Responsible Conduct of Research: Data ManagementKristin Briney
This presentation was given by myself and Brad Houston (http://www.slideshare.net/herodotusjr), for UWM's Responsible Conduct of Research (RCR) series in Fall of 2013. It covers data management plans and practical data management tips. The corresponding handout is also available on Slideshare: http://www.slideshare.net/kbriney/rcr-data-management-handout
Prof George Alter, UMich, ICPSR, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17.
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
2) Prof George Alter, (Research Professor, ICPSR and Visiting Professor, ANU) George will share the benefit of over 50 years of experience in managing sensitive social science data in the ICPSR: https://www.icpsr.umich.edu/icpsrweb/
More about ICPSR: -- ICPSR (USA) maintains a data archive of more than 250,000 files of research in the social and behavioral sciences. It hosts 21 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields. -- ICPSR collaborates with a number of funders, including U.S. statistical agencies and foundations, to create thematic collections: see https://www.icpsr.umich.edu/icpsrweb/content/about/thematic-collections.html
Workshop - finding and accessing data - Cambridge August 22 2016Fiona Nielsen
Finding and accessing human genomic data for research
University of Cambridge, United Kingdom | Seminar Room G
Monday, 22 August 2016 from 10:00 to 12:00 (BST)
Charlotte, Nadia and Fiona presented an overview of data sources around the world where you can find genomics data for your research and gave examples of the data access application for dbGaP and EGA with specific details relevant for University of Cambridge researchers.
Managing sensitive data at the Australian Data ArchiveARDC
Dr Steven McEachern, Director, Australian Data Archive, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
1) Dr Steve McEachern (Director, Aust Data Archive) Stevediscussed how the Australian Data Archive manages and publishes sensitive social science data.
More about ADA: -- The Australian Data Archive (ADA) provides a national service for the collection and preservation of digital research data and to make these data available for secondary analysis by academic researchers and other users. -- The ADA is comprised of seven sub-archives - Social Science, HIstorical, Indigenous, Longitudinal, Qualitative, Crime & Justice and International. -- ADA data is free of charge to all users -- The archive is managed by the ADA central office based in the ANU Centre for Social Research and Methods at the Australian National University (ANU).https://www.ada.edu.au/
The world of research data: when should data be closed, shared or openheila1
That research data should be shared with the rest of the world has become almost evangelical in nature. This paper will try to answer the following questions:
• What are the (real) reasons for ‘forcing’ scientists to open their data, even if they are not ready to do so?
• What right have non-scientists (and scientists) to push indiscriminately for the sharing of data without taking the nuances of research into consideration?
Physical characteristics of research data before it can be shared
Modes of data sharing
Case study: public humiliation in the name of Open Science
Advantages and disadvantages of sharing research data
AI to the rescue of open research articles?
In conclusion
Jonathan Tedds Distinguished Lecture at DLab, UC Berkeley, 12 Sep 2013: "The ...Jonathan Tedds
http://dlab.berkeley.edu/event/open-research-challenge-peer-review-and-publication-research-data
A talk by Dr. Jonathan Tedds, Senior Research Fellow, D2K Data to Knowledge, Dept of Health Sciences, University of Leicester.
PI: #BRISSKit www.brisskit.le.ac.uk
PI: #PREPARDE www.le.ac.uk/projects/preparde
The Peer REview for Publication & Accreditation of Research data in the Earth sciences (PREPARDE) project seeks to capture the processes and procedures required to publish a scientific dataset, ranging from ingestion into a data repository, through to formal publication in a data journal. It will also address key issues arising in the data publication paradigm, namely, how does one peer-review a dataset, what criteria are needed for a repository to be considered objectively trustworthy, and how can datasets and journal publications be effectively cross-linked for the benefit of the wider research community.
I will discuss this and alternative approaches to research data management and publishing through examples in astronomy, biomedical and interdisciplinary research including the arts and humanities. Who can help in the long tail of research if lacking established data centers, archives or adequate institutional support? How much can we transfer from the so called “big data” sciences to other settings and where does the institution fit in with all this? What about software?
Publishing research data brings a wide and differing range of challenges for all involved, whatever the discipline. In PREPARDE we also considered the pre and post publication peer review paradigm, as implemented in the F1000 Research Publishing Model for the life sciences. Finally, in an era of truly international research how might we coordinate the many institutional, regional, national and international initiatives – has the time come for an international Research Data Alliance?
Responsible Conduct of Research: Data ManagementKristin Briney
This presentation was given by myself and Brad Houston (http://www.slideshare.net/herodotusjr), for UWM's Responsible Conduct of Research (RCR) series in Fall of 2013. It covers data management plans and practical data management tips. The corresponding handout is also available on Slideshare: http://www.slideshare.net/kbriney/rcr-data-management-handout
Prof George Alter, UMich, ICPSR, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17.
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
2) Prof George Alter, (Research Professor, ICPSR and Visiting Professor, ANU) George will share the benefit of over 50 years of experience in managing sensitive social science data in the ICPSR: https://www.icpsr.umich.edu/icpsrweb/
More about ICPSR: -- ICPSR (USA) maintains a data archive of more than 250,000 files of research in the social and behavioral sciences. It hosts 21 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields. -- ICPSR collaborates with a number of funders, including U.S. statistical agencies and foundations, to create thematic collections: see https://www.icpsr.umich.edu/icpsrweb/content/about/thematic-collections.html
Workshop - finding and accessing data - Cambridge August 22 2016Fiona Nielsen
Finding and accessing human genomic data for research
University of Cambridge, United Kingdom | Seminar Room G
Monday, 22 August 2016 from 10:00 to 12:00 (BST)
Charlotte, Nadia and Fiona presented an overview of data sources around the world where you can find genomics data for your research and gave examples of the data access application for dbGaP and EGA with specific details relevant for University of Cambridge researchers.
Managing sensitive data at the Australian Data ArchiveARDC
Dr Steven McEachern, Director, Australian Data Archive, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
1) Dr Steve McEachern (Director, Aust Data Archive) Stevediscussed how the Australian Data Archive manages and publishes sensitive social science data.
More about ADA: -- The Australian Data Archive (ADA) provides a national service for the collection and preservation of digital research data and to make these data available for secondary analysis by academic researchers and other users. -- The ADA is comprised of seven sub-archives - Social Science, HIstorical, Indigenous, Longitudinal, Qualitative, Crime & Justice and International. -- ADA data is free of charge to all users -- The archive is managed by the ADA central office based in the ANU Centre for Social Research and Methods at the Australian National University (ANU).https://www.ada.edu.au/
The world of research data: when should data be closed, shared or openheila1
That research data should be shared with the rest of the world has become almost evangelical in nature. This paper will try to answer the following questions:
• What are the (real) reasons for ‘forcing’ scientists to open their data, even if they are not ready to do so?
• What right have non-scientists (and scientists) to push indiscriminately for the sharing of data without taking the nuances of research into consideration?
Physical characteristics of research data before it can be shared
Modes of data sharing
Case study: public humiliation in the name of Open Science
Advantages and disadvantages of sharing research data
AI to the rescue of open research articles?
In conclusion
Jonathan Tedds Distinguished Lecture at DLab, UC Berkeley, 12 Sep 2013: "The ...Jonathan Tedds
http://dlab.berkeley.edu/event/open-research-challenge-peer-review-and-publication-research-data
A talk by Dr. Jonathan Tedds, Senior Research Fellow, D2K Data to Knowledge, Dept of Health Sciences, University of Leicester.
PI: #BRISSKit www.brisskit.le.ac.uk
PI: #PREPARDE www.le.ac.uk/projects/preparde
The Peer REview for Publication & Accreditation of Research data in the Earth sciences (PREPARDE) project seeks to capture the processes and procedures required to publish a scientific dataset, ranging from ingestion into a data repository, through to formal publication in a data journal. It will also address key issues arising in the data publication paradigm, namely, how does one peer-review a dataset, what criteria are needed for a repository to be considered objectively trustworthy, and how can datasets and journal publications be effectively cross-linked for the benefit of the wider research community.
I will discuss this and alternative approaches to research data management and publishing through examples in astronomy, biomedical and interdisciplinary research including the arts and humanities. Who can help in the long tail of research if lacking established data centers, archives or adequate institutional support? How much can we transfer from the so called “big data” sciences to other settings and where does the institution fit in with all this? What about software?
Publishing research data brings a wide and differing range of challenges for all involved, whatever the discipline. In PREPARDE we also considered the pre and post publication peer review paradigm, as implemented in the F1000 Research Publishing Model for the life sciences. Finally, in an era of truly international research how might we coordinate the many institutional, regional, national and international initiatives – has the time come for an international Research Data Alliance?
Towards a Threat Hunting Automation Maturity ModelAlex Pinto
Threat Hunting has been commonly definable as a series of investigative actions that should be performed by analyst teams to cover detection gaps where automated tools fail. However, as those techniques become more and more widespread and standardized, wouldn’t it be the case that we can automate a large part of those threat hunting activities, creating a definition oxymoron?
In this session, we will demonstrate how some threat hunting techniques can be automated or constructed to augment human activity by encoding analyst intuition into repeatable data extraction and processing technologies. Those techniques can be used to simplify the triage stage and get actionable information from potential threats with minimal human interaction. We then present a Hunting Automation Maturity Model (HAMM) that organizes these techniques around capability milestones, including internal and external context and analytical tooling.
The Emerging Discipline of Data Science: Principles and Techniques for Data-Intensive Analysis, Keynote, 2nd Swiss Workshop on Data Science – SDS|2015, Winterthur, Switzerland, 12 June 2015
Abstract and other presentations at: http://michaelbrodie.com/?page_id=17
Jim Wojno: Incident Response - No Pain, No Gain!centralohioissa
Say incident response to 10 people and odds are you'll get 10 different opinions on how to do it right. When evaluating tools and procedures for enterprise Incident Response it's helpful to understand how to approach this in a way that will cause the adversary maximum pain. This talk will review the essential requirements for IR tools and procedures in a vendor / tool neutral approach. Find out the right questions to ask and the strategies to make sure you get the most out of your incident response team.
No Free Lunch: Metadata in the life sciencesChris Dwan
This presentation covers some challenges and makes suggestions to support the work of creating flexible, interoperable data systems for the life sciences.
Reforming Medical Device approval processes especially in software requires careful consideration of shifting risks to patients without adequate protections.
I gave this prezo to Auckland Regional Clinical IS Leadership Group on Feb 21, 2014. It shows how difficult it can be to deal with certain kinds of health information when developing systems by an impressive example (originally from Dr. Sam Heard). Therefore we need rigorous and scientific methods to tackle this - in this case using openEHR's multi-level modelling approach to create a single content model from which all health information exchange payload definitions will be derived. New Zealand's Interoperability Reference Architecture (HISO 10040) is underpinned by openEHR Archetypes to create this content model. The bottom line of the prezo is that almost every national programme starts health information standardisation from the wrong place; most of them are complex technical speficifications, like CDA, which are almost impossible for clinicians to comprehend and provide feedback. The process is flawed! Instead it should start from simple to understand representations, such as simple diagrams, mindmaps etc.and then handed over to techies once clinical validity and utility is agreed upon.That's the beauty of Archetype approach - great tooling and the Clinical Knowledge Manager (CKM) enable clinicians and other domain experts to collaborate and develop clinical models easily.
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.
Elaine Martin, MSLS, DA, Donna Kafel, RN, MSLS, and Andrew Creamer, MaEd, MSLS of UMass Medical School''s Lamar Soutter Library present Best Practices for Managing Data. The presentation features the importance of managing data for research projects, and tactical best practice initiatives to create a data management and sharing plan, including how to preserve label, secure, store, and preserve data. Issues, such as licensing, data dictionaries, regulations, and metadata are addressed in the presentation.
Supporting a Collaborative R&D Organization with a Dynamic Big Data SolutionSaama
Nikhil Gopinath presents regarding big data solutions at the Big Data and Analytics for Healthcare and Life Sciences Summit on October 18, 2017 in San Francisco, CA.
Large amounts of antibiotics used for human therapy result in the selection of pathogenic bacteria resistant to multiple drugs, creating a burden on medical care in hospitals, especially for patients admitted to intensive care units (ICU).
Employing Machine learning techniques and building models, better approaches and preventive ways can thus be introduced to lower mortality rates & costs
ASSESSMENT OF BIOMEDICAL LITERATURE
Components of internal and external validity of controlled clinical trials
Internal validity — extent to which systematic error (bias) is minimized in clinical trials
Selection bias: biased allocation to comparison groups
Performance bias: unequal provision of care apart from treatment under evaluation
Detection bias: biased assessment of outcome
Attrition bias: biased occurrence and handling of deviations from protocol and loss to follow up
Requirements, needs
Planning, direction
Information collection
Information Assessment
- Evaluation for accuracy, correctness, relevance, usefulness
- Source reliability assessment (competency and past behavior based)
- Bias assessment (motivators, interests, funding, objectives)
- Conflicts of interest
- Sources of funding, important business relationships
- Grading of individual items (study, report, analysis, article)
Collation of information
- Exclusion of irrelevant, incorrect, and useless information
-Arrangement of information in a form which enables real-time analysis
- System for rapid retrieval of information
External validity — extent to which results of trials provide a correct basis for generalization to other circumstances
Patients: age, sex, severity of disease and risk factors, comorbidity
Treatment regimens: dosage, timing and route of administration, type of treatment within a class of treatments, concomitant treatments
Settings: level of care (primary to tertiary) and experience and specialization of care provider
Modalities of outcomes: type or definition of outcomes and duration of follow up
How to successfully provide the pre-hospital medical oversight that EMS professionals want so they can improve patient outcomes while enhancing EMS agency operations with limited resources.
How to 'hack' the data world without having a computer expert on standby. Why the professionalization of paramedicine is important? When will we be professionals? How will professionalization affect the future of EMS?
Towards a Threat Hunting Automation Maturity ModelAlex Pinto
Threat Hunting has been commonly definable as a series of investigative actions that should be performed by analyst teams to cover detection gaps where automated tools fail. However, as those techniques become more and more widespread and standardized, wouldn’t it be the case that we can automate a large part of those threat hunting activities, creating a definition oxymoron?
In this session, we will demonstrate how some threat hunting techniques can be automated or constructed to augment human activity by encoding analyst intuition into repeatable data extraction and processing technologies. Those techniques can be used to simplify the triage stage and get actionable information from potential threats with minimal human interaction. We then present a Hunting Automation Maturity Model (HAMM) that organizes these techniques around capability milestones, including internal and external context and analytical tooling.
The Emerging Discipline of Data Science: Principles and Techniques for Data-Intensive Analysis, Keynote, 2nd Swiss Workshop on Data Science – SDS|2015, Winterthur, Switzerland, 12 June 2015
Abstract and other presentations at: http://michaelbrodie.com/?page_id=17
Jim Wojno: Incident Response - No Pain, No Gain!centralohioissa
Say incident response to 10 people and odds are you'll get 10 different opinions on how to do it right. When evaluating tools and procedures for enterprise Incident Response it's helpful to understand how to approach this in a way that will cause the adversary maximum pain. This talk will review the essential requirements for IR tools and procedures in a vendor / tool neutral approach. Find out the right questions to ask and the strategies to make sure you get the most out of your incident response team.
No Free Lunch: Metadata in the life sciencesChris Dwan
This presentation covers some challenges and makes suggestions to support the work of creating flexible, interoperable data systems for the life sciences.
Reforming Medical Device approval processes especially in software requires careful consideration of shifting risks to patients without adequate protections.
I gave this prezo to Auckland Regional Clinical IS Leadership Group on Feb 21, 2014. It shows how difficult it can be to deal with certain kinds of health information when developing systems by an impressive example (originally from Dr. Sam Heard). Therefore we need rigorous and scientific methods to tackle this - in this case using openEHR's multi-level modelling approach to create a single content model from which all health information exchange payload definitions will be derived. New Zealand's Interoperability Reference Architecture (HISO 10040) is underpinned by openEHR Archetypes to create this content model. The bottom line of the prezo is that almost every national programme starts health information standardisation from the wrong place; most of them are complex technical speficifications, like CDA, which are almost impossible for clinicians to comprehend and provide feedback. The process is flawed! Instead it should start from simple to understand representations, such as simple diagrams, mindmaps etc.and then handed over to techies once clinical validity and utility is agreed upon.That's the beauty of Archetype approach - great tooling and the Clinical Knowledge Manager (CKM) enable clinicians and other domain experts to collaborate and develop clinical models easily.
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.
Elaine Martin, MSLS, DA, Donna Kafel, RN, MSLS, and Andrew Creamer, MaEd, MSLS of UMass Medical School''s Lamar Soutter Library present Best Practices for Managing Data. The presentation features the importance of managing data for research projects, and tactical best practice initiatives to create a data management and sharing plan, including how to preserve label, secure, store, and preserve data. Issues, such as licensing, data dictionaries, regulations, and metadata are addressed in the presentation.
Supporting a Collaborative R&D Organization with a Dynamic Big Data SolutionSaama
Nikhil Gopinath presents regarding big data solutions at the Big Data and Analytics for Healthcare and Life Sciences Summit on October 18, 2017 in San Francisco, CA.
Large amounts of antibiotics used for human therapy result in the selection of pathogenic bacteria resistant to multiple drugs, creating a burden on medical care in hospitals, especially for patients admitted to intensive care units (ICU).
Employing Machine learning techniques and building models, better approaches and preventive ways can thus be introduced to lower mortality rates & costs
ASSESSMENT OF BIOMEDICAL LITERATURE
Components of internal and external validity of controlled clinical trials
Internal validity — extent to which systematic error (bias) is minimized in clinical trials
Selection bias: biased allocation to comparison groups
Performance bias: unequal provision of care apart from treatment under evaluation
Detection bias: biased assessment of outcome
Attrition bias: biased occurrence and handling of deviations from protocol and loss to follow up
Requirements, needs
Planning, direction
Information collection
Information Assessment
- Evaluation for accuracy, correctness, relevance, usefulness
- Source reliability assessment (competency and past behavior based)
- Bias assessment (motivators, interests, funding, objectives)
- Conflicts of interest
- Sources of funding, important business relationships
- Grading of individual items (study, report, analysis, article)
Collation of information
- Exclusion of irrelevant, incorrect, and useless information
-Arrangement of information in a form which enables real-time analysis
- System for rapid retrieval of information
External validity — extent to which results of trials provide a correct basis for generalization to other circumstances
Patients: age, sex, severity of disease and risk factors, comorbidity
Treatment regimens: dosage, timing and route of administration, type of treatment within a class of treatments, concomitant treatments
Settings: level of care (primary to tertiary) and experience and specialization of care provider
Modalities of outcomes: type or definition of outcomes and duration of follow up
How to successfully provide the pre-hospital medical oversight that EMS professionals want so they can improve patient outcomes while enhancing EMS agency operations with limited resources.
How to 'hack' the data world without having a computer expert on standby. Why the professionalization of paramedicine is important? When will we be professionals? How will professionalization affect the future of EMS?
Finding The Answers That Are Right Under Your FeetNick Nudell
As an EMS executive, keeping up with the burden of requirements for contractual reasons or accountability while preparing your operation for the future has your time stretched thin. With so much of your organization geared towards collecting and reporting information to others, finding the time and responsive tools for your own internal benchmarking and performance improvement can be a challenge. Furthermore, with new national performance standards coming, combined with the pressures on your existing operation, performance improvement driven by your internal data may seem daunting. This talk looks at the potential that executives have today to harness the data in their organization and transform it into information that highlights the areas of friction in your organization. Nick Nudell will share his insights on some of the various analytical tools and methods that EMS executives can use today track their clinical, operational, and safety performance in real time today for actionable positive change in their organization.
EMS Compass Overview Call For Measures May 2015Nick Nudell
The EMS Compass Initiative opened a call for measures to be submitted during May 2015. This provides an overview of the project and how these performance measures will be designed by EMS and used by EMS providers. The measures will demonstrate the value of EMS care for a community and for patients.
Paramedic Information Privacy Security and Assurance Alliance iCERT 2015Nick Nudell
Paramedic data systems supporting clinical and business operations are now very sophisticated. Managing these systems requires special training and credentialing for safe and secure paramedic operations. The Paramedic Information Privacy Security & Assurance Alliance (PIPSAA) introduced this subject to the Industry Council for Emergency Response Technologies (iCERT) 2015 forum on Cybersecurity.
Electronic Patient Tracking Intro For Healthcare 2005Nick Nudell
Tracking of patients is important. Here's a presentation describing the first application of electronic technologies for patient tracking - that I authored as an employee of the City and County of San Francisco in 2004.
Through the EMS Compass initiative, the EMS community will develop tools that can be used to measure EMS system performance and the quality of patient care. This will lead to unprecedented capability for local EMS agencies, systems, regions and states to assess conditions and embark on widespread improvement.
This session provides a comprehensive overview of the latest updates to the Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards (commonly known as the Uniform Guidance) outlined in the 2 CFR 200.
With a focus on the 2024 revisions issued by the Office of Management and Budget (OMB), participants will gain insight into the key changes affecting federal grant recipients. The session will delve into critical regulatory updates, providing attendees with the knowledge and tools necessary to navigate and comply with the evolving landscape of federal grant management.
Learning Objectives:
- Understand the rationale behind the 2024 updates to the Uniform Guidance outlined in 2 CFR 200, and their implications for federal grant recipients.
- Identify the key changes and revisions introduced by the Office of Management and Budget (OMB) in the 2024 edition of 2 CFR 200.
- Gain proficiency in applying the updated regulations to ensure compliance with federal grant requirements and avoid potential audit findings.
- Develop strategies for effectively implementing the new guidelines within the grant management processes of their respective organizations, fostering efficiency and accountability in federal grant administration.
Understanding the Challenges of Street ChildrenSERUDS INDIA
By raising awareness, providing support, advocating for change, and offering assistance to children in need, individuals can play a crucial role in improving the lives of street children and helping them realize their full potential
Donate Us
https://serudsindia.org/how-individuals-can-support-street-children-in-india/
#donatefororphan, #donateforhomelesschildren, #childeducation, #ngochildeducation, #donateforeducation, #donationforchildeducation, #sponsorforpoorchild, #sponsororphanage #sponsororphanchild, #donation, #education, #charity, #educationforchild, #seruds, #kurnool, #joyhome
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Up the Ratios Bylaws - a Comprehensive Process of Our Organizationuptheratios
Up the Ratios is a non-profit organization dedicated to bridging the gap in STEM education for underprivileged students by providing free, high-quality learning opportunities in robotics and other STEM fields. Our mission is to empower the next generation of innovators, thinkers, and problem-solvers by offering a range of educational programs that foster curiosity, creativity, and critical thinking.
At Up the Ratios, we believe that every student, regardless of their socio-economic background, should have access to the tools and knowledge needed to succeed in today's technology-driven world. To achieve this, we host a variety of free classes, workshops, summer camps, and live lectures tailored to students from underserved communities. Our programs are designed to be engaging and hands-on, allowing students to explore the exciting world of robotics and STEM through practical, real-world applications.
Our free classes cover fundamental concepts in robotics, coding, and engineering, providing students with a strong foundation in these critical areas. Through our interactive workshops, students can dive deeper into specific topics, working on projects that challenge them to apply what they've learned and think creatively. Our summer camps offer an immersive experience where students can collaborate on larger projects, develop their teamwork skills, and gain confidence in their abilities.
In addition to our local programs, Up the Ratios is committed to making a global impact. We take donations of new and gently used robotics parts, which we then distribute to students and educational institutions in other countries. These donations help ensure that young learners worldwide have the resources they need to explore and excel in STEM fields. By supporting education in this way, we aim to nurture a global community of future leaders and innovators.
Our live lectures feature guest speakers from various STEM disciplines, including engineers, scientists, and industry professionals who share their knowledge and experiences with our students. These lectures provide valuable insights into potential career paths and inspire students to pursue their passions in STEM.
Up the Ratios relies on the generosity of donors and volunteers to continue our work. Contributions of time, expertise, and financial support are crucial to sustaining our programs and expanding our reach. Whether you're an individual passionate about education, a professional in the STEM field, or a company looking to give back to the community, there are many ways to get involved and make a difference.
We are proud of the positive impact we've had on the lives of countless students, many of whom have gone on to pursue higher education and careers in STEM. By providing these young minds with the tools and opportunities they need to succeed, we are not only changing their futures but also contributing to the advancement of technology and innovation on a broader scale.
ZGB - The Role of Generative AI in Government transformation.pdfSaeed Al Dhaheri
This keynote was presented during the the 7th edition of the UAE Hackathon 2024. It highlights the role of AI and Generative AI in addressing government transformation to achieve zero government bureaucracy
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
A process server is a authorized person for delivering legal documents, such as summons, complaints, subpoenas, and other court papers, to peoples involved in legal proceedings.
Many ways to support street children.pptxSERUDS INDIA
By raising awareness, providing support, advocating for change, and offering assistance to children in need, individuals can play a crucial role in improving the lives of street children and helping them realize their full potential
Donate Us
https://serudsindia.org/how-individuals-can-support-street-children-in-india/
#donatefororphan, #donateforhomelesschildren, #childeducation, #ngochildeducation, #donateforeducation, #donationforchildeducation, #sponsorforpoorchild, #sponsororphanage #sponsororphanchild, #donation, #education, #charity, #educationforchild, #seruds, #kurnool, #joyhome
Canadian Immigration Tracker March 2024 - Key SlidesAndrew Griffith
Highlights
Permanent Residents decrease along with percentage of TR2PR decline to 52 percent of all Permanent Residents.
March asylum claim data not issued as of May 27 (unusually late). Irregular arrivals remain very small.
Study permit applications experiencing sharp decrease as a result of announced caps over 50 percent compared to February.
Citizenship numbers remain stable.
Slide 3 has the overall numbers and change.
3. Real World Problem
3
• What action to take?
• Where to do it?
• Who should do it?
• How quickly does it need to be done?
• Why was it done?
• Decisions!
paramedicfoundation.org
twitter.com/paramedicfound
facebook.com/ParamedicFoundation
4. Data Sources
4
• Caller phone number: call routing information, mobile/fixed,
single/multiple user (like an IP address), GPS/tower, eCall/Automatic
Crash Notification
• Resources/system status: what people, vehicles, equipment, etc.
• Environment: Weather, crowding & traffic (granular to the device),
street corner/high rise/wilderness, ferry/train/plane schedules
• Call center, paramedics, hospital, police records, fire records, public
health
• Social media: twitter, facebook, instagram, etc
paramedicfoundation.org
twitter.com/paramedicfound
facebook.com/ParamedicFoundation
5. Existing research
5
• 50 years of Operations Research / Management
• 25 years of decision tool/tree validation
• 10 years of clinical registry prediction tool validation
• 15 years of decision support in emergency calling “appropriateness”
• 6 months of deep data mining exploratory work
paramedicfoundation.org
twitter.com/paramedicfound
facebook.com/ParamedicFoundation
6. Why is it so complex?
6
• Chinese city with 9 million residents
• 2.5 calls per resident over 5 years (0.5/person/year)
• Repeat callers average 2.09 calls per year
• USA with 320 million residents
• 240 million 911 calls per year (0.75/person/year)
• 41,000 calls per Public Safety Answering Point
• $4.51 per call, just to maintain the ICT & dispatching system
• 10,000+ ICD10 diagnosis codes
• 19,000 EMS services across 50 states & 6 territories
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7. Categorization
7
• Started in 1978…
• 36 Families of problem types
• Level of Urgency: Hot or Not
• Omega, Alpha, Bravo, Charlie, Delta, Echo
• Nuanced descriptors help determine what
kind of first-aid instructions are to be given
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9. Decision Tree – Manual Deductive Reasoning
9
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• Dispatching priority relies on standardized keywords compared to
a known list of static scenarios
• IF
• Shooting THEN
• Urgently send police, apply tourniquet, stop bleeding.
• Not breathing/pulseless THEN
• Start CPR, urgently send paramedics
• Cardiac history THEN
• Urgently send paramedics, take aspirin, stay calm
• Known as clustering in computer science
10. Questions / Prioritization / Instructions
10
• Priorities designed to purposefully over-triage rather than increase
specificity as risk management tool
• Lots of vehicles / fewer vehicles
• Lights & Sirens / no L&S
• Queuing theory using probabilistic expected delays for paramedics,
police, or fire department responders
• Targeting the slowest delay possible because time=money
• Knowledge discovery opportunities are overlooked!
• Crowdsource trained people for faster response
• Electronic medical records describe historical risk
• Caller behavior, word choice, history, location, etc are untapped indicators
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11. Queuing Theory – Planning to Disappoint
11
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• Operations Research, Management Science, & Computer Science
disciplines rely on probabilistic calculations
• A model is constructed so that queue lengths and waiting time
can be predicted
• Interarrival time & service times are independent random variables
• Designed to select next task to perform
• The most commonly used laws are:
• FIFO - First In First Out: who comes earlier leaves earlier
• LIFO - Last Come First Out: who comes later leaves earlier
• RS - Random Service: the customer is selected randomly
• Priority
12. Erlang Call Center Algorithm
12
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Source: http://www.erlang.com/calculator/call/
Estimate how many agents you
need in your call center for
each hour during an eight hour
day…
How many taxis for a particular
time of day?
How many hospital beds? Fire
trucks? Paramedics? Police?
13. Natural Language Processing
13
• Machine learning to determine semantic meaning
• Based on ontologies and probabilistic decisions
• “Understanding” of words, meanings, intents
• Better suited for structured, grouped or otherwise trained text such as
physician narratives or same language categorization
• Excels at spelling, grammar, and Named Entity Recognition that are relatively
structured attributes
• Well suited for classifying/parsing simple or common statements
• Generally “trained” by humans (expensive)
• Handling unstructured data, stemming, bag of words, TF/IDF, topic modeling.
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14. Machine Learning - Inductive
14
• Learns from the information itself
• Classifier accuracy is similar to human experts
• Common Algorithm Types
• K-nearest neighbors (KNN)
• Linear regression
• Logistic regression
• Naive Bayes
• Decision trees, bagged trees, boosted trees, boosted stumps
• Random Forests
• AdaBoost
• Neural networks
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15. Comparing Supervised Learning Algorithms
15
Algorithm
Problem
Type
Results
interpretabl
e by you?
Easy to
explain
algorithm
to others?
Average
predictive
accuracy
Training speed
Prediction
speed
Amount of
parameter
tuning needed
(excluding
feature
selection)
Performs well
with small
number of
observations?
Handles lots of
irrelevant
features well
(separates signal
from noise)?
Automaticall
y learns
feature
interactions?
Gives
calibrated
probabilities
of class
membership?
Parametric
?
Features
might need
scaling?
KNN Either Yes Yes Lower Fast
Depends
on n
Minimal No No No Yes No Yes
Linear
regression
Regression Yes Yes Lower Fast Fast
None (excluding
regularization)
Yes No No N/A Yes
No (unless
regularized)
Logistic
regression
Classification Somewhat Somewhat Lower Fast Fast
None (excluding
regularization)
Yes No No Yes Yes
No (unless
regularized)
Naive Bayes Classification Somewhat Somewhat Lower
Fast (excluding
feature
extraction)
Fast
Some for feature
extraction
Yes Yes No No Yes No
Decision trees Either Somewhat Somewhat Lower Fast Fast Some No No Yes Possibly No No
Random
Forests
Either A little No Higher Slow Moderate Some No
Yes (unless noise
ratio is very high)
Yes Possibly No No
AdaBoost Either A little No Higher Slow Fast Some No Yes Yes Possibly No No
Neural
networks
Either No No Higher Slow Fast Lots No Yes Yes Possibly No Yes
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https://docs.google.com/spreadsheets/d/16i47Wmjpj8k-
mFRk-NnXXU5tmSQz8h37YxluDV8Zy9U/edit#gid=0
16. Support Vector Machine (SVM)
16
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Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011).
Natural language processing: an introduction. Journal of the
American Medical Informatics Association : JAMIA, 18(5), 544–
551. http://doi.org/10.1136/amiajnl-2011-000464
17. Algorithm Quality
17
• Very similar level of accuracy
between algorithms
• Will use similar attributes for
scoring
• May vary when categorical vs
continuous data
• Primary difference is in efficiency
• Big-O Notation is a relative
representation of the complexity of
an algorithm
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18. Random Forest
18
• Advantages
• It has been widely shown that random forests
are one of the most accurate existing
classification methods
• It can deal with a huge number of features
• It runs efficiently on large datasets
• It can help estimate which variables are
important in classification
• It can be extended to an unsupervised version
to work with unlabeled data.
• It is relatively robust to noise
• Disadvantages
• They tend to overt noisy data.
• Not as intuitive as some other classification
methods
• Might take a while to build the forest (but once
it's built classification is very fast)
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19. The Turing Test
19
• In 1950 Alan Turing wondered ‘Can computers think?’
• Proposed The Imitation Game
• Interrogator and two players, one human and one computer
• Based on typewritten responses the interrogator was to guess which
player was the computer
• He believed having adequate storage was the primary limiting factor
with speed being next
• Learning machine is like a child being taught
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Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.
20. Research Questions
20
• Can an a priori algorithmic, inductive reasoning based approach be
developed to:
• improve the speed of the decision making process during emergency call
taking and dispatching?
• improve the accuracy of the resource assignment for emergency call
dispatching?
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21. Discussion – Present Considerations
21
• Flowchart/Tree: veracity of the reporting party, socio-economic and
demographic factors of the patient/victim, the capability of the
responding unit, the quality of services provided by the responding
individual, and the specificity of the dispatching algorithm itself are
not factored into the decision model.
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22. Discussion – Future Considerations & Research
22
• Future research: develop an AI, ML based approach.
• Obtain detailed 911 call and electronic Patient Care Records for approximately
five million patients where an outcome is identified.
• unfounded/no merit, patient treated but not transported, patient treated and
transported, and patient transferred to another responder.
• The clinical condition at the time of the outcome will be determined based on standard
paramedic coding practices.
• Data split by randomization to a training dataset and test dataset.
• A Random Forest model built from training dataset then applied to test
dataset.
• Comparative statistics to evaluate the resource assignments, reduced
demand, and potential savings of the new model
• New knowledge model is a dynamic and real-time application
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