1. The document discusses some early observations from the Associate Director for Data Science at the National Institutes of Health regarding data at NIH.
2. It notes that NIH does not fully understand how existing data is used, has focused more on why data should be shared rather than how to share it, and lacks plans for long-term sustainability of data.
3. Potential solutions discussed include developing a biomedical commons, modifying the review process, improving education in data science, and expanding the Big Data to Knowledge initiative. The goal is to create a digital research enterprise that better connects all aspects of the research lifecycle.
This document provides an overview of Philip Bourne's early observations and thoughts regarding data management at the NIH. Some of the key points are: 1) Existing data resources are not well understood in terms of how they are used; 2) There is a need to focus on how data will be managed and shared, not just why it should be; 3) There is no NIH-wide sustainability plan for data management; 4) Training in biomedical data science is inconsistent. The document discusses some potential solutions such as establishing a NIH data commons and improving training programs.
What Can Happen when Genome Sciences Meets Data Sciences?Philip Bourne
The document discusses the intersection of genome sciences and data sciences. It provides context on data science definitions, relevant examples at NIH, and challenges. The author argues that fully integrating diverse biomedical data sources through open platforms could accelerate research by enabling new discoveries. However, changing entrenched work practices and incentivizing platform use are challenges. The DSI is working to break down silos through collaboration and practical training to help advance open data and digital integration of research workflows.
This document discusses where open science is headed and provides one perspective on this issue. It notes that the answer depends on who you ask and then outlines the speaker's background and bias as being from biomedicine and an advocate for open science. It asks what the endpoint of open science should be and discusses some implications of the democratization of science, such as more scrutiny, new types of rewards, and removing artificial boundaries. The speaker then provides some personal examples to illustrate these implications.
Genome sharing projects around the world nijmegen oct 29 - 2015Fiona Nielsen
Genome sharing projects across the world
Did you ever wonder what happened to the exponential increase in genome sequencing data? It is out there around the world and a lot of it is consented for research use. This means that if you just know where to find the data, you can potentially analyse gigabytes of data to power your research.
In this talk Fiona will present community genome initiatives, the genome sharing projects across the world, how you can benefit from this wealth of data in your work, and how you can boost your academic career by sharing and collaboration.
by Fiona Nielsen, Founder and CEO of DNAdigest and Repositive
With a background in software development Fiona pursued her career in bioinformatics research at Radboud University Nijmegen. Now a scientist-turned-entrepreneur Fiona founded DNAdigest and its social enterprise spin-out Repositive Ltd. Both the charity and company focus on efficient and ethical sharing of genetics data for research to accelerate diagnostics and cures for genetic diseases.
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.
This document provides an introduction to machine learning and its applications in genomics and biology. It discusses how biology and genomics data have become "big data" due to technological advances in sequencing and data generation. Machine learning is well-suited for analyzing these large, multidimensional datasets and addressing complex biological questions. The document outlines different machine learning approaches like supervised and unsupervised learning, and provides examples of real-world applications. R and Python are introduced as popular programming languages for machine learning.
Abstract: http://j.mp/1MhWWei
Healthcare applications now have the ability to exploit big data in all its complexity. A crucial challenge is to achieve interoperability or integration so that a variety of content from diverse physical (IoT)- cyber (web-based)- and social sources, with diverse formats and modality (text, image, video), can be used in analysis, insight, and decision-making. At Kno.e.sis, an Ohio Center of Excellence in BioHealth Innovation, we have a variety of large, collaborative healthcare/clinical/biomedical projects, all involving domain experts and end-users, and access to real world data that include: clinical/EMR data (of individual patients and that related to public health), data from a variety of sensors (IoT) on and around patients measuring real-time physiological and environmental observations), social data (Twitter, Web forums, PatientsLikeMe), Web search logs, etc. Key projects include: Prescription drug abuse online-surveillance and epidemiology (PREDOSE), Social media analysis to monitor cannabis and synthetic cannabinoid use (eDrugTrends), Modeling Social Behavior for Healthcare Utilization in Depression, Medical Information Decision Assistant and Support (MIDAS) with application to musculoskeletal issues, kHealth: A Semantic Approach to Proactive, Personalized Asthma Management Using Multimodal Sensing (also for Dementia), and Cardiology Semantic Analysis System (with applications to Computer Assisted Coding and Computerized Document Improvement).
This talk will review how ontologies or knowledge graphs play a central role in supporting semantic filtering, interoperability and integration (including the issues such as disambiguation), reasoning and decision-making in all our health-centric research and applications. Additional relevant information is at the speaker’s HCLS page. http://knoesis.org/amit/hcls
1. The document discusses some early observations from the Associate Director for Data Science at the National Institutes of Health regarding data at NIH.
2. It notes that NIH does not fully understand how existing data is used, has focused more on why data should be shared rather than how to share it, and lacks plans for long-term sustainability of data.
3. Potential solutions discussed include developing a biomedical commons, modifying the review process, improving education in data science, and expanding the Big Data to Knowledge initiative. The goal is to create a digital research enterprise that better connects all aspects of the research lifecycle.
This document provides an overview of Philip Bourne's early observations and thoughts regarding data management at the NIH. Some of the key points are: 1) Existing data resources are not well understood in terms of how they are used; 2) There is a need to focus on how data will be managed and shared, not just why it should be; 3) There is no NIH-wide sustainability plan for data management; 4) Training in biomedical data science is inconsistent. The document discusses some potential solutions such as establishing a NIH data commons and improving training programs.
