1) S3IT provides science IT support services to researchers at UZH, including access to infrastructure, software tools, expertise, and project support.
2) They are organized with embedded experts who work directly with research groups, and site teams that provide local support and some centralized services.
3) S3IT implements a hybrid cloud strategy using an OpenStack-based private cloud for secure workloads along with bursting to public clouds as needed. They aim to consolidate workload onto their cloud to reduce total cost of ownership for researchers.
The document provides an overview of research data management and the importance of avoiding a "DATApocalypse" or data disaster. It discusses the definition of research data, why data management is important, questions to consider, best practices for data management planning, documentation, and long-term preservation. The goal is to help researchers and institutions properly manage data to enable sharing and preservation, as required by most major funders.
Getting to grips with research data management Wendy Mears
This document provides an overview of research data management. It defines research data management and discusses its importance. It also outlines the data lifecycle model and provides guidance on sharing data, working with data, planning for data management, and useful resources for research data management. The document aims to help researchers effectively manage the data created throughout the research process.
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2017-02-15. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
This slideshow was used in a research data management planning course taught at IT Services, University of Oxford, on 2017-02-01. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
Research Data (and Software) Management at Imperial: (Everything you need to ...Sarah Anna Stewart
A presentation on research data management tools, workflows and best practices at Imperial College London with a focus on software management. Presented at the 2017 session of the HPC Summer School (Dept. of Computing).
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2017-02-22. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
The document provides an overview of research data management and the importance of avoiding a "DATApocalypse" or data disaster. It discusses the definition of research data, why data management is important, questions to consider, best practices for data management planning, documentation, and long-term preservation. The goal is to help researchers and institutions properly manage data to enable sharing and preservation, as required by most major funders.
Getting to grips with research data management Wendy Mears
This document provides an overview of research data management. It defines research data management and discusses its importance. It also outlines the data lifecycle model and provides guidance on sharing data, working with data, planning for data management, and useful resources for research data management. The document aims to help researchers effectively manage the data created throughout the research process.
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2017-02-15. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
This slideshow was used in a research data management planning course taught at IT Services, University of Oxford, on 2017-02-01. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
Research Data (and Software) Management at Imperial: (Everything you need to ...Sarah Anna Stewart
A presentation on research data management tools, workflows and best practices at Imperial College London with a focus on software management. Presented at the 2017 session of the HPC Summer School (Dept. of Computing).
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2017-02-22. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2016-11-16. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
The document discusses the importance of digital data preservation and ethics. It notes that data loss is worse than commonly known, with estimates that 1 in 1,500 files become corrupt on average and 3-500 files corrupt on each hard drive. Proper data preservation is important for responsibilities to colleagues, research subjects, and the public. Key aspects of preservation include having backup copies stored in different locations and formats, as well as periodically checking the integrity of stored data. University libraries can assist with data management planning, curation, archiving, and publishing to help researchers properly preserve their important digital data.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
SPARC Repositories conference in Baltimore - Nov 2010Jisc
1. The document discusses the reasons for and vision of creating a global network of repositories to openly share knowledge and data.
2. Key reasons for a global network include enabling open access to information, supporting science through linked data, and aligning with universities' responsibilities to the public.
3. The ideal vision is to build socio-technical infrastructure similar to what was created in the 1880s to support electricity, in order to manage and share linked, open, and trusted data globally through repository networks.
presented by Stuart Macdonald at the College of Science and Engineering - "What's new for you in the Library“, Murray Library, Kings Buildings, University of Edinburgh. 28 May 2014
Covers research data, research data management, funder policies and the University's RDM policy, RDM services and support, awareness raising, training, progress so far.
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Data collection is the process of systematically gathering information to answer research questions. Accurate data collection is essential to maintaining research integrity. Issues that can compromise integrity include errors in data collection instruments or procedures. Quality assurance and quality control help ensure integrity. Quality assurance occurs before data collection through standardized protocols and manuals. Quality control occurs during and after collection through review and validation of data. Maintaining integrity supports accurate conclusions and prevents wasted resources.
The document summarizes a workshop on planning for research data management. It discusses what research data management is, including definitions and lifecycle models. It emphasizes the importance of planning for RDM from the beginning of a research project, including developing a data management plan that addresses data collection, documentation, storage, sharing, and long-term preservation. The workshop also covered naming conventions, file formats, metadata, and tools and resources available to support RDM.
A collaborative approach to "filling the digital preservation gap" for Resear...Jenny Mitcham
A presentation given by Jenny Mitcham at the Northern Collaboration Conference on 10th September 2015 at Leeds. It describes work underway in the "Filling the Digital Preservation Gap" project using Archivematica to preserve research data
SLIDES | 12 time-saving tips for research supportLibrary_Connect
The document provides 25 tips for using various tools to work smart, work together, and stay up-to-date as a researcher. The tips include creating a document library, downloading and marking up documents, using an electronic lab notebook, joining a research ecosystem, setting alerts, following researchers, analyzing search results, and more. The overall message is that new tools can help researchers organize the growing amount of data, connect with collaborators, and maintain novelty in their work.
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.
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.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
Big Data and Data Mining - Lecture 3 in Introduction to Computational Social ...Lauri Eloranta
Third lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
1) The document discusses best practices for managing research data, including organizing files, documenting data with metadata, storing data securely both internally and externally, and presenting data through tables, charts, and text for publication and sharing.
2) Key recommendations for data management include using logical file naming conventions, non-proprietary file formats, and documenting data with standard metadata fields. External repositories can increase data accessibility and preservation.
3) Effective data presentation involves using tables and charts to clearly visualize quantitative and qualitative findings. Graphs should have clear titles and labels while tables should have logical data placement. Text should concisely summarize results.
The state of global research data initiatives: observations from a life on th...Projeto RCAAP
The document discusses research data management and provides guidance on best practices. It defines research data management as the active management of data over its lifecycle. It recommends writing a data management plan to document how data will be created, stored, shared, and preserved. It also provides tips for making data accessible and reusable through use of metadata standards, documentation, open licensing, and depositing data in repositories with persistent identifiers. The goal is to help researchers manage and share their data effectively to increase access and reuse.
This slideshow was used in a data management planning course taught at IT Services, University of Oxford, on 2016-11-09. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
The document discusses evaluating opportunities for developing multiplex cinemas. It begins by defining a multiplex as an entertainment complex showing multiple films under one roof, along with supporting businesses like restaurants and shops. Demographic analysis of the movie exhibition industry shows that the 12-29 age group accounts for half of annual admissions. Frequent moviegoers, defined as those who watch movies at least once a month, make up 81% of total admissions. The document then provides calculations for estimating demand for a multiplex based on population and per capita income and spending figures. It concludes that demographic data for Noida indicates a population and growth supportive of a movie theater, and that lifestyle characteristics of Noida residents show a strong tendency towards
Daddy Yankee es un cantante puertorriqueño de reggaetón cuyo nombre real es Ramón "Raymond" Ayala. El documento expresa que al autor le gusta Daddy Yankee como artista musical.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2016-11-16. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
The document discusses the importance of digital data preservation and ethics. It notes that data loss is worse than commonly known, with estimates that 1 in 1,500 files become corrupt on average and 3-500 files corrupt on each hard drive. Proper data preservation is important for responsibilities to colleagues, research subjects, and the public. Key aspects of preservation include having backup copies stored in different locations and formats, as well as periodically checking the integrity of stored data. University libraries can assist with data management planning, curation, archiving, and publishing to help researchers properly preserve their important digital data.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
SPARC Repositories conference in Baltimore - Nov 2010Jisc
1. The document discusses the reasons for and vision of creating a global network of repositories to openly share knowledge and data.
2. Key reasons for a global network include enabling open access to information, supporting science through linked data, and aligning with universities' responsibilities to the public.
3. The ideal vision is to build socio-technical infrastructure similar to what was created in the 1880s to support electricity, in order to manage and share linked, open, and trusted data globally through repository networks.
presented by Stuart Macdonald at the College of Science and Engineering - "What's new for you in the Library“, Murray Library, Kings Buildings, University of Edinburgh. 28 May 2014
Covers research data, research data management, funder policies and the University's RDM policy, RDM services and support, awareness raising, training, progress so far.
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Data collection is the process of systematically gathering information to answer research questions. Accurate data collection is essential to maintaining research integrity. Issues that can compromise integrity include errors in data collection instruments or procedures. Quality assurance and quality control help ensure integrity. Quality assurance occurs before data collection through standardized protocols and manuals. Quality control occurs during and after collection through review and validation of data. Maintaining integrity supports accurate conclusions and prevents wasted resources.
The document summarizes a workshop on planning for research data management. It discusses what research data management is, including definitions and lifecycle models. It emphasizes the importance of planning for RDM from the beginning of a research project, including developing a data management plan that addresses data collection, documentation, storage, sharing, and long-term preservation. The workshop also covered naming conventions, file formats, metadata, and tools and resources available to support RDM.
A collaborative approach to "filling the digital preservation gap" for Resear...Jenny Mitcham
A presentation given by Jenny Mitcham at the Northern Collaboration Conference on 10th September 2015 at Leeds. It describes work underway in the "Filling the Digital Preservation Gap" project using Archivematica to preserve research data
SLIDES | 12 time-saving tips for research supportLibrary_Connect
The document provides 25 tips for using various tools to work smart, work together, and stay up-to-date as a researcher. The tips include creating a document library, downloading and marking up documents, using an electronic lab notebook, joining a research ecosystem, setting alerts, following researchers, analyzing search results, and more. The overall message is that new tools can help researchers organize the growing amount of data, connect with collaborators, and maintain novelty in their work.
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.
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.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
Big Data and Data Mining - Lecture 3 in Introduction to Computational Social ...Lauri Eloranta
Third lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
1) The document discusses best practices for managing research data, including organizing files, documenting data with metadata, storing data securely both internally and externally, and presenting data through tables, charts, and text for publication and sharing.
2) Key recommendations for data management include using logical file naming conventions, non-proprietary file formats, and documenting data with standard metadata fields. External repositories can increase data accessibility and preservation.
3) Effective data presentation involves using tables and charts to clearly visualize quantitative and qualitative findings. Graphs should have clear titles and labels while tables should have logical data placement. Text should concisely summarize results.
The state of global research data initiatives: observations from a life on th...Projeto RCAAP
The document discusses research data management and provides guidance on best practices. It defines research data management as the active management of data over its lifecycle. It recommends writing a data management plan to document how data will be created, stored, shared, and preserved. It also provides tips for making data accessible and reusable through use of metadata standards, documentation, open licensing, and depositing data in repositories with persistent identifiers. The goal is to help researchers manage and share their data effectively to increase access and reuse.
This slideshow was used in a data management planning course taught at IT Services, University of Oxford, on 2016-11-09. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
The document discusses evaluating opportunities for developing multiplex cinemas. It begins by defining a multiplex as an entertainment complex showing multiple films under one roof, along with supporting businesses like restaurants and shops. Demographic analysis of the movie exhibition industry shows that the 12-29 age group accounts for half of annual admissions. Frequent moviegoers, defined as those who watch movies at least once a month, make up 81% of total admissions. The document then provides calculations for estimating demand for a multiplex based on population and per capita income and spending figures. It concludes that demographic data for Noida indicates a population and growth supportive of a movie theater, and that lifestyle characteristics of Noida residents show a strong tendency towards
Daddy Yankee es un cantante puertorriqueño de reggaetón cuyo nombre real es Ramón "Raymond" Ayala. El documento expresa que al autor le gusta Daddy Yankee como artista musical.
This study examined the effects of mouthpiece use on gas exchange parameters during steady-state exercise in college students. Sixteen participants performed two 10-minute treadmill runs under three conditions: with a custom-fitted mouthpiece, without a mouthpiece, and nose breathing only. Oxygen consumption, oxygen consumption per kg of body weight, and carbon dioxide production were significantly higher when using a mouthpiece compared to the other two conditions. The findings suggest that mouthpiece use improves specific gas exchange parameters during exercise.
The document discusses several topics including singing, color guard, acoustic guitar, running, and disliking soccer. It provides repetitive lists with 5 entries for singing, color guard, running and mentions disliking soccer.
Managing online presence is crucial as it is our identity online. Go and google your name and see what you get out of it.
For business, it is the same too. If you want your customer to start finding you online, pay attention to your online presence because you never know.
This document provides an overview of digital preservation challenges and strategies. It defines key terms and outlines issues like vast data volumes, technological dependencies, and approaches to preservation including bit preservation, migration, and emulation. Non-technical challenges are also discussed, like collaboration, costs, and legal issues. Personal archiving, digital forensics, and working with current and obsolete digital data are additional topics.
The document discusses a Faculty Development Program (FDP) on database management systems that was held on December 6, 2018 at the University College of Engineering Tindivanam in Tindivanam, India. The FDP covered recent research perspectives in different database management systems and the importance of database management systems in Digital India. It was conducted by Dr. A. Karthirvel, Professor and Head of the Computer Science and Engineering Department at MNM Jain Engineering College in Chennai.
Session presented by Judith Carr, Research Data Manager at the University of Liverpool on Research Data Management and your PhD.
Aim:- To show how research data management can contribute to the success of your PhD.
Covers:
* What is research data and why it is important?
* The Research Data lifecycle
Research Data – more than just your results
* FAIR data and Open Research
DMP online tool
The document provides an overview of data science, big data, data mining, and data mining techniques. It defines data science as a multi-disciplinary field that uses scientific methods to extract knowledge from structured and unstructured data. Big data is described as large, diverse datasets that are too large for traditional databases to handle. Common data mining tasks like prediction, classification, clustering and association rule mining are summarized. Finally, specific techniques like decision trees, k-means clustering, and association rule mining are overviewed.
This document describes a design challenge to create a system for managing data flows and access within computational social science studies in a privacy-aware manner. The system should support multiple studies conducted by different researchers while reusing common functions like user management, informed consent processes, and data access controls. It should allow multiple users in different studies to continuously view collected data and manage their consent and authorizations. Privacy-aware approaches are needed as sensitive personal data is increasingly collected at scale, but current solutions are minimal; the goal is a simple yet effective system like Funf for data collection from phones.
This document provides guidance on creating a data management plan (DMP). It explains that DMPs are required by many funders to help researchers better organize, document, and preserve their data. The key parts of a DMP include describing the data, metadata standards, data security, archiving and preservation, and access. The presenter provides tips for addressing each part, such as using open formats and partnering with repositories. Resources for creating a DMP at the University of Wisconsin-Milwaukee are also listed.
Meeting Federal Research Requirements for Data Management Plans, Public Acces...ICPSR
These slides cover evolving federal research requirements for sharing scientific data. Provided are updates on federal agency responses to the 2013 OSTP memo, guidance on data management plans, resources for data management and curation training for staff/researchers, and tips for evaluating public data-sharing services. ICPSR's public data-sharing service, openICPSR, is also presented. Recording of this presentation is here: https://www.youtube.com/watch?v=2_erMkASSv4&feature=youtu.be
Data science applications can be found in many domains including business, healthcare, urban planning, and more. In business, data science is used to optimize operations and customer experiences. In healthcare, data science aims to improve efficiency, reduce readmissions, and enable earlier disease detection. For urban areas experiencing rapid growth, data science combines with urban informatics to help address challenges. Case studies show how data science is used in cancer research by leveraging large datasets and algorithms, in healthcare by Stanford and Google to advance precision medicine, in political elections through micro-targeting, and with the growing Internet of Things to analyze data from billions of connected devices.
Data Mining and Big Data Challenges and Research OpportunitiesKathirvel Ayyaswamy
The document discusses 10 challenging problems in data mining research. It summarizes each problem with 1-2 paragraphs explaining the challenges. Some of the key problems discussed include developing a unifying theory of data mining, scaling up for high dimensional and streaming data, mining complex relationships from interconnected data, ensuring privacy and security of data, and dealing with non-static and unbalanced data. The document advocates that more research is needed to address these issues and better integrate data mining with database systems and domain knowledge.
Real-time applications of Data Science.pptxshalini s
This document provides an overview of data science through discussing big data challenges, defining data science, contrasting it with other fields, and presenting case studies. It explains that data science uses theories from fields like computer science, mathematics, and statistics to analyze large, complex data sets and help organizations make better decisions. Example applications discussed include using data science in healthcare to improve patient care, in elections to micro-target voters, and in cities to address urban challenges through data-driven solutions.
This document provides an overview of concepts related to information technology, economic development, and the adoption of information. It defines key terms like data, information, knowledge, wisdom. It also discusses the spectrum of knowledge, information overload and its causes and solutions. Regarding economic development, it defines economic growth and describes the stages of economic transformation from agrarian to industrial to information society. It also discusses Rogers' stages of adoption process and categories of users in adopting information.
Data science applications and use cases were discussed. Examples included using data science in business for tasks like optimizing operations, healthcare to improve efficiency and care, and urban planning to address challenges in cities. Data science contrasts with other disciplines by combining technical skills from computer science, mathematics, and statistics to analyze large datasets. Case studies demonstrated data science applications in domains like cancer research using patterns in biomedical data, healthcare to power precision medicine, political campaigns using social media microtargeting, and the growing Internet of Things producing large volumes of data.
This document provides an overview of data mining. It discusses the introduction to data mining, its importance and applications. The key techniques of data mining discussed include classification, prediction, clustering, association and summarization. Examples of data mining applications mentioned are in healthcare, banking/finance, retail and web mining. The document concludes with discussing future trends in data mining involving new algorithms and data types, as well as computing resources like cloud computing.
