Good afternoon everyone, welcome to the webinar this afternoon. My name is Amir Aryani, I'm working for ANZ and with myself today I have Dr. Jinbo Wang from NCI who will be the co-presenter in this talk. We are going to talk about the Neo4j technology that we use as part of the research data switchboard and I will give some background, we will talk about some technical aspects of this and then Jinbo will talk about the NCI implementation of this technology. So the agenda for the talk would be the background on the research data switchboard and the research graph which is the data modeling behind this. Then we will talk about the Neo4j queries, I have allocated something about 10 to 15 minutes on the technical side of this and then we go to the NCI implementation and at the end we will have time for questions and answers. The background on this work is it is started from the challenge of cross-platform discovery of research data, it goes back to 2004 in the research data alliance working group when we had the problem of finding the related connections. So some of you might have seen this slide, it's actually one of the earlier slides in this work and I actually kept using that because it actually shows the earlier stage of the problem and although we have solved this in the research data strategy at a great degree but so many repositories already have this issue. If you have a dataset and you want to know what else in the scholarly communication can be linked to this information, usually the keyboard search is not an efficient way. So in this case for example in the ANTS page in 2014 we had a dataset that was actually in the page for that we had a cross link to a dataset and we were doing the cross keyword search for title and related keywords. Now the problem was the queries that comes back from that was including a lot of false positives. We had in this result we have more than 1000 records connected to the dataset that was supposed to be a recommendation for the researcher that these are related information to this dataset. In practice that is not very useful when you have so many recommendations. So one of the initial ideas behind this was how the Amazon or other retail stores do this by looking at a service because it see also service. If you look at the book they tell you do you want to look at these five other books by the same author or by the same publisher or someone who have already purchased this book also purchased those other books. So usually those recommendations are very precise and are very limited. So you recommend three or five options not 1000 options to the end user. In this context we started a working group in the early days. These are the initial partners that joined the group at the time to basically address this cross platform discovery challenge. We had a significant contribution from the dry out certain data site and later on we had open air and other partners joined us. In Australia we had the collaboration of NCI and also University of Sydney. And also there are other universities who have been involved in this project like RMIT or Vivo corner in earlier stages of the working group. Now this is about the research data alliance and usually when I get to this part of the presentation, I talk about the structure of research data alliance. I think in the scope of this talk we don't have that much time for this. But just as a brief note that research data alliance is a joint venture by main funders that they invest in the data infrastructure. And the main goal is that the people who actually work on different projects, they need to coordinate and collaborate. Now there are different working groups and each working group have different area that they work on those. It's almost like projects. Now in this environment of the research data alliance we had a working group which started from 2014 and concluded the main deliverables in 2015. And we are at the moment continuing to maintain the work and extending the platform. Now the working group, the main recommendation after multiple prototypes was the data sets can be connected using the co-authorship model. And although in principle it's a simple idea but in practice it requires connecting information across different infrastructures. Now when we were doing this at a time we were looking at the first stage of this process was looking at how this can be done by librarians. And that's where we actually went to the process of asking a librarian or actually we had a case study group to look at this process and tell us what they will do so we learn from their practice. So in this context this is what they call it new version of the research data alliance after couple of updates unfortunately I didn't get the screen shot from the same data set in 2014 so I have to go have a new photo. This data set is from University of Sydney and when you as a researcher or someone works in a library you look at this data set you can identify the researcher that has worked or contributed to this data set. Well you can actually search for that person on Google. You can find the researcher and the profile page of the researcher including publications. When you go to the publication list you can if you want read every single paper and in the content of those paper you will find data sets in other repositories as been cited or mentioned. So in this case we have a data set from the drawout repository. Now in the drawout repository you can actually see the same researcher although with different name abbreviation and the publication is basically disconnected from University of Sydney. So the logo of University of Sydney are just put together in this slide to emphasize the connection but in practice when you are in drawout environment you don't know that this work is connected to a researcher in University of Sydney. So if we go to the chain of all of these connections we will get something like we go from a data set in hands to a researcher in University of Sydney with article in plus one to a data set in drawout repository. Now this process the first activity of the group was to actually demonstrate that it works so we went around about we had about 250 collections at a time. Then we did a study around that and we established the links. But obviously it is not a scalable model you cannot do this for every single data set and every single researcher. So we decided to use machines and in this context we so the goal here was we wanted to have a solution without spending too much time on the research and inventing the standards. So we adopted all the standards that we could from other groups and other platforms and we tried to implement something that it is simple to adopt by others and also easy to maintain. So the overall structure is basically have three different layers. The first one is a harvesting layer. It's basically is OEI-PMA and it reads a couple of different formats Dublin Core is the most obvious one but also it reads FCS, it reads Mark 21 and DLI, DDI and couple of other ones from the international repository. Now the list of these actually is in the working group list which I believe I have a link to this further in the slides. The harvesting layer put the information into a set of machines that in this case we implemented them in Amazon but you can do this on Nexar or any other high performance computing platform. The main function that's required from those platforms that they should be able to run Java programs because everything is implemented in Java. And what those programs do is that they basically read the information from all of those points and connect them together when the connection is possible. It resolves the identifiers, it uses the cross-reference integration to get the metadata from the DOIs, the same thing for data side, it does the same thing for ORCID, it has a Google API integration when we have the grant or paper and we actually search for certain domains in universities to find the profile pages. We use this one for some level of disambiguation and then we are doing something called node linking that goes across the graph and link the nodes that there is a there's an inference component for those connections to happen. Again I cannot actually go to the detail of this, there is a document in the working group recommendation that talks about a relationship that's called known as. So if you find two different elements that those elements either have the same identifier that includes any kind of UI or there are the same title with the similarity in other elements, we link them as known as elements and then in the node linking a stage they actually link them together. Now the point is all of that information would get together in one database and in the case of our project we use Neo4j. The main reason for that was one, the simplicity of implementation, it was very easy to hire programmers who can actually code Java and who can actually write program for Neo4j and also the performance. So the speed of querying the database from Neo4j is much, much quicker than the if you implement something using the RDMS or use the same thing like a triple store. So the Neo4j was the main point of aggregating all of these connections for us. And in the data access layer after doing something that we call metadata harmonization which basically harmonizing the names as much as possible or by names on the property names as much as possible toward a doubling core metadata we ended up with a graph model that we call it research graph at the time. Now there is one characteristic of this meta model which is different from the other schemas that you're harvesting. This one, first of all it's run mainly by URI, we have the URI for most of our elements where it is possible to convert information to URI. The example of that is converting the DOI to URI, converting the grant ID to parallel. Everywhere that we had the option to come to convert something to URI we have done it. And that enables the scalability of this graph to a distributed graph across multiple platforms. And the other thing is we have the separate relation object which if some of you are familiar with FCS we have the party record, we have a data which is a collection, we have services, we have activities. In this model also we have a relation object. And the advantage, the main advantage of doing this is that it enables connecting the nodes to a URI which actually don't exist in our ecosystem. Example of this is that if you are looking at the data repository in a university you might have actually orchid record as a related identifier. And in this model we just put the relation object that says this record is linked to orchid and we don't have to resolve it. It sits there by the time later on in our inference model we can resolve the record to that orchid identifier and then the graph system actually handles this as a bi-directional relationships between nodes. Now I were not supposed to talk about too much about architecture because we have a lot to talk about the actual Neo4j queries so I'll just go ahead quickly on this topic. There are these are examples of multi-degrees of separation I'll just go through one of them. So two database can be connected if for example if a dataset has the contributor which is an author and that author actually published a paper and that paper cited another dataset. This is what we call three degrees of separation and we have multiple of those based on different scenarios. This is a link to the research graph model in the interest of time I think I will skip this slide without talking about the individual elements there's a link to the schema and if you have a time at the end of the webinar and if you have questions about that I can actually come back and talk about the schema later. And these are the links the slides will be available so you can actually you don't have to write this write the links later on you will get the slides and you can click on these links. The next part of the talk is about Neo4j so that was actually the main motivation for this another review of the switchboard project if you like. We implemented the research data switchboard in multiple different institutions in Australia NCI has adopted that University of Sydney has adopted that ANZ is using this and also in Europe we have multiple partners who are using this technology. Now we came up with the same questions again and again and these are different queries that people ask how can I do this? How can I find my data set using DUI? How can I find all the data sets that are from this particular publisher? So in this part of this presentation I'm gonna walk you through some of these scenarios. Now one thing about the Neo4j is that it has a graph browser and for the purpose of this presentation and also for the research data switchboard we have the extended version of this browser website which has some built-in functionality for the queries related