What Can Happen when Genome Sciences Meets Data Sciences?Philip Bourne
The document discusses the intersection of genome sciences and data sciences. It provides context on data science definitions, relevant examples at NIH, and challenges. The author argues that fully integrating diverse biomedical data sources through open platforms could accelerate research by enabling new discoveries. However, changing entrenched work practices and incentivizing platform use are challenges. The DSI is working to break down silos through collaboration and practical training to help advance open data and digital integration of research workflows.
This document discusses where open science is headed and provides one perspective on this issue. It notes that the answer depends on who you ask and then outlines the speaker's background and bias as being from biomedicine and an advocate for open science. It asks what the endpoint of open science should be and discusses some implications of the democratization of science, such as more scrutiny, new types of rewards, and removing artificial boundaries. The speaker then provides some personal examples to illustrate these implications.
Genome sharing projects around the world nijmegen oct 29 - 2015Fiona Nielsen
Genome sharing projects across the world
Did you ever wonder what happened to the exponential increase in genome sequencing data? It is out there around the world and a lot of it is consented for research use. This means that if you just know where to find the data, you can potentially analyse gigabytes of data to power your research.
In this talk Fiona will present community genome initiatives, the genome sharing projects across the world, how you can benefit from this wealth of data in your work, and how you can boost your academic career by sharing and collaboration.
by Fiona Nielsen, Founder and CEO of DNAdigest and Repositive
With a background in software development Fiona pursued her career in bioinformatics research at Radboud University Nijmegen. Now a scientist-turned-entrepreneur Fiona founded DNAdigest and its social enterprise spin-out Repositive Ltd. Both the charity and company focus on efficient and ethical sharing of genetics data for research to accelerate diagnostics and cures for genetic diseases.
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.
This document provides an introduction to machine learning and its applications in genomics and biology. It discusses how biology and genomics data have become "big data" due to technological advances in sequencing and data generation. Machine learning is well-suited for analyzing these large, multidimensional datasets and addressing complex biological questions. The document outlines different machine learning approaches like supervised and unsupervised learning, and provides examples of real-world applications. R and Python are introduced as popular programming languages for machine learning.
Abstract: http://j.mp/1MhWWei
Healthcare applications now have the ability to exploit big data in all its complexity. A crucial challenge is to achieve interoperability or integration so that a variety of content from diverse physical (IoT)- cyber (web-based)- and social sources, with diverse formats and modality (text, image, video), can be used in analysis, insight, and decision-making. At Kno.e.sis, an Ohio Center of Excellence in BioHealth Innovation, we have a variety of large, collaborative healthcare/clinical/biomedical projects, all involving domain experts and end-users, and access to real world data that include: clinical/EMR data (of individual patients and that related to public health), data from a variety of sensors (IoT) on and around patients measuring real-time physiological and environmental observations), social data (Twitter, Web forums, PatientsLikeMe), Web search logs, etc. Key projects include: Prescription drug abuse online-surveillance and epidemiology (PREDOSE), Social media analysis to monitor cannabis and synthetic cannabinoid use (eDrugTrends), Modeling Social Behavior for Healthcare Utilization in Depression, Medical Information Decision Assistant and Support (MIDAS) with application to musculoskeletal issues, kHealth: A Semantic Approach to Proactive, Personalized Asthma Management Using Multimodal Sensing (also for Dementia), and Cardiology Semantic Analysis System (with applications to Computer Assisted Coding and Computerized Document Improvement).
This talk will review how ontologies or knowledge graphs play a central role in supporting semantic filtering, interoperability and integration (including the issues such as disambiguation), reasoning and decision-making in all our health-centric research and applications. Additional relevant information is at the speaker’s HCLS page. http://knoesis.org/amit/hcls
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.
Reproducibility and Scientific Research: why, what, where, when, who, how Carole Goble
This document discusses the importance of reproducibility in scientific research. It makes three key points:
1. For results to be considered valid, scientific publications should provide clear descriptions of methods and protocols so that other researchers can successfully repeat and extend the work.
2. Many factors can undermine reproducibility, such as publication pressures, poor training, disorganization, and outright fraud. Ensuring reproducible research requires transparency across experimental designs, data, software, and computational workflows.
3. Achieving reproducible science is challenging and poorly incentivized due to the resources and time required to prepare materials for independent verification. Overcoming these issues will require collective effort across the research community.
Smart Data in Health – How we will exploit personal, clinical, and social “Bi...Amit Sheth
The document discusses using big health data from personal, clinical, and social sources to better understand health outcomes through tools like the Kno.e.sis research center, which analyzes data from sensors, medical records, and social media to provide personalized health information and recommendations to improve care. It also describes specific projects like kHealth, which monitors asthma patients using mobile and sensor data, and PREDOSE, which tracks prescription drug abuse using information extracted from social media.
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Biomedical Research as an Open Digital EnterprisePhilip Bourne
The document discusses the challenges and opportunities facing biomedical research as it transitions to becoming a fully digital open enterprise. It notes issues around reproducibility, limited funding, and the need to better connect different elements of the research lifecycle like data capture, analysis, and publication. The author proposes the "Commons" as a conceptual framework to help address these issues by providing shared resources like cloud-based storage and computing, tools to discover and access data and software, and standards to improve reproducibility. The goal is to foster an ecosystem that maximizes the benefits of digital technologies for biomedical research.
presents the foundational aspects of web analytics and some specifics such as the hotel problem. Discusses trace data, behaviorism, and other cool web analytics stuff
Research in the time of Covid: Surveying impacts on Early Career ResearchersRebecca Grant
The document summarizes key findings from a survey of nearly 5,000 researchers conducted between March and July 2020 regarding the impact of COVID-19 on research. Some of the main findings include:
- Early career researchers reported being more impacted by COVID-19 than other career stages, with 33% saying their research was extremely or very impacted
- Half of respondents expect to reuse open data from other labs during lockdown and 65% expect to reuse their own data
- Data reuse is seen as important for allowing research to continue given restrictions, with 10-15% increase in intention to reuse data during and after the pandemic
- While early career researchers were generally more supportive of data sharing than other career stages, concerns around mis
Data Management Lab: Data management plan instructionsIUPUI
This document provides instructions for creating a data management plan. It outlines 17 components that should be addressed in a data management plan, including a description of the data, how it will be stored and backed up, how it will be shared and preserved, ethical and legal obligations, and a budget. Each component includes several questions to consider to ensure all relevant aspects are covered. Resources are also provided to help with developing policies for topics like data sharing, privacy, and legal requirements. The goal is to create a comprehensive plan for managing research data throughout its entire lifecycle and use.
