Agricultural Research Data Network . ARDN Conference and Workshops. June 7-9, 2022 ARDN History and Overview. Presented by Cheryl Porter, University of Florida. Thanks. So, let’s just highlight some of the technical agriculture research institutes, agricultural research institutions, indian journal of agricultural research difficulties. This is the first in-person meeting at the National Agricultural Library since pre-pandemic time, so we are getting all of the bugs out of the technical agriculture research institutes, agricultural research institutions, indian journal of agricultural research systems and learning what works and what doesn’t work so it’s been a very interesting week. But this this picture shows our kickoff meeting in December 2019 for the current project agriculture research institutes, agricultural research institutions, indian journal of agricultural research that we re funded on that allowed us to develop the tools and protocols and methods for ARDN – the Agricultural Research Data Network. So that date is a little auspicious. That’s, you know, a couple months later we were all locked down into these little boxes, and for the remainder of the project agriculture research institutes, agricultural research institutions, indian journal of agricultural research until today, or until this week, we were doing our collaborative research solely through Zoom, right, so and I know everybody around the world has had the same exact problems, but we found ways to make it work by holding these mini hackathons. We would get together on Zoom once a month for pretty much all-day meeting to work through the design of the infrastructure, implementation, and the data, protocols that we’ve been developing. But the ARDN story goes back long before the beginning of this current NIFA-funded project agriculture research institutes, agricultural research institutions, indian journal of agricultural research. So, it a lot of the tools and methods that we’re using come from AgMIP, which is the Agricultural Model agriculture research institutes, agricultural research institutions, indian journal of agricultural research Intercomparison Improvement Project agriculture research institutes, agricultural research institutions, indian journal of agricultural research, and even before that. So, prior to AgMIP, model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching groups around the world didn’t always get along well together or cooperate on things. There was little to no protocols for applying model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, and comparing model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, and improving model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs. It wasn’t quite as bad as the shootout at that OK Corral like this picture shows, but you know… these groups were not working well together. So, in 2008 at a workshop in St. Pete Beach, Florida, the origins of AgMIP were conceived in a hallway conversation between Jim Jones, Cynthia Rosenzweig, Jerry Hatfield, and John Antle, and they decided that we need a long-term rigorous way of improving model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, improving all model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, and coming up with ways that we can better characterize the responses that we model agriculture research institutes, agricultural research institutions, indian journal of agricultural research to things like climate change.
And without that rigorous long-term systematic approach, we weren’t going to be able to solve some of the big problems that we face. And so, that began AgMIP. First funding came through in 2010 from ARS to get the first workshop going. And of course, what we’re concerned with here is that AgMIP has a lot of moving parts. But what we’re concerned but here in ARDN is the data interoperability protocols that were developed to support ensemble model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching activities at AgMIP. And so AgMIP really is a global community of science. There were scientists from all over the world, many different model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching communities. And so, that included climate model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching, crop and livestock model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching, and socio-economic levels, so what it is all about crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching. And this just shows some of the pictures, group pictures of meetings around the world. A few very high-profile papers have come out of the AgMIP crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural research comparisons. This is the first of those papers, where a lot of the protocols were hammered out. This is Senthold Asseng publishing in, and I think about 50 co-authors, publishing in Nature Climate Change , about the outcome of ensemble model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, so they did a wheat model agriculture research institutes, agricultural research institutions, indian journal of agricultural research intercomparison, and there were 30 wheat model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs that they used. Nobody even knew 30 wheat model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs existed in the world at that time, but they all got together, they did the intercomparison and learn some pretty interesting things. One of them being that the median of the crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural research predictions was more accurate than any single model agriculture research institutes, agricultural research institutions, indian journal of agricultural research across many different environments. Nobody wanted to do that. And we got consistent messages from other model agriculture research institutes, agricultural research institutions, indian journal of agricultural research intercomparisons for rice and maze. And these intercomparisons are continuing largely on funded volunteer work.