to the scholarly works. Basically if you can think about this as an extended graph explorer for Neo4j. In this environment one of the things that you can do as an example of the Cypher queries is that you can search for a data set. And what I'm going to do is that I'm gonna search for the same data set from Dryer that we just look at it in our example. So here I can say I want to see the DUI where, well let's still look DUI from the slides. So what I said is that give me a data set from Dryer when there is a DUI and that DUI equal to this. When I do that it basically comes back with the kind of red slash orange dot that we present that data's record. You can look at the content of this here. It tells you this is a record from Dryer, this is a title and these are a list of authors. Now what we can do in this environment we can actually query this. But are it double clicking on that or just do one click and then click on the expand button. So here you will get the other information. There are four other data sets in the Dryer environment linked to this. There is a paper which is this one is from plus one article. This was also one of the earlier slides and this is a researcher. Now in this environment I can keep expanding the notes and what they call this traversing through the graph. So here I can see all the publications for that record, for that researcher which in this case is catching the love and then all the grants that you got. And also for these grants some of them actually have connections and I believe one of them is also connected to another data sets. Now this data set, now in this environment if I want to look at the metadata of the record I can actually expand this thing in the bottom of the page and here I can see the title which this is the first data set that you started from it. So back to the point of multi degrees of separation. Here I have a data set that it is linked to a researcher grant and goes all the way back to the initial data set. Now in this domain you can do a set of queries and I actually have a list of things that we need to look at it. So we will going to look at the how to find a data set, how to find a publication grant and researcher, how to find links to orchid record, how to find data sets that have DOI, how to find DOIs using prefix, find highly connected data sets that is using a number of edges on the graph, connections with multiple degrees of separation and find shortest path between two researchers. Now this might end up to be a bit overwhelming with going through all of these cipher queries. What I will do is that if some of these things feel complicated the slides will be available online so you can actually always go and try them and then basically send me an email and we can have an offline conversation about the syntax of the queries. In this case, so we did already check finding a data set by DOI but we can also find the data set by title. So the way that it works is that any node in our research graph model has a property called title and in this case the title for the data set is the one that we get from the metadata record. Now this is a simple query. You can do the same thing for publication. You can actually get the publication record. This is one publication query from a certain database. Now I can go here. I can actually post the same query here and then I can actually get this record from the database. Now one thing I want to point out is that if you see the query that you run it actually can take longer than usual because the size of the graph is a big database and in this case you have about six million nodes in this graph database. And what we have just run here is that it's asked for a string search in a graph database. For some of us who are familiar with the architecture design of databases the graph database is not designed for string search. Now there's a trick about this and then I was doing this I thought it would be a very, very good example. This in this case it goes actually to the six million nodes to find the related element but we could actually do this much better by just making this query more precise. If you know it is from CERN we could just add the CERN namespace here and also we can add the limit number at the end. What it does it says the first one that you found it come back don't go for the rest of this. I know it's only one instance of that. So what I do here if I hit the enter button it comes back immediately. So the way that you make the query have a direct impact on the performance that you get from the graph database. Now this is another example of how you can find the title. This is the same as a data set so there's no complexity about that. There is a, I want to find a record from CERN that is a publication and it has a title and this is a title for that. Now for the grant we have another property that it is useful and that is called Per persisting URL. In Australia for all the ARC and NHMRC grants we have Per which basically is this name space and the grant ID is at the end. Basically you will have Per.org slash AU research slash grants and then a slash if it is ARC would be ARC dash grant number. If it is NHMRC it's gonna be NHMRC dash grant number. So I can actually copy the same query and go here and paste it. But before I paste it I want to show you another thing. Every query that you type you can actually hit this button and add that to your favorite. So I have the list of the favorites of all the queries that I want to run just in case that I forget to type one of them. So in this case I can actually go to this list and click on that it's the same query. I can run it and just to make it quick I say just give me one. Now this is our grant and you can look at the content of the grant here. Now the same thing you can actually search for grant by title obviously. Now for the researchers there are a lot of options. You can search the researcher by first name or last name. You can search the researcher by ORCID and also you can search the researcher by ScopusID. In this system we actually get the ScopusIDs which are linked to ORCID and we index them. So if you are looking for a researcher with a ScopusID you can actually find that and the query would look like this. So you say I want a researcher with a ScopusID equal to this and give me the researcher. Now this query I think now we are getting to the queries that are more complicated. So what we are going to do here we are going to find a connection. So we are not only looking for a particular node I'm looking for a nodes that satisfy the specific criteria. In this case we are looking for a record from DRAIA that is linked to a record from ORCID. Now one thing is if you ever try to copy this information and copy this cycle queries into PowerPoint presentation or Word or other text editors what they do they fiddle around with the format of this dash and other specific characters. So just be mindful on that. When you copy and paste sometimes they are actually being distorted. So back to the topic. In this case we have a dataset and this is from DRAIA and this syntax basically says I want the only nodes from DRAIA that are linked to ORCID and then I want to get the count. I want to know how many they are. So this is actually introduction of another syntax here which is a count element. You can actually rather than returning the nodes you can return the number of nodes. So in this case we have 1,231 DRAIA datasets that are actually, well actually dataset and publications that are included in the ORCID profiles. And the way that we can actually see what they are you can just say return n. Just give me 10 of those records. I get these ones. I can expand one of them randomly. So this publication, now actually this is another approach here I'll explain another thing. Now when you look at the node in the research graph model there is multiple labels for each node. The labels identify the source. In this case we have one publication which came from ORCID and crossref. That means we have two different sources for information that have the metadata for this record and then we merge them. Now in one of the early presentations someone asked me what will happen if there's a conflict about this metadata? What will happen is a title in ORCID is different from title in crossref. The way that we manage this is that there's a priority. It's basically there are different sources that have authority about different information. For example, anything that is related to DOI if that DOI is registered with crossref the information from crossref will override other nodes. So let's say in this case the title in the ORCID record might be modified by the researcher. When we ingest this and then we do the integration with crossref we actually overwrite the information from ORCID by the crossref when it gets to the DOI. Now I know this might actually lead to false negatives but this is actually for us was the most practical way to manage that. Now this publication we can expand it. It goes to the ORCID record. And if we expand the ORCID record we might get some information attached to that which in this case we didn't. So that is just one ORCID record linked to one publication. Going back to our slides. Now this is another type of query that might be useful for many of us is how do I find the records in ANS that are linked to ORCID? So in this case I want to look for records in University of Sydney that been contributed to the Research Data Australia and they have a connection to ORCID. So the only difference here is that I use a new element that called ANS group. Now there is another characteristic of our graph model is that unlike many different, well some schemas have a concept of profile in the graph model we do not have actually a concept of profiling. What we do is that we have a standard sort of field but also the repositories with their own domain specific fields we can ingest them into the system. And that's actually one of the examples that we did for NCR ingesting the organization nodes into the graph structure as they had in the geonetwork data model. And it works because the system is actually agnostic to the metadata elements in the individual nodes. So basically in the graph database you can have a hybrid model. So in this case we have ANS group which is a metadata element from ANS and we say okay that is equal to the University of Sydney and give me 10 records of that. So these are the records from the University of Sydney in ANS which they have a connection to Orchid. So for example this is a dataset. I can expand this. These publications are Crossref publications. I don't see any Orchid records here. Actually yes it is. So this is our Orchid record. Okay so this is another example of actually this is another useful thing. One of the things that you can do in the graph data is you can search for properties. So I want to know all the datasets that they have DUI. And I can actually copy and paste in our graph database system and we can hit the enter button and see how many datasets we have here that has DUI registered with. So the answer is 57,000 records in our database has a property called DUI. Now you can replace this with other properties that you say I want to see all the grants actually they have pearl. And so it's 45,000 grants. Now the other example or one of the queries that we had especially from our European partners was finding the datasets by the prefix under DUI. Basically the goal of this is you want to actually find the datasets from particular journal or from particular publisher. So this actually uses a syntax that's called regular expression. In Neo4j you put a tilde after the equal sign and then you put the dot start at the end which that means everything after that is acceptable. So when I do that it will return a lot of data actually here so I put a limit 10 and it gives me 10 records that those records have this DUI. So you can individually look at the metadata or I can just click on rows and I look at the record for this. All of them have the DUI that matches my criteria. Now another example here would be how we can find the highly connected datasets. So the things are getting more and more complicated here. And I have the good news I have only two of these complicated queries to go. So here what we have done is says, okay I want to see all the datasets based on the number of connections that they have. What we have done is that we say, okay I want to get a dataset actually this query is for and so I want to see all the ands datasets that have the most number of connections. So I say, okay well give me the key for that record give me the title of that so I want to know what it is and I want you to actually count the number of connections that it has sort them by the number of those connections in the reverse order. Do that it goes into the system and comes back quickly. So here we find all the datasets that we have by