A first approach for handling uncertainty in citizen science

Citizen Science is coming to the forefront of scientific research as a valuable method for large-scale processing of data. New technologies in fields such as astronomy or bio-sciences generate tons of data, for which a thorough expert analysis is no longer feasible. In contrast, communities of volunteers coordinated by the Internet are showing a great potential in completing such analysis in a reasonable time. However, this approach brings uncertainty and the spread of biases within the data, since amateur participants are usually non-experts on the subject and count with variable skills and expertise. This means lack of accuracy in results coming from Citizen Science projects. This work presents a novel approach to handle uncertainty in Citizen Science. We focus on leveraging this uncertainty in the data pursuing a refinement of results. We distinguish between two types of uncertainty: a first one due to the lack of consensus between amateurs, and another one quantified by amateurs themselves during the course of the project. We test our method using the Galaxy Zoo, a project which aims for the labelling of a huge dataset of galaxy images. Considering available expert classifications to validate our experiments, the proposed method is able to improve current accuracy and classify a greater number of images

A first approach for handling uncertainty in citizen science

Citizen Science is coming to the forefront of scientific research as a valuable method for large-scale processing of data. New technologies in fields such as astronomy or bio-sciences generate tons of data, for which a thorough expert analysis is no longer feasible. In contrast, communities of volunteers coordinated by the Internet are showing a great potential in completing such analysis in a reasonable time. However, this approach brings uncertainty and the spread of biases within the data, since amateur participants are usually non-experts on the subject and count with variable skills and expertise. This means lack of accuracy in results coming from Citizen Science projects. This work presents a novel approach to handle uncertainty in Citizen Science. We focus on leveraging this uncertainty in the data pursuing a refinement of results. We distinguish between two types of uncertainty: a first one due to the lack of consensus between amateurs, and another one quantified by amateurs themselves during the course of the project. We test our method using the Galaxy Zoo, a project which aims for the labelling of a huge dataset of galaxy images. Considering available expert classifications to validate our experiments, the proposed method is able to improve current accuracy and classify a greater number of images

Novel automated classification approaches for citizen science

Citizen science, traditionally known as the engagement of amateur participants in research, is showing a great potential for large-scale processing of data. In areas such as astronomy, ecology, or geo-sciences, where emerging technologies generate huge volumes of data, citizen science projects enable image classification at a rate not possible to accomplish by experts alone. Using the power of the web, virtual communities of volunteers sharing a common goal are able to coordinate the classification of hundreds of thousands of images in a reasonable amount of time. However, expert evaluations usually reveal biases and uncertainty in the results, since the participants involved are typically inexperienced in the task and hold variable skills and backgrounds. Consequently, the research community tends to distrust citizen science outcomes, claiming a generalised lack of accuracy and validation, and leaving the major part of the resulting data unemployed after the finalisation of the projects. Citizen science also offers a great amount of labelled data at a reduced cost for the training of machine learning classifiers. Nonetheless, current efforts attempting the exploitation of citizen science outcomes with machine learning tools have ignored the inherent uncertainty in results as well as the potential of expert classifications to ameliorate this issue. The ultimate goal has mainly been to replicate the amateur endeavours, thus propagating their biases and limitations in the automated classification. Similarly, the potential behind the learning from unlabelled data to alleviate this uncertainty has also been disregarded. This framework claims for a solution that can take advantage of all levels of knowledge: expert classifications, citizen science data, and unlabelled data. However, the synergy between these sources of data remains unexplored, waiting for the development of new methodologies that may lead to an enhanced automated classification. This thesis focuses on the development of automated approaches for classification problems aided by citizen science projects on the web, aiming to leverage the inherent uncertainty in the results and all levels of knowledge available about the problem. As a case study, we select the longest running implementation of a scientific problem aided by modern citizen science: the classification of galaxies from images. We exploit the results of the first edition of the Galaxy Zoo, a citizen science project that nowadays represents the largest galaxy image database manually annotated. The research is completed through three progressive stages. First, we introduce a novel multi-stage approach to handle the uncertainty within data labelled in the course of citizen science projects. Our method proposes a set of transformations that leverage the uncertainty in amateur classifications in conjunction with a hybridisation strategy that provides the best aggregation of the transformed data for improving the quality and confidence in the results. The second stage comprises a thorough study of machine learning methods for image classification, introducing the use of autoencoders to learn from unlabelled data, and exploring the learning from amateur and expert classifications by the exploitation of pre-training and fine-tuning of convolutional neural networks. Finally, in the third stage of the research, the previous findings are combined to propose a solution to the novel learning paradigm defined that is able to exploit data either labelled by experts and amateurs in the course of citizen science projects, and unlabelled data. In summary, the research conducted here introduces a set of novel mechanisms towards an improved automated classification based on citizen science data, expert classifications, and raw data. As a result, the proposed method for handling the uncertainty boosts the accuracy and is able to classify a higher number of images in comparison with previous approaches. This is accomplished by taking advantage of the uncertainty measured by participants themselves. The use of autoencoders greatly speeds up feature extraction with respect to state-of-the-art methods, also revealing the potential behind the exploitation of amateur and expert classifications by deep learning-based classifiers. In last place, a novel approach leverages all insights previously found and presents an innovative setting to learn from expert and amateur classifications and unlabelled data that surpasses the performance obtained using such label sets separately or joint. These results have also signified a global study of the automated classification of galaxy images problem that, from state-of-the-art approaches, have contributed new methods built on the boundary amongst citizen science, astroinformatics, and machine learning fields of study

