8th International Digital Curation Conference #idcc13 IDCC brings together those who create and manage data and information, those who use it and those who research and teach about curation processes. Our view of ‘data’ is a broad one – video games and virtual worlds are of just as much interest as data from laboratory instruments or field observation. Whether the information originates in the arts, humanities, social or experimental sciences the issues faced are cross-disciplinary. Digital curators maintain, preserve, and add value to digital information throughout its life, reducing threats to its long-term value, mitigating the risk of digital obsolescence, and enhancing the potential for reuse for all purposes. If you are a curator, if you teach or train future curators, or if you depend on them for your work, IDCC is for you. There will be a programme of workshops on Monday 14 January and Thursday 17 January, the main conference will run from 15-16 January 2013 Accommodation Registration Conference registration fees N.B. Register now
EDUCATION, TRAINING, & OUTREACH - DataFOUR (D4)--Data Federation of University Research A list of resources that provide information and tools for people interested in learning more about research data management. Curriculum Data Management, Information Literacy and DaMSSI– This collection of documents contains a final report, a mapping of RDM training projects against the "Researcher Development Framework" and "Seven Pillars of Information Literacy," and career profiles showing the relevance and utility of research data management skills to a variety of careers. DataONE - Education Modules Eleven Educational modules in PowerPoint format that cover various data management topics. DigitalPreservation in a Box – The National Digital Stewardship Alliance created a toolkit addressed to a more general audience to convey basic concepts of digital preservations, as well as resources and tools in the area. DMPToolWebinar Series – An education model targeted towards librarians and information professionals, which promote the use of the DMPTool. Workshops
Forschungsdaten.org UK Institutional data policies For many institutions, effective research data management requires formal policy for support and guidance. Some will take the view that existing policies on such matters as records management or library collecting policies are sufficient; others will amend such policies to specifically address research data; and others will create new policies to address the roles and responsibilities of institutions and the researchers who work with them. The DCC is collecting examples of explicit policies on research data and examples of existing policies amended to encompass research data. If you are looking to create your own policies, you are likely to find these examples useful. Policies from non-UK institutions appear in the policy guidance page. Get in touch if your institution has a data policy and we'll add the details. If you're planning to develop an institutional policy or strategy, attend a DCC regional roadshow to get an idea of how we can help you develop it, cost it or implement it.
The Science Data Literacy Project Curation Lifecycle Model (Digital Curation Centre) The model enables granular functionality to be mapped against it: to define roles and responsibilities and build a framework of standards and technologies to implement. It can be used to help identify additional steps that may be required – or actions not required by certain situations or disciplines – and to ensure that processes and policies are adequately documented. Click on the model below to find out more about specific steps or to download the Curation Lifecycle Model. ** This publication is available in print and can be ordered from our online store ** Key elements of the DCC Curation Lifecycle Model Data, any information in binary digital form, is at the centre of the Curation Lifecycle. This includes: Databases: structured collections of records or data stored in a computer system. Preservation Planning Plan for preservation throughout the curation lifecycle of digital material. Conceptualise Conceive and plan the creation of data, including capture method and storage options.
Developments in Research Funder Data Policy | Jones Developments in Research Funder Data Policy Sarah Jones 2012, Vol. 7, No. 1, pp. 114-125 doi:10.2218/ijdc.v7i1.219 Abstract This paper reviews developments in funders’ data management and sharing policies, and explores the extent to which they have affected practice. Full Text: PDF Research Data Mgmt Systems | STELLA Unconference FACILITATOR: Eugene Barsky, UBCNOTES: Michal Strutin, SCU The NSF mandate for submitting data management plans with a grant proposal is a game-changer. Yet, their mandate does not have teeth and no way to assess if data plans have been carried out. How to work with faculty to meet their data needs: storage, organization, preservation. At the RDAP (Research Data Access and Preservation) Summit, they heard representatives from NOAA, NIH, etc. Will researchers be able to reuse their own data? NSF points researchers to different directorates for data management. New England Libraries have an eScience Portal and a New England eScience Day. OSTP (White House Office of Science and Technology Policy): all agencies must come out with plans to make data more available. Has anyone’s institution said, “Data management is our responsibility and we’re going to do it.”? Someone else is using ePrints. Eugene: UCB has 8 terabytes (accommodating videos, etc.) and their library absorbs the cost, for now.
Stanford University Libraries: Data management plans About data management plans (DMPs) A data management plan (DMP) is a written document that describes the data you expect to acquire or generate during the course of a research project, how you will manage, describe, analyze, and store those data, and what mechanisms you will use at the end of your project to share and preserve your data. You may have already considered some or all of these issues with regard to your research project, but writing them down helps you formalize the process, identify weaknesses in your plan, and provide you with a record of what you intend(ed) to do. Data management is best addressed in the early stages of a research project, but it is never too late to develop a data management plan. Requirements, examples, and review Funding agency requirements Many funding agencies require a DMP with every funding request. SPARC's Data Sharing Requirements by Federal Agency You can also consult the section below with sample agency-specific plans. Sample agency-specific plans
Science as an open enterprise - Report 21 June 2012 The Spanish Cucumber E. Coli. This genome was analysed within weeks of its outbreak because of a global and open effort; data about the strain’s genome sequence were released freely over the internet as soon as they were produced. The Science as an open enterprise report highlights the need to grapple with the huge deluge of data created by modern technologies in order to preserve the principle of openness and to exploit data in ways that have the potential to create a second open science revolution. Exploring massive amounts of data using modern digital technologies has enormous potential for science and its application in public policy and business. Areas for action Six key areas for action are highlighted in the report: Videos Professor GUO Huadong, President of CODATA - the International Council for Science's Committee on Data for Science and Technology
Education Modules | DataONE Below are links to education modules in powerpoint format that you can download and incorporate into your teaching materials. Materials are licensed as CC0 and you may enhance and reuse for your own purposes. All slides can be previewed in the embedded slideshare viewer below. The topics covered include: Lesson 01: Why Data Management (.pptx) Trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data life cycle. Lesson 02: Data Sharing(.pptx) Data sharing in the context of the data life cycle, the value of sharing data, concerns about sharing data, and methods and best practices for sharing data. Lesson 03: Data Management Planning(.pptx) Benefits of a data management plan (DMP), DMP components, tools for creating a DMP, NSF DMP information, and a sample DMP. Lesson 04: Data Entry and Manipulation (.pptx) Best practices for data entry, data entry and data manipulation tools. Slide previews