the number of connections that they actually link to them. So if you actually look at this particular node we will end up 757 nodes linked to that particular dataset. Now this is the last of our queries and this is a scenario actually not sorry I did a lie there is one more. This is an example of finding the links that goes through the multiple degrees of separation. The application of this query is a scenario like this. I am working for ands I'm thinking how many connections do we have to draw up? Well we might have connections directly but we might also have connections indirectly. The example of indirect connections would be I have a dataset or paper in ands registry that is actually claimed by a record or a profile in orbit and that profile in orbit also has connections to draw up. So the syntax for this would be something like this that says I want to get the older connections between one to three. So if I run the query like this and the outcome is return title and the key and only 25 of them what it does it actually returns the node title so this is a link from ands and then we have a dataset key from the draw up. Now the last of our queries are I found this one interesting for a lot of people in the publication domain when they are looking at the researchers or datasets or two different datasets and they're wondering are these two actually connected? What you can do in a graph database you can search for something that's called shortest path. Oh that's an example of the copying from the browser is not a good idea I don't know how these characters end up here actually I go back to my list and I'll pick this one as the last query I don't have this one safe, okay? So we have to fix it. So what this query has done it says okay I want to have a dataset from draw up with this DOI and I want to have a dataset from ands with this DOI and find me the shortest path within this two and it does it looks like this. So these two datasets are linked using a researcher and a publication. Now in this case you can actually replace a dataset with a researcher and instead of DOI you can use ORPIT. We can replace a dataset with a grant and instead of DOI you can use PERL. Now if you use the extended version of Neo4j with the research graph model you will actually get this last tab here which you can open it and it has a template for all of these queries. So for example for the shortest path I can click here and it fill up the box automatically for me and I only need to actually fill up the DOIs that I need and we are actually extending this further. So at the moment we have about 10 queries here as example and we are planning to add more and more queries to this template. So this is basically the last slide for the Neo4j queries. What we will do is I hope at the end of the presentation we will have a time to have a discussion about this question Q&A. The next part of the talk is by Dr. Jinbo Wang about the NCI. I believe I did not actually introduce Dr. Wang. Dr. Jinbo Wang is actually working as a data collection manager for NCI which is located in ANU in Canberra. Her background is, this is actually what the tricky one which we tried before the presentation. I believe she got her PhD in the seismologies as a seismologist which is in the geoscience and basically her mission at the moment in NCI is connecting information across different platforms by basically providing this ecosystem for researchers and make the research environment more efficient. Now with that introduction I will hand over this presentation to Jinbo. Hello everyone. I will use about 10 to 15 minutes to share my experience as a user of RD switchboard. And I wanted to show the graph connection experience using our NCI metadata. And this talk has also been presented two weeks ago in the first reproduced science workshop in Hanover, Germany. It's got positive feedback. So for people who doesn't know much about NCI, NCI is in short is for national computational infrastructure. So we are the national level super computer center physically located at Australian National University campus. From 2013, we received a big chunk of money to store the research data. The motivation is some of the data getting bigger and bigger to gigabyte terabyte even much larger especially in our domain such as environment. It's growing so fast. Individual PC or hard disk cannot store large scale data and transferring data and share the data became a problem. That's why we got this funding as one of the eight node storage to support the research data infrastructure. At the moment, we have more than 10 petabyte research data as you can see in this figure our data including from the space astronomy observation to satellite images and climate model, climate change research ecosystem onto the ground like geophysics exploration and even deeper mental core geodynamic processing data. With the funding of being one of the research data infrastructure, we make use of the advantages to work on the data that we collected to make a seamless connection across different discipline. So as you can see here, we care about data formats because we want to make use of the HPC facility and we care about provide open access to researchers because the large scale data is impossible for them to download to their local machine and do the processing. It's better to provide some kind of virtual environment for them to log in and do a big processing at our center. Because we have this so much data, we need to organize the catalog for people to know what data set available at NCI. So this is one of the common question researchers care about. The catalog we build based on a rational relationship between researcher data grant and the paper. For example, if you see the first line, it says researcher A use data one supported by grant A generate paper one and two. Similarly, for each line, we have one record. However, the obvious thing here is we can see the redundancy of researcher B appear twice, data one appear twice, grant B appear twice. If every single node is in our database, it creates a lot of redundancy. So the idea of adopting at the switchboard is we use the idea of identifier. And we use the identifier of the same researcher like ORCID. We use the same identifier of the data like a DOI. We use the same identifier like a pearl of a grant, et cetera. Now, after we merge those different nodes with the same identifier, actually each entity of researcher data grants and paper are now connected through a graphical relationship. I think that's my understanding as a user how RD switchboard can help us to make the connection because with this graph view, what I can answer question, for example, like what is the usage of NCI's dataset? It can be translated directly to an RD switchboard query that Amiya just to show you a little while ago, how many datasets published at NCI are being referenced in the research journal articles. Another question such as what is awareness of the available datasets within the research community? It can translate to a query question that is how many researchers, institutes are connected to the datasets and so on. So the third question, which is even more specific, if I would like to know more about this dataset, who should I contact? Who generate this data? Who use the data to publish the paper? And what is the previous research has been done using this dataset? I believe this is a very common question for a researcher that when they start a new topic, they would do this kind of research like myself doing a Google search first. But if we provide this kind of infrastructure, it will make the literature review much easier. And now I will use two slides to explain exactly how we organize our catalog and then adopting RD switchboard technically. So we organize our catalog in a hierarchy structure. On the top node, as you can see here, it's an NCI geo network node, which is only about the top collection level, high level summary of the data collection. At the moment, we have more than 200. And on the middle level, you can see every single project has its own geo network catalog. Geo network is our metadata display interface, but you can use other interface as well. So at NCI, for each individual project, we might have thousands of records for in the file level or granular level. It's not appropriate to have all these different granularity catalog in a single node because then it's hard to separate them and it's harder to aggregate it by research domain, for example. So we use this structure. It provides flexibility for us to do more aggregation at a later stage. You can check out our main geo network website using this link. So what RD switchboard do is, every single geo network has its own individual database and we dump those database into the RD switchboard graph database. And the connection has been made. Here, I don't show the exactly how it does, but that's the magic where it happens in this box when the identifier was used to merge the different nodes so that the connection has been made. This screenshot is the current status that using NCS metadata, we find some connections, for example, between the datasets researcher and institute. But I also noticed that there are nodes are disconnected in my follow up processing. I actually found out they are actually connected, but in our metadata, because it's lack of some critical information. So when I present this database in a graphical view, it's disconnected. It means that I have to correct some of the metadata information in our database. So, so far as I explained, the RD switchboard helped me identify some missing critical metadata entries, which I can provide, I can make it more complete. Sometimes it's also helped me identify the arrows in the catalog and I can easily fix it. But without RD switchboard, it's almost impossible, because we have hundreds and thousands records. It's hard to check manually, but RD switchboard can tell me immediately. RD switchboard graph view provides analytical view of how research data has been used so far. This has been a very common question being asked many times by our user, because they care about who use their data and they care about how to make the data even more public to make more connections to the external world. And RD switchboard is an ideal tool to make it happen. It also can help me evaluate the impact of the datasets researchers and institute based on assumption that more connection an entity has, it has a bigger impact if you like. Finally, as you see in the example in Amir's demonstration, if a researcher doesn't have an ORCID, those query wouldn't work. So it's really a good motivational or encouragement for researcher to register an ORCID, for data manager to mentor DOI for their dataset, for data repository provide persistent identifier to increase the accessibility of the dataset. So this is our experiences so far. I would like to end up my presentation by giving you a real example that how I feel it is really helpful from a data repository point of view. And that is the basic question, what dataset are connected to each other? So we have a group from the Bureau of Meteorology and they download the climate reanalysis data from US because it's too large and there are a number of people want to use that data. So they approved by NCI to store at NCI so that they can use it. After a little while, another group from Cyro also climate research group, they are downloading the climate reanalysis data from the same source, but different portion, different subset and they won't do some research. However, those two groups don't know each other but they both came to me, would like to find some storage at NCI to support their research. And I suggest them since you share the common interest, why don't you talk to each other? So group C, if group A already downloaded some data that group B can use and vice versa and then they start talking to each other. After a few months, a third group which is also from the Bureau of Meteorology but to different branch and they are asking something very similar, some question very, very similar about using and sharing reanalysis data. And I suggest the same thing. However, as a human being, as a communication hub, it's very difficult and it's hard and it's time consuming. I can see a good chance for ID switchboard now play my role as a communication hub to present those connections automatically in the graph database. So people can go there anytime 24 seven check the connection of their dataset and start talking to each other without talking to myself. It also reduce, it might also motivate a collaboration from different group when they see the connections. So that's my hope that the ID switchboard when NC are adopting it, so we can offer those kind of services to our user. So in summary, the ID switchboard is a great tool to create a linkage among researchers, datasets and the publications. The Neo4j Graph View is a very eye catching and straightforward to understand complex interconnections within the research community. I also feel this data management is a joint effort by the whole research community and the librarian community. And that's the end of my talk. I will hand over to Amir as a presenter. Thanks Jingbo, thank you very much. What will happen after this? After this webinar, we will have a BAF in e-research conference. That will be one place that you can find us. I believe Jingbo would be there as well. And we also have Nataniel from University of Sydney. We will talk about this technology further in the BAF and for some of you who are interested to get that Neo4j database that I was using for demo, that's actually quite a big file. I can give you that one with the USB disk. So that's one way of getting that. If, so that is a quick way. In next week, if you come to the research conference, if you find me, I can give you the file. If that doesn't happen, then Jingbo and I, we are actually putting that database on the NCI platform as open access repository. So other people will be able to download it. That is a publication that you are in the middle of it. So I don't know exactly how long does it take for that dataset to appear. It should be that one also soon. Regardless, the slides will be available online. If you have any further questions about this Neo4j technology, you can send me the email on this email address. The entire code for both research data switchboard and the research graph database structure all on GitHub. So the links are on the earlier slide. I can also bring the screenshot from this. So what will happen is the first thing you probably would need to do if you want to create a graph database, you might want to go to Neo4j and download the Neo4j source code and compile it. But the easiest way would be we have a Neo4j repository here that's actually compiled. So every plugin, everything has been built. We can just download it and write. So that would be your database. Ready to go? Your schema is on the schema repository here. And also there is a page on the web that explains how the schema works. There are some crosswalks on the repository that you can look how you can do the crosswalk if you want to import it. In the Neo4j structure, if you remember there was a harvesting point. So when you do the harvesting, the information needs to go to the switchboard and the switchboard data is all on the GitHub repository under the switchboard name. We have a multiple instances also here. So multiple repositories here. And the code that as Jean said, make the magic happens is in the influence repository. Now we have about five minutes that we can actually allocate to question and answer. So we have one question from Christopher. So the question is that what processing power is required for the queries? Okay, so the answer to this is that it's very much depends on your two things. Actually graph size is the obvious element. And the other one is your indexing of elements for your properties. So in the ideal world, if you index everything, then that obviously makes it much, much quicker. That would reduce the computation power, but requires more storage. In the tradeoff would be you can actually index less properties and then you will actually need to have more computation power allocated to that. But the example that I've shown, it was actually in my MacBook Pro, which has a i5 processor. So it has a six million graph database, I'm running the go-to-vib in our software plus power presentation and also I have a Neo4j running on the background. So that is not expensive to run. The thing which is expensive to run is an inference engine. That one, it requires a high performance computing and a lot of memory because it is a multi-thread processing. So in our case in the Amazon, we have a machine which I believe it is in one of the earlier slides. That machine has 32, 36 cores and 60 gigabytes of RAM. And that machine takes about 72 hours to complete the pipeline. But if you have a machine, so the way that it works for the way that the pipeline works is if you have a machine with half of the power, it doesn't take twice the time. It actually make eight times longer. So for running the inference engine on the large graph database, you need a very powerful machine or a set of powerful machine as a cluster. Now, this actually opens a conversation about something that called distributed graph. I briefly mentioned that and that is why this graph project are now more adopting the idea of having the cluster of graph running on different platforms. And that is something that we probably need to open the conversation in another webinar or another technical meeting. So I have one question here. It says for RDS switchboard, is a form-based search option planned rather than the search query swing? Yes, the answer is yes. We are working on a couple of different options for this. One is that at the institutional repositories, we are at the moment exploring the idea of having the integration right into the repositories like DSPACE that enables just a whole platform to work like a plugin. And also in the research graph website, you are working on the idea of providing, as you said, form-based search so you can actually type your queries and get to the graph without actually loading the whole Neo4j. Okay, so the question says, is there any study comparing the Neo4j graph with other traditional views? And there are two different comparison here about, so the question here is, is there any study about comparing the Neo4j technology and other technologies? There are a lot of actual studies on the web which is search Neo4j compare and it's easy to do this on Google. It brings lots of other options. So there are studies with the comparing Neo4j with traditional databases like SQL database. There are comparison with Neo4j with the non-SQL databases like MongoDB. And then there are comparison between Neo4j and the triple store studies. So in this context, well, the first one is quite obvious. The SQL databases are tuned to the string-based search and they'll have different structure. The problem of those databases for this kind of scenario is that finding the chain of relationships is very, very expensive process to do. No SQL databases, well, Neo4j is one of those. So you are comparing different items in the same category. There are a number of those options. There is another, for example, similar product in this category called OrientDB. There is another one called TitanDB. So there is a couple of those in these groups. You can actually search and develop for finding the different performance differences. It is that the main differences are the performance, the simplicity of the use, and interoperability with other tools and platforms. And the semantic web, the main difference between Neo4j and a triple store is on the inference model. I would say Neo4j is far less capable of making a complex logic, but at the same time, it provides you with the simplicity of implementation and the performance in query. So it's much quicker to get the data from Neo4j. However, there are different triple stores. So different technologies. And I remember in 2014, when we were doing the project for the first time, we did some cases studies at a time. Our experience suggested that the triple store technology requires more computation power, but that might have been changed in the last two years. Okay, so the question is that is the complexity of inference improved the search quality? The short answer is yes, it does. So the last question is, is there already a switch for implementable in the search data alliance? In a way, yes, it was a collaborative project that was initially started by Ant, Dryad, and Sen, and then other people joined. So we had the infrastructure contribution. We had data contribution. We had coding contribution from different partners. And overall, we can say this is the implementation of what the working group recommended. So the working group came up with a recombination of different connections, and then we implemented that in the switchboard model. And the question was about the research data Australia. So the actual question was, is are the switchboard implemented in research data Australia? Which I mess read that. So the answer to this one is that we use the research data switchboard to enrich the research data Australia. It's one of the linking capabilities that Ant has, but at the moment we do not have the graph visualizer. This is one of the items in our pipeline, which we already have this one planned into our development cycle. And in the future versions of the research data Australia, we are working to have a graph visualizer that provides some of this information. Okay, thank you everyone. I believe I'm four minutes over the time of the webinar. So I would like to thank everyone for attending the webinar.


I have clients come to me and they say, Ross, I don't get it. I'm a successful business person. I built my business from nothing to a multi-million dollar business. No one handled me anything. I had to be strategic in my thinking and consistent and intelligent. Why can't I use that same intelligence to figure out women? I'm spinning in confusion, I have been for years. Has anyone here ever felt confusion in their dealings with women?  Raise both hands and say, hallelujah, Ross has got it. My students actually do that. They'll actually do that when I don't do. So I understand the profound confusion. And what does this profound confusion lead to? It leads to a approach to life where you won't take a step unless you're absolutely guaranteed you're gonna succeed. And it leads to running data in your head rather than exploring out into the world. I continue to see this in my seminars. It's very interesting. Same people, not the same face, but they always sit in the third row on this side. I am not making this up. And they always ask questions like this. Ross, do you remember on your 1994 New Jersey seducing master's tapes? Tape four, side B, 34 seconds in. You said the following pattern. And I'll say, stop, I don't remember that. Did you actually try the pattern? How many times did you try it? Once, what year was that tape series made? 1994, and what is today's date? 2007, and you tried it once? Yes, but I anticipate within another year there's a 3.5. Do you understand what he's doing? He thinks that he needs to be certain before he makes the move. He's looking for performance confidence before he does any performing. Let me unpack what I mean by the term performance confidence. Let's say you walk the tightrope at the circus. You've done it a thousand times before. It's reasonably certain that a thousand and one will be a good experience. True, yes? You could fall off, but it's true. So these guys want performance confidence before they do any performing. So they wind up never having any performance and never having any confidence. Now, that's what I call a real problem, that here's the greater, more subtle problem. Here's the much more subtle problem. My meditation teacher taught me subtle is significant. It leads to a learning strategy where for every one point of action you do a thousand points of rumination, a thousand points of thinking. Has anyone ever known anyone who might be guilty of something like that in an intimate area of life? You don't have an attractiveness problems, my friend. You don't have an attraction problem, my friends.  It's a learning strategy issue, not an attractiveness issue. I could take anyone, in fact, when I work with private clients, I give them assignments. I gave a client this assignment.  If I give you an assignment to do, I promise you it's not illegal, dangerous, or fattening, will you do it? He said yes. I said, get a journal. Where do you live? Chicago. Is there a lot of people in Chicago? Yes. I want you to go to a place at noon where you can observe people like the financial district and pick out a guy who looks like he's got the world by the bait seam. Anyone speak Hebrew? Bait seam, balls. Got the word world by the balls. Someone who looks like he's an out the mail. I don't believe there is such a critter, but that's a discussion for another day. Someone who looks like he's got the word world by the balls, he's dressed nice. He looks like he's got money, hot chicks.  And I want you to write me an essay of the way his life will look and feel 90 days after having your shitty programming. Make it a 10 page essay.  And he came back to me and he went, oh, it's just my programming in my head. It never was about me.  Look what you had to pay me to get to that realization. So, I want to take a completely different angle and say it's not an attraction problem and attracting this problem.   And again, it's got nothing to do with attractiveness or gain, oddly enough because I teach game. So, here's the lesson embedded in all of this, the paradox.


