La Trobe University Library partnered with our Health Sciences academics to procure datasets from two Victorian regional health service providers in 2014/15 and from these created a publically available, healthy communities data collection for research purposes
International perspective for sharing publicly funded medical research dataARDC
Presentation by Olivier Salvado, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Steve McEachern, ADA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Davina Ghersi, NHMRC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Prof Lisa Askie, ANZCTR, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Adrian Burton, ARDC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Merran Smith, PHRN, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
CINECA webinar slides: Making cohort data FAIRCINECAProject
Cohort studies, which recruit groups of individuals who share common characteristics and follow them over a period of time, are a robust and essential method in biomedical research for understanding the links between risk factors and diseases. Through questionnaires, medical assessments, and other interactions, voluminous and complex data are collected about the study participants. While cohort studies present a treasure trove of data, the data is often not FAIR (findable, accessible, interoperable and reusable). First, due to the sensitive and private nature of medical information, cohort data are often access controlled. Due to the lack of information about the studies (metadata), often one needs to dig deep to know what data is available in a cohort study. Therefore, many cohort datasets suffer from the findable and accessible issues. Second, often data collection is performed with instruments and data specifications tailored to the study. As a result, combining data across cohorts, even ones with similar characteristics, is difficult, making interoperability and reusability a challenge. In this presentation, we will explore several informatics techniques, such as the use of ontology, to make cohort data more FAIR. We will also consider the implications of making cohort data more open and the ethical and governance issues associated with open science benefit sharing.
This webinar is part of the “How FAIR are you” webinar series and hackathon, which aim at increasing and facilitating the uptake of FAIR approaches into software, training materials and cohort data, to facilitate responsible and ethical data and resource sharing and implementation of federated applications for data analysis.
The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions.
This webinar took place on 17th February 2021 and is part of the CINECA webinar series.
For previous and upcoming CINECA webinars see:
https://www.cineca-project.eu/webinars
International perspective for sharing publicly funded medical research dataARDC
Presentation by Olivier Salvado, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Steve McEachern, ADA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Davina Ghersi, NHMRC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Prof Lisa Askie, ANZCTR, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Adrian Burton, ARDC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Merran Smith, PHRN, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
CINECA webinar slides: Making cohort data FAIRCINECAProject
Cohort studies, which recruit groups of individuals who share common characteristics and follow them over a period of time, are a robust and essential method in biomedical research for understanding the links between risk factors and diseases. Through questionnaires, medical assessments, and other interactions, voluminous and complex data are collected about the study participants. While cohort studies present a treasure trove of data, the data is often not FAIR (findable, accessible, interoperable and reusable). First, due to the sensitive and private nature of medical information, cohort data are often access controlled. Due to the lack of information about the studies (metadata), often one needs to dig deep to know what data is available in a cohort study. Therefore, many cohort datasets suffer from the findable and accessible issues. Second, often data collection is performed with instruments and data specifications tailored to the study. As a result, combining data across cohorts, even ones with similar characteristics, is difficult, making interoperability and reusability a challenge. In this presentation, we will explore several informatics techniques, such as the use of ontology, to make cohort data more FAIR. We will also consider the implications of making cohort data more open and the ethical and governance issues associated with open science benefit sharing.
This webinar is part of the “How FAIR are you” webinar series and hackathon, which aim at increasing and facilitating the uptake of FAIR approaches into software, training materials and cohort data, to facilitate responsible and ethical data and resource sharing and implementation of federated applications for data analysis.
The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions.
This webinar took place on 17th February 2021 and is part of the CINECA webinar series.
For previous and upcoming CINECA webinars see:
https://www.cineca-project.eu/webinars
Managing Ireland's Research Data - 3 Research MethodsRebecca Grant
Slides providing an overview of the research methods used in the author's thesis, "Managing Ireland's Research Data: Recognising Roles for Recordkeepers". The methods discussed are online surveys, comparative case studies, and autoethnography.
Licensed as CC-BY.
Survey of research data management practices up2010digschol2011heila1
An analysis of data management practices at a large South African university was conducted through interviews with researchers and students to identify needs and challenges. The findings showed that while data collection methods vary, data storage is often ad hoc with no centralized support or resources. Researchers expressed a need for a central university server or repository for secure data storage and assistance with time constraints. It was concluded that a formal research data management program and staff support are needed to improve current practices.
The document provides an overview of the Research Capability Programme (RCP) which aims to enable use of NHS data for research purposes. It discusses the RCP's enabling phase where governance structures and stakeholder engagement were established. The implementation phase will develop infrastructure to provide research support services including access to data sources, cohort management, and anonymization/coding of data. Key challenges include ensuring opportunities are maximized, improving data linkage and quality, and navigating complex information governance issues.
CINECA webinar slides: Open science through fair health data networks dream o...CINECAProject
Since the FAIR data principles were published in 2016, many organizations including science funders and governments have adopted these principles to promote and foster true open science collaborations. However, to define a vision and create a video of a Personal Health Train that leverages worldwide FAIR health data in a federated manner is one step. To actually make this happen at scale and be able to show new scientific and medical insights for it is quite another!
In this webinar, we will dive into the basics of FAIR health data, but also take stock of the current situation in health data networks: after a year of frantic research and collaborations and many open datasets and hackathons on COVID-19, has the situation actually improved? Are we sharing health data on a global scale to improve medical practice, or is quality medical data still only accessible to researchers with the right credentials and deep pockets?
This webinar is part of the “How FAIR are you” webinar series and hackathon, which aim at increasing and facilitating the uptake of FAIR approaches into software, training materials and cohort data, to facilitate responsible and ethical data and resource sharing and implementation of federated applications for data analysis.
The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions.
This webinar took place on 21st January 2021 and is part of the CINECA webinar series.
For previous and upcoming CINECA webinars see:
https://www.cineca-project.eu/webinars
NordForsk Open Access Reykjavik 14-15/8-2014:Finnish data-initiativeNordForsk
This document summarizes Finland's Research Data Initiative from 2009-2017. The initiative aimed to develop a sustainable research data infrastructure in Finland by providing services like data storage, metadata, and long-term preservation. It also sought to encourage open data sharing and reuse. The initiative progressed from early planning projects to establishing core services. Lessons learned include the importance of flexible governance, permanent preservation, embracing change through openness, and addressing cultural shifts around data sharing. The initiative aims to enhance research through improved access, collaboration and reuse of scientific data.
Principles, key responsibilities, and their intersectionARDC
Dr Daniel Barr from RMIT University presented at the University of Technology Sydney's RIA Data Management Workshop on 21 June 2018. In partnership with the Australian Research Council, the National Health and Medical Research Council, the Australian Research Data Commons, and RMIT University, this is part of a national workshop series in data management for research integrity advisors.
Libraries and Research Data Management – What Works? Lessons Learned from the...LIBER Europe
This presentation by Dr Birgit Schmidt was given at the Scholarly Communication and Research Infrastructures Steering Committee Workshop. The workshop title was Libraries and Research Data Management – What Works?
Research Integrity Advisor and Data ManagementARDC
Dr Paul Wong from the Australian Research Data Commons presented at the University of Technology Sydney's RIA Data Management Workshop on 21 June 2018. In partnership with the Australian Research Council, the National Health and Medical Research Council, the Australian Research Data Commons, and RMIT University, this is part of a national workshop series in data management for research integrity advisors.
Horizon 2020 open access and open data mandatesMartin Donnelly
This document summarizes the key requirements for open access and open data under the Horizon 2020 framework. It outlines the mandate for open access to publications, requiring deposit in a repository and granting open access rights. It also describes the open data pilot, defining research data and the FAIR principles of findable, accessible, interoperable and reusable data. Projects must submit a data management plan addressing data collection, sharing and preservation. Compliance involves depositing data in a repository and applying an open license.
NordForsk Open Access Reykjavik 14-15/8-2014: BbmriNordForsk
1. The Nordic countries have a unique advantage for biobank collaboration due to their ability to link biobanks to comprehensive nationwide health registries using personal identification numbers.
2. The BBMRI Nordic pilot project aims to build a large-scale study of colon cancer in the Nordic countries by systematically identifying cases and controls from biobanks and retrieving associated data and samples.
3. While the project faced some initial bottlenecks related to legal and scientific cooperation issues, it has established that large-scale joint Nordic biobank research is possible within the current ethical and legal framework.
Presentation given by Kate LeMay at the 'Sharing Health-y Data: Challenges and Solutions' workshop, held at The Menzies Research Institute (Hobart, Tasmania) on 28th June 2016. The event was co-hosted by ANDS and the University of Tasmania library
The document summarizes a pilot project at the University of Edinburgh to support the development of a UK Research Data Discovery Service. PhD interns engaged with researchers from various schools to describe and deposit research datasets in the university's systems to be harvested by the discovery service. Observations found mixed results across schools, with humanities researchers less comfortable sharing data due to copyright and reluctance to share interpretations. Other schools had established data repositories causing less interest in the university's system. Building research data management practices will require tailored approaches and more training over time.
Topics covered at the workshop address basic questions related to Research Data Management for open data, which include preparing a Research Data Management (RDM) plan, licensing data and intellectual property, metadata and contextual description (documentation), ethical and legal aspects of sharing sensitive or confidential data, anonymizing research data for reuse, data archiving and long-term preservation, and data security and storage.
Event: http://conferences.nib.si/AS2015/default.htm
Related material: http://conferences.nib.si/AS2015/BookAS15.pdf
Research Ethics and Use of Restricted Access Datalibbiestephenson
Presentation given to the California Center for Population Research on principles of research ethics, data management for protection of privacy and confidentiality, and applying for access to restricted data in social science research.
This document summarizes research on incentives for researchers to share their data. It discusses findings from qualitative interviews and quantitative surveys. Key findings include:
- Individual researchers are motivated by benefits to their own research, career, and discipline's norms. They are influenced by funder and journal policies.
- Institutional supports like data infrastructure, funding, and training also influence researchers' data sharing practices. Funder requirements and assistance with data management increase sharing.
- Studies found the main individual motivations are career benefits and research impact. The main institutional factors are skills training, support services, and policies that ensure proper data reuse and acknowledgement.
Why science needs open data – Jisc and CNI conference 10 July 2014Jisc
This document discusses the importance of open data in science. It provides 4 key reasons why open data is important:
1) It allows for identification of patterns in large datasets that could not be found otherwise.
2) It enables data modeling through iterative integration of initial models with observational data.
3) It facilitates deeper integration and analysis of diverse linked datasets.
4) It supports exploitation of networked sensor data through acquisition, integration, analysis and feedback.
However, open data needs to be "intelligently open" through being discoverable, accessible, intelligible, assessable and reusable to realize its full potential. Mandating such intelligent open data is important to drive an open data infrastructure ecology.
This document discusses open data in healthcare. It provides background on open health data initiatives in several European countries like the UK, France, Portugal, and Italy. It notes both opportunities and challenges with opening up health data, including issues around governance, strategy, and privacy. The document advocates for a patient-centered approach and emphasizes the potential for open health data to empower citizens and fuel innovation when various stakeholders collaborate.
Managing Ireland's Research Data - 3 Research MethodsRebecca Grant
Slides providing an overview of the research methods used in the author's thesis, "Managing Ireland's Research Data: Recognising Roles for Recordkeepers". The methods discussed are online surveys, comparative case studies, and autoethnography.
Licensed as CC-BY.
Survey of research data management practices up2010digschol2011heila1
An analysis of data management practices at a large South African university was conducted through interviews with researchers and students to identify needs and challenges. The findings showed that while data collection methods vary, data storage is often ad hoc with no centralized support or resources. Researchers expressed a need for a central university server or repository for secure data storage and assistance with time constraints. It was concluded that a formal research data management program and staff support are needed to improve current practices.
The document provides an overview of the Research Capability Programme (RCP) which aims to enable use of NHS data for research purposes. It discusses the RCP's enabling phase where governance structures and stakeholder engagement were established. The implementation phase will develop infrastructure to provide research support services including access to data sources, cohort management, and anonymization/coding of data. Key challenges include ensuring opportunities are maximized, improving data linkage and quality, and navigating complex information governance issues.
CINECA webinar slides: Open science through fair health data networks dream o...CINECAProject
Since the FAIR data principles were published in 2016, many organizations including science funders and governments have adopted these principles to promote and foster true open science collaborations. However, to define a vision and create a video of a Personal Health Train that leverages worldwide FAIR health data in a federated manner is one step. To actually make this happen at scale and be able to show new scientific and medical insights for it is quite another!
In this webinar, we will dive into the basics of FAIR health data, but also take stock of the current situation in health data networks: after a year of frantic research and collaborations and many open datasets and hackathons on COVID-19, has the situation actually improved? Are we sharing health data on a global scale to improve medical practice, or is quality medical data still only accessible to researchers with the right credentials and deep pockets?
This webinar is part of the “How FAIR are you” webinar series and hackathon, which aim at increasing and facilitating the uptake of FAIR approaches into software, training materials and cohort data, to facilitate responsible and ethical data and resource sharing and implementation of federated applications for data analysis.
The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions.
This webinar took place on 21st January 2021 and is part of the CINECA webinar series.
For previous and upcoming CINECA webinars see:
https://www.cineca-project.eu/webinars
NordForsk Open Access Reykjavik 14-15/8-2014:Finnish data-initiativeNordForsk
This document summarizes Finland's Research Data Initiative from 2009-2017. The initiative aimed to develop a sustainable research data infrastructure in Finland by providing services like data storage, metadata, and long-term preservation. It also sought to encourage open data sharing and reuse. The initiative progressed from early planning projects to establishing core services. Lessons learned include the importance of flexible governance, permanent preservation, embracing change through openness, and addressing cultural shifts around data sharing. The initiative aims to enhance research through improved access, collaboration and reuse of scientific data.
Principles, key responsibilities, and their intersectionARDC
Dr Daniel Barr from RMIT University presented at the University of Technology Sydney's RIA Data Management Workshop on 21 June 2018. In partnership with the Australian Research Council, the National Health and Medical Research Council, the Australian Research Data Commons, and RMIT University, this is part of a national workshop series in data management for research integrity advisors.
Libraries and Research Data Management – What Works? Lessons Learned from the...LIBER Europe
This presentation by Dr Birgit Schmidt was given at the Scholarly Communication and Research Infrastructures Steering Committee Workshop. The workshop title was Libraries and Research Data Management – What Works?
Research Integrity Advisor and Data ManagementARDC
Dr Paul Wong from the Australian Research Data Commons presented at the University of Technology Sydney's RIA Data Management Workshop on 21 June 2018. In partnership with the Australian Research Council, the National Health and Medical Research Council, the Australian Research Data Commons, and RMIT University, this is part of a national workshop series in data management for research integrity advisors.
Horizon 2020 open access and open data mandatesMartin Donnelly
This document summarizes the key requirements for open access and open data under the Horizon 2020 framework. It outlines the mandate for open access to publications, requiring deposit in a repository and granting open access rights. It also describes the open data pilot, defining research data and the FAIR principles of findable, accessible, interoperable and reusable data. Projects must submit a data management plan addressing data collection, sharing and preservation. Compliance involves depositing data in a repository and applying an open license.
NordForsk Open Access Reykjavik 14-15/8-2014: BbmriNordForsk
1. The Nordic countries have a unique advantage for biobank collaboration due to their ability to link biobanks to comprehensive nationwide health registries using personal identification numbers.
2. The BBMRI Nordic pilot project aims to build a large-scale study of colon cancer in the Nordic countries by systematically identifying cases and controls from biobanks and retrieving associated data and samples.
3. While the project faced some initial bottlenecks related to legal and scientific cooperation issues, it has established that large-scale joint Nordic biobank research is possible within the current ethical and legal framework.
Presentation given by Kate LeMay at the 'Sharing Health-y Data: Challenges and Solutions' workshop, held at The Menzies Research Institute (Hobart, Tasmania) on 28th June 2016. The event was co-hosted by ANDS and the University of Tasmania library
The document summarizes a pilot project at the University of Edinburgh to support the development of a UK Research Data Discovery Service. PhD interns engaged with researchers from various schools to describe and deposit research datasets in the university's systems to be harvested by the discovery service. Observations found mixed results across schools, with humanities researchers less comfortable sharing data due to copyright and reluctance to share interpretations. Other schools had established data repositories causing less interest in the university's system. Building research data management practices will require tailored approaches and more training over time.
Topics covered at the workshop address basic questions related to Research Data Management for open data, which include preparing a Research Data Management (RDM) plan, licensing data and intellectual property, metadata and contextual description (documentation), ethical and legal aspects of sharing sensitive or confidential data, anonymizing research data for reuse, data archiving and long-term preservation, and data security and storage.
Event: http://conferences.nib.si/AS2015/default.htm
Related material: http://conferences.nib.si/AS2015/BookAS15.pdf
Research Ethics and Use of Restricted Access Datalibbiestephenson
Presentation given to the California Center for Population Research on principles of research ethics, data management for protection of privacy and confidentiality, and applying for access to restricted data in social science research.
This document summarizes research on incentives for researchers to share their data. It discusses findings from qualitative interviews and quantitative surveys. Key findings include:
- Individual researchers are motivated by benefits to their own research, career, and discipline's norms. They are influenced by funder and journal policies.
