which the different the sector can exchange information and that's been happening wildly in the chat where some people have already chipped in and given their own experience I'll try and sift through some of these questions that are mostly about the actual phrasing of the code itself and if we don't get to all of them then we'll pass them on to Justin or we'll be able to either address them at the coming workshops or privately there are a couple of questions towards the beginning about I'll read one of them out here is the requirement to have proper storage facilities something that can be shared between institutions there's no reason why can't you know particularly for you know big massive data sets that are generated by collaborative research it really depends on the sort of arrangements and negotiations that are put in place between institutions but there's no reason look we don't want duplication of big data sets there's no reasoning for that to occur but as the NHMRC has pointed out sometimes all you need is some metadata that signpost the existence of the large data set which and where it can be found and that doesn't necessarily have to be within a single institution another questioner well I bet not quite the same in a situation where you have researchers working for various institutes and they're nested into a collaborative entity where does the primary responsibility for adherence to the code sit well good practice would mean that there's a data management plan in place at the start of the project and that data management plan would be based on an agreement or negotiations between all players generally speaking though if from the ARC perspective if the administering organization that's the organization that's applied for and won the funding and would be managing a larger project on the basis of other players the responsibility for data management etc would rest with that with that institution but the data management plan could specify particular nuances about how the data is managed in access but there's a couple of questions about sort of compliance and spot checks so I read out one or two of those does the ARC or NHMRC ever do proactive spot check audits or is it only based on alleged breaches as I said a little later in my presentation we do have a system of institutional visits or reviews as we call them so we generally visit two sometimes up to four institutions a year on a sort of a rolling basis it used to be called an audit but you know it's not really that formal we will come out and look at as part of that process look at a number of projects that have been undertaken by the institution making sure that they conform with with the the funding agreement and requirements and as of 2020 of course there needs to be a data management plan in place before that project can start so I would think that that one of the questions asked moving forward would be for those projects to look at can you please show me your data management plan before the project started as far as potential breaches of of the code and the guide regarding data we aren't necessarily or we're not considered an investigative body as I mentioned it's a self-regulated system so it's a case of either self-reporting or if there are instances of potential breaches of the code regarding data and someone reports that to us either openly or anonymously we will look at a little bit of due diligence to see whether there's any merit in that allegation and then we will refer it on to the institution to investigate themselves and our policies specify that if we do so they need to report back within a 12-week window on the outcomes of their preliminary assessment as to whether or not there's been a breach of the code in relation to data management and there was another question about the repercussions of a breach but I think what you're saying there is that then the the consequences are put in place by the institutions themselves no no well that's the that the institutions themselves will will look at the the issue and allegation and determine whether there's a breach of the code and in essence the severity of that breach institutions can use can can apply the term research misconduct they want to but we recognize in the guide and investigation guide and their policies that breaches of the code occur on a spectrum from being minor and inadvertent to systemic or planned fraud or integrity breaches which for all intensive purposes research misconduct whether you label it or not so once we're advised of the outcomes of the institution's consideration we will look at it on some merits and we will determine from our perspective whether we need to apply a sanction or a control ranging from maybe asking for a double certification for that researcher for a couple of years regarding data management when they apply for funding all the way through to ceasing their access to ARC funding for a number of years and recalling the the grant amount from the institution so it's a case of working closely with institutions and of course institutions themselves if they want can apply their own sanctions to that researcher as well but we will look specifically on the impact of the the project from our perspective the potential damage to the ARC reputation and act accordingly great as a question about the data retention periods and the potential for there to be inconsistency or or just not the same data retention period required by let's say there's an example of a state government requirement and potentially the federal requirement another retention requirements of different institutions perhaps just the questions you know it's really an observation that there seems to be the opportunity for a disparity yeah look I said that the guides it's a guide it's not written in law I know although there are some specific reference periods that are strongly suggested the ones that I went through about genomics data etc etc and also I kept going to the point