Data stewardship for Open Science: implementing FAIR principles
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
2021
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xviii, 226 Seiten Illustrationen 24 cm |
| ISBN: | 9781032095707 |
Staff View
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Contents List of Figures xiii Preface XV Author xvii Chapter 1 ■ Introduction 1.1 DATA STEWARDSHIP FOR OPEN SCIENCE 1 1.2 INTRODUCTION BY THE AUTHOR 6 1.3 DEFINITIONS AND CONTEXT 29 1.4 THE LINES OF THINKING 36 1.5 THE BASICS OF GOOD DATA STEWARDSHIP 38 Chapter 2 ■ Data Cycle Step 1 : Design of Experiment 63 2.1 IS THERE PRE-EXISTING DATA? 63 2.2 WILL YOU USE PRE-EXISTING DATA (INCLUDING OPEDAS)? 65 2.3 WILL YOU USE REFERENCE DATA? 66 2.4 WHERE IS IT AVAILABLE? 68 2.5 WHAT FORMAT? 69 2.6 IS THE DATA RESOURCE VERSIONED? 70 2.7 WILL YOU BE USING ANY EXISTING (NON-REFERENCE) DATASETS? 2.8 • 1 2.9 72 WILL OWNERS OF THAT DATA WORK WITH YOU ON THIS STUDY? 73 IS RECONSENT NEEDED? 74 vii
viii ■ Contents 2.10 DO YOU NEED TO HARMONIZE DIFFERENT SOURCES OF OPEDAS? 2.11 2.12 WHAT/HOW/WHO WILL INTEGRATE EXISTING DATA? 76 2.11.1 Will you need to add data from the literature? 77 2.11.2 Will you need text-mining? 78 2.11.3 Do you need to integrate or link to a different type of data? 79 WILL REFERENCE DATA BE CREATED? 2.12.1 2.12.2 2.13 75 What will the IP be like? How will you maintain it? WILL YOU BE STORING PHYSICAL SAMPLES? 2.13.1 2.13.2 81 82 84 85 Where will information about samples be stored? 87 Will your data and samples be added to an existing collection? 88 2.14 WILLYOU BE COLLECTING EXPERIMENTAL DATA? 90 2.15 ARE THERE DATA FORMATTING CONSIDERATIONS? 91 92 2.15.2 What is the volume of the anticipated dataset? What data formats do the instruments yield? 2.15.3 What preprocessing is needed? 94 2.15.3.1 Are there ready-to-use workflows? 2.15.3.2 What compute is needed? 95 2.15.1 2.16 93 96 2.15.4 Will you create images? 98 ARE THERE POTENTIAL ISSUES REGARDING DATA OWNERSHIP AND ACCESS CONTROL? 99 2.16.1 Who needs access? 100 2.16.2 What level of data protection is needed? 101 2.16.2.1 Is the collected data privacy sensitive? 102 2.16.2.2 Is your institutes’ security sufficient for storage? 103 Chapter 3.1 3 ■ Data Cycle Step 2: Data Design and Planning 105 ARE YOU USING DATA TYPES USED BY OTHERS, TOO? 106
Contents ■ ix 3.1.1 3.2 3.3 3.4 WILL YOU BE USING NEW TYPES OF DATA? 107 108 3.2.1 3.2.2 Are there suitable terminology systems? Do you need to develop new terminology systems? Ill 109 3.2.3 How will you describe your data format? 112 HOW WILL YOU BE STORING METADATA? 114 3.3.1 Did you consider how to monitor data integrity? 115 3.3.2 Will you store licenses with the data? METHOD STEWARDSHIP 3.4.1 3.5 What format(s) will you use for the data? 119 Is all software for steps in your workflow prop erly maintained? 120 STORAGE (HOW WILL YOU STORE YOUR DATA?) 3.5.1 117 123 Storage capacity planning. 123 3.5.1.1 Will you be archiving data for long term preservation? 124 3.5.1.2 Can the original data be regenerated? 126 3.5.1.3 If your data changes over time, how frequently do you do backups? 3.5.2 When is the data archived? 3.5.3 Re-use considerations: Will the archive need to be online? 130 3.5.4 Will workflows need to be run locally on the stored data? 131 3.5.4.1 127 129 Is there budget to enable supported reuse by others (collaboration/coauthorship)? 133 3.5.5 How long does the data need to be kept? 135 3.5.6 Will the data be understandable after a long time? 137 How frequently will you archive data? 138 3.6 IS THERE (CRITICAL) SOFTWARE IN THE WORKSPACE? 139 3.7 DO YOU NEED THE STORAGE CLOSE TO COMPUTER CAPACITY? 141 3.5.7
