 in speeding up the process of choosing the right research methodology. Before we begin, I would like to mention that you can send us your questions using the questions tab in the control panel, and our expert speakers would be happy to answer all your queries at the end of the discussion. Today we have with us three expert speakers, Dr. Gavin Hogan, Dr. Sourish Das Gupta, and Dr. Anupama Kapadia. Dr. Hogan is a highly published author and researcher with 20 plus years of extensive experience in manifold science field, such as bioinformatics, health information technology, sociology, and cross-cultural Japanese gerontology. Currently, he's appointed as a senior research scientist at the Center for Home Care Policy and Research at the Visiting Nurse Service, New York, USA. He has held many senior level projects at the strategic level in training and development of health outcome researchers, program evaluators, clinicians, social workers, nurses, and other scientists. To date, Dr. Hogan has 150 plus publications with credit, which includes research papers, books, book chapter, review article, and book reviews. He is also an expert editor and reviewer of peer-reviewed journals, grants, and books. At Anago Academy, Dr. Hogan conducts basic and advanced session for guiding research scholars successfully through their publication journey. Professor Das Gupta, the founder of RAGS, is an ever-curious researcher and an educator with deep-rooted entrepreneurial bug. He did his PhD in computer science from the University of Missouri, Kansas City, USA, and currently works as a professor of artificial intelligence at DAICT India. He is particularly sensitive about various shortcomings in education and research. Through his research and entrepreneurship, Professor Das Gupta wishes to contribute in making top-class research and education opportunities available at all strata of society. He loves studying and discussing artificial intelligence, theoretical linguistics, culinary art, and comparative religion. Dr. Kapadia is a licensed specialist in physical medicine and rehabilitation orthopedics by qualification and scholarly publishing profession by vocation. She has been working in and around the editorial paradigm for 15-plus years now and is actively mentoring and training academic professional in the writing and editing field. To date, she has led several projects related to fulfilling newer requirements of clients and publishers by enhancing the editorial, peer review systems and author services of Enago. She is a member of well-renowned scholarly associations such as Co, ISMTE, and Ease, which focus on increasing professional networking within the scholarly publishing community and help establish best practices among publishers and journalists. She is also a volunteer in different committees of ISMTE and Ease. We extend a warm welcome to our speakers and thank them for joining us today. Now, without any further delay, I will hand over the mic to Dr. Anupama to begin the panel discussion. Thank you so much, Neha. Welcome again, Dr. Gavin and Dr. Suresh to this ever-interesting discussion. Thank you so much for agreeing to come on to this panel. And just quickly, I want to say that we already have around 600-plus attendees on her webinar and they are from different universities. I can see at least 10 countries, so we have good representation and this will be a well-organized, lively webinar. So, let's make it an interesting one for our attendees. All right, here we go. So, as we know, the topic is about research methodology. It's about de-mystifying the field of research methodology or the segment of research methodology, something that all researchers encounter at different stages of their life. So now, my first question to both of you would be, yeah. So, basically, in terms of a researcher's life cycle, how should they begin? Dr. Gavin, I think the first question is for you. How should they begin to identify the gaps and how should they begin to compare research papers? Because this is like the most basic thing that they should be doing and getting it right. So, Dr. Gavin, what are your thoughts? Thank you, Dr. Anu and to Winago for inviting me and for including me in this esteemed panel. I really appreciate it. Thank you. So, the way I think about this is at a couple of levels. If you're a college student and you're trying to do a research paper that's going to entail possibly some different steps than if you're a junior researcher or senior researcher and you're diving into your field. Either way, the gold standard is really immersion into the field. And you get that by reading the journals and becoming familiar with the field as a whole and then drilling down deeper and deeper into the topic that you're interested in. There are some structured ways of finding gaps in the literature and there's no need to reinvent the wheel every time. And in fact, many full-time researchers employ, let's call them some shortcuts, to identify the gaps in a particular field where you would want to potentially strike out in a new direction or to pick a new project. One of the ways is, of course, doing literature searches and literature reviews, staying up with the literature and you don't need to read every paper in your field, but you can skim the abstracts of some of the leading journals in your field and papers that are coming out and you'll get a good idea of what's going on in the field. And in that kind of, let's call it a browsing search, it's not a structured search yet. You're going to come up every once in a while, you'll come up with something called an agenda-setting review. And this is where experts have come together in a structured way to identify the gaps in a particular field. What are the questions that still need some work? Where do we want to put our resources? And of course the funding agencies also produce such kind of agenda-setting papers. The big national healthcare entities, for example, in the United States, the National Institutes of Health, they put out white papers from time to time about the needs of the field or subfields to move forward. And so there are some structured ways to do it and I'm sure Dr. Suresh will help us in some of the more automated ways. But essentially that's how I would start. Yes, of course I'd completely agree. And actually my question to Dr. Suresh would be, based on what Gavin just described, is it easy to do a structured search? What is your thought about that? How can young researchers get used to a structured search so that they get to the results faster or get to publishing? So I think what I think, first of all, thanks a lot for your inviting. What I think is that when you go into structured searching, part of that is the process of the spiral loop of how you pick up one paper and that can be a survey paper, that can be a position paper, that can be a white paper. And what Gavin told us is pretty much we are trying to figure out, can we automate some of that process, the standard methods that we actually pick up the gaps and can we emulate some of those process and speed it up inside racks. So that is the entire thing. So it's not a new process that we are trying to bring it in. We're trying to figure out whether we can automate or semi-automate something. Now, if you look at racks, if you use racks, you will see that while you're reading some paper, you can figure out what are all the other papers which are like survey papers or which are like review articles and so on. It can specifically go to certain aspects of the paper which racks can grab out and say, oh, these are the problem statements. This is the research question. These are the assumptions and this is the novelty and so on and so forth. And you can actually select those sections and then again look for papers which have similar research problem or similar novelty or related novelty or similar assumptions. Now, one thing I tell my students is that there are, in our field at least, there are like four different ways in which you can pick up the gaps. One is that the problem formulation or the way the research question has been formulated itself may have a gap, which means it's not completing the different aspects, the important aspects, some of the important aspects are missing and you might actually reformulate or redesign or complete that research question. And so that's one gap. Another gap is the assumptions. Maybe the assumptions are invalid or some of the assumptions are not true under certain kind of conditions or maybe you want to lift some of those assumptions or explore more carefully by adding or modifying some of those assumptions and that itself becomes a research question. The gap may be in the approach also. There's something called approach gap where something doesn't make sense or maybe there is a loophole and it's not always about the gap, by the way. It's also about the opportunity, especially in engineering and computer science. A lot of times you're not really like there's a gap or there is something lacking, but it's like the building block, like a thing of Lego where you're trying to incrementally trying to build something, improvement more to say rather than a gap. And then in many cases, RACs will also extract out the limitations or the future scope. It tries to detect those parts while you're reading a paper and makes it very explicit in front of you that these are the limitations and these are the speculations and these are the future scope and the authors. Some of the findings, right? Some of the findings of the previous studies. So it dissects it for you according to the different arguments or the narrative structure of the paper. Yes. Completely, I agree. I think you've already met the curiosity of the audience with respect to RACs. We have to introduce that a little bit later for them, but that quickly that discussion, what you just said, that quickly leads me on to the next question and we have the next slide. Yeah. So once researchers know what they are going to study, right? Or have identified an approach gap or a method gap or a logical gap or an argumentative gap, right? How in your best, in your best knowledge, Dr. Gavin, I think the question for you first, how should they collect their findings and how should they begin writing or documenting their literature review? So what are your thoughts? Well, I mean, there's no one good right correct way to do this. A lot of this has to do with the personality of the researcher and their style and also the resources available. I mean, because some of these projects can be quite extensive and time consuming, access to the bibliographic literature is sometimes restricted or not as good as we might think or hope it would be. But there are, again, some structured kind of, let's say, pen and paper types of ways of organizing one's literature review to find similarities, for example, across papers. You want to find a particular experimental design or result across a number of different papers or differences. You want to compare the differences and line up the differences or you want to find which particular method, which method did they use or to piggyback on a very important but often overlooked shadow factor in this kind of work is to look at the assumptions. Look at the assumptions or sometimes even the unspoken assumptions that are