that in relation to the management of data you also need to give consideration to other legislation and requirements as well so you need to look holistically at that and I think from an ARC and HMRC perspective if you meet the the minimum requirements that were specified in the guide it's not too much of a problem and I'm sorry I can't give you a straight answer on that but there's a guidance material only institutions are there able to interpret that and what they expect their researchers to adhere to and in doing so institutions would also give reference I think to their state legislation requirements as well yeah that's right um great let me just down here we haven't got a lot of time so just a couple more questions there and again skip over some of the ones that might be implementation stuff that can be covered in the other in the workshops uh let's have a look here covered that one does the DC do spot checks in situations where you have researchers working no we've already done that one appraisal and sentencing of research data is actually one of the most difficult aspects who should make the final call on appraisal and therefore retention period now I'm sure we'll get into this tomorrow but is there anything specific from the code point of view do you think Justin no not nothing specific but I think it's all part of the of the process that there needs to be some decisions and considerate some consideration of these issues prior to the commencement of the project involving all players and institutions should be there who I guess provides some assistance um you know the ARC also I mean part of its education remit too is to provide some I guess some assistance in in some of those considerations as well good now I might go on some of the more um practical ones as we're coming right to the end here uh the will the slides be shared yes you can make this um I think ARC will make the slides available afterwards yep not a problem we'll make them available from the ARDC website uh will the record is it being recorded will the recording be made available yes we will make the recording available uh in on the ARDC website as well checking any others here sure that we've got any others uh one last question here all universities adhere to the code they must they must but they don't always in fact quite often it's the observation have policies or procedures for data management or data storage let me start again all universities adhere to the code they must but they don't always in fact quite often have policies or procedures for data management or data storage and don't provide infrastructure for data storage does it mean all the responsibilities fall on the researchers no no institutions are required to have policies in place at a bare minimum and the support infrastructure so look I think some universities have been well prepared historically and supporting their researchers in this space but moving forward you know there is the expectation and requirement that these the support mechanisms and policies are in place at institutions we have one of the reasons that we you know the guide was released in 2018 but didn't come into force into 2019 was the need for institutions to prepare themselves for the for the sweeter policies often institutions too have shared arrangements in relation to or can in relation to storage of data and management of data but what I'm in saying short is no the responsibility for researchers are to adhere to the code but any possible shortcomings in their respect would be considered contextually in the support that's reported by institutions as well good thank you very much Justin that's really nice of you to have allocated the time I think so on behalf of the audience consider a very very loud clap at the moment thank you very much for giving us all that information Justin and clarifying it with questions as well and I also look at I encourage and I understand I think most of you guys are from research offices I think it's really important that you share information and ideas with each other there are some particular groups in place that do so already and I know that working with those people in those groups they are very very open to providing assistance to others and ensuring you know a consistent approach in supporting and underpinning the research integrity effort in Australia so I think these forums are fantastic opportunities to to make some of these links if you haven't done so already and I'm always always willing to take questions and hopefully point you in the right direction of others that might better provide assistance if I can't do it myself yes good all right and then there are a lot of organizations that are the code and all the different guides touch lots of different parts and of the research system in Australia what I DC is trying to do here is build up a community of practice if you like of the people and responsible for the implementation of the the data components of the code and its guide we've got these two workshops that we've planned again just reiterate that for tomorrow for universities and in one week for medical research institutes Keith is putting a link in there if you haven't quite registered for that yet the registrations are still open so you can participate I strongly recommend you do and other people at your organization can come in the idea here is that you know we can learn from each other and consider as a group you know some of those shades of gray and really help ourselves to implement this really nice code and the policy framework so I DC will continue to with the think of these for two workshops as the as a start here and we have a lot of communities of practice to support data management and institutional responsibilities as well as researcher responsibilities so we're very keen to contribute and to keep doing that if anyone has any suggestions for us and how we can help to support the can the help to support the sector helping itself then don't hesitate to contact us as well so I think we come to the end of our time look forward to working with you tomorrow on some of the problem solving around how to do this again thanks very much to Justin and thanks very much to the very active audience that's really helped with the questions and answers thank you very much everyone we'll see you tomorrow


