 Hi everyone, welcome to my talk. My name is Elina Tacolo and I'm a PhD student in Friedrichschilding University of Jena in Germany. Today I'll present to you our attempt to create a synthesis of the ecological niche concept using the research weaving framework. Ecological niche is one of the most fundamental concepts in ecology. It is based on the very old idea that each organism occupies a distinct place in the environment. However, the study of ecological niche practice has proven to be complicated. How do we identify a species place? Is it the species habitat? Is it its functional role in the community? Or is it some mathematical construct, like a multi-dimensional hypervolume? Since its introduction in scientific literature in the beginning of the 20th century, the concept has been studied through many different prisons. In a recent review we wrote, we included a non-exhaustive list with more than 30 definitions of ecological niche that we found in literature. It is just clear that ecological niche is a very diverse concept, which spreads over multiple subfields of ecology and there is clearly an ongoing scientific debate regarding its components. For this reason we decided to use the research weaving framework because it allows for both evidence and influence synthesis in order to explore and map the diverse literature of ecological niche. Thankfully the offers of research weaving provided a list of recommended tools for each part of the analysis which came in handy when planning our project. So in short our aim was to quantitatively analyze in a reproducible way the ecological niche literature and identify temporal trends. Here are the components of the research weaving framework. This is a list and each analysis does not depend on the previous or next one. Each step is independent. In the next slides I will go through this list that explain what we did in every step of this analysis. First of all we compiled the data set of more than 30,000 publications along with their metadata. Then we had to identify the study species. Doing this manually for 30,000 publications is a very painful process so we decided to use an algorithm which is called GN Finder. The mechanism of GN Finder is based on the assumption that all taxonomic names, classes, orders, families, etc. start with a first capital letter. The algorithm has two steps. At first it identifies all words beginning with a capital letter in a text and then it compares the extracted words with two online taxonomic databases and Cyclopedia of Life and NCBI. If the word exists in one of those databases then it's a taxonomic name. If not, it's considered a normal word. Here you can see an example with an abstract from our data set. GN Finder would first extract the words in bold and then after comparing the results with the databases it would keep the green words and remove the yellow words. The performance of the algorithm was very good. Only 5% of the words in the final output were not taxonomic names. Then we classified the studies according to their type. In particular we had to identify whether they were experimental, observational, theoretical or meta-science studies. I have to admit here that this is the only part of the research weaving framework that we haven't fully automated. We couldn't find an algorithm that can make an inference about the methodology of a study. So we resorted in an artificial intelligence online platform called Ryan. We used Ryan in order to label our abstracts one by one. So if you have any idea of how we can avoid this please drop me an email. The good thing about this though is that we can use more detailed labels and more importantly we can classify the studies according to the source of their data, paying it database, fieldwork, simulated data etc. Next up is a part of temporal trends. This step requires a plot with a number of publications per year. As you can see ecological niche studies show an exponential increase over time. Since our dataset extends over a period of almost 100 years we decided to break it down to smaller subsets. Every analysis you see in this presentation has been run once for the complete dataset and once for each 10 year subset. This allows us to identify temporal trends in all the components of the research weaving framework. Moving on we have the spatial patterns. For this step we use the bibliometric side package to extract the country name from the affiliations of the authors. With this information we can construct networks of collaborations between countries and identify clusters. However due to phenomena such as helicopter science the affiliations of the authors do not always correspond with the places where the studies actually took place. So we decided to take this one step further and create a process in order to identify study areas. The identification of country names in the abstracts can be easily done with the help of string R and maps or packages. But that was not enough. Quite often the authors instead of mentioning specific countries they mention biogeographical regions. For example a study might have taken place in the Amazon rainforest, the Alps or the Mediterranean. For this reason we decided to use text mining again in order to identify location names in the abstract. This is still work in progress. The goal is to build a global heat map in order to identify underrepresented study areas in the ecological means research. The next component of the research waving framework is content analysis. Here we use topic modeling which is a text mining method in order to identify conceptual topics in the abstracts of our publications. Essentially these topics represent research communities inside the ecological means literature. We identified 10 topics and here you can see their evolution over time. Next up is the analysis of terms. Terms refer to the keywords of each publication and luckily the keywords are included in the metadata downloaded from online databases such as Web of Science or Scopus. Using their occurrence frequencies we can build networks like these ones. Here each node is a keyword and the edges indicate coherence of two keywords. In this slide you can see such networks created with bibliometrics and iGraph are packages. Furthermore in order to quantify temporal trends we calculated network indices for each 10 year subset which I won't explain in detail today due to limited time. We created similar networks and calculated their indices for the next two components as well. So similarly to terminal analysis we created networks based on co-authorship patterns. Here each node is an author and each edge indicates shared authorship. Again we have calculated the network indices for each 10 year subsets to show the temporal trends of co-authorship patterns. Here you can see the publication networks which were created based on co-citation patterns. Co-citation is defined as the frequency with which two documents are cited together by other documents. So here each node is a paper and each edge indicates two papers that tend to be frequently cited together. As with previous analysis we calculated the network indices for each 10 year subset. To sum up we here try to apply the research weaving framework on the extensive literature of the ecological niche concept. We use many tools recommended by the authors as well as additional text mining algorithm and we conducted eight different analysis in order to construct the conceptual map of the ecological niche concept. The ultimate goal of this project is to bring everything together into a nice automated workflow. Stay tuned for the preprint and thank you very much for your attention.