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Abstract: In this presentation, the existing open data practices in high energy astro-, particle and astroparticle physics are presented. Open data has become fundamental in astrophysics, particle, and astroparticle physics, enhancing collaboration, reproducibility, and transparency, while accelerating innovation. A recent shift toward openness, marked by data-sharing initiatives and accessible resources, is driving breakthroughs like the multi-messenger observation of GW170817, a neutron star merger detected in both gravitational waves and gamma rays, and the identification of blazar TXS 0506+056 as a high-energy neutrino source. Across these fields, robust efforts are underway to develop and implement FAIR-compliant data policies, with a wide array of supportive tools, standards, protocols, and software already in use (Virtual Observatory in astrophysics, CERN’s Open Data Portal in particle physics, ...). The challenges of astroparticle physics data, often more complex than traditional astrophysics or particle physics data, call for additional coordination and technical advancements to meet FAIR principles effectively. Machine learning also plays a transformative role in these domains, enhancing the analysis of both proprietary and open data to reveal new insights and optimize research methodologies.
Keywords: open data, FAIR data, astrophysics, high-energy particle physics, astroparticle physics, multi-messenger astronomy
Published in RUNG: 06.01.2025; Views: 183; Downloads: 1
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Abstract: Data Quality has become the base for any analytical operation or modelling. Poor Quality of data can lead to poor analytical modeling, which in turn can lead to poor decision making and predictions, which can finally impact the revenue and working of an organization. This is true for both public and private sector organizations. With rise in E-Governance, lot of nations and their respective public sector units are making use of publicly available datasets. But are these datasets reliable and have good quality. This is the major research question studied in this paper. The study collected publicly available datasets from Open Government Data platforms across 8 different nations around the world. More than 300 datasets having roughly 3.5 million rows were assessed for various data quality measures. The various parameters studied for the data were valid data types, correctness, completeness, statistical features, variability, comparability, duplicacy and the likes. Script was written in R to check the value for various measures. It was found that different countries had advantages on different parameters. Not one country was found to have all the parameters to be of high quality. Different ranges were found for the dataset for various parameters which was helpful in determining the overall quality of the dataset. This will be helpful for various public and private sector organizations in assessing the quality of datasets they intend to work on. Substantial efforts and resources can be saved on Advanced Analytics if the quality of dataset can be determined in advance. The proposed data quality assessment model can be applied on any private or public dataset. Different industry and organizations can set different threshold values for the parameters to benchmark their analytical process. Both practitioners and researchers can be benefitted from this research work.
Keywords: data quality assessment, open government datasets, e-governance, data quality measures
Published in RUNG: 05.04.2021; Views: 2993; Downloads: 0
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