already known by the advanced researchers in the field. They might not even feel they need to address the assumptions but could nevertheless be quite important in how the paper is structured and the results that come out of it. So I will say that there are some tried and true standard paper and pencil types of ways of abstracting information from across a number of papers and then slowly kind of boiling it down to something digestible. The Sage publications, for example, their big famous publisher, they have dozens and dozens and dozens of monographs about how to do each of these little things and then quite digestible. So I do recommend the Sage series on kind of the old fashioned ways of doing some of this work. Yes, and that's very important, right? To make it digestible because it has to be interesting not only to the researchers from the same field but with interdisciplinary research with the laypersons and also maybe the trans-review committees, corporates who are interested in research for them to digest whatever you put it out as researchers. It becomes extremely important. Dr. Suresh, similar thoughts? Yeah, absolutely. But what we also realized that a lot of the times when you're writing down your notes and putting things together, but you're also reading a lot of things, right? And a lot of things are also in your pending, you know, like your to-do list which gets filed up as well, right? So what happens is that we don't know what we don't know in the sense like there may be some really good, interesting, important things that are similar or related or complementary or contradictory even, right? But it's still there in somewhere in my pile of papers and I don't know about that, right? Or it might be a deja vu moment that, hey, I've read something very similar or related but I'm not able to remember which part of which paper, you know, or maybe I have paper but it's kind of like lost somewhere or maybe I don't even remember that I don't remember in the sense that, you know, it seems to be new to me but actually not. I have read something two months back or five months back or seven months back that's kind of quite related to what I am for engineering. So what we did was we came up with this feature called contrast whenever you have this deja vu moment or whenever you feel like comparing something you just select it, click on a button and then all the papers with those particular sections which are related from a narrative point of view from a semantic point of view which are related to what you selected all those papers will come up and you can pin them like a sticky note which bidirectionally pins, you know, both the sides together sort of like you stitch the sections of one paper to the other sections of the paper and it can bring it like a network of whenever you find those things. And you look delighted when you talk about this and you should be, I think it's so I think it will be very helpful, something like this because the information nowadays I think that research is exposed to through open access and also infrastructure online, digital infrastructure there is too much information and that brings me to the next question so can we have the next slide? So yeah, so basically they're also exposed to different perspectives, different arguments different findings like we just discussed too so maybe a set of new researchers versus experienced researchers presenting different arguments and different perspectives in their papers so especially early career researchers and the newer authors how should they manage such different perspectives and arguments and how should they make logic out of it? Dr. Gavin your thoughts? Yeah, I mean this is this is quite hard to grasp argument or issue sometimes because we when we're meeting certain things we sometimes don't even really realize that the author is coming from a particular perspective or not sometimes again the assumptions that are driving the argument are flavoring the paper or the item in ways that we don't quite even that are not obvious in other words so one way to try and work around it or at least to kind of counteract this idea of different perspectives having sometimes let's call it non-objective goals or hidden goals sometimes is to use a process called triangulation so we want to triangulate on our issue by reading and absorbing and taking account of papers or journals or ideas that are coming from different sources and different perspectives so that we can somehow sort of triangulate on some middle ground that we might call something closer to the truth and that is kind of the old fashioned way of doing it and it's tedious sometimes but the good thing about going through in sort of this way in my opinion is that you sometimes find the as Dr. George says the thing you didn't know you didn't know you find the things that you didn't realize were connected to what you're thinking about that's often why you'll find me wandering deep into the bookshelves of the library you find the book that you want but then three titles over is another book that you never heard but that happens to be extremely relevant so this is I think part of the fun part of yes it's the fun part you're finding all of these interesting things that are related and that are building the connections and building the ideas and building the arguments and then of course to getting back to what you want to do giving you more fodder and more ammunition and more food for thought in moving your own work forward. Yes the process of discovery I mean it has to be an enlightening one it has to be I think every five steps that you take if the sixth step is a surprise step then I think you would