We need to call upon Gohan's rage again. What, Dato, again? How many times do you plan on trying to wake up level three? It can't work every time, but you're wrong. It works every time. Simple as that, really. How's it going, everybody? Dato Dui here, back with another round of Dragon Ball Fighter's ranked matches. And today we're playing a team of Videl, Bardock, and Yamcha. Obviously, this is a team designed to facilitate one another Bardock and Videl more so than Yamcha. But Videl's really good with both these assists. Bardock is really good with both these assists. So it really doesn't matter who you have out, as long as it's not Yamcha. You know, Yamcha's okay. He just, uh... Man, he's so good in the back. Why would you... Oh, Super Dash right out of the gate. Uh, should we... Oh, never mind. I was gonna say should we down tech, but we're sliding. We can still down tech, though. We can still get a little cheeky with it. Oh, no tech. Oh, God. All right. The frameage is going down, so that's good. It's gonna throw us back in the corner. All right, we guard cancel. We have to get out there. All right, there we go. Stagger the low lights and we're in. This is a good chance for us to catch up. Vigino players always click buttons on wake up. There we go. Oh, no. Oh, we let him get away. Overhead Yamcha. Yes, sir, we knew it would hit. It's just a confidence thing. All right, now we're pretty much safe to go back out to Videl. No, countered. All right, you know what? We have to take out an old friend. All right, let's hope this works, eh, boys? The only way we can win now is through the help of Gohan's rage. You should have never tested us. Get him, Gohan. Oh, I'm sorry. I just get a little bit worked up during times like this. You know, you hate to see Videl get messed up like that. That was cheap. Oh, wake up, level 3s. If they're designed into the character. Down heavy. Yes, it just made off. Everything makes so much sense in Dragon Ball fighters. Should we extend this the all natural way? Let's go. Why not? Oh, we drop it. That's fine. That's fine. That's absolutely fine. I didn't want to drop that though. Man, the confidence is just through the roof. You can, I can just make. All right, should we be a bar doc player? Wow, it's amazing. It's amazing how becoming a bar doc player was the worst thing I ever did. Right there. I really, I left my brain at the door. You hate. No, I can't. I can't keep saying you hate to see it. We have to mix it up. You can't get too predictable and commentary. Oh my God. Okay. Early spark. Well, not early actually. What am I talking about? No. Okay. We. Oh, woo. Super Dash. Oh my goodness. We'd like to see that. Should we go hard knockdown? I don't see why not. Right. We get a lot of time off sparking. We get to set the pace of the game. We're definitely going to go with a down medium here because we got to at least condition them a little bit. Switch. Oh, no bar though. So we don't get sliding. Yeah. I'm going to hit buttons. I'm going to hit buttons because I got two characters left and I am not afraid to lose them. Can we kill here? We absolutely can. This is going to be a level one.  How do I say this? It's a little less damage than just going out into another characters level one. Shout out to Gohan. Go on really clutch that entire game out for us. All right, Vigito. Are you going to Super Dash right away? Not right away, but it was pretty close. Moon Assault. Wow, Spirit Sword hits me a lot lower than expected. All right, we're going to pop up here. Okay. Okay. Gar cancel. We out. Oh, no. Why did I bring in Yamcha? It's because I'm a Yamcha main. Ah, a dumb Yamcha main at that. I didn't finish the full title. I like to leave some off the resume. You know, I got to try to get jobs out here in this economy. Oh, yikes. I throw me back in the corner. Yep. Classic Vigito stuff level. He knows. Oh, he knows that I don't come on, man. It's just one assist. All right, Spark. You probably could have gotten out of there the old fashion way too. Spirit Sword. You can't run into those just like I'm doing right now. Guard cancel out. We're playing too slow though. You see this like, okay, this is, um, not the best to say the least Gohan. Oh, um, we need to call upon Gohan's rage again. What doctor again? How many times do you plan on trying to wake up level three? Can't work every time, but you're wrong. It works every time. Simple as that really. What's the science behind that? How could it work every time? Well, to tell you the truth, even I don't fully know. Even obviously, right? Because I'm getting we're tagging. Please be enough. Oh, baby. Oh my God. Feels good, man. This dude is never going to pressure me on knockdown again, but I have three bars. He has stayed a full football field length away. He's like, I'm not touching. I'm not going near this dude. He's going to wake up with Gohan's rage. Don't snap me out. Oh, you would. You Vigito players are all the same.  Oh, my God. This is the most satisfying game of fighters I've ever played. No, that was supposed to be a justice combination like that. Okay. Oh, no, wrong move. Oh, we have to block. Oh, we bad Super Dash. Moon Assault. Come on. No, come on. No. How many bad mistakes can I make? Genuinely, how bad could I be? Get me out. All right, we jabbed him out. I don't even know how we fit a jab in between that vanish, but I find it's best not to ask questions. It's best just to let sleeping dogs lie. Ah, goodbye, Yamcha, my friend. It was so nice having you around. All right, we don't have sparking, so we really do have to play safe here. Let's go. Couldn't block that. Let's go. I saw the Super Dash coming. Don't hum without the bandana looking slick. Yeah. Just got the glasses on Justice Combo from all the way downtown. Let's go. One, two, three. One, two, three. We are the best. Hold on. Everybody don't panic. I got a plan.  Ah, work it, Gohan. That was beautiful. This might be the best Dragon Ball Fighters right batch I've ever uploaded. This might be one of the best Dragon Ball Fighters right matches I've ever uploaded. Whoa, whoa, get that out of here, bro. That was a little too wild for even my standards. Gohan Bingo. Oh, no. Oh, no. Oh, we're good. We're good. We're back in the bad territory. Back in the bad. All right, we're OK. Guard cancel saves us there. He didn't block the low. He should have looked out for it. Honestly, I can't blame him. I don't think I would have either. That was just something in the moment I was kind of feeling. How long is that? I was looking for that. It's just like I saw it. I was like, nice. OK, now I'll hit. Boom, counter. What? Vegeta, how long are you planning on holding that counter out? Three bars. We out. All right. We might need to call upon Gohan's rage here. He has to be looking out for it now, right? He's like, no way. No way, man. I ain't falling for the same old trick. I ain't falling for it. But guess what? He's safe jump. Don't go on pushing back enough to tag out though. I'll take it as a win.  And I'm on this.  But it's working.  We should all accept that now. Miss, this is going to be a hard one to come back from. Honestly, perhaps we could be a Bardock player here. Take a shot every time that doesn't work. Of water, of water, YouTube. I saw you looking at the demonetization button. You ain't slick. Damn, level three. OK, so he knows we're going to spark here. But because we know he knows, we're just going to do it anyway. This was the worst round of the set so far. This was not the greatest set for me. OK, can he kill? Probably, right? He just needs to throw on some level ones. He's just buying time for the sparking to go away, which is smart. It's definitely not going to last too much longer. All right, come on. Come on, Yoncha. I've seen, wait a second, wait a second. That's a lot of Dragon Balls. If you're a longtime viewer of the channel, you know every time I look at the Dragon Balls, it's a wrap. The game's over. But what? Why am I feeling differently this time? I don't know. I'm getting locked out pretty hard. Oh, DP. Drop it. You've got to drop it, dude. You have to drop this. He is not dropping anything tonight. Why is this so hard? Why is landing three auto combos the hardest thing any Dragon Ball fighters player can do? OK, that's one. Why, Yoncha? Damn, if we could have just ran up and got in between that frame, I think we might have been able to do it. That's so hard, though. Oh, without a sis, that is really a task, dude.  Well, that's a door just making up words now. Oh, come on. Yoncha, stop him. Oh, we're getting caught by Super Dashers now. I'm getting too frantic. Knock me down. Oh, Dragon Rush. Oh, he's playing good. He's playing good. And when he's playing good, he's playing great. Can you not put me back in the corner? OK, understandable. I know I'm, I don't have much leverage here. We out. There we go. Don't click any buttons. It's my turn. It's my time to shine, baby. Let's get him, Bardock. Let's get him. Let's get him. Let's get him. Oh, this, it wasn't even that late. It was kind of, it was a late Super Dash. There we go.  Oh, baby. Madel. OK, will he level three here? Not right. Oh, I had to read. I had to read, dude. I had to read.  No, get off of me. OK, OK. That was somehow awesome for us. Switch. Damn it. Oh, get that out of here. Oh, Bardock just slammed this man to the ground, Bardock.  Where'd you learn to do that? Oh, why did I dash forward? Why did I dash forward in truly my time of need? OK, that's fine. He'll get us more to the corner. Oh, that's a big punish. I actually think he could have done a better punish than that. But my knowledge of Team Gohan is pretty limited. Oh, God, we should have sparked years ago. We can't let this kind of rampage go on for too long. Oh, we're dead. Yikes. Wait a minute. Hold on. Wait a minute. No, we're dead. We're dead. We're dead. Yikes, why'd you buff at ArcSys? I know it was such a bad move, but. Come on. There we go. Is that a reset? Oh, that might. No, it's not enough. Oh, God, he got caught by the. You should have. You should have either blocked that. You should have let that hit you or just not done anything at all, dude. Actually, that's one of the same overhead. No, I'm who I think I am Lotus. And sliding. Let's go. Oh, God, he kept the price where we might lose. I don't know why Team Gohan scares me. That's a big punish. No, don't guard canceling the clutch. Just don't do it, dude. Just don't do it. Now we got to play real defense. That's going to be the end of this rank session video. Hopefully you enjoyed it. Yamcha comes through in the clutch. Videl also helped out. Oh, this was a great one. This was a great Videl video. Like I said, if you enjoyed it, make sure to go down in the comments. Let me know your favorite moment if you have one while you're down there. If you like this video and channel, make sure to hit the like button and subscribe. It helps you keep keep up to date on all things fighting games and Dragon Ball Fighter season two.  Give those a watch for you then catch your interest. Other than that, thank you so much for watching this video. I'm Datsudoya. I'll see you in the next one.


