Data creator

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

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.

This guide covers the basics of sampling as well as other related topics such as clustering, stratification and weighting. 

This guide explains the main reasons for using weights, how weights work and how to use weighting variables in statistical analysis. 

This guide aims to help researchers utilise extensive survey data available. In particular, this guide is designed to support those starting small research projects, especially students doing dissertations. 

The guide includes materials to read, worksheets for getting started and questions to think about and answer.

This guide aims to show the strength of using Census Microdata for a variety of research purposes, via a worked example taken from real-life research. This guide assumes some familiarity with microdata, mapping and statistical software. 

This presentation was delivered virtually for Botswana Open University Library on 17th May 2021 as part of a Foundational Data Stewardship Workshop. It is primarily aimed at data stewards but can also be useful to researchers and RDM service providers and should be viewed in conjunction with these two other presentations that were part of the same workshop:

• DOI:10.5281/zenodo.4665390 (Open and Responsible Research: Roles and Responsibilities for Data Stewards)
• DOI:10.5281/zenodo.4561728 (Developing and Implementing a Research Data Policy)

The UK Data Service and the Office for National Statistics (UK) held a business data conference in 2021. This resource provides all slides, recordings and other useful materials from the conference. 

The conference included a mixture of presentations from the  Office for National Statistics  (ONS), UK Data Service, and researchers who have used data from the UK Surveys covering the areas of business, industry and trade.