The need for reliable test data to verify scientific theories together with the necessity to calibrate code‐oriented expressions and their level of safety have pushed researchers to develop test databases. Such databases collect the work of multiple independent scientific initiatives compiling experimental evidences on similar test configurations but with different parameters such as geometry, size of the component tested, boundary conditions, and/or mechanical properties of the materials. Such databases, usually collected for specific purposes, have traditionally faced maintenance, and especially consistency issues. Also, they have given rise to conflicting values due to different interpretations of the same data entries in databases elaborated by different researchers. In addition, such databases are frequently designed and configured to serve for one purpose, although their data could be used in multiple manners. Recent developments regarding machine learning and artificial intelligence have opened the door to exploiting historically collected test results by data mining techniques. In an effort to improve the current situation concerning databases, the fib has launched a wide and open initiative on test data management. It consists of a common data structure, sufficiently flexible to serve for multiple purposes, hosting very different test setups, and meant to be used for cross analyses of data. This allows reusing data originally developed for one purpose to analyze other aspects, enhancing the value of current experimental work. Also, a consistent management structure is defined taking advantage of the fib network, with database editors, collectors and users, allowing for a transparent and documented peer‐review process while incorporating new test data. This paper presents the main basis grounding the test data management as well as details on the first two completed applications, referring to fiber‐reinforced concrete (material data) and to punching of slab‐column connections (structural response), showcasing the framework versatility and universality.