Table 1
Main results for conceptualization and contextualization identified in international policy documents.
| CS is predominantly referred to as a data collection tool (Bonney et al 1996), while few policy documents relate to CS as an approach to democratise science (Irwin 1995) |
| Documents embrace the variety of CS approaches and different levels of engagement |
| Documents provide descriptive understandings of CS through describing tasks of participants in CS activities |
| Policy application areas are mainly biodiversity and environment related, e.g., with reference to environmental policies or health risk management |
| CS is linked to Open Science and Crowdsourcing |
| CS is viewed as an inclusive approach to joint research bridging academia and societal actors and linked to education |
| Digital technologies are perceived as main driver for facilitating CS |
Table 2
Policy areas of CS in policy documents (for each document only one passage is mentioned regardless of the frequency of mentioning).
| Policy area | Total | Source |
|---|---|---|
| Astronomy, e.g. asteroid detection | 3 | 19:12, 25:10, 27:20 |
| Biodiversity assessment, management and strategies | 9 | 2:189, 4:3, 15:3, 19:50, 24:2, 25:10, 26:21, 29:4, 34:21 |
| Environmental monitoring and reporting | 4 | 19:11, 21:8, 39:10, 40:15 |
| wildlife monitoring and management | 3 | 4:3, 15:3, 30:1 |
| Environmental science and policies/policymaking | 6 | 4:1, 11:2, 15:3, 20:5, 36:2, 37:14 |
| health-related | 4 | 6:33, 13:1, 15:7, 19:8 |
| natural resources management | 1 | 29:4 |
| biological conservation | 2 | 15:6, 19:12 |
| Environmental health risks management | 1 | 9:1 |
| pest and disease outbreaks | 5 | 4:3, 10:57, 19:44, 25:6, 30:1 |
| biosecurity, pest animals | 2 | 2:156, 33:17 |
| disaster mitigation/planning | 1 | 25:8 |
| hazard mapping, pollution breaches | 1 | 19:50 |
| littering | 3 | 11:1, 16:10, 20:9 |
| noise, air quality/pollution | 5 | 11:3, 15:4, 20:36, 25:8, 30:1 |
| discovery of new species | 4 | 4:3, 10:11, 15:3, 36:4 |
| invasive species | 3 | 14:11, 15:16, 20:12 |
| soil health | 1 | 15:24 |
| Medical research | 2 | 5:49, 25:6 |
| epidemiology | 3 | 19:8, 25:8, 37:14 |
| biomedicine | 1 | 28:61 |
| public health risks | 1 | 9:1 |
| Open Science, Open Data, Big data | 3 | 20:5, 21:34, 18:46 |
| Weather information | 4 | 4:3, 5:49, 25:7, 30:1 |
| Others | ||
| Cultural heritage digital social innovation, digital government | 1 | 20:37 |
| urban life | 1 | 20:9 |
| consumer strategies | 1 | 26:21 |
| social sciences | 1 | 37:14 |
| smart cities, incl. ICT, energy and transport infrastructures | 1 | 21:3 |
| geographical information and mapping, e.g. school districts | 2 | 37:14, 25:9 |
| environmental justice | 1 | 15:17 |
Table 3
Main benefits of CS for science, society, and policy analysed in international policy documents.
| Science | Members of Society | Policy |
|---|---|---|
Science Project level
| Increase of
| Improvement of
|
Table 4
Main challenges for CS in policy perspectives.
| Data quality and management | Organisation and governance | Policy implementation |
|---|---|---|
| – Reliability and quality of CS data – Re-usability of solutions – Standardisation of data and meta-data | – Interconnection, knowledge exchange and synergies between CS projects and communities – Access and interoperability of data – Communication, motivation and volunteer collaboration – Internal project standards and use of tools – Exclusion through digital technology | – Recognition of CS by science and policy – Evaluation of CS projects – Uptake of CS data by policy – Expectation management – Participation bias – Publication bias towards successful projects – Temporal gaps between scientific process and policy needs – Lag of management action on findings |