Qualitative data describes qualities, characteristics, and concepts related to people, their experiences, and our socially constructed nature of reality.
Examples of research that involve qualitative data: interviews, focus groups, observation, field notes, oral histories, primary source research with items such as diaries and journals, and any other research endeavor that allows a subject to express qualities and characteristics of their experience.
Below is a list of some great resources to look at:
"Free" datasets and software may have limits on how you can use them.
Here are some of the basic types of licenses for datasets:
"Open Source" is talking about the software involved: open source software (OSS) is freely available online for download and use; this term does not refer to a license of any kind. Some basic types of licenses for OSS include:
When you create a data set, you want to make sure that it is "good" data in that it is accurate, complete, organized, and ultimately, reusable.
Here are some tools that can help:
Creating good data : a guide to dataset structure and data representation by
This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected.
Creating and verifying data sets with Excel by
This is a practical step-by-step guide to using Excel in the data preparation process. The book includes plentiful screenshots, text boxes summarizing important points, examples from across the social sciences, and questions at the end of each chapter. In addition, a sample dataset will be available online for students to download and use for all the examples and exercises throughout the text. This brief book is intended to familiarize students, teachers, and researchers with the Excel toolkit, and provide strategies that ease the task of data creation and analysis.
Data Information in Online Environments by
This book constitutes the refereed post-conference proceedings of the First International Conference on Data and Information in Online Environments, DIONE 2020, which took place in Florianópolis, Brazil, in March 2020. DIONE 2020 handles the growing interaction between the information sciences, communication sciences and computer sciences. The 18 revised full papers were carefully reviewed and selected from 37 submissions and focus on the production, dissemination and evaluation of contents in online environments. The goal is to improve cooperation between data science, natural language processing, data engineering, big data, research evaluation, network science, sociology of science and communication communities.
A Guide to Responsible Research by
This Open Access book is a guide to good, responsible research at each step of the process of research discovery, so that a researcher at the beginning of a scientific career has a clear pathway to doing good research and producing reliable results. The textbook will give context to the practices described in the European Code of Conduct for Research Integrity, guided by the fundamental principles or research integrity - reliability, honesty, respect, and accountability. Although we base the book on the European Code, the principles are the same in the global research community, such as those outlined in Fostering Integrity in Research from the US National Academies; Engineering and Medicine. The chapters in the book follow good research practices, give practical advice and address basic principles. In this way, the book is applicable to different research fields. It directs readers to various sources for further and updated information, particularly drawing from the resources available at The Embassy of Good Science, the European platform for research integrity and ethics.
How to create a data management plan for an online research project by
Data management plans (DMPs) offer a clear pathway for data through every step of the research process. This guide introduces DMPs, why they are useful, and covers the various components of DMPs, including management, security, ethics, data retention, and sharing. While no one document can cover all the differences in data, methods, and norms that different research types and fields require for DMPs, this guide offers a first step to understanding and creating DMPs. Specific guidance for different fields and funders can be found through the web resources listed in this guide or through specific institutional, field, or funder portals.