be delighted so Dr. Suresh then from an automation point of view does this process become easier if you I'm very delighted to tell you that you know we are kind of closing a long going research that we are doing on a particular feature called Advanced Minor and what it does is that let's say that you're reading this paper and it's like show me papers which have different approaches for the same problem. Show me papers which have diverse approaches show me papers which use the same approach but for a different problem and you can do all these sort of combinations like I call it like making your own subway where you can have same problem different approach same problem different experiment different data sets used on our sample studies used right or the same sample studies you same problem and you can do also the combination sort of to dig into narrow down into those things which otherwise is very difficult to get that out from a standard discovery engine so to say. Yeah but at the end of this will you be able to triangulate like Dr. Devan said. Right so once you once you discover those things and then probably what we want to do next is that to generate that sort of like how to put it like what you see in Amazon the product comparison sort of table so we generate that for you saying that okay this is how they are similar this is how they are related this is reinforcing the other or this is how they are you know they are complimenting each other or they are one is taking the other as a supportive evidence to establish something and all those sort of constructs of how these are different or similar and so on and so forth you know sort of so that's the next step of you know once we can at least retrieve you out that okay these are the similar things these are the different things with the divers that's out there you know you can filter them in terms of publishers rather venues you can filter them in terms of the prestige value of the conference of the channel you can filter them in terms of authors and so on and so forth yes and there are sorry Dr. Anu there are structured ways of assessing the credibility of evidence and out there I give some credit to the business management and operations research community for helping to structure some of that as well and of course in healthcare I'm very familiar with healthcare literature is they're very keen on assessing the credibility of research in doing things like meta-analyses which is a mathematical way of combining evidence across different studies such that you end up with you know sample sizes that could never be accomplished within a single study but you are cooling the mathematical results across a number of studies and so yes so there are there are some you know more or less formal ways of doing what we're talking about on the qualitative side as well yes yes and I think that's a wonderful sorry these are the kind of these are the kind of things that we want to do going ahead yes yes I think Dr. Gavin is providing great insights and I think Dr. Sourish's team is doing a wonderful job of anticipating these challenges and trying to make it easier for the researchers but what Dr. Hogan just said it is a wonderful segue into the next question which is about systematic literature reviews so it is one of the formal ways of correlating data and long term studies etc so how should researchers correlate findings from these systematic literature reviews and how should they integrate them into their research arguments or their research methodologies so Dr. Gavin well I'm not sure exactly what you mean by correlate but but I can just say a few words about this in any way I mean for many people producing the systematic literature review is the first step of a line of research for other people that's what they do they specialize in producing the literature reviews so it kind of depends on what your end goal is but as I said there are formal ways of assessing data across these systematic literature reviews that are already out there and no need to reinvent the wheel so the idea of extracting data in a structured way from across the literature space is one that we've already discussed but I also mention another point that we have not mentioned yet and that is we're talking so far about the publications that are widely referenced and abstracted in the various online services now there is also something called the grade literature and the grade literature is harder to find but this is where you will find often the studies that are negative studies or studies that had equivocal findings and so in the published literature you'll find a bias towards positive findings so to help counteract this the more sophisticated and time and resources that you have to devote to this you should try and find these unpublished or white papers or association reports it's a lot harder to find but as you dig deeper you'll find that adding those sources will enrich your literature review very much so quick question does doing that improve your chances with the publisher or the journal too or is that a risk that the researcher is taking in terms of looking at the negative studies, finding or negative results studies and then trying to combat them or something like that can it go either way I think it could go either way I personally would think it's a lean towards being a plus because as long as you call it out and are transparent in your process and how you did it I think it's a plus I think it's a wonderful way for an experienced researcher to sort of do this because I think with newer researchers they are still trying to find their ground so if they have a mentor or a guide to help them with this