Zum Wandel der wissenschaftlichen Wissensproduktion durch Big Data: Welche Rolle spielt Citizen Science?

2019 ◽  
Vol 44 (S1) ◽  
pp. 15-35 ◽  
Author(s):  
Martina Franzen
Keyword(s):  
Big Data ◽  
2017 ◽  
Vol 16 (02) ◽  
pp. C05
Author(s):  
Stuart Allan ◽  
Joanna Redden

This article examines certain guiding tenets of science journalism in the era of big data by focusing on its engagement with citizen science. Having placed citizen science in historical context, it highlights early interventions intended to help establish the basis for an alternative epistemological ethos recognising the scientist as citizen and the citizen as scientist. Next, the article assesses further implications for science journalism by examining the challenges posed by big data in the realm of citizen science. Pertinent issues include potential risks associated with data quality, access dynamics, the difficulty investigating algorithms, and concerns about certain constraints impacting on transparency and accountability.


Author(s):  
Tarun Reddy Katapally

UNSTRUCTURED Citizen science enables citizens to actively contribute to all aspects of the research process, from conceptualization and data collection, to knowledge translation and evaluation. Citizen science is gradually emerging as a pertinent approach in population health research. Given that citizen science has intrinsic links with community-based research, where participatory action drives the research agenda, these two approaches could be integrated to address complex population health issues. Community-based participatory research has a strong record of application across multiple disciplines and sectors to address health inequities. Citizen science can use the structure of community-based participatory research to take local approaches of problem solving to a global scale, because citizen science emerged through individual environmental activism that is not limited by geography. This synergy has significant implications for population health research if combined with systems science, which can offer theoretical and methodological strength to citizen science and community-based participatory research. Systems science applies a holistic perspective to understand the complex mechanisms underlying causal relationships within and between systems, as it goes beyond linear relationships by utilizing big data–driven advanced computational models. However, to truly integrate citizen science, community-based participatory research, and systems science, it is time to realize the power of ubiquitous digital tools, such as smartphones, for connecting us all and providing big data. Smartphones have the potential to not only create equity by providing a voice to disenfranchised citizens but smartphone-based apps also have the reach and power to source big data to inform policies. An imminent challenge in legitimizing citizen science is minimizing bias, which can be achieved by standardizing methods and enhancing data quality—a rigorous process that requires researchers to collaborate with citizen scientists utilizing the principles of community-based participatory research action. This study advances SMART, an evidence-based framework that integrates citizen science, community-based participatory research, and systems science through ubiquitous tools by addressing core challenges such as citizen engagement, data management, and internet inequity to legitimize this integration.


2021 ◽  
pp. 76-88
Author(s):  
Graeme F. Clark ◽  
Jordan Gacutan ◽  
Robert Lawther ◽  
Emma L. Johnston ◽  
Heidi Tait ◽  
...  

2020 ◽  
Author(s):  
Giuseppina Monacelli ◽  
Carlo Cipolloni ◽  
Lorenza Babbini ◽  
Maria Chiara Sole ◽  
Alessandro Lotti ◽  
...  

<p>Water and environmental monitoring, observation and decision support systems (DSS) are being transformed by a wealth of open and big data that are increasingly available, accurate and timely. Consolidated technologies of earth observation, remote sensing, geospatial modelling and visualization systems are stimulating earth, hydrological and environmental sciences that are reacting not only with increasing scientific production, but with novel solutions-oriented methods, tools and algorithms. Procedures, methods and tools are more and more available for analysis, interpretation and mapping of river and basin coastal landscape features and hydro-environmental dynamics. Citizen science are further empowering the capabilities of DSS by gathering and sharing data on the human behaviour component to better understand the nature-human-urban interplay. Citizens, empowered by mobile devices, act as data and information producers, receivers and transmitters supporting the assessment of the effects of human-derived observations, feedbacks and actions sensing. Emerging hardware and software technologies (e.g. machine learning, artificial intelligence, IoT, etc.) are creating amazing opportunities for these DSS linked to the development of the human-machine interface and its use for promoting practical environmental and social actions to manage and mitigate natural hazard and climatic risks. The National System for Environmental Protection (SNPA) by the Italian Institute for Environmental Protection and Research (ISPRA) is supporting and implementing a wide and diverse range of research, applied research, learning and communication activities, both at the national and international level, in collaborating with leading academic, professional and international organizations, for integrating citizen science, open data and big data into next generation water and environmental decision support systems. This contribution, while depicting the overall SINA framework (Italian Environmental Information System) and ongoing and planned activities by ISPRA SNPA and SINA, presents recent outcomes of research initiatives developed within the Water JPI, UNEP INFORAC, National Plan for Climate Adaptation (PNACC), Marine pollution, Biodiversity, the Water, Food and Energy Nexus among others. Insights from joint research efforts and working groups are presented and shared while pursuing further synergies and stimulate the discussion on this crucial topic for national and international agencies, like ISPRA, that seek to transfer research data, models and tools into institutional and operational activities.</p>


AMBIO ◽  
2015 ◽  
Vol 44 (S4) ◽  
pp. 601-611 ◽  
Author(s):  
Steve Kelling ◽  
Daniel Fink ◽  
Frank A. La Sorte ◽  
Alison Johnston ◽  
Nicholas E. Bruns ◽  
...  