Increasing transparency in Medical Education through Open Data Rebecca Grant
Slides presented at the AMEE Virtual Conference 2021, introducing the MedEdPublish platform and data policies. Approaches to sharing sensitive human data, and particulary qualitative data, are discussed.
Big Data and machine learning are increasingly important in biomedical science and clinical practice. Big Data refers to large and complex datasets that are too large for traditional tools to handle. Machine learning involves algorithms that can recognize patterns in data without being explicitly programmed. Some challenges of working with big data and machine learning include issues with data volume, variety, and veracity. However, techniques like distributed analysis, standards, and validation can help address these challenges.
Do Open data badges influence author behaviour? A case study at Springer NatureRebecca Grant
Digital badges have previously been shown to incentivise journal authors to share their data openly. In this paper we introduce an Open data badging project at the Springer Nature journal BMC Microbiology. The development of the Open data badge is described, as well as the challenges of developing standard badging criteria and ensuring authors’ awareness of the badges. Next steps for the badging project are outlined, which are based on the experiences of the team assessing the badges, the number of badges awarded at the journal to date, and the results of an author survey.
Data Science in Biomedicine - Where Are We Headed?Philip Bourne
The document discusses the future of data science in biomedicine. It notes that we are currently at a point of deception, where digitization is occurring but disruption has not yet fully happened. It outlines implications such as open collaborative science becoming more important, and data/analytics increasing in scholarly value. Initiatives like the Precision Medicine Initiative and Big Data to Knowledge are aiming to improve research efficiency and enable precision medicine through large datasets and new methodologies. The future will require cooperation across funders and changes to training to address new skills needed.
Bibliometric is a method for measuring, monitoring, and studying scientific outputs in a given area for various purposes, such as prospecting research opportunities and substantiating scientific research. Bibliometric is one family of measures that uses a variety of approaches for counting publication, citation, co-citation, bibliographic coupling, keyword co-occurrence, and co-authorship networks. Information technology (IT) tools can be used to assist the process of searching for relevant scientific contents, collecting scientific data, and summarizing the results obtained. Bibliometric paper can be written before writing a literature review article and at the introduction section of any research papers. In this workshop, you will get familiar with “How to Write Your First Bibliometric Paper”.
Capturing Context in Scientific Experiments: Towards Computer-Driven Sciencedgarijo
Scientists publish computational experiments in ways that do not facilitate reproducibility or reuse. Significant domain expertise, time and effort are required to understand scientific experiments and their research outputs. In order to improve this situation, mechanisms are needed to capture the exact details and the context of computational experiments. Only then, Intelligent Systems would be able help researchers understand, discover, link and reuse products of existing research.
In this presentation I will introduce my work and vision towards enabling scientists share, link, curate and reuse their computational experiments and results. In the first part of the talk, I will present my work for capturing and sharing the context of scientific experiments by using scientific workflows and machine readable representations. Thanks to this approach, experiment results are described in an unambiguous manner, have a clear trace of their creation process and include a pointer to the sources used for their generation. In the second part of the talk, I will describe examples on how the context of scientific experiments may be exploited to browse, explore and inspect research results. I will end the talk by presenting new ideas for improving and benefiting from the capture of context of scientific experiments and how to involve scientists in the process of curating and creating abstractions on available research metadata.
Enhancing Our Capacity for Large Health Dataset AnalysisCTSI at UCSF
Overview of UCSF-CTSI Comparative Effectiveness Large Dataset Analysis Core, which offers resources for the analysis of large, public data sets on health and health care.
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
“Research Tools” can be defined as vehicles that broadly facilitate research and related activities. “Research Tools” enable researchers to collect, organize, analyze, visualize and publicized research outputs. Dr. Nader has collected over 700 tools that enable researchers to follow the correct path in research and to ultimately produce high-quality research outputs with more accuracy and efficiency. It is assembled as an interactive Web-based mind map, titled “Research Tools”, which is updated periodically.
Research Skills Session 8: Avoid Scientific MisconductNader Ale Ebrahim
One of the most important research ethical issues that should be taken into consideration is “scientific misconduct” such as fabrication, falsification and plagiarism. Plagiarism can occur at any stage of the research activities such as reporting, communicating, authoring, and peer review. The purpose of this workshop is to engage researchers in their responsibility to conduct an ethical research.
This document provides an overview of FAIR data principles and the FAIR data ecosystem. It discusses what FAIR data is, including that FAIR data aims to support communities in publishing and utilizing scientific data and knowledge in a findable, accessible, interoperable, and reusable manner. It then describes the different levels of the FAIR data ecosystem, including normative principles, standards in the FAIR data protocol, FAIR data resources that comply with these standards, and systems/tools that use FAIR data. It provides examples of converting raw data into FAIR data resources and the potential applications of a FAIR data ecosystem.