This is the latest model agriculture research institutes, agricultural research institutions, indian journal of agricultural research intercomparison table that I’m aware of that just came out a few months ago on the soybean intercomparison. Just to illustrate what it is, and AgMIP is still very active. So, what did we learn from AgMIP. A couple of the things we learned is the need for uniform data sources to provide to these ensembles of model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs for model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching activities. And so, AgMIP had to develop methods that would allow that to happen. We needed a consistent vocabulary. So, not just for model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching, crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching data, but for model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchers of different domains to talk to each other. For instance, we went from climate data and model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs to crop and livestock model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs to socio-economic model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, and just a term is as benign as baseline means something totally different to the crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchers and to socio-economic model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchers, so we had to lay down this common vocabulary. Another thing we learned is that everybody already had certain way of doing things, we you know we came when we started out talking about data interoperability and AgMIP, we really thought we could impose a new standard that everybody would conform to, and everybody would be happy about doing that. Well, we quickly learned that that’s not the case, and that there’s already enough standards out there. One more standard is just going to cause you know create one more standard, so what we needed to do was learn how to work with people the way that they already do this and be able to transfer data from one protocol to another easily. And another lesson we learned is that the protocols that were developed by a community for a community result in acceptance and adoption by that community. So somewhere along the way, during the AgMIP times we had a USDA AgMIP data harmonization workshop, right here, in this building, in this room at NAL back in May 2015, for the purposes of taking some of these AgMIP protocols, applying them to USDA data. I can remember Jerry Hatfield himself coding up his data for use in AgMIP, like he didn’t even delegate it to somebody else, he did it all by himself at this workshop.
So that was pretty cool. We talked about vocabularies for different agricultural domains, we worked with a dairy person. We work with pests and disease data. And so that was, that was one of the first times that we took AgMIP protocols and trying to really work with a wider community. Then about 2017, we started working with the CGIAR, the Consultative Group for the International Agricultural Research, so these are the big international agricultural research facilities around the world, and they were developing this big data platform. And so Medha Devare was Project agriculture research institutes, agricultural research institutions, indian journal of agricultural research Manager for one component of that big data platform, and we worked with her on these data annotation protocols for their system, that now have been wrapped-up into what we’re doing in the current project agriculture research institutes, agricultural research institutions, indian journal of agricultural research, ARDN. Medha is going to talk right after lunch today. She’s going to present how, how they have implemented VMapper and some of our other ARDN tools into their share data workflow within CGIAR. So it’s really cool that what we’re doing here with USDA is compatible with what’s being done at international research institutions. And then, 2019, we started this three year and NIFA funded project agriculture research institutes, agricultural research institutions, indian journal of agricultural research for Agricultural Research Data Network. So, it’s building on all of these other things. It’s building on what was done In AgMIP and what was done with the CGIAR. We extended the data annotation protocols for use on Ag Data Commons, built some more robust translators. And basically, we’re wrapping up, at this meeting today we’re wrapping-up that three-year project agriculture research institutes, agricultural research institutions, indian journal of agricultural research and presenting the work that we’ve done. And just one more thing to note, IFDC the International Fertilizer Development Center, has been a partner from the beginning, when we were working with AgMIP, all the way through present day with co-developing methods and tools and also sort of being our guinea pig for implementing some of these translators and making the vocabulary more robust for the types of data that they collect with their , especially with micronutrients and such.
