20 resultados para project sesame

em JISC Information Environment Repository


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Mishka Fielding, Learning Resources Manager with the help of Anthony Beal, e-Learning Adviser, RSC Northwest, created a one-hour interactive session to promote Learning Resources and Information Literacy to staff and students within the College. These sessions successfully promoted the department, increasing their stock circulation by 50% and their usage of e-books, putting them 1st out of 200 in the ‘Jisc Collections Project’.

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Cloud-based infrastructure essentially comprises two offerings, cloud-based compute and cloud-based storage. These are perhaps best typified for most people by the two main components of the Amazon Web Services (AWS)1 public cloud offer, the Elastic Compute Cloud (EC2)2 and the Simple Storage Service (S3)3, though, of course, there are many other related services offered by Amazon and many other providers of similar public cloud infrastructure across the Internet.

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CAMEL is short for Collaborative Approaches to the Management of e-Learning and was a project funded by the HEFCE Leadership, Governance and Management programme. It set out to explore how institutions who were making effective use of e-learning and who were collaborating in regional lifelong learning partnerships might be able to learn from each other in a Community of Practice based around study visits to each of the partner institutions. This short publication highlights some of the things CAMEL participants found out about e-learning and about each other.

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The DADAISM project brings together researchers from the diverse fields of archaeology, human computer interaction, image processing, image search and retrieval, and text mining to create a rich interactive system to address the problems of researchers finding images relevant to their research. In the age of digital photography, thousands of images are taken of archaeological artefacts. These images could help archaeologists enormously in their tasks of classification and identification if they could be related to one another effectively. They would yield many new insights on a range of archaeological problems. However, these images are currently greatly underutilized for two key reasons. Firstly, the current paradigm for interaction with image collections is basic keyword search or, at best, simple faceted search. Secondly, even if these interactions are possible, the metadata related to the majority of images of archaeological artefacts is scarce in information relating to the content of the image and the nature of the artefact, and is time intensive to enter manually. DADAISM will transform the way in which archaeologists interact with online image collections. It will deploy user-centred design methodologies to create an interactive system that goes well beyond current systems for working with images, and will support archaeologists’ tasks of finding, organising, relating and labelling images as well as other relevant sources of information such as grey literature documents.

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Social scientists have used agent-based models (ABMs) to explore the interaction and feedbacks among social agents and their environments. The bottom-up structure of ABMs enables simulation and investigation of complex systems and their emergent behaviour with a high level of detail; however the stochastic nature and potential combinations of parameters of such models create large non-linear multidimensional “big data,” which are difficult to analyze using traditional statistical methods. Our proposed project seeks to address this challenge by developing algorithms and web-based analysis and visualization tools that provide automated means of discovering complex relationships among variables. The tools will enable modellers to easily manage, analyze, visualize, and compare their output data, and will provide stakeholders, policy makers and the general public with intuitive web interfaces to explore, interact with and provide feedback on otherwise difficult-to-understand models.

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An output from the online promotion of research expertise project:

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This document is a report from Spotlight Data to Jisc. It describes the project findings and steps required to build a prototype journal research data policy system.