Description
Data Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field.
The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard.
Table of Contents
Chapter 1. Introduction
1.1 Data stewardship for open science
1.2 Introduction by the author
1.3 Definitions and context
1.4 The lines of thinking
1.5 The basics of good data stewardship
Chapter 2. Data cycle step 1: Design of experiment
2.1 Is there preexisting data?
2.2 Will you use preexisting data (including opedas)?
2.3 Will you use reference data?
2.4 Where is it available?
2.5 What format?
2.6 Is the data resource versioned?
2.7 Will you be using any existing (nonreference) data sets?
2.8 Will owners of that data work with you on this study?
2.9 Is reconsent needed?
2.10 Do you need to harmonize different sources of opedas?
2.11 What/how/who will integrate existing data?
2.12 Will reference data be created?
2.13 Will you be storing physical samples?
2.14 Will you be collecting experimental data?
2.15 Are there data formatting considerations?
2.16 Are there potential issues regarding data ownership and access control?
Chapter 3. Data cycle step 2: Data design and planning
3.1 Are you using data types used by others too?
3.1.1 What format(s) will you use for the data?
3.2 Will you be using new types of data?
3.3 How will you be storing metadata?
3.4 Method stewardship
3.5 Storage (how will you store your data?
3.6 Is there (critical) software in the workspace?
3.7 Do you need the storage close to compute capacity?
3.8 Compute capacity planning
Chapter 4. Data cycle step 3: Data Capture (equipment phase)
4.1 Where does the data come from? Who will need the data?
4.2 Capacity and harmonisation planning
Chapter 5. Data cycle step 4: Data Processing and Curation
5.1 Workflow development
5.2 Choose the workflow engine
5.3 Workflow running
5.4 Tools and data directory (for the experiment)
Chapter 6. Data cycle step 5 Data Linking and ‘Integration’
6.1 What is the approach you will use for data integration?
6.2 Will you make your output semantically interoperable data?
6.3 Will you use a workflow e.g. with tools for database access or conversion?
Chapter 7. Data cycle step 6: Data Analysis, Interpretation
7.1 Will you use static or dynamic (systems) models?
7.2 Machine learning?
7.3 Will you be building kinetic models?
7.4 How will you make sure the analysis is best suited to answer your biological question?
7.5 How will you ensure reproducibility?
7.6 Will you be doing (automated) knowledge discovery?
Chapter 8. Data cycle step 7: Information and insight in publishing
8.1 How much will be open data/access?
8.2 Who will pay for open access data publishing?
8.3 Legal issues
8.4 What technical issues are associated with hpr?
8.5 Will you publish also if the results are negative?