A Lifecycle Approach to Information PrivacyMicah Altman
The document discusses challenges in privacy across the lifecycle of data from collection to dissemination and proposes taking a lifecycle approach. It analyzes how concepts like differential privacy could address issues raised at different stages and questions that approach generates regarding legal and technical issues. The goal is to advance interdisciplinary research at the intersection of law, social science, public policy, data collection methods, data management, statistics, and computer science.
Services for the use of human data in cross-border collaborations. Presentation at ECCB 2016 confenrence. Introducing outcomes of Nordic Tryggve project.
This document provides an introduction to big data, including definitions and key characteristics. It discusses how big data is defined as extremely large and complex datasets that cannot be managed by traditional systems due to issues of volume, velocity, and variety. It outlines three key characteristics of big data: volume (scale), variety (complexity), and velocity (speed). Examples are given of different types and sources of big data. The document also introduces cloud computing and how it relates to big data management and processing. Finally, it provides an overview of topics to be covered, including frameworks, modeling, warehousing, ETL, and specific analytic techniques.
Presentation given at the Consorcio Madrono conference on Data Management Plans in Horizon 2020 http://www.consorciomadrono.es/info/web/blogs/formacion/217.php
Similar to Service and Support for Science IT-Peter Kunzst, University of Zurich (20)
Aleida Alcaide, European cloud partnership. Iniatives of the European Public ...Mind the Byte
The European public sector is taking several initiatives to advance cloud computing. The European Cloud Strategy aims to boost cloud adoption, cut costs, and create jobs. It identifies three key actions: cutting through standards confusion; developing model contract terms; and establishing a European Cloud Partnership for public procurement. The Partnership's "Trusted Cloud Europe" paper proposes enabling safe, cost-effective public sector cloud use. The Cloud for Europe project will run a pre-commercial procurement to test cloud solutions.
Progress towards security in the Cloud-Héctor Sánchez, MicrosoftMind the Byte
This document discusses cybersecurity and cloud computing. It summarizes that Microsoft is addressing privacy, security, and customer data handling with Office 365. Microsoft is signing EU Model Clauses with all customers and implementing rigorous ISO27001 security controls to enable customers to comply with local regulations. The document also notes Microsoft is aggressively rolling out encryption of customer content between data centers and implementing Perfect Forward Secrecy to protect user communications from interception.
IP strategies to protect your cloud technology-Florian Michalek, Bardehele Pa...Mind the Byte
This document discusses strategies for protecting cloud technology with intellectual property (IP). It begins by explaining that computer-implemented inventions involving cloud computing can be patentable if they have a technical character and meet patentability requirements. It then provides examples of patented cloud computing inventions and guidelines from the European Patent Office. The document also addresses issues of confidentiality for cloud-based research and prior art disclosures on the internet.
Shared Clinical History of Catalonia-Ignasi Garcia-Milà, Fundació TICSalutMind the Byte
TicSalut is an agency in Catalonia that promotes ICT use in healthcare. The region has a decentralized healthcare system with various providers using different information systems. Catalonia has created a Shared Medical Record (SMR) that stores patient health information from various sources in a central repository. This allows healthcare providers to access patient information. The SMR is now used widely across Catalonia. Catalonia is also developing a Personal Health Channel for citizens to access their health information online.
Alfons Nonell-Canals is the CEO of Mind the Byte, a research company specialized in providing cloud-based in silico drug discovery solutions. He introduces cloud computing as the delivery of computing resources as a service over the network. Major cloud providers like Amazon Web Services offer infrastructure services like EC2 for compute and S3 for storage, as well as platform services for databases, applications, and management. AWS allows users to easily set up the infrastructure needed for tasks like high-performance computing, web services, and computational drug discovery through its console and APIs.
The document discusses cloud computing and how it can benefit scientific research. It notes that cloud computing provides on-demand access to shared computing resources over the internet, allowing users to scale their resources up or down flexibly based on needs. The company Mind the Byte provides cloud services including high-performance computing, autoscaling systems, software development tools, and their own scientific software programs like Hurakan for virtual screening and iMols for drug discovery. Cloud computing allows scientists to reduce costs, handle spikes in workload, ensure backups and uptime, and get connectivity and replacement of hardware.
Byte is a unit of digital information that typically consists of eight bits. Historically, a byte represented the number of bits used to encode a single character. It is the basic addressable element in many computer architectures.
Mind the Byte is a consultancy that provides computational scientific solutions for researchers. They can handle all scientific computation needs, from large data analysis to drug discovery and prediction of compound activity. Their services include drug discovery, molecular modelling, support, education, and cloud computing.
