Did you know that the roots of data integration trace back to the 1980s? Data sharing is an even older practice. Let鈥檚 explore the journey from data sharing to integrated data, with a spotlight on meteorology.
馃对锔�&苍产蝉辫;Meteorology: A Historical Precedent
Before the era of satellites and supercomputers, meteorologists relied on shared data to predict weather patterns. The first international standards for weather observation were adopted in the late 19th century. Countries worldwide contributed daily meteorological data, fostering collaboration and advancing the science of weather prediction.
Fast-forward to today. The World Meteorological Organization (WMO) governs the exchange of weather data, research, and information. The WMO facilitates stakeholder representation and international cooperation. It collaborates with other meteorological services, research institutions, and international bodies to support initiatives like the Integrated Surface Database (ISD) and the Integrated Data Viewer (IDV).
鈿栵笍&苍产蝉辫;Legal and Ethical Considerations Sharing data across borders has its challenges. National security interests, data accuracy, and storage complexities posed hurdles. Today, the practice of sharing all types of data is well-established, thanks to robust legal and ethical guidelines.
馃殌&苍产蝉辫;Modern Technology Advancements in technology have revolutionized data integration. Satellites capture real-time weather data, while supercomputers process massive datasets. AI algorithms predict storms, droughts, and climate shifts. The integration of international meteorological data fuels our understanding of global phenomena.
馃敆&苍产蝉辫;Linking the Past to the Present Whether in meteorology or economics and social research, lessons from this history resonate. Data integration isn鈥檛 just about algorithms; it鈥檚 about collaboration, ethics, and a shared vision for a better world.
馃寪 From Meteorology to Social Sciences: The Feedback Dilemma In natural sciences like meteorology, predictions yield quick, tangible feedback (e.g., did it rain?). In contrast, social sciences face delayed feedback (e.g., program impact assessment), emphasizing the importance of having a pipeline of connected, integrated, data.
(, 2015)