The European Competence Framework for Researchers describes important skills and abilities that help researchers succeed in various disciplines. It is divided into seven main domains, reflecting the broad and diverse nature of modern research.
ResearchComp is a tool intended to: (1) assist researchers in evaluating and improving their transversal skills; (2) guide higher education institutions and training providers in adapting their offerings for researchers; (3) inform employers about the wide range of competencies researchers possess.
ResearchComp establishes a shared language for understanding researchers’ transversal competencies, and anyone may use it voluntarily. Specifically: (1) researchers can identify the competencies needed to pursue careers in various socio-economic fields, assess their current proficiency, and determine where further growth is needed to enhance their careers; (2) universities, research organizations, and training providers can create or refine their programs to equip researchers with strong transversal skills early on, or through continuous training; (3) employers will better recognize the comprehensive skill sets researchers bring, helping them recruit highly qualified candidates; (4) policy makers can track researchers’ competencies more effectively and craft targeted policies that support mobility across different sectors.
Select competences by clickiing. Choose only those competences for which you can imagine 1–3 examples of how you applied them in the past. Then analyse the summary by clicking on "Report". You can (1) print the summary, (2) save it as an html-file with selected answers, (3) send it by email, and (4) copy it to clickboar (for example, to paste it into your favourite AI chat).
Foundational competences are given in black, intermediate – purple, advanced – blue, expert – blue-green.
This tool includes text from the European Competence Framework for Researchers PDF, which is © European Union. Reproduction or reuse of that text may require acknowledgment of the source.
The HTML, CSS, and JavaScript code for this interactive self-evaluation tool is released under the CC0 1.0 Universal license. The code is created by Vladislav Ivanistsev and OpenAI ChatGPT (o3 model). The code is stored in a github/researchcomp repository – in case of any error, please create an issue there.
This interactive tool is provided as-is, without any official endorsement by or affiliation with the European Commission. Consider liking the code on github and subscribing to youtube@doublelayer.
Adapt the following promt to analyse your summary with AI:
For every sub‑category in the given {text, pdf}, assign a level exactly as specified under “Level‑assignment rules”.
For each main category, convert levels to numbers (Foundational = 1, Intermediate = 2, Advanced = 3, Expert = 4), average the numbers of its sub‑categories, round to the nearest whole number, and convert the result back to a level name.
Report that rounded level as the category’s average level.
Level‑assignment rules (apply independently to every sub‑category):
* Inspect levels in this order: Expert → Advanced → Intermediate → Foundational.
* Assign the first level encountered that contains at least two competencies.
* If a level has only one competency, continue downward until one with two is found.
* If no level contains two competencies, assign the level that has one competency.
* If none have any competencies, mark the sub‑category “No level assigned”.
Output format
Main Category (average level) - Sub‑category (assigned level) - Sub‑category (assigned level) ...
List every sub‑category exactly as it appears in the PDF, each preceded by "- " and indented four spaces beneath its main category.