Widening the Circle of Engagement Around Environmental Issues using Cloud-based Tools

Environmental data are being generated and collected at unprecedented rates. However, the diversity in form and format of these environmental assets poses challenges for collaborative and reproducible science. Moreover, access constraints that surround environmental data lead to difficulty in use and interpretation of results. Cloud computing offers high potential to break down such barriers and engender collaboration, attribution, reuse, and reproducibility. In this article we review the design of the Environmental Virtual Observatory pilot (EVOp) that was conceived as a cloud-enabled virtual research space for different users interested in environmental science, ranging from domain specialists to the general public. We discuss the key technologies and processes used: a hybrid cloud infrastructure; standard service interfaces; a unified service delivery platform; and a test-driven development cycle. We also discuss the methodology by showcasing one of the exemplars developed in EVOp, stressing the importance of weaving stakeholder engagement from the beginning and throughout the process. We also briefly highlight some of the lessons learnt of working in an interdisciplinary team.

[1]  P M Haygarth,et al.  A cloud based tool for knowledge exchange on local scale flood risk. , 2015, Journal of environmental management.

[2]  María-del-Mar Gallardo,et al.  A practical use of model checking for synthesis: generating a dam controller for flood management , 2011, Softw. Pract. Exp..

[3]  Giuseppe Procaccianti,et al.  Green ICT Research and Challenges , 2016, EnviroInfo.

[4]  A. Budden,et al.  Big data and the future of ecology , 2013 .

[5]  Manish Parashar,et al.  Enabling on-demand science via cloud computing , 2014, IEEE Cloud Computing.

[6]  Katie A. Barnas,et al.  Why is Data Sharing in Collaborative Natural Resource Efforts so Hard and What can We Do to Improve it? , 2014, Environmental Management.

[7]  François Bousquet,et al.  Modelling with stakeholders , 2010, Environ. Model. Softw..

[8]  Patricia Lago,et al.  Aligning economic impact with environmental benefits: A green strategy model , 2012, 2012 First International Workshop on Green and Sustainable Software (GREENS).

[9]  Joost Buurman,et al.  The California drought: coping responses and resilience building. , 2017 .

[10]  Jáchym Čepický PyWPS 2.0.0: The presence and the future , 2007 .

[11]  Richard G Baraniuk,et al.  More Is Less: Signal Processing and the Data Deluge , 2011, Science.

[12]  Muzafar Ahmad Bhat,et al.  Cloud Computing: A solution to Geographical Information Systems (GIS) Cloud Computing and GIS , 2011 .

[13]  Dick Botteldooren,et al.  Multiagent-Based Data Fusion in Environmental Monitoring Networks , 2012, Int. J. Distributed Sens. Networks.

[14]  Lori N. K. Leonard,et al.  Data-intensive hydrologic modeling: A Cloud strategy for integrating PIHM, GIS, and Web-Services , 2010 .

[15]  Cassidy Johnson,et al.  The data gap: An analysis of data availability on disaster losses in sub-Saharan African cities☆ , 2016 .

[16]  Mario Piattini,et al.  Interactions between environmental sustainability goals and software product quality: A mapping study , 2018, Inf. Softw. Technol..

[17]  Paul Quinn,et al.  Environmental Virtual Observatory Pilot Project , 2014 .

[18]  Ruzanna Chitchyan,et al.  Uncovering sustainability concerns in software product lines , 2017, J. Softw. Evol. Process..

[19]  B. Timbal,et al.  The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society , 2013 .

[20]  Nancy Wilkins-Diehr,et al.  The global impact of science gateways, virtual research environments and virtual laboratories , 2019, Future Gener. Comput. Syst..

[21]  Dimitri Solomatine,et al.  Cloud and cluster computing in uncertainty analysis of integrated flood models , 2013 .

[22]  Gene Whelan,et al.  Design of a component-based integrated environmental modeling framework , 2014, Environ. Model. Softw..

[23]  Marco Aiello,et al.  What IS Can Do for Environmental Sustainability: A Report from CAiSE'11 Panel on Green and Sustainable IS , 2012, Commun. Assoc. Inf. Syst..

[24]  Peter E. Thornton,et al.  A functional test platform for the Community Land Model , 2014, Environ. Model. Softw..

[25]  Alexander Y. Sun Enabling collaborative decision-making in watershed management using cloud-computing services , 2013, Environ. Model. Softw..

