2nd KAPTUR Steering Group, presentation on RDM policy development at Goldsmiths College, University of London by Tahani Nadim, Project Officer for KAPTUR
Urban Forestry and Wood Utilization Assessment for Raleigh, NCArbor Day Foundation
The document discusses using an industry cluster model to support wood product businesses in Raleigh, North Carolina. It outlines key aspects of successful clusters like links between groups in a geographic region that mutually benefit each other. The document recommends that Raleigh identify a leader committed to collaboration to determine what cluster elements are present or absent and develop strategies like adding more log yards. The key takeaways are that clusters rely on entrepreneurship, innovation, and leadership with commitment to collaboration.
Meeting Success - Research-Based Ways to Improve Your Next MeetingBrian Lynch
Prepared by Brian Lynch for the October meeting of the Charlottetown Project & Product Manager and Business Analyst Group, hosted at Invesco, Charlottetown. This presentation provides a number of current, research-based recommendations for optimizing the effectiveness of any business meeting.
Visit the PMBA group at: https://www.meetup.com/Charlottetown-Product-Project-Management-and-BA-Meetup/
The technical report summarizes work from the KAPTUR project including:
1) An environmental assessment report analyzed researcher data practices and found they want to share research but with privacy.
2) A technical analysis reviewed 17 systems and recommended piloting Figshare and DataStage.
3) A costing model analyzed institutional vs cloud hosting and identified risks and integration challenges to cloud computing.
4) The meeting presented on piloting Figshare, DataStage, EPrints and CKAN as research data management systems.
Presentation given by Robin Burgess, KAPTUR Project Officer for The Glasgow School of Art, at the DCC Roadshow Northeast Scotland, University of Dundee, 5th December 2012
This presentation was given by John Murtagh, Project Officer for University of the Arts London at the KAPTUR training event held on Monday 19th November and supported by DCC through the Institutional Engagement project.
This technical report summarizes work integrating two data repository systems, DataStage and Figshare, with EPrints. [1] It outlines pros and cons of each system as well as lessons learned from testing their integration. [2] Key challenges remaining are fixing issues with SWORD submissions between DataStage and EPrints, getting feedback, and assessing upgrade issues. [3] The next steps are to gather experts to address SWORD transfer issues between systems and obtain outside help with SWORD2 if needed.
Urban Forestry and Wood Utilization Assessment for Raleigh, NCArbor Day Foundation
The document discusses using an industry cluster model to support wood product businesses in Raleigh, North Carolina. It outlines key aspects of successful clusters like links between groups in a geographic region that mutually benefit each other. The document recommends that Raleigh identify a leader committed to collaboration to determine what cluster elements are present or absent and develop strategies like adding more log yards. The key takeaways are that clusters rely on entrepreneurship, innovation, and leadership with commitment to collaboration.
Meeting Success - Research-Based Ways to Improve Your Next MeetingBrian Lynch
Prepared by Brian Lynch for the October meeting of the Charlottetown Project & Product Manager and Business Analyst Group, hosted at Invesco, Charlottetown. This presentation provides a number of current, research-based recommendations for optimizing the effectiveness of any business meeting.
Visit the PMBA group at: https://www.meetup.com/Charlottetown-Product-Project-Management-and-BA-Meetup/
The technical report summarizes work from the KAPTUR project including:
1) An environmental assessment report analyzed researcher data practices and found they want to share research but with privacy.
2) A technical analysis reviewed 17 systems and recommended piloting Figshare and DataStage.
3) A costing model analyzed institutional vs cloud hosting and identified risks and integration challenges to cloud computing.
4) The meeting presented on piloting Figshare, DataStage, EPrints and CKAN as research data management systems.
Presentation given by Robin Burgess, KAPTUR Project Officer for The Glasgow School of Art, at the DCC Roadshow Northeast Scotland, University of Dundee, 5th December 2012
This presentation was given by John Murtagh, Project Officer for University of the Arts London at the KAPTUR training event held on Monday 19th November and supported by DCC through the Institutional Engagement project.
This technical report summarizes work integrating two data repository systems, DataStage and Figshare, with EPrints. [1] It outlines pros and cons of each system as well as lessons learned from testing their integration. [2] Key challenges remaining are fixing issues with SWORD submissions between DataStage and EPrints, getting feedback, and assessing upgrade issues. [3] The next steps are to gather experts to address SWORD transfer issues between systems and obtain outside help with SWORD2 if needed.
The document outlines a conference on research data in the visual arts. It discusses the objectives of the KAPTUR project to investigate the nature of research data in the visual arts, develop appropriate policies and systems, and showcase good practices. The project aims to address challenges such as the varied nature of research outputs and lack of research data management infrastructure in arts institutions. The conference will include discussions on defining research data in the arts, policy adoption, infrastructure requirements, and next steps.
