@article{10.1145/3705863,
author = {Hamed, Naeima and Rana, Omer and Orozco-terWengel, Pablo and Goossens, Beno\^{\i}t and Perera, Charith},
title = {A Comparison of Open Data Observatories},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1936-1955},
url = {https://doi.org/10.1145/3705863},
doi = {10.1145/3705863},
abstract = {Open Data Observatories refer to online platforms that provide real-time and historical data for a particular application context, e.g., urban/non-urban environments or a specific application domain. They are generally developed to facilitate collaboration within one or more communities through reusable datasets, analysis tools, and interactive visualizations. Open Data Observatories collect and integrate various data from multiple disparate data sources—some providing mechanisms to support real-time data capture and ingest. Data types can include sensor data (soil, weather, traffic, pollution levels) and satellite imagery. Data sources can include Open Data providers, interconnected devices, and services offered through the Internet of Things. The continually increasing volume and variety of such data require timely integration, management, and analysis, yet presented in a way that end-users can easily understand. Data released for open access preserve their value and enable a more in-depth understanding of real-world choices. This survey compares thirteen Open Data Observatories and their data management approaches - investigating their aims, design, and types of data. We conclude with research challenges that influence the implementation of these observatories, outlining some strengths and limitations for each one and recommending areas for improvement. Our goal is to identify best practices learned from the selected observatories to aid the development of new Open Data Observatories.},
note = {Just Accepted},
journal = {J. Data and Information Quality},
month = nov,
keywords = {Urban and non-urban data observatories, FAIR Open Data principles, Data integration, Data platforms.}
}