- Institutional supports like data infrastructure, funding, and training also influence researchers' data sharing practices. Funder requirements and assistance with data management increase sharing.
- Studies found the main individual motivations are career benefits and research impact. The main institutional factors are skills training, support services, and policies that ensure proper data reuse and acknowledgement.
Why science needs open data – Jisc and CNI conference 10 July 2014Jisc
This document discusses the importance of open data in science. It provides 4 key reasons why open data is important:
1) It allows for identification of patterns in large datasets that could not be found otherwise.
2) It enables data modeling through iterative integration of initial models with observational data.
3) It facilitates deeper integration and analysis of diverse linked datasets.
4) It supports exploitation of networked sensor data through acquisition, integration, analysis and feedback.
However, open data needs to be "intelligently open" through being discoverable, accessible, intelligible, assessable and reusable to realize its full potential. Mandating such intelligent open data is important to drive an open data infrastructure ecology.
This document discusses open data in healthcare. It provides background on open health data initiatives in several European countries like the UK, France, Portugal, and Italy. It notes both opportunities and challenges with opening up health data, including issues around governance, strategy, and privacy. The document advocates for a patient-centered approach and emphasizes the potential for open health data to empower citizens and fuel innovation when various stakeholders collaborate.
This document summarizes an agenda for sessions on DAOS (Domino Attachment and Object Service) and ID Vault. It discusses configuring and best practices for DAOS, including enabling it, using the estimator tool, and location considerations. It also covers configuring ID Vault, including requirements, the setup process, selecting administrators and organizations, and viewing uploaded IDs.
November 7, 2014: Brent Anthony, Co-Founder/Organizer of Health 2.0 NC Triangle, and Ketan Mane, Senior Research Scientist at Renaissance Computing Institute (RENCI), presents "ReThink to Uncover Potential for Open Health Data"
When you limit your data, you limit our potential. Emerging Trends to Accelerate our Health & Business.
The ODDC Network hosted an Open Session at the ICT for Development Conference in Cape Town, South Africa - 10th December 2013. These slides present an overview of the discussions.
SciDataCon 2014 Data Papers and their applications workshop - NPG Scientific ...Susanna-Assunta Sansone
Part of the SciDataCon14 workshop on "Data Papers and their applications" run by myself and Brian Hole to help attendees understand current data-publishing journals and trends and help them understand the editorial processes on NPG's Scientific Data and Ubiquity's Open Health Data.
Exploration of open health data at the federal, state, and local levels—including syndicated web content, downloadable datasets, and API-accessible information.
The health datapalooza story building an open data ecosystem for healthAman Bhandari
How we launched the Health Datapalooza under Todd Park and Aneesh Chopra. We used the open data and open gov mandate to release data and use that to fuel entrepreneurship and innovation in healthcare.
Joanna Lord, CMO of BigDoor, gave a presentation at the SEL Summit in 2014 about the future of brands. She discussed how brands have evolved from simply being trademarks and logos to representing the sum of a product's attributes and what customers think of when they hear the brand name. Top brands today, like Nike and Google, create intelligent connections with customers, are agile, empower consumers to build the brand with them through permission marketing, and understand loyalty goes both ways. The biggest brands of tomorrow will get this and be set up to focus on delivering value beyond just products to succeed.
Health Datapalooza IV: June 3rd-4th, 2013
Sanofi US Data Design Diabetes Demo Day
The “2013 Sanofi US Data Design Diabetes Innovation Challenge – Prove It!” invites innovators to develop solutions that use or produce data for decision-making to help improve health outcomes for people living with diabetes. Through baseline knowledge models, evidence-based practice, or predictive analysis, Prove It! asks innovators to think creatively about how to effectively harness data to address diabetes in the United States. During this hour, the final teams will live pitch their product to a panel of judges on the Main Stage with one winner to be presented with $100,000 on Tuesday, June 4.
Presenter: Sara Holoubek, Chief Executive Officer, Luminary Labs
This document discusses the potential for open health data to disrupt and improve healthcare systems. It provides examples of how open health data initiatives in the United States, United Kingdom, and Netherlands are fueling innovation and entrepreneurship in healthcare. Open health data encourages app and startup development that can enhance patient care, research, and transparency while reducing healthcare costs. Challenges to open health data include institutional resistance, privacy concerns, and ensuring data is accessible and usable across markets.
This qualitative overview of the Open Health Data initiatives is meant to showcase the importance of open health data, social as well as economic impacts across US, UK and a select set of Western European countries. This overview is not meant to be a comprehensive report on all the global initiatives, funding models and tracking of open health data. There are tremendous efforts across the globe to change our global healthcare system and we believe that open health data is one of the keys to bridge the gap between digital citizens & governments. Also, please note that if your country, initiative or product was not mentioned, it is in no way meant to diminish the impact of the efforts. Please feel free to share, discuss and contribute to the list of ongoing efforts and initiatives on one of our global communities or on openhealthdata.org.
Connecting the Dots: How Open Health Data will Accelerate Care Delivery Innov...Apigee | Google Cloud
Former CTO of the Obama administration, Aneesh Chopra, joins Apigee’s health care transformation strategist, Aashima Gupta, to discuss how to foster an application ecosystem that leverages connected apps and data to implement patient-centric reform.
In this webcast we will discuss:
- imperatives to open up data across multiple EHRs, payers, government and remote monitoring services
- Project Argonaut, an open, industry-led “code sprint” to support FHIR-based APIs for patient data interoperability
- how APIs built on a secure platform will enable expanded information sharing for patient health records
- value and viability of low cost inter-operability through FHIR-based APIs at healthcare systems
This document discusses using open data to enhance natural capital. It acknowledges contributors to the discussion and notes the presentation will be available online. The overview previews topics on open data definitions, current state, community wellness, natural capital, and moving forward. Open data is defined as content or data anyone can freely use, reuse, and redistribute subject to attribution and/or sharing requirements. Current global, national, and provincial open data initiatives are highlighted. The value proposition of open data for community analytics and improvements through data reuse applications is presented.