x ■ Contents 3.8 COMPUTE CAPACITY PLANNING 3.8.1 Chapter 143 Determine needs in memory/CPU/IO ratios 4»Data Cycle Step 3: Data Capture (Equipment Phase) 147 4.1 WHERE DOES THE DATA COME FROM? WHO WILL NEED THE DATA? 150 4.2 CAPACITY AND HARMONISATION PLANNING 4.2.1 5.1 Case report forms? 153 155 5 ■ Data Cycle Step 4: Data Processing and Curation 157 157 WORKFLOW DEVELOPMENT 5.1.1 5.2 152 Will you use non֊equipment data capture (i.e., questionnaires, free text)? 4.2.1.1 Chapter 143 Will you be running a bulk/routine workflow? CHOOSE THE WORKFLOW ENGINE 158 159 5.2.1 Who are the customers that use your workflows? 162 5.2.2 Can workflows be run remotely? 163 5.2.3 Can workflow decay be managed? 164 5.2.4 Verify workflows repeatedly on the same data 165 5.3 WORKFLOW RUNNING 5.4 TOOLS AND DATA DIRECTORY (FOR THE EXPERIMENT) 168 Chapter 6.1 166 6 ■ Data Cycle Step 5: Data Linking and 'Integration' 171 WHAT APPROACH WILL YOU USE FOR DATA INTEGRATION? 175 6.2 WILL YOU MAKE YOUR OUTPUT SEMANTICALLY INTEROPERABLE? 176 6.3 WILL YOU USE A WORKFLOW OR TOOLS FOR DATABASE ACCESS OR CONVERSION? 179 Chapter 7.1 7 ■ Data Cycle Step 6: Data Analysis, Interpretation 181 WILL YOU USE STATIC OR DYNAMIC (SYSTEMS) MODELS? 182
Contents ■ xi 7.2 MACHINE-LEARNING? 183 7.3 WILL YOU BE BUILDING KINETIC MODELS? 185 7.4 HOW WILL YOU MAKE SURE THE ANALYSIS IS BEST SUITED TO ANSWER YOUR BIOLOGICAL QUESTION? 186 HOW WILL YOU ENSURE REPRODUCIBILITY? 187 7.5 7.6 Chapter 7.5.1 Will this step need significant storage and com pute capacity? 188 7.5.2 How are you going to interpret your data? 7.5.3 How will you document your interpretation steps? 191 189 WILL YOU BE DOING (AUTOMATED) KNOWLEDGE DISCOVERY? 192 8 ■ Data Cycle Step 7: Information and Insight Publishing 195 8.1 HOW MUCH WILL BE OPEN DATA/ACCESS? 8.2 WHO WILL PAY FOR OPEN ACCESS DATA PUBLISHING? 199 8.3 LEGAL ISSUES 8.3.1 8.4 202 203 Where to publish? WHAT TECHNICAL ISSUES ARE ASSOCIATED WITH HPR? 206 8.4.1 What service will be offered around your data? 207 8.4.2 Submit to an existing database? 209 8.4.3 Will you run your own access Web service for data? 210 How and where will you be archiving/ cataloguing? 212 8.4.4 8.5 196 WILL YOU STILL PUBLISH IF THE RESULTS ARE NEGATIVE? 213 8.5.1 Data as a publishable unit? 214 8.5.2 Will you publish a narrative? 215 Bibliography 219 Index 223 |
| adam_txt |
Contents List of Figures xiii Preface XV Author xvii Chapter 1 ■ Introduction 1.1 DATA STEWARDSHIP FOR OPEN SCIENCE 1 1.2 INTRODUCTION BY THE AUTHOR 6 1.3 DEFINITIONS AND CONTEXT 29 1.4 THE LINES OF THINKING 36 1.5 THE BASICS OF GOOD DATA STEWARDSHIP 38 Chapter 2 ■ Data Cycle Step 1 : Design of Experiment 63 2.1 IS THERE PRE-EXISTING DATA? 63 2.2 WILL YOU USE PRE-EXISTING DATA (INCLUDING OPEDAS)? 65 2.3 WILL YOU USE REFERENCE DATA? 66 2.4 WHERE IS IT AVAILABLE? 68 2.5 WHAT FORMAT? 69 2.6 IS THE DATA RESOURCE VERSIONED? 70 2.7 WILL YOU BE USING ANY EXISTING (NON-REFERENCE) DATASETS? 2.8 • 1 2.9 72 WILL OWNERS OF THAT DATA WORK WITH YOU ON THIS STUDY? 73 IS RECONSENT NEEDED? 74 vii
viii ■ Contents 2.10 DO YOU NEED TO HARMONIZE DIFFERENT SOURCES OF OPEDAS? 2.11 2.12 WHAT/HOW/WHO WILL INTEGRATE EXISTING DATA? 76 2.11.1 Will you need to add data from the literature? 77 2.11.2 Will you need text-mining? 78 2.11.3 Do you need to integrate or link to a different type of data? 79 WILL REFERENCE DATA BE CREATED? 2.12.1 2.12.2 2.13 75 What will the IP be like? How will you maintain it? WILL YOU BE STORING PHYSICAL SAMPLES? 2.13.1 2.13.2 81 82 84 85 Where will information about samples be stored? 87 Will your data and samples be added to an existing collection? 88 2.14 WILLYOU BE COLLECTING EXPERIMENTAL DATA? 90 2.15 ARE THERE DATA FORMATTING CONSIDERATIONS? 91 92 2.15.2 What is the volume of the anticipated dataset? What data formats do the instruments yield? 2.15.3 What preprocessing is needed? 94 2.15.3.1 Are there ready-to-use workflows? 2.15.3.2 What compute is needed? 95 2.15.1 2.16 93 96 2.15.4 Will you create images? 98 ARE THERE POTENTIAL ISSUES REGARDING DATA OWNERSHIP AND ACCESS CONTROL? 99 2.16.1 Who needs access? 100 2.16.2 What level of data protection is needed? 101 2.16.2.1 Is the collected data privacy sensitive? 102 2.16.2.2 Is your institutes’ security sufficient for storage? 103 Chapter 3.1 3 ■ Data Cycle Step 2: Data Design and Planning 105 ARE YOU USING DATA TYPES USED BY OTHERS, TOO? 106
Contents ■ ix 3.1.1 3.2 3.3 3.4 WILL YOU BE USING NEW TYPES OF DATA? 107 108 3.2.1 3.2.2 Are there suitable terminology systems? Do you need to develop new terminology systems? Ill 109 3.2.3 How will you describe your data format? 112 HOW WILL YOU BE STORING METADATA? 114 3.3.1 Did you consider how to monitor data integrity? 115 3.3.2 Will you store licenses with the data? METHOD STEWARDSHIP 3.4.1 3.5 What format(s) will you use for the data? 119 Is all software for steps in your workflow prop erly maintained? 120 STORAGE (HOW WILL YOU STORE YOUR DATA?) 3.5.1 117 123 Storage capacity planning. 123 3.5.1.1 Will you be archiving data for long term preservation? 124 3.5.1.2 Can the original data be regenerated? 126 3.5.1.3 If your data changes over time, how frequently do you do backups? 3.5.2 When is the data archived? 3.5.3 Re-use considerations: Will the archive need to be online? 130 3.5.4 Will workflows need to be run locally on the stored data? 131 3.5.4.1 127 129 Is there budget to enable supported reuse by others (collaboration/coauthorship)? 133 3.5.5 How long does the data need to be kept? 135 3.5.6 Will the data be understandable after a long time? 137 How frequently will you archive data? 138 3.6 IS THERE (CRITICAL) SOFTWARE IN THE WORKSPACE? 139 3.7 DO YOU NEED THE STORAGE CLOSE TO COMPUTER CAPACITY? 141 3.5.7
x ■ Contents 3.8 COMPUTE CAPACITY PLANNING 3.8.1 Chapter 143 Determine needs in memory/CPU/IO ratios 4»Data Cycle Step 3: Data Capture (Equipment Phase) 147 4.1 WHERE DOES THE DATA COME FROM? WHO WILL NEED THE DATA? 150 4.2 CAPACITY AND HARMONISATION PLANNING 4.2.1 5.1 Case report forms? 153 155 5 ■ Data Cycle Step 4: Data Processing and Curation 157 157 WORKFLOW DEVELOPMENT 5.1.1 5.2 152 Will you use non֊equipment data capture (i.e., questionnaires, free text)? 4.2.1.1 Chapter 143 Will you be running a bulk/routine workflow? CHOOSE THE WORKFLOW ENGINE 158 159 5.2.1 Who are the customers that use your workflows? 162 5.2.2 Can workflows be run remotely? 163 5.2.3 Can workflow decay be managed? 164 5.2.4 Verify workflows repeatedly on the same data 165 5.3 WORKFLOW RUNNING 5.4 TOOLS AND DATA DIRECTORY (FOR THE EXPERIMENT) 168 Chapter 6.1 166 6 ■ Data Cycle Step 5: Data Linking and 'Integration' 171 WHAT APPROACH WILL YOU USE FOR DATA INTEGRATION? 175 6.2 WILL YOU MAKE YOUR OUTPUT SEMANTICALLY INTEROPERABLE? 176 6.3 WILL YOU USE A WORKFLOW OR TOOLS FOR DATABASE ACCESS OR CONVERSION? 179 Chapter 7.1 7 ■ Data Cycle Step 6: Data Analysis, Interpretation 181 WILL YOU USE STATIC OR DYNAMIC (SYSTEMS) MODELS? 182
