At first glance, the concepts of circular economy and open data ecosystems may
seem unrelated. However, a recent visit to the Thermochemical Processes
Group (GPT) at the University of Zaragoza in Spain during the 2nd ODECO
Training Week revealed some striking similarities between the two. In this post,
we will explore how the principles of circular economy and open data
ecosystems can work in tandem to create sustainable solutions and generate
value for stakeholders.
1. A Glimpse into the Circular Economy at GPT:
The GPT Waste Processing Unit is a prime example of circular economy
principles in action. The GPT researchers focus on recycling and recovering
waste from animal sources, such as fur, manure, urine, and faunal remains.
They convert this waste into valuable products like bio-oil and biogas, which
can then be used for fuel or electricity generation. That is, the GPT researchers
create value from what would otherwise be considered a waste product.
2. The Role of Open Data in the Circular Economy:
The GPT researchers rely on open data sources to discover chemical reactions
with potential as recycling technologies. Open data providers, such as the Food
and Agriculture Organization (FAO) and Global Open Data for Agriculture &
Nutrition (GODAN), provide key information that helps GPT research. This use
of open data in research highlights the potential for data-driven approaches to
contribute to the circular economy and to promote sustainable development.
After completing their experiments, GPT researchers publish the data associated
with their results through journal papers. By doing this, other researchers may
benefit from the new possibilities for recycling waste materials using chemical
processes under certain conditions.
3. The Interconnectedness of Stakeholders in Open Data Ecosystems:
The success of both circular economy practices and open data ecosystems
depends on the collaboration of various stakeholders, such as data providers,
users, and intermediaries. These parties play a critical role in improving data
quality, fostering innovation, and driving user-centric open data initiatives. By
working together as an ecosystem, stakeholders can improve the accessibility
and usability of open data, leading to better-informed decision-making and
more sustainable practices.
4. Challenges and Opportunities in Open Data Quality:
However, challenges exist in ensuring the quality of open data for research.
GPT researchers highlighted that many available open datasets are not of
optimal quality for being reused. This issue emphasizes the need for an open
data ecosystem where stakeholders work together to improve data quality and
facilitate the user-driven provision of open data. In addition, the publication of
data associated with experiments through journal papers seems to be not the
best way of disseminating information to other researchers. Ideally, FAO,
GODAN and other data repositories should facilitate more direct ways to get
feedback from researchers about their published datasets, or to ingest new
datasets generated by researchers and related to the existing ones in the
Conclusion:
The circular economy and open data ecosystems share a common goal: creating
value and benefits for all involved stakeholders. By fostering collaboration and
innovation, these two paradigms can work hand-in-hand to promote sustainable
development and improve the quality of life. The takeaway message is clear: a
well-developed open data ecosystem can lead to a thriving circular economy,
generating value and benefits for all parties involved. To achieve this,
stakeholders must work together to improve data quality and facilitate the user-
driven provision of open data, ultimately leading to a more sustainable future.
Authors:
Mohsan Ali
University of the Aegean, Greece
Abdul Aziz
University of Zaragoza, Spain
