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RDM Training

RDM Training
Related:  Research EcosystemData Management

Fort Lauderdale Principles | Large-Scale Data Sharing in Biological Research In response to requests from the large-genome sequencing scientific community, the Wellcome Trust sponsored an international meeting in January 2003 to discuss pre-publication data release. Held in Fort Lauderdale, Florida, the meeting primarily brought together representatives of the producers of large-genome sequence data, the users of such data, funding agencies and scientific journals. The Wellcome Trust supports the recommendation made at the meeting for high-throughput, large-genome projects, which we consider to be community resource projects. Typically these will be multicentred, multifunded and international projects such as the Human Genome Project and the Mouse Genome Project. The Trust is pleased with the outcome of the meeting and the reaffirmation of the Bermuda Principles as regards large-genome sequencing projects. Download the full report of the Florida meeting, 'Sharing Data from Large-scale Biological Research Projects' [PDF 36KB].

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 Public Health Information & Data Tutorial The Public Health Information and Data Tutorial provides instruction for members of the public health workforce on issues related to information access and management. There are no copyright restrictions on the contents of this tutorial and users are free to adapt or duplicate any portion. The contributors and authors of this tutorial’s content represent city, county, state and federal agencies. They establish clear connections to recognized competencies in public health and provide examples representing much of the diversity inherent in the practice of public health. Learning objectives: Stay informed of developments and events related to public health; Find reliable and authoritative consumer-oriented materials to support health education; Retrieve statistical information and access data sets relevant to public health; and Retrieve and evaluate information in support of evidence-based practice. This tutorial is based on Public Health Information and Data: A Training Manual.

Toronto Stmt: Prepublication Data Sharing Nature 461, 168-170 (10 September 2009) | doi:10.1038/461168a; Published online 9 September 2009 Open discussion of ideas and full disclosure of supporting facts are the bedrock for scientific discourse and new developments. Traditionally, published papers combine the salient ideas and the supporting facts in a single discrete 'package'. With the advent of methods for large-scale and high-throughput data analyses, the generation and transmission of the underlying facts are often replaced by an electronic process that involves sending information to and from scientific databases. One of the lessons from the Human Genome Project (HGP) was the recognition that making data broadly available prior to publication can be profoundly valuable to the scientific enterprise and lead to public benefits. The principles for rapid release of genome-sequence data from the HGP were first formulated at a meeting held in Bermuda in 1996; these were then implemented as policy by several funding agencies.

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. Only UK policies are listed here, and only for institutions which manage data themselves. 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.

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.

AHRQuality Indicators™ | Agency for Healthcare Research & Quality Mission The Quality Indicators (QIs) are measures of health care quality that use readily available hospital inpatient administrative data. AHRQ develops Quality Indicators to provide health care decisionmakers with tools to assess their data. Visit the AHRQuality Indicators™ web site. Relevance The QIs are used to highlight potential quality concerns, identify areas that need further study and investigation, and track changes over time. Products The current AHRQ QI modules represent various aspects of quality: Prevention Quality Indicators, Inpatient Quality Indicators, Patient Safety Indicators, and Pediatric Quality Indicators. The AHRQ QIs are used in free software distributed by AHRQ. Audiences The QIs are designed for program managers, researchers, and others at the Federal, State, and local levels interested in health care quality measurement. Settings of Care Acute care hospitals Partners

DARIAH-DE - | TextGrid 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 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