[26]  Wouter Buytaert,et al.  Environmental Virtual Observatories (EVOs): prospects for knowledge co-creation and resilience in the Information Age , 2016 .

[27]  C. Tenopir,et al.  Data Sharing by Scientists: Practices and Perceptions , 2011, PloS one.

[28]  Gordon S. Blair,et al.  A Cloud-based Virtual Observatory for Environmental Science , 2011 .

[29]  Faiza Samreen,et al.  Cloud Brokerage , 2018, ACM Comput. Surv..

[30]  Anne E. Trefethen,et al.  The Data Deluge: An e-Science Perspective , 2003 .

[31]  Erik Jagroep,et al.  Awakening Awareness on Energy Consumption in Software Engineering , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Society Track (ICSE-SEIS).

[32]  Keith Beven,et al.  Cloud-enabled Web Applications for Environmental Modelling , 2012 .

[33]  Yehia El-khatib,et al.  Web technologies for environmental Big Data , 2015, Environ. Model. Softw..

[34]  Bob Gill,et al.  Magic Quadrant for Cloud Infrastructure as a Service , Worldwide 03 , 2016 .

[35]  M. Ragan-Kelley,et al.  The Jupyter/IPython architecture: a unified view of computational research, from interactive exploration to communication and publication. , 2014 .

[36]  Wouter Buytaert,et al.  Web-based environmental simulation: bridging the gap between scientific modeling and decision-making. , 2012, Environmental science & technology.

[37]  Jacqueline Corbett,et al.  Green IS Research: A Modernity Perspective , 2016, Commun. Assoc. Inf. Syst..

[38]  Hossein Saiedian,et al.  A Leveled Examination of Test-Driven Development Acceptance , 2007, 29th International Conference on Software Engineering (ICSE'07).

[39]  Witold F. Krajewski,et al.  Towards an integrated Flood Information System: Centralized data access, analysis, and visualization , 2013, Environ. Model. Softw..

[40]  Maurizio Morisio,et al.  Exploring initial challenges for green software engineering: summary of the first GREENS workshop, at ICSE 2012 , 2013, SOEN.

[41]  Patricia Lago,et al.  Creating Environmental Awareness in Service Oriented Software Engineering , 2010, ICSOC Workshops.

[42]  Matthias Jarke,et al.  Preparing Research Projects for Sustainable Software Engineering in Society , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Society Track (ICSE-SEIS).

[43]  Anthony J. Jakeman,et al.  Ten iterative steps in development and evaluation of environmental models , 2006, Environ. Model. Softw..

[44]  Yehia El-khatib Mapping Cross-Cloud Systems: Challenges and Opportunities , 2016, HotCloud.

[45]  Ethan P. White,et al.  Nine simple ways to make it easier to (re)use your data , 2013 .

[46]  Keith Beven,et al.  The Environmental Virtual Observatory (EVO) local exemplar: A cloud based local landscape learning visualisation tool for communicating flood risk to catchment stakeholders , 2013 .

[47]  Gordon S. Blair,et al.  Complex Distributed Systems: The Need for Fresh Perspectives , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[48]  Christopher J. A. Macleod,et al.  Modellers' roles in structuring integrative research projects , 2013, Environ. Model. Softw..

[49]  Dongyao Wu,et al.  Building Pipelines for Heterogeneous Execution Environments for Big Data Processing , 2016, IEEE Software.

[50]  M. Brugnach,et al.  Why are decisions in flood disaster management so poorly supported by information from flood models? , 2014, Environ. Model. Softw..

[51]  Gordon S. Blair,et al.  SE in ES: Opportunities for Software Engineering and Cloud Computing in Environmental Science , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS).

[52]  Matthew S. Mayernik,et al.  Advanced Technologies and Data Management Practices in Environmental Science: Lessons from Academia , 2012 .

[53]  Martyn P. Clark,et al.  Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models , 2008 .

[54]  Oliver Bates,et al.  HCI and Environmental Public Policy: Opportunities for Engagement , 2017, CHI.

[55]  Bryn Nelson Data sharing: Empty archives , 2009, Nature.

[56]  P. Longley,et al.  Geographic information portals--a UK perspective , 2005, Comput. Environ. Urban Syst..

[57]  Sheila Greene,et al.  A geospatial framework to support integrated biogeochemical modelling in the United Kingdom , 2015, Environ. Model. Softw..