Alexander von Humboldt-Foundation - Georg Forster Research Fellowship Please send us your application, if you are a researcher with above average qualifications in a developing or transition country (see list of countries), if you intend to carry out long-term research of your own choice (6 to 24 months) at a research institution in Germany together with an academic host you have chosen yourself, if your research outline includes aspects that are important for the continued development of your country or region of origin and if you want to contribute to the exchange of knowledge and methods between Germany and your country of origin. We offer you Up to 95 Georg Forster Research Fellowships can be granted in 2016. In the last few years, about one third of applications were successful (see also positive selection decisions since March 2013). In addition, the Humboldt Foundation grants Georg Forster Research Awards every year to leading researchers from developing countries.
Our Lessons Availability All of our lessons are freely available under the Creative Commons - Attribution License. You may re-use and re-mix the material in any way you wish, without asking permission, provided you cite us as the original source (e.g., provide a link back to this website). Contributing If you have questions about contributing to particular lessons, please contact their maintainers (listed below). To Share or not to Share? That is the (Research Data) Question… While public access to research articles is a fact of life for much of the scholarly community, access to research data – while a top priority for many governments and other funders, who see it as the key to future economic growth – remains a challenge. There are many reasons for this, both practical (eg, lack of infrastructure) and professional (eg, lack of credit, getting scooped). The publishing community can and does already help with the former, for example through support for NISO, CrossRef, CODATA, and other organizations and, increasingly, the development of data sharing and management solutions. Resolving the professional issues, however, will almost certainly require action by research funders and institutions.
Impact A Time of Planetary Change We live in a unique time in the history of our planet. Human activity has become the largest driver of change on the Earth. The systems that enable human flourishing—indeed all life—have come under unprecedented stress. And these stresses amplify new forms of connected change, volatility and risk. In areas as wide ranging as food security, environmental monitoring, urban planning, disaster response, public health and climate resilience, better stewardship of humanity’s impacts has never been more important. Data Science Seminars - BD2K Training Coordinating Center The BD2K Guide to the Fundamentals of Data Science Series Every Friday beginning September 9, 2016 12pm - 1pm Eastern Time / 9am - 10am Pacific Time
Data repositories - Open Access Directory From Open Access Directory This list is part of the Open Access Directory. This is a list of repositories and databases for open data. Human Frontier Science Program The registration site for the 2017 Long-Term and Cross-Disciplinary Fellowships is not open yet. The application guidelines with the 2017 eligibility criteria will be available on our website in May. FOR REFERENCE (2016 competition) Research Data MANTRA - Library Training Introduction During autumn and winter 2012-13, data librarians at the University of Edinburgh (Robin Rice and Anne Donnelly) led a pilot course for four University academic service librarians on Research Data Management (RDM) covering five topics involving reading assigments from the MANTRA course, reflective writing, and 2-hour face-to-face training sessions, including group exercises from the UK Data Archive (UKDA). The course was deemed successful by participants and Information Services managers, and was delivered to all the University's academic service librarians. Here we share our training for small groups of librarians anywhere who wish to gain confidence and understanding of research data management. The DIY Training Kit is designed to contain everything needed to complete a similar training course on your own (in small groups) and is based on open educational materials.
How to create a good data management plan Data management plans are growing in importance in the world of research. Not only are funders increasingly making these plans a requirement when researchers submit their grant applications, they are also a very useful tool to save researchers time and effort when running experiments. Additionally, they add value to the wider scientific community as well-organized data provides a great starting point for other researchers to carry out further analysis. But what are they? Data management plans simply describe how data will be acquired, treated and preserved both during and after a research project.
A Practical introduction to acquisition, validation, quality control and access to (biodiversity) data. (June 2016) NIOZ-Yerseke (alternatively: Zandvoort), the Netherlands, 14th – 15th June 2016 (2 days) Application deadline: 15 April 2016. Places: 20 candidates maximum (European and International applicants welcome). Places will need to be confirmed by 10 May 2016. Funds for travel, accommodation and subsistence will be provided (subject to limits*) Target
Research Data Blog [Reposted from The University of Edinburgh, like many other universities, is currently undertaking extensive work to build infrastructure that supports and enables good practice in the area of Research Data Management. This infrastructure ranges from large-scale research storage facilities to data management planning tools. One aspect of Research Data Management highlighted in the University’s RDM Roadmap is ‘Data stewardship: tools and services to aid in the description, deposit, and continuity of access to completed research data outputs.’ To help describe how these systems fit together yet how they differ from each other, I use a model with two axes to differentiate what they hold, and who can access them.
Converting data between wide and long format Problem You want to do convert data from a wide format to a long format. Many functions in R expect data to be in a long format rather than a wide format. Programs like SPSS, however, often use wide-formatted data. Solution There are two sets of methods that are explained below:
How to write a good codebook - McGill University Codebook cookbook A guide to writing a good codebook for data analysis projects in medicine 1. Introduction Writing a codebook is an important step in the management of any data analysis project.