Closing Data Gaps with Citizen Science? Findings from the Danube Region †
2.1. Study Area
2.2. Danube Reference Data and Services Infrastructure (DRDSI)
2.2.1. Policy Context
2.2.2. DRDSI Implementation
2.3. Citizen Science
2.3.1. What Is Citizen Science?
2.3.2. Emerging Organisational Structures for Citizen Science
2.3.3. Citizen Science Networks in the Danube Region
3. Analysis of Existing Data Gaps
3.1. Data Discovery
3.1.1. Open Data Portal
3.2. Gap Analysis
4. Citizen Science Contribution
4.1. Identification of Existing Activities
- The CITI-SENSE project  developed a rich web portal and toolbox (http://co.citi-sense.eu/) that can be re-used to measure air pollution and the human perception of air quality at any given occasion by using low-cost sensors and mobile phone applications. Methods and tools are provided to capture information about both, indoor and outdoor air quality.
- enviroCar is a Citizen Science platform for analysing and mapping crowd-sourced car sensor data . The research project proposes an innovative approach towards the monitoring of car-related air pollution. Data is collected directly from the cars of volunteers. It is afterwards contributed to an open platform (https://envirocar.org/). An application programming interface (API) is made available to ensure easy access to the citizen-generated content. From our perspective this alternative approach to data collection is highly promising. Mashing up the resultant observations with other data sources (e.g., air quality models, data from monitoring stations, etc.) would lead to improved data coverage for air related matters.
- The iSPEX project  established a measurement network for particulate matter through the use of a smartphone app equipped with a specialised add-on. The project engaged with a vast number of volunteers, and illustrated the benefits of Citizen Science for obtaining a high resolution representation of the studied phenomenon.
4.2. Development of a New Citizen Science Application
4.2.1. App Rationale
4.2.2. App Conceptualisation
- Citizens (site owners) that do not have to organize burning activities.
- Industry (biomass producers) that get access to more biomass.
- Civil society for which (i) security is increased by less uncontrolled burning activities, and (ii) pollution from combustion is decreased.
- Research organisations that get access to one more spatial dataset about bioenergy in the Danube region.
- Governmental organisations that have less data gaps and can better support developments in the region.
4.2.3. Waste2Fuel Initial Development
- CORINE Land Cover 2012—Land cover inventory obtained through satellite image and in situ data processing .
- Land Parcel Identification Systems (LPIS)—systems, put in place by European Union Member States for identification of land plots based on high resolution aerial imagery.
- Open Land Use Map—Open, harmonised and seamless database of open land use in the Danube region obtained through combining input from heterogeneous data sources .
- Land Use/Cover Area frame statistical Survey (LUCAS)—sample statistical survey carried out by Eurostat every three years .
4.2.4. App Functionality
5.1. Citizens’ Impact on Policy Making
- Data coverage. The heterogeneous spatio-temporal coverage of citizen data might swamp any signal, or produce spurious signals of change where none exists . At the same time, the coverage of data that fits policy makers’ needs is (i) hard to define, and (ii) difficult to implement in practice. Data from Citizen Science apps, for example, is closely bound to where a community is involved for a particular reason. It often hardly exceeds the local dimension (e.g., bounds of a municipality). Community building activities are thus likely to be required in order to close a particular data gap on higher administrative levels or less-urban areas.
- Combined use of data. The integration (or mashing-up) of data, even if feasible and relatively easy from a technical point of view, should be based on solid theoretical foundations and where possible cross-checked with other data sources. This is important for Citizen Science approaches because it is, for example, very difficult to determine whether a particular high concentration (or lack) of data for a particular area, is objectively representing the studied phenomenon, or is related to a particularly active (or inactive) community. Notably, tools and methods from multiple projects might be combined to best address a data need. Also, in some cases, Citizen Science might be the only possible way to collect a particular data set, for example, for mapping uncharted territories (see for example ) that cannot be surveyed through traditional methods such because of military conflicts, dictatorships, or environmental crises.
- Data validation and quality. Validation and quality assurance of gathered data are a common issue for any data collection, but are frequently challenged in relation to Citizen Science activities. Related challenges and solutions are widely discussed in the literature (for example [48,49,50,51]). Whereas evidence shows that, for example, for species identification, non-professionals may reach a high level of expertise and eventually even outperform professional scientists. It might also be feasible to use Citizen input as a proxy for resource optimization, as, for example, done in a collaboration between the Scottish Environmental Protection Agency (SEPA) as part of the Anglers’ Riverfly Partnership (http://www.riverflies.org/rp-riverfly-monitoring-initiative).
- Fitness for purpose. Specific analysis of the appropriateness of a particular Citizen Science approach with respect to the concrete policy making needs would be needed when addressing any of the identified data gaps. The suitability of a solution that has already been developed will most essentially depend on two factors. The tools that are provided by the project, e.g., (i) Can a concrete air quality sensor meet the quality needs that are required in order to improve the model of environmental pollution at a regional level (also considering the density and costs of already existing measurement networks)? (ii) Does the smartphone for monitoring selected species deliver reliable occurrence records? And the methodology required by the project in order to mobilize or gather data, e.g., (a) Can the approach cover an entire region or country at a given resolution? (b) Can the data gathering be repeated in well-defined cycles in order to support monitoring? These investigations need to be carried out on a case-by-case basis (per required data set and per possible suitable Citizen Science project). They might be accompanied by a cost-benefit analysis.