What we mean by elasticity of scale is actually related to the returns to scale. In returns to scale we have studied that when there will be the total increase in the amount of the inputs or all the inputs there are scaled up or scaled down then what will be the impact in the total amount of the output. So keeping in view now that returns to scale we are going to measure the proportionate change in the output due to change in that scale. So proportionate change in the output y due to proportionate change in the scale is called elasticity of scale and if we show here that is this change in y due to y will be equal to change in the output and when it is divided by change in scale due to that that will be proportionate change in scale. So when this change in y by y is divided by change in s by s so we invert this and it comes like this s by ds and it is explained here. So proportionate change in the output due to change in the scale will be called elasticity of scale and coming to this now we say that what is the basic significance for this. We want to assess here that either change in the inputs or the scaling has caused in the output change more than a certain point or less than that certain point. So that either that change or the scaling was feasible or it was beneficial that and to want to decide that if he is enhancing the amount of his inputs, increasing all of them, then is there really an increase in the output and either that increase is as much as it is required or the input is above it. So if the amount of the input increases, the more the output increases, then we will say that at least there was no loss. But this is also a possibility that on all the inputs, we say that the scaling is going up and increasing, but the output is decreasing in the output. So here the entrepreneur has to see one thing, the main thing is that the output is increasing but the output is not increasing in the output of that output. But if we look at the overall society, then we have to see it here as in the planning setup, because all the capital and resources that the entrepreneur or the producer uses and if the output is not increased, then there is an opportunity cost for them who have to pay the society and who have to pay it themselves, then those inputs, they can be utilized at any other place in a better way. So from here our concept of opportunity cost is also developed. And from here, if we look at the aspect of externalities on our environmental aspect, that along with all these things, that is supporting our society in a better form or giving negative conditions there. So that is why when elasticity of scale is looking at our resource saving, our efficiency setup and our welfare aspects, its study is very important. Now if we look at it graphically, then in front of us, there are three kinds of isoquants shown here and if we look at these three isoquants, then there are various combinations of inputs between which there is possibility of switching. Now if we look here, then these straight lines from origin are basically joining these three points over these three isoquants and they actually show those combinations where if we bring these three together, then it means it shows the constant returns along this OB line and at the same time this shows the constant returns along this OA line. So these are the combinations where the more inputs are increased, then here this particular point is also increasing in the output at that point. And if we look at it in this form, then by expressing these three points, we say that this is constant returns along our show. Now similarly, the possibility is that instead of constant, our actual whenever it is, the possibility is that this is not constant or the amount of input scaling done in the output proportionate change is more or less than that. So the three forms of returns to scale that are coming in front of us will be clearly explained. Now if we look at the output, the amount of input done by scale, if it is more or less or equal, then we will have either increasing returns to scale or constant returns to scale or decreasing returns to scale. So it is possible that whatever combination we have in all these forms, when we explain it, then this will explain what we have earlier. So this will be when we can say there are the increasing returns to scale, that we are enhancing the inputs, but the output that is increasing in the output of enhancing the inputs is also increasing. Now if we look at it here, then the increase is a vertical show. But if we look at it along with the vertical show, then if we look at the slope, then this slope is also showing us that it is increasing. Means if we take its first derivative, then it is also showing its curvature in the increasing rate form. In the second form, if we look at the proportionate change, then when we explain the elasticity, then it will show us that there is a straight line. Change in the output due to change in the total scaling will be equal to the straight line or the same. So this is our constant returns to scale. Now similarly, if we look at another graph, then it is showing us upward. But this upward line, but upward decreasing means that is increase in output at decreasing rate.  When this is our less than one equation. Now here possibility can be that it is not necessary that from the start onwards, either same increasing rate of return or constant or decreasing, there is a possibility that the change of production function will change shape or curvature. So this is possible in this form. If we go to this part, then there is a point of inflection coming up to that point of inflection. This was increasing at increasing rate. And after this point, it was decreasing at decreasing rate. So initially increasing rate and after that it is decreasing. And similarly, we see that we can draw one more which can be that initially it is increasing and afterwards it takes the shape of decreasing. So this can be a shape like that. So there is a possibility in which we can say that this elasticity of scale may vary from one aspect to other aspect. Thank you. .


The horse with the speed of light, a cloud of dust, and a hardy high old silver, the lone ranger. With his faithful Indian companion, Tonto, the masked rider of the planes led the fight for law and order in the early west in the United States. The stories of his strength and courage, his daring and resourcefulness, have come down to us through the generations, and nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoofbeats of a great horse, Silver. The lone ranger rides again. Come on, Silver. We're heading for Leadville. Oh, Silver! Oh, Silver! Oh, Silver! Oh, Silver! The whole vast stretch of Tomahawk Basin was an armed camp. The honest cattle owners knew that Benjamin Steele, owner of the Tomahawk ranch, was an unscrupulous man, dangerous as a rattlesnake, whose one desire was to own every head of cattle that ranged the grasslands, and who didn't care how he did it. That's what I'm saying to all you men. My name is Fergus. You all can who I am. I come over to this country seeking new land, a new place to dwell. I come to Tomahawk Basin, and there sit down my roots. I homesteaded. I live there, and there I intend to stay.  Aye, not even that man who would be loud of the valley, who would drive us all away.  I'm a peaceable man. Then you doubt it. But I fought for what was right in Scotland, and I won't do otherwise here. Have we got to let one man take a cattle without saying a name? Senor, that is very pretty speech you make, no? It is what I believe. Your name she is Fergus, no? Andrew Fergus. Aye, there's no need to tell me yours. It is a name as black as the heart of the man you work for. Your name is Breed. I work for Senor Benjamin Steele. You better tell him what you heard her saying. Aye, no. See, I tell him, I tell him plenty. He's very funny. Your hombres think you can whip Mr. Steele. That is very funny. Fergus, look out, he's wearing guns. Aye, I wear guns. I am all so fast on trigger. When the lone Ranger comes back, we'll drive Steele into the hills or into jail. The lone Ranger is not here. The Sheriff of Leadville, he also is not here. So perhaps you'll take the law into your own hands, eh? Aye, wear guns, Breed, because it is the custom. I am a peaceable man, and I do not like to use them. But if necessary, I will use them. Perfect. Look out, there's going to be gun smoke.  It's a beastly one. I'm looking for the Sheriff of Leadville. I want to see him prone over. Well, I'll be. See you, Senor, I also... Look out, here it comes. Hey, Seldom, get down. Shooting's slowing up. Wrecking, Breed, and other gender, all out of bullets. Aye, I wonder if anyone would like to know if Breed's a dangerous hombre with a six gun. Aye, that he is. But who in places was that other gent? They didn't know, Ken, exactly, but still. Soft man, here comes one of them. Steady, boy, steady. Where's the Sheriff? He's gone. He won't be back in Leadville for a few days. Are you hurt, lad? I want to see the Sheriff, nothing else matters. Hurry up, get up there. There goes one of them. If he finished Breed, all I can say is it's good riddance to the whole community. Oh, so you're alive, Breed. Your opponent has left, and I'd advise you to do likewise. See, I go. I go. But, Senors, I tell you now, I work for Mr. Steele, his biggest lunch at Old Tomahoe Basin.  