Okay so, to describe what are motivating you know what really motivates our desire to work on data interoperability for agricultural research data is that we recognize that there’s a huge gap between the potential value of data that we get from agricultural research, and the value that we’re actually getting from those data. And so, as an example the data are collected in an experiment, there used to, for some very specific research purpose. You write a paper, and you get it published people cite it and the data behind that often are lost after the original research is over. And so, we feel like if we can collect those data across many different years across many different locations, different genetics, different management and pull them all together and use it as a collective resource so those data could have much greater value. And so, in the FAIR data principles, we are working primarily in this with Interoperable and Reusable. So, what we’re trying to do is bridge the gap between the supply of data on one hand, and the demand for data on the other. And with some of the challenges being that these data are in different physical locations they use different vocabularies, different formats, schemas, and so on. So how can we take these data in the condition that they exist, but still be able to interpret them, combine them and use them for further research. So, we started with some existing AgMIP translators. So, this is sort of the end user side, the data demand side. We added these annotation files using standard vocabularies. We interpret the contents and the format of the datasets in an automated way: once we annotated them, we understand what is contained within the datasets. We added some new translators for conversion from the raw data to this AgMIP format this standardized format that was designed for AgMIP and add some additional translators for end users.
So, this slide shows, one of the use cases that we developed for the project agriculture research institutes, agricultural research institutions, indian journal of agricultural research. In this case, we have a crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researcher who wants to do a search for her study in Tanzania and this this depiction is with the Guardian CGIAR data and publication portal. It could just as easily have been Ag Data Commons; I m showing you because the concept is exactly the same. So she puts in a few search terms, she wants her data to be organized, she wants it to be compatible with ARDN so that she can get model agriculture research institutes, agricultural research institutions, indian journal of agricultural research ready data out of it. So within the Guardian API and search engine, it goes out and checks through her search terms, it’s looking at these data annotation files that are available for data sets on the portal, identifying some external data sets are sitting in remote repositories, perhaps, and bringing the data together, and then translating it for the specific crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs that she was interested in. It’s never quite this cut and dry. For example, even very, very detailed datasets, don’t have every single piece of information that you need to run a model agriculture research institutes, agricultural research institutions, indian journal of agricultural research. So there’s always sort of an iterative process to prepare those data, fill in the missing gaps, and go about your model agriculture research institutes, agricultural research institutions, indian journal of agricultural research, but the ARDN tools can get us, you know, 90 95% of the way there. The second use case that we use to drive the work that we’re doing, is that we envision that if you could collect enough information from field crop experiments that you could combine across the environment and management and genetics, that that could help drive the development of new gene based model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, because now if we could take these data and combine them with genetic data, you can actually build model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs that will take the genetic data and predict what would happen in any environment, even without growing a crop there. So there are folks working on this already, there’s a lot of work that have been done on this. It’s sort of in its infancy, but it requires a tremendous amount of data. OK, so the elements that we need to build a system.
First of all, we wanted to use the existing technology that was available through AgMIP. We want to what the system to work with legacy data sets. So that is a data set that may have been archived in some repository it’s sitting out there with a DOI, you can’t really change the format of the data set, easily because it’s, you know, it’s a permanent archive, but we can annotate it. We also want it to work with new data set. So, as we’re collecting new data, if you can do that with the proper vocabulary and the correct format, you may not need the annotation at all, but we can still use the AgMIP tools to translate it. We want the system to be not proprietary, easily deployed, and platform independent. So, I think we’ve checked all of those. We wanted to work with existing standards, using a hub and spoke configuration. So, on the right-hand side I ve got an illustration of what that means. So, if you see the blue circles as being sources of data, and the orange circles as being somebody who demands data in a certain format, the point to point would require a lot of translators to get from every single format on the left to the every single format on the right. Whereas the hub and spoke allows us to go to the centralized hub format, which is the AgMIP crop experiment format, and have a few translators for a few data sources and a few translators for a few end-user formats, much more efficient way of handling data. Okay, so I talked about this data annotation and this slide describes what that means. So, we’ve got in this case we re showing the Ag Data Commons is being the central repository. There’s some core metadata that already sits on Ag Data Commons with every dataset, and we propose to add some additional you can think of it as extended metadata in the form of these three sidecar files. OK, so this file, the sidecar file one, has variables in the dataset map to ontological terms. So, this allows data contents to be discovered through the Semantic Web.
