Findability, accessibility, interoperability, and reusability (FAIR) are the key principals for modern research data.
While data-driven science started to implement these principals for measurement and simulation data, it is not completely immanent how these principles translate to (pure) mathematics research. Although mathematics research results are time independent, a new generation of mathematicians demands a timely research infrastructure. In this symposium, we discuss the question of FAIR data for mathematics from two perspectives:
1) What are the needs of future mathematicians and
2) what is technically possible today?
|10:00||Michael Kohlhase||Mathematical Data wants to be Deep FAIR|
|10:24||Fabian Rack||Openess and FAIRness for mathematical research data: the legal framework|
|10:48||Moritz Schubotz||Collecting Datasets by Analyzing References in the zbMATH Database|
|11:12||Wolfgang Dalitz||alsoMATH - A Database for Mathematical Algorithms and Software|
|11:36||Christian Himpe||Cultivating Cooperation in a Competitive Community|
|16:00||Lars Kastner||Confirmable workflows in polymake|
|16:24||Claus Fieker||Storage of Number Fields and Related Objects|
|16:48||Thomas Koprucki||Modal Pathway Diagrams for the Representation of Mathematical Models|
|17:12||Howard S. Cohl||From the A&S handbook to a digital mathematics platform: reusing the DLMF for the DRMF|