Can coffee help me lose weight? Yes, 25,422 users in the USA use it for that ...nirahealhty
The South Beach Coffee Java Diet is a variation of the popular South Beach Diet, which was developed by cardiologist Dr. Arthur Agatston. The original South Beach Diet focuses on consuming lean proteins, healthy fats, and low-glycemic index carbohydrates. The South Beach Coffee Java Diet adds the element of coffee, specifically caffeine, to enhance weight loss and improve energy levels.
This particular slides consist of- what is hypotension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is the summary of hypotension:
Hypotension, or low blood pressure, is when the pressure of blood circulating in the body is lower than normal or expected. It's only a problem if it negatively impacts the body and causes symptoms. Normal blood pressure is usually between 90/60 mmHg and 120/80 mmHg, but pressures below 90/60 are generally considered hypotensive.
LGBTQ+ Adults: Unique Opportunities and Inclusive Approaches to CareVITASAuthor
This webinar helps clinicians understand the unique healthcare needs of the LGBTQ+ community, primarily in relation to end-of-life care. Topics include social and cultural background and challenges, healthcare disparities, advanced care planning, and strategies for reaching the community and improving quality of care.
We are one of the top Massage Spa Ajman Our highly skilled, experienced, and certified massage therapists from different corners of the world are committed to serving you with a soothing and relaxing experience. Luxuriate yourself at our spas in Sharjah and Ajman, which are indeed enriched with an ambiance of relaxation and tranquility. We could confidently claim that we are one of the most affordable Spa Ajman and Sharjah as well, where you can book the massage session of your choice for just 99 AED at any time as we are open 24 hours a day, 7 days a week.
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DECODING THE RISKS - ALCOHOL, TOBACCO & DRUGS.pdfDr Rachana Gujar
Introduction: Substance use education is crucial due to its prevalence and societal impact.
Alcohol Use: Immediate and long-term risks include impaired judgment, health issues, and social consequences.
Tobacco Use: Immediate effects include increased heart rate, while long-term risks encompass cancer and heart disease.
Drug Use: Risks vary depending on the drug type, including health and psychological implications.
Prevention Strategies: Education, healthy coping mechanisms, community support, and policies are vital in preventing substance use.
Harm Reduction Strategies: Safe use practices, medication-assisted treatment, and naloxone availability aim to reduce harm.
Seeking Help for Addiction: Recognizing signs, available treatments, support systems, and resources are essential for recovery.
Personal Stories: Real stories of recovery emphasize hope and resilience.
Interactive Q&A: Engage the audience and encourage discussion.
Conclusion: Recap key points and emphasize the importance of awareness, prevention, and seeking help.
Resources: Provide contact information and links for further support.
Hypertension and it's role of physiotherapy in it.Vishal kr Thakur
This particular slides consist of- what is hypertension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is summary of hypertension -
Hypertension, also known as high blood pressure, is a serious medical condition that occurs when blood pressure in the body's arteries is consistently too high. Blood pressure is the force of blood pushing against the walls of blood vessels as the heart pumps it. Hypertension can increase the risk of heart disease, brain disease, kidney disease, and premature death.
MBC Support Group for Black Women – Insights in Genetic Testing.pdfbkling
Christina Spears, breast cancer genetic counselor at the Ohio State University Comprehensive Cancer Center, joined us for the MBC Support Group for Black Women to discuss the importance of genetic testing in communities of color and answer pressing questions.
Comprehensive Rainy Season Advisory: Safety and Preparedness Tips.pdfDr Rachana Gujar
The "Comprehensive Rainy Season Advisory: Safety and Preparedness Tips" offers essential guidance for navigating rainy weather conditions. It covers strategies for staying safe during storms, flood prevention measures, and advice on preparing for inclement weather. This advisory aims to ensure individuals are equipped with the knowledge and resources to handle the challenges of the rainy season effectively, emphasizing safety, preparedness, and resilience.
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Service and Support for Science IT-Peter Kunzst, University of Zurich
1. Service and Support for Science IT
Scientific Cloud Experiences
Dr. Peter Kunszt
Director S3IT
2. Outline
• Introduction
– What is Science IT
– How are we organized
• UZH ScienceCloud Infrastructure and
Implementation
• Science Data and Security/Privacy
3. Challenge : Scale Up
• High Throughput Instruments
– Much larger data volumes
– Increased data complexity
• Large Collaborations
– More people
– More experiments and measurements
– More coverage
4. Fire and forget...
• Scientists do not want to be bothered with
infrastructure details
• IT JUST NEEDS TO WORK!
