Abstract:

Novel and more affordable technologies are allowing new actors to engage increasingly in the monitoring of hydrological systems and the assessment of water resources. This trend may shift data collection from a small number of mostly formal institutions (e.g., statutory monitoring authorities, water companies) toward a much more dynamic, decentralized, and diverse network of data collectors (including citizens and other nonspecialists). Such a move toward a more diverse and polycentric type of monitoring may have important consequences for the generation of knowledge about water resources and the way that this knowledge is used to govern these resources.

An increasingly polycentric approach to monitoring and data collection will change inevitably the relation between monitoring and decision-making for water resources. On a technical level, it may lead to improve availability of, and access to, data. Furthermore, the opportunity for actors to design and implement monitoring may lead to data collection strategies that are tailored better to locally specific management questions. However, in a policy context the evolution may also shift balances of knowledge and power. For example, it will be easier to collect data and generate evidence to support specific agendas, or for nonspecialists to challenge existing agreements, laws, and statutory authorities.

We identify strong links with polycentric models of river basin management and governance. Polycentric models (Ostrom 2010) recognize the existence of multiple centers of decision-making within a catchment and provide a potential alternative to the top-down centralizing tendencies of integrated water resources management. Although polycentric systems are often associated with data scarcity, we argue that citizen science provides a framework for data collection in such systems and that it provides opportunities for knowledge generation, institutional capacity building and policy support, in particular in basins that are faced with multiple challenges, stressors, and resource scarcity.

Source: Citizen Science for Water Resources Management: Toward Polycentric Monitoring and Governance?

Grizz Tracker

Grizz Tracker was developed in partnership with Peace Region’s Operations Division staff, Alberta Environment and Parks (AEP) in collaboration with industrial stakeholders (Daishowa-Marubeni International Ltd., Boucher Bro Lumber Ltd., Canfor, Canadian Natural Resources Ltd., Manning Diversified Forest Products) and the Miistakis Institute to enable industrial personal working in the Lower Peace Region to collect sightings information on grizzly bears. The collaborative efforts of all parties to mobilize this work is truly a success in itself!

This program formalized existing efforts by Industrial personal to report grizzly bear sightings to Peace Region AEP staff. Past reporting efforts have assisted AEP in better understanding grizzly bear presence and helped to inform the locations of hair snag monitoring sites, used to identify individual grizzly bears through genetic analysis of individual hair samples.

Grizz Tracker includes the development of a smartphone app and associated on-line mapping tool to increase efficiency, accuracy and ease of data collection, and ultimately generate a dataset to be used to inform grizzly bear management.

Source: Grizz Tracker

I was really surprised the first time someone asked — I think it was in a review of a proposal — about whether it was ethical to do citizen science. “Isn’t this exploitation?” was how the concern was phrased. Getting unpaid people to do what was previously paid work might seem problematic. As citizen science has grown, so too has thoughtful criticism of the practice.

The term ‘citizen science’ covers such a wide range of activities that I think it’s hard to address the ‘ethics of citizen science’ broadly. As citizen science is broad, so too are its ethics, covering everything from completely ethical to unethical.

Source: Is citizen science ethical?

Abstract:

This article explores the tensions between game play and contributing to science within Foldit (http://fold.it/portal/), an online puzzle game and participatory science project in which participants fold proteins in novel ways. No prior scientific knowledge is required in order to play, but solutions developed by players have led to important scientific discoveries. Based on analysis of online exchanges and interviews with a number of players, we examine the tensions between the experience and pleasure of playing a game and the desire to work and contribute to scientific activity. We examine our players’ experiences in terms of Stebbins’ (1982, 2007) notion of serious leisure.

Source: When Does Leisure Become Work

One of the wonderful things about citizen science is the innate coupling of science research with science education – learning while doing. But does this equation actually hold up? This article reviews four citizen science categories in the context of whether public understanding of science is obtained and comes to the conclusion that while there are many instances in which increased public understanding of science occurs through participation in citizen science, the field can be more purposeful in future project delivery to enable a higher level of public understanding of science. –LFF

Abstract:

Over the past 20 years, thousands of citizen science projects engaging millions of participants in collecting and/or processing data have sprung up around the world. Here we review documented outcomes from four categories of citizen science projects which are defined by the nature of the activities in which their participants engage – Data Collection, Data Processing, Curriculum-based, and Community Science. We find strong evidence that scientific outcomes of citizen science are well documented, particularly for Data Collection and Data Processing projects. We find limited but growing evidence that citizen science projects achieve participant gains in knowledge about science knowledge and process, increase public awareness of the diversity of scientific research, and provide deeper meaning to participants’ hobbies. We also find some evidence that citizen science can contribute positively to social well-being by influencing the questions that are being addressed and by giving people a voice in local environmental decision making. While not all citizen science projects are intended to achieve a greater degree of public understanding of science, social change, or improved science -society relationships, those projects that do require effort and resources in four main categories: (1) project design, (2) outcomes measurement, (3) engagement of new audiences, and (4) new directions for research.

