Data Stewardship for Open Science
-10%
portes grátis
Data Stewardship for Open Science
Implementing FAIR Principles
Mons, Barend
Taylor & Francis Inc
03/2018
226
Dura
Inglês
9780815348184
15 a 20 dias
Descrição não disponível.
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?
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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?
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