The document summarizes the Jisc Managing Research Data Programme which aims to support universities in improving research data management. It discusses why managing research data is important, highlighting funder policies and the benefits of open data. It provides an overview of Jisc's activities including training projects, guidance resources, and funding for institutional infrastructure services and repositories. The presentation emphasizes the importance of institutional policies, support services, skills development and cultural change to effectively manage research data in line with funder expectations.
The document summarizes the outputs and findings of the KAPTUR project. It produced four main outputs: an environmental assessment report, technical analysis report, costing model, and pilot demonstration service. The technical analysis report analyzed 17 data management systems and recommended further analysis of four top systems. The pilot used EPrints, Figshare, and DataStage to test supporting visual arts research data. The project identified challenges for researchers in managing and preserving their data.
The document summarizes the process of raising the profile of research data management (RDM) at the University of Chichester (UCA) through their participation in the Kaptur Project. It describes conducting interviews and analysis to understand current RDM practices, developing an RDM policy through discussion and testing a repository model, and obtaining approval of the draft policy. It reflects on the project's successes in establishing RDM processes and importance of ongoing communication and collaboration around RDM.
This document summarizes Goldsmiths' efforts to develop a research data management policy. A working group was formed to review existing policies, discuss data storage and training. They drafted a policy addressing the research data lifecycle, responsibilities of researchers, and the college's role in preserving access to data. A data repository was also created. Key recommendations include identifying stakeholders, being practical, and tying the policy to the university's strategic goals. The overall aim is to improve research support through better research data management.
Dr. Robin Burgess developed a research data management policy for the Glasgow School of Art to raise awareness of the importance of managing research data. Burgess conducted interviews that found arts research data takes many complex forms and is difficult to define. A policy was created through collaboration and defined research data broadly. It addressed roles, preservation, and tools to support implementation. Challenges included building support and understanding of data management, but the policy provides guidance tailored to the arts.
The document summarizes the development of a research data management (RDM) policy at University of the Arts London (UAL). A working group was formed and conducted surveys and interviews to understand research practices and data types. They determined practice-based research has unique data needs. The group defined research data for visual arts and drafted a RDM policy. Training was provided and the policy was approved, establishing procedures for archiving research data and processes at UAL.
The document provides a template for institutions to develop business, financial, and sustainability plans to support research data management (RDM) best practices after the end of the JISC KAPTUR project. The template includes sections for background, objectives, stakeholders, strategic alignment, options appraisal, risk management, cost analysis, and evaluation. It is intended to help institutions outline how they will take RDM best practices forward and ensure ongoing support beyond the project lifespan.
This document discusses research data in the context of visual arts research. It defines research data, discusses its importance and challenges in the visual arts domain. Key points covered include the heterogeneous nature of visual arts data, principles of data curation and preservation, and the need for data management planning and assistance with archiving. Examples of types of visual arts research data are provided.
This document summarizes drivers for research data management in UK higher education, including policies from research funders like RCUK and AHRC. It also describes resources for supporting research data management, such as the Jisc Managing Research Data programme, the Digital Curation Centre (DCC), and projects funded through the Jisc programme like CAiRO and KAPTUR. The DCC provides guidance on data management planning, training, and curation best practices. Research data is broadly defined as any digital evidence used or created during the research process to generate new knowledge.
This document provides guidance on questions to consider when developing a technical plan or data management plan for a research funding application. It covers four sections: (1) digital outputs and technologies used in the project; (2) technical methodology including standards, formats, hardware/software, and data processing; (3) technical support and experience; and (4) preservation, sustainability and access including preservation methods, continued access, and intellectual property considerations. The questions aim to ensure digital outputs are well-planned, fit-for-purpose, and preserved/accessible after the project ends.
Presentation given by Leigh Garrett about the KAPTUR project and the importance of effective RDM practice at the UCA RDM training workshop, 16th January 2013.
Presentation given by Anne Spalding, KAPTUR Project Officer for University for the Creative Arts as part of the UCA RDM training workshop given on 16th January 2013.
This document outlines a method for estimating the IT costs of research data management systems over 10 years. It describes costing two types of systems: an externally hosted cloud-based system (Amazon Web Services) and an internally hosted open source system. Key factors that are costed include storage, hardware, software, staffing, and annual inflation. The document also notes some limitations, such as development costs being excluded and cloud pricing changing over time. An accompanying Excel spreadsheet model allows entering storage and other variables to calculate total costs for each system.
The document outlines a conference on research data in the visual arts. It discusses the objectives of the KAPTUR project to investigate the nature of research data in the visual arts, develop appropriate policies and systems, and showcase good practices. The project aims to address challenges such as the varied nature of research outputs and lack of research data management infrastructure in arts institutions. The conference will include discussions on defining research data in the arts, policy adoption, infrastructure requirements, and next steps.
The document summarizes the Jisc Managing Research Data Programme which aims to support universities in improving research data management. It discusses why managing research data is important, highlighting funder policies and the benefits of open data. It provides an overview of Jisc's activities including training projects, guidance resources, and funding for institutional infrastructure services and repositories. The presentation emphasizes the importance of institutional policies, support services, skills development and cultural change to effectively manage research data in line with funder expectations.
The document summarizes the outputs and findings of the KAPTUR project. It produced four main outputs: an environmental assessment report, technical analysis report, costing model, and pilot demonstration service. The technical analysis report analyzed 17 data management systems and recommended further analysis of four top systems. The pilot used EPrints, Figshare, and DataStage to test supporting visual arts research data. The project identified challenges for researchers in managing and preserving their data.
The document summarizes the process of raising the profile of research data management (RDM) at the University of Chichester (UCA) through their participation in the Kaptur Project. It describes conducting interviews and analysis to understand current RDM practices, developing an RDM policy through discussion and testing a repository model, and obtaining approval of the draft policy. It reflects on the project's successes in establishing RDM processes and importance of ongoing communication and collaboration around RDM.
This document summarizes Goldsmiths' efforts to develop a research data management policy. A working group was formed to review existing policies, discuss data storage and training. They drafted a policy addressing the research data lifecycle, responsibilities of researchers, and the college's role in preserving access to data. A data repository was also created. Key recommendations include identifying stakeholders, being practical, and tying the policy to the university's strategic goals. The overall aim is to improve research support through better research data management.
Dr. Robin Burgess developed a research data management policy for the Glasgow School of Art to raise awareness of the importance of managing research data. Burgess conducted interviews that found arts research data takes many complex forms and is difficult to define. A policy was created through collaboration and defined research data broadly. It addressed roles, preservation, and tools to support implementation. Challenges included building support and understanding of data management, but the policy provides guidance tailored to the arts.
The document summarizes the development of a research data management (RDM) policy at University of the Arts London (UAL). A working group was formed and conducted surveys and interviews to understand research practices and data types. They determined practice-based research has unique data needs. The group defined research data for visual arts and drafted a RDM policy. Training was provided and the policy was approved, establishing procedures for archiving research data and processes at UAL.
The document provides a template for institutions to develop business, financial, and sustainability plans to support research data management (RDM) best practices after the end of the JISC KAPTUR project. The template includes sections for background, objectives, stakeholders, strategic alignment, options appraisal, risk management, cost analysis, and evaluation. It is intended to help institutions outline how they will take RDM best practices forward and ensure ongoing support beyond the project lifespan.
This document discusses research data in the context of visual arts research. It defines research data, discusses its importance and challenges in the visual arts domain. Key points covered include the heterogeneous nature of visual arts data, principles of data curation and preservation, and the need for data management planning and assistance with archiving. Examples of types of visual arts research data are provided.
This document summarizes drivers for research data management in UK higher education, including policies from research funders like RCUK and AHRC. It also describes resources for supporting research data management, such as the Jisc Managing Research Data programme, the Digital Curation Centre (DCC), and projects funded through the Jisc programme like CAiRO and KAPTUR. The DCC provides guidance on data management planning, training, and curation best practices. Research data is broadly defined as any digital evidence used or created during the research process to generate new knowledge.
This document provides guidance on questions to consider when developing a technical plan or data management plan for a research funding application. It covers four sections: (1) digital outputs and technologies used in the project; (2) technical methodology including standards, formats, hardware/software, and data processing; (3) technical support and experience; and (4) preservation, sustainability and access including preservation methods, continued access, and intellectual property considerations. The questions aim to ensure digital outputs are well-planned, fit-for-purpose, and preserved/accessible after the project ends.
Presentation given by Leigh Garrett about the KAPTUR project and the importance of effective RDM practice at the UCA RDM training workshop, 16th January 2013.
Presentation given by Anne Spalding, KAPTUR Project Officer for University for the Creative Arts as part of the UCA RDM training workshop given on 16th January 2013.
This document outlines a method for estimating the IT costs of research data management systems over 10 years. It describes costing two types of systems: an externally hosted cloud-based system (Amazon Web Services) and an internally hosted open source system. Key factors that are costed include storage, hardware, software, staffing, and annual inflation. The document also notes some limitations, such as development costs being excluded and cloud pricing changing over time. An accompanying Excel spreadsheet model allows entering storage and other variables to calculate total costs for each system.
4. Next steps
Working group with DCC on Wednesday, 25 July 2012
Drafting
October: Information Management and Systems Committee
November: Research and Enterprise Committee
Academic Board