The SSH Training Discovery Toolkit provides an inventory of training materials relevant for the Social Sciences and Humanities.
Use the search bar to discover materials or browse through the collections. The filters will help you identify your area of interest.
|Australian Research Data Commons Training Materials||
The ARDC is a transformational initiative that enables Australian research community and industry access to nationally significant, leading edge data intensive eInfrastructure, platforms, skills and collections of high-quality data. The purpose of the ARDC is to provide Australian researchers with competitive advantage through data, providing access to leading edge eResearch collections, tools, infrastructure and services. Its mission is to accelerate research and innovation by driving excellence in the creation, analysis and retention of high-quality data assets.
|Cite the data||
Citing a dataset correctly is just as important as citing articles, books, images and websites - each dataset is a source of evidence to support our argument.
This resource includes videos, presentations and top ten tips for citing data. These can be used by trainers to teach and highlight the importance of data citation.
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.
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.
|OpenAIRE FAQ on Open Science, RDM||
FAQ by OpenAIRE answering various questions around Open Science and RDM.
|Use cases of OpenAIRE services for different stakeholders||
OpenAIRE is preparing use case narratives with scenarios of Open Science services offered by OpenAIRE targeting different users: researchers, research communities/research infrastructures, content providers, funders, managers of research.
The main goal is to showcase how OpenAIRE services are contributing to embed Open Science into researchers' daily workflow and to implement and align Open Science policies and infrastructures across European research institutions.
Benefits and assumptions of those who already use the OpenAIRE services and several best practices on Open Access publishing, sharing research data and enriching the repositories content will be made available on the website
|H2020 OpenAIRE Fact sheets||
In an effort to make open access for publications and data simple for everyone, OpenAIRE is creating factsheets with a brief overview of how to comply with H2020 OA mandates and how to use OpenAIRE services where available.
|Delivering RDM Services - Roadmaps||
One of the topic of the MOOC Delivering Research Data Management Services. This part helps the learner to develop a roadmap for delivering research data services at their own institution.
|Delivering RDM Services - Introduction||
First topic of the MOOC Delivering Research Data Management Services.
This part introduces the importance of research data management and places it in the context of the global movement towards Open Research, and ultimately Open Science.
|Research Data Management and Sharing||
This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories.