Abstract
We present a cloud-computing environment, referred to as AtomPy, based on Google-Drive Sheets and Pandas (Python Data Analysis Library) DataFrames to promote community-driven curation of atomic data for astrophysical applications, a stage beyond database development. The atomic model for each ionic species is contained in a multi-sheet workbook, tabulating representative sets of energy levels, A-values and electron impact effective collision strengths from different sources. The relevant issues that AtomPy intends to address are: (i) data quality by allowing open access to both data producers and users; (ii) comparisons of different datasets to facilitate accuracy assessments; (iii) downloading to local data structures (i.e., Pandas DataFrames) for further manipulation and analysis by prospective users; and (iv) data preservation by avoiding the discard of outdated sets. Data processing workflows are implemented by means of IPython Notebooks, and collaborative software developments are encouraged and managed within the GitHub social network. The facilities of AtomPy are illustrated with the critical assessment of the transition probabilities for ions in the hydrogen and helium isoelectronic sequences with atomic number Z ≤ 10.
Highlights
This report is concerned with problems of atomic data assessment and the development of cloud-computing tools to make it more efficient in the current data-intensive and collaborative research enterprise
The AtomPy software is fairly simple: it is essentially confined to an Application Programming Interface (API) for data downloading from Google Drive to the user disk space and a series of Python utilities developed by community members to be shared in the GitHub [66] social network
In the present work we have made an attempt to bring out and discuss new scientific research directions that are driven by collaborative data-intensive projects, and in this context, some of the problems that compromise reliable atomic data assessments for the astrophysical community
Summary
This report is concerned with problems of atomic data assessment and the development of cloud-computing tools to make it more efficient in the current data-intensive and collaborative research enterprise. Union: the Virtual Atomic and Molecular Data Centre (VAMDC) [29,30,31,32], aimed at implementing a cyberinfrastructure capable of interconnecting several (more than 15) of such databases In this project an international group of physicists and computer scientists defined and implemented interoperability standards and protocols, virtual node architectures, XML schemata, query languages and web portals. The implementation of any spectral modeling code generally involves lengthy searches of atomic parameters in the aforementioned databases in order to piece together a master dataset as complete and accurate as possible Such a task is usually reserved to experts capable of reviewing the available data, a job that may take up several years.
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