Objective India reported its first COVID-19 case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, this unstructured data limits upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets. Materials and Methods Through a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. This was further visualized as a dashboard through a frontend web application and a JSON repository, which serves as an API for the frontend. Results From the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for non-specialists. To ensure longevity and reusability, the dataset was deposited in an open-access public repository for future analysis. Finally, we provide outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak. Discussion We anticipate that our dataset can form the basis for future studies, supplemented with clinical and epidemiological data from the individuals affected with COVID-19 in Kerala. Conclusion We reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals.

Context. Galaxies are biased tracers of the underlying network of dark matter. The strength of this bias depends on various galaxy properties, as well as on redshift. One of the methods used to study these dependences of the bias are measurements of galaxy clustering. Such studies are made using galaxy samples from various catalogues -frequently bearing their own problems related to sample selection methods. It is therefore crucial to understand how sample choice influences the clustering measurements, and which galaxy property is the most direct tracer of the galaxy environment. Aims. We investigate how different galaxy properties -luminosities in u, g, r, J, K-bands, stellar mass, star formation rate and specific star formation rate trace the environment in the local universe. We also study the effect of survey flux limits on galaxy clustering measurements. Methods. We measure the two-point correlation function (2pCF) and marked correlation functions (MCFs) using the aforementioned properties as marks. We use nearly stellar-mass-complete galaxy sample in the redshift range 0.1 < z < 0.16 from the Galaxy And Mass Assembly (GAMA) survey with a flux limit of r < 19.8. Further, we impose a brighter flux limit of r < 17.8 to our sample and repeat the measurements to study how this affects galaxy clustering analysis. We compare our results to measurements from the Sloan Digital Sky Survey (SDSS) with flux limits of r < 17.8 and r < 16.8. Results. We show that the stellar mass is the most direct tracer of galaxy environment, the K-band luminosity being a good substitute, although such a proxy sample misses close pairs of evolved, red galaxies. We also show that the u-band luminosity can be a proxy of star formation rate in the context of galaxy clustering. We observe an effect of the survey flux limit on clustering studies -samples with a higher flux limit (smaller magnitude) miss some information about close pairs of starburst galaxies.

Objective: India reported its first COVID-19 case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, this unstructured data limits upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets. Materials and Methods: Through a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. This was further visualized as a dashboard through a frontend web application and a JSON repository, which serves as an API for the frontend. Results: From the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for non-specialists. To ensure longevity and reusability, the dataset was deposited in an open-access public repository for future analysis. Finally, we provide outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak. Discussion: We anticipate that our dataset can form the basis for future studies, supplemented with clinical and epidemiological data from the individuals affected with COVID-19 in Kerala. Conclusion: We reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals.

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