Live from the Hilton at Bonnet Creek, Orlando, Florida. Extracting the signal from the noise. It's theCUBE, covering Vision 2015. Brought to you by IBM. And now your hosts, Dave Vellante and Jeff Frick. Welcome back to Vision 2015 everybody. I'm Dave Vellante with Jeff Frick and we're here. This is theCUBE, we're live on the ground. Two days of wall-to-wall coverage of IBM Vision, hanging with the CFOs, performance management, sales management, governance, compliance, risk. John Cothart is here. He is involved in the UI, the user experience around Watson, Watson analytics. The shiny new toy, the secret weapon of IBM. John, welcome to theCUBE. Thank you very much Dave, Jeff, great to be here. Yeah, so tell us about your role. You've got product experience and design in your title. That's kind of cool. It's a great place to be these days. So what does that mean? Well, I definitely have probably one of the funnest jobs for the team. So I straddle a number of different initiatives and really it's trying to bring the pretty pictorials of what we think we imagine the software to be into reality. It's how you do that translation from the pictographs of we think it needs to be designed in this particular way and we think the user is going to want to do this, that and the other. And then how do we build the continuum? So I get to basically play with information sets and ideas and concepts all day long thinking about how is a business user going to get value? And going out to these business users and actually spending time working with them on what are they trying to achieve and make sure that that experience flows from the design elements, from all that prettiness all the way through into something that's usable and tightly harnessed to getting them more analytics and insight. So is your role also all the way from ideation all the way through execution? Right, so I'm the bridge. I'm the bridge from the ideation team all the way into the product management and development teams and try to bring things backwards and forwards wherever possible so that if something's, you know, when we get into the development lifecycle in software you end up realizing that maybe some of those ideations need to be tweaked and adjusted and it's how do we do that in a consistent manner and allow that continuum. So I sort of act a little bit like the glue to bring those teams together and make it happen. So in the keynotes this morning you could see some of the sort of older products and you'd see them and you go, okay, that's nice, the tables and so forth. And then all of a sudden you see this new look and feel and the speakers tell us that now we're going to bring in the Watson experience, wow. And it really shines. So first of all, you must be excited to see that come to light. But what's the essence of what you guys are trying to achieve there? Well, you know, earlier today we talked about analytics for all and maybe we'd take it a step further and talk about citizen data scientists. You know, one of the biggest challenges that Dr. Jing Shear was talking about was there's a shortage of these types of resources that are really remembering their stats courses from university and other. And there's also this penchant for wanting to easily get at information. So how do you take something that's deep and complex like optimization and predictive analytics and make it super simple? Make it as easy for just any user to adopt to. And so it's trying to bridge that gap of, you know, all these users have data. They have information, but they may not have insights and they may not be able to drive those insights into action. And so when we talk about things like the citizen data scientists, it's about trying to get all of that capability into the hands of someone who's probably not seen this type of technology ever before. And it really transcends this need to design around a lot of different use cases as well in terms of mobility and in terms of how people actually work, you know, getting into what they're trying to accomplish and making it simple and straightforward with value. I mean, it's kind of been the Achilles' heel of decision support for decades, decision management. Very powerful, but only a handful of people in the organization can actually leverage the tool set. And it's kind of been a failing, you guys don't use the big data theme, but we do all the time, but it's sort of been a failing of the big data world is that the data really isn't, and the insights aren't in the hands of everyday business users. How is that changing? What is IBM, you know, what are you forecasting in terms of being able to actually achieve that? Well, I think Watson Analytics is the starting point and really it's taking, like you said, we've got these other great capabilities in some of our more long-serving products for us in the business intelligence platform, SPSS and the like. And it's really trying to harmonize and blend the capabilities that people who use those every day, day in and day out with that business user who probably sees them, if you think about forecasting just as an example, people that do forecasting for marketing or human resources or operations, they might do that once a quarter, maybe, maybe once every half year. And it's how do you actually get them to see the insights and then know what to do with that data? So I think Watson Analytics starts creating that environment for them. It creates those line of business professionals, the ability to see that information in a different way and start really engaging in the process and engaging in making that transformation from analytics. And I think when we really look at how we want to transform, it's so that the people that need the information and insight can get at it and they don't need to have a PhD in stats. They don't need to go and be a master data management type person to curate information. And Watson Analytics is starting that for us. It's a journey, though, as Dr. Jing Shear said earlier this morning. We're in a great point where we can create value for individuals today, but there's so much more we want to do. There's so much more we want to extend this out to to really bring that capability to make the citizen data scientists. There was a slide this morning that struck me and I'm trying to cut through sort of the marketing and I wonder if you could put it into perspective. Something to the effect of so that everybody in the room can be as smart as the smartest person. Something along those lines, I'm not getting it exact. And I thought, at first I thought, I got a knee-jerk reaction and I said, well, that can never happen. It's always going to be somebody smarter. But then I said, well, actually, if I've got a helper like Watson giving me the insights, I'm going to elevate the rising tide lifts all ships. So what does that mean? I mean, I'm probably botching the tagline, but what does that mean that everybody in the room is as smart as the smartest person? So I think for us it means that when you're working, you're collaborating. And the whole organization needs to collaborate together and I think what's been the challenge in that whole vacuum of sending things over to the data scientist team and getting them to work on models and getting them to figure out what the right algorithm is and then bringing it back and saying, business, this is what we should do. The idea here is that what we really want to see happen is that you take the hundreds of questions that the smart business people you have working for you, allow them to ask them. Allow them to ask them in natural language. Allow them to have something like Watson actually take that information, take all the plethrough and corpus of data that you've got for your organization. Join it with external data, whether that might be social media data, weather data if you're impacted by that, and actually start bringing all of that information to visual insights. And really what we're trying to do is then, like you said, it's trying to raise that tide, trying to bring everyone up to a level where they can have a more intelligent conversation, right? You talk to a marketer and you say, well, what's a great customer? Who's going to give you the highest customer lifetime value? Well, today they may have a gut feel and in many cases, most of the long serving marketers will have that gut feel pretty much nailed. But what if we could give them 10% incremental insight and make them that much smarter by giving them slightly different visualizations and guided discovery to know what are those other variables that I didn't know about? What's actually impacting my customer lifetime value? If you're an insurer and you start only focusing on premiums and you start focusing on the highest level of premiums, are you missing the boat, right? Could it be that there's a specific population that is a long serving customer of yours and they may only have basic rate coverage? It's those types of insights that we're trying to make really visible across the organization so that when that marketer then has the answer and they can have that conversation with the C level executive, that they can actually all be talking the same language and further when they go to put this in place in action in a decision management system, as you rightly put, which have been lacking, is actually a continuum that allows you to say, here's what we want to really focus on. Now my data scientist, the one that really has spent all that time getting the PhD, can sit there and actually work on something that's more meaningful. It really comes down to time to value as well as that upgrade of people's knowledge and awareness, really of seeing something that really makes sense to their business, to their questions. And I think the natural language interaction, the ability to create the insights dynamically for them and sort of point them to things that they didn't necessarily see in terms of the patterns in the data, I think that's going to be very compelling in bringing and elevating those users up to just a different level of ability to execute for their businesses. So John, how does it actually work? I mean, you're sitting down with an analyst who maybe's never played with one of these tools or even a line of business person. How do you get them started? And what's your kind of experiences to their kind of aha moment? Do they have to have an aha moment? Do they generally find one? Is it the natural language aspect based on their experience with Google and kind of their not work world? How does it actually work on the ground to get that person to try it and then to get some positive feedback so they get into a nice loop? Well, I think we started it out with offering it in a freemium model. We started it out for that reason because we do believe that people will maybe not have an aha moment the first time they use it. We want them to. I mean, we're designing it to try to do that. So what's really happening in the solution and in the service is we're actually interrogating all that data that they bring to the table. And most times a professional has some type of list of information, some type of rectangular, commoner sort of set of data. And when they load that into Watson Analytics, what it's really doing right away is it's unpacking that and it's looking at it and interrogating it. And with our experience of analytics over time, it's applying algorithms to say, what are some of the pieces here that really matter? And so what we hope some of the first aha moments would be is when people see what kind of quality their data is.  