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Analysis of human resource management challenges in implementation of industry 4.0 in Indian automobile industry

Top management green commitment and green intellectual capital as enablers of hotel environmental performance: the mediating role of green human resource management, the study of knowledge employee voice among the knowledge-based companies: the case of an emerging economy.

PurposeA review of previous studies on the voices of employees and knowledge workers clarifies that paying attention to employees' voice is critical in human resource management. However, limited studies have been conducted on it, and much less emphasis has been placed compared to other human resource management activities such as human resource planning. Therefore, the voice of knowledge employees has been one of the critical issues that have attracted a great deal of attention recently. Nonetheless, there is no evidence of various comprehensive and integrated voice mechanisms. As a result, this study aims to design knowledge workers' voice patterns in knowledge-based companies specialising in information and communication technology (ICT) in Iran in May and June 2020.Design/methodology/approachThis study is a qualitative grounded theory research. We collected the data from a target sample of 15 experts in knowledge-based ICT companies using in-depth semi-structured interviews. Since all the participants had practised the employee voice process, they were regarded as useful data sources. Data analysis was also performed using three-step coding (open, axial and selective) by Atlas T8, which eventually led to identifying 14 components and 38 selected codes. We placed identified components in a paradigm model, including Personality Characteristics, Job Factors, Economic Factors, Cultural Factors, Organisational Policies, Organisational Structure, Climate Of Voice in the Organisation, Management Factors, Emotional Events, Communications and Networking, Contrast and Conflict and, etc. Then, the voice pattern of the knowledge staff was drawn.FindingsThe results showed that constructive knowledge voice influences the recognition of environmental opportunities and, additionally, it helps the competitive advantages among the employees. By forming the concept of knowledge staff voice, it can be concluded that paying attention to knowledge staff voice leads to presenting creative solutions to do affairs in critical situations. The presentation of these solutions by knowledge workers results in the acceptance of environmental changes, recognition and exploitation of new chances and ideas, and sharing experiences in Iranian knowledge-based companies.Practical implicationsStrengthening and expanding the voice of employees in knowledge-oriented companies can pave the way to growth and development towards a higher future that prevents the waste of tangible and intangible assets.Originality/valueCompanies' ability to engage in knowledge workers is a vital factor in human resource management and strategic management. However, the employee voice has not been involved integrally in the context of corporate.

Achieving Human Resource Management Sustainability in Universities

The sustainability of human resource management (HRM) is the basis for an organization’s future growth and success. This study aims to investigate achieving HRM sustainability in universities. We use a quantitative research method design to investigate the factors that affect HRM sustainability at universities. The study was conducted during the spring and summer of 2020 at Iranian state universities. As the study’s statistical population included 2543 employees, a sample size of 334 employees was calculated using the Cochran formula. A questionnaire with 32 statements based on a 5-point Likert scale was used to collect the data, which were analyzed using PLS3 software. The findings show that human resource practices, social factors, psychological factors, employer branding, and economic factors have positive and significant effects on HRM sustainability at universities. Findings indicate that it is essential to consider the implementation of adequate HRM practices and related socio-economic and psychological supports for HRM sustainability in universities that can lead to the competitiveness of the higher education institutions such as universities.

Relationship Model between Human Resource Management Activities and Performance Based on LMBP Algorithm

The research on the relationship between human resource management activities and performance is an important topic of enterprise human resource management research. There are some errors between the relationship between human resource management activities and performance and the real situation, so it is impossible to accurately predict the performance fluctuation. Therefore, the relationship model between human resource management activities and performance based on the LMBP algorithm is constructed. Using the Levenberg–Marquardt (LM) algorithm and BP (back-propagation) neural network algorithm to establish a new LMBP algorithm, control the convergence of the new algorithm, optimize the accuracy of the algorithm, and then apply the LMBP algorithm to predict the risk of performance fluctuation under human resource management activities of enterprises, the indicators of human resource management activities of enterprises are determined, to complete the mining of enterprise performance data, the grey correlation analysis is combined, and the relationship model between human resource management activities and performance is built. The experimental samples are selected from CSMAR database, and the simulation experiment is designed. Using different algorithms to forecast the fluctuation of enterprise performance, the experimental results show that the LMBP algorithm can more accurately reflect the relationship between enterprise HRM and performance.

Inclusive human resource management in freelancers' employment relationships: The role of organizational needs and freelancers' psychological contracts

Student leadership programme: igniting the young minds.

Learning outcomes This case will help students to understand the following: Develop a basic understanding of competency building processes. Learn about the mentoring process and its application in leadership development. Develop awareness about the methodology for assessment of the effectiveness of training. Case overview/synopsis Dr A. R. K. Pillai founded the Indian Leprosy Foundation in 1970 in response to the national call by late Mrs Indira Gandhi, prime minister of India, to the public-spirited people to take up leprosy eradication. It collaborated with international agencies to reduce leprosy drastically in India from four million, in 1982 to around a hundred thousand cases in 2006. In 2006, the Indian Leprosy Foundation was renamed as Indian Development Foundation (IDF) as the trustees decided to expand the work of IDF in the areas of health, children’s education and women’s empowerment. Dr Narayan Iyer, Chief Executive Officer (CEO) of IDF initiated a leadership development intervention called the Students’ leadership programme (SLP) for children in the age group of 12 to 14, from the urban poor households in 2014. It was a structured mentoring programme spanning over three months in collaboration with the schools. It aimed at incubating skills in the areas of leadership, teamwork, personality, behavioural traits and provided career guidance. It had a humble beginning in 2014 with a coverage of 50 students. Initially, IDF welcomed executives from the corporate sector as mentors. As there was a need to rapidly expand the scope of SLP to the other cities of India, IDF tied up with the graduate colleges and invited the students to be the mentors. The other objective behind this move was to create social awareness among the students from more affluent strata of society. IDF was able to dramatically increase the participation of the students through SLP by approximately up to 100,000 by 2020. However, rapid progress threw up multiple challenges. The teachers complained about the non-availability of the students for regular classes to teach the syllabus as the students were busy with SLP. The schools forced IDF to shorten the duration of SLP to two months. Also, many undergraduate mentors were unable to coach the participants due to lack of maturity and found wanting to strike a rapport with them. There was a shortage of corporate executives who volunteered for the mentoring, due to work pressures. Dr Narayan, CEO & National Coordinator and Ms Mallika Ramchandran, the project head of SLP at IDF, were worried about the desired impact of SLP on the participants and its sustainability due to these challenges. So, with the support of Dr Narayan, she initiated a detailed survey to assess the ground-level impact of SLP. The objective was to get clarity about what was working for SLP and what aspects needed to improve, to make the programme more effective. Overall feedback from the survey was very positive. The mothers had seen very positive changes in the participants’ behaviour post-SLP. The teachers had specific concerns about the effectiveness of undergraduate mentors. The need for a refresher course to inculcate ethical behaviour and the inadequacy of the two-month duration of the SLP to reinforce values were highlighted. Respondents also voiced the requirement to build responsible citizenship behaviours among the participants. Mallika was all for preparing a model to further enhance the effectiveness of SLP. Dr Narayan and Mallika embraced the challenge and they were raring to go to develop SLP as a cutting-edge leadership programme and to take it to new heights. Complexity academic level This case can be used in courses on human resource management in postgraduate and graduate management programmes. It can also be used in the general and development management courses and during executive education programmes to teach methodologies for evaluating the effectiveness of the training interventions, with emphasis on the voluntary sector. Supplementary materials Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management.

Sustainable human resource management: six defining characteristics

Socially responsible human resource management and employee ethical voice: roles of employee ethical self‐efficacy and organizational identification, feasibility of implementing the human resource payroll management system based on cloud computing.

PurposeThe present study is descriptive research in terms of purpose, descriptive analysis in terms of nature and cross-sectional research in terms of time. The study’s statistical population includes all employees and managers of the China City Organization selected as sample members using random sampling method and Krejcie table of 242 people. The questionnaire was modified and revised based on the goals, tasks and mission of the target organization to collect information. In data analysis, due to the normality of data distribution, the structural equation modeling method is used to evaluate the causal model, reliability and validity of the measurement model. Evaluation and validation of the model are done through the structural equation model. Questionnaire-based model and data are analyzed using Smart PLS 3.0. The main purpose of this study is to assess the feasibility of implementing the human resource payroll management system based on cloud computing technology.Design/methodology/approachNew technologies require innovative approaches for creating valuable opportunities in an organization to integrate the physical flows of goods and services and financial information. Today, cloud computing is an emerging mechanism for high-level computing as a storage system. It is used to connect to network hosts, infrastructure and applications and provide reliable services. Due to advances in this field, cloud computing is used to perform operations related to human resources. The role, importance and application of cloud computing in human resource management, such as reducing the cost of hardware and information software in hiring, job planning, employee selection, employee socialization, payroll, employee performance appraisal, rewards, etc., is raised. This way, human resource management teams can easily view resumes, sort candidates and observe and analyze their performance. Cloud computing is effective in implementing human resource payroll management systems. Therefore, the primary purpose of this study is to assess the feasibility of implementing the human resource payroll management system based on cloud computing technology.FindingsTesting the research hypotheses shows that the dimension desirability of ability and acceptance is provided in dimensions related to the minimum conditions required to implement cloud computing technology in the organization. For this reason, the feasibility of implementing the systems based on cloud computing in companies must be considered.Research limitations/implicationsThis study also has some limitations that need to be considered in evaluating the results. The study is limited to one region. It cannot be assured that the factors examined in other areas are effective. The research design for this study is a cross-sectional study. It represents the static relationship between the variables. Since cross-sectional data from variable relationships are taken at a single point in time, they are collected in other periods. As a proposal, future researchers intend to investigate the impact of Enterprise Resource Planning (ERP) systems based on cloud computing.Practical implicationsThe research also includes companies, departments and individuals associated with systems based on cloud computing.Originality/valueIn this paper, the feasibility of implementing the human resource payroll management system based on cloud computing is pointed out, and the approach to resolve the problem is applied to a practical example. The presented model in this article provides a complete framework to investigate the feasibility of implementing the human resource payroll management system based on cloud computing.

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Human Resource Research

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human resources research paper

Human Resource Research ( HRR ) is an international, double-blind peer-reviewed, open-access journal published by Macrothink Institute. HRR carries original and full-length articles that reflect the latest research and developments in both theoretical and practical aspects of Human Resource. It provides an academic platform for professionals and researchers to contribute innovative work in the field. 

  • Compensation Management
  • Employee Relations
  • Human Resource Development
  • Human Resource Management
  • Knowledge Management
  • Leadership and Team Management
  • Learning and Development
  • Organizational Learning
  • Organizational Management
  • Organizational Staffing
  • Performance Management
  • Professional Development and relevant subjects.
  • Strategic HRM
  • Strategic Management
  • Training and Development. 

Paper Selection and Publication Process

a). Upon receipt of paper submission, the Editor sends an E-mail of confirmation to the corresponding author within 1-3 working days. If you fail to receive this confirmation, your submission/e-mail may be missed. Please contact the Editor in time for that.

b). Peer review. We use double-blind system for peer-review; both reviewers and authors’ identities remain anonymous. The paper will be peer-reviewed by three experts; two reviewers from outside and one editor from the journal typically involve in reviewing a submission. The review process may take 4-10 weeks .

c). Notification of the result of review by E-mail.

d). The authors revise paper and pay   article processing charge (100USD).

e). E-journal in PDF is available on the journal’s webpage, free of charge for download. 

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We follow the  Gold Open Access  way in journal publishing. This means that authors publish in the journals that provide immediate open access for readers to all articles on the publisher’s website. The readers pay nothing, while authors (or their institutions or funders) pay a publishing fee to maintain the journal operation.

All articles published are open-access articles distributed under the terms and conditions of the  Creative Commons Attribution license .

Vol 6, No 1 (2022)

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Human Resource Research   ISSN 1948-5441   E-mail: [email protected]

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Human Resource Management Research Paper Topics

Academic Writing Service

Human resource management research paper topics are a critical area of study for students and professionals aiming to understand and advance the field of Human Resource Management (HRM). With the rise of complex organizational structures, diverse workplace environments, and evolving employment laws, HRM has become an essential part of any successful organization. This abstract provides an overview of the multifaceted world of HRM research and introduces a comprehensive list of research paper topics that cater to various aspects of HRM. From talent acquisition to employee retention, performance evaluation, training, and legal compliance, the following sections will offer detailed insights into these areas. Students interested in pursuing research in HRM will find these topics engaging and highly relevant to the current organizational landscape. Additionally, they will be introduced to iResearchNet’s writing services that provide expert assistance in producing custom HRM research papers, ensuring quality, depth, and adherence to academic standards.

100 Human Resource Management Research Paper Topics

Human Resource Management (HRM) is a field that delves into the multifaceted interactions between employees and organizations. The role of HRM has evolved over time to include not only the management of recruitment and employee relations but also strategic planning, legal compliance, and organizational development. Here, we present a comprehensive list of Human Resource Management research paper topics divided into 10 essential categories, each containing 10 specific topics.

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HRM is a dynamic and broad field that demands multidimensional approaches to study. As students explore various topics, they will find intricate connections between management strategies, organizational behavior, and employee satisfaction. The following list serves as an inclusive guide to inspire research and academic inquiry.