Systems Biology & Pharmacology from a Structural PerspectivePhilip Bourne
Philip Bourne gave a talk on his past research leading data science at the NIH and his current and future research. Some of his past work includes developing methods for determining binding site similarity across protein space using geometric potentials and sequence-order independent profile-profile alignment. His current research focuses on using these methods for applications like predicting drug efficacy, toxicity and repurposing old drugs for new uses. He discussed challenges like the large search space and flexibility of proteins. Going forward, he hopes cloud computing environments can help make data and tools more accessible and reusable to advance systems pharmacology research.
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.
Reproducibility and Scientific Research: why, what, where, when, who, how Carole Goble
This document discusses the importance of reproducibility in scientific research. It makes three key points:
1. For results to be considered valid, scientific publications should provide clear descriptions of methods and protocols so that other researchers can successfully repeat and extend the work.
2. Many factors can undermine reproducibility, such as publication pressures, poor training, disorganization, and outright fraud. Ensuring reproducible research requires transparency across experimental designs, data, software, and computational workflows.
3. Achieving reproducible science is challenging and poorly incentivized due to the resources and time required to prepare materials for independent verification. Overcoming these issues will require collective effort across the research community.
Smart Data in Health – How we will exploit personal, clinical, and social “Bi...Amit Sheth
The document discusses using big health data from personal, clinical, and social sources to better understand health outcomes through tools like the Kno.e.sis research center, which analyzes data from sensors, medical records, and social media to provide personalized health information and recommendations to improve care. It also describes specific projects like kHealth, which monitors asthma patients using mobile and sensor data, and PREDOSE, which tracks prescription drug abuse using information extracted from social media.
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Biomedical Research as an Open Digital EnterprisePhilip Bourne
The document discusses the challenges and opportunities facing biomedical research as it transitions to becoming a fully digital open enterprise. It notes issues around reproducibility, limited funding, and the need to better connect different elements of the research lifecycle like data capture, analysis, and publication. The author proposes the "Commons" as a conceptual framework to help address these issues by providing shared resources like cloud-based storage and computing, tools to discover and access data and software, and standards to improve reproducibility. The goal is to foster an ecosystem that maximizes the benefits of digital technologies for biomedical research.
presents the foundational aspects of web analytics and some specifics such as the hotel problem. Discusses trace data, behaviorism, and other cool web analytics stuff
Research in the time of Covid: Surveying impacts on Early Career ResearchersRebecca Grant
The document summarizes key findings from a survey of nearly 5,000 researchers conducted between March and July 2020 regarding the impact of COVID-19 on research. Some of the main findings include:
- Early career researchers reported being more impacted by COVID-19 than other career stages, with 33% saying their research was extremely or very impacted
- Half of respondents expect to reuse open data from other labs during lockdown and 65% expect to reuse their own data
- Data reuse is seen as important for allowing research to continue given restrictions, with 10-15% increase in intention to reuse data during and after the pandemic
- While early career researchers were generally more supportive of data sharing than other career stages, concerns around mis
Data Management Lab: Data management plan instructionsIUPUI
This document provides instructions for creating a data management plan. It outlines 17 components that should be addressed in a data management plan, including a description of the data, how it will be stored and backed up, how it will be shared and preserved, ethical and legal obligations, and a budget. Each component includes several questions to consider to ensure all relevant aspects are covered. Resources are also provided to help with developing policies for topics like data sharing, privacy, and legal requirements. The goal is to create a comprehensive plan for managing research data throughout its entire lifecycle and use.
Increasing transparency in Medical Education through Open Data Rebecca Grant
Slides presented at the AMEE Virtual Conference 2021, introducing the MedEdPublish platform and data policies. Approaches to sharing sensitive human data, and particulary qualitative data, are discussed.
Big Data and machine learning are increasingly important in biomedical science and clinical practice. Big Data refers to large and complex datasets that are too large for traditional tools to handle. Machine learning involves algorithms that can recognize patterns in data without being explicitly programmed. Some challenges of working with big data and machine learning include issues with data volume, variety, and veracity. However, techniques like distributed analysis, standards, and validation can help address these challenges.
Do Open data badges influence author behaviour? A case study at Springer NatureRebecca Grant
Digital badges have previously been shown to incentivise journal authors to share their data openly. In this paper we introduce an Open data badging project at the Springer Nature journal BMC Microbiology. The development of the Open data badge is described, as well as the challenges of developing standard badging criteria and ensuring authors’ awareness of the badges. Next steps for the badging project are outlined, which are based on the experiences of the team assessing the badges, the number of badges awarded at the journal to date, and the results of an author survey.
Data Science in Biomedicine - Where Are We Headed?Philip Bourne
The document discusses the future of data science in biomedicine. It notes that we are currently at a point of deception, where digitization is occurring but disruption has not yet fully happened. It outlines implications such as open collaborative science becoming more important, and data/analytics increasing in scholarly value. Initiatives like the Precision Medicine Initiative and Big Data to Knowledge are aiming to improve research efficiency and enable precision medicine through large datasets and new methodologies. The future will require cooperation across funders and changes to training to address new skills needed.
Bibliometric is a method for measuring, monitoring, and studying scientific outputs in a given area for various purposes, such as prospecting research opportunities and substantiating scientific research. Bibliometric is one family of measures that uses a variety of approaches for counting publication, citation, co-citation, bibliographic coupling, keyword co-occurrence, and co-authorship networks. Information technology (IT) tools can be used to assist the process of searching for relevant scientific contents, collecting scientific data, and summarizing the results obtained. Bibliometric paper can be written before writing a literature review article and at the introduction section of any research papers. In this workshop, you will get familiar with “How to Write Your First Bibliometric Paper”.
