DiSCmap : digitisation of special collections mapping, assessment, prioritisation. Final project report

Traditionally, digitisation has been led by supply rather than demand. While end users are seen as a priority they are not directly consulted about which collections they would like to have made available digitally or why. This can be seen in a wide range of policy documents throughout the cultural heritage sector, where users are positioned as central but where their preferences are assumed rather than solicited. Post-digitisation consultation with end users isequally rare. How are we to know that digitisation is serving the needs of the Higher Education community and is sustainable in the long-term? The 'Digitisation in Special Collections: mapping, assessment and prioritisation' (DiSCmap) project, funded by the Joint Information Systems Committee (JISC) and the Research Information Network (RIN), aimed to:- Identify priority collections for potential digitisation housed within UK Higher Education's libraries, archives and museums as well as faculties and departments.- Assess users' needs and demand for Special Collections to be digitised across all disciplines.- Produce a synthesis of available knowledge about users' needs with regard to usability and format of digitised resources.- Provide recommendations for a strategic approach to digitisation within the wider context and activity of leading players both in the public and commercial sector.The project was carried out jointly by the Centre for Digital Library Research (CDLR) and the Centre for Research in Library and Information Management (CERLIM) and has taken a collaborative approach to the creation of a user-driven digitisation prioritisation framework, encouraging participation and collective engagement between communities.Between September 2008 and March 2009 the DiSCmap project team asked over 1,000 users, including intermediaries (vocational users who take care of collections) and end users (university teachers, researchers and students) a variety of questions about which physical and digital Special Collections they make use of and what criteria they feel must be considered when selecting materials for digitisation. This was achieved through workshops, interviews and two online questionnaires. Although the data gathered from these activities has the limitation of reflecting only a partial view on priorities for digitisation - the view expressed by those institutions who volunteered to take part in the study - DiSCmap was able to develop:- a 'long list' of 945 collections nominated for digitisation both by intermediaries andend-users from 70 HE institutions (see p. 21);- a framework of user-driven prioritisation criteria which could be used to inform current and future digitisation priorities; (see p. 45)- a set of 'short lists' of collections which exemplify the application of user-driven criteria from the prioritisation framework to the long list (see Appendix X):o Collections nominated more than once by various groups of users.o Collections related to a specific policy framework, eg HEFCE's strategically important and vulnerable subjects for Mathematics, Chemistry and Physics.o Collections on specific thematic clusters.o Collections with highest number of reasons for digitisation

Manufacturing and Management Paradigms, Methods and Tools for Sustainable Industry 4.0-Oriented Manufacturing Systems

In the current Industry 4.0 era (I4.0, for short) [1–3], manufacturing and management (M&M) paradigms, methods and tools should be wisely developed to fulfil sustainability issues, regarding not just economic but also social and environmental concerns by simultaneously fostering organizational efficiency excellence and transformational initiatives, therefore contributing to enduring business results.This Special Issue aims to provide new insights regarding manufacturing or management paradigms (strategies), methods, tools, and practices aligned with the contemporary necessity to create and implement sustainable I4.0 manufacturing systems. Each of the five selected papers of this Special Issue makes a novel contribution to that aim.This work was supported by national funds through the FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, UIDB/50014/2020, and EXPL/EME-SIS/1224/2021.info:eu-repo/semantics/publishedVersio

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

What is Mizzou Advantage?

This booklet provides detailed information about the Mizzou Advantage and the projects awarded grants in rounds 1 and 2 of the program

Collaborative and intelligent networks and decision systems and services for supporting engineering and production management

Collaborative networks and systems (CNS) have received much attention in recent decades to reach a competitive advantage [...]This work was supported by national funds through the FCT-Fundacao para a Ciencia e Tecnologia, through the R&D Units Project Scopes: UIDB/00319/2020, UIDB/50014/2020, and EXPL/EME-SIS/1224/2021

LIBER 41st Annual Conference : Mobilising the Knowledge Economy for Europe

Conference Programme. 27–30 June 2012, Tart

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/