This was really important for the CGIAR, and that was developed during the phase where we worked with the big data platform. It was less important for the National Agricultural Library, so we have not fully implemented that one in this project agriculture research institutes, agricultural research institutions, indian journal of agricultural research. Sidecar file 2 is where we spent most of our time. This is the roadmap for translation. This gives us the ability to annotate the contents of a dataset. Physically, where is the data located a URL where the data can the raw data exist, where in the data set, which files in that data set are appropriate for this particular end use, were physically on each sheet which robot starts and ends with and also mapping of each column to an ICASA variable. So ICASA, and I’ll get into that a little bit later, is our vocabulary that’s been adopted for AgMIP. Sidecar file three is a subset of the data, data which allows us to search, and index, the data, allowing fast complex search and discovery of datasets, even without going out to the remote repository, where the data may be physically located. So ARDN data has a few characteristics. These are typically Agronomic Research Data from field crop experiments, perhaps from farm survey data or household survey data. They are point based, you know, each piece of information is associated with a latitude and longitude so that is opposed to the sort of aggregated statistical type data that you might get from NASS, for example, these are point-based data. These are quantitative data. So we’re interested in data that can be used in analytics, or model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching. And they are associated, because they re point data, they re associated with an environment, which is described by soil, soil properties, and typically daily weather data to describe that environment. A lot of experimental data do not include the soil and weather data, well, you know, it’s still acceptable to ARDN-ize” data but ideally, it would include these environmental data as well. And the vocabulary that we use to describe ARDN data is defined by the ICASA data dictionary.
So ICASA stands for the International Consortium for Agricultural Systems Analysis, that entity doesn’t exist anymore, but it left this rich legacy of a data dictionary that was originally developed to document field crop experiments, but was I feel also for use in describing the kinds of terms that we use in AgMIP, and ARDN. And we feel that these the methods, and the software developed for ARDN could be applied to other domains fairly easily. You can you know, the idea is that you can kind of unplug a vocabulary and plug-in a different vocabulary but still use these tools to get the same goals of data harmonization. So over on the right we have in the box there is a list of a few of the data sets that we have ARDN-ized as part of this project agriculture research institutes, agricultural research institutions, indian journal of agricultural research. Most of these are up on the Ag Data Commons now with the annotation files. So, these vary a lot in extent, and the type of research that was done with these data, including Long-term Ecological Research Data from the Kellogg Biological Station, the corn cap data it has been annotated and it’s up on the Ag Data Commons now, variety trials, so this is quite different from detailed field experiments, but extremely valuable and make you have cultivar information from multiple environments over multiple years and there’s lots of data. The TERRA-REF is high throughput phenotypic data from a field in Maricopa, Arizona. So also, very valuable data, and what’s interesting about it is the data is fed from the breeding API, or BrAPI, BrAPI endpoint, and, which makes it a little bit different on access of these data. And there s a few datasets that have been added, as we ve gone through this process. So, this is the ARDN data sharing workflows. So, there’s four interconnecting workflows that are shown here. Starting with the ARDN data provider tools. So, prior to today, prior to the conference today, we had two one-day workshops, working with ARDN tools. So, on a Tuesday workshop we had groups, and you know many of you may have been involved in that, but we had groups working with these data provider tools, VMapper in particular, to create the annotations for a data set or multiple datasets.
So, yeah, that was a good opportunity, will hear more about how the workshop went a little bit later today. And then we have a search and discovery workflow, where data from ARDN datasets are made available through these additional sidecar files one and three to the greater world. So, these are these are search tools that are based on the additional sidecar files that were developed for ARDN. On the upper right, we have the data end-user tools. So, mostly these were developed with AgMIP data interoperability. So these are geared towards individual crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, so it translates from the central format, ACEB format, to the very specific format required by different crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs. And we’ve added some tabular data outputs for analytics. Also, we’ve talked to data analysts, and they really like the ACEB format itself, it’s a JSON format, and they really prefer to do analytics directly from the JSON format. So, there’s various different end-user format, they’re not even all listed on this figure. And then sitting in the middle of everything is the data Translation API. So this takes the raw data and the annotation in the sidecar file two – so remember that sort of the roadmap for translation – and it has a translator that puts it into this hub format, that ACEB format. It also has in AgMIP translators that allow data to be prepared for end users, and it has that sidecar file three extractor, which extracts out of the ACEB the data needed for search and discovery. So I’m going to talk a little bit about the data provider tools. We’ve got ARDN Data Tools at data.agmip.org/ardn/. And the VMapper tool like I said, we spent the workshop on Tuesday talking about VMapper, and then Wednesday, yesterday, we worked on what’s called the Data Factory. Those of you who are were involved in some of the AgMIP regional integrated assessments, remember a tool called QuadUI Desktop tool.
This Data Factory here is actually QuadUI in a browser-based application. So let’s look at VMapper a little bit. So VMapper, it’s like this is the screenshot from it. It brings up your data, it allows you to annotate column by column the raw data headers to the ICASA variable names. It allows you to convert your units. It checks for dimensional capability, or compatibility. It allows you, if you’ve got your data organized in multiple tables, which many datasets many of the large, complex extensive datasets do, it will detect relational linkages between your tables and allow you also to check that and create your own linkages. You can do things like creating new very new columns of variables from existing columns of variables, if your data set doesn’t have exactly the right variable to map to ICASA and allows you to do some user-defined data transformation. So, if it’s a pretty nice tool, it’s something we did not have in AgMIP in the early days, when we were developing data interoperability tools, and it’s something that we wanted for a long time, and this project agriculture research institutes, agricultural research institutions, indian journal of agricultural research finally was able to allow us to get there. And a little bit about AgMIP end-user tools. So, these were designed to bring data in from multiple inconsistent data sources into a hub format, which you can see I know all of you in Zoom can’t see my laser pointer but in the middle is this harmonized data format that we’re bringing data into, so that’s sort of that hub for data exchange. And in addition to the data from the these multiple data sources, we wanted a way to provide data that was missing from the data sets, but we need it for model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching and so the little Einstein guy over there on the right, that’s our that’s our local expert, who knows the agronomic systems that were studying, and who we asked questions about..well, what is the row spacing of peanut in northern , things like that that you don’t get from the data but maybe you need for a model agriculture research institutes, agricultural research institutions, indian journal of agricultural research.
So that set of assumptions is combined with the actual data into the crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural research translators that then translate information to the very specific formats needed for multiple model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs. And we went a little bit further with AgMIP and we harvested the outputs from the crop model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs and put them into a harmonized format as well. All this was done in a series of hackathons. This one shows it’s at Texas Advanced Computer Center in 2013. And we ended up with a library or libraries of data translation tools for all these different model agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs that could be then deployed in desktop applications, and in breeding model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching applications, and in browser-based application, so it was very flexible way to deploy these tools. Okay, so the next thing I want to show is the Data Factory This is I’ve mentioned it already This is where we had a workshop on Wednesday, yesterday, to take the data that had been harmonized using our tools, and actually apply it and model agriculture research institutes, agricultural research institutions, indian journal of agricultural research so basically using the AgMIP translators to do that. This is a screenshot of what that Data Factory looks like. It’s, again, available at data.agmip.org/ardn. You re loading your data that you’ve prepared previously. You load in what we call a field overlaid DOME, which is the best assumptions for the data that are missing from the dataset, linkage file to combine those two data streams in the correct way, and then finally, it allows us to output to whatever formats we want that are within translators that we have for end users. So, in this case we have translators selected for DSSAT, APSM, and STICS. Okay so through this data workflow process that we’ve got, you know this end-to-end where you annotate data, and then translate it to end-user formats, we end up with some artifacts. And so, we start out with the raw data, and so we archive, and we annotate that data with our sidecar files 1, 2, and 3, those are all in JSON format. They’re not particularly human-readable but typically a human doesn t have to read them.