Are they all some risk? Gents, looks like we got a range war on our hands. Fergus is only one thing to do. Aye, Thatcher, we take the law into our own hands. Right. From now on, we're banded together against Steele. Vigilantes. It's not far now, Silver. We'll be on the rim of Tomahoe Basin soon. Tuddo, this way, Silver. Hurry! Roll, Silver, steady, boys, steady. Tuddo, you came as hurry. You're rolling ahead of me, Tuddo. Why didn't you continue? There's plenty trouble in Tomahoe Basin since we go away. We've only been gone a week. That's right. Like you're saying, time to come to basin ahead of you. Last night, me ride into Leadville. Yes. Me hear bullets from Café. What was it about? A fellow named Breed there. Him may keep big talk and reach for guns to shoot Fergus. Fergus? Ah. Fergus is one of the most peaceful ranchers in the basin. Him not that way anymore. What do you mean by that? He's not dead. No, him live. Four Breed shoot. Stranger come door. Breed reach for guns. Stranger shoot lights out. There are plenty noise, but it's dark and nobody hurt. Stranger him leave town. Breed him leave town, too. Who was this stranger? Well, Tom did not know that. He masked for sheriff. I see. Go on then. What? Uh, King Sabi. What mean vigilante? Vigilante? Ah. I know. That's the name. Men use out west when they take the law under their own hands to deal out justice. It means that honest men turn almost as bad as the criminals they go after. Vigilante. Ah. That's what Tonto think it mean. What about Kerry and Fergus and Thatcher? Them all join. Them ride with rifle across saddle. Range war start and never stop till steel him dead. Or until the honest ranchers stop his bullets. Ah. What we do, King Sabi? The sheriff's got to handle this. He's got to be done honestly and legally. The sheriff not here. Then we ride, Tonto. Fergus lives across the basin. They get to his place several hours after sundown. Oh. Silver. Get him out of this town. Oh, Silver. Hover. You can say that the ranchers are banded together as vigilantes are wipe us out. Is that it, breed? Yes. Arrive from Leadville to seguir in your steer. That is why you and me we leave ranch. By them vigilantes haven't got a chance. We can out shoot them any day. We're gunfighters. They ain't. That's right, Rosti. So you and me we have a job of work to do you, Sabi? Yeah, I serve you, all right. They're riding toward Fergus's ranch. Senor Fergus and his daughter, they are alone. We get their son go down. We shoot him. Can ambush him as he heads from the crowd to his house? You're smart, hombre, Rosty. After that, we have another job of work. Yeah? You hear, maybe I have gone fighting late, will you? Yeah, some stranger, wasn't it? Walked in the cafe, and you and him started blasting. Then he lights out. Yeah, that was no stranger, Rosty. Huh? I know who that man is. Senor Steeley also know. You and me, we got job to find that man and to finish him. Sounds important. We must finish him before he get the shot if he gets it. That's easy to say, but where's he campin' out? There's valleys and all mighty big struts.  But first, right now, we go take care of Fergus.  No, Senor Steeley, he give the orders. Now we go, eh?  The sun has already gone down and the crickets are starvin'. Meg, why you out here at the cryo? What about the evening meal? supper's been ready for an hour. I came out to wait for you. Hey, dinner like being disobeyed, lass. Father, I, I heard shooting off to the west. Meg, you're a good girl. Now don't bother about what's not your business. Besides, it was not I. I have not yet used my rifle. I'm glad, Father. Oh, wash make. Get back to the house now. Didn't he understand? We have sworn a range war against steel and his cutthroats. You might have been shot.  They'd shoot anybody. I'll help you put the corral bar back. Yes, stubborn lass. Why not, Father? The scotch have always been stubborn. Hey, yeah, have me there, lassie. I'll get this handle off the horse. And now, the corral bar. How tis what a few steps to the house? Come along. Father. Keep walking. Father, it's dark, but I thought I saw something. Over there.  Keep walking. Father. Hey, run for it, Meg! Run! It was three hours later when the lone ranger and toddler reached the Fergus ranch house. As they galloped up, the windows were dark. It was not a sound except the whisper of the crickets and the thud of their horse's hooves. But as they stopped in front of the porch... Daddy Silver, study boy. Hello, boy. Lift your hands and stay right in your saddles. We're friends, Fergus. Here we'll soon see. Light the lantern, Meg. It is you, the lone ranger, and Tonto. We'd better get inside, Father. Hey, come in! Come in! What's that? The men who tried to ambush us just after sundown. They were driven off. It was dark, but we thought that you... It wasn't us. Helen, I just arrived. We've been running hard all the way. Sundown, we were miles from here. Who tried to ambush you? We don't know. It must have been Steele's men. And you don't know who drove them off? No, it was dark. All at once they were shooting behind them. Then Father redoubled his fire. They were caught in between so they ran for their horses and got away. Whoever was helping us went after them. Aye, it was one man. Fergus, you've got to stop this.  I'm talking about taking the law into your own hands. Starting a vigilante movement against Steele. Steele is a murderous criminal. Then let the law deal with him. It's too late. The sheriff wasn't here, and we took the law into our own hands. By the totten of the clan Fergus, that's it. What, Father? I have been wool-gathering last. The man who helped us at Sundown. Why, I should have kinned that horse. He rode, looked familiar. Who was it? His name I never got. But he was the stranger who was in Leadville last night. He asked for the sheriff. Ask for the...  That depends. Where are you going? Todd and I are going to trail the man who helped you. But it's a dark mask man. How can you find his trail? Hmm, come up soon. It's your way. Yes. Suppose the man he's following turned on him. He wouldn't have a chance. Breed! Breed! Bring them behind his mosquito. No, they're right. They come after us. They're right here. Bring up the dog on your hide. You're going local, isn't it? No, I ain't going local. The moon's come up, Breed. Take a squint along the back trail.  I did. There's only one man following us. Yeah, just one. We've got buffaloed into running breed. We've got to strut them off easy. Fair up, sir. Sure. We could have got Fergus after all. Now we'll have it to do all over again. Wait. This hombre will come. It is man we look for. See, I recognize horse. That fellow will shoot at me last night at Leadville. Pronto Rusty, back of the trail. Take only one shot to get rid of him. Any fool now? That's easy to follow, huh? We're ganging on them, Todd. These tracks are fresher. One rinder up ahead. Him just now disappear over ridge. We'll be up with him in a minute. Teddy Silver's got him.  He must stop me. One shot. Only one, Todd. Come along, Frank.  Back to Silver. It comes from that way. Over the ridge. What's that? There he is. From the ground beside his horse. May not see anybody else. Oh, they heard us coming. They rode off. Here we are, steady boy. Whoa, Silver.  Easy, Mr. Him shot. Yeah, yeah, engine. From ambush, I didn't have a chance. I wanted to see the sheriff. But now, I'm cashing in my chips. Reckon I'm done for. The curtain falls on the first act of our Lone Ranger story. Before the next exciting scenes, please permit us to pause for just a few moments. Now to continue our story. It was nearly dawn. The moon had set, and it was dark outside Andrew Frigg's ranch house. But still, he and his daughter sat tensely trying to pierce the gloom. Father, I hear a horse. Just one horse. And he's walking, not galloping. Who could it be? Don't move, Meg. Wait. Hi. It's all right, Meg. Light the lantern. It's Tonto. You've been a long time gone, Tonto. And why did you come so slow? Me. Me need help. Are you alone? Where's the masked man? Masked friend. He'll be here soon. Him follow trail of bad men. I don't under.  Him shot. Hurry, in the house. Here, over here. Who is that? Me, not an old name. Him follow ask for sheriff. Don't put him here on bed. They go. What deals you? Father, that's Jeff Daryl. Jeff Daryl. He was a stranger to me. I was in love with him. Tonto, he's. Is he dead? Poor Silver Steady Boy. I. I, Tonto. How was he? Him inside. No, no, you not go in. Why not? Girl in there. She know Daryl long time. Daryl, that's his name. She tell me story. Him follow name Jeff Daryl. Him own ranch, other side of Tomahawk Basin near Leadville. They meet when she go into town to buy supplies for father. He left the basin? Him leave sudden cause of trouble with Benjamin Steele. So that's it. And that must mean that. Tonto, he wanted to see the sheriff. Well, girl think maybe Daryl have evidence to put Steele in jail. No wonder Breeden Rusty shot him. What, that? Breed, Rusty? Yes, I do brought Daryl back here. I trail them. They had us straight for the Tomahawk Ranch. Hell, I recognize them when Don came up, but I was too late to go after them. Oh, plenty men on ranch, huh? Oh, yes, Kimo Sabi. Steele's ranch is an armed camp. Now where's Fergus? It's going to be a wide open range war unless we persuade him to call off this vigilante business. And too late to do that. Too late? When Fergus learned girl loved Daryl, him take rifle and horse. Him go get other ranchers to fight Steele. They can't do that. Take the law into their own hands. Besides, they're outnumbered. Unless they use strategy, they haven't a chance. Let me say that, too. But Fergus, him too mad to think straight. Don't do it. What is it, Miss Meg? Meg. Meg.  