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'Open Data' is gaining momentum in literature and language for research. 'Open Data' is a collection of easily accessible data that is to be available for general use without any restrictions. The Open Data analysis has been taken up in contemporary period by almost every government in the world. This paper describes how some of the Urdu researchers' tasks could be conducted, bringing a new actor in the ecosystem. The paper investigates are the objectives of opening the data in Urdu Literature, and how can researchers get the maximum benefit? Both qualitative and quantitative data collection methods are used. Interviews and survey research strategies are employed and data collected from four user groups, including popular websites like Rekhta, Urdu media, and Urdu research journals. The study implied the deductive method, which incorporated a top-down approach. There are plenty of data sources that might provide a different perspective and large public datasets from various authors, countries, and literary agencies. The results show the positive impact of 'Open Data' with context to the Urdu Literature.

BACKGROUND: The objective of this study was to create the Canadian Food Environment Dataset (Can-FED) and to demonstrate its validity. DATA AND METHODS: Food outlet data were extracted from Statistics Canada's Business Register (BR) in 2018. Retail food environment access measures (both absolute and relative measures) were calculated using network buffers around the centroid of 56,589 dissemination areas in Canada. A k-medians clustering approach was used to create categorical food environment variables that were easy to use and amenable to dissemination. Validity of the measures was assessed by comparing the food environment measures from Can-FED with measures created using Enhanced Points of Interest data by DMTI Spatial Inc. and data from a municipal health inspection list. Validity was also assessed by calculating the geographic variability in food environments across census metropolitan areas (CMAs) and assessing associations between CMA-level food environments and CMA-level health indicators. RESULTS: Two versions of Can-FED were created: a researcher file that must be accessed within a secure Statistics Canada environment and a general-use file available online. Agreement between Can-FED food environment measures and those derived from a proprietary dataset and a municipal health inspection list ranged from rs=0.28 for convenience store density and rs=0.53 for restaurant density. At the CMA level, there is wide geographic variation in the food environment with evidence of patterning by health indicators. INTERPRETATION: Can-FED is a valid and accessible dataset of pan-Canadian food environment measures that was created from the BR, a data source that has not been explored fully for health research.