5. Widening Complexity Gap: IT-Research
Local IT
Resources
Research Labs
Core Facilities
Miracle
SCIENCE IT
6. What is Science IT ?
FILL THE GAP
Dedicated Support Center
for Science IT
• SPEED : faster time to solution
• ACCESS : to infrastructure,
software, expertise
• ENABLE : use IT technology and
software for new ideas
Speed
Access
Enablement
7.
8. Supporting Science
• Be a partner to research projects for Science IT
• Provide services to individual researchers, groups and
consortia
– Consultancy for advanced usage of IT in Science
– Research software development and support
– Access to competitive IT infrastructure
– Access to a library of tools and software
– Project management and collaboration support
– Training and education on the usage of infrastructure and software
• Collaborate internally, nationally and internationally with
partners, suppliers and other Science IT units
• Maintain high level of internal expertise on topics relevant to
Science IT
• Advise UZH Governance on evolution of needs, assist in
prioritization
10. S3IT Organization
Core
Team
Site
Team
Site
Team
EE
EE
EE
EE
EE
...
...
EE = Embedded Expert
Working directly in projects
or on-site in groups on
specific tasks
Site Teams
Joint teams with other units
providing local support and
some global services
Core Team
Directorate, Office, core
services, central
infrastructure and
consultancy, project mgmt
12. S3IT Core Business: Project
Support
• Infrastructure is important but ‚just‘ a means to an end
• Science IT Support: Applications, access, integration
• Data analysis
• Simulations
• Data Integration
• Application scaling, making use of big infrastructures
• Workflows, automation
• Visualization
• Software design and usage advice, Code Clinic
• Training and education
• ...
14. Mapping Security and Privacy
• Most science follows 3 stages
– Conception, preparation, proposition stage – private
– Project stage (3-5y) – share in group
– Publication of results – open to all
• Some have additional constraints (regulations)
– Medicine – patient data records need consent
(different per country)
– Law and business – confidentiality in projects
– Engineering, pharmacology, etc.. – patents
15. Infrastructure
• Supercomputing
– Used as a scientific instrument by
• theoretical physics, astrophysics, mathematics, computational
chemistry, biochemistry, quantum chemistry
• Continuous usage
• Cluster computing
– Used as a workhorse by many groups
• Life science, biochem, geoscience, medicine, digital humanities,
banking and finance, art history, ...
• Data analysis, statistical analysis, parameter studies, etc
• Non-continuous usage
• Server computing
– Used as interactive computers by many groups
• All groups. Interactive processing, visualization, steering of
computation. Commercial and open-source tools.
• Daily usage, non-continuous.
16. Storage Classes
• Large, cheap data store for projects O(xPB)
– No need to be backed up: Easy to regenerate but
time-consuming
• Reliable project data store O(1PB)
– With secondary copy
– Only addition, no changes
• Working storage O(x100TB)
– Active data, databases, server-side processes
• Fast storage for streaming analysis O(100TB)
– Fast changing data, immediate analysis, rare!
17. Datacenter Consolidation
OCI – S3IT
ZMB
BIOC
MATH
PHYS
IMLS / Neuro
Consolidate
into
Central
Datacenter
Aim: Scale and Secure!
18. UZH ScienceCloud Implementation
• OpenStack – based on Canonical
• Deployment using Ansible
• Vagrant-like system for configuration:
Elasticluster (developed at UZH)
• Flexible submission and workflow framework
for job control: GC3pie (developed at UZH)
• Database management framework openBIS
for data lifecycle management (developed at
ETH/SystemsX.ch)
19. Business Model
• Supercomputing
– Investment every 4 years into the system
– Research groups to find 3rd party funding
• Commodity Cloud and Storage
– Subscription / year : Cores, TB
– Per use fee
– Subsidized, not TCO – covering operations
• Servers / Pets
– Yearly or monthly fee
– Size matters
• Yearly acquisition / rollover
– Easy to plan
20. Experience so far:
• Supercomputing needed only by few groups
– Can be completely outsourced to national center, done as of 2015
• Cloud is suitable for most Science Workloads
– User support scales well
– Can cover very many use cases
– Build dedicated boxes for exceptions, don‘t be driven by them
– Flexibility is key
• Must use local infrastructure for secure, data intensive and
memory intensive workloads
– Data locality needed for COST and (rarely) policy reasons –
exception: medical data
– Hybrid cloud – burst available for CPU intensive jobs
– Deal with heterogeneity
21. Future Cloud Strategy: HYBRID
• Run sizeable local cloud infrastructure for internal
workloads
• Burst peak loads to public cloud providers
– For selected workloads coherent with policy and cost
Advantages
• Plannable local infrastructure (plan for full usage)
• Flexibility in scaling, quick provisioning of needed
capacity
22. Open Questions
• Policies. What workloads can be burst to public clouds?
Under what conditions
– Calculations, simulations usually OK
– Data analyis: depends on data (network issues being
resolved)
– Check compliance of cloud providers. ISO, HIPAA, etc
– Adherence to swiss cantonal data protection regulations
• Cost. How to buy public cloud services?
– Public procurement of agreements?
– How not to be bound to a single provider?
– Is this necessary at all?
• How do i charge my users?
– For internal and for external use?
– Aim: consolidate their workload into our cloud. No TCO!
23. Comments on Security in
academia
• Users in academia are super smart. They remove
barriers faster than you can erect them.
• Do risk assessment and risk analysis instead of
prevention.
• Don‘t do anything ‚for security reasons‘, always qualify
with real risk numbers
• Public Clouds are MUCH MORE secure than our own
– Amazon, Microsoft, IBM etc have whole teams of security
experts – they hired our best students for this
• It is a question of TRUST
– Regulations by countries
– Do we trust the US not to do industrial and academic
espionage, forcing their own companies to give out our
data?
24. Scientific Requirements
• Know your workload: Data, Privacy, Science,
Sharing aspects are tightly connected
• Lots of hidden complexity and contradicting
requirements
29
25. 1. What Data?
• Different kinds of ‚BIG‘ data
• Volume, Variety, Velocity, Veracity
• Understanding is Knowledge is Science
– Data vs. Information and Knowledge
– What are the right questions?
– What should be protected, till when?
– How to navigate, explore, evolve
30
WHO OWNS THE DATA?
For science, proprietary data is a hindrance
26. 2. Data Reuse
• Currently a wealth of data is not reused for
new discovery
• Lots of potential! Regulators need to be told..
• Data repositories with computing and search
capability – perfect for Cloud Model
• Do the computation where the data is –
Private, public, hybrid Cloud
31
IP on TOOLS, ease of data USE, not DATA itself.
27. 3. Motivate to annotate
• Scientists publish what is necessary and
prescribed by the journals, not more –
mandate better annotation
• Provide more recognition for producing ´good´
datasets – Data Citation
• Check Data quality – bad quality or
data without annotation has no value
32
Creation of well annotated, sustained public
resources
28. 4. Standard Formats
• Too many ‚Standards‘ or not used
– Instrument vendors often at fault
• Protection of data by proprietary formats
– Data is lost to research
• Do not pay for data in nonstandard
formats
– Data value is zero if unusable
33
Mandate standard formats for domain data
29. 5. Data Sharing/Publishing
• Share in collaborative mode
• Avoid Data Loss
• Motivate and enable data publication
• Establish business model for data publication
(reward/career benefit)
• Journals adapt, see Scientific Data
http://www.nature.com/scientificdata
New role for Archives and Libraries
30. 6. Patient Data Records
• Legal issues of data privacy
• People are not in control of their own data
• Difficult to get consent
• NSA effect – trust
Put citizens back in control
31. Patient Data Records
• TRUST
– Swiss Cooperative: citizen owned
• NEUTRALITY
– A simple e-Banking system for any personal health data. Same
level of security
• TRACTION
– Volume: it is free, it‘s rewarded
• IMPACT
– Request data directly, avoid legal issues
36
32. • It is a cooperative, not a business
• Funding by running campaigns to ask people to
participate in research & surveys
• Participants are REWARED for sharing their data or
providing new data
• Build tools on top
• Currently seeking funding
– H2020, foundations
– Projects with hospitals, clinics
37
33. Approach at S3IT
• Early involvement with Research Groups
– Proposal writing, partnership
– Advice on Data Management, infrastructure, standards
• Strong cooperation with Libraries
– Early involvement with publishers, archives
– Joint information to research groups on data management
plans, data citations
• Seeking contact with funding bodies and decision
makers
– Communicate business plan for Science IT ‚project
consumables‘
– Evaluation of projects based on technology cost and
feasibility
– Usage of public and each others‘ cloud resources for cash
34. Links
• www.s3it.uzh.ch - Science IT at UZH
• www.sybit.net - Systems Biology IT, SystemsX.ch
• www.erasysapp.eu - Systems Biology, DMMCore
project
• www.healthbank.ch - Public Cooperative being
set up for patient-owned data. Seeking funding
(H2020, pending, and other sources)