Оглавление
Группа авторов. Digital Transformation of the Laboratory
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Digital Transformation of the Laboratory. A Practical Guide to the Connected Lab
Preface
Inspiration
Knowledge Base
Practical
Case Studies
Continuous Improvement
Vision of the Future and Changing the Way We Do Science
Part I Inspiration
1 The Next Big Developments – The Lab of the Future
1.1 Introduction
1.2 Discussion
1.2.1 People/Culture
1.2.2 Process
1.2.3 Lab Environment and Design
1.2.4 Data Management and the “Real Asset”
1.2.4.1 Data in the Hypothesis‐driven, Research Lab
1.2.4.2 Data in the Protocol‐driven Lab
1.2.4.3 New Data Management Developments
1.2.5 New Technology
1.2.5.1 Lab Automation Integration and Interoperability
1.2.5.2 Quantum Computing and the Lab of the Future
1.2.5.3 Impact of AI and ML
1.2.6 New Science
1.2.6.1 New Science in Health Care
1.2.6.2 New Science in the Life Sciences Domain
1.2.6.3 Other Important New Science Areas
1.3 Thoughts on LotF Implementation
1.4 Conclusion
References
Part II Knowledge Base
2 Crucial Software‐related Terms to Understand
2.1 Digital Revolution
2.2 Computers
2.2.1 Programs, Instructions, and Programming Languages
2.2.2 Hardware and Software
2.2.3 Operating Systems
2.2.4 Abstraction
2.2.5 Virtualization
2.3 Internet
2.3.1 World Wide Web (WWW)
2.3.2 Web Applications
2.3.3 Web Applications in Comparison With Traditional Applications
2.4 Cloud Computing
2.4.1 Classification of Cloud Services
2.4.1.1 IaaS (infrastructure as a service)
2.4.1.2 PaaS (platform as a service)
2.4.1.3 SaaS (software as a service)
2.4.2 Cloud Deployment Models
2.4.2.1 Public Cloud
2.4.2.2 Private Cloud
2.4.2.3 Hybrid Cloud
2.4.3 Issues and Considerations
2.5 Computer Platforms
2.5.1 Desktop/Laptop/PC
2.5.1.1 Desktop Applications
2.5.2 Mobile
2.5.2.1 Mobile Applications
2.5.3 Server/Web
2.5.3.1 Web Browser
2.5.4 Embedded
2.5.5 Cross‐platform
2.6 Applications
2.7 Values of Software
2.7.1 Features
2.7.2 Design
2.8 Software Development
2.9 Software Product Lifecycle
2.10 Software Design
2.10.1 Code
2.10.2 Data
2.11 Software Quality
2.12 Software Integration
2.12.1 API
2.12.2 Middleware
2.12.3 Authentication and Authorization
2.12.4 Internet of Things
2.13 Data‐flow Modeling for Laboratories
2.14 Software Licensing
2.14.1 Proprietary Software Licenses
2.14.2 Open Source
References
3 Introduction to Laboratory Software Solutions and Differences Between Them
3.1 Introduction
3.2 Types of Software Used in Laboratories. 3.2.1 Electronic Lab Notebook (ELN)
Example
Example
Example
3.2.2 Laboratory Information Management System (LIMS)
Example
Example
Example
3.2.3 Laboratory Execution System (LES)
3.2.4 Laboratory Data Management System (LDMS)
3.2.5 Chromatography Data Management System (CDMS)
Example
3.2.6 Process Analytical Technology (PAT) Software
3.2.7 Automation Scheduling Software
3.2.8 Laboratory Instrument Software
3.2.9 Middleware and Robotic Process Automation (RPA)
3.2.10 Data Analysis Software
3.2.11 Enterprise Resource Planning (ERP)
References
4 Data Safety and Cybersecurity
4.1 Introduction
4.1.1 Magnetic Storage
4.1.2 Solid‐state Drives
4.2 Data Safety
4.2.1 Risks
4.2.2 Measures
4.2.2.1 Backups
4.2.2.2 Data Replication
4.3 Cybersecurity
4.3.1 Threat Model
4.3.1.1 Untargeted/Opportunistic Attacks
4.3.1.2 Targeted Attacks
4.3.2 Risks
4.3.2.1 Physical Access
4.3.2.2 Software Access
4.3.2.3 Privileged Users
4.3.2.4 Data in Transit
4.3.2.5 Social Engineering
4.3.3 Measures
4.3.3.1 Physical Protection
4.3.3.2 Software and Infrastructural Measures
Penetration Testing, Consulting
4.3.3.3 Encryption
Encryption of Data at Rest
Encryption of Data in Transit
VPN
4.3.3.4 Policies and Processes
4.3.3.5 Education
4.3.3.6 Third‐party Security Review
References
5 FAIR Principles and Why They Matter
5.1 Introduction
5.2 What Is the Value of Making Data FAIR?
5.3 Considerations in Creating Lab‐based Data to Prepare for It to Be FAIR
5.4 The FAIR Guiding Principles Overview
References
6 The Art of Writing and Sharing Methods in the Digital Environment
6.1 Introduction
6.2 Tools and Resources for Tracking, Developing, Sharing, and Disseminating Protocols
6.2.1 Tools for Organizing and Tracking Your Protocols
6.3 Making Your Protocols Public
6.4 The Art of Writing Methods
References
Part III Practical
7 How to Approach the Digital Transformation
7.1 Introduction
7.2 Defining the Requirements for Your Lab. 7.2.1 Digitization Versus Digitalization Versus Digital Transformation
Example
Example
Example
7.2.2 Defining the Approach and Scope for Your Lab – Digitization, Digitalization, or Digital Transformation?
7.2.2.1 Which Challenges Do I Have Now?
Example
7.2.2.2 Which Challenges Need My Immediate Attention?
7.2.2.3 Which Challenges Do I See in the Future?
Example
7.2.2.4 What is My Long‐term Business Strategy?
7.2.2.5 How Will Changes Affect My Current Business?
Example
7.2.2.6 How Will I Manage Legacy Data?
Example 1 Complete migration
Example 2 Archive everything
Example 3 Hybrid approach
7.2.2.7 How Will I Get People to Cooperate?
7.3 Evaluating the Current State in the Lab
7.3.1 Defining the Overall Goals of the Digitalized Laboratory. 7.3.1.1 Example
Goal 1: Improve the Data Management by Implementing Digital Tools
Goal 2: Increase the Efficiency of the Laboratories by 25%
Goal 3: Improve Data Integrity by Eliminating Manual Steps from Data Flows
Goal 4: The Acquisition of New Technologies Should Be 100% and Should Be Easy for the Users to Start Using the New Tools
Goal 5: Project Should Be Finished in 12 Months
7.3.2 Defining the Data Flows
7.3.3 Describing the Processes
7.3.4 Identifying the Bottlenecks
7.3.4.1 Bottlenecks in Data Flow Optimization
7.3.4.2 Efficiency and Integrity of Data Flows
Example
Example
Example
7.3.4.3 Example: Make Data Machine Readable
Example
7.3.5 Opportunities in Process Optimization
7.3.5.1 Time‐consuming Processes
7.3.5.2 General Laboratory Processes
7.3.6 Gap Analysis
7.3.6.1 Example
References
8 Understanding Standards, Regulations, and Guidelines
8.1 Introduction
8.2 The Need for Standards and Guidelines
8.3 How Does Digitalization Relate to Standards and Guidelines
8.3.1 Standards Should Affect the Selection of the Tools for Digitalization
Example
8.3.2 Digital Tools Promote Good Practices
8.4 Challenges Related to Digitalization in Certified Laboratories
8.5 Can Digital Strategy be Implemented without Certification?
Example
References
9 Interoperability Standards
9.1 SiLA
9.2 AnIML
9.3 Allotrope
9.4 Conclusion
10 Addressing the User Adoption Challenge
10.1 Introduction
10.2 Identify Key Stakeholders and Explain the Reasons for Change
Example
10.3 Establish a Steering Committee
10.4 Define the Project Objectives, Expected Behaviour, and Timeline
10.5 Check for Understanding and Encourage Debate
Example
10.6 Acknowledge Ideas and Communicate Progress
10.7 Provide a Feedback Mechanism
10.8 Set Up Key Experience Indicators and Monitor Progress
10.8.1 Happiness
10.8.2 Engagement
10.8.3 Adoption
10.9 Gradually Expand to a Larger Scale
10.10 Conclusions
References
11 Testing the Electronic Lab Notebook and Setting Up a Product Trial
11.1 Introduction
11.2 The Product Trial
11.3 The Importance of a Product Trial
11.4 Setting Up a Product Trial. 11.4.1 Phase I: Planning
11.4.2 Phase II: Conceptualization
11.4.3 Phase III: Testing
Example
11.4.4 Phase IV: Reporting
11.5 Good Practices of Testing a Product
11.5.1 Taking the Time for Planning
11.5.2 Having a Bigger Picture in Mind
11.5.3 Keeping Your Testers Motivated
11.5.4 Systematic Evaluation of Products
11.5.5 Cooperating with Vendors
11.6 Conclusions
References
Part IV Case Studies
12 Understanding and Defining the Academic Chemical Laboratory's Requirements: Approach and Scope of Digitalization Needed
12.1 Types of Chemistry Laboratory
12.2 Different Stages of Digitalization
12.3 Preparatory Stage
12.3.1 Digitalization Requirements
12.3.2 Issues and Barriers to Adoption
12.3.3 Suggested Solutions
12.4 Laboratory Stage
12.4.1 Digitalization Requirements
12.4.2 Issues and Barriers to Adoption
12.4.3 Suggested Solutions
12.5 Transferal Stage
12.5.1 Digitalization Requirements
12.5.2 Issues and Barriers to Adoption
12.5.3 Suggested Solutions
12.6 Write‐up Stage
12.6.1 Digitalization Requirements
12.6.2 Issues and Barriers to Adoption
12.6.3 Suggested Solutions
12.7 Conclusions and Final Considerations
References
13 Guidelines for Chemistry Labs Looking to Go Digital
13.1 Understanding the Current Setup
13.2 Understanding Your Scientists and Their Needs
13.3 Understanding User‐based Technology Adoption
13.4 Breaking Down the Barriers Between Science and Technology
13.5 Making Your Laboratory Team Understand Why This Is Necessary
13.6 Working with Domain Experts
13.7 Choosing the Right Software
13.8 Changing Attitude and Organization
References
14 Electronic Lab Notebook Implementation in a Diagnostics Company
14.1 Making the Decision
14.2 Problems with Paper Notebooks
14.3 Determining Laboratory's Needs
14.4 Testing
14.5 A Decision
14.6 How to Structure the ELN
14.7 Conclusion
15 Identifying and Overcoming Digitalization Challenges in a Fast‐growing Research Laboratory
15.1 Why Going Digital?
15.2 Steps to Introduce ELNs in Lab Practice
15.2.1 Step 1: Getting to Know the Market or What We Can Expect of an ELN
15.2.2 Step 2: Defining the Needs of Our Lab and Our Requirements for an ELN
15.2.2.1 Data Structure
15.2.2.2 Compatibility with Databases
15.2.2.3 Flexibility of Documentation Style
15.2.2.4 Report Options
15.2.2.5 Speed
15.2.3 Step 3: Matching Steps 1 and 2 and Testing Our Best Options
15.2.4 Step 4: Getting Started in Implementing the ELN
15.3 Creating the Mindset of a Digital Scientist
15.4 The Dilemma of Digitalization in Academia
16 Turning Paper Habits into Digital Proficiency
16.1 Five Main Reasons for the Implementation of a Digital System to Manage the Research Data
16.1.1 Scale‐up of the Laboratory
16.1.2 Protocol Management Issues
16.1.3 Environmental and Financial Factors
16.1.4 Introducing the Benefits of Technology to Younger Employees
16.1.5 Remote Access to Data by Authorized Supervisors
16.2 The Six‐step Process of Going from Paper to Digital
16.2.1 Defining the Specific Needs of the Laboratory
16.2.2 Testing the Software and Defining the Standard Way to Use It
16.2.3 Organizing the Collaboration Between Lab Members and Supervisors
16.2.4 Managing Projects and Setting Up Work Processes
16.2.5 Versioning of Protocols and Keeping the Protocol Repository Up to Date
16.2.6 Choosing to Digitize Only New Projects
16.3 Onboarding All Team Members and Enhancing the Adoption of the New Technology in the Lab
16.4 Benefits of Switching from Paper to Digital
17 Going from Paper to Digital: Stepwise Approach by the National Institute of Chemistry (Contract Research)
17.1 Presentation of our CVTA Laboratory
17.2 Data Management Requirements Explained in Detail
17.2.1 Meaning of ALCOA
17.2.2 FDA and CFR 21 Part 11
17.2.3 MHRA and GxP Data Integrity Guidance and Definitions
17.2.4 Definition of Terms and Interpretation of Requirements
17.3 Going from Paper to Digital
17.4 Implementation of SciNote (ELN) to CVTA System
17.4.1 Some of CVTA user's Requirements (URS)
17.4.2 From Documentation Review and Approval to ELN Implementation
17.4.3 Step‐by‐Step Implementation of Change Control Management in SciNote
17.4.3.1 Creating Projects in SciNote
17.4.3.2 Creating a Workflow
17.4.3.3 Creating the Tasks and Protocol Steps
17.4.3.4 Filtering, Overview of Data and Inventory for Change Control Management
17.4.3.5 Audit Trail of Changes
17.4.3.6 Overview of all Activities
17.4.4 Organization and Signing of CVTA Documentation in ELN SciNote Due to User Roles and Permissions. 17.4.4.1 Managing the Team Roles and Responsibilities within SciNote
17.4.4.2 Managing Projects for Efficient Work with Clients
17.5 Suggestions for Improvements and Vision for the Future
References
18 Wet Lab Goes Virtual: In Silico Tools, ELNs, and Big Data Help Scientists Generate and Analyze Wet‐lab Data
18.1 CRISPR‐Cas9 Explained
18.2 Introduction of the Digital Solutions and ELN into the Laboratory
18.3 The Role of the ELN and In Silico Tools in the Genome‐editing Process. 18.3.1 Designing sgRNA
18.3.2 Issues with Paper‐based Processes and the Use of ELN
18.3.3 High‐content Imaging for the Target Discovery
18.3.4 Plant Virtual Laboratory
18.4 The Role of the ELN and In Silico Tools in the Protein Design Process
18.4.1 Protein Modeling
18.4.2 Protein Redesign
18.4.3 Importance of Keeping the Electronic Records
18.4.4 Development of Therapeutic Antibodies
18.4.5 Importance of Electronic Lab Notebook for Communication Between Team Members
References
Note
19 Digital Lab Strategy: Enterprise Approach
19.1 Motivation
19.1.1 Which Problem Do We Want to Solve?
19.1.2 New Problems Require New Answers
19.2 Designing a Flexible and Adaptable Architecture
19.3 There is Only One Rule: No Rules
19.4 The Lab Digitalization Program Compass
19.5 Conclusion
References
Part V Continuous Improvement
20 Next Steps – Continuity After Going Digital
20.1 Are You Ready to Upgrade Further?
20.2 Understanding the Big Picture
20.3 What to Integrate First?
20.3.1 Integrations
20.3.2 Laboratory Equipment – Concepts of IoT and Lab 4.0
20.3.2.1 Does the Equipment Support Integrations?
20.3.2.2 How Often Is the Instrument Being Used?
20.3.2.3 Is There a High Chance for Human Error?
20.3.2.4 Do You Need One‐ or Two‐way Sync?
20.3.2.5 Is the Equipment Using Any Standards?
20.3.2.6 Is Equipment Cloud Connected?
20.3.3 Data Repositories
20.3.4 Data Analytics Tools
20.3.5 Other Types of Integrations
20.3.5.1 Scientific Search Engines and Literature Management
20.3.5.2 Data Sharing
20.3.5.3 Publishing
20.3.5.4 Upgrading Plans
20.4 Budgeting
20.5 Continuous Improvement as a Value
References
Part VI Vision of the Future and Changing the Way We Do Science
21 Artificial Intelligence (AI) Transforming Laboratories
21.1 Introduction to AI
21.1.1 Opportunities
21.1.2 Needs
21.1.3 Challenges
21.2 Artificial Intelligence in Laboratories
21.2.1 Data Preprocessing
21.2.2 Data Analytics
21.3 Process Monitoring
21.4 Discussion – Human in the Loop
References
22 Academic's Perspective on the Vision About the Technology Trends in the Next 5–10 Years
22.1 Hybrid Solutions
22.2 Voice Technologies
22.3 Smart Assistants
22.4 Internet of Things
22.5 Robot Scientists
22.6 Making Science Smart – Incorporating Semantics and AI into Scientific Software
22.7 Conclusions
References
23 Looking to the Future: Academic Freedom Versus Innovation in Academic Research Institutions
23.1 Introduction
23.2 Corporate Culture Versus Academic Freedom
23.3 Spoiled for Choice, but Still Waiting for the Perfect Solution
23.4 Building a Single, Shared Infrastructure for Research Data Management
23.5 A Journey of a Thousand Miles Begins with a Single Step
Reference
24 Future of Scientific Findings: Communication and Collaboration in the Years to Come
24.1 Preprints: Reversing the Increased Time to Publish
24.2 Virtual Communities
24.3 Evolving Publishing Models
24.4 Funders Are Starting to Play a Role in Facilitating and Encouraging Rapid Sharing and Collaboration
24.5 Conclusion
References
25 Entrepreneur's Perspective on Laboratories in 10 Years
25.1 Data Recording
25.2 Recognition of Voice and Writing
25.3 Data Recording in the Future
25.4 Experimental Processes
25.5 Research Project Management
25.6 Experimental Planning
25.7 Virtual Reality
25.8 Smart Furniture
25.9 Experiment Execution
25.10 Laboratory Automation Trends
25.11 Cloud Laboratories
25.12 Data Analysis Trends
25.13 Artificial Intelligence
25.14 Data Visualizations and Interpretation
25.15 Databases
Example
25.16 Conclusion
References
Index. a
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