Photo Credit: EOL (CC BY). Citizen Science in action: Learning about biodiversity through games.

Source: Can citizen science enhance public understanding of science?

Abstract:

Citizen Science is part of a broader reconfiguration of the relationship between science and the public in the digital age: Knowledge production and the reception of scientific knowledge are becoming increasingly socially inclusive. We argue that the digital revolution brings the “problem of extension” — identified by Collins and Evans in the context of science and technology governance — now closer to the core of scientific practice. In order to grasp the implications of the inclusion of non-experts in science, the aim of this contribution is to define a role-set of non-certified knowledge production and reception, serving as a heuristic instrument for empirical clarifications.

Source: The “Problem of Extension” revisited: new modes of digital participation in science

Abstract:

The ability of volunteers to undertake different tasks and accurately collect data is critical for the success of many conservation projects. In this study, a simulated herpetofauna visual encounter survey was used to compare the detection and distance estimation accuracy of volunteers and more experienced observers. Experience had a positive effect on individual detection accuracy. However, lower detection performance of less experienced volunteers was not found in the group data, with larger groups being more successful overall, suggesting that working in groups facilitates detection accuracy of those with less experience. This study supports the idea that by optimizing survey protocols according to the available resources (time and volunteer numbers), the sampling efficiency of monitoring programs can be improved and that non-expert volunteers can provide valuable contributions to visual encounter-based biodiversity surveys. Recommendations are made for the improvement of survey methodology involving non-expert volunteers.

Source: How useful are volunteers for visual biodiversity surveys? An evaluation of skill level and group size during a conservation expedition

Here we have a real existential crisis – one that those of us in the field grapple with continuously – a search for the true meaning of the term  “citizen science”. This term has different meanings to various stakeholders, but to accurately track the contributions that are being made academically (and otherwise) by various forms of citizen science, we need to obtain more precise context-driven definitions of the term “citizen science”. This article is an excellent place to start. –LFF

Abstract:

The concept of citizen science (CS) is currently referred to by many actors inside and outside science and research. Several descriptions of this purportedly new approach of science are often heard in connection with large datasets and the possibilities of mobilizing crowds outside science to assists with observations and classifications. However, other accounts refer to CS as a way of democratizing science, aiding concerned communities in creating data to influence policy and as a way of promoting political decision processes involving environment and health.In this study we analyse two datasets (N = 1935, N = 633) retrieved from the Web of Science (WoS) with the aim of giving a scientometric description of what the concept of CS entails. We account for its development over time, and what strands of research that has adopted CS and give an assessment of what scientific output has been achieved in CS-related projects. To attain this, scientometric methods have been combined with qualitative approaches to render more precise search terms.Results indicate that there are three main focal points of CS. The largest is composed of research on biology, conservation and ecology, and utilizes CS mainly as a methodology of collecting and classifying data. A second strand of research has emerged through geographic information research, where citizens participate in the collection of geographic data. Thirdly, there is a line of research relating to the social sciences and epidemiology, which studies and facilitates public participation in relation to environmental issues and health. In terms of scientific output, the largest body of articles are to be found in biology and conservation research. In absolute numbers, the amount of publications generated by CS is low (N = 1935), but over the past decade a new and very productive line of CS based on digital platforms has emerged for the collection and classification of data.

Source: What Is Citizen Science? – A Scientometric Meta-Analysis

Human-machine collaboration is the key to solving the most complex issues of the world, an editorial published recently in the journal Science suggested.

Championing “human computation”— a system that combines the artificial intelligence of machines and talents of humans, the authors claim the system could successfully tackle complex issues like climate change and geopolitical conflicts.

Authors Pietro Michelucci and Janis Dickinson also claim that the “human computation’ system could help solve the issues without the existential risks that are posed by artificial intelligence and the technological singularity.

The idea is to develop understanding of real-world problems online, and test possible solutions to those problems in this computational space. Then, the new knowledge should be applied back in the real world to bring desired changes.

Source: Human-machine collaboration could tackle world’s toughest issues

An article published in PLOS One tracking academic papers mentioning ‘citizen science’ caused a lot of discussion in the last month. My take is here, but Caren Cooper’s blog does a much better job of exploring the issues. –CJL

Citizen science is skyrocketing in popularity. Not just among participants (of which there are millions), but also in its visibility in academic journals. A new article in PLOS ONE by Ria Follett and Vladimir Strezov tracks trends in academic articles containing the term “citizen science.” The authors deciphered patterns based on 888 articles summoned with the keyword search “citizen science” and revealed adoption of the term over time in different disciplines and for different purposes.

“Citizen science,” by that specific phrase, first appeared in academic publications in 1997. After 2003, articles about methods and data validity began to appear. Papers about projects tricked into the literature until 2007, at which time the skyrocketing began. I suspect momentum was slowly building since about 2002 as more and more projects and their data started being accessible online: more access likely equates to more use, assuming the patterns in “citizen science” are a vague proxy for an actual increase in adoption of citizen science.

Photo Credit: Cascades Butterfly Team, by Karlie Roland, NPS