Transforming data to transform the environment

Technological innovation has also enabled extensive growth in the number of citizen scientists mobilising to collect environmental data, from measuring soil health to water quality to wildlife sightings.

Declining government investment in environmental protection is leading to a lack of evidence around aspects of the environment. There is a need to supplement sparse datasets with new information to support targeted action and measure effectiveness of policies and interventions. Citizen science data can be combined with other data sources, such as remote sensor data and infrastructure-level data, to support powerful insights in environmental science. 

This can be a useful tool for filling data gaps in national datasets, as well as empowering and building environmental citizenship among members of the public through ‘hands-on’ experience of environmental work. Increasing public connection with the environment supports other important pathways to improving the impact of environmental work, supporting behavioural change and community-led action. Citizen science can democratise the process of evidence gathering and gather new insights into different individual and cultural approaches to evidence gathering and analysis. 

The rise of citizen science data does not come without challenges. There are many schemes nationwide, with great diversity in how they are implemented and managed, and varying levels of quality assurance protocols. Although citizen science data can lead to locally rich pictures of environmental status, the data landscape as a whole is fragmented due to the range in quality of citizen science projects and the resulting data.

In order to maximise the vast potential benefits of the work of citizen scientists in the future, it is important to put in place a national standardised framework for citizen science projects which can support the development of schemes that meet the scientific rigour needed to support evidence-based change.

There is already a vast quantity of data available on the environment around us. However, a large amount of this is proprietary, locked behind paywalls, or hidden in different areas of the internet. To ensure that we are not just collecting data, but actually translating its findings into learning and action, an increase in the amount of open data will be essential for unlocking the value of large datasets. Open data is incredibly important for supporting systemic change and allows for the same dataset to be put to myriad uses, providing a much more efficient and equitable system.

Data should be managed in a centralised database to maximise accessibility. To make the most of the data available, we also need to ensure that is interoperable so that it can be used across systems for a multitude of stakeholders and applications. Application Programme Interfaces (APIs) should also be developed to support data analysis and visualisation across the sector. 

To support the establishment of centralised databases, it is vital that open data standards are developed. This will support stakeholders in collecting, publishing, accessing and sharing data that is of better quality and aligned with an agreed ‘common language’ for data. This is also a useful tool for sharing data across disciplines, where there may be different norms around dataset formats — essential for supporting interdisciplinary science. One of our immediate goals should be to move towards coherence and standardisation of environmental data to ensure validity and consistency of data, and to improve transparency. 

Transforming data into actionable information requires powerful visualisation techniques which can highlight insights, such as trends, anomalies and statistically significant findings into easily digestible information for a variety of stakeholders. Decision makers need data to inform evidence-based policy, often in fields in which they are not specialists. This means that data needs to be presented in a robust but understandable format. 

Data can also be a powerful tool for engaging with the public, helping to cut through scientific jargon to deliver targeted messages that can support awareness-raising and behavioural change. Data visualisations should be used to their full potential in these areas, whilst ensuring that insights are not oversimplified or misinterpreted.

Increasing the evidence-base for environmental science is a fundamental requirement for quantifying and mitigating the impact of human activity and supporting national and local policy development. Real-time environmental data can provide evidence on the success of interventions at the hyperlocal, local and national levels. 

Data and evidence also play an important role in accountability and ensuring that stakeholders are meeting their requirements, targets or pledges. Increasingly sophisticated data collection can also pinpoint harmful practices and hold perpetrators accountable, helping to trace environmental incidents and sources of pollution. This can play an important role in supporting the ‘polluter pays’ principle in environmental regulation.

Current environmental goals and targets will never materialise into environmental improvement in the future, unless we can measure progress towards them and scale up or adapt approaches in response to findings. If we continue with ‘business as usual’ whereby we focus our data insights on informing target setting, rather than target delivery, we will not achieve the environmental improvement needed. 

Monitoring methodologies and data collection must therefore be an integral part of environmental projects, to report and monitor progress towards environmental goals, review the effectiveness of policy interventions, and allow for continuous improvement.

Assessing progress towards long-term environmental improvement will need to go beyond purely monitoring data. Integrated assessment approaches will be needed to address effectiveness of interventions on complex, systemic environmental issues. This will also support a move towards more interdisciplinary science by combining insights across disciplines.