A dataset is generally a collection of data.
A database is an organized collection of data, allowing different kinds of queries.
A dashboard is a graphical user interface that is employed to make sense of a dataset that is too large or complex to be simply visualized in a small number of plots.
We all know crystallographic databases have been around for a long time, and an effort is now ongoing to include the results of materials modelling in databases too. My question is however on graphically navigating experimental data on molecular materials beyond X-ray crystallography.
I have found several specialized databases and dashboards, oriented towards accelerated materials discovery, for "solid state" (extended) materials , e.g. this topological materials database or this dashboard for Metal Organic Frameworks (see figure 1 below). By collecting existing data, they facilitate finding trends and exploring in new directions. There are of course also many similar efforts in the pharmaceutical industry.
It seems however that in other fields of chemistry and molecular materials science these tools are less common. In our group we recently manually data-mined the bibliography to build a dataset and a dashboard for experimental data visualization in molecular nanomagnets (see figure 2 below). We plan to use this to open new routes for materials modelling in this field. I am sure there have to be other examples beyond solid state materials and molecules oriented towards pharmaceutical applications, but am having a hard time finding them.
Can people point towards analogous dashboards beyond these two fields? A couple of example screenshots are included to give a graphical idea beyond the very brief definition for dashboard above.
Figure 1: Dashboard for Metal Organic Frameworks at MOF Data Explorer. (Moghadam, P.Z., Islamoglu, T., Goswami, S. et al. Computer-aided discovery of a metal–organic framework with superior oxygen uptake. Nat Commun 9, 1378 (2018). https://doi.org/10.1038/s41467-018-03892-8)
Figure 2: Dashboard for Single Ion Magnets at SIMDAVIS App. (Duan, Y., Rosaleny, L.E., Coutinho, J.T. et al. Data-driven design of molecular nanomagnets. Nat Commun 13, 7626 (2022). https://doi.org/10.1038/s41467-022-35336-9)