This slide deck is basically an extended and updated version of the "Microservices - stress-free and without increased heart-attack risk" slide deck - yet quite a lot of extensions and updates.
The deck is organized in three parts: Why, What and How.
The first part addresses the question if and when you should use microservices at all. It tries to create a bigger picture by explaining changing (business) markets and a changing role of IT and fits microservices into that picture. When looking at this picture it also becomes clear that microservices always should accompanied by other measures like DevOps, Cloud and some more if you really want to leverage its benefits. Otherwise you usually only get the downsides of microservices with harvesting their benefits.
The second part revisits the famous blog post from James Lewis and Martin Fowler, using it to explain what characteristics the microservice architecural style has. It turns out that this post contains quite a lot of information and that quite often only a subset of the characteristics get implemented.
The third part, the "How" dives deeper into the challenges and pitfalls that you usually encounter if you decide to adopt microservices. While of course not being complete and not being a perfect guide that makes everything easy, it should at least help you tho avoid the most common problems and pitfalls.
As always the voice track is missing which contains most of the information (it is a 90 min talk after all), but hopefully also the slides alone contain some helpful information.
Open Research Data Frameworks: Lessons for the Global SouthAnup Kumar Das
The presentation titled "Open Research Data Frameworks: Lessons for the Global South" was delivered in the National Symposium on Improving eGovernance using Big Data Analytics, held at Department of Management Studies, Indian Institute of Technology Delhi, India, on 28th February 2017. The symposium was a run up event of ICEGOV2017 (10th International Conference on Theory and Practice of Electronic Governance), held at New Delhi. I briefly discussed the global initiatives such as UNESCO's Global Open Access Portal (GOAP), Re3Data.org (Registry of Research Data Repositories), GODAN (Global Open Data for Agriculture and Nutrition), Research Data Alliance (RDA), ICSSR Data Service, and self-archiving of scientific data on data repositories.
This document discusses La Trobe University's efforts to capture and manage research datasets through its Research Online repository and an ANDS project. It provides examples of datasets already collected, including spreadsheet and visual data. Drivers for changing research practices to share data, such as funder policies, are outlined. Issues around informed consent, data types, and curating data for the repository through a standard questionnaire are also covered. The aim is to properly manage and preserve research data in a central location.
Incentives for sharing research data – Veerle Van den Eynden, UK Data Service
Incentives to innovate – Joe Marshall, NCUB
Incentives in university collaboration - Tim Lance, NYSERNET
Giving researchers credit for their data – Neil Jefferies, The Bodleian Digital Library Systems and Services (BDLSS)
Jisc and CNI conference, 6 July 2016
Presentation at the La Trobe University Open Access Seminar on 25 October 2013. What are the benefits of exposing research data? Why is La Trobe University doing this? What tools does the Library provide to help with this?
In order to be reused, research data must be discoverable.
The EPSRC Research Data Expectations* requires research organisations to maintain a data catalogue to record metadata about research data generated by EPSRC-funded research projects.
Universities are increasingly making research data assets available through repositories or other data portals.
The requirement for a UK research data discovery service has grown as universities become more involved in RDM and capacity develops.
Survey of research data management practices up2010heila1
The document summarizes the findings of a survey conducted by the University of Pretoria Library Services department from October 2009 to March 2010. The survey interviewed 52 researchers and students to evaluate current research data management practices. It found that while support for research activities is good, data management practices are ad hoc and informal. Top needs identified were a central data repository and increased storage options. The report recommends establishing a research data manager position and exploring partnerships with national data initiatives.
1) The document discusses reflections on cohorts and longitudinal population studies, focusing on their strengths and weaknesses. It summarizes a survey of 77 cohort studies across 32 low and middle-income countries.
2) Key recommendations include improving data linkage, coordination between studies, use of emerging technologies, capacity building, data sharing, standardization, and translation of research outputs.
3) Barriers to effective data sharing are discussed, as well as initiatives by the Wellcome Trust to address priorities like data discoverability, incentives for data sharing, and ensuring ethical standards.
This document outlines a briefing on research data management (RDM) at LSBU. It defines RDM and research data, discusses why RDM has gained increased interest and attention due to factors like funder policies and legislative changes. It describes the benefits of RDM for researchers and institutions. It then outlines LSBU's RDM policy, which includes requirements for data management plans, data storage, sharing, and citation. The document discusses next steps for LSBU, including a survey of current practices, case studies, interviews, and launching an institutional data repository in 2016. It notes both opportunities, like training workshops, and challenges to implementing RDM, such as changing researcher behaviors and incentives.
Rachel Bruce UK research and data management where are we nowJisc
The document discusses the state of research data management in UK universities. It finds that while areas like data cataloguing and access/storage systems are progressing, governance of data access/reuse and digital preservation/planning are lagging. Barriers to progress include low researcher priority, funding availability, and lack of staff/infrastructure. Gaps include defining responsibilities, standards, costs, and tools. Coordination and sharing resources across institutions is needed to help universities advance research data management.
Stuart Macdonald steps through the process of creating a robust data management plan for researchers. Presented at the European Association for Health Information and Libraries (EAHIL) 2015 workshop, Edinburgh, 11 June 2015.
The document provides information on creating a data management plan (DMP) for grant applications. It discusses what a DMP is, why they are important, and what funders require in a DMP. A DMP outlines how research data will be collected, documented, stored, shared, and preserved. The document recommends addressing six key themes in a DMP: data types and standards; ethics and intellectual property; data access, sharing and reuse; short-term storage and management; long-term preservation; and resourcing. Developing a strong DMP helps researchers manage data effectively and makes data available and reusable by others.
