Research Data Management

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.

This is privacy by design in research! The aim of this training material is to show researchers how to perform a data protection impact assessment for an innovative research scenario to enable responsible re-use of archived speech corpora.

The project website provides help to develop learning goals.

Taken from: Teaching with CLARIN: https://www.clarin.eu/content/privacy-design-research

The goal of the course in sociolinguistics is to show students the possibilities and challenges offered by oral history archives for (socio)linguistic research. The course is intended as a research framework that will guide students during their future research work. The lectures allow students to become acquainted with the CLARIN infrastructure, and to present them with software tools that will allow them to carry out their own thesis research independently. The course offers guidance for the following steps that must be addressed during a (research) project dealing with oral archives: i) reviewing ethical and legal issues arising from using and reusing legacy data; ii) use of metadata to provide the appropriate level of description for the dataset; iii) automatic and manual transcription of the speech material, using the CLARIN infrastructure; iv) the selection and use of the appropriate CLARIN software and tools depending on the research goals (phonetic, lexical, discourse analysis, etc.).

Taken from: Teaching with CLARIN: https://www.clarin.eu/content/oral-archives-sociolinguistic-research

This is a five-week course for individuals that support researchers to manage and share their data, including librarians, IT and information specialists, data stewards, and research office staff. You’ll discover common research support services, how to create data management plans, and how to develop your own research data management roadmap. You will learn from experts in the field from the internationally-recognised Digital Curation Centre and Research Data Netherlands and build your confidence in supporting researchers and preserving data. This course has been certified by the CPD Certification Service as conforming to continuing professional development principles.

QAMyData is an easy-to-use, open source tool that provides a health check for numeric data. The tool uses automated methods to detect and report on some of the most common problems in survey or numeric data, such as missingness, duplication, outliers and direct identifiers.

The tool offers a number of configurable tests that have been categorised into four types: file, metadata, data integrity, and identifiers, which can be run on popular file formats, including SPSS, Stata, SAS and CSV. A standard config file has default settings for each test, such as a threshold for pass or fail on various tests (e.g. detect value label that are truncated, email addresses identified as a string, or undefined missing values) which can be easily adapted to meet the user’s own desired thresholds. The configuration feature allows the creation of a unique Data Quality Profile. The software creates a ‘data health check’ that details errors and issues as both a summary and detailed report, providing a location of the failed test. New tests can easily be added. Data depositors and publishers can act on the results and resubmit the file until a clean bill of health is produced.

Guide designed to be used as a resource for encouraging better data management and research integrity in undergraduate dissertations, and is aimed at RDM trainers and teachers supervising research projects and students undertaking them. The resource consists of a number of practical templates and exemplars, and a link to the slides for teaching is also included. These can be adapted and modified to suit various project needs and different disciplines.

QualiBank is an online tool for browsing, searching and citing the content of selected qualitative data collections held at the UK Data Service.

DMPonline is a web-based tool that supports researchers to develop data management and sharing plans. It contains the latest funder templates and best practice guidelines to support users to create quality DMPs.

Within the tool you will find custom guidance and example answers to help you develop your DMP. These are offered by the DCC, research funders and many research organisations. You can also browse the growing list of public DMPs published by other users of the tool for inspiration.

The service is free at the point of use for researchers to develop DMPs regardless of whether their institution or funder is a customer. DMPonline is also available on subscription for institutions that require a customised service.

This resource is a guide that proposes a different approach to data management. It aims at giving practical advice for arts and humanities scholars who are willing to take their first steps in research data management but don't know where to begin. Our approach to data management views it as a reflective process that exposes and tweaks existing behaviours, rather than one that introduces specific tools. It is intended to encourage awareness of one’s own processes and mindfulness about how they could be more open and how and how small changes across three points in your research workflow can make big differences.