And so starting with data quality, starting with the ability to start visualizing starting points where I think everyone comes to the table with a hypothesis, they come with a question in mind, now they can ask it, right? And sometimes that's just the aha that I ask it and the visualization coach says, oh, you mentioned state and you mentioned this, let me show you a map to represent what you're looking for. Or let me show you a tree map or a heat map. And it dynamically does that. It does that without the user having to really know too much about the system. So sometimes that's it. But then you get other users and I'm thinking about Legends Hospitality who's got a publicly available quote for us and their big thing was they were spending months and months trying to find the variables that mattered to a particular business problem. And what they found in a week's time in days of playing with Watson analytics and looking at their data slightly differently than they had before, they got the answer that they were trying to accomplish for the last six months. And so when you start hearing those types of messaging from customers, you start realizing that the all-home moment may not be right away. But definitely in terms of time, we can start creating that all-home moment that much faster for them in trying to get the insight. And so for that basic analyst, someone who's not used these tools outside of maybe a spreadsheet type tool, maybe a visualization tool that helps and build pretty graphics, they can now interact with it. And we can guide them. We can give them that guidance, which I think is starting to make that difference for them. What about? I'm sorry. I always had my visualization question, which I think is funny. I was just going to ask that. A billion of, I'm just wondering how do you visually represent a billion of anything to get a meaningful something on a picture? You know, it's such a great question, guys. And it's something that we continue to work on. I mean, this is part of the journey for us. So when we started down this project, we do a lot of research and development at IBM, as you guys know. We started the visualization engine sort of coach, if you will. We started that project about four years ago. So it's definitely matured a lot in the four years before we brought it to market in Watson Analytics. But what we really do is we're using a series of algorithms and there's actually a whole set of standards that a German body has kind of created to say, what's the best way to display things? What are the right colors? And we're adopting those into our rapidly adapted visualization engine. And what that allows us to do is when someone asks the question and there's break down, we're immediately going to start leaning towards giving you a breakdown, which is really a tree map. You know, when they do compare, I mean, that's when you want to see the side-by-side bar. When you want to see a ratio, right? That might be a donut chart or something where you can sow the percentage of the hole and the different metrics. So we've learned and we've taken all of that learning over the last four years, plus, you know, all the corpus of information that's available to us from standard bodies and whatnot, and pushed it into a smart algorithm. And so it's hard. It's not easy to get it right, but we'd like to think that we're at least giving you a series of relevant opportunities to visualize what you were looking for. And the visualization engine is part of Watson, is that right? Yes, that's right. And that's relatively new for this whole space. Yeah, yeah, exactly. It's not just, you know, an engine that creates pie charts. It's an engine that actually thinks about the data set. It thinks about what the user's asked.  So as business users, you can start writing that much more into it and how you want to present your information and create a common way for your users to adopt visualization so that you can actually create standards inside of a business. So I got to ask the competitive question because there's a lot of upstarts, talk to speaking of visualization, that sort of take pot shots at the Cognos and the whole BI world and building cubes and it's slow and cumbersome lacks the visualization. How do you respond to that? Well, I'd say that we're entering into a fairly new market in terms of almost a data-less approach, model-less approach, maybe might even be more accurate. And so for us, the hard part is to make sure that we can carry all those customers that we've got into this new world into a new way of thinking and a new way of managing their information and a new way to know. And I would say that what we're really trying to design is that model by which you can take all the greatness you've created over time and use it, but also be able to work in a much more flexible situation. One thing you'll notice is when you start working with Watson Analytics and you're exploring the data, just as an example, we dynamically create hierarchies where we think they make sense, but we also give you the ability to make the hierarchy for yourself. So you think of retailers and a traditional hierarchy in a more older school technology is likely to put year, then months and days, right, and weeks and everything in that chain. Well, there are lots of times when you actually want to invert that and you want to actually look at the same week or the same month across time, across the years. And in more traditional tools, that's a very rigid and hard thing to do. With Watson Analytics, you can do it on the fly yourself really quickly. And when you ask the questions, we'll actually dynamically flex the model to get there. So I'd say that we have learned a lot about what our customers are trying to do and we're trying to execute that in Watson Analytics to transform the way they're thinking about their data. Try to map the tool to the behavior as opposed to try to map the behavior to the tool. Exactly. What I tell what you call clients, what I call practitioners is if you look at IBM's portfolio, it's kind of better to have overlap than it is to have gaps, right?  