Lessons from the UK: Data access, patient trust & real-world impact with heal...Varsha Khodiyar
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The art of depositing social science data: maximising quality and ensuring go...Louise Corti
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Research Data, or: How I Learned to Stop Worrying and Love the PolicyTorsten Reimer
1) The document summarizes the development of Imperial College London's research data management policy. It involved investigating current practices through surveys and interviews, piloting small projects, and taking a flexible approach focused on practical solutions rather than strict compliance.
2) A key finding was that researchers want secure but accessible storage and sharing of research data. The policy implemented flexible infrastructure using existing tools like Box, GitHub, Zenodo and Symplectic to meet researchers' needs.
3) The approach was to make practical progress initially while continuing to learn and adapt the solutions, rather than waiting for perfect solutions or strict funder compliance.
This document discusses data publishing and management. It introduces the advantages of publishing research data, including increasing citations, recognition and meeting grant requirements. It outlines best practices for data management planning and provides examples of data publishing platforms like SHaRED. The document advises that major journals and funding bodies now require data publication in open repositories to promote open access and data sharing in science.
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5. 5La Trobe University
Research Data Managment
• La Trobe University Working
party from various business
depts
• Most universities grappling with
the same issues
• Managing research data can be
complex & messy
• There’s always exceptions to the
norm
Sam Searle, Content and Discovery Services, Griffith University
6. 6La Trobe University
What are research data?
6
“It is not possible to apply a uniform definition of research data across all
disciplines. Research data may be numerical, textual, audio-visual,
digital or physical, depending on the discipline and the nature of the
research.”
Source: University of Sydney Research Data Management Policy 2014 http://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2013/337
7. 7La Trobe University
Why now?
• Good practice
• Funder requirements
• Compliance
• Increased citation
Australian Code for the Responsible Conduct of
Research, states:
Policies are required that address the ownership
of research materials and data, their storage,
their retention beyond the end of the project,
and appropriate access to them by the research
community.
8. 8La Trobe University
General issues to manage when dealing with research data…
• Research data can be digital or analogue
• Dealing with sensitive data
• Copyright & Licensing
• Not all research data will be suitable for Open Access
• Issues relating to validation/review of research data (established principles &
criteria for journal articles & thesis)
• Data Management plans
9. 9La Trobe University
Population Health data collection
for the City of Greater Bendigo
http://dx.doi.org/10.4225/22/55BAE9DBD9670
10. 10La Trobe University
Some key issues/terms
• Research
• Ethics
• Privacy
• Confidentiality
• Consent
• Risk
• Harm
• Identifier
• Databank
• De-identified data
• CC-BY
11. 11La Trobe University
ANDS Major Open Data Collections (MODC) project
• The project was funded by the Australian National Data Service as part of its
Open Data Collections program to support partner institutions make available an
internationally significant open research data collection.
12. 12La Trobe University
ANDS Major Open Data Collections (MODC) project
• La Trobe University Library partnered with the Building Healthy Communities
Research Focus Area (RFA) to trial processes to develop a Healthy Communities
Data Collection.
• Funders requirements:
• There is a demonstrated research need for the data beyond the scope of the
institution
• The data should be well described and well linked having richly connected data
collections and sub-collections
• The open data collection must be discoverable through appropriate means including
Research Data Australia as well as institutional, international and discipline specific
portals
13. 13La Trobe University
The Bendigo Health Population data collection
• Health researchers have a major interest
in accessing clinical data to support
research to inform and improve
population health and health services.
• Cardiovascular disease is the leading
cause of death in Australia, being
responsible for 33.7 per cent of all deaths
in 2008 (ABS 2010). Furthermore,
cardiovascular disease is the second
leading cause of the disease burden (18
per cent of the total burden) (Begg et al.
2007).
• Curate & identify connections
between disparate data sets.
14. 14La Trobe University
Secondary research data from regional health service providers
• Bendigo Health: 48,000 patient
records relating to Circulatory
system diseases (ICD code range of
I00–I99) for patients over 40 years
of age
• Loddon Mallee Murray Medicare
Local: 245,000 patient records from
General Practices within the
Loddon/Murray/Mallee catchment
area for a broad range of health
issues
• Other sources: ABS, AIHW
MODC
Data
collection
BH
LMMML
Other
data
Curate & identify connections
between disparate data sets.
15. 15La Trobe University
Key issues for project team
• No precedence for publication of secondary data
(methodology, workflow, supporting templates)
• Obligations when handling sensitive health data
requires systems and process to ensure the security
and integrity of the data is managed
• Collection, Use & Disclosure of data involving human
subjects is subject to ethics approvals
• The O in MODC is for Open: third-party material to be
licenced under conditions that support re-use & re-
purposing
16. 16La Trobe University
Some project house keeping: systems & templates to develop
• Schemas / Data dictionary
• Data sample
• Data extraction plan
• Ethics
• Secure storage / Access restricted by roles
• License agreement
• Metadata
• Research data management plan
• Sensitive data
17. 17La Trobe University
Stage one: data acquisition
• Preliminary meetings
• Positive response from both health services
• “Define area of research interest and we can
extract and supply the data”
• La Trobe Human Research Ethics Committee
approved
• More meetings with Health IT Manager, La Trobe
Epidemiologist and Library repository staff
18. 18La Trobe University
Stage one: define area of interest
Bendigo Health (BH) CIO required detailed scope of intention
& data planning for the project, including:
• Clear statement of intent – including inputs/outputs
• Area of interest (scope)
• Any internal resources required
• Roadmap & Timeline
• May require approval from BH Human Research Ethics
Committee (HREC)
19. 19La Trobe University
Stage one: Human Research Ethics
• The primary role of Bendigo Health’s Human
Research Ethics Committee is to protect the welfare
and rights of participants in research.
