Abstract
In the context of atomic data computations for astrophysical applications, we review four different types of databases we have implemented for data dissemination: a database for nebular modeling; TIPTOPbase; OPserver; and AtomPy. The database for nebular plasmas is briefly discussed as a study case of a successful project. TOPbase and the OPserver were developed during the Opacity Project, an international consortium concerned with the revision of astrophysical opacities, while TIPbase was part of the Iron Project to calculate radiative transition probabilities and electron impact excitation collision strengths for iron-group ions. AtomPy is a prototype for an open, distributed data-assessment environment to engage both producers and users. We discuss design strategies and implementation issues that may help in the undertaking of present and future scientific database projects.
Highlights
Since the mid 1970s I have been involved in the calculation of atomic data for astrophysical applications, a specialized research field relevant to the spectral modeling of the plasmas associated with the wide variety of astronomical objects currently observed
If the computing of atomic data is in itself a life-time dedication, the design and implementation of efficient online database management systems (DBMS) require engineering skills originally alien to CPU-based scientific computing, which in most situations involve steep learning curves in research environments driven by fast changing information and communications technologies (ICT)
Due to the large data volumes involved in the calculation of atomic data for astrophysical applications and to rapidly evolving ICT, data producers have been compelled to develop online databases to facilitate data dissemination
Summary
Since the mid 1970s I have been involved in the calculation of atomic data for astrophysical applications, a specialized research field relevant to the spectral modeling of the plasmas associated with the wide variety of astronomical objects currently observed. The end products are not always warmly received by the data-user communities, and their long-term maintenance and upgrade are underfunded compromising sustainability. Despite such deterrents, the growth and diversity of distributed data repositories since the 1970s, boosted in the early 1990s by the emergence of the World Wide Web, have given rise to an unprecedented data deluge [2]. The growth and diversity of distributed data repositories since the 1970s, boosted in the early 1990s by the emergence of the World Wide Web, have given rise to an unprecedented data deluge [2] To illustrate this diversity I review four different case studies—four of a kind—I have https://www.sdss.org/. Atoms 2020, 8, 30 been involved with in order to highlight important issues to consider in the design, implementation, and maintenance stages of scientific databases
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