  • The role of HRM in organizational strategy
  • Integrating HRM and business planning
  • Talent management strategies
  • Outsourcing HR functions: Pros and cons
  • Technology and HRM
  • Cross-cultural HRM
  • Mergers and acquisitions: HRM challenges
  • Strategic HR planning and organizational success
  • The future of strategic HRM
  • Best HR practices in top-performing companies
  • Innovative recruitment techniques
  • Bias and discrimination in the recruitment process
  • Role of artificial intelligence in recruitment
  • Recruitment marketing strategies
  • Social media as a recruitment tool
  • Ethics in employee selection
  • Assessing the effectiveness of recruitment strategies
  • Diversity and inclusion in recruitment
  • Remote hiring practices
  • Campus recruitment strategies
  • The effectiveness of training programs
  • Employee development and organizational growth
  • The role of mentors in employee growth
  • E-learning and virtual training methods
  • Personalized training approaches
  • Training evaluation methods
  • Cross-training and skill development
  • The future of corporate training
  • Impact of continuous learning culture
  • Leadership development programs
  • Modern performance appraisal techniques
  • 360-degree feedback system
  • Employee engagement and performance
  • Performance management and job satisfaction
  • Aligning performance goals with organizational objectives
  • Challenges in performance evaluation
  • Performance-based rewards
  • Emotional intelligence and employee performance
  • Performance management in remote work environments
  • Real-time performance tracking systems
  • Building trust and collaboration among employees
  • Conflict resolution strategies
  • Impact of organizational culture on employee engagement
  • Managing generational differences in the workplace
  • Role of leadership in fostering engagement
  • Employee wellness programs
  • The psychology of employee engagement
  • Communication strategies for employee relations
  • Remote employee engagement tactics
  • Work-life balance initiatives
  • Salary negotiation techniques
  • The psychology of compensation
  • Pay equity and gender wage gap
  • The impact of benefits on employee retention
  • Flexible compensation models
  • Global compensation strategies
  • Linking compensation to performance
  • Employee stock ownership plans (ESOPs)
  • Non-monetary benefits and motivation
  • Compensation transparency
  • Labor law compliance in multinational corporations
  • Whistleblowing and ethical considerations
  • HRM in unionized workplaces
  • Workplace harassment laws
  • Employee rights and employer responsibilities
  • Managing employee terminations ethically
  • Diversity and anti-discrimination policies
  • Legal aspects of employee benefits
  • Remote work and legal challenges
  • Ethical dilemmas in HRM
  • Building a diverse workforce
  • Strategies for fostering inclusion
  • The impact of diversity on team performance
  • Gender diversity in leadership roles
  • Managing cultural diversity
  • Age diversity in the workplace
  • Disability inclusion strategies
  • LGBT+ inclusion in the workplace
  • Ethnic diversity and organizational success
  • Bias reduction training
  • The role of HRM in shaping organizational culture
  • Employee behavior and organizational success
  • Workplace norms and values
  • Emotional labor in organizations
  • Organizational change management
  • Strategies for building a positive work environment
  • Employee motivation and organizational culture
  • The psychology of workplace relationships
  • Corporate social responsibility (CSR) and culture
  • The role of leadership in defining organizational culture
  • Emerging Trends in HRM
  • HRM in the gig economy
  • Artificial intelligence and HRM
  • Employee mental health and well-being
  • Sustainability and HRM
  • The future of remote work
  • Integrating HRM and corporate social responsibility (CSR)
  • Blockchain in HRM
  • Personal branding in HR
  • The role of big data analytics in HRM
  • HRM challenges in the post-pandemic world

The list of human resource management research paper topics presented above offers a rich and diverse avenue for exploration. Each category delves into core aspects of HRM, reflecting the ever-changing nature of this field. As students embark on their research journey, they will discover a world that intricately connects people, organizations, and societal values. Whether focusing on traditional practices or emerging trends, these topics provide the starting point for meaningful inquiry and the creation of knowledge that contributes to the continued growth and evolution of HRM.

Human Resource Management and the Range of Research Paper Topics

Human Resource Management (HRM) is an interdisciplinary field that integrates aspects of management, psychology, sociology, economics, and legal studies. It is the art and science of managing people within an organization to maximize their performance, well-being, and alignment with strategic goals. As a broad and multifaceted domain, HRM opens doors to a wide array of research opportunities. This article will explore the essence of HRM, its historical evolution, theoretical frameworks, practical applications, and the myriad of research paper topics it offers.

Historical Background

The history of HRM can be traced back to the early 20th century, during the rise of the industrial revolution. The scientific management theory introduced by Frederick Taylor sought to apply scientific principles to worker productivity. As the business environment grew more complex, the Hawthorne studies emerged, highlighting the importance of social factors and human relations in the workplace. The evolution from personnel management to modern HRM reflects a shift from viewing employees as mere resources to recognizing them as valuable assets.

Theoretical Frameworks

HRM is underpinned by several key theories that guide practice:

  • Resource-Based View (RBV): Emphasizes the role of human resources as a competitive advantage.
  • Equity Theory: Focuses on fairness and justice in employee relations.
  • Expectancy Theory: Explains how employees are motivated by the expected outcomes of their actions.
  • Human Capital Theory: Regards employees as assets whose value can be enhanced through training and development.

These theories offer diverse perspectives for research, ranging from organizational behavior to strategic HRM.

Key Functions and Practices

The scope of HRM encompasses various functions that address the needs of both the organization and its employees:

  • Recruitment and Selection: Designing and implementing processes to attract and hire suitable candidates.
  • Training and Development: Enhancing employee skills and knowledge through continuous learning.
  • Performance Management: Assessing and managing employee performance to align with organizational goals.
  • Compensation and Benefits: Structuring pay and rewards to motivate and retain talent.
  • Labor Relations: Navigating the legal landscape and fostering healthy employee-employer relationships.

Contemporary Challenges

Modern HRM faces several challenges that provide fertile grounds for research:

  • Diversity and Inclusion: Creating a workforce that represents various backgrounds, beliefs, and perspectives.
  • Technology and Automation: Leveraging technology to enhance HR processes while considering its impact on jobs.
  • Globalization: Managing HR practices across different cultures and jurisdictions.
  • Ethical Considerations: Balancing organizational needs with ethical treatment of employees.

Emerging Trends

The ever-changing business landscape leads to new trends in HRM:

  • Remote Work: The rise of virtual workplaces and the associated management challenges.
  • Well-Being and Mental Health: Prioritizing employee health and well-being as part of HR strategy.
  • Sustainability: Integrating social responsibility into HR practices.

Range of Research Paper Topics

The complexity and diversity of HRM lead to an abundance of research paper topics. Here are examples from different areas:

  • Strategic HRM: Examining the alignment of HR practices with business strategy.
  • Employee Engagement: Exploring factors that influence engagement and its impact on performance.
  • Legal Aspects of HRM: Investigating laws and regulations affecting HR practices.
  • Organizational Culture and Behavior: Analyzing the influence of culture on employee behavior and organizational success.

Human Resource Management is a vast and dynamic field that intertwines various disciplines, theories, practices, and challenges. From historical roots to contemporary issues, HRM offers a rich tapestry of research opportunities. Whether investigating traditional functions or delving into emerging trends, students and scholars can find a wealth of topics that resonate with their interests and contribute to our understanding of human interactions within organizational contexts. The spectrum of human resource management research paper topics reflects the depth and breadth of a field that continues to evolve, shaping the way we work, lead, and thrive in an ever-changing world.

How to Choose Human Resource Management Research Paper Topics

Selecting the right topic for a research paper in Human Resource Management (HRM) is a critical step that can shape the entire trajectory of your project. The topic you choose should align with your interests, academic level, the specific requirements of the assignment, and the current trends in the field. Here’s a guide to help you navigate the decision-making process and pinpoint a topic that resonates with you.

The realm of Human Resource Management is vast and diverse, encompassing various theories, functions, challenges, and emerging trends. Choosing a suitable research paper topic within this multifaceted field requires careful consideration and strategic thinking. This section will outline ten essential tips to guide you in selecting a meaningful, relevant, and engaging topic for your research.

  • Identify Your Interests: Begin by reflecting on what aspects of HRM intrigue you. Are you passionate about organizational behavior, talent acquisition, employee welfare, or strategic HRM? Your research will be more enjoyable if it aligns with your interests.
  • Understand the Assignment Requirements: Review the guidelines and grading criteria provided by your instructor. Consider the scope, length, and expected complexity of the paper.
  • Conduct a Preliminary Literature Review: Explore existing research in areas that interest you. Identify gaps, controversies, or emerging trends that could form the basis for your study.
  • Consider the Target Audience: Think about who will read your paper. Tailoring the topic to your audience’s interests, knowledge level, and expectations can enhance its impact.
  • Evaluate Available Resources: Assess the availability of data, tools, and resources needed for your research. The feasibility of a topic depends on your ability to access relevant information and support.
  • Align with Current Trends: Consider choosing a topic that relates to contemporary issues or recent developments in HRM. This alignment can make your research more relevant and appealing.
  • Seek Guidance from Instructors or Peers: Don’t hesitate to consult with your instructor, classmates, or academic advisors. They may offer valuable insights, feedback, or suggestions.
  • Ensure Ethical Consideration: Ensure that your chosen topic complies with ethical standards, particularly if it involves human subjects, sensitive data, or controversial subjects.
  • Consider the Broader Impact: Reflect on how your research could contribute to the field of HRM. A topic with potential practical implications or theoretical advancements can add value to your work.
  • Create a Shortlist and Evaluate: Draft a list of potential topics and weigh them against the criteria outlined above. This systematic approach can help you identify the most suitable option.

Selecting a research paper topic in Human Resource Management is a thoughtful and iterative process that requires introspection, exploration, and strategic thinking. By considering your interests, academic requirements, available resources, current trends, ethical considerations, and potential impact, you can identify a topic that not only resonates with you but also contributes to the vibrant discourse in HRM. Remember that your choice is not set in stone; it’s a starting point that you can refine and adapt as you delve into your research. Embrace the journey, for the right topic is a gateway to discovery, learning, and growth in the multifaceted world of human resource management.

How to Write a Human Resource Management Research Paper

Writing a research paper on Human Resource Management (HRM) is a complex task that requires a clear understanding of the subject matter, a methodical approach to research, and strong writing skills. The following section will guide you through the process of crafting a well-structured, insightful, and academically rigorous research paper in HRM.

Human Resource Management is at the core of organizational success, shaping the way businesses attract, retain, and develop talent. As a field that intertwines with psychology, sociology, business strategy, and law, writing a research paper on HRM is both challenging and rewarding. The following guide provides a step-by-step approach to help you navigate the research, writing, and revision stages, ensuring that your paper is thorough, coherent, and impactful.

  • Understand the Assignment: Before diving into research and writing, clarify the assignment’s objectives, scope, format, and grading criteria. Ensure you understand what is expected in terms of content, structure, style, and depth of analysis.
  • Choose a Relevant Topic: Select a topic that aligns with your interests, the course objectives, and current HRM trends. Refer to Section IV for guidance on choosing the right topic.
  • Conduct Comprehensive Research: Utilize reputable sources such as academic journals, books, and industry reports to gather data, theories, and insights related to your topic. Evaluate the credibility and relevance of each source.
  • Develop a Thesis Statement: Craft a clear and concise thesis statement that outlines the central argument or focus of your paper. The thesis should guide the reader on what to expect and provide a roadmap for your analysis.
  • Create an Outline: Develop a detailed outline that breaks down the main sections and sub-sections of your paper. An outline will help you organize your thoughts, maintain coherence, and ensure a logical flow of ideas.
  • Write the Introduction: Begin with an engaging introduction that introduces the topic, provides background information, highlights its significance, and presents the thesis statement.
  • Develop the Body Paragraphs: Divide the body of your paper into clear sections and subsections. Use headings and subheadings to guide the reader. Each paragraph should have a clear topic sentence, supporting evidence, and a concluding sentence that links back to the thesis.
  • Include Practical Insights and Case Studies: Where appropriate, include practical examples, case studies, or industry insights that illustrate your points. This application of theory to real-world scenarios can enhance the depth and relevance of your paper.
  • Write the Conclusion: Summarize the key findings, restate the thesis in light of the evidence, and discuss the implications, limitations, and recommendations for future research or practice.
  • Revise and Edit: Review your paper multiple times to check for clarity, coherence, grammar, and formatting errors. Consider seeking feedback from peers or instructors, and use plagiarism check tools to ensure originality.

Writing a research paper in Human Resource Management is a multifaceted process that requires careful planning, diligent research, critical analysis, and thoughtful writing. By following the tips outlined above, you can create a paper that not only meets academic standards but also contributes valuable insights to the dynamic field of HRM. Remember that writing is a process of continuous refinement; embrace revisions, seek feedback, and strive for clarity and depth. The journey of crafting an HRM research paper is an opportunity to deepen your understanding, hone your skills, and contribute to the ongoing discourse in a field that shapes the heart of organizations around the world.

iResearchNet Writing Services

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  • Expert Degree-Holding Writers: At the core of our services, you’ll find our team of experts. These are not just writers but highly qualified professionals who hold advanced degrees in Management, Business, and related fields. Their qualifications and in-depth knowledge ensure that your HRM research paper is handled with the required expertise and understanding of nuanced academic requirements.
  • Custom Written Works: Beyond the qualifications of our writers, we prioritize the creation of each research paper from scratch. This means that we take your specific guidelines, your unique instructions, and the expectations of your academic level into account when crafting your paper. This unique, individualized approach results in a piece that is as distinctive as the student it represents.
  • In-Depth Research: A cornerstone of our services is our commitment to comprehensive research. Our writers don’t just skim through the surface of your chosen topic. Instead, they delve deep, exploring various reputable sources and making sure that they provide a robust and critical analysis. This commitment to in-depth exploration ensures your work aligns with the academic rigor expected in HRM studies.
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  • Customized Solutions: iResearchNet operates on the principle that each student and each research paper is unique. We value your individual needs and academic goals and believe in a personalized approach to our writing services. Our writers work closely with you, tailoring their approach to resonate with your unique requirements.
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Personal privacy VS. public safety: A hybrid model of the use of smart city solutions in fighting the COVID‐19 pandemic in Moscow

Sergey a. revyakin.

1 HSE University, Moscow Russia

Associated Data

Data derived from public domain resources.

Technological advancements and big data have brought many improvements to smart city infrastructure. During the COVID‐19 outbreak, smart city technologies were considered one of the most effective means of fighting the pandemic. The use of technology, however, implies collecting, processing personal data, and making the collected data publicly available which may violate privacy. While some countries were able to freely use these technologies to fight the pandemic, many others were restricted by their privacy protection legislation. The literature suggests looking for an approach that will allow the effective use of smart city technologies during the pandemic, while complying with strict privacy protection legislation. This article explores the approach applied in Moscow, Russia, and demonstrates the existence of a hybrid model that might be considered a suitable tradeoff between personal privacy and public health. This study contributes to the literature on the role of smart city technologies during pandemics and other emergencies.

1. INTRODUCTION

Along with effective urban management, smart city technologies are useful for effective emergency management (Ekman,  2019 ; Liu & Li,  2020 ). The COVID‐19 outbreak and the associated government restrictions triggered a significant increase in the use of smart city technologies to fight the pandemic (Inn,  2020 ; Markotkin,  2021 ). According to the literature (Inn,  2020 ; WHO,  2019 ), smart city technologies can be useful in identifying, tracking, and forecasting outbreaks through big data analytics, enhancing public security via improved facial recognition and infrared technologies, delivering supplies, and assisting surveillance. The investment in smart city technologies improved the quality of planning, preparation, and forecasting during the pandemic (Sharifi et al.,  2021 ).

The benefits of using smart city technologies are evident, however, the municipalities of many countries can not use such technologies to the fullest to fight the pandemic because they raise concerns about the “erosion of privacy” and could violate privacy protection regulations (Kummitha,  2020 ). That is why such countries have to apply the restrictions on using these technologies, applying “a human‐driven” approach (Kummitha,  2020 ; Kupferschmidt & Cohen,  2020 ). Some other countries were able to immediately and forcefully activate the full capacity of smart city technologies to keep the pandemic under control by applying “the techno‐driven” approach. The literature describes the pros and cons of these approaches when using smart city technologies during pandemics but advises looking for a tradeoff between personal privacy and public safety (Kitchin,  2020 ). The article demonstrates one of those tradeoffs represented by a hybrid approach applied in Moscow. Based on the above and considering that Moscow has the most advanced smart city technologies in Russia and strict privacy regulations at the same time, the article answers the following research question: How did Moscow use its smart city technologies to fight against COVID‐19?

To answer the research question, the author studied the literature, government reports and decrees, WHO reports, newspaper articles, other websites, and tested the functionality of the federal and Moscow authorities' mobile applications. The key finding of the research is that Moscow adopted a hybrid approach that combines the features of both the human‐driven and techno‐driven approaches. That approach has not been described in the literature and could be considered as a potential compromise between the concerns about the privacy of individuals and public safety. This approach could be applied by other countries with strict privacy regulations. The article also proposes a qualification matrix, which can be used to define the type of approach applied by a municipality.

The article is organized as follows. The second section contains a literature review aimed at developing qualification criteria to distinguish between human‐driven and technology‐driven approaches to using smart city technologies and define which smart city devices support specific types of government measures (active surveillance, issuing warnings, identification of the infected, isolation, lockdown, and quarantine). The third section describes the research methodology, while the fourth section describes the results (the approach adopted by Moscow authorities). The fifth section contains the overall discussion and considers the theoretical and practical implications of this research and suggests areas for future study. The sixth section provides conclusions and limitations.

2. LITERATURE REVIEW

2.1. techno‐and human‐driven approaches during a pandemic: criteria for qualification.

A smart city requires collecting and integrating data obtained from sensors, physical devices, software applications, personal cameras, the Internet, smartphones, and similar devices (Quijano‐Sánchez et al.,  2020 ) for further analysis using artificial intelligence algorithms. It requires opening the data for public consideration to increase the transparency on the virus outbreak, which would decrease privacy (Janssen & van den Hoven,  2015 ). In countries with advanced privacy protection regulations, the use of smart city technologies to track people during the pandemic were perceived as a significant increase in digital control (Markotkin,  2021 ) and a form of government overreach (The Wall Street Journal,  2020 ). The literature suggests that the free flow of information and data collection makes the technology work effectively (Kummitha,  2020 ), but “the challenge is how much data is enough”? (The New York Times,  2020a ). When looking for trade‐offs, this is one of the critical questions to be theoretically explored and practically addressed using liberty‐friendly principles of the adoption of technologies (Kitchin,  2020 ).

In line with the difference between “technology‐push” and “demand‐pull” theories of social change and technological innovation (Kim & Lee,  2009 ), Kummitha ( 2020 ) suggested the key differences between the two approaches to using smart city technologies during a pandemic (Table  1 ).

The key differences between Techno‐ and Human‐driven approaches during a pandemic: criteria for qualification

The approach to using smart city technologies during a pandemic represents a type of the decision the authorities need to make. That means, within the same technological equipment the decision made (the approach applied) could be different. The techno‐driven approach suggests the immediate and forceful use of the full capacity of smart city technologies to keep the pandemic under control. It requires the synchronization and replication at all levels of government at once (Kummitha,  2020 ) as well as the collection and the use of citizens' data (Cabestan,  2020 ). China was the first country that used smart city technologies to track citizens, which was solely aimed at fighting the pandemic (Selinger,  2020 ). The activation of all the technologies available in smart cities allowed the Chinese government to apply effective non‐pharmaceutical measures to stop the spread of COVID‐19 (Kummitha,  2020 ). As reported by The Wall Street Journal, “in South Korea, investigators scan smartphone data to find within 10 min people who might have caught coronavirus from someone they met. Israel has tapped its Shin Bet intelligence unit, usually focused on terrorism, to track down potential coronavirus patients through telecom data”. (The Wall Street Journal,  2020 ). The techno‐driven approach requires citizens to follow the protocols and does not consider the context (Janssen & Kuk,  2016 ). This is one of the reasons why it raises concerns about “erosion of privacy” and freedom (The Wall Street Journal,  2020 ). The literature doubts that the techno‐driven approach adopted in China could be replicated anywhere else in the world (Kupferschmidt & Cohen,  2020 ).

Western democracies adopted a human‐driven approach (Kummitha,  2020 ) (Table  1 ). This approach adopted when the government has restrictions on the collection and use of citizens' data (personal data protection and privacy laws) (Kupferschmidt & Cohen,  2020 ) and therefore has to take the context into account and be very selective when using technologies because of the many sensitive limitations (Kummitha,  2020 ).

Thus, the approach chosen by municipalities depends on a number of factors: the extent to which the regulations allow tracking citizens, collecting personal data, and addressing an individual if specifically required for public safety (Table  2 ).

Techno‐and human‐driven approaches during a pandemic: additional criteria for qualification

As shown in Table  2 , the techno‐driven approach implies data collection and processing at an individual level, while the human‐driven approach deals with aggregated data. The literature points out that the techno‐driven approach is more effective during a pandemic than the human‐driven approach (Kummitha,  2020 ; WHO,  2019 ), because, for example, relying on anonymous data may not be as effective as collecting data from individuals (Stamati et al.,  2015 ). Relying on quantitative analysis, Yang and Chong (Yang & Chong,  2021 ) concluded that the investment in smart cities decreases the number of COVID‐19 cases. That is one of the reasons why “technology” is considered a key factor in predicting and controlling a pandemic such as COVID‐19 (Yang & Chong,  2021 ). However, along with the benefits, the use of AI and Big Data (for the techno‐driven approach) could raise concerns, because increased transparency (making the collected data publicly available) may violate privacy (Janssen & van den Hoven,  2015 ). The literature also specifies other reasons why technology alone could not be an effective solution in the public sector (Kuziemski & Misuraca,  2020 ). While the opportunities are well‐described, the literature points out that “the risks and downsides are given less attention” and “the effects are hard to predict and accountability requires both the curation of data and algorithm” (Janssen & Kuk,  2016 , p. 376). That is why “evidence‐based policies are not a panacea for many reasons” (Nam,  2020 , p. 1). The literature does not conclude which approach (techno‐ or human‐driven) is preferable for smart city authorities during a pandemic and suggests tailoring it to the local needs and resources available (Inn,  2020 ). Considering that each approach has its pros and cons, Kitchin ( 2020 ) suggests that governments should try to respect both civil liberties (not to collect, process, and share personal data—the human‐driven approach) and public health (collect, process, and share personal data—the techno‐driven approach). Therefore, further research of possible trade‐offs is required to make the use of smart city technologies during a pandemic more liberty‐friendly.

2.2. Types of measures municipalities may implement during a pandemic

Based on WHO‐recommended strategies for the prevention and control of communicable diseases (WHO,  2001 ), which is aligned with the epidemic theory (Patten & Arboleda‐Flórez,  2004 ), there are four types of measures that municipalities can implement during a pandemic: (1) active surveillance and issuing warnings; (2) identification of the infected; (3) isolation; and (4) lockdown and quarantine.

Active surveillance and warnings are measures aimed at preventing citizens from being infected. Identification is the measure aimed at identifying possibly infected citizens for further tests and decisions on isolation and quarantine. Once an infected person is identified, they need to be isolated from society. Isolation is a process of instructing a person on the next steps toward quarantine. Lockdown and quarantine are the measures aimed at preventing infected persons from infecting others.

These four measures could be supported by smart city technologies to a different extent. Table  3 demonstrates that the “Active surveillance and issuing warnings” type of measures relies on the largest number of smart city devices: “As advised by WHO and learned from the Chinese context, early surveillance is the most effective strategy available for the prevention of transmission” (Kummitha,  2020 , p. 8).

Available smart city technologies to support the four types of measures during a pandemic

Therefore, the approach municipalities choose is based on:

  • Whether a municipality activates all available smart city components to fight the pandemic, or uses them selectively because of some limitations;
  • Whether a municipality uses smart city components to support the four types of measures or only some of them.

I will elaborate upon the above in Section  4 to explore the approach adopted by the Moscow authorities.

3. RESEARCH APPROACH

This section describes the data selected for the study, the methods, and the analytical approach adopted. This paper is a part of a more extensive research project 1 that focuses on the analysis of changes in public administration driven by digital technologies.

3.1. Case selection

As mentioned, while the techno‐driven approach was predominantly used in China (Kupferschmidt & Cohen,  2020 ), Western countries adopted a human‐driven approach (Kummitha,  2020 ). Russia is geographically located between China and Western countries and has an advanced smart city infrastructure and strict privacy regulations at the same time. Russia was one of the most severely affected countries in the world (as of May 8, 2020, Russia had the third‐largest number of new coronavirus cases identified in the world (Worldometer,  2020 )). Considering the above, the author decided to study Russia's experience in using smart city technologies in fighting the COVID‐19 pandemic. The study analyzes Moscow's experience for three main reasons.

Firstly, being the capital city, Moscow has the most advanced smart city technologies in Russia, and the article aims to explore whether the authorities were able to use the technologies in line with the privacy protection laws in place. In terms of smart city devices, nowadays, Moscow authorities collect data from surveillance cameras (installed on public buses, the subway, and at traffic lights, 193,000 cameras in total) (Forbes,  2020a ; Moscow Department of Information Technology,  2020 ), taxi and car‐sharing services, transport (transport card transactions), GLONASS sensors, and Caesar‐Satellite anti‐theft systems (BBC,  2020b ). The AI system allows for finding a person's location in the city based on a photo. The main source of photos has been doctors, who are required to take pictures of infected (quarantined) citizens when visiting them (BBC,  2020b ). The cameras are also used to identify the elderly who left their home. However, the system has reportedly had some problems identifying individuals wearing face masks (BBC,  2020b ). Since 2015, Moscow authorities have had access to geolocation data from mobile providers and have been collecting voice samples of citizens calling the city hotline (BBC,  2020b ). Free Wi‐Fi points, the mos. ru, and other city services were used as data sources; and since 2017, even if a person turned on the incognito mode in a browser, the system would recognize the person and collect data (BBC,  2020b ). It was declared that Moscow authorities have no direct access to the bank transaction history of the residents; but the authorities have access to the data on citizen's property and related payments (BBC,  2020b ). In some countries, such as South Korea, the government monitored both the phones and credit cards of the infected and quarantined citizens after informing them about these measures (Lee & Lee,  2020 ). Moscow is equipped with similar types of smart city devices as China and South Korea, except for robots and temperature screening systems (sensors) for public places.

Secondly, due to the high population concentration, Moscow had the largest number of the infected in Russia and there was an urgency to use any available means to combat the spread of the disease.

Finally, at first glance, it was hard to determine whether the approach applied by the Moscow authorities was techno‐driven or human‐driven. During the outbreak, the Russian government did not introduce equal and synchronized measures for all Russian cities. However, the Moscow authorities experimented with measures and technologies to keep the pandemic under control. Upon the introduction of certain measures in Moscow, other cities and regions adopted some of the measures as well (Vedomosti,  2020b ).

3.2. Identification of the data

The following data sources were used to answer the research question: scholarly articles, news articles, government reports and decrees, mobile applications of the Moscow and federal authorities, and WHO reports.

WHO reports were found using Google, by filtering the search results of trusted sources. Research articles about the role of technologies in tackling COVID‐19 transmission were found using a search engine in Scopus on May 8, 2020, and updated on May 12, 2021, with search phrases such as “coronavirus OR COVID‐19 AND “smart technology” OR “smart city”” (1688 documents in Scopus, 49,576 results in Science Direct), ““Smart City” AND Moscow AND COVID‐19 OR pandemic” (23 documents in Scopus, 83,269 results in Science Direct). The results were narrowed down by using filters, searching within search results, using recommended and cited articles to find information on the article's scope. To find newspaper articles on the devices that were used in smart cities around the world, I looked through news items that covered the first 5 months of 2020. The search was conducted in Google and Yandex News, and the results were limited to popular and trusted media sources. The search for the use of specific devices of the smart city system was performed using Google, with search phrases such as “Moscow coronavirus temperature sensors public places”. Government reports (both nationwide and Moscow‐specific), and decrees on the measures during the pandemic in Russia were found on the relevant government websites or trusted law databases using Google. Google and the App Store were used to explore websites, infection maps, and applications. In total 59 sources (articles, newspapers, websites) were selected and cited in this paper.

3.3. Analysis

To assess whether a techno‐ or human‐driven approach was applied by Moscow, I considered Kummitha's ( 2020 ) definition of the key differences between the approaches (Table  1 ). I defined three key criteria for determining the approach to using smart city technologies during a pandemic based on the literature (Table  2 ). Also based on the literature, I distributed the types of smart city components of the four types of municipal measures during a pandemic that they could support (Table  3 ). Using the qualification matrices to explore the approach described in Tables  2 and ​ and3, 3 , I would conclude that the approach used by Moscow authorities was technology‐driven, if:

  • It was characterized by the immediate and forceful activation of all 2 available smart city devices for all 3 types of measures.
  • The authorities collected the personal data of citizens (data from surveillance cameras, geolocation, temperature screening systems, QR‐codes, etc.).
  • The authorities openly shared data processing results (the data on the travel history/paths of the infected citizens), based on that the authorities contacted those who got infected or possibly got infected to apply the measures.

I would conclude that the approach applied by Moscow authorities was human‐driven, if:

  • 4 It was characterized by selective and time distributed activation of smart city devices.
  • 5 The authorities did not collect the personal data of citizens, and would rather collect anonymous data.
  • 6 The authorities were not openly sharing data after processing; the authorities predominantly were focused on sharing anonymous aggregated data on the infected citizens and warnings to society.

In order to determine the approach adopted by Moscow authorities, I explored the smart city components that were available in Moscow and the extent to which they had been used to fight the pandemic (Tables  4 and ​ and5). 5 ). Based on the results, I made a conclusion on the type of the approach that was adopted by Moscow authorities and suggested the qualification matrix to define the approach applied by the authorities during the pandemic (Table  6 ).