Capturing Context in Scientific Experiments: Towards Computer-Driven Sciencedgarijo
Scientists publish computational experiments in ways that do not facilitate reproducibility or reuse. Significant domain expertise, time and effort are required to understand scientific experiments and their research outputs. In order to improve this situation, mechanisms are needed to capture the exact details and the context of computational experiments. Only then, Intelligent Systems would be able help researchers understand, discover, link and reuse products of existing research.
In this presentation I will introduce my work and vision towards enabling scientists share, link, curate and reuse their computational experiments and results. In the first part of the talk, I will present my work for capturing and sharing the context of scientific experiments by using scientific workflows and machine readable representations. Thanks to this approach, experiment results are described in an unambiguous manner, have a clear trace of their creation process and include a pointer to the sources used for their generation. In the second part of the talk, I will describe examples on how the context of scientific experiments may be exploited to browse, explore and inspect research results. I will end the talk by presenting new ideas for improving and benefiting from the capture of context of scientific experiments and how to involve scientists in the process of curating and creating abstractions on available research metadata.
Enhancing Our Capacity for Large Health Dataset AnalysisCTSI at UCSF
Overview of UCSF-CTSI Comparative Effectiveness Large Dataset Analysis Core, which offers resources for the analysis of large, public data sets on health and health care.
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
“Research Tools” can be defined as vehicles that broadly facilitate research and related activities. “Research Tools” enable researchers to collect, organize, analyze, visualize and publicized research outputs. Dr. Nader has collected over 700 tools that enable researchers to follow the correct path in research and to ultimately produce high-quality research outputs with more accuracy and efficiency. It is assembled as an interactive Web-based mind map, titled “Research Tools”, which is updated periodically.
Research Skills Session 8: Avoid Scientific MisconductNader Ale Ebrahim
One of the most important research ethical issues that should be taken into consideration is “scientific misconduct” such as fabrication, falsification and plagiarism. Plagiarism can occur at any stage of the research activities such as reporting, communicating, authoring, and peer review. The purpose of this workshop is to engage researchers in their responsibility to conduct an ethical research.
This document provides an overview of FAIR data principles and the FAIR data ecosystem. It discusses what FAIR data is, including that FAIR data aims to support communities in publishing and utilizing scientific data and knowledge in a findable, accessible, interoperable, and reusable manner. It then describes the different levels of the FAIR data ecosystem, including normative principles, standards in the FAIR data protocol, FAIR data resources that comply with these standards, and systems/tools that use FAIR data. It provides examples of converting raw data into FAIR data resources and the potential applications of a FAIR data ecosystem.
Systems Biology & Pharmacology from a Structural PerspectivePhilip Bourne
Philip Bourne gave a talk on his past research leading data science at the NIH and his current and future research. Some of his past work includes developing methods for determining binding site similarity across protein space using geometric potentials and sequence-order independent profile-profile alignment. His current research focuses on using these methods for applications like predicting drug efficacy, toxicity and repurposing old drugs for new uses. He discussed challenges like the large search space and flexibility of proteins. Going forward, he hopes cloud computing environments can help make data and tools more accessible and reusable to advance systems pharmacology research.
Health Policy and Management as it Relates to Big DataPhilip Bourne
Big data and data science are having major implications for health policy and management. Precision medicine initiatives that collect and analyze large amounts of genomic and health data from many individuals could lead to medical breakthroughs but also raise issues regarding data sharing, attribution, security, intellectual property, and ethics. As data-driven technologies continue advancing, it will be important to address the ethical, legal and societal implications of areas like algorithm development, artificial intelligence, open science, and business models to ensure data science research benefits society.
Moving Forward with Open Data Science - SWOT AnalysisPhilip Bourne
This document summarizes the strengths, weaknesses, opportunities, and threats of open data science based on a symposium discussion. The strengths included government support for open data and science, contributions from young people, and pre-competitive engagement from the commercial sector. Weaknesses consisted of needs for better search capabilities, overcoming cultural gaps, issues with existing business models and academic evaluation. Opportunities involved openness enabling more advancement, potential for public health impacts, and emerging open platforms and partnerships. Threats centered around incentive and reward structures, potential rollbacks of initiatives, and risks of recentralization.
This document outlines Philip Bourne's vision for data science at NIH through 2020. It discusses the commitment to continuing the Big Data to Knowledge (BD2K) program, and views BD2K as part of a broader data science strategy. This includes a vibrant research program, developing a sustainable data ecosystem around the FAIR principles and commons, increased workforce training, and a changing governance model. The goal is new innovations from large data, evidence of real applications, broad commons adoption improving sharing and reuse, and policies supporting an effective balance between data spending and gains.
Ten Simple Rules for Building and Maintaining a Scientific ReputationPhilip Bourne
Originally a 2011 article in PLOS Comp Biol, http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002108 presented as a lecture to the Morgridge Institute for Research, Madison, WI on December 14, 2016
Internal NIH Seminar to the BISTI Team on some early thoughts from the Associate Director for Data Science (ADDS). These ideas are for discussion only and in no way reflect what might happen subsequently. Presented April 1, 2014 (the date is purely a coincidence).
The Thinking Behind Big Data at the NIHPhilip Bourne
- The document discusses the challenges and opportunities presented by big data in biomedical research. It highlights issues like lack of reproducibility, need for data sharing and standards, and ensuring sustainability of data resources.
- The NIH is organizing various initiatives through the Big Data to Knowledge program to address these issues. This includes developing a biomedical research data commons, training programs, funding for innovation, and modified review processes.
- The goal is to improve data accessibility, support for workflows, relationships with publishers, and metrics to measure reproducibility and reward data sharing. This will help close the research lifecycle loop and advance biomedical discovery.