- Business models. Following from the above mentioned assessment of cost-effectiveness, we also want to emphasise on the need for robust business models. Citizen Science activities require much more resources than possible technology (hardware and software). It also requires capacity building, community management and sustainability of the gathered results. Here, we should separate between the funding models of Citizen Science projects as such—see, for example —from operational (and possibly long-term) use. While equal needs might be argued for other scientific and governmental approaches to data collection, analysis and dissemination, Citizen Science requires a different set of skills in order to, for instance, meet certain ethical requirements. However, as diverse as Citizen Science is in itself, as diverse are possible business models. Especially the embodied concept of shared responsibility offers a new range of possibilities, for example, Non-Governmental Organisations (NGOs) enter the scene, which might provide support in community outreach, and in some cases Citizen Science communities entail their community managers. Equally, a Citizen Science approach might help to optimise and concentrate available resources where they are most needed. Last but not least, Citizen Science approaches may also help reaching other goals of the involved parties, such as the modernisation of public services or the improved relationship between people and governments. Resource pooling is thus an option.
- Expectation management. Governmental support of a Citizen Science application (e.g., via a mobile phone app) raises expectations. Especially, citizens are likely to expect an immediate (re)action after having provided information. However, in regional policies across country borders, reactions are not always immediate, particular not if it is used as extended scientific advice in evidence based policy making. Decisions will take place along the policy cycle and diverse/heterogeneous political and cultural landscapes each add their own temporal constraints. The use of the data and according feedback mechanisms have to be clearly communicated from the beginning of each Citizen Science initiative.
- Participant incentives. The incentive of citizens to contribute to data collection initiatives beyond the initial curiosity is to be investigated. Not only need people be mobilized and attracted to participate in a Citizen Science activity, but active contributors have to be retained, too. Multiple mechanisms exists, but have to be selected carefully. One of the options would be to benefit from already active communities and engage with them in order to make a data collection exercise valuable for all participants in the process.
6.2. Main Lessons
- Are there existing data which are possible to ‘open’ in order to close the gap?
- If yes, how much does it cost to open the data?
- If other sources are too expensive, is there a Citizen Science activity (completed, ongoing, or planned) that might close the gap?
- Would the quality of data from citizens be satisfactory?
- How to ensure sustainability of the data collection process?
6.3. Future Work
- Data gaps and possible Citizen Science contributions need to be further analysed on a case by case basis. This should include closer investigations of the data needs (e.g., in terms of coverage and quality), possibilities and limitations of using an already existing Citizen Science approach (transportability, need of community building etc.), but also a critical reflection on possibly underlying assumptions (e.g., on social needs).
- The potential of the app described in Section 4.2 is not explored to its full potential. Its possible use as a standalone tool, or in combination with one or more of the traditional approaches should be further investigated. Also, the deployment in the involved communities should be tested with a limited set of participants before approaching broader use.
- Methodologies for mobilizing and retaining citizens to contribute on a regular basis (i.e., beyond the initial curiosity) should be tested and then rolled out more widely. This will have to account for repeated promotion activities, but equally for feedback provision and ensured use of the inputs provided by the citizen.
- A scientifically sound methodological framework for utilisation of citizen contributions still needs to be developed and tested in real world conditions. Such investigations might need to reflect about the quality criteria to be applied and the relationship to e.g., official statistics and indicator-based assessment.
- Successful organizational settings to orchestrate the engagement processes need to be identified, shared and adopted where needed. When talking about regional development policy, the amount and diversity of involved stakeholders makes this exercise particularly channelling and highly interesting.
Conflicts of Interest
|CKAN||Comprehensive Knowledge Archive Network|
|CORDIS||Community Research and Development Information Service|
|DRDSI||Danube Reference Data and Services Infrastructure|
|EUSDR||European Union Strategy for the Danube Region|
|ECSA||European Citizen Science Association|
|JRC||Joint Research Centre, European Commission|
|OGC||Open Geospatial Consortium|
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|3.||Irrigation and agricultural development|
|7.||Land and Soil|
|9.||Others (none of the above)|
|Austria||Power plants||Energy production|
|Livestock farms||Irrigation and agriculture|
|Bosnia & Herzegovina||unknown (The country report does not contain substantial evidence|
that can to be used for identification of possible data gaps.)
|Bulgaria||Bio-heat on local scale||Bioenergy|
|Data on utilities||Energy production|
|Livestock farms||Irrigation and agriculture|
|Forest mills||Irrigation and agriculture|
|Data on health||Others|
|Land use master plans||Land and Soil|
|Croatia||Data on bioenergy||Bioenergy|
|Czech republic||Land use in rural areas||Land and Soil|
|Montenegro||Renewable energy sources||Energy production|
|Energy production data||Energy production|
|Data on transportation||Navigability|
|Romania||Data on energy||Energy production|
|Slovakia||Multi-modal transportation network||Navigability|
|Production and use of energy resources||Energy production|
|Culture and tourism||Other|
|Slovenia||EUSDR priority areas well covered|
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Lisjak, J.; Schade, S.; Kotsev, A. Closing Data Gaps with Citizen Science? Findings from the Danube Region. ISPRS Int. J. Geo-Inf. 2017, 6, 277. https://doi.org/10.3390/ijgi6090277
Lisjak J, Schade S, Kotsev A. Closing Data Gaps with Citizen Science? Findings from the Danube Region. ISPRS International Journal of Geo-Information. 2017; 6(9):277. https://doi.org/10.3390/ijgi6090277Chicago/Turabian Style
Lisjak, Josip, Sven Schade, and Alexander Kotsev. 2017. "Closing Data Gaps with Citizen Science? Findings from the Danube Region" ISPRS International Journal of Geo-Information 6, no. 9: 277. https://doi.org/10.3390/ijgi6090277