Citizen science, the active participation of the public in scientific research projects, is a rapidly expanding field in open science and open innovation. It provides an integrated model of public knowledge production and engagement with science. As a growing worldwide phenomenon, it is invigorated by evolving new technologies that connect people easily and effectively with the scientific community. Catalysed by citizens' wishes to be actively involved in scientific processes, as a result of recent societal trends, it also offers contributions to the rise in tertiary education. In addition, citizen science provides a valuable tool for citizens to play a more active role in sustainable development.
This book identifies and explains the role of citizen science within innovation in science and society, and as a vibrant and productive science-policy interface. The scope of this volume is global, geared towards identifying solutions and lessons to be applied across science, practice and policy. The chapters consider the role of citizen science in the context of the wider agenda of open science and open innovation, and discuss progress towards responsible research and innovation, two of the most critical aspects of science today.
14 pages, 2 figures ; Citizen Science (CS) is a prominent field of application for Open Science (OS), and the two have strong synergies, such as: advocating for the data and metadata generated through science to be made publicly available [1]; supporting more equitable collaboration between different types of scientists and citizens; and facilitating knowledge transfer to a wider range of audiences [2]. While primarily targeted at CS, the EU-Citizen. Science platform can also support OS. One of its key functions is to act as a knowledge hub to aggregate, disseminate and promote experience and know-how; for example, by profiling CS projects and collecting tools, resources and training materials relevant to both fields. To do this, the platform has developed an information architecture that incorporates the public participation in scientific research (PPSR)—Common Conceptual Model①. This model consists of the Project Metadata Model, the Dataset Metadata Model and the Observation Data Model, which were specifically developed for CS initiatives. By implementing these, the platform will strengthen the interoperating arrangements that exist between other, similar platforms (e.g., BioCollect and SciStarter) to ensure that CS and OS continue to grow globally in terms of participants, impact and fields of application ; The EU-Citizen.Science project received funding from the EU's Horizon 2020 Framework Program for Research and Innovation under grant agreement No. 824580. The research described in this paper is partly supported by the project "Citizen Science to promote creativity, scientific literacy, and innovation throughout Europe" (COST Action), which received funding from the EU's Horizon 2020 Framework Program for Research and Innovation under grant agreement No. 15212 ; With funding from the Spanish government through the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S) ; Peer reviewed
Citizen science is an important vehicle for democratizing science and promoting the goal of universal and equitable access to scientific data and information. Data generated by citizen science groups have become an increasingly important source for scientists, applied users and those pursuing the 2030 Agenda for Sustainable Development. Citizen science data are used extensively in studies of biodiversity and pollution; crowdsourced data are being used by UN operational agencies for humanitarian activities; and citizen scientists are providing data relevant to monitoring the sustainable development goals (SDGs). This article provides an International Science Council (ISC) perspective on citizen science data generating activities in support of the 2030 Agenda and on needed improvements to the citizen science community's data stewardship practices for the benefit of science and society by presenting results of research undertaken by an ISC-sponsored Task Group. ; ISSN:2624-9553
Citizen science is an important vehicle for democratizing science and promoting the goal of universal and equitable access to scientific data and information. Data generated by citizen science groups have become an increasingly important source for scientists, applied users and those pursuing the 2030 Agenda for Sustainable Development. Citizen science data are used extensively in studies of biodiversity and pollution; crowdsourced data are being used by UN operational agencies for humanitarian activities; and citizen scientists are providing data relevant to monitoring the sustainable development goals (SDGs). This article provides an International Science Council (ISC) perspective on citizen science data generating activities in support of the 2030 Agenda and on needed improvements to the citizen science community's data stewardship practices for the benefit of science and society by presenting results of research undertaken by an ISC-sponsored Task Group.
Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals. ; This paper emerged from the first two workshops of the Horizon 2020 project GLOBIS‐B (GLOBal Infrastructures for Supporting Biodiversity research; http://www.globis‐b.eu/). Financial support came from the European Commission (grant 654003). C. A. additionally received funding from the LifeWatchGreece infrastructure (MIS 384676), funded by the Greek Government under the General Secretariat of Research and Technology (GSRT), ESFRI Projects and National Strategic Reference Framework (NSRF). M. O. was supported by the Swedish LifeWatch project funded by the Swedish Research Council (Grant no. 829‐2009‐6278), and J.E. by the Australian Research Council (grant FT0991640).