A Successful Academic Medical Center Must be a Truly Digital EnterprisePhilip Bourne
This document discusses how academic medical centers must become truly digital enterprises to succeed in the future. It outlines how data sharing and use of data analytics will become increasingly important in biomedical research. Academic medical centers will need to improve efficiency, embrace open collaboration, and ensure current training prepares researchers for working with large, diverse data sources. However, balancing accessibility and security of data will also be critical as these digital transformations occur. The implications discussed could shape opportunities, scientific practices, and the value of data and analytics for academic medical institutions.
This document discusses the NIH's efforts to create a sustainable ecosystem for biomedical data sharing and analysis. It outlines Philip Bourne's perspective as Associate Director for Data Science at NIH. Key points include:
1) NIH spends an estimated $1 billion per year on data but has little understanding of how much it should spend.
2) ADDS aims to foster a digital biomedical research enterprise through developing infrastructure, policy, training and business models.
3) Example initiatives include establishing a "Commons" for shared data storage and analysis, harmonizing clinical data standards, and creating sustainable business models for data sharing.
4) The goals are to improve efficiency, collaboration, reproducibility and discovery through better management and
The document summarizes NIH's approach to data science and the ADDS mission. It discusses establishing a data ecosystem through community, policy, and infrastructure. The goals are to foster sustainability, efficiency, collaboration, reproducibility, and accessibility. NIH plans to seed the ecosystem through existing resources and funding. Example initiatives include establishing a data commons, standards, and training programs to develop a diverse data science workforce. The overall aim is to support a "digital enterprise" that enhances biomedical research and health outcomes.
Finding and Accessing Human Genomics DatasetsManuel Corpas
This document summarizes a workshop about finding and accessing human genomic datasets. The workshop covered various data sources such as public repositories, case studies on accessing data from the University of Cambridge, and a demonstration of the Repositive platform which aims to simplify accessing genomic data through a single search. Hands-on sessions allowed participants to search for genomic data themes in small groups using Repositive and report their results. Overall the workshop aimed to educate researchers on challenges of accessing genomic data and introduce Repositive as a tool to help address fragmentation and simplify the workflow for discovering and accessing genomic datasets.
The NIH as a Digital Enterprise: Implications for PAGPhilip Bourne
The document discusses the NIH's vision of becoming a digital enterprise to enhance biomedical research. It outlines how research is becoming more digital and data-driven. The NIH aims to foster open sharing of data and tools through its Commons platform to facilitate collaboration and reproducibility. It also stresses the importance of training the next generation of data scientists to enable the digital enterprise. The end goal is to accelerate discovery and improve health outcomes through more integrated and data-driven research.
Why is the NIH investing $100M at the intersection of data science and health research? The NIH seeks to invest in ways to help researchers easily find, access, analyze, and curate research data. Researchers want visual analytics, and to build the database into a “social network” – being able to “friend” or “like” the data.
1. The document discusses a librarian's project using their hospital's EHR data in their medical school curriculum.
2. It describes the librarian's education in biomedical informatics and their discovery of databases like i2b2 and REDCap that can be used to analyze EHR data for research and teaching.
3. The librarian aims to incorporate the use of these databases into the medical school curriculum to help students learn how to retrieve and analyze patient data from EHRs.
Philip Bourne discusses the opportunities for data science in addressing diabetes. Data science involves using diverse digital data to ask and answer relevant questions, arriving at statistically significant conclusions not otherwise possible. It also involves sharing findings in a way that can improve lives. Diabetes is well-suited for data science approaches due to increasing data from genomics, wearables, electronic health records, and predictive modeling successes. However, data science must be done carefully with input from experts to account for confounders and ensure accurate outcomes for complex health issues like diabetes.
The document summarizes a workshop hosted by the NIH Associate Director for Data Science to discuss charting the future of data science at NIH. The workshop goals were to get input from all stakeholders, identify strategic directions, policies, and funding initiatives, and have participants leave as advocates and supporters. The agenda included providing background, open discussion, identifying topics for breakout groups, subgroup discussions, and reporting back. The document provides context on current NIH data science efforts and examples of collaborators in building a biomedical research digital enterprise.
Will Biomedical Research Fundamentally Change in the Era of Big Data?Philip Bourne
This document discusses how biomedical research may fundamentally change in the era of big data. It notes that biomedical research has always been data-driven, but the scope, variety, complexity and volume of data is now much greater. It also discusses the need for more open data sharing and new tools and methods for large-scale analysis. The document suggests biomedical research may move towards a more collaborative "platform" model, as seen with companies like Airbnb, with the goal of improving data access, reuse and reproducibility of research. However, overcoming challenges like incentives, trust and work practices will be important for any new platform to succeed.
Philip Bourne presented his viewpoint on the future of open science at an NIAID workshop. He argued that as science becomes more democratized, it will lead to more scrutiny of research, a need for new types of rewards beyond publications and citations, and a removal of artificial boundaries between fields. As an example, he discussed how open science allowed two researchers working in different areas to connect via common data references in their notebooks. Bourne believes this digitization and interconnection of research will accelerate, transforming institutions into digital enterprises where digital assets are identifiable and interoperable. However, fully realizing this vision will require coordinating tools across the research lifecycle through common frameworks and developing new support structures.
Presented in the workshop session "What Bioinformaticians Need to Know about Digital Publishing Beyond the PDF" at ISMB 2013 in Berlin. https://www.iscb.org/cms_addon/conferences/ismbeccb2013/workshops.php
Data Science and AI in Biomedicine: The World has ChangedPhilip Bourne
This document discusses the changing landscape of data science and AI in biomedicine. Some key points:
- We are at a tipping point where data science is becoming a driver of biomedical research rather than just a tool. Biomedical researchers need to become data scientists.
- Data science is interdisciplinary and touches every field due to the rise of digital data. It requires openness, translation of findings, and consideration of responsibilities like algorithmic bias.
- Advances like AlphaFold2 show the power of large collaborative efforts combining data, computing resources, engineering, and domain expertise. This points to the need for public-private partnerships and new models of open data sharing.
- The definition of
AI in Medical Education A Meta View to Start a ConversationPhilip Bourne
- AI has the potential to significantly impact medical education and healthcare.
- Chatbots and large language models can provide a rich training ground for students to learn, while augmented reality may change the student-patient dynamic.
- AI tools like predictive analytics and imaging analysis can assist in research, diagnosis, and personalized treatment, but models are still limited and education of implications is needed.
- If developed responsibly with oversight, AI could help democratize healthcare and create new industries, but history shows technology disruptions can also lead to deception if misused. The impacts and timeline of AI in medicine remain uncertain.
AI+ Now and Then How Did We Get Here And Where Are We GoingPhilip Bourne
The document discusses the past, present, and future of artificial intelligence (AI). It describes how AI has advanced due to increases in data and improvements in algorithms and computing technology. An example of AI, ChatGPT, is discussed as using large language models, pre-training, and transformers to generate language. The future of AI is uncertain but could involve neural networks that mimic the brain more closely. AI may disrupt many industries like education and research in the coming years or decades through forces of digitization, disruption, and other factors. The impacts and timeline of AI progress are difficult to predict precisely.
Thoughts on Biological Data SustainabilityPhilip Bourne
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.
The document discusses FAIR data and its importance. FAIR stands for Findable, Accessible, Interoperable, and Reusable. The author argues that data science is becoming a major driver in many fields due to the large amounts of digital data being created. For data and data science to reach their full potential, data needs to be FAIR so it can be easily discovered, accessed, integrated and reused. An example is given of a researcher combining health and vehicle crash data using techniques from data science to improve emergency care. Making data FAIR enables greater collaboration, public-private partnerships and opportunities for translation.
Data Science Meets Biomedicine, Does Anything ChangePhilip Bourne
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.
Presented online as part of the NASM series in Advancing Drug Discovery see https://www.nationalacademies.org/event/40883_09-2023_advancing-drug-discovery-data-science-meets-drug-discovery
Biomedical Data Science: We Are Not AlonePhilip Bourne
This document discusses biomedical data science and the opportunities and challenges presented by new developments in data science. Some key points:
- 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
BIMS7100-2023. Social Responsibility in ResearchPhilip Bourne
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.
What Data Science Will Mean to You - One Person's ViewPhilip Bourne
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.
The document provides an overview of the School of Data Science at the University of Virginia and its approach to collaborating with Novo Nordisk on diabetes research. It discusses that the School of Data Science aims to catalyze discovery through interdisciplinary research, educate a diverse workforce, and serve the community by applying data science. It also provides examples of using artificial intelligence to recognize patterns related to diabetes and details potential areas of collaboration between the School and Novo Nordisk, including student projects, visiting fellows, faculty partnerships, and PhD mentorship.
Towards a US Open research Commons (ORC)Philip Bourne
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.
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.
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 ScalesPhilip Bourne
3 basic points when establishing a new biomedical initiative. Presented at Frontiers of Computing in Health and Society, George Mason University, September 21, 2021.
The document discusses the importance of social responsibility in research. It makes three key points:
1. Research should maximize benefit to society by making findings accessible and usable by the public who funds the research.
2. Under certain conditions like privacy, all research should be openly accessible so others can build upon it.
3. Most research data is lost within 10-15 years of publication according to studies, highlighting the need for open data standards to ensure long-term availability.
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
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تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
PhRMA Some Early Thoughts
1. PhRMA: Some Early Thoughts
Philip E. Bourne Ph.D.
Associate Director for Data Science
National Institutes of Health
2. Prior History
Professor UCSD - Research structural bioinformatics,
systems pharmacology
Assoc. Vice Chancellor for Innovation UCSD
Co-directed the RCSB Protein Data Bank
Co-founded PLOS Computational Biology; First EIC
3. Disclaimer: I only started March 3,
2014
…but I had been thinking about this prior to my
appointment
http://pebourne.wordpress.com/2013/12/
8. NIH Data & Informatics Working
Group
In response to the growth of large biomedical
datasets, the Director of NIH established a
special Data and Informatics Working Group
(DIWG).
9. Big Data to Knowledge (BD2K)
1. Facilitating Broad Use
2. Developing and Disseminating Analysis
Methods and Software
3. Enhancing Training
4. Establishing Centers of Excellence
http://bd2k.nih.gov
10. Policies
PubMed Central
Data sharing policy
OSTP Directive
– Genomic data sharing policy
Leading edge of further policies for different data
types including clinical data eg to address issues with
DbGap
13. * http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm
Jan. 2008 Jan. 2009 Jan. 2010Jul. 2009Jul. 2008 Jul. 2010
1RUZ: 1918 H1 Hemagglutinin
Structure Summary page activity for
H1N1 Influenza related structures
3B7E: Neuraminidase of A/Brevig Mission/1/1918
H1N1 strain in complex with zanamivir
[Andreas Prlic]
Consider What
Might Be Possible
14. We Need to Learn from Industries Whose
Livelihood Addresses the Question of Use
15. Some Early Observations
1. We don’t know enough about how existing data are
used
2. We have focused on the why, but not the how
16. 2. We have focused on the why, but
not the how
The OSTP directive is the why
The how is needed for:
– Any data that does not fit the existing data resource model
• Data generated by NIH cores
• Data accompanying publications
• Data associated with the long tail of science
17. Considering a Data Commons to
Address this Need
AKA NIH drive – a dropbox for NIH investigators
Support for provenance and access control
Likely in the cloud
Support for validation of specific data types
Support for mining of collective intramural and
extramural data across IC’s
Needs to have an associated business model
http://100plus.com/wp-content/uploads/Data-Commons-3-1024x825.png
18. Some Early Observations
1. We don’t know enough about how existing data are
used
2. We have focused on the why, but not the how
3. We do not have an NIH-wide sustainability plan for
data (not heard of an IC-based plan either)
19. 3. Sustainability
Problems
– Maintaining a work force – lack of reward
– Too much data; too few dollars
– Resources
• In different stages of maturity but treated the same
• Funded by a few used by many
– True as measured by IC
– True as measured by agency
– True as measured by country
• Reviews can be problematic
20. 3. Sustainability
Solutions
– Establish a central fund to support
– New funding models eg open submission and review
– Split innovation from core support and review separately
– Policies for uniform metric reporting
– Discuss with the private sector possible funding models
– More cooperation, less redundancy across agencies
– Bring foundations into the discussion
– Discuss with libraries, repositories their role
– Educate decision makes as to the changing landscape
21. Some Early Observations
1. We don’t know enough about how existing data are
used
2. We have focused on the why, but not the how
3. We do not have an NIH-wide sustainability plan for
data (not heard of an IC-based plan either)
4. Training in biomedical data science is spotty
22. 4. Training in biomedical data science
is spotty
Problem
– Coverage of the domain is unclear
– There may well be redundancies
Solution
– Cold Spring Harbor like training facility(s)
• Training coordinator
• Rolling hands on courses in key areas
• Appropriate materials on-line
23. Some Early Observations
1. We don’t know enough about how existing data are
used
2. We have focused on the why, but not the how
3. We do not have an NIH-wide sustainability plan for
data (not heard of an IC-based plan either)
4. Training in biomedical data science is spotty
5. Reproducibility will need to be embraced
25. Long Term I think of these solutions
as something I call the digital
enterprise
Any institution is a candidate to be a digital
enterprise, but lets explore it in the context
of the academic medical center
26. Components of The Academic Digital
Enterprise
Consists of digital assets
– E.g. datasets, papers, software, lab notes
Each asset is uniquely identified and has
provenance, including access control
– E.g. publishing simply involves changing the access control
Digital assets are interoperable across the enterprise
27. Life in the Academic Digital Enterprise
Jane scores extremely well in parts of her graduate on-line neurology class.
Neurology professors, whose research profiles are on-line and well described, are
automatically notified of Jane’s potential based on a computer analysis of her scores
against the background interests of the neuroscience professors. Consequently,
professor Smith interviews Jane and offers her a research rotation. During the
rotation she enters details of her experiments related to understanding a widespread
neurodegenerative disease in an on-line laboratory notebook kept in a shared on-line
research space – an institutional resource where stakeholders provide metadata,
including access rights and provenance beyond that available in a commercial
offering. According to Jane’s preferences, the underlying computer system may
automatically bring to Jane’s attention Jack, a graduate student in the chemistry
department whose notebook reveals he is working on using bacteria for purposes of
toxic waste cleanup. Why the connection? They reference the same gene a number
of times in their notes, which is of interest to two very different disciplines – neurology
and environmental sciences. In the analog academic health center they would never
have discovered each other, but thanks to the Digital Enterprise, pooled knowledge
can lead to a distinct advantage. The collaboration results in the discovery of a
homologous human gene product as a putative target in treating the
neurodegenerative disorder. A new chemical entity is developed and patented.
Accordingly, by automatically matching details of the innovation with biotech
companies worldwide that might have potential interest, a licensee is found. The
licensee hires Jack to continue working on the project. Jane joins Joe’s laboratory,
and he hires another student using the revenue from the license. The research
continues and leads to a federal grant award. The students are employed, further
research is supported and in time societal benefit arises from the technology.
From What Big Data Means to Me JAMIA 2014 21:194
28. Life in the NIH Digital Enterprise
Researcher x is made aware of researcher y through
commonalities in their data located in the data commons.
Researcher x reviews the grants profile of researcher y and
publication history and impact from those grants in the past 5
years and decides to contact her. A fruitful collaboration ensues
and they generate papers, data sets and software. Metrics
automatically pushed to company z for all relevant NIH data and
software in a specific domain with utilization above a threshold
indicate that their data and software are heavily utilized and
respected by the community. An open source version remains,
but the company adds services on top of the software for the
novice user and revenue flows back to the labs of researchers x
and y which is used to develop new innovative software for
open distribution. Researchers x and y come to the NIH training
center periodically to provide hands-on advice in the use of their
new version and their course is offered as a MOOC.
29. To get to that end point we have to
consider the complete research
lifecycle
30. The Research Life Cycle will Persist
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
31. Tools and Resources Will Continue To
Be Developed
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
32. Those Elements of the Research Life Cycle will
Become More Interconnected Around a Common
Framework
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
33. New/Extended Support Structures Will
Emerge
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
Commercial &
Public Tools
Git-like
Resources
By Discipline
Data Journals
Discipline-
Based Metadata
Standards
Community Portals
Institutional Repositories
New Reward
Systems
Commercial Repositories
Training
34. We Have a Ways to Go
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
Commercial &
Public Tools
Git-like
Resources
By Discipline
Data Journals
Discipline-
Based Metadata
Standards
Community Portals
Institutional Repositories
New Reward
Systems
Commercial Repositories
Training
35. How PhRMA Can Be Engaged?
Representation on ADDS Advisory?
Participate in forthcoming workshops
– High level strategy
– Implementation
Other ideas?