because I have encountered many researchers during my experience where authors and researchers actually struggle to make some sense out of the negative findings they are not sure whether they want to place any arguments or they want to support the negative findings they are not really sure I think you were saying something I was just about to say that until and unless you find something if you can provide some new insights that becomes a contribution to the community out of the negative finding even if that's a negative finding your analysis and how you interpret and what's the new insight what is the takeaway for the community that has to be placed and positioned in a very good way in a very positive way so it's a true crime I think I don't know if it's contextual but I think around 20 years ago the lack of DNA evidence it generally resulted in negative findings more or less but now for the community you can definitely publish research cases based on DNA findings because now that is a positive something like that that benefits everyone I think that supports the literature also researchers will gain more confidence about doing this I mean there's there's kind of an infinite regress on going backwards in this in what we're talking about and it's all driven partially by the curiosity of the researcher you find a bunch of negative findings and then you also find a bunch of positive findings why are we getting these negative findings that's your next problem that's your next research question so it can just it can really help you to find your research problem is to look at the negative findings yes I think we'll cover this in the research gap question also depends on the personality of the researcher completely true and so basically over to the next question it is sort of related to what we just discussed but we might as well cover the topic in terms of experimental designs or datasets that are more concrete now so we're not talking about papers that are subjective or qualitative but quantitative research what are your tips or what are your suggestions for researchers and researchers who want to do this comparing contrasting research papers containing such datasets well I mentioned it already but this is where you would really want to get more structured types of abstracting and pulling data out in a structured way it can become quite intensively mathematical and statistically driven process for how to do this and the economists are actually quite good at this as well but you can really start out really with just kind of constructing little two by two tables you know yes no yes no on two of your dimensions of interest and start plugging in some numbers 14 papers say yes yes another 10 papers say yes no 5 papers say no no and you start to get an idea qualitatively about sort of what the field is saying in an easy to apprehend way I'm all for doing these little side noodles of two by two or three by three tables or little tiny little charts and tables and graphs for myself and it opens up another whole area for discussion which is visualization of your data is getting to know your data through many many ways so you're looking at it qualitatively you're looking at the words that you're maybe doing word counts and word frequencies and then you're using little miniature scatter plots to see what's happening across all the different papers and there are a lot of good ways like that that are simple sort of back of the envelope heuristics that will help you understand the scope of what you're looking at that's interesting so Dr. Sarish does RACS also help researchers with something like this I have already talked about the compare and contrast thing but at a very basic level you can go to the experiment section of the papers and then you can actually select certain things even the data set or the results whatever is the measure or the metrics or the sample how the experiment design has been done for example study or clinical trials or whatever that is and you can pick up all the papers that are out there that are similar or related experimental design and you can pick that up and then you can add it into a reading list and then you can also find the connections at a deeper level with the tool or what you already have read which is already there in your collection so at that level it's already there to like let's say there are two tables or two figures and to actually automate the process of how you interpret these two figures or two tables and how they connect each other and apply some sort of statistical stuff or some sort of mathematical stuff so these are the next things that people are doing in our area by the way in NLP and all people are trying to figure these things out and that we would also want to do in the patient hold that thought in the graphics but I'm going to come to that yes Dr. Gemma did you have a question did you have something to add? I was just going to say in terms of finding these related papers Dr. Sorsch's tools are perhaps an extension of some baby versions that are already out there and available free so for example when you go look at Google Scholar or through PubMed in the healthcare literature and you find an article of interest those platforms will also generate related titles or titles that are citing the research forward in time so you published a paper in 2020 and then you pull it up and then it's going to start showing you the papers that have cited that paper so there are some tools like that that are already there that are citations that work in one of that and we also have by the way we forgot to mention we also have that thing so you can actually see who cited this paper and