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Modeling the underlying academic data as an RDF knowledge graph (KG) is one efficient method. This makes standardization, visualization, and interlinking with LinkedData resources easier. As a result, scholarly KGs are essential for converting document-centric academic material into linked and automatable knowledge structures.
In the most generic terms, every project starts with raw data, which comes from observations and measurements i.e. it is directly downloaded from instruments. It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions.
Data storage : Store the data in a Snowflake data warehouse by creating a data pipe between AWS and Snowflake. Data Extraction, Preprocessing & EDA : Extract & Pre-process the data using Python and perform basic Exploratory DataAnalysis. Please refer to this documentation link.
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