And then when we translate those data to that hub format, we get an ACEB file, ACEB stands for AgMIP Crop Experiment Binary. So that’s another artifact that we end up with. We have supplemental data files that allow us to provide those assumptions, so filling-in missing missing data before we try to model agriculture research institutes, agricultural research institutions, indian journal of agricultural research this experiment. We can end up with model agriculture research institutes, agricultural research institutions, indian journal of agricultural research-ready input files so that’s what that Data Factory gives us, as it takes the DOME and ACEB and translates it to a model agriculture research institutes, agricultural research institutions, indian journal of agricultural research-ready format. And then finally we end up with model agriculture research institutes, agricultural research institutions, indian journal of agricultural research output files after you run the simulations. So, all of these data artifacts are related to that original data. Okay, we want to have the ability to archive those and link them to the data that they originally came from and give credit to that original dataset. So, this, this workflow is a little bit messy but the big thing I want to emphasize here is the gray box on the right, that says Ag Data Commons data, metadata, and workflow. So, what we have here is within Ag Data Commons, we have the original data set. But we also have a separate dataset that is ARDN products. Okay. and these are .they are linked. This is this is linked as a in Ag Data Commons terminology it s a resource, it’s a data resource. And so because it is a data resource and it’s linked to the original data, and it can include the sidecar file two and the sidecar file three, the ACEB. It can even include the model agriculture research institutes, agricultural research institutions, indian journal of agricultural research input files, and a model agriculture research institutes, agricultural research institutions, indian journal of agricultural research output file, it could include multiple FCTs, might have two people that made different assumptions about the model agriculture research institutes, agricultural research institutions, indian journal of agricultural research. So, all of these things are associated with that original dataset on Ag Data Commons. That’s how it’s all linked together. We also in the yellow box there is sort of a procedure for iteratively preparing that sidecar file two, and when it’s ready, all your data and your sidecar file two can be archived and so on. So, all of the ARDN tools, data, protocols, apps, information, methods, everything is open source.
We want people to access these, we want them to be used. We hope that people will use them and add to them, and give back, you know, using to the GitHub repositories. So, these software products were developed by and for the community of users. We’ve gotten a lot of input from people on how to do this. That came out of AgMIP, which definitely was, you know, a global community to develop these methods and tools. So back in the early days of AgMIP, we joked that we were building the plane in flight, because the tools that we were developing at that time were needed right away by the people who were doing regional integrated assessment. So, a lot of we always felt like we didn’t have enough time to build this infrastructure. Now this ARDN project agriculture research institutes, agricultural research institutions, indian journal of agricultural research has allowed us to create this sleek new product. And I’m totally kidding. This is where we’d like to be. We’re not there yet, right, we were on our way there. We have a long way to go still. . Yeah, but I think this is what we have, actually, is this firm foundation for Now we’ve got the basic tools that allow us to annotate data, but we’ve got a long way to go in implementing these tools and getting a critical mass of datasets, organized such that they really are useful. We have to have some demonstration project agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs, where we actually use these data and big model agriculture research institutes, agricultural research institutions, indian journal of agricultural researching project agriculture research institutes, agricultural research institutions, indian journal of agricultural researchs. So, we still have a long way to go, but I do feel like now we’ve got this firm foundation that we can work from. And this afternoon, our, our wrap-up session will be about just this -where do we need to go next, what.. what’s left and how can we accomplish it. So, I hope that everybody can join at that time. So, I don’t know how our time is doing but do we have any time for questions? Okay. So, are there any questions from anybody? I know most of the folks in this room have heard this, all before, but hopefully it might be something new to some folks that are connected remotely.
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