Citizen Science for the SDGs – Aligning Citizen Science outcomes to the UN Sustainable Development Goals

The overall objective of the TG on Citizen Science for the SDGs is to study the feasibility of aligning the data generated by Citizen Science projects and platforms to the specific requirements of the Result Framework proposed by the United Nations (UN) 2030 Agenda; namely, the indicators associated with the Sustainable Development Goals (SDG). This alignment would facilitate and encourage the inclusion of such data in the official monitoring of the SDGs at local, national, and global levels. Furthermore, the TG will complete the work of the prior CODATA–WDS TG on Citizen Science and the Validation, Curation, and Management of Crowdsourced Data.

In 2015, the UN adopted 17 SDGs with the ambition by the year 2030 to end poverty, promote prosperity and wellbeing for all, and protect the planet. Each goal includes a list of priorities, or targets (for a total of 169 targets), and each target has associated quantities, or indicators, to monitor progress, inform policy, and ensure accountability of all stakeholders. In terms of data, the backbone for monitoring progress towards the SDGs and its target is provided by national statistical offices. However, many gaps exist in the required data, asmeasurement of progress requires accessible, timely, and reliable disaggregated data at scales not possible by conventional scientific approaches.

The role of Citizen Science in supporting this global effort is evident, as citizens are in a unique position to provide disaggregated data and the required scale and resolution. Citizen Science provides a powerful methodology: high-quality open data are collected using sensors and ubiquitous low-cost technologies such as smartphones; in turn, accessible web-based tools enable all stakeholders to track progress at a local, regional, or even global level. However, data generated by Citizen Science projects are not yet included in the official framework to monitor the SDGs, despite the abundant literature illustrating that Citizen Science can contribute to high-quality research. 

The TG on Citizen Science for the SDGs seeks to facilitate and encourage this inclusion by envisaging common practices, simple data policies, and fitness-for-use standards aimed at facilitating the mapping of data to the specific requirements of the SDG framework. Mapping will provide visibility to Citizen Science generated data and their use in filling some of the official data gaps, while challenging the scientific community to identify targeted methods and data to tackle the remaining gaps. Sharing of ‘SDG-mapped’ data will produce benefits well beyond scientific results, strengthen the science-policy interface, and help amplify the societal impact of Citizen Science.

Names of the co-chairs

  • Caren B Cooper (Associate Professor), North Carolina State University, Raleigh Country: USA
  • Alex de Sherbinin (Associate Director for Science Applications; Vice-chair of WDS Scientific Committee), CIESIN, Columbia University Country: USA
  • Rosy Mondardini (Managing Director), Citizen Science Center Zurich Country: Switzerland

Key envisaged outputs that the TG intends to deliver over the two years

  1. Identify a shortlist of effective practices for data validation, curation, and use that will enable Citizen Science projects to make their data open and FAIR (Findable, Accessible, Interoperable, and Reusable).
  2. Identify a shortlist of open data repositories that can support short- and longer-term data curation.
  3. Through the above activities, identify the Citizen Science datasets that are best suited to alignment with the SDG framework.
  4. Promote proper data stewardship practices by Citizen Science and Crowdsourcing initiatives, based on findings from the CODATA–WDS TG on Citizen Science and the Validation, Curation, and Management of Crowdsourced Data.
  5. Explore possible ways to map existing and historic Citizen Science data to the indicators framework, including the possibility to propose new indicators inferred by the data and more relevant to people’s life and experience.
  6. Explore the potential for data on human capital in volunteer activity from Citizen Science platforms; namely, data on the engagement of volunteers and subsequent learning/social/civic outcomes to support indicators. This incorporates issues of inclusiveness in monitoring and data collection, thus ensuring ‘leaving no one behind’.
  7. Collaborate with UN statistical offices to gather requirements and develop shared glossaries to support the inclusion of Citizen Science in the list of accepted ‘non-official’ data providers for the SDGs.
  8. Work with the UN, including the UN Environment and Development Programmes, to continue to gain support for Citizen Science and strengthen the science–policy interface.

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