Governance and information governance: some ethical considerations within an expanding information society

Governance and information governance ought to be an integral part of any government or organisations information and business strategy. More than ever before information and knowledge can be produced, exchanged, shared and communicated through many different mediums. Whilst sharing information and knowledge provides many benefits it also provides many challenges and risks to governments, global organisations and the individual citizen. Information governance is one element of a governance and compliance programme, but an increasingly important one, because many regulations apply to how information is managed and protected from theft and abuse, much of which resides with external agencies usually outside the control of the individual citizen. This paper explores some of the compliance and quality issues within governance and information governance including those ethical concerns as related to individual citizens and multiple stakeholders engaged directly or indirectly in the governance process

From Sensor to Observation Web with Environmental Enablers in the Future Internet

This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities’ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)

Capturing mink and data : Interacting with a small and dispersed environmental initiative over the introduction of digital innovation

This case study was carried out by Koen Arts1, Gemma Webster1, Nirwan Sharma1, Yolanda Melero2, Chris Mellish1, Xavier Lambin2 and René van der Wal1. We thank two anonymous reviewers for their suggestions, and Chris Horrill from SMI for his very helpful and insightful comments on previous drafts of this manuscript. The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Case study for 'Responsible Research & Innovation in ICT' platformPostprin

Risks, alternative knowledge strategies and democratic legitimacy: the conflict over co-incineration of hazardous industrial waste in Portugal.

The decision to incinerate hazardous industrial waste in cement plants (the socalled ‘co-incineration’ process) gave rise to one of the most heated environmental conflicts ever to take place in Portugal. The bitterest period was between 1997 and 2002, after the government had made a decision. Strong protests by residents, environmental organizations, opposition parties, and some members of the scientific community forced the government to backtrack and to seek scientific legitimacy for the process through scientific expertise. The experts ratified the government’s decision, stating that the risks involved were socially acceptable. The conflict persisted over a decade and ended up clearing the way for a more sustainable method over which there was broad social consensus – a multifunctional method which makes it possible to treat, recover and regenerate most wastes. Focusing the analysis on this conflict, this paper has three aims: (1) to discuss the implications of the fact that expertise was ‘confiscated’ after the government had committed itself to the decision to implement co-incineration and by way of a reaction to the atmosphere of tension and protest; (2) to analyse the uses of the notions of ‘risk’ and ‘uncertainty’ in scientific reports from both experts and counter-experts’ committees, and their different assumptions about controllability and criteria for considering certain practices to be sufficiently safe for the public; and (3) to show how the existence of different technical scientific and political attitudes (one more closely tied to government and the corporate interests of the cement plants, the other closer to the environmental values of reuse and recycling and respect for the risk perception of residents who challenged the facilities) is closely bound up with problems of democratic legitimacy. This conflict showed how adopting more sustainable and lower-risk policies implies a broader view of democratic legitimacy, one which involves both civic movements and citizens themselves