Contents ■ xi 7.2 MACHINE-LEARNING? 183 7.3 WILL YOU BE BUILDING KINETIC MODELS? 185 7.4 HOW WILL YOU MAKE SURE THE ANALYSIS IS BEST SUITED TO ANSWER YOUR BIOLOGICAL QUESTION? 186 HOW WILL YOU ENSURE REPRODUCIBILITY? 187 7.5 7.6 Chapter 7.5.1 Will this step need significant storage and com pute capacity? 188 7.5.2 How are you going to interpret your data? 7.5.3 How will you document your interpretation steps? 191 189 WILL YOU BE DOING (AUTOMATED) KNOWLEDGE DISCOVERY? 192 8 ■ Data Cycle Step 7: Information and Insight Publishing 195 8.1 HOW MUCH WILL BE OPEN DATA/ACCESS? 8.2 WHO WILL PAY FOR OPEN ACCESS DATA PUBLISHING? 199 8.3 LEGAL ISSUES 8.3.1 8.4 202 203 Where to publish? WHAT TECHNICAL ISSUES ARE ASSOCIATED WITH HPR? 206 8.4.1 What service will be offered around your data? 207 8.4.2 Submit to an existing database? 209 8.4.3 Will you run your own access Web service for data? 210 How and where will you be archiving/ cataloguing? 212 8.4.4 8.5 196 WILL YOU STILL PUBLISH IF THE RESULTS ARE NEGATIVE? 213 8.5.1 Data as a publishable unit? 214 8.5.2 Will you publish a narrative? 215 Bibliography 219 Index 223 |
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| index_date | 2024-07-03T17:49:27Z |
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| spelling | Mons, Barend 1957- Verfasser (DE-588)1197630112 aut Data stewardship for Open Science implementing FAIR principles Barend Mons Boca Raton ; London ; New York CRC Press 2021 © 2018 xviii, 226 Seiten Illustrationen 24 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Open Science (DE-588)1197084231 gnd rswk-swf Open Access (DE-588)7525775-0 gnd rswk-swf Wissenschaftskommunikation (DE-588)1031114947 gnd rswk-swf Information resources management Database management Data protection Business enterprises / Computer networks / Management Information technology / Management Open Science (DE-588)1197084231 s Open Access (DE-588)7525775-0 s Wissenschaftskommunikation (DE-588)1031114947 s DE-604 Äquivalent Druck-Ausgabe, Hardcover 978-0-8153-4818-4 Erscheint auch als Online-Ausgabe 978-1-3153-5114-8 (DE-604)BV046888765 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032788943&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Mons, Barend 1957- Data stewardship for Open Science implementing FAIR principles Open Science (DE-588)1197084231 gnd Open Access (DE-588)7525775-0 gnd Wissenschaftskommunikation (DE-588)1031114947 gnd |
| subject_GND | (DE-588)1197084231 (DE-588)7525775-0 (DE-588)1031114947 |
| title | Data stewardship for Open Science implementing FAIR principles |
| title_auth | Data stewardship for Open Science implementing FAIR principles |
| title_exact_search | Data stewardship for Open Science implementing FAIR principles |
| title_exact_search_txtP | Data stewardship for Open Science implementing FAIR principles |
| title_full | Data stewardship for Open Science implementing FAIR principles Barend Mons |
| title_fullStr | Data stewardship for Open Science implementing FAIR principles Barend Mons |
| title_full_unstemmed | Data stewardship for Open Science implementing FAIR principles Barend Mons |
| title_short | Data stewardship for Open Science |
| title_sort | data stewardship for open science implementing fair principles |
| title_sub | implementing FAIR principles |
| topic | Open Science (DE-588)1197084231 gnd Open Access (DE-588)7525775-0 gnd Wissenschaftskommunikation (DE-588)1031114947 gnd |
| topic_facet | Open Science Open Access Wissenschaftskommunikation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032788943&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT monsbarend datastewardshipforopenscienceimplementingfairprinciples |