Open Science

An introduction to R for non-programmers using gapminder data

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

Spanish version available here

Github repository available here

This introductory webinar is for anyone who is involved in the collection of data and is considering making (some of) their data available in accordance with funders’ requirements. More and more funders are requiring that research data be made available after completion of the research project, usually through the archiving of data in a trusted repository. However, research teams often still lack the appropriate skills and knowledge regarding how to properly prepare their data for archiving and sharing. This webinar aims to raise awareness about relevant key data management practices for sharing, specifically regarding data documentation, gaining consent, and data anonymisation. Addressing each of these three topics, it provides a short theoretical introduction, including what FAIR means and how it is implemented, as well as practical illustrations drawing on a large-scale cross-national survey (the European Social Survey). It also provides some practical tips with respect to data archiving, in particular how to choose an appropriate archive or repository.

This webinar is intended for everyone who wants to learn about ways of finding and reusing research data. Managing your research data in a FAIR and transparent manner is important. It helps researchers to meet requirements of funding institutions and ensures long-term re-usability of their data. The webinar introduces the CESSDA ERIC Data Catalogue as a means of finding and accessing research data. It enables participants to understand conditions for reuse (licenses) and introduces use-cases. This event is part of a workshop/webinar series organised by members of the SERISS project.

Description

DDI-Codebook is a more light-weight version of the standard, intended primarily to document simple survey data. Originally DTD-based, DDI-C is also available as an XML Schema.

Applications

Documentation of a simple study. Basic descriptive content for variables, files, source material, and study level information. Supports discovery, preservation, and the informed use of data. 

DDI-Lifecycle is designed to document and manage data across the entire life cycle, from conceptualization to data publication, analysis and beyond. It encompasses all of the DDI-Codebook specification and extends it. Based on XML Schemas, DDI-Lifecycle is modular and extensible. This version also supports improvements in Classification management (based on GSIM / Neuchatel), non-survey data collection (Measurements), sampling, weighting, questionnaire Design and support for DDI as a Property Graph.

The DDI Controlled Vocabularies Group (CVG) has created a set of controlled vocabularies that can be used with DDI as well as for other purposes and applications. Select DDI Alliance vocabularies are already in use at organizations like the Finnish Social Science Data Archive (FSD), the GESIS - Leibniz Institute for the Social Sciences, the Inter-university Consortium for Political and Social Science (ICPSR), Mathematica Policy Research,  the UK Data Archive (UKDA), and the University at Bielefeld, Germany. Nesstar Publisher (http://www.nesstar.com/) now incorporates the controlled vocabularies for Analysis Unit and Time Method.

The ORION Open Science Train-the-Trainer course is intended to guide you in how to facilitate and run training on Open Science. The course covers the theoretical underpinnings of adult education as well as practical methods and techniques to use in training events. From didactics to video creation, from audience profiles to Brainwalking. There are a range of materials, media, and activities intended to strengthen your abilities as a training facilitator, both face-to-face and online.

This module is a part of the introductory online course “Data Steward Training”. In this course learners will develop the skills and gain knowledge on how to be a successful data steward. 

By the end of this module, learners will:

• Understand different types of RDM services which their institution or organisation may need to provide. 

• Be able to use the RISE model to plan and develop your RDM services.

This module is suitable for data stewards and trainers seeking introductory learning material. It will take on average one hour to complete the module. 

The materials include a video presentation, a full video transcript, a PowerPoint presentation and a learning activity to support learning.   

This learning material has been developed in collaboration with the FAIRsFAIR and EOSC Synergy projects. The majority of the material has been used in previous online data steward instructor training workshops in 2020 and 2021.

This module is a part of the introductory online course “Data Steward Training”. In this course learners will develop the skills and gain knowledge on how to be a successful data steward. 

By the end of this module, learners will:

• Understand the role of pedagogy and learning activities to teach effectively. 

• Be able to critically assess the reasons for organising training activities.  

• Be able to create training which meets their aims and the needs of their learners.

This module is suitable for data stewards and trainers seeking introductory learning material. It will take on average one hour to complete the module. 

The materials include a video presentation, a full video transcript, a PowerPoint presentation and various learning activities and resources to support learning.   

This learning material has been developed in collaboration with the FAIRsFAIR and EOSC Synergy projects. The majority of the material has been used in previous online data steward instructor training workshops in 2020 and 2021. 

This module is a part of the introductory online course “Data Steward Training”. In this course learners will develop the skills and gain knowledge on how to be a successful data steward. 

By the end of this module, learners will:

• Be able to explain the difference between FAIR and Open Data to researchers.

• Be able to make data FAIR by using a set of practical guidelines and tools.

• Understand the range of skills and knowledge associated with data stewardship.

• Be aware of different approaches to stewardship service provision.

• Be able to identify key elements that help make research data discoverable, accessible, interoperable and reusable.

• Be able to practise making data FAIR. 

This module is suitable for data stewards and trainers seeking introductory learning material. It will take around 1 hour and 30 minutes to complete the module. 

The materials include video presentations, full video transcripts, PowerPoint presentations and various learning activities and resources to support learning.