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`,t,a,_,s,i,k,p,V=`

FAQ

What are your data-sources?

Primarily, the current OpenAlex + snapshot, but we extend it with journal classification and impact factor estimates + from + Scimago

How soon does a new paper make it into the data?

We aim to recalculate the complete dataset at least once a month, so as soon as a paper has + been indexed by OpenAlex, it will appear in our database within a month.

Do you filter the data?

All works published after 2010 in Q1 or Q2 (classified at the time of publication by + Scimago) are considered, with citations and author + affiliations provided by OpenAlex. Institutions are filtered to those that have at least 1000 + such works and subsequently, only citations among these institutions are considered.

How do you classify papers and journals?

OpenAlex provides a hierarchy of concepts + and assigns them to each piece of work, based on the contents, while Scimago classifies journals + into a hierarchy of 27 major thematic areas and 309 specific subject categories based on + Scoups + categorization.

How do you measure impact?

Our impact flows are based on citations, but in some cases we make use of coathorships to + visualize relationships between institutions. In all cases, the number of citations are noted + in the charts.

What is specialization?

We use a modified version of the Revealed comparative + advantage metric. To calculate RCA, we begin by selecting a meaningful reference + group from the dataset. + This reference group provides a baseline against which we evaluate the specialization of the + entity + in question. The RCA score is computed by comparing the proportion of an entity's impact in a + specific + field to the proportion of impact in the same field for the chosen reference group. + Mathematically, + RCA is expressed as: RCA = (Proportion of Entity's Impact in Field) / (Proportion of Reference + Group's Impact in Field). We also introduce a correction term, so that branches that have very + low expected impact don't dominate specialization.

What are the papers that actually make up the relationship between two institutiions in a + certain field?

You can not see the actual list of papers at the moment that make up a branch of impact, but + we are working on implementing it. Due to the nature of the data with hundreds of millions of + citations, and our current architecture of statically serving everything, showing a concrete + list of papers is challenging.

Do you weigh the impact of an institution based on number of authors?

Currently, no. All impact is measured tha same, even if one of 100 authors has an affiliation + to an institution or 3 of 3. We plan to change this, or at least provide the option to view + different ways but it is not straightforward to specify

What kind of visualization engine do you use?

All our visualizations are hand-rolled SVGs with css transitions, made with svelte.

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We use a large, open, up-to-date dataset.

OpenAlex logo Scimago logo

A daily-updated snapshot of OpenAlex, a complex, open - catalog of scholarly works is processed in a transparent way, to create our profiles and - visualizations. Currently, TODO this might change! - all works published after 2010 in Q1 or Q2 (classified at the time of publication by - Scimago) are considered, with citations and author - affiliations provided by OpenAlex. Institutions are filtered to those that have at least 1000 - such works and subsequently, only citations among these institutions are considered.

`,g,o,b,a,h,y=`

OpenAlex provides a hierarchy of concepts - and assigns them to each piece of work, based on the contents, while Scimago classifies journals - into a hierarchy of 27 major thematic areas and 309 specific subject categories based on - Scoups categorization. These hierarchies of concepts and journal classifications serve as the foundation for our - analysis. They allow us to define fields of study, ensuring that one can evaluate entities - within their relevant academic contexts. By leveraging these openly available resources, we're - able to offer users a comprehensive, data-driven exploration of academic impact, - specialization, and expertise across diverse fields and subject categories.

The Revealed comparative advantage metric allows us to assess the relative specialization or expertise of various entities within - the academic landscape. To calculate RCA, we begin by selecting a meaningful reference group from - dataset. This reference group provides a baseline against which we evaluate the specialization - of the entity in question. The RCA score is computed by comparing the proportion of an entity's - impact in a specific field to the proportion of impact in the same field for the chosen reference - group. Mathematically, RCA is expressed as: RCA = (Proportion of Entity's Impact in Field) / (Proportion - of Reference Group's Impact in Field)

It's important to note that the choice of impact definition and reference group can vary - depending on the specific analysis goals and context. In some cases, we might measure impact - in terms of the number of papers published, whereas in others, we may use the number of - citations received. Additionally, the reference group itself can be tailored to the specific - question at hand. For example, when assessing the impact of a research entity within a on a - specific university, the reference group may consist exclusively of entities from that same - country, providing a localized context for the analysis.

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