Smart city technologies of Moscow in action during the pandemic

The approach of the Moscow authorities to use smart city technologies during the outbreak of COVID‐19

Qualification matrix for the approach applied by the authorities during the pandemic

The first infected person in Russia was identified in Moscow (Ministry of DDCMM of the Russian Federation,  2020 ) and Moscow was the city with the largest number of infected citizens in Russia. In this section, I explore the extent to which Moscow used its smart technologies to fight COVID‐19. I describe the key features of the approach used by the Russian government and focus on the comparison between the available and utilized components of Moscow's smart city system.

4.1. COVID‐19 outbreak: Key features of the government's approach

There are two main features of the approach adopted by the Russian government during the COVID‐19 outbreak. First, a State of Emergency was not declared, lockdown or quarantine measures were not introduced in Russia; secondly, each region could introduce its own measures (Vedomosti,  2020b ). When the federal government launched a mobile application designed for self‐identification and for using QR passes, it was not mandatory for use by citizens or regional governments (Ministry of DDCMM of the Russian Federation,  2020 ). As a result, the application was not widely and systematically used. The literature points out that the transfer of measures from the national to municipal level was one of the success factors in fighting the pandemic in other countries (Huynh et al.,  2020 ).

When the situation began to deteriorate, Moscow authorities introduced a self‐isolation regime for all citizens. Moscow authorities developed several scenarios for COVID‐19 transmission in Russia and introduced measures for each possible scenario; all other regions considered the Moscow's experience the best practice (Vedomosti,  2020a ). Moscow Mayor Sobyanin signed a decree on regulation and restrictions, but despite the pandemic, a State of Emergency was not introduced in Moscow, along with the lockdown and quarantine measures. At the same time, quarantine was introduced for elderly people in Moscow — adults 65 or older being most vulnerable (Forbes,  2020b ). The common protocol of the identification of infected people in Moscow was self‐identification. Moscow authorities distributed instructions (the websites of Moscow's Mayor and Moscow's Government,  2020 ) stating that if a citizen has SARS or seasonal allergy symptoms, they should call a doctor for further instructions. Moscow authorities were quite transparent in sharing the information daily through a special website and in the media (The official portal of the Moscow's Mayor and Moscow's Government,  2020 ).

4.2. Smart city components used during the pandemic in Moscow

The Smart City concept is quite popular in Russia. Based on the IQ Index of Russian cities 4 (covering 191 cities), Moscow has the highest urban digitalization index, followed by Kazan and Saint Petersburg (Russian newspaper [Rossiyskaya Gazeta],  2020 ). The Smart City concept of Moscow is similar to other projects all around the world and is aimed at “the development of urban management by increasing the efficiency and transparency of urban management; improving the life quality of the Moscow population by the large‐scale use of information and communication technologies in the social sphere, in the sphere of ensuring the integrated security of the city of Moscow and in other spheres of city administration in Moscow, as well as in the citizens' everyday life” (Ruzina,  2020 ). Moscow authorities have been implementing smart city technologies since 2010 based on the Singapore model (International Telecommunication Union,  2018 ). Moscow was ranked 72nd in the Smart City Index 2019 (The IMD World Competitiveness Center,  2019 ), which ranks 102 cities worldwide and measures how citizens perceive the impact of policies on their daily lives. Singapore holds the 1st place, Zurich and Oslo are in 2nd and 3rd, respectively.

When the pandemic began and the self‐isolation regime was introduced, Moscow authorities announced that they would use the current smart city system to monitor citizens (RBC,  2021 ). The wide use of smart city technologies to fight the pandemic was possible due to changes in federal legislation that were introduced in April 2020 (Markotkin,  2021 ). These changes allowed the Moscow authorities to conduct experiments involving the use of smart city technologies to improve the life of the citizens and the efficiency of governance (Markotkin,  2021 ), however, these new amendments guaranteed the protection of privacy during the experiments.

However, there was no forceful and immediate activation of available smart city technologies for active surveillance, identification, and isolation of infected persons—the approach was time distributed and selective (not all available smart city technologies were used, the measures differ from the ones in other regions of Russia) (Table  4 ).

For instance, high alert mode was put into place in Moscow on March 16 (Moscow Government,  2020 ). A “social monitoring” application to track infected citizens was launched on April 2, 2020 ( Mjerija Moskvy Gotova Primenjat’ QR‐Kody Dlja Kontrolja Rezhima Izoljacii [The Moscow City Government Is Ready to Apply QR Codes to Control the Mode of Isolation],   2020 ), and from April 13, 2020, the QR‐pass system was introduced for those who needed to leave their residence ( Sobjanin Ob'javil o Vvedenii v Moskve Specpropuskov. Chto Jeto Znachit [Sobyanin Announced the Introduction of Special Passes in Moscow. What Does It Mean],   2020 ).

Not all available smart city technologies were activated in Moscow to keep the virus under control. The technologies were mostly focused on the isolation and quarantine of infected citizens and less focused on active surveillance to issue warnings and tracking to identify potentially infected persons to isolate them for further lockdown and quarantine. Evidence of the wide use of the technologies (cameras, for instance) for active surveillance and issuing warnings for COVID‐19‐related purposes in Moscow has not been found. However, these technologies were used in urgent situations: surveillance cameras were used to track potentially infected citizens when the first infected person was identified (Vedomosti,  2020a ). Based on surveillance camera records and geolocation data from mobile phones, all persons that the infected person contacted (including family members) were identified and quarantined. Text messages were used to inform potentially infected citizens of their status and the need to self‐isolate (RIA Novosti,  2020b ). This demonstrates that the data were collected and processed on an individual level and were not anonymized. However, smart city technologies were used in cases of emergency. Moscow authorities were sharing aggregated (anonymized) data on Internet websites only and never published the data on the travel history of the infected citizens. Publicly available maps for Russia (Ministry of Health of the Russian Federation,  2020 ) and Moscow (Mash,  2020 ) show the addresses from where infected persons were taken to a hospital.

The approach introduced by Moscow authorities was issue‐based—smart city technologies were used only based on evidence (when an infected person was identified). The most active measure of the authorities to prevent the disease among citizens was to issue a recommendation to self‐isolate. Self‐identification mode was required for infected citizens (who had symptoms and were suspected of being infected). While WHO recommendations and the Chinese experience show that “governments need to impose lockdowns as early as possible” (Kummitha,  2020 , p. 8), lockdowns and quarantines were not imposed in Moscow.

The business community tried to contribute by offering technologies to the authorities. For instance, AT Consulting VOSTOK developed a solution to identify infected citizens (including the potentially infected) (ComNews,  2020 ). Drone producers suggested using drones for monitoring public places (to avoid crowds) and infrastructure during an emergency shutdown of enterprises for alerting the citizens about antiviral activities, for the expedited delivery of medical tests, samples, vaccines, and drugs, and for applying antiseptics and disinfectants to potentially infected areas (RBC,  2020 ). However, none of those suggestions were accepted by Moscow authorities.

As mentioned in Section  4.1 , no uniform anti‐COVID‐19 measures were implemented in all Russian regions (Vedomosti,  2020b ). Moscow was leading the way in introducing the regulation to get COVID‐19 under control, and the regions were adopting some of them. For instance, in Tatarstan, it was required that citizens receive a text message with a permit to leave their homes. The permit was valid for only 1 hour, and it could be obtained only twice a day. QR‐code identification was launched in the Nizhny Novgorod region (The website of the Moscow Mayor and Moscow Government, 2020). In many other regions of Russia, AI systems were used to quickly collect data on the number of free hospital beds, ambulance crews online, etc (RIA Novosti,  2020a ). These data were used to make forecasts and arrange medical assistance effectively. Using AI allows the reduction in the number of people involved in the collection and analysis of information (from 100 to 200 specialists at a call‐center to 10–15).

In order to assess people's perception of smart technologies applied by the Moscow authorities during the pandemic, a number of surveys were conducted. One survey reported, “The population does not believe in an easy solution, as in H.G. Wells' The War of the Worlds; on the contrary, the more the screws are tightened, the quieter the crisis is perceived” (translated by the author) ( How Do Russians Respond to the Epidemic? Polling Stories [Kak Rossijane Reagirujut Na Jepidemiju? Istorii Oprosov], 2020 ). Citizens experienced issues with some of the technologies, for instance, with the Social Monitoring application aimed at tracking home‐treated infected citizens (issues with downloading the app, registration, issues with uploading a photo, etc.) even when mobile devices were provided to the infected by the mayor's office ( “Social Monitoring”: How Moscow Mocks the Quarantined Sick [“Social'nyj Monitoring”: Kak Moskva Izdevaetsja Nad Zapertymi v Karantin Bol'nymi], 2020 )). As a consequence, people were fined for violating the regulations and were not happy about it (gave the application low scores and negative reviews) ( Mobile Application “Social Monitoring” Reviews, 2021 ).

5. DISCUSSION

5.1. the approach of the moscow authorities: neither techno‐driven nor human‐driven.

The approach of the Moscow authorities could be qualified as human‐driven because it was selective and time distributed, and the authorities shared aggregated (anonymized) data on the infected citizens (Table  5 ).

In other regions of Russia, AI systems were used to quickly collect data to make forecasts and arrange medical assistance effectively (RIA Novosti,  2020a ). However, this practice was not common for all Russian regions. The absence of harmonized country‐wide measures to fight the pandemic (Vedomosti,  2020b ) also proves that the approach of the authorities was rather selective.

However, the approach introduced by Moscow authorities also has the attributes of the technology‐driven approach (Table  5 ), because it allows for collecting the personal data along with the ability to contact infected or potentially infected individuals when required (based on the data processing results). For instance, the Moscow authorities were collecting the personal data from surveillance cameras, mobile phones, and so on, and were using them when they needed to find, track, or inform (by a text message) the infected or potentially infected persons.

Thus, I concluded that Moscow authorities adopted a hybrid model that combines features of the techno‐driven and human‐driven models (Table  5 ). Smart city technologies in Moscow were used selectively and were mostly focused on the isolation and quarantine of the infected and less focused upon active surveillance to issue warnings, identify potentially infected persons and to isolate them for further lockdown and quarantine. These technologies allowed the authorities to collect the personal data and use them when there was a need to find, track, or inform the infected or potentially infected person, but it was shared only in an anonymized form. A State of Emergency was not declared, lockdown and quarantine were not introduced in Russia, and there were no uniform country‐wide measures in place (each of the regions was able to introduce their own measures to fight the pandemic).

5.2. Theoretical and practical implications

“Human history has always been about keeping up with technological advances to make life more comfortable (fire), easier (the wheel), more productive (the printing press, steam power), and more mobile (the car)”. (Done,  2012 , p. 53). Humanity has achieved fantastic results in the development of technology, but during the pandemic, it faced the paradox of the inability to use it at full capacity. This is because along with the development of the technologies, humanity was developing the concept of key civil rights and liberties, which resulted in the implementation of legislation such as the European Convention on Human Rights (Glas,  2013 ) or General Data Protection Regulation (Otto,  2018 ). “But in emergencies like pandemics, privacy must be weighed against other considerations, like saving lives”, said Mila Romanoff, the data and governance lead for United Nations Global Pulse (The New York Times,  2020a ). “I am more and more convinced the greatest battle of our time is against the “religion of privacy”. It literally could get us all killed”, said the former Portuguese Europe Minister Bruno Macaes (BBC,  2020a ).

Authorities around the world were not ready for the COVID‐19 outbreak and when it happened, they used the means that were available in each specific country or municipality. As demonstrated in Section  2.1 , the available literature defines the techno‐driven and human‐driven approaches used by the authorities during the pandemic. The techno‐driven approach is considered more effective in fighting the pandemic (Kummitha,  2020 ; WHO,  2019 ), but it cannot be replicated in countries with strict privacy regulations (Kupferschmidt & Cohen,  2020 ). The active use of technologies during the pandemic was criticized for overreach and the “erosion of privacy” (The Wall Street Journal,  2020 ) because “the increased surveillance and health data disclosures have also drastically eroded people's ability to keep their health status private” (The New York Times,  2020a ). The governments were also expected to find ways to use technologies while complying with data protection laws at the same time, and to reconsider the balance between personal privacy and public safety (The New York Times,  2020a ). The technologies are developing very rapidly and the literature suggests that a trade‐off model is needed to harmonize civil liberties and public health (Kitchin,  2020 ).

In this regard, the article demonstrates the existence of a hybrid model that could represent a new generation of approaches aimed at finding a meaningful balance between privacy and public safety, using the benefits of technology. The literature shows that technology alone could not be an effective solution in the public sector (Kuziemski & Misuraca,  2020 ) and a hybrid model of the use of smart city technologies significantly resonates with this statement. The model relies on the strength of the technology and acknowledges its role in fighting the pandemic, allowing the authorities for temporary tracking of the infected persons for the sake of public safety. However, using such a model might require amending the legislation in time to make it work, which might be quite difficult to do in some countries. This is one of the limitations of the hybrid model. The existence of emergency protocols for the use of smart city technologies could be a solution for such countries. The hybrid model is selective in using technologies (not all available technologies are used at all stages of fighting the pandemic, and the protocols used could differ from one region to the next) and it is cautious with data collection (for many reasons). For instance, Russia “lacks the vast troves of user data possessed by China” (Goode,  2020 , p. 1).

The existence of hybrid models is important for several reasons. First, from a theoretical point of view, the hybrid model adopted in Moscow demonstrates the existence of alternative models other than the two main model types identified in the literature (Table  5 ). Further research could focus on developing a classification of hybrid models and analyzing the factors that shape them in different countries. Based on the demand for the trade‐off between civil rights and public safety, hybrid models need to be explored further. At the same time, the findings of the article contributes to the studies of the public administration model in Russia.

Secondly, from a practical point of view, the hybrid model will allow governments to have a third option and use smart city technologies effectively while meeting the requirements of local regulations on privacy. That means that authorities do not need to choose one of the two main approaches but could consider a hybrid model (Table  5 ). There are clear practical intentions from the countries that were not satisfied with the human‐driven models to find such a hybrid approach. “These are strange times. Germany, perhaps the most privacy‐conscious nation on Earth, is considering a mobile phone app that would trace the contacts of anyone infected with COVID‐19” (BBC,  2020a ). During the emergency, former New York Governor Cuomo “got the unlimited authority to rule by executive order during state crises like pandemics and hurricanes” (The New York Times,  2020a ). Another example of such an approach comes from Israel where the government was allowed to use mobile provider data of infected people within 30 days: “We have to maintain the balance between the rights of the individual and needs of general society, and we are doing that”, said former Israeli Prime Minister Benyamin Netanyahu at the time (The New York Times,  2020b ). Nevertheless, when looking for a balance the authorities would need to decide on how much data is enough, and further research and practical experiments should help in answering this question.

Thirdly, the existence of a hybrid model is important from a political point of view, because using a techno‐driven approach that violates freedoms could negatively affect the political reputation of governors even if it is successful from a healthcare point of view. The use of a flexible and meaningful approach could bring many benefits for the politicians who could, for instance, arrange public participation in choosing the extent of using the technologies in emergency situations.

The results described in Table  5 could be presented as a Qualification matrix for the approach applied by the authorities during a pandemic (Table  6 ).

The Qualification matrix could be useful for the theoretical analysis of models applied in other countries (regions) and classifying them. The matrix is also useful for a self‐audit and policy development within a region and a country. “The pandemic may, finally, humanize the use of high‐tech in cities. The smart city models of a generation ago were all about regulation and control—the state online. What's emerging in this pandemic are good programs and protocols which create community”, stated Richard Sennett, Professor of Urban Studies at MIT (Digital Leaders,  2020 ). Therefore, the exploration of new hybrid models of a government approach to pandemics, including the limitations and new trade‐offs, could be popular for some time, because many questions remain to be answered both in theory and in practice.

6. CONCLUSION

Many countries implemented smart city technologies, but during the COVID‐19 outbreak in 2020, some countries were able to use its full capacity (the techno‐driven approach), while others could do this only selectively (the human‐driven approach) because of strict privacy protection legislation. The literature suggests that along with these two approaches, an alternative model would add value. The Russian Federation has advanced smart city infrastructure and strict legislation on privacy protection simultaneously. This paper explored the approach of the Moscow authorities to using smart city technologies during the COVID‐19 outbreak in 2020 and concluded that the authorities used a hybrid approach which demonstrates the features of both human‐driven and techno‐driven approaches. The author developed a Qualification matrix to define the approach used by authorities during the pandemic.

This research was based on publicly available sources of information and did not rely on any internal data of the authorities that could potentially influence the findings. For instance, only publicly available data were used when assessing whether smart city devices were utilized for the specific government measure. That may mean that other devices can also be used, but no information about such devices was available via the open sources. This is the main limitation of this research. As the next step, the results of the research could be validated through interviews with the managers of the smart city system of Moscow.

CONFLICT OF INTEREST

This article is a part of a research project implemented as part of the Basic Research Program at HSE University. The research was undertaken independently by the author.

ACKNOWLEDGEMENTS

I would like to thank the editor and reviewers for their encouragement and guidance throughout the review process. The paper has significantly benefited from their comments. I also thank Rama Krishna Reddy Kummitha, Michael Revyakin, Keld Pedersen, Joel Cumberland and David Connolly for their discussions on the drafts of this paper.

Revyakin, S. A. (2022). Personal privacy VS. public safety: A hybrid model of the use of smart city solutions in fighting the COVID‐19 pandemic in Moscow . Public Administration and Development , 42 ( 5 ), 281–292. 10.1002/pad.1997 [ CrossRef ] [ Google Scholar ]

1 Under the Basic Research Program at the HSE University.

2 Where applicable.

3 Where applicable.

4 Index of digitalization of the urban economy.

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  • Published: 19 February 2024

Genomic data in the All of Us Research Program

The all of us research program genomics investigators.

Nature ( 2024 ) Cite this article

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  • Genetic variation
  • Genome-wide association studies

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics 1 , 2 , 3 , 4 . The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health 5 , 6 . Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Comprehensively identifying genetic variation and cataloguing its contribution to health and disease, in conjunction with environmental and lifestyle factors, is a central goal of human health research 1 , 2 . A key limitation in efforts to build this catalogue has been the historic under-representation of large subsets of individuals in biomedical research including individuals from diverse ancestries, individuals with disabilities and individuals from disadvantaged backgrounds 3 , 4 . The All of Us Research Program (All of Us) aims to address this gap by enrolling and collecting comprehensive health data on at least one million individuals who reflect the diversity across the USA 5 , 6 . An essential component of All of Us is the generation of whole-genome sequence (WGS) and genotyping data on one million participants. All of Us is committed to making this dataset broadly useful—not only by democratizing access to this dataset across the scientific community but also to return value to the participants themselves by returning individual DNA results, such as genetic ancestry, hereditary disease risk and pharmacogenetics according to clinical standards, to those who wish to receive these research results.

Here we describe the release of WGS data from 245,388 All of Us participants and demonstrate the impact of this high-quality data in genetic and health studies. We carried out a series of data harmonization and quality control (QC) procedures and conducted analyses characterizing the properties of the dataset including genetic ancestry and relatedness. We validated the data by replicating well-established genotype–phenotype associations including low-density lipoprotein cholesterol (LDL-C) and 117 additional diseases. These data are available through the All of Us Researcher Workbench, a cloud platform that embodies and enables programme priorities, facilitating equitable data and compute access while ensuring responsible conduct of research and protecting participant privacy through a passport data access model.

The All of Us Research Program

To accelerate health research, All of Us is committed to curating and releasing research data early and often 6 . Less than five years after national enrolment began in 2018, this fifth data release includes data from more than 413,000 All of Us participants. Summary data are made available through a public Data Browser, and individual-level participant data are made available to researchers through the Researcher Workbench (Fig. 1a and Data availability).

figure 1

a , The All of Us Research Hub contains a publicly accessible Data Browser for exploration of summary phenotypic and genomic data. The Researcher Workbench is a secure cloud-based environment of participant-level data in a Controlled Tier that is widely accessible to researchers. b , All of Us participants have rich phenotype data from a combination of physical measurements, survey responses, EHRs, wearables and genomic data. Dots indicate the presence of the specific data type for the given number of participants. c , Overall summary of participants under-represented in biomedical research (UBR) with data available in the Controlled Tier. The All of Us logo in a is reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Participant data include a rich combination of phenotypic and genomic data (Fig. 1b ). Participants are asked to complete consent for research use of data, sharing of electronic health records (EHRs), donation of biospecimens (blood or saliva, and urine), in-person provision of physical measurements (height, weight and blood pressure) and surveys initially covering demographics, lifestyle and overall health 7 . Participants are also consented for recontact. EHR data, harmonized using the Observational Medical Outcomes Partnership Common Data Model 8 ( Methods ), are available for more than 287,000 participants (69.42%) from more than 50 health care provider organizations. The EHR dataset is longitudinal, with a quarter of participants having 10 years of EHR data (Extended Data Fig. 1 ). Data include 245,388 WGSs and genome-wide genotyping on 312,925 participants. Sequenced and genotyped individuals in this data release were not prioritized on the basis of any clinical or phenotypic feature. Notably, 99% of participants with WGS data also have survey data and physical measurements, and 84% also have EHR data. In this data release, 77% of individuals with genomic data identify with groups historically under-represented in biomedical research, including 46% who self-identify with a racial or ethnic minority group (Fig. 1c , Supplementary Table 1 and Supplementary Note ).

Scaling the All of Us infrastructure

The genomic dataset generated from All of Us participants is a resource for research and discovery and serves as the basis for return of individual health-related DNA results to participants. Consequently, the US Food and Drug Administration determined that All of Us met the criteria for a significant risk device study. As such, the entire All of Us genomics effort from sample acquisition to sequencing meets clinical laboratory standards 9 .

All of Us participants were recruited through a national network of partners, starting in 2018, as previously described 5 . Participants may enrol through All of Us - funded health care provider organizations or direct volunteer pathways and all biospecimens, including blood and saliva, are sent to the central All of Us Biobank for processing and storage. Genomics data for this release were generated from blood-derived DNA. The programme began return of actionable genomic results in December 2022. As of April 2023, approximately 51,000 individuals were sent notifications asking whether they wanted to view their results, and approximately half have accepted. Return continues on an ongoing basis.

The All of Us Data and Research Center maintains all participant information and biospecimen ID linkage to ensure that participant confidentiality and coded identifiers (participant and aliquot level) are used to track each sample through the All of Us genomics workflow. This workflow facilitates weekly automated aliquot and plating requests to the Biobank, supplies relevant metadata for the sample shipments to the Genome Centers, and contains a feedback loop to inform action on samples that fail QC at any stage. Further, the consent status of each participant is checked before sample shipment to confirm that they are still active. Although all participants with genomic data are consented for the same general research use category, the programme accommodates different preferences for the return of genomic data to participants and only data for those individuals who have consented for return of individual health-related DNA results are distributed to the All of Us Clinical Validation Labs for further evaluation and health-related clinical reporting. All participants in All of Us that choose to get health-related DNA results have the option to schedule a genetic counselling appointment to discuss their results. Individuals with positive findings who choose to obtain results are required to schedule an appointment with a genetic counsellor to receive those findings.

Genome sequencing

To satisfy the requirements for clinical accuracy, precision and consistency across DNA sample extraction and sequencing, the All of Us Genome Centers and Biobank harmonized laboratory protocols, established standard QC methodologies and metrics, and conducted a series of validation experiments using previously characterized clinical samples and commercially available reference standards 9 . Briefly, PCR-free barcoded WGS libraries were constructed with the Illumina Kapa HyperPrep kit. Libraries were pooled and sequenced on the Illumina NovaSeq 6000 instrument. After demultiplexing, initial QC analysis is performed with the Illumina DRAGEN pipeline (Supplementary Table 2 ) leveraging lane, library, flow cell, barcode and sample level metrics as well as assessing contamination, mapping quality and concordance to genotyping array data independently processed from a different aliquot of DNA. The Genome Centers use these metrics to determine whether each sample meets programme specifications and then submits sequencing data to the Data and Research Center for further QC, joint calling and distribution to the research community ( Methods ).

This effort to harmonize sequencing methods, multi-level QC and use of identical data processing protocols mitigated the variability in sequencing location and protocols that often leads to batch effects in large genomic datasets 9 . As a result, the data are not only of clinical-grade quality, but also consistent in coverage (≥30× mean) and uniformity across Genome Centers (Supplementary Figs. 1 – 5 ).

Joint calling and variant discovery

We carried out joint calling across the entire All of Us WGS dataset (Extended Data Fig. 2 ). Joint calling leverages information across samples to prune artefact variants, which increases sensitivity, and enables flagging samples with potential issues that were missed during single-sample QC 10 (Supplementary Table 3 ). Scaling conventional approaches to whole-genome joint calling beyond 50,000 individuals is a notable computational challenge 11 , 12 . To address this, we developed a new cloud variant storage solution, the Genomic Variant Store (GVS), which is based on a schema designed for querying and rendering variants in which the variants are stored in GVS and rendered to an analysable variant file, as opposed to the variant file being the primary storage mechanism (Code availability). We carried out QC on the joint call set on the basis of the approach developed for gnomAD 3.1 (ref.  13 ). This included flagging samples with outlying values in eight metrics (Supplementary Table 4 , Supplementary Fig. 2 and Methods ).

To calculate the sensitivity and precision of the joint call dataset, we included four well-characterized samples. We sequenced the National Institute of Standards and Technology reference materials (DNA samples) from the Genome in a Bottle consortium 13 and carried out variant calling as described above. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations 14 . The overall sensitivity for single-nucleotide variants was over 98.7% and precision was more than 99.9%. For short insertions or deletions, the sensitivity was over 97% and precision was more than 99.6% (Supplementary Table 5 and Methods ).

The joint call set included more than 1 billion genetic variants. We annotated the joint call dataset on the basis of functional annotation (for example, gene symbol and protein change) using Illumina Nirvana 15 . We defined coding variants as those inducing an amino acid change on a canonical ENSEMBL transcript and found 272,051,104 non-coding and 3,913,722 coding variants that have not been described previously in dbSNP 16 v153 (Extended Data Table 1 ). A total of 3,912,832 (99.98%) of the coding variants are rare (allelic frequency < 0.01) and the remaining 883 (0.02%) are common (allelic frequency > 0.01). Of the coding variants, 454 (0.01%) are common in one or more of the non-European computed ancestries in All of Us, rare among participants of European ancestry, and have an allelic number greater than 1,000 (Extended Data Table 2 and Extended Data Fig. 3 ). The distributions of pathogenic, or likely pathogenic, ClinVar variant counts per participant, stratified by computed ancestry, filtered to only those variants that are found in individuals with an allele count of <40 are shown in Extended Data Fig. 4 . The potential medical implications of these known and new variants with respect to variant pathogenicity by ancestry are highlighted in a companion paper 17 . In particular, we find that the European ancestry subset has the highest rate of pathogenic variation (2.1%), which was twice the rate of pathogenic variation in individuals of East Asian ancestry 17 .The lower frequency of variants in East Asian individuals may be partially explained by the fact the sample size in that group is small and there may be knowledge bias in the variant databases that is reducing the number of findings in some of the less-studied ancestry groups.

Genetic ancestry and relatedness

Genetic ancestry inference confirmed that 51.1% of the All of Us WGS dataset is derived from individuals of non-European ancestry. Briefly, the ancestry categories are based on the same labels used in gnomAD 18 . We trained a classifier on a 16-dimensional principal component analysis (PCA) space of a diverse reference based on 3,202 samples and 151,159 autosomal single-nucleotide polymorphisms. We projected the All of Us samples into the PCA space of the training data, based on the same single-nucleotide polymorphisms from the WGS data, and generated categorical ancestry predictions from the trained classifier ( Methods ). Continuous genetic ancestry fractions for All of Us samples were inferred using the same PCA data, and participants’ patterns of ancestry and admixture were compared to their self-identified race and ethnicity (Fig. 2 and Methods ). Continuous ancestry inference carried out using genome-wide genotypes yields highly concordant estimates.

figure 2

a , b , Uniform manifold approximation and projection (UMAP) representations of All of Us WGS PCA data with self-described race ( a ) and ethnicity ( b ) labels. c , Proportion of genetic ancestry per individual in six distinct and coherent ancestry groups defined by Human Genome Diversity Project and 1000 Genomes samples.

Kinship estimation confirmed that All of Us WGS data consist largely of unrelated individuals with about 85% (215,107) having no first- or second-degree relatives in the dataset (Supplementary Fig. 6 ). As many genomic analyses leverage unrelated individuals, we identified the smallest set of samples that are required to be removed from the remaining individuals that had first- or second-degree relatives and retained one individual from each kindred. This procedure yielded a maximal independent set of 231,442 individuals (about 94%) with genome sequence data in the current release ( Methods ).

Genetic determinants of LDL-C

As a measure of data quality and utility, we carried out a single-variant genome-wide association study (GWAS) for LDL-C, a trait with well-established genomic architecture ( Methods ). Of the 245,388 WGS participants, 91,749 had one or more LDL-C measurements. The All of Us LDL-C GWAS identified 20 well-established genome-wide significant loci, with minimal genomic inflation (Fig. 3 , Extended Data Table 3 and Supplementary Fig. 7 ). We compared the results to those of a recent multi-ethnic LDL-C GWAS in the National Heart, Lung, and Blood Institute (NHLBI) TOPMed study that included 66,329 ancestrally diverse (56% non-European ancestry) individuals 19 . We found a strong correlation between the effect estimates for NHLBI TOPMed genome-wide significant loci and those of All of Us ( R 2  = 0.98, P  < 1.61 × 10 −45 ; Fig. 3 , inset). Notably, the per-locus effect sizes observed in All of Us are decreased compared to those in TOPMed, which is in part due to differences in the underlying statistical model, differences in the ancestral composition of these datasets and differences in laboratory value ascertainment between EHR-derived data and epidemiology studies. A companion manuscript extended this work to identify common and rare genetic associations for three diseases (atrial fibrillation, coronary artery disease and type 2 diabetes) and two quantitative traits (height and LDL-C) in the All of Us dataset and identified very high concordance with previous efforts across all of these diseases and traits 20 .

figure 3

Manhattan plot demonstrating robust replication of 20 well-established LDL-C genetic loci among 91,749 individuals with 1 or more LDL-C measurements. The red horizontal line denotes the genome wide significance threshold of P = 5 × 10 –8 . Inset, effect estimate ( β ) comparison between NHLBI TOPMed LDL-C GWAS ( x  axis) and All of Us LDL-C GWAS ( y  axis) for the subset of 194 independent variants clumped (window 250 kb, r2 0.5) that reached genome-wide significance in NHLBI TOPMed.

Genotype-by-phenotype associations

As another measure of data quality and utility, we tested replication rates of previously reported phenotype–genotype associations in the five predicted genetic ancestry populations present in the Phenotype/Genotype Reference Map (PGRM): AFR, African ancestry; AMR, Latino/admixed American ancestry; EAS, East Asian ancestry; EUR, European ancestry; SAS, South Asian ancestry. The PGRM contains published associations in the GWAS catalogue in these ancestry populations that map to International Classification of Diseases-based phenotype codes 21 . This replication study specifically looked across 4,947 variants, calculating replication rates for powered associations in each ancestry population. The overall replication rates for associations powered at 80% were: 72.0% (18/25) in AFR, 100% (13/13) in AMR, 46.6% (7/15) in EAS, 74.9% (1,064/1,421) in EUR, and 100% (1/1) in SAS. With the exception of the EAS ancestry results, these powered replication rates are comparable to those of the published PGRM analysis where the replication rates of several single-site EHR-linked biobanks ranges from 76% to 85%. These results demonstrate the utility of the data and also highlight opportunities for further work understanding the specifics of the All of Us population and the potential contribution of gene–environment interactions to genotype–phenotype mapping and motivates the development of methods for multi-site EHR phenotype data extraction, harmonization and genetic association studies.

More broadly, the All of Us resource highlights the opportunities to identify genotype–phenotype associations that differ across diverse populations 22 . For example, the Duffy blood group locus ( ACKR1 ) is more prevalent in individuals of AFR ancestry and individuals of AMR ancestry than in individuals of EUR ancestry. Although the phenome-wide association study of this locus highlights the well-established association of the Duffy blood group with lower white blood cell counts both in individuals of AFR and AMR ancestry 23 , 24 , it also revealed genetic-ancestry-specific phenotype patterns, with minimal phenotypic associations in individuals of EAS ancestry and individuals of EUR ancestry (Fig. 4 and Extended Data Table 4 ). Conversely, rs9273363 in the HLA-DQB1 locus is associated with increased risk of type 1 diabetes 25 , 26 and diabetic complications across ancestries, but only associates with increased risk of coeliac disease in individuals of EUR ancestry (Extended Data Fig. 5 ). Similarly, the TCF7L2 locus 27 strongly associates with increased risk of type 2 diabetes and associated complications across several ancestries (Extended Data Fig. 6 ). Association testing results are available in Supplementary Dataset 1 .

figure 4

Results of genetic-ancestry-stratified phenome-wide association analysis among unrelated individuals highlighting ancestry-specific disease associations across the four most common genetic ancestries of participant. Bonferroni-adjusted phenome-wide significance threshold (<2.88 × 10 −5 ) is plotted as a red horizontal line. AFR ( n  = 34,037, minor allele fraction (MAF) 0.82); AMR ( n  = 28,901, MAF 0.10); EAS ( n  = 32,55, MAF 0.003); EUR ( n  = 101,613, MAF 0.007).

The cloud-based Researcher Workbench

All of Us genomic data are available in a secure, access-controlled cloud-based analysis environment: the All of Us Researcher Workbench. Unlike traditional data access models that require per-project approval, access in the Researcher Workbench is governed by a data passport model based on a researcher’s authenticated identity, institutional affiliation, and completion of self-service training and compliance attestation 28 . After gaining access, a researcher may create a new workspace at any time to conduct a study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is regularly audited and made accessible publicly on the All of Us Research Projects Directory. This streamlined access model is guided by the principles that: participants are research partners and maintaining their privacy and data security is paramount; their data should be made as accessible as possible for authorized researchers; and we should continually seek to remove unnecessary barriers to accessing and using All of Us data.

For researchers at institutions with an existing institutional data use agreement, access can be gained as soon as they complete the required verification and compliance steps. As of August 2023, 556 institutions have agreements in place, allowing more than 5,000 approved researchers to actively work on more than 4,400 projects. The median time for a researcher from initial registration to completion of these requirements is 28.6 h (10th percentile: 48 min, 90th percentile: 14.9 days), a fraction of the weeks to months it can take to assemble a project-specific application and have it reviewed by an access board with conventional access models.

Given that the size of the project’s phenotypic and genomic dataset is expected to reach 4.75 PB in 2023, the use of a central data store and cloud analysis tools will save funders an estimated US$16.5 million per year when compared to the typical approach of allowing researchers to download genomic data. Storing one copy per institution of this data at 556 registered institutions would cost about US$1.16 billion per year. By contrast, storing a central cloud copy costs about US$1.14 million per year, a 99.9% saving. Importantly, cloud infrastructure also democratizes data access particularly for researchers who do not have high-performance local compute resources.

Here we present the All of Us Research Program’s approach to generating diverse clinical-grade genomic data at an unprecedented scale. We present the data release of about 245,000 genome sequences as part of a scalable framework that will grow to include genetic information and health data for one million or more people living across the USA. Our observations permit several conclusions.

First, the All of Us programme is making a notable contribution to improving the study of human biology through purposeful inclusion of under-represented individuals at scale 29 , 30 . Of the participants with genomic data in All of Us, 45.92% self-identified as a non-European race or ethnicity. This diversity enabled identification of more than 275 million new genetic variants across the dataset not previously captured by other large-scale genome aggregation efforts with diverse participants that have submitted variation to dbSNP v153, such as NHLBI TOPMed 31 freeze 8 (Extended Data Table 1 ). In contrast to gnomAD, All of Us permits individual-level genotype access with detailed phenotype data for all participants. Furthermore, unlike many genomics resources, All of Us is uniformly consented for general research use and enables researchers to go from initial account creation to individual-level data access in as little as a few hours. The All of Us cohort is significantly more diverse than those of other large contemporary research studies generating WGS data 32 , 33 . This enables a more equitable future for precision medicine (for example, through constructing polygenic risk scores that are appropriately calibrated to diverse populations 34 , 35 as the eMERGE programme has done leveraging All of Us data 36 , 37 ). Developing new tools and regulatory frameworks to enable analyses across multiple biobanks in the cloud to harness the unique strengths of each is an active area of investigation addressed in a companion paper to this work 38 .

Second, the All of Us Researcher Workbench embodies the programme’s design philosophy of open science, reproducible research, equitable access and transparency to researchers and to research participants 26 . Importantly, for research studies, no group of data users should have privileged access to All of Us resources based on anything other than data protection criteria. Although the All of Us Researcher Workbench initially targeted onboarding US academic, health care and non-profit organizations, it has recently expanded to international researchers. We anticipate further genomic and phenotypic data releases at regular intervals with data available to all researcher communities. We also anticipate additional derived data and functionality to be made available, such as reference data, structural variants and a service for array imputation using the All of Us genomic data.

Third, All of Us enables studying human biology at an unprecedented scale. The programmatic goal of sequencing one million or more genomes has required harnessing the output of multiple sequencing centres. Previous work has focused on achieving functional equivalence in data processing and joint calling pipelines 39 . To achieve clinical-grade data equivalence, All of Us required protocol equivalence at both sequencing production level and data processing across the sequencing centres. Furthermore, previous work has demonstrated the value of joint calling at scale 10 , 18 . The new GVS framework developed by the All of Us programme enables joint calling at extreme scales (Code availability). Finally, the provision of data access through cloud-native tools enables scalable and secure access and analysis to researchers while simultaneously enabling the trust of research participants and transparency underlying the All of Us data passport access model.

The clinical-grade sequencing carried out by All of Us enables not only research, but also the return of value to participants through clinically relevant genetic results and health-related traits to those who opt-in to receiving this information. In the years ahead, we anticipate that this partnership with All of Us participants will enable researchers to move beyond large-scale genomic discovery to understanding the consequences of implementing genomic medicine at scale.

The All of Us cohort

All of Us aims to engage a longitudinal cohort of one million or more US participants, with a focus on including populations that have historically been under-represented in biomedical research. Details of the All of Us cohort have been described previously 5 . Briefly, the primary objective is to build a robust research resource that can facilitate the exploration of biological, clinical, social and environmental determinants of health and disease. The programme will collect and curate health-related data and biospecimens, and these data and biospecimens will be made broadly available for research uses. Health data are obtained through the electronic medical record and through participant surveys. Survey templates can be found on our public website: https://www.researchallofus.org/data-tools/survey-explorer/ . Adults 18 years and older who have the capacity to consent and reside in the USA or a US territory at present are eligible. Informed consent for all participants is conducted in person or through an eConsent platform that includes primary consent, HIPAA Authorization for Research use of EHRs and other external health data, and Consent for Return of Genomic Results. The protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program. The All of Us IRB follows the regulations and guidance of the NIH Office for Human Research Protections for all studies, ensuring that the rights and welfare of research participants are overseen and protected uniformly.

Data accessibility through a ‘data passport’

Authorization for access to participant-level data in All of Us is based on a ‘data passport’ model, through which authorized researchers do not need IRB review for each research project. The data passport is required for gaining data access to the Researcher Workbench and for creating workspaces to carry out research projects using All of Us data. At present, data passports are authorized through a six-step process that includes affiliation with an institution that has signed a Data Use and Registration Agreement, account creation, identity verification, completion of ethics training, and attestation to a data user code of conduct. Results reported follow the All of Us Data and Statistics Dissemination Policy disallowing disclosure of group counts under 20 to protect participant privacy without seeking prior approval 40 .

At present, All of Us gathers EHR data from about 50 health care organizations that are funded to recruit and enrol participants as well as transfer EHR data for those participants who have consented to provide them. Data stewards at each provider organization harmonize their local data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, and then submit it to the All of Us Data and Research Center (DRC) so that it can be linked with other participant data and further curated for research use. OMOP is a common data model standardizing health information from disparate EHRs to common vocabularies and organized into tables according to data domains. EHR data are updated from the recruitment sites and sent to the DRC quarterly. Updated data releases to the research community occur approximately once a year. Supplementary Table 6 outlines the OMOP concepts collected by the DRC quarterly from the recruitment sites.

Biospecimen collection and processing

Participants who consented to participate in All of Us donated fresh whole blood (4 ml EDTA and 10 ml EDTA) as a primary source of DNA. The All of Us Biobank managed by the Mayo Clinic extracted DNA from 4 ml EDTA whole blood, and DNA was stored at −80 °C at an average concentration of 150 ng µl −1 . The buffy coat isolated from 10 ml EDTA whole blood has been used for extracting DNA in the case of initial extraction failure or absence of 4 ml EDTA whole blood. The Biobank plated 2.4 µg DNA with a concentration of 60 ng µl −1 in duplicate for array and WGS samples. The samples are distributed to All of Us Genome Centers weekly, and a negative (empty well) control and National Institute of Standards and Technology controls are incorporated every two months for QC purposes.

Genome Center sample receipt, accession and QC

On receipt of DNA sample shipments, the All of Us Genome Centers carry out an inspection of the packaging and sample containers to ensure that sample integrity has not been compromised during transport and to verify that the sample containers correspond to the shipping manifest. QC of the submitted samples also includes DNA quantification, using routine procedures to confirm volume and concentration (Supplementary Table 7 ). Any issues or discrepancies are recorded, and affected samples are put on hold until resolved. Samples that meet quality thresholds are accessioned in the Laboratory Information Management System, and sample aliquots are prepared for library construction processing (for example, normalized with respect to concentration and volume).

WGS library construction, sequencing and primary data QC

The DNA sample is first sheared using a Covaris sonicator and is then size-selected using AMPure XP beads to restrict the range of library insert sizes. Using the PCR Free Kapa HyperPrep library construction kit, enzymatic steps are completed to repair the jagged ends of DNA fragments, add proper A-base segments, and ligate indexed adapter barcode sequences onto samples. Excess adaptors are removed using AMPure XP beads for a final clean-up. Libraries are quantified using quantitative PCR with the Illumina Kapa DNA Quantification Kit and then normalized and pooled for sequencing (Supplementary Table 7 ).

Pooled libraries are loaded on the Illumina NovaSeq 6000 instrument. The data from the initial sequencing run are used to QC individual libraries and to remove non-conforming samples from the pipeline. The data are also used to calibrate the pooling volume of each individual library and re-pool the libraries for additional NovaSeq sequencing to reach an average coverage of 30×.

After demultiplexing, WGS analysis occurs on the Illumina DRAGEN platform. The DRAGEN pipeline consists of highly optimized algorithms for mapping, aligning, sorting, duplicate marking and haplotype variant calling and makes use of platform features such as compression and BCL conversion. Alignment uses the GRCh38dh reference genome. QC data are collected at every stage of the analysis protocol, providing high-resolution metrics required to ensure data consistency for large-scale multiplexing. The DRAGEN pipeline produces a large number of metrics that cover lane, library, flow cell, barcode and sample-level metrics for all runs as well as assessing contamination and mapping quality. The All of Us Genome Centers use these metrics to determine pass or fail for each sample before submitting the CRAM files to the All of Us DRC. For mapping and variant calling, all Genome Centers have harmonized on a set of DRAGEN parameters, which ensures consistency in processing (Supplementary Table 2 ).

Every step through the WGS procedure is rigorously controlled by predefined QC measures. Various control mechanisms and acceptance criteria were established during WGS assay validation. Specific metrics for reviewing and releasing genome data are: mean coverage (threshold of ≥30×), genome coverage (threshold of ≥90% at 20×), coverage of hereditary disease risk genes (threshold of ≥95% at 20×), aligned Q30 bases (threshold of ≥8 × 10 10 ), contamination (threshold of ≤1%) and concordance to independently processed array data.

Array genotyping

Samples are processed for genotyping at three All of Us Genome Centers (Broad, Johns Hopkins University and University of Washington). DNA samples are received from the Biobank and the process is facilitated by the All of Us genomics workflow described above. All three centres used an identical array product, scanners, resource files and genotype calling software for array processing to reduce batch effects. Each centre has its own Laboratory Information Management System that manages workflow control, sample and reagent tracking, and centre-specific liquid handling robotics.

Samples are processed using the Illumina Global Diversity Array (GDA) with Illumina Infinium LCG chemistry using the automated protocol and scanned on Illumina iSCANs with Automated Array Loaders. Illumina IAAP software converts raw data (IDAT files; 2 per sample) into a single GTC file per sample using the BPM file (defines strand, probe sequences and illumicode address) and the EGT file (defines the relationship between intensities and genotype calls). Files used for this data release are: GDA-8v1-0_A5.bpm, GDA-8v1-0_A1_ClusterFile.egt, gentrain v3, reference hg19 and gencall cutoff 0.15. The GDA array assays a total of 1,914,935 variant positions including 1,790,654 single-nucleotide variants, 44,172 indels, 9,935 intensity-only probes for CNV calling, and 70,174 duplicates (same position, different probes). Picard GtcToVcf is used to convert the GTC files to VCF format. Resulting VCF and IDAT files are submitted to the DRC for ingestion and further processing. The VCF file contains assay name, chromosome, position, genotype calls, quality score, raw and normalized intensities, B allele frequency and log R ratio values. Each genome centre is running the GDA array under Clinical Laboratory Improvement Amendments-compliant protocols. The GTC files are parsed and metrics are uploaded to in-house Laboratory Information Management System systems for QC review.

At batch level (each set of 96-well plates run together in the laboratory at one time), each genome centre includes positive control samples that are required to have >98% call rate and >99% concordance to existing data to approve release of the batch of data. At the sample level, the call rate and sex are the key QC determinants 41 . Contamination is also measured using BAFRegress 42 and reported out as metadata. Any sample with a call rate below 98% is repeated one time in the laboratory. Genotyped sex is determined by plotting normalized x versus normalized y intensity values for a batch of samples. Any sample discordant with ‘sex at birth’ reported by the All of Us participant is flagged for further detailed review and repeated one time in the laboratory. If several sex-discordant samples are clustered on an array or on a 96-well plate, the entire array or plate will have data production repeated. Samples identified with sex chromosome aneuploidies are also reported back as metadata (XXX, XXY, XYY and so on). A final processing status of ‘pass’, ‘fail’ or ‘abandon’ is determined before release of data to the All of Us DRC. An array sample will pass if the call rate is >98% and the genotyped sex and sex at birth are concordant (or the sex at birth is not applicable). An array sample will fail if the genotyped sex and the sex at birth are discordant. An array sample will have the status of abandon if the call rate is <98% after at least two attempts at the genome centre.

Data from the arrays are used for participant return of genetic ancestry and non-health-related traits for those who consent, and they are also used to facilitate additional QC of the matched WGS data. Contamination is assessed in the array data to determine whether DNA re-extraction is required before WGS. Re-extraction is prompted by level of contamination combined with consent status for return of results. The arrays are also used to confirm sample identity between the WGS data and the matched array data by assessing concordance at 100 unique sites. To establish concordance, a fingerprint file of these 100 sites is provided to the Genome Centers to assess concordance with the same sites in the WGS data before CRAM submission.

Genomic data curation

As seen in Extended Data Fig. 2 , we generate a joint call set for all WGS samples and make these data available in their entirety and by sample subsets to researchers. A breakdown of the frequencies, stratified by computed ancestries for which we had more than 10,000 participants can be found in Extended Data Fig. 3 . The joint call set process allows us to leverage information across samples to improve QC and increase accuracy.

Single-sample QC

If a sample fails single-sample QC, it is excluded from the release and is not reported in this document. These tests detect sample swaps, cross-individual contamination and sample preparation errors. In some cases, we carry out these tests twice (at both the Genome Center and the DRC), for two reasons: to confirm internal consistency between sites; and to mark samples as passing (or failing) QC on the basis of the research pipeline criteria. The single-sample QC process accepts a higher contamination rate than the clinical pipeline (0.03 for the research pipeline versus 0.01 for the clinical pipeline), but otherwise uses identical thresholds. The list of specific QC processes, passing criteria, error modes addressed and an overview of the results can be found in Supplementary Table 3 .

Joint call set QC

During joint calling, we carry out additional QC steps using information that is available across samples including hard thresholds, population outliers, allele-specific filters, and sensitivity and precision evaluation. Supplementary Table 4 summarizes both the steps that we took and the results obtained for the WGS data. More detailed information about the methods and specific parameters can be found in the All of Us Genomic Research Data Quality Report 36 .

Batch effect analysis

We analysed cross-sequencing centre batch effects in the joint call set. To quantify the batch effect, we calculated Cohen’s d (ref.  43 ) for four metrics (insertion/deletion ratio, single-nucleotide polymorphism count, indel count and single-nucleotide polymorphism transition/transversion ratio) across the three genome sequencing centres (Baylor College of Medicine, Broad Institute and University of Washington), stratified by computed ancestry and seven regions of the genome (whole genome, high-confidence calling, repetitive, GC content of >0.85, GC content of <0.15, low mappability, the ACMG59 genes and regions of large duplications (>1 kb)). Using random batches as a control set, all comparisons had a Cohen’s d of <0.35. Here we report any Cohen’s d results >0.5, which we chose before this analysis and is conventionally the threshold of a medium effect size 44 .

We found that there was an effect size in indel counts (Cohen’s d of 0.53) in the entire genome, between Broad Institute and University of Washington, but this was being driven by repetitive and low-mappability regions. We found no batch effects with Cohen’s d of >0.5 in the ratio metrics or in any metrics in the high-confidence calling, low or high GC content, or ACMG59 regions. A complete list of the batch effects with Cohen’s d of >0.5 are found in Supplementary Table 8 .

Sensitivity and precision evaluation

To determine sensitivity and precision, we included four well-characterized control samples (four National Institute of Standards and Technology Genome in a Bottle samples (HG-001, HG-003, HG-004 and HG-005). The samples were sequenced with the same protocol as All of Us. Of note, these samples were not included in data released to researchers. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations. We use the high-confidence calling region, defined by Genome in a Bottle v4.2.1, as the source of ground truth. To be called a true positive, a variant must match the chromosome, position, reference allele, alternate allele and zygosity. In cases of sites with multiple alternative alleles, each alternative allele is considered separately. Sensitivity and precision results are reported in Supplementary Table 5 .

Genetic ancestry inference

We computed categorical ancestry for all WGS samples in All of Us and made these available to researchers. These predictions are also the basis for population allele frequency calculations in the Genomic Variants section of the public Data Browser. We used the high-quality set of sites to determine an ancestry label for each sample. The ancestry categories are based on the same labels used in gnomAD 18 , the Human Genome Diversity Project (HGDP) 45 and 1000 Genomes 1 : African (AFR); Latino/admixed American (AMR); East Asian (EAS); Middle Eastern (MID); European (EUR), composed of Finnish (FIN) and Non-Finnish European (NFE); Other (OTH), not belonging to one of the other ancestries or is an admixture; South Asian (SAS).

We trained a random forest classifier 46 on a training set of the HGDP and 1000 Genomes samples variants on the autosome, obtained from gnomAD 11 . We generated the first 16 principal components (PCs) of the training sample genotypes (using the hwe_normalized_pca in Hail) at the high-quality variant sites for use as the feature vector for each training sample. We used the truth labels from the sample metadata, which can be found alongside the VCFs. Note that we do not train the classifier on the samples labelled as Other. We use the label probabilities (‘confidence’) of the classifier on the other ancestries to determine ancestry of Other.

To determine the ancestry of All of Us samples, we project the All of Us samples into the PCA space of the training data and apply the classifier. As a proxy for the accuracy of our All of Us predictions, we look at the concordance between the survey results and the predicted ancestry. The concordance between self-reported ethnicity and the ancestry predictions was 87.7%.

PC data from All of Us samples and the HGDP and 1000 Genomes samples were used to compute individual participant genetic ancestry fractions for All of Us samples using the Rye program. Rye uses PC data to carry out rapid and accurate genetic ancestry inference on biobank-scale datasets 47 . HGDP and 1000 Genomes reference samples were used to define a set of six distinct and coherent ancestry groups—African, East Asian, European, Middle Eastern, Latino/admixed American and South Asian—corresponding to participant self-identified race and ethnicity groups. Rye was run on the first 16 PCs, using the defined reference ancestry groups to assign ancestry group fractions to individual All of Us participant samples.

Relatedness

We calculated the kinship score using the Hail pc_relate function and reported any pairs with a kinship score above 0.1. The kinship score is half of the fraction of the genetic material shared (ranges from 0.0 to 0.5). We determined the maximal independent set 41 for related samples. We identified a maximally unrelated set of 231,442 samples (94%) for kinship scored greater than 0.1.

LDL-C common variant GWAS

The phenotypic data were extracted from the Curated Data Repository (CDR, Control Tier Dataset v7) in the All of Us Researcher Workbench. The All of Us Cohort Builder and Dataset Builder were used to extract all LDL cholesterol measurements from the Lab and Measurements criteria in EHR data for all participants who have WGS data. The most recent measurements were selected as the phenotype and adjusted for statin use 19 , age and sex. A rank-based inverse normal transformation was applied for this continuous trait to increase power and deflate type I error. Analysis was carried out on the Hail MatrixTable representation of the All of Us WGS joint-called data including removing monomorphic variants, variants with a call rate of <95% and variants with extreme Hardy–Weinberg equilibrium values ( P  < 10 −15 ). A linear regression was carried out with REGENIE 48 on variants with a minor allele frequency >5%, further adjusting for relatedness to the first five ancestry PCs. The final analysis included 34,924 participants and 8,589,520 variants.

Genotype-by-phenotype replication

We tested replication rates of known phenotype–genotype associations in three of the four largest populations: EUR, AFR and EAS. The AMR population was not included because they have no registered GWAS. This method is a conceptual extension of the original GWAS × phenome-wide association study, which replicated 66% of powered associations in a single EHR-linked biobank 49 . The PGRM is an expansion of this work by Bastarache et al., based on associations in the GWAS catalogue 50 in June 2020 (ref.  51 ). After directly matching the Experimental Factor Ontology terms to phecodes, the authors identified 8,085 unique loci and 170 unique phecodes that compose the PGRM. They showed replication rates in several EHR-linked biobanks ranging from 76% to 85%. For this analysis, we used the EUR-, and AFR-based maps, considering only catalogue associations that were P  < 5 × 10 −8 significant.

The main tools used were the Python package Hail for data extraction, plink for genomic associations, and the R packages PheWAS and pgrm for further analysis and visualization. The phenotypes, participant-reported sex at birth, and year of birth were extracted from the All of Us CDR (Controlled Tier Dataset v7). These phenotypes were then loaded into a plink-compatible format using the PheWAS package, and related samples were removed by sub-setting to the maximally unrelated dataset ( n  = 231,442). Only samples with EHR data were kept, filtered by selected loci, annotated with demographic and phenotypic information extracted from the CDR and ancestry prediction information provided by All of Us, ultimately resulting in 181,345 participants for downstream analysis. The variants in the PGRM were filtered by a minimum population-specific allele frequency of >1% or population-specific allele count of >100, leaving 4,986 variants. Results for which there were at least 20 cases in the ancestry group were included. Then, a series of Firth logistic regression tests with phecodes as the outcome and variants as the predictor were carried out, adjusting for age, sex (for non-sex-specific phenotypes) and the first three genomic PC features as covariates. The PGRM was annotated with power calculations based on the case counts and reported allele frequencies. Power of 80% or greater was considered powered for this analysis.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The All of Us Research Hub has a tiered data access data passport model with three data access tiers. The Public Tier dataset contains only aggregate data with identifiers removed. These data are available to the public through Data Snapshots ( https://www.researchallofus.org/data-tools/data-snapshots/ ) and the public Data Browser ( https://databrowser.researchallofus.org/ ). The Registered Tier curated dataset contains individual-level data, available only to approved researchers on the Researcher Workbench. At present, the Registered Tier includes data from EHRs, wearables and surveys, as well as physical measurements taken at the time of participant enrolment. The Controlled Tier dataset contains all data in the Registered Tier and additionally genomic data in the form of WGS and genotyping arrays, previously suppressed demographic data fields from EHRs and surveys, and unshifted dates of events. At present, Registered Tier and Controlled Tier data are available to researchers at academic institutions, non-profit institutions, and both non-profit and for-profit health care institutions. Work is underway to begin extending access to additional audiences, including industry-affiliated researchers. Researchers have the option to register for Registered Tier and/or Controlled Tier access by completing the All of Us Researcher Workbench access process, which includes identity verification and All of Us-specific training in research involving human participants ( https://www.researchallofus.org/register/ ). Researchers may create a new workspace at any time to conduct any research study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is made accessible publicly on the All of Us Research Projects Directory at https://allofus.nih.gov/protecting-data-and-privacy/research-projects-all-us-data .

Code availability

The GVS code is available at https://github.com/broadinstitute/gatk/tree/ah_var_store/scripts/variantstore . The LDL GWAS pipeline is available as a demonstration project in the Featured Workspace Library on the Researcher Workbench ( https://workbench.researchallofus.org/workspaces/aou-rw-5981f9dc/aouldlgwasregeniedsubctv6duplicate/notebooks ).