• HREC review research proposals and make judgments
on whether risks of the research are justified by the
potential benefits.
• Must meet the requirements for ethical research
within National Statement on Ethical Conduct in
Human Research (2007)
• Also familiarize yourself with the relevant
Commonwealth and State legislation to ensure your
project complies with human research and privacy
laws
21. 21La Trobe University
What is human research?
• Human research is conducted with or about people through:
• Taking part in surveys, interviews or focus groups
• Undergoing psychological, physiological or medical testing or treatment
• Being observed by researchers
• Researchers having access to their personal documents or other materials
• The collection and use of their body organs, tissues or fluids (eg skin,
blood, urine, saliva, hair, bones, tumour and other biopsy specimens) or
their exhaled breath
• Access to their information as part of an existing published or unpublished
source or database
22. 22La Trobe University
National Statement on
Ethical Conduct in
Human Research, 2007
(Updated May 2015)
• Two themes must always be
considered in human research:
the risks and benefits of
research, and participants’
consent.
• The National Statement allows for
different levels of ethical review
of research, reflecting the
difference in degree of risk
involved.
23. 23La Trobe University
NS 2.1: Risk and Benefit
• The expression low risk research describes research in which the only
foreseeable risk is one of discomfort.
• The expression negligible risk research describes research in which there is no
foreseeable risk of harm or discomfort; and any foreseeable risk is no more than
inconvenience.
GUIDELINE 2.1.2: Risks to
research participants are
ethically acceptable only if they
are justified by the potential
benefits of the research.
24. 24La Trobe University
NS 2.2: General requirements for Consent
• Consent to participate in research must be
voluntary and based on sufficient information
and adequate understanding of both the
proposed research and the implications of
participation in it.
• Depending upon the circumstances of an
individual project it may be justifiable to
employ an opt-out approach or a waiver of the
requirement for consent, rather than seeking
explicit consent.
GUIDELINE 2.2.1
The guiding principle for
researchers is that a person’s
decision to participate in
research is to be voluntary, and
based on sufficient information
and adequate understanding of
both the proposed research
and the implications of
participation in it.
GUIDELINE 2.2.2
Participation that is voluntary
and based on sufficient
information requires an
adequate understanding of the
purpose, methods, demands,
risks and potential benefits of
the research.
25. 25La Trobe University
NS 3.2 Databanks
• The National Statement defines databanks as
“[A] systematic collection of data … If data are
being collected, aggregated and stored with a
view to use for future related or as yet
unspecified research, this may involve ‘banking’
the participants’ data.”
• The term databanks includes databases.
• Types of research that commonly make use of
databanks include epidemiology, pathology,
genetics and social sciences.
GUIDELINE 3.2.1
When planning a databank,
researchers should clearly
describe how their research
data will be collected, stored,
used and disclosed, and outline
how that process conforms to
this National Statement,
particularly the requirements
for consent set out in
paragraphs 2.2.14 to 2.2.18.
GUIDELINE 3.2.3
Researchers’ use of data from
databanks must comply with
conditions specified by the
providers of the data; in
particular, any conditions on
the identifiability of the data
(see paragraphs 2.2.14 to
2.2.18).
26. 26La Trobe University
NS 5 (Processes of research governance and ethical review)
Institutional responsibilities
• Research involving no more than low risk
can be exempted from review
• Institutions may choose to exempt from
ethical review research that:
a) is negligible risk research (as defined
in paragraph 2.1.7); and
b) involves the use of existing collections
of data or records that contain only
non-identifiable data about human
beings.
• Deciding to exempt research from ethical
review still means the research must meet
the requirements of the National Statement
and be ethically acceptable.
GUIDELINE 5.1.8
Research that carries only
negligible risk (see paragraph
2.1.7) and meets the
requirements of paragraphs
5.1.22 and 5.1.23 may be
exempted from ethical review.
28. 28La Trobe University
Data identifiability
• Individually identifiable data: where the identity of
a specific individual can reasonably be ascertained.
Examples of identifiers include the individual’s name,
image, date of birth or address
• Re-identifiable data: from which identifiers have
been removed and replaced by a code, but it remains
possible to re-identify a specific individual by, for
example, using the code or linking different data sets
• Non-identifiable data: where no specific individual
can be identified, as the data has never been labelled
with individual identifiers or from which identifiers
have been permanently removed
29. 29La Trobe University
Legal requirements
• Every project will involve the collection, use or disclosure of some piece of
information.
• Researchers should review ALL Privacy Principles in the relevant legislation to
ensure that their project is fully compliant with all aspects of the law.
• Researchers are responsible for identifying the relevant Act and guidelines
under which an application for approval of a project is made.
• If more than one Act (or set of guidelines) applies, all relevant legislative
requirements will need to be met, including the obtaining of any necessary
approvals from a Human Research Ethics Committee. The statutory guidelines
referred to above are not identical, as they must reflect the various statutes
under which they are made and any different requirements must be adhered to.
30. 30La Trobe University
Victorian Laws
• In Victoria there is a requirement to comply with legislation relevant to human
research involving information privacy (Information Privacy Act 2000) and
health information (Health Records Act 2001).
• The Health Records Act 2001 (Victoria) applies to all health information handled
by the Victorian public sector and private sector. There are eleven Health
Privacy Principles (HPPs). HPP 1 and 2 govern the collection, use and
disclosure of health information, including for the purposes of research.
• The Information Privacy Act 2000 (Victoria) regulates the responsible collection
and handling of personal information – which includes “sensitive information”
but excludes health information by organisations in the Victorian public sector,
including universities. Sets out ten Information Privacy Principles (IPPs). IPPs 1,
2 and 10 deal with the collection, use and disclosure of this information for
the purposes of research.
31. 31La Trobe University
Commonwealth Law
• The Privacy Act 1988 (Cth) outlines thirteen Australian
Privacy Principles, which establish requirements for the
collection, storage, use and disclosure of personal
information and health information.
