Toward a Sociocultural Learning Theory Framework to Designing Online learning Communities in Citizen Science

How can sociocultural learning theory inform design principles for citizen science online learning communities to inspire local environmental action? The purpose of this article is to identify themes in sociocultural learning theory that could inform the use and development of highly collaborative online learning communities that utilize community informatics tools for citizen science to enable on-the-ground environmental actions. Applying previously established socio-cultural theories provides an opportunity to build on what’s already known about how people learn and collaborate. Finally, this article explains how communities of practice theory, knowledge building theory, and place-based education theory can be woven together to create the basis for development of a conceptual framework

To have your citizen science cake and eat it? Delivering research and outreach through Open Air Laboratories (OPAL)

Background: The vast array of citizen science projects which have blossomed over the last decade span a spectrum of objectives from research to outreach. While some focus primarily on the collection of rigorous scientific data and others are positioned towards the public engagement end of the gradient, the majority of initiatives attempt to balance the two. Although meeting multiple aims can be seen as a ‘win–win’ situation, it can also yield significant challenges as allocating resources to one element means that they may be diverted away from the other. Here we analyse one such programme which set out to find an effective equilibrium between these arguably polarised goals. Through the lens of the Open Air Laboratories (OPAL) programme we explore the inherent trade-offs encountered under four indicators derived from an independent citizen science evaluation framework. Assimilating experience from the OPAL network we investigate practical approaches taken to tackle arising tensions. Results: Working backwards from project delivery to design, we found the following elements to be important: ensuring outputs are fit for purpose, developing strong internal and external collaborations, building a sufficiently diverse partnership and considering target audiences. We combine these ‘operational indicators’ with four pre-existing ‘outcome indicators’ to create a model which can be used to shape the planning and delivery of a citizen science project. Conclusions: Our findings suggest that whether the proverb in the title rings true will largely depend on the identification of challenges along the way and the ability to address these conflicts throughout the citizen science projec

The Trail, 1954-03-02

https://soundideas.pugetsound.edu/thetrail_all/1680/thumbnail.jp

Spelman Messenger November 1963 vol. 80 no. 1

The Atlanta University Center Robert W. Woodruff Library acknowledges the generous support of the Council on Library and Information Resources (CLIR) in supporting the processing and digitization of a number of historic collections as part of the projectOur Story: Digitizing Publications and Photographs of the Historically Black Atlanta University Center Institutions

A Big Data perspective on Cyber-Physical Systems for Industry 4.0: modernizing and scaling complex event processing

Doctoral program in Advanced Engineering Systems for IndustryNowadays, the whole industry makes efforts to find the most productive ways of working and it already understood that using the data that is being produced inside and outside the factories is a way to improve the business performance. A set of modern technologies combined with sensor-based communication create the possibility to act according to our needs, precisely at the moment when the data is being produced and processed. Considering the diversity of processes existing in a factory, all of them producing data, Complex Event Processing (CEP) with the capabilities to process that amount of data is needed in the daily work of a factory, to process different types of events and find patterns between them. Although the integration of the Big Data and Complex Event Processing topics is already present in the literature, open challenges in this area were identified, hence the reason for the contribution presented in this thesis. Thereby, this doctoral thesis proposes a system architecture that integrates the CEP concept with a rulebased approach in the Big Data context: the Intelligent Event Broker (IEB). This architecture proposes the use of adequate Big Data technologies in its several components. At the same time, some of the gaps identified in this area were fulfilled, complementing Event Processing with the possibility to use Machine Learning Models that can be integrated in the rules' verification, and also proposing an innovative monitoring system with an immersive visualization component to monitor the IEB and prevent its uncontrolled growth, since there are always several processes inside a factory that can be integrated in the system. The proposed architecture was validated with a demonstration case using, as an example, the Active Lot Release Bosch's system. This demonstration case revealed that it is feasible to implement the proposed architecture and proved the adequate functioning of the IEB system to process Bosch's business processes data and also to monitor its components and the events flowing through those components.Hoje em dia as indústrias esforçam-se para encontrar formas de serem mais produtivas. A utilização dos dados que são produzidos dentro e fora das fábricas já foi identificada como uma forma de melhorar o desempenho do negócio. Um conjunto de tecnologias atuais combinado com a comunicação baseada em sensores cria a possibilidade de se atuar precisamente no momento em que os dados estão a ser produzidos e processados, assegurando resposta às necessidades do negócio. Considerando a diversidade de processos que existem e produzem dados numa fábrica, as capacidades do Processamento de Eventos Complexos (CEP) revelam-se necessárias no quotidiano de uma fábrica, processando diferentes tipos de eventos e encontrando padrões entre os mesmos. Apesar da integração do conceito CEP na era de Big Data ser um tópico já presente na literatura, existem ainda desafios nesta área que foram identificados e que dão origem às contribuições presentes nesta tese. Assim, esta tese de doutoramento propõe uma arquitetura para um sistema que integre o conceito de CEP na era do Big Data, seguindo uma abordagem baseada em regras: o Intelligent Event Broker (IEB). Esta arquitetura propõe a utilização de tecnologias de Big Data que sejam adequadas aos seus diversos componentes. As lacunas identificadas na literatura foram consideradas, complementando o processamento de eventos com a possibilidade de utilizar modelos de Machine Learning com vista a serem integrados na verificação das regras, propondo também um sistema de monitorização inovador composto por um componente de visualização imersiva que permite monitorizar o IEB e prevenir o seu crescimento descontrolado, o que pode acontecer devido à integração do conjunto significativo de processos existentes numa fábrica. A arquitetura proposta foi validada através de um caso de demonstração que usou os dados do Active Lot Release, um sistema da Bosch. Os resultados revelaram a viabilidade da implementação da arquitetura e comprovaram o adequado funcionamento do sistema no que diz respeito ao processamento dos dados dos processos de negócio da Bosch e à monitorização dos componentes do IEB e eventos que fluem através desses.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the Doctoral scholarship PD/BDE/135101/2017 and by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039479; Funding Reference: POCI-01- 0247-FEDER-039479]