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Acknowledgements

The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers (OT2 OD026549; OT2 OD026554; OT2 OD026557; OT2 OD026556; OT2 OD026550; OT2 OD 026552; OT2 OD026553; OT2 OD026548; OT2 OD026551; OT2 OD026555); Inter agency agreement AOD 16037; Federally Qualified Health Centers HHSN 263201600085U; Data and Research Center: U2C OD023196; Genome Centers (OT2 OD002748; OT2 OD002750; OT2 OD002751); Biobank: U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: U24 OD023163; Communications and Engagement: OT2 OD023205; OT2 OD023206; and Community Partners (OT2 OD025277; OT2 OD025315; OT2 OD025337; OT2 OD025276). In addition, the All of Us Research Program would not be possible without the partnership of its participants. All of Us and the All of Us logo are service marks of the US Department of Health and Human Services. E.E.E. is an investigator of the Howard Hughes Medical Institute. We acknowledge the foundational contributions of our friend and colleague, the late Deborah A. Nickerson. Debbie’s years of insightful contributions throughout the formation of the All of Us genomics programme are permanently imprinted, and she shares credit for all of the successes of this programme.

Author information

Authors and affiliations.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Alexander G. Bick & Henry R. Condon

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

Ginger A. Metcalf, Eric Boerwinkle, Richard A. Gibbs, Donna M. Muzny, Eric Venner, Kimberly Walker, Jianhong Hu, Harsha Doddapaneni, Christie L. Kovar, Mullai Murugan, Shannon Dugan, Ziad Khan & Eric Boerwinkle

Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

Kelsey R. Mayo, Jodell E. Linder, Melissa Basford, Ashley Able, Ashley E. Green, Robert J. Carroll, Jennifer Zhang & Yuanyuan Wang

Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Lee Lichtenstein, Anthony Philippakis, Sophie Schwartz, M. Morgan T. Aster, Kristian Cibulskis, Andrea Haessly, Rebecca Asch, Aurora Cremer, Kylee Degatano, Akum Shergill, Laura D. Gauthier, Samuel K. Lee, Aaron Hatcher, George B. Grant, Genevieve R. Brandt, Miguel Covarrubias, Eric Banks & Wail Baalawi

Verily, South San Francisco, CA, USA

Shimon Rura, David Glazer, Moira K. Dillon & C. H. Albach

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Robert J. Carroll, Paul A. Harris & Dan M. Roden

All of Us Research Program, National Institutes of Health, Bethesda, MD, USA

Anjene Musick, Andrea H. Ramirez, Sokny Lim, Siddhartha Nambiar, Bradley Ozenberger, Anastasia L. Wise, Chris Lunt, Geoffrey S. Ginsburg & Joshua C. Denny

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

I. King Jordan, Shashwat Deepali Nagar & Shivam Sharma

Neuroscience Institute, Institute of Translational Genomic Medicine, Morehouse School of Medicine, Atlanta, GA, USA

Robert Meller

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Mine S. Cicek, Stephen N. Thibodeau & Mine S. Cicek

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Kimberly F. Doheny, Michelle Z. Mawhinney, Sean M. L. Griffith, Elvin Hsu, Hua Ling & Marcia K. Adams

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

Evan E. Eichler, Joshua D. Smith, Christian D. Frazar, Colleen P. Davis, Karynne E. Patterson, Marsha M. Wheeler, Sean McGee, Mitzi L. Murray, Valeria Vasta, Dru Leistritz, Matthew A. Richardson, Aparna Radhakrishnan & Brenna W. Ehmen

Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

Evan E. Eichler

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Stacey Gabriel, Heidi L. Rehm, Niall J. Lennon, Christina Austin-Tse, Eric Banks, Michael Gatzen, Namrata Gupta, Katie Larsson, Sheli McDonough, Steven M. Harrison, Christopher Kachulis, Matthew S. Lebo, Seung Hoan Choi & Xin Wang

Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA

Gail P. Jarvik & Elisabeth A. Rosenthal

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Dan M. Roden

Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA

Center for Individualized Medicine, Biorepository Program, Mayo Clinic, Rochester, MN, USA

Stephen N. Thibodeau, Ashley L. Blegen, Samantha J. Wirkus, Victoria A. Wagner, Jeffrey G. Meyer & Mine S. Cicek

Color Health, Burlingame, CA, USA

Scott Topper, Cynthia L. Neben, Marcie Steeves & Alicia Y. Zhou

School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Eric Boerwinkle

Laboratory for Molecular Medicine, Massachusetts General Brigham Personalized Medicine, Cambridge, MA, USA

Christina Austin-Tse, Emma Henricks & Matthew S. Lebo

Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA

Christina M. Lockwood, Brian H. Shirts, Colin C. Pritchard, Jillian G. Buchan & Niklas Krumm

Manuscript Writing Group

  • Alexander G. Bick
  • , Ginger A. Metcalf
  • , Kelsey R. Mayo
  • , Lee Lichtenstein
  • , Shimon Rura
  • , Robert J. Carroll
  • , Anjene Musick
  • , Jodell E. Linder
  • , I. King Jordan
  • , Shashwat Deepali Nagar
  • , Shivam Sharma
  •  & Robert Meller

All of Us Research Program Genomics Principal Investigators

  • Melissa Basford
  • , Eric Boerwinkle
  • , Mine S. Cicek
  • , Kimberly F. Doheny
  • , Evan E. Eichler
  • , Stacey Gabriel
  • , Richard A. Gibbs
  • , David Glazer
  • , Paul A. Harris
  • , Gail P. Jarvik
  • , Anthony Philippakis
  • , Heidi L. Rehm
  • , Dan M. Roden
  • , Stephen N. Thibodeau
  •  & Scott Topper

Biobank, Mayo

  • Ashley L. Blegen
  • , Samantha J. Wirkus
  • , Victoria A. Wagner
  • , Jeffrey G. Meyer
  •  & Stephen N. Thibodeau

Genome Center: Baylor-Hopkins Clinical Genome Center

  • Donna M. Muzny
  • , Eric Venner
  • , Michelle Z. Mawhinney
  • , Sean M. L. Griffith
  • , Elvin Hsu
  • , Marcia K. Adams
  • , Kimberly Walker
  • , Jianhong Hu
  • , Harsha Doddapaneni
  • , Christie L. Kovar
  • , Mullai Murugan
  • , Shannon Dugan
  • , Ziad Khan
  •  & Richard A. Gibbs

Genome Center: Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine

  • Niall J. Lennon
  • , Christina Austin-Tse
  • , Eric Banks
  • , Michael Gatzen
  • , Namrata Gupta
  • , Emma Henricks
  • , Katie Larsson
  • , Sheli McDonough
  • , Steven M. Harrison
  • , Christopher Kachulis
  • , Matthew S. Lebo
  • , Cynthia L. Neben
  • , Marcie Steeves
  • , Alicia Y. Zhou
  • , Scott Topper
  •  & Stacey Gabriel

Genome Center: University of Washington

  • Gail P. Jarvik
  • , Joshua D. Smith
  • , Christian D. Frazar
  • , Colleen P. Davis
  • , Karynne E. Patterson
  • , Marsha M. Wheeler
  • , Sean McGee
  • , Christina M. Lockwood
  • , Brian H. Shirts
  • , Colin C. Pritchard
  • , Mitzi L. Murray
  • , Valeria Vasta
  • , Dru Leistritz
  • , Matthew A. Richardson
  • , Jillian G. Buchan
  • , Aparna Radhakrishnan
  • , Niklas Krumm
  •  & Brenna W. Ehmen

Data and Research Center

  • Lee Lichtenstein
  • , Sophie Schwartz
  • , M. Morgan T. Aster
  • , Kristian Cibulskis
  • , Andrea Haessly
  • , Rebecca Asch
  • , Aurora Cremer
  • , Kylee Degatano
  • , Akum Shergill
  • , Laura D. Gauthier
  • , Samuel K. Lee
  • , Aaron Hatcher
  • , George B. Grant
  • , Genevieve R. Brandt
  • , Miguel Covarrubias
  • , Melissa Basford
  • , Alexander G. Bick
  • , Ashley Able
  • , Ashley E. Green
  • , Jennifer Zhang
  • , Henry R. Condon
  • , Yuanyuan Wang
  • , Moira K. Dillon
  • , C. H. Albach
  • , Wail Baalawi
  •  & Dan M. Roden

All of Us Research Demonstration Project Teams

  • Seung Hoan Choi
  • , Elisabeth A. Rosenthal

NIH All of Us Research Program Staff

  • Andrea H. Ramirez
  • , Sokny Lim
  • , Siddhartha Nambiar
  • , Bradley Ozenberger
  • , Anastasia L. Wise
  • , Chris Lunt
  • , Geoffrey S. Ginsburg
  •  & Joshua C. Denny

Contributions

The All of Us Biobank (Mayo Clinic) collected, stored and plated participant biospecimens. The All of Us Genome Centers (Baylor-Hopkins Clinical Genome Center; Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine; and University of Washington School of Medicine) generated and QCed the whole-genomic data. The All of Us Data and Research Center (Vanderbilt University Medical Center, Broad Institute of MIT and Harvard, and Verily) generated the WGS joint call set, carried out quality assurance and QC analyses and developed the Researcher Workbench. All of Us Research Demonstration Project Teams contributed analyses. The other All of Us Genomics Investigators and NIH All of Us Research Program Staff provided crucial programmatic support. Members of the manuscript writing group (A.G.B., G.A.M., K.R.M., L.L., S.R., R.J.C. and A.M.) wrote the first draft of this manuscript, which was revised with contributions and feedback from all authors.

Corresponding author

Correspondence to Alexander G. Bick .

Ethics declarations

Competing interests.

D.M.M., G.A.M., E.V., K.W., J.H., H.D., C.L.K., M.M., S.D., Z.K., E. Boerwinkle and R.A.G. declare that Baylor Genetics is a Baylor College of Medicine affiliate that derives revenue from genetic testing. Eric Venner is affiliated with Codified Genomics, a provider of genetic interpretation. E.E.E. is a scientific advisory board member of Variant Bio, Inc. A.G.B. is a scientific advisory board member of TenSixteen Bio. The remaining authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Timothy Frayling and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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Extended data figures and tables

Extended data fig. 1 historic availability of ehr records in all of us v7 controlled tier curated data repository (n = 413,457)..

For better visibility, the plot shows growth starting in 2010.

Extended Data Fig. 2 Overview of the Genomic Data Curation Pipeline for WGS samples.

The Data and Research Center (DRC) performs additional single sample quality control (QC) on the data as it arrives from the Genome Centers. The variants from samples that pass this QC are loaded into the Genomic Variant Store (GVS), where we jointly call the variants and apply additional QC. We apply a joint call set QC process, which is stored with the call set. The entire joint call set is rendered as a Hail Variant Dataset (VDS), which can be accessed from the analysis notebooks in the Researcher Workbench. Subsections of the genome are extracted from the VDS and rendered in different formats with all participants. Auxiliary data can also be accessed through the Researcher Workbench. This includes variant functional annotations, joint call set QC results, predicted ancestry, and relatedness. Auxiliary data are derived from GVS (arrow not shown) and the VDS. The Cohort Builder directly queries GVS when researchers request genomic data for subsets of samples. Aligned reads, as cram files, are available in the Researcher Workbench (not shown). The graphics of the dish, gene and computer and the All of Us logo are reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Extended Data Fig. 3 Proportion of allelic frequencies (AF), stratified by computed ancestry with over 10,000 participants.

Bar counts are not cumulative (eg, “pop AF < 0.01” does not include “pop AF < 0.001”).

Extended Data Fig. 4 Distribution of pathogenic, and likely pathogenic ClinVar variants.

Stratified by ancestry filtered to only those variants that are found in allele count (AC) < 40 individuals for 245,388 short read WGS samples.

Extended Data Fig. 5 Ancestry specific HLA-DQB1 ( rs9273363 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight ancestry specific consequences across ancestries.

Extended Data Fig. 6 Ancestry specific TCF7L2 ( rs7903146 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight diabetic consequences across ancestries.

Supplementary information

Supplementary information.

Supplementary Figs. 1–7, Tables 1–8 and Note.

Reporting Summary

Supplementary dataset 1.

Associations of ACKR1, HLA-DQB1 and TCF7L2 loci with all Phecodes stratified by genetic ancestry.

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human resources research paper

Unlocking Agricultural Innovation: A Roadmap for Growth and Sustainability

  • Published: 22 February 2024

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  • Elahe Davoodi Farsani 1 ,
  • Shahla Choobchian   ORCID: orcid.org/0000-0003-2750-1094 1 &
  • Moslem Shirvani Naghani 2  

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Agricultural innovation is crucial for navigating the dynamic market landscape and overcoming the diverse challenges posed by the current economic climate. It serves as a primary driver for both social advancement and economic prosperity, embodying the transformative force necessary for sustainable progress. Specifically, eco-friendly innovation not only boosts productivity but also promotes the responsible use of natural resources, marking a significant stride towards environmental stewardship. Identifying innovation indicators in agriculture is essential for achieving sustainable development and providing a benchmark for assessing progress in this pivotal industry. Recognizing the existing gap in this area, this paper aims to formulate and evaluate innovation indicators within the agricultural sector. Using the qualitative content analysis method with a deductive approach, the study meticulously examines a comprehensive research sample comprising 32 articles, three theses, and one book. The findings reveal eight primary factors that serve as key indicators of innovation in agriculture, covering aspects such as human capital and research, institutional frameworks, market sophistication, infrastructure, knowledge and technology outputs, and creative and innovative endeavors. This research represents a critical step in providing immediate insights into the effectiveness of political strategies and initiatives aimed at fostering innovation in the agricultural industry. By equipping policymakers with a thorough understanding of the current state of innovation in this sector, these insights have the potential to significantly alleviate food insecurity and stimulate economic growth. Thus, empowering policymakers with actionable insights derived from this study can pave the way for transformative change, driving the agricultural sector towards enhanced resilience and prosperity.

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Farsani, E.D., Choobchian, S. & Naghani, M.S. Unlocking Agricultural Innovation: A Roadmap for Growth and Sustainability. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01860-w

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Abstract: Animating virtual characters has always been a fundamental research problem in virtual reality (VR). Facial animations play a crucial role as they effectively convey emotions and attitudes of virtual humans. However, creating such facial animations can be challenging, as current methods often involve utilization of expensive motion capture devices or significant investments of time and effort from human animators in tuning animation parameters. In this paper, we propose a holistic solution to automatically animate virtual human faces. In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients. This method offers the flexibility of generating animations with characters of different appearances and blendshape topologies. Second, a practical toolkit was developed using Unity 3D, making it compatible with the most popular VR applications. The toolkit accepts both image and video as input to animate the target virtual human faces and enables users to manipulate the animation results. Furthermore, inspired by the spirit of Human-in-the-loop (HITL), we leveraged user feedback to further improve the performance of the model and toolkit, thereby increasing the customization properties to suit user preferences. The whole solution, for which we will make the code public, has the potential to accelerate the generation of facial animations for use in VR applications.

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