Scientific conferences play a crucial role in the field of Computer Science by promoting the cross-pollination of ideas and technologies, fostering new collaborations, shaping scientific communities, and connecting research efforts from academia and industry. However, current systems for analysing research data do not provide a good representation of conferences. Specifically, these solutions do not allow to track research trends, to compare conferences in similar fields, and to analyse the involvement of industrial sectors. In order to address these limitations, we developed the AIDA Dashboard, a tool for exploring and making sense of scientific conferences which integrates statistical analysis, semantic technologies, and visual analytics.
If you want to know more about this research initiative please refer to the following paper: Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature (published at ISWC 2022).
The AIDA Dashboard allows the users to analyze different entities in Computer Science. In the current version only conferences are searchable (soon we will allow users to search conferences by topics, stay tuned).
A conference can be searched through its name or its acronym using the search bar in the middle of the home page. Then, the system will automatically suggest the closest matches. Clicking on one of the suggested conferences the system will redirect the users to the conference page
The conference page (e.g. Neurips - Neural Information Processing Systems) is organized in different sections:
Left chart:
Blue bars $$average\_citation_y = \frac{CIT_y}{|paper_y|}$$ where y is a year in which the venue receipt publications, and CITy are the number of citations received by those papers in the year y
Black line $$impact\_factor_y = \frac{Citations_y}{Publications_{y-1} + {Publications_{y-2}}}$$. It’s important to note that Citationsy are only the citations received in the year y by the publications written in the years (y−1)&(y−2)
Right chart: This chart represent the ranking trend for the venue computed for every associated focus area using the average citation value. This means that rank(X)y = Position(X,focus_area)y which means that is the position of venue X among all the venues classified in the focus_area in the year y. This representation can be view in rank view (absolute rank) or in a percentile view, which say in which percentile the venue X was placed in the year y.
All Citations: is the summary of all the citations received in the selected time frame (e.g. last 5 year).
AVERAGE Citations: $$average\_citations_{tf} = \frac{Citations_{tf}}{Publications_{tf}}$$ where tf is the selected time frame (e.g. last 5 years), Citationstf is the total number of citations received by the papers written in the selected tf, and Publicationstf is the total number of articles written in tf
All Citations: $$\sum{Citations_{y}}$$ where y is an year. This means that in Citations2021 we can have papers written in 2021 who cites article written in every year.
AVERAGE Citations: $$average\_citations_{y} = \frac{\sum{Citations_{y}}}{Publications_{y}}$$ Here we compute the average number of citations received by papers written in a year y. For example, the average citations in 2019 is computed as follows: We retrieve all the papers written in 2019. We count all the citations received by those papers in years ≥ 2019 and we divide the number of citations by the number of articles obtaining the average number of citations received by papers written in 2019 by an entity