Speaker 1: I used to absolutely hate trying to find papers to support my research, but now it is easier than ever to get a sense of what's out there in your research field with connected papers. Connected papers is a really easy and simple tool to use that I use very often, even outside of academia these days. This is what it's like. Essentially, it is a way to visually explore academic papers in a visual graph, and that's what I love about it. It gives you a snapshot of a research. In the past, you would have to read loads of peer-reviewed papers and get a sixth sense of your field. Now, that sixth sense can be obtained from this tool. You search initially for keywords, a title, a DOI, which is a document object identifier, or a URL of a paper. I've got this paper here. This is one of my papers, and I'm going to search for it by title. I'm going to copy and paste that into the connected paper search bar, and I'm going to say, build a graph. This is what the core functionality is all about, building this graph. This is the paper that I want to select. You can see that it's got some other ones that maybe I meant, but I meant this one, and then it creates this graph. If it hasn't created it recently, it will create one, and there will be a waiting page, but ultimately, this is what you end up with, no matter what. What you end up with is this three-panel layout. The first panel over here is a list of all of the papers that it's found. In the middle, you get this kind of network graph, and on the side, you get an overview of the paper that you've selected. Here it's in purple, and that's our seed graph, or origin paper, as they call it. Here you can see this is the abstract, and with this abstract, we can get a sense of what the paper is actually about. However, this is probably the most interesting panel, the one in the middle, but not as all as it seems, because this isn't a citation graph. It's not about these papers citing each other. In fact, it's much more powerful. Just takes a little bit of getting used to, like what it really means. I always click down here, and this is how to read the graph. I pull this up nearly every time I use this, just to refresh my memory, and papers are arranged according to their similarity, not citations. That's the first very important point you need to understand, is this is based on whether they think certain papers have similar understandings and similar research fields. They do that, I think, by looking at the citations across a range of papers, and those that cite the most papers together are kind of grouped into a research field. That means we're not just relying on citations directly, which means that we get a really nice clustering of similar articles. Here you can see, this is the seed paper, we've got this paper, one of my other papers, one of these papers, and you can see that they're connected by these lines. The thicker the line, and we'll go here to see that here, the similar papers have strong connecting lines and cluster together. Look at my sad little lonely paper right here, all alone, whereas this little cluster over here could be something that I could explore because I haven't really got much of a strong connection to those at the moment. When I hover over one of these bubbles, you'll see the connection that it's made. It goes from this one to Stapleton to Stapleton, so there's three connections. I could probably have a look at this cluster and see if I could make my research more aligned with this little sort of information bubble right here, but we're not finished there. First of all, they're collected based on how similar the papers are. The second thing you need to know is that the node size is the number of citations, so that means really heavily cited articles, ones that have got a lot of interest in the scientific realm, are bigger. This one is bigger, this one is smaller, this one is small, this is my paper, it doesn't have many citations. All of these ones are actually more heavily cited than my paper because the node is bigger. The third thing that's really important is the color. The color relates to the time in which or the date on which it was published. The lighter the color, the further in the past. What you really want is a nice big node that is dark in color. That tells you that a lot of people are citing it and it's a relatively recent paper. Those are the ones I'd be looking at if I was in a research field because I want to know those papers and find out why they're so exciting. Maybe there's something in there that can inform my research. But you can see here that this one's large, it's lighter, this one's large, it's lighter. We'd expect that correlation. The longer a research paper has been around, the more citations it could attract. But it's these ones that are more interesting to me, so a nice big dark one that's nice and fat. These ones over here look pretty good. This one's really nice and dark. It's recent but it hasn't got many citations, not huge. The bigger, the better. That's what I'd be looking for in my initial viewpoint and my first exploration of this network graph. That's the important things you need to know. It's not a citation tree. In fact, it's much more powerful than that. This is how I would use it. I would start looking here and I really like the list view. If I go up here to list view, you can see then we've got all of these that are similar to my paper and this is what I'm interested in over here. Similarity to origin, which is how they cluster it. But we can also sort by year, by citations, by references and we can download the lot into any one of my reference managers using the bib file. Probably the two most important places on this for me at the moment are these two areas, prior works and derivative works. If I want to see what happened before a certain paper, this is where I would go. I want to go in here and have a look to see if there's anything that is really heavily cited and therefore I must know because it is seminal work, it is very important foundational work for my field of research. Up here it gives you a little bit of a blurb on what this really means. Then we've also got derivative works. These are papers that are cited by many of the papers in the graph and it tells you that it could be a very important add-on from the work that you are initially interested in. I would want to know that because that is more up-to-date. Now, you'll notice that some of these are in blue and you may be like, why are they in blue? This is the least understood thing about connected papers, I think, in my opinion. If we click on one of them, you can see that these end up in blue. If we click off on this, you can see then we've got these in blue and what it means is that it is cited by the selective derivative work. It means that if we click on something, we can see what in our network actually is citing that piece of work. That tells us then that this is heavily connected and that it is probably something we should be spending a bit more time reading because they are directly citing it. Because remember, this is not a citation graph, this is a similarity graph. So if a paper in this graph is highly connected to my initial research paper and is directly cited by something in this graph, that means I want to know about it. That would be headed straight to Zotero or whatever reference manager you use. The last panel you should know about is this one over here. You've got a range of things you can do. You can click on any one of these references and it will pull up an abstract if it's got it or you can go out and find it in other places. So I clicked on this one by Woo and you can see that we can open it in Semantic Scholar, we can look at the publisher page, we can go to Google Scholar, we can go to Publish, PubMed, we can also report a mistake if it's your paper and you realize they've got it wrong and you can save it. Now I've saved a number of different papers but I don't think I would use this to manage my references. I'd use Mendeley, Zotero, EndNote, something like that. Go check out my other videos where I talk about using all of those. But you can go up here and you can go to Saved Papers and they will appear in your Saved Papers list just in case it's something that you don't want to miss out on. But I'm going to go back to this network graph and yeah, that is ultimately all of the stuff you need to know about connected papers. You can also filter by keyword, whether or not PDF is available, open access and filter by year. So there's two important things you can do with each of these papers in this network graph. The first one is by clicking on it and going to Open Graph, you can create an own graph with its own origin paper. You see how there's only one origin paper here? Essentially, it's created a whole new graph with that one as the origin. The second thing you can do is go to Add Origin over here and what that does is it adds it to this list of origins. So it essentially creates a thicker and more connected and bigger network graph because you're adding more origins, i.e. more papers that you want to find similar papers of. Yes, I think that makes sense. So it means now that this network graph is not only looking for my paper but it's also looking for this one, this was my paper down here, and it's also looking for this one because we've added it as an origin paper and you can see that there's very, very few connections to this paper. In fact, Yang has got more and more connections that are similar to it than my paper all the way out over here. And then if we're not happy with that, you can see the origin ones are in this purple color so we can click it and we can say Remove Origin and then it will reconnect and recalculate that graph without that origin in it. So those are two important tools that you need to know about but I would be getting these graphs and I would be putting them into a reference manager for easy access and use later on. If you like this video and you love references, go check out this one where I talk about how to use Mendeley like a pro. It's a great watch.