You get better, huh? I reckon so, Ingen. Meg says she says you're a doctor and saved my hide. I don't know how to thank you. Steele forced me out of the valley. He used unlawful means. Rec and I was a coward for a while, but now I've come back, looking for the sheriff so as he can put Steele and all the coyotes who work for him where they belong, in jail where they... Don't try to get up, Daryl. We'll get the sheriff for you, and we'll get Steele. Well, where's father? I've got to tell him. Well, what's wrong? Where is he? You didn't notice? Notice what? He's gone, Miss Meg. Gone? Well, you were busy watching over Daryl. Your father left. Unless he can be stopped, he and the rest will try to attack the Steele ranch. Oh, no, it would be suicide. Yes. Unless? Unless what? Toto, the sheriff is on his way to Leadville. I'm going to meet him and hurry him up. Now, here's what you must do. Aye, that's what I'm saying. That's why I gathered you all together. You've got horses, you've got guns. Have you got the courage to fight against us? Go, yes, sir! We're put to handful against Steele and his crew, but my daughter told me what Steele did to young Daryl, who's lying at death's door right now at my ranch. I'm a patient man, but I cannot abide treachery and greed and tyranny. I fought against them in Scotland, and I'll fight against them here in the valley. And whether we live or die, who will ride against him? Aye, aye, aye, aye, aye, aye, aye! Well, you wait. What is it? It's Toto! Toto, where's the low ranger? We can use him now. Long range is safe. You wait. Him ride for Sarah.  It's not bad to die, but we want to die fighting against a foe, not shot down from the dark like Daryl. Daryl's not dead. Him get better. You wait for Sarah.  You don't have chance against Steele if you're not waiting. Aye, then Toto, we'll wait. But we can't wait for long. If the lone ranger does not return by sundown, then legal or no legal, odds are no odds. We fight! That's right, right, right, right, right! A mask man. Rain up or I'll shoot. Sarah, close your fire! The lone ranger. Mr. Almos pulled trigger. Sarah, there's trouble at Tomahawk Basin. Ferguson's small cattle owners have formed a vigilante outfit. They can't do that. It's not lawful. There's no time to argue. For all I know, they may be shooting now. Those men are ranchers, not fighters. They don't understand how to fight. And Steele's got more men. They're gunfighters, dead shots. Ferguson hasn't got a chance. Doggone, if I only had legal proof to go after Steele and his crew, I'd get- You have legal evidence. Huh? It's waiting for you at the Basin. Cut your horses loose from the traces and ride with me. I sure will. Here, slap this saddle on the bay. Right. I'll cut him loose. There we are. I'll put on the saddle. Let me tighten this cinch. There. Right. If I ride one and let the other trail along free, maybe I can keep up with Silver. Kino, Mr. Let's travel.  Get up there. Hold him, Silver. Let those zombies try to shoot it out with us. We'll fill them so full of lead they'll weigh more in a Texas steer. See? They will come from over the hill. Steele said we wait for the tank from there. What about the mask man? The long range. Yeah.  What did he report to Steele? He reported the ranchers. They don't wait for long range. They won't, eh? Sure not. They wait till Sun set that he's all. And look, see for yourself. Sun she has already going down, no? What about it? No. You wait for mask man. We've been waiting half the day. Let's go get him. Oh, Vegas, no wait. Now, Tantra Bailey is right. We've waited too long. Up men, gallop your horses. We'll ride all the ridge and swoop down on them like a robber to Bruce. Gallop your horses. Silent and grim against the low-ring sky, the ranchers headed across the rangeland. Anger and the fierce determination sealed their lips tight. Tantra riding with them tried to argue and to argue again with Fergus, but in vain. Fergus had only the one answer. The time for waiting is past. Now is the time for guns. And whether we live or die, at least we shall strike a blow for freedom. The galloping hooves thud along the grassland, under the broad sweep of their stetsons, the men's eyes were fastened on the ridge, behind which way to the gun that would shatter the night's silence. But then, out of the west, out of the west from Leadville, flashed a great white stallion, a voice echoed across the night. Hold the silhouette, hold the line. Neck-low ranger, Tantra Bailey. Fergus, Tantra, don't stop. You're too late, mask man. Stop, you've got to listen to me. Hold, hold, hold, hold, hold, hold, hold, hold, hold. Mr. Eucannus, where was from a purpose? We mean to fight, and we mean to fight, no. Fergus, the sheriff was only 10 minutes behind me. Yeah, we can't offer the sheriff, Mr. Laughalano. We mean to make steel pay for his crimes. If you attack this way, you'll be down like trees before a hurricane. The bullets will sweep you from the saddles. We will not listen. You will listen. You'll listen because the sheriff is on your side. What's that? We're breaking the law. Oh, you're not breaking the law. You see this badge I'm wearing? Well, that's the sheriff's badge. Deputy sheriff, Bailey. The sheriff is making you all deputies. And why not? Jeff, they've only got proof against steel. Aye, that's so. Then what deputies? On one condition, give up their suicidal attempt to ride down on them. Go about it another way. You're not asking us to quit. No, we're going to attack. But we're going to surprise them. Allison, four men will go ahead to the ridge. When they hear the howl of a coyote, they'll start shooting. The rest of you will follow Toto and me. It's getting on, Breed. I thought they were going to come after us. The boys are ready, but... Sheriff's Day. We'll keep them. Boys are beginning to get nervous. Oh, you're called nervous.  Hey, wait. I didn't mean you. You better not. Just a little... What you say is very peculiar. What happened to them? Yeah. Yeah, it's nothing. It's always the same like this before the gun starts, eh? What was that? A coyote howling at the moon. There ain't no moon. It is the attack. This is its own place. Let them have it. They're up on the ridge. Make sure we pull it. Fire the gun-flashing. Four men on the ridge, fired as fast as they could work their guns. The gun fighters below report a withering fire up in the darkness. From the dry stream, bed in the rear, the lone ranger and toddler and the others can hear guns. I came as hurry. You make plenty good time. Listen to guns. All steel men, they fire up on the way. Yes, hello. All right, Fergus. Bailey, the rest of you, this is it. The shooting will cover our approach. We'll take them in the rear before they know what hit them. Remember, the sheriff wants them captured, not dead. Aye! Lead the way! Follow me! Come on, Silverhead! Silver and Scout forged ahead of the posse raced up the bed of the dry stream. While the bad men had time to turn, the posse swept over them like a tide. Guns were knocked from their hands. Ropes looped around their bodies. In a few moments, it was all over.  You got us! Don't shoot!  Aye! That's your do! Tie them up, men! We'll have them ready for the sheriff when he gets here. Wait a moment. Where's Breed, Rusty? Huh? Breed was here with you. What's happened to him? I don't know. I'll be dog-gone. Is that yellow skunk just pulled up states and left us? How fit? You do nothing. You go to jail. Maybe Breed in ranch house with the steel. That's where you're wrong, isn't it? What's that, Bailey? Then you cover the house. Sure, mister. We went there, but Breed wasn't there. Steel's gone, too. Both of them were there most as soon as the trouble started. There's no telling where they headed. Well, never mind, man. We got the rest. We've broken Steel's power in Tomahawk Basin. And from now on, every man can walk in peace and friendship with his neighbor. And if it had not been for the Lone Ranger's help, we... He's gone, Andrew, while you were talking. Gone? Where to, man? I don't know. Perhaps after Steel and Breed. That's the way the Lone Ranger is. He's one gent who never waits around for thanks. I've just heard as a copyrighted feature of the Lone Ranger Incorporated.


Now he's gone.   Did he go goddamn it?  I'm going later guys


Desi did it as a result of Lucy being pregnant. And so because she was pregnant and couldn't go on the air anymore, he was like, well, I have been sitting on this footage. I own it, not the studio, they own it. So what they did was like, they were like, well, let's replay it. And at the time, no one knew. That sounds like a bad idea. At the time, no one had done that before. Will people tune in for this? And it was such a hit. And so he kind of rewrote how the industry operated after that with the ability of rerunning IP that they had already invented.