again how good they are in terms of the prestige value of where they have been right and the prestige and the impact factors of the journal and the quality of them and so on yes alright I think let's go to the next question but we seem to have already covered this topic more or less but if you still want to if either of you wants to add anything in terms of exploring and organizing resources or references to enrich specific ideas in terms of the research gaps or the research question you can still cover this question but if we are going to repeat some topic then we can go to the next question which is about graphics so I leave that to you Dr. I will just say that one thing that we haven't talked about I think yet is this idea of cross fertilization across disciplines even and this is something that I think you'll find in some of the more productive academic communities you'll have people who are so curious about the world that they're reading the engineering literature or the bioinformatics literature but they also happen to be interested in 18th century British folk dancing and they say well how do I use what I'm reading in the engineering literature to analyze folk dancing patterns in 18th century Britain and so they start bringing the methods from one field into analyzing things in another field and in fact that's one of the things that mark the paper that is up on the handout by the sequence analysis paper where I use the sequence analysis methods from biology and genomics and computer science to analyze series and strings of clinical events and so that's an example of using methods from one field to create new knowledge in another field yes absolutely I think public health is also one such field I think where public health and epidemiology where I think history matters, present matters, science matters, people matter so an interesting thing to add on to what Gavin said one of our early that algorithm we don't use but one of our early algorithm to figure out where the two documents, two papers are similar to what extend we actually adapted the Smith-Waterman algorithm which is the kind of gene sequencing algorithm and I'll tell you what really happened I was a little early to one of my PhD students' defense and there was another student's defense going on and I said then what the heck just go in and let's see and that guy was in from bioinformatics and he was talking about needleman motion algorithm which is another kind of gene sequencing algorithm and he was talking about that and it suddenly struck me that you know what like I can actually look at two sentence similarity you know align sentences at a semantic level maybe in that way so what we did in RACS is that if you are reading a particular paper and going into a methodology or something there is something called new suggestions so what we do is we throw out this sort of more serendipity surprise means sort of things which are you know which are related to what is your current focus and try to bring up also those papers which helps you to expand or broaden out your scope not just to narrow down but also to broaden out so it's kind of like a mix of both yes yes I appreciate that very much that we're sharing methods already we're coming from completely different research traditions and yet I just understood exactly what Dr. Das Gupta said even though I'm not a computer scientist at all Thank you so much for bringing in your perspectives about interdisciplinary research I'm glad we touched upon that point I think we did not discuss it yes and Dr. Suresh next slide please this question is first for you so basically you were talking about tables and graphics and visuals so we have this plan like can all your paper collection across different projects maybe even in collaboration and I'll touch on that later and how can I visualize them as a graph let's say and can I zoom in and zoom out and figure out what are the more finer relationships and group them together into let's say similar research methods and I group them together into one place you know with their similar problems similar application domain application where this technique has been happening so this is possible with only graphics only figures yes and this is also possible with the data sets that you have because we are already doing that from the papers that you upload we are extracting the tables we are extracting the papers we are able to look into each and every single cell of the table and so on and so forth so we have the semantics we can extract the equations also you know from the you know if you have equations or something like that from the paper and you know in fact tables and equations they're easier to understand because they have a fixed semantics you know and then we can actually try to figure out what is in their line and so on and so forth so that's what we want to do that's also part of the research roadmap but visualization is pretty much a very important focus for the product team also for us also we know how important it's going to be going completely and also Dr. Gavin I think I was told that this topic is close to your heart so what do you have to say about the use of graphics yeah I already started to discuss it a little bit in terms of exploring your data using various sorts of visual media and you can do it on the back of an envelope with you know pen and paper or you know you can be a little fancier about it but it's also extremely relevant in terms of presentation of your results and there are extremely creative and beautiful ways of transmitting a lot a highly dense amount of information visually that would be much more difficult to do in words or in tables and there are some amazing resources out there to get one's mind stimulated in this area the one that comes to my mind is Edward Tufty T-U-F-T-E and he's written a series of wonderful books about how to visualize your data for maximum readability and transmission of information so I highly commend you not just for data exploration but for presentation as well alright I think yeah that sounds beautifully so I think that's the end of our time discussion unfortunately I don't know how the time flew by but yeah so I would like to take this opportunity now because Dr. Sarish did mention Rax a lot of times and I'm sure all of you might be curious on what Rax is all about so let me just quickly play a very short video so that you understand what Rax is all about so yeah here you go I'm not able to hear the video I'm not sure if the other panelists are able to hear the video no I am not able to so the team is quickly checking that in the meanwhile are there any other points that you all think we should be discussing in this panel webinar because if we do not have questions we can discuss those but I think we have questions already so yeah okay so let's get the questions alright so fine we can do that too so team are you all checking the audio I think there is a caption over there so probably the narrative has a caption I guess alright I think the audience questions are coming in so let me just quickly let's take them so okay it's about Rax actually so the audience wants to know I'm not able to hear you yeah now it's okay yes what was the question the question is does Rax work with languages in English enough now no we want to this is also very much part of the pipeline but what kind of language what do you we want I mean what language what do you mean so I think there is no language specific language that is mentioned but I can see a lot of universities from the Latin America I think attending this webinar so maybe Spanish and Portuguese would be right yes so right now we don't try to understand papers which are written in other languages we won't be able to do a very good intelligent work we will pass it we will give you something but we won't be able to do really good semantic understanding we won't be able to do it right now and I understand why they are asking this because I think it is a librarian that is asking this one of the attendees because I think the research output for Latin America is also very high and they do publish a lot of local language papers in local journals I think if a solution can be provided to them understanding that very much usable from a technological point of view is just that we didn't get enough language to pass I understand but I think they will look forward to this the next question is I think Dr. Gavan and Dr. Sourish how should researchers keep doing literature review when writing a dissertation so I think it is a student that is asking this question literature review for thesis or desertations do you recommend a specific process I am sorry can you repeat the first part so yes how should researchers do literature review from beginning till the end of writing a dissertation or a thesis well this is something that is really going to be highly field specific and even university school specific some schools want highly formalized literature review as one of the chapters in the dissertation others are little less formal about it and just want to see that you have incorporated the right literature for the research question at hand either way so it is going to be hard to provide a general answer that will fit every situation but what I would say is that one good way to think about it would be to go to the library at your school and look at a whole bunch of the recent dissertations and see what is expected and what is sort of a good model for that it is your research question and your department style so that is how I would start Dr. Suresh, anything to add to that I mean because it is kind of like summing up and threading some of the entire story when you are doing a dissertation so make sure that every chapter which will cover specific topics should have adequate literature support or references and there should be a good quality references and don't miss on citations where given the citations so if you are using a fact as an evidence to support a claim then make sure that the fact is cited and also make sure of course this depends upon discipline but in our discipline at least we need to make sure that the citations or the references are pretty new as much new in the sense relatively speaking should be contemporary to what you are doing a lot of work has been done all through maybe the last 3 years, 4 years, 5 years but if you are citing something from 1990 and if that is the idea you are covering that's not a good idea exactly so team are we ready to play the video now can you pause the video please I am not sure if we can hear the audio so what I suggest is we share the link with all the authorities and the registries but I do want Dr. Saresh to talk about tracks and let's not lose value of time so I want you to talk to the audience and tell them how researchers can disseminate and discuss their findings using tracks so basically in tracks if you open up tracks it's a very simple procedure just sign up just like how you would do on facebook or LinkedIn or something and you have what we call as a reading room where you are a paper and you have things to figure out what are other things associated with those papers and so on and so forth similar problems same problems surveys articles related to the people that you are reading and then the thing that you want to discuss with people like