It seems to me that Watson is this glue that lays over that portfolio and allows you to sort of unlock the power through simpler analytics, through visualization, through just more powerful cognitive computing. Is that the right way to think of it that you're unlocking that power or is that integration sort of far off? It's definitely not far off. Some of it's already done and completed and in market, which is great. And I think it is the right way to think about how we want to transform analytics, how we want to make sure that we can get easy analytics, we can get the simple and smart analytics. Because really, when we look at all the wealth of, my good friend Doug Barton who works for us calls it an embarrassment of riches. I mean, when you look at all the capabilities we've got, it really is phenomenal. And what we're trying to do is we're trying to, as we evolve how users are working, as we create a new model for them to be working, we need to make sure that we unlock their potential. And some of that means that we've got to rely on some of these more heritage longer serving products for IBM as we make that transition. So what you see is the ability to right away right now go in and actually connect to your Cognos environment and pull in information and do further exploration. Some of the visual storytelling is actually overlapped very nicely where when you go into business intelligence and you're looking at workspace and then you go and look at assemble, there's a lot of those same concepts of dragging and dropping and asking or doing intent-based offering where you're asking for data to just serve you with the visualization coach. So we are trying to make Watson analytics that forefront for the masses. And as we're doing that, we want to make sure that the people who have invested with us on some of these other tools also get some of that capability. And obviously we're here at Vision and sort of a relatively narrow focus on sales performance management and GRC, but then there's this whole Hadoop space that IBM plays in. That's another corpus of data that Watson analytics can analyze. Exactly, I mean, we at some point, at different points we talk about the styles of data that we're really trying to deal with. Most of the analytics people want to do today in terms of the line of business is definitely in more structure than unstructured. But there is a huge plethora of unstructured data out there. If you look at what we did with the Twitter relationship and why it's in Watson analytics, so that we can actually curate something that's meaningful. Just looking at the tweets over time is probably not as effective as if you could look at the tweets and the surrounding hashtags that were also in that tweet along with sentiment, along with being able to look at some of the demographics of who's actually communicating with those hashtags is important. So we like to think that anything that is related around the big data story, the Hadoop platform, anything that's unstructured, there's immediate value to present it in a slightly different way. We work very closely with the Watson Explorer team and a few other of the Watson teams as well as the big data insights teams to make sure that we can actually have a nice handshake of when we're looking at data in different formats and different styles. So we're out of time, John, but last question. Break out the binoculars or even the telescope. Where do you see this business going in the next five to 10 years? I mean, technology is moving so fast. Cognitive computing is really, you know, coming to real, it's becoming real. Where do you see this business going in the next five or 10 years? Well, I think probably the single biggest thing that I could say is that we are truly going to transform the way that people work and where they work and how they work. And I guess what I'm really trying to get at is form factor does not matter and it hasn't mattered for a while. But now you're getting to the point where you're going to want to be able to do voice search and get answers and insights quickly, immediately. You're going to want to have assist solution that allows you to ask a question and you may not have access to all the information today but one that suggests to you the information that will matter to your analysis. So if you're one of our good customers who talks about us every once in a while, the Cincinnati Zoo who's been up here at Vision a few times for us, talks about the fact that they started with our technology stack and being able to understand that actually having the ice cream bar open at 11 in the morning before lunchtime actually made them just as much money as having it open all afternoon. So they might as well have it open at the right times. But now imagine if you could be factoring in all this other external influencing data, what's happening politically around the region where that particular environment is, what's happening and whether, all of these things. So I think we're going to move into a model where people are truly asking questions of their data and the cognitive computing pieces going to be resolving that question and guiding you to information you didn't know you should have access to. And that's, you know, it's not about the form factor, it's about the corpus of data that we're going to present to you and how you end up using it. IBM is a camelot of analytics for the embarrassment of riches. John, thanks very much for coming on theCUBE. It was really a pleasure having you. That's great, Dave, Jeff. Thank you so much for having us and I hope you enjoy Vision 2015. Absolutely. All right, keep it right there, everybody. We'll be back with our next guest. This is theCUBE, we're live, Vision 2015. Be right back.