Sections 16A and 16B of the Privacy Act set out certain
circumstances in which it is permissible to collect, use and
disclose personal information and health information for
the purposes of research.
32. 32La Trobe University
Definitions by law
• Collection: an organisation or individual collects information if it gathers,
acquires or obtains information from any source and by any means, whether
that information has been requested or not. Questionnaires, surveys, interviews,
focus groups and requests for information held in databases, data sets or
institutional records are all examples of how information may be collected.
• Use: an organisation or individual uses information if it handles the information
in any way. Use of information includes any form of quantitative or qualitative
analysis and any inclusion of the information in any form of publication.
• Disclosure: an organisation or individual discloses information when it releases
information to other organisations or individuals (that is, outside of those who
collected the information in the first instance).
33. 33La Trobe University
Step 2: Data cleansing and merging
• Data preparation and linkage:
• Filter / Screen fields (eg: Pensioners, ATSI)
• Aggregation / Band fields (eg: DOB)
• BH- and ABS- data were joined on SLA- (‘Statistical Local Area’) codes
• ML- and ABS- data were joined on SLA+ML (Medicare-Local) codes
• How much data in total?
• ML - 221,268 patient records
• BH - 40,237 patient records
• Other tables incl Measurement; Medication, Diagnosis.
34. 34La Trobe University
Step 3: Deposit to La Trobe repository
• Supported by data dictionary & reusable format
• Metadata created to describe collection and distribute through La Trobe repository:
• Research Data Australia
• National Library’s TROVE service
• DataCite
• Google
35. 35La Trobe University
http://hdl.handle.net/1959.9/319746
LTU Research Online repository
Title: Population Health data collection for the City of Greater Bendigo.
Keywords: Health informatics; Epidemiology; Heart disease; Circulatory system disease; Health data analysis
Description: This data collection contains de-identified clinical health service utilisation data from Bendigo Health and the General
Practitioners Practices associated with the Loddon Mallee Murray Medicare Local. The collection also includes associated population
health data from the ABS, AIHW and the Municipal Health Plans. Health researchers have a major interest in how clinical data can be
used to monitor population health and health care in rural and regional Australia through analysing a broad range of factors shown to
impact the health of different populations. The Population Health data collection provides students, managers, clinicians and
researchers the opportunity to use clinical data in the study of population health, including the analysis of health risk factors, disease
trends and health care utilisation and outcomes.
37. 37La Trobe University
Funders supporting the re-use and re-purposing of open
research data
The Australian Research Council (ARC) Open Access Policy:
• “Any publications arising from an ARC supported research Project must be
deposited into an open access institutional repository within a twelve (12)
month period from the date of publication.”
http://www.arc.gov.au/arc-open-access-policy
38. 38La Trobe University
Accessing and Using Publicly Funded Data for Health Research
The National Health and Medical Research Council has drafted a framework of
principles for researchers and data custodians to consider when requests or
applications are made for access to existing health and health-related datasets for
research purposes.
1. Research use of publicly held health and health-related data should be
maximised
2. Data custodians should recognise their responsibilities and accountabilities
when providing access to data for research.
3. Researchers should recognise their responsibilities and accountabilities when
accessing and using publicly held health and health related datasets
39. 39La Trobe University
Lessons learnt
• No such thing as a free lunch: Open access projects still require investments of
time, money and expertise
• Relationships: Bendigo hospital, Loddon Mallee Murray Medicare Local ANDS
• New model for releasing secondary data: little precedence for open
publication of data alone
• Technical: Disparate data from different proprietary technical systems
• Managing risk: dealing with sensitive health data under an open access model
40. 40La Trobe University
Key terms (National Statement on Ethical Conduct
in Human Research 2007)
• Research: Includes at least investigation undertaken to gain knowledge
and understanding or to train researchers
• Ethics: The concepts of right and wrong, justice and injustice, virtue and
vice, good and bad, and activities to which these concepts apply
• Privacy: A domain within which individuals and groups are entitled to be
free from the scrutiny of others
• Confidentiality: The obligation of people not to use private information –
whether private because of its content or the context of its communication -
for any purpose other than that for which it was given to them
• Consent: A person’s or group’s agreement, based on adequate knowledge
and understanding of relevant material, to participate in research
41. 41La Trobe University
Key terms (National Statement on Ethical Conduct
in Human Research 2007)
• Risk: The function of the magnitude of a harm and the probability
that it will occur
• Harm: that which adversely affects the interests or welfare of an
individual or a group. Harm includes physical harm, anxiety, pain,
psychological disturbance, devaluation of personal worth and social
disadvantage
• Identifier: Details attached to data, such as name and/or contact
information, that identify an individual
• Databank: A systematic collection of data, whether individually
identifiable, re-identifiable or non-identifiable
• De-identified data: NS avoids term as it’s meaning is unclear.
• CC-BY: Attribution Creative Commons license
42. 42La Trobe University
Resources
• ANDS ‘Publishing and Sharing Sensitive Data’ -
http://ands.org.au/guides/sensitivedata.html
• ANDS ‘Ethics, consent and data sharing’ - http://ands.org.au/guides/ethics-working-
level.html
• How to confidentialise data: the basic principles, National Statistical Service -
http://www.nss.gov.au/nss/home.nsf/pages/Confidentiality+-
+How+to+confidentialise+data:+the+basic+principles
• The National Statement on Ethical Conduct in Human Research (2007) -
http://www.nhmrc.gov.au/guidelines-publications/e72
• [DRAFT] Principles for Accessing and Using Publicly-Funded Data for Health Research -
http://consultations.nhmrc.gov.au/public_consultations/funded-data
• The Australian Code for the Responsible Conduct of Research -
http://www.nhmrc.gov.au/_files_nhmrc/publications/attachments/r39.pdf