Using the collaborative model of STORM for impact and environmental engagement

The environment needs communications and engagement programs that are as professional as other environmental projects. STORM (Stormwater Outreach for Regional Municipalities) offers a model for environmental professionals on: 1. Crowd sourcing engagement and action, 2. Sharing a learning environment on best practices, 3. How collaboration adds up to resources, partners, training and impact. Projects of any size can use these approaches to improve outreach. STORM collaborates to design, create and evaluate programs that engage diverse audiences in environmental actions. Over 83 jurisdictions and nonprofit partners use the collective action model to deliver award winning programs. Examples of these techniques will come from those programs: Don\u27t Drip and Drive, Puget Sound Starts Here, Natural Yard Care, STORM Fest, LatinX audience research, and the Resource Reservoir, an online library and web strategy. The teams use social marketing frameworks to develop effective, measurable programs that follow steps familiar to environmental projects: research, design, strategy, implementation, evaluation. Those resources are available to partners and the public to use. Historically, effective outreach design has not been a focus of environmental investment, research or training. The STORM collaborative fills this gap by working systematically to empower members and residents to address environmental issues. Membership work groups chose to focus on specific issues, and share resources that allow a greater impact and reach. The STORM collaborative helps jurisdictions to share the load of addressing large-scale regional water quality issues and engagement. It is a model for working together for environmental action and making the work of environmental professionals relevant to their communities. There is the perception that social marketing or behavior change programs cannot be measured or do not work, that inclusion of new communities is challenging. Like many environmental issues, change can be incremental, but powerful, and new practices can be used address new challenges

Models in the Cloud: Exploring Next Generation Environmental Software Systems

There is growing interest in the application of the latest trends in computing and data science methods to improve environmental science. However we found the penetration of best practice from computing domains such as software engineering and cloud computing into supporting every day environmental science to be poor. We take from this work a real need to re-evaluate the complexity of software tools and bring these to the right level of abstraction for environmental scientists to be able to leverage the latest developments in computing. In the Models in the Cloud project, we look at the role of model driven engineering, software frameworks and cloud computing in achieving this abstraction. As a case study we deployed a complex weather model to the cloud and developed a collaborative notebook interface for orchestrating the deployment and analysis of results. We navigate relatively poor support for complex high performance computing in the cloud to develop abstractions from complexity in cloud deployment and model configuration. We found great potential in cloud computing to transform science by enabling models to leverage elastic, flexible computing infrastructure and support new ways to deliver collaborative and open science

Kresge Foundation 2010-2011 Annual Report

Contains an introduction to Kresge's strategy; board chair's letter; president's letter; foundation timeline; program information; grant summary, including geographic distribution; grants lists; financial summary; and lists of board members and staff

Arctic air pollution: Challenges and opportunities for the next decade

The Arctic is a sentinel of global change. This region is influenced by multiple physical and socio-economic drivers and feedbacks, impacting both the natural and human environment. Air pollution is one such driver that impacts Arctic climate change, ecosystems and health but significant uncertainties still surround quantification of these effects. Arctic air pollution includes harmful trace gases (e.g. tropospheric ozone) and particles (e.g. black carbon, sulphate) and toxic substances (e.g. polycyclic aromatic hydrocarbons) that can be transported to the Arctic from emission sources located far outside the region, or emitted within the Arctic from activities including shipping, power production, and other industrial activities. This paper qualitatively summarizes the complex science issues motivating the creation of a new international initiative, PACES (air Pollution in the Arctic: Climate, Environment and Societies). Approaches for coordinated, international and interdisciplinary research on this topic are described with the goal to improve predictive capability via new understanding about sources, processes, feedbacks and impacts of Arctic air pollution. Overarching research actions are outlined, in which we describe our recommendations for 1) the development of trans-disciplinary approaches combining social and economic research with investigation of the chemical and physical aspects of Arctic air pollution; 2) increasing the quality and quantity of observations in the Arctic using long-term monitoring and intensive field studies, both at the surface and throughout the troposphere; and 3) developing improved predictive capability across a range of spatial and temporal scales

Analysis and control of complex collaborative design systems

This paper presents a novel method for modelling the complexity of collaborative design systems based on its analysis and proposes a solution to reducing complexity and improving performance of such systems. The interaction and interfacing properties among many components of a complex design system are analysed from different viewpoints and then a complexity model for collaborative design is established accordingly. In order to simplify complexity and improve performance of collaborative design, a general solution of decomposing a whole system into sub-systems and using unified interface mechanism between them has been proposed. This proposed solution has been tested with a case study. It has been shown that the proposed solution is meaningful and practical

Moving Ideas and Money: Issues and Opportunities in Funder Funding Collaboration

Presents an overview of funder collaboratives, ranging from information exchange, co-learning, informal and formal strategic alignments to pooled funding, joint ventures, and hybrid networks. Discusses elements of success, outcomes, and challenges

Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper