Big Data in Nursing
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The document discusses how information technology has profoundly impacted how we live and work over the past 20-30 years. Research has accelerated due to these changes with implications for both healthcare providers and patients. We are now entering an era where the world's knowledge is freely available at our fingertips online. This era of open data has the potential to decentralize and democratize information. It is predicted that institutions will become seamless digital enterprises and healthcare will become more personalized and focused on preventative care as big data and technologies like 3D printing continue to disrupt many industries.
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globalisation, religious
chauvinism and cultural
nationalism, financialisation of
the world economy, right wing
sectarian political leadership
both locally and globally,
valorisation of toxic patriarchy
and hyper masculinity with
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by globally controlled media
barons have intensified
complexities in governance,
polity, livelihood and survival
struggles in the midst of
climate change, for the mass of
rural and urban workers,
peasants, forest dwellers/
tribals, fisher folks, petty
traders, small scale
industrialists, sexual minorities
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This document discusses approaches to improving biological data sustainability. It proposes moving from the current BDS 1.0 model to a BDS 2.0 model. BDS 1.0 is characterized by increasing data and costs but decreasing funds for innovation. BDS 2.0 would recognize the monetary value of data and embrace public-private partnerships and a data economy. It suggests a "data credits" system where data curation is a service with monetary value. The document provides examples of how this could work for the Protein Data Bank (PDB) and more globally. It argues BDS 2.0 could encourage competition, globalization, and private sector engagement to better foster sustainable and FAIR biological data.
What is FAIR Data and Who Needs It?
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Data Science Meets Biomedicine, Does Anything Change
Data science is driving major changes in biomedical research by enabling new types of integrative, multi-scale analyses. However, biomedical research may no longer lead data science due to a lack of comprehensive data infrastructure and cultural barriers. Responsible data science that balances openness, ethics, and benefiting patients could help establish biomedicine's continued leadership role. Major challenges include limited resources, attracting diverse talent, and prioritizing strategic initiatives over conforming to traditional models of research.
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- We are at a tipping point where biomedical research is no longer the sole leader in data science due to advances in many other fields. Biomedical researchers need to become data scientists to stay relevant.
- Data science is being driven by the massive growth of digital data and requires an interdisciplinary approach. It is touching every field and attracting many students.
- Developing effective data systems and infrastructure is a major challenge to enable open sharing and analysis of data. Initiatives are underway but more collaboration is needed across sectors.
- Advances in machine learning, like Alpha
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Social responsibility in research refers to conducting studies that benefit society while avoiding harm. It involves considering risks and benefits to human and animal subjects, ensuring transparency and integrity, and engaging stakeholders. Socially responsible research also addresses equity, diversity and inclusion. Data sharing is an important aspect of social responsibility, as it enables reproducibility and collaborative research. However, data must be shared in a FAIR manner and maintained over time to realize its full benefits. Researchers should consider social responsibility throughout the entire research lifecycle.
AI from the Perspective of a School of Data Science
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What Data Science Will Mean to You - One Person's View
This document provides an overview of data science from the perspective of Philip Bourne. Some key points:
- Data science is disruptive to higher education and all disciplines are being impacted by large amounts of digital data.
- Data science can be defined using a 4+1 model focusing on value, design, systems, analytics, and practice.
- Principles of excellence, inclusivity, openness, and fairness should guide data science work.
- Lessons from advances in computational biology and AlphaFold2 show the importance of open data, collaboration, and engineering challenges.
- A data science school should focus on responsible data practices while balancing open research that benefits patients.
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Towards a US Open research Commons (ORC)
On August 2nd, 2021, US scientists and officials met to discuss establishing a US Open Research Commons (ORC) to make research data and computing resources more accessible and interoperable across public and private sectors. Currently, US resources are siloed and limited in discoverability. Other countries have established similar initiatives that the US is not formally represented in. An ORC could pool resources to benefit a more diverse group of researchers in addressing societal challenges, but establishing one requires overcoming cultural and institutional barriers between agencies through policy leadership. Immediate action is needed for the US to remain competitive in open science.
COVID and Precision Education
This document discusses opportunities for precision education arising from the move to digital education during the COVID pandemic. It notes that for the first time, essentially all educational materials were digital, creating opportunities to make content findable, accessible, interoperable, and reusable. This could improve content quality through transparency and ratings similar to academic publishing. It also enables aggregated views of content and student performance, improved content and syllabi, and recommender systems. Challenges include issues around content ownership, sharing rules, bias, privacy, security, and adoption of next generation learning management systems.
One View of Data Science
The document discusses the rise of data science and its disruptive impact on higher education. It analyzes precedents like bioinformatics that were enabled by new digital data sources and technologies. The author advocates that universities should embrace data science by establishing interdisciplinary collaborations, investing in data infrastructure, and ensuring research has societal value and responsibility.
Cancer Research Meets Data Science — What Can We Do Together?
Presentation by Phil Bourne, Lei Xie, Can Mura and Zheng Zhao to the University of Virginia Cancer Center on Nov 19, 2021
Data Science Meets Open Scholarship – What Comes Next?
TRANSPARENT DATA FOR EFFECTIVE RESEARCH
BEILSTEIN OPEN SCIENCE SYMPOSIUM 2021
OCTOBER 5 - 7, 2021
ONLINE
Data to Advance Sustainability
Philip Bourne presented on how data can advance sustainability. He discussed how high throughput DNA digital data changed biomedicine and spawned the new field of data science. Data science now touches all domains, including helping achieve UN Goal 10 of reducing inequalities through projects like using data to understand the history of Native American displacement. While data presents endless opportunities, it also has weaknesses like being messy and non-conclusive, and threats like bias and lack of training. Bourne advocates for building trust through evidence and creating an open data environment to realize data's potential, while acknowledging that sustaining open data faces challenges around proprietary concerns, security, and driving social change.
Frontiers of Computing at the Cellular and Molecular Scales
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- 1. Nursing’s Big Data philip.bourne@nih.gov FNINR Oct. 15, 2014
- 3. A Brief View of Where we Have Come from and Where we Are Going
- 5. In the past 20-30 years we have seen a profound impact of information technology on the way we live and work Research ha s accelerated with implications for healthcare providers and patients alike
- 7. The World’s Knowledge is at Our Fingertips and Free! http://upload.wikimedia.org/wikipedia/commons/2/26/EnwikipediaArt.PNG
- 8. The Era of Open Has The Potential to Deinstitutionalize & Democratize Daniel Hulshizer/Associated Press
- 9. An Example of That Potential: The Story of Meredith http://fora.tv/2012/04/20/Congress_Unplugged_ Phil_Bourne
- 10. The Era of Open Has The Potential to Deinstitutionalize & Democratize Daniel Hulshizer/Associated Press
- 11. We are entering an era of further disruption
- 12. Others Think So Too Others provide a more compelling argument: – Google car – 3D printers – Waze – Robotics
- 13. From the Second Machine Age From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
- 14. So what will this look like for an institution? Institutions will become seemless digital enterprises
- 16. What Does This Mean to Nursing Research? Healthcare becomes patient centric for the first time Personalized medicine is the norm rather than the exception Preventive care accelerates
- 17. Larry Smarr the Measured Man
- 18. NNIIHH…… philip.bourne@nih.gov TTuurrnniinngg DDiissccoovveerryy IInnttoo HHeeaalltthh