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
- Title Page
- Copyright Page
Table of Contents
- List of Figures
- 1 Introduction
2 Data Cycle Step 1: Design of Experiment
- 2.1 Is There Pre-Existing Data?
- 2.2 Will you use Pre-existing 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 (Non-Reference) Datasets?
- 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?
3 Data Cycle Step 2: Data Design and Planning
- 3.1 Are You Using Data Types Used by Others, Too?
- 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?)
- 3.5.1 Storage capacity planning
- 3.5.2 When is the data archived?
- 3.5.3 Re-use considerations: Will the archive need to be online?
- 3.5.4 Will workflows need to be run locally on the stored data?
- 184.108.40.206 Is there budget to enable supported reuse by others (collaboration/coauthorship)?
- 3.5.5 How long does the data need to be kept?
- 3.5.6 Will the data be understandable after a long time?
- 3.5.7 How frequently will you archive data?
- 3.6 Is There (Critical) Software in the Workspace?
- 3.7 Do You Need The Storage Close To Computer Capacity?
- 3.8 Compute Capacity Planning
- 4 Data Cycle Step 3: Data Capture (Equipment Phase)
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)
- 6 Data cycle step 5: Data linking and ‘integration’
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?
8 Data cycle step 7: Information and insight 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 still publish if the results are negative?
- Title: Data Stewardship for Open Science
- Release date: March 2018
- Publisher(s): Chapman and Hall/CRC
- ISBN: 9781315351148
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