a virtual study circle sort of or a journal club sort of then you can actually share that with your supervisor or with your lab so peers and you can jointly start a thread or even comment certain aspects of the people and start a thread like a comment thread sort of and people can even discuss it out remotely even and that's a great way of doing literature critical reading and literature which is a review you know which is collectively jointly as a team and also you can do critical reading and you know there's something called critical templates so we also already create some templates for you and you can actually while you read you can start down your notes like bullets on that template which helps which helps you to ask the right questions you know what should be the right questions when you are actually reading a paper and then again you can share that with your collaborator outside even external people also and they can give comments on how you have reviewed your paper so while you are reading a very important paper you can immediately on a split screen sort of way and you can actually start putting your thoughts and reviewing the paper and then you can share that with your team or with your lab or with your supervisor and get some comments on your own review and then finally polish it up and you can publish it also later on as a systematic review or a review that makes sense somebody has also asked Ravija Srivastava she has asked how do you do this pinning on rats how do you pin papers or how do you pin research when you use the right tools so we have you know what you can do is that if you find a particular statement to be very important you can actually pin that statement and it goes right at the top of the paper or actually there is something called keen size and summarizer and the summarizer is like a section by summary which means it summarizes every section of the paper every section of the subsection and section of the paper and then you can go over there and if you find that this is a very important statement and you really want to highlight or pin you can pin that section and it gets pinned at the top of the paper which means that's kind of like a flash card you are like designing your own flash cards for every paper that you are reading inside rats and then you can share that and all of those things can happen so that's one thing the other thing when I was talking about the methodology and you want to know all the other people that are related to that specific methodology in that context of the paper so you select that section place get all the papers and whatever people you feel like reading you can staple it and it gets staple like a sticky note that the other people gets stapled with that paragraph on that section like that and then you can again go back to that thing and get back to that people we see stapled and start reading and again the same process that's nice Dr. I was just going to say that I tried RACS myself recently I can't say that I'm an expert user but it's very intuitive and easy to use and easy to start so I would just give you a plug and a thumbs up for making a really nice product that's nice I was just about to ask you would you recommend this to your students would you recommend this to them definitely I would definitely encourage the students to try it out and I think there are some free options and some other options so try it out and I think a lot of questions actually about how so if there's a question if a researcher or a group of researchers want to access the RACS tools how should they do that Dr. Suresh so you know there's Suresh Suresh is the best person to contact us there's info at the redbrax.io there's a contact page also if you want institutional institutional licenses then you can contact Suresh or marketing head if you want to do just individual subscriptions it doesn't take much of it doesn't pinch your purse that much it's just $5 a month and you can go and you can subscribe through spiping your credit card or whatever that is you can take monthly subscription you can also take a whole year year-long subscription but then the good thing for students is that it's heavily discounted and we do our installments like monthly auto installments which we are doing for a whole year which means that you don't have to like for the entire year it just gets ordered in the app yes I think we are reaching the end of the webinar we have almost reached it so basically what I wanted to say was I think this entire panel discussion has been extremely extremely enlightening even for someone like me not being from the research field but always wanting to help authors and researchers publish their work in whatever best way that they can I think we've sort of fulfilled that objective in this webinar but it has led to so many more questions that I hope we will cover in the next few sessions so with that thank you so much Dr. Gavin for your time for your expertise we appreciate it so so much and Dr. Sourish thank you so much for your insights yes thank you so much for everyone thanks a lot for joining us and for taking care of yourself and do great research great that's a good good good way to end it yes and to all our attendees like the other panelists said thank you so much for attending this session my team will be contacting all of you with every information which that is necessary for you all to know related to the webinar they will also share the handouts that Dr. Gavin has kindly provided they will also share the links that Dr. Sourish mentioned in his discourse so thank you so much everyone have a nice day have a nice evening wherever you are thank you so much bye bye