Scientific conferences and journals play a crucial role by promoting the cross-pollination of ideas and technologies, fostering new collaborations, shaping scientific communities, and connecting research efforts from academia and industry. However, bibliometric systems and academic search engines provide a limited support for analysing scientific venues in similar fields, and to analyse the involvement of industrial sectors. This led to the creation of the AIDA Dashboard, an innovative tool for exploring and making sense of scientific venues which integrates statistical analysis, semantic technologies, and visual analytics.
The AIDA Dashboard allows the users to analyze different entities in Computer Science: conferences, journals and research areas.
Conferences and journals can be searched through their name or their acronym using the search bar in the middle of the home page. Then, the system will automatically suggest the closest matches. When searching, the dashboard prompts also research areas, e.g., The Web. Clicking on one of the suggested items the system will redirect users to either the venue panel (for conferences and journals) or advanced search panel (for topics).
The venue panel (e.g., International Semantic Web Conference) is organized in different sections:
The advanced search panel (e.g., Semantic Web) allows users to browse and compare venues according to their fields. The user can browse the different fields using the selection menus and switch between journals and conferences with the button in the upper right. Journals and conferences can be ranked according several metrics, including:
The last two metrics are not typically offered by alternative systems, but are very useful to identify emergent venues that are attracting strong research groups but may not have yet received a good number of citations.
Here we list the different metrics displayed in the different tabs that we derived from citations.
Left chart:
Blue bars $$average\_citation_y = \frac{Citations_y}{|Publications_y|}$$ where Publicationsy is the number of published papers at the venue in the year y, and Citationsy is the number of citations received by those papers the same year.
Black line $$impact\_factor_y = \frac{Citations_y}{Publications_{y-1} + {Publications_{y-2}}}$$ It is important to mention that Citationsy are only the citations received in the year y by the publications written in the years (y−1) and (y−2).
Right chart:
This chart displays how the venue is ranked in each focus area over time. To attain this, for each focus area we computed the average citation value, as: rank(venue)y = Position(venue,focus_area)y which determines the position of the venue among all the venues classified in the same 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 was placed in the year y.
All Citations: $$∑Citations_{tf}$$ is the summation of all the citations received in the selected time frame tf (e.g. last 5 year).
AVERAGE Citations: $$average\_citations_{tf} = \frac{Citations_{tf}}{Publications_{tf}}$$ where Publicationstf and Citationstf represent the total number of publications and citations received in the selected time frame tf (e.g. last 5 years).
All Citations:
Counts the yearly citations $$∑Citations_y(venue)$$ where y is an year. For instance, with Citations2021(venue) we identify the overall number of papers written in 2021 citing papers published at the venue in previous years.
AVERAGE Citations: $$average\_citations_{y} = \frac{\sum{Citations_{y}}}{Publications_{y}}$$ which computes the average number of citations received by papers written in a year y. For example, to compute the average citations in 2019, we first retrieve all the papers written in 2019 at the venue. Then, we count all the citations received by those papers and finally, we divide it by the number of articles.