2020 ◽  
Author(s):  
Steffen Fritz

<p>In September 2015, the United Nations ratified the 17 Sustainable Development Goals (SDGs), which are comprised of a further 169 targets and 232 indicators for monitoring progress on poverty, well-being and major environmental and socio-economic problems, both nationally and globally. Much of the data used for SDG monitoring comes from censuses, surveys and other administrative data provided by national statistical offices, government agencies and international organizations. However, traditional data collection can be costly and infrequent, and the information can become outdated very quickly. Moreover, reporting is generally at the national level, so spatial variations of indicators within a country are not often available, yet this information is critical for effective spatial planning. Without knowing where issues are occurring in space, we cannot implement targeted solutions. Hence, there is currently a lack of data needed for effective monitoring and implementation of the SDGs.</p><p>Non-traditional data sources such as those arising from citizen science and geospatial big data, e.g., satellite imagery, mobile phone data, social media, etc. are part of the current ‘data revolution’, all of which have potential use in SDG monitoring and implementation. This lecture will provide an overview of these new and emerging non-traditional data sources in monitoring the SDGs, providing a range of examples from citizen science, Earth Observation (including the work of the Group on Earth Observations) and mobile phone data, among others. Where relevant, we will touch upon disaster risk reduction. Finally, actions will be presented that are currently happening to promote the data revolution for sustainable development and what is still needed to make tangible progress on SDG implementation using these new data sources as well as how the engagement of citizens in data collection can trigger transformative and behavioral change.</p>


2021 ◽  
pp. 1-13
Author(s):  
Graeme F. Clark ◽  
Jordan Gacutan ◽  
Robert Lawther ◽  
Emma L. Johnston ◽  
Heidi Tait ◽  
...  

Author(s):  
Nikita S. Panchariya ◽  
Andrew J. DeStefano ◽  
Varsha Nimbagal ◽  
Revathi Ragupathy ◽  
Serkan Yavuz ◽  
...  
Keyword(s):  
Big Data ◽  

2020 ◽  
Author(s):  
Tarun R Katapally

UNSTRUCTURED The coronavirus disease (COVID-19) pandemic is an extremely complex existential threat that requires cohesive societal effort to address health system inefficiencies. When our society has faced existential crises in the past, we have banded together by using the technology at hand to overcome them. The COVID-19 pandemic is one such threat that requires not only a cohesive effort, but also enormous trust to follow public health guidelines, maintain social distance, and share necessities. However, are democratic societies with civil liberties capable of doing this? Mobile technology has immense potential for addressing pandemics like COVID-19, as it gives us access to big data in terms of volume, velocity, veracity, and variety. These data are particularly relevant to understand and mitigate the spread of pandemics such as COVID-19. In order for such intensive and potentially intrusive data collection measures to succeed, we need a cohesive societal effort with full buy-in from citizens and their representatives. This article outlines an evidence-based global digital citizen science policy that provides the theoretical and methodological foundation for ethically sourcing big data from citizens to tackle pandemics such as COVID-19.


2016 ◽  
Vol 29 (3) ◽  
pp. 10-27
Author(s):  
Yu-Wei Lin ◽  
Jo Bates ◽  
Paula Goodale

This paper investigates the embodied performance of ‘doing citizen science’. It examines how ‘citizen scientists’ produce scientif c data using the resources available to them, and how their socio-technical practices and emotions impact the construction of a crowdsourced data infrastructure. We found that conducting citizen science is highly emotional and experiential, but these individual experiences and feelings tend to get lost or become invisible when user-contributed data are aggregated and integrated into a big data infrastructure. While new meanings can be extracted from big data sets, the loss of individual emotional and practical elements denotes the loss of data provenance and the marginalisation of individual ef orts, motivations, and local politics, which might lead to disengaged participants, and unsustainable communities of citizen scientists. The challenges of constructing a data infrastructure for crowdsourced data therefore lie in the management of both technical and social issues which are local as well as global.Keywords: crowdsourcing, big data infrastructure, citizen science


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