Lancaster University's IT Security Manager outlines key aspects of the university's security overview in May 2017. It discusses external requirements like Cyber Essentials Plus certification, meeting the standards of the Information Governance Toolkit, and working towards ISO 27001. The document also covers how the university classifies information, including personal and sensitive personal data. Guidelines are provided around securely transferring, storing, and disposing of information to protect data.
State of the Art Informatics for Research Reproducibility, Reliability, and...Micah Altman
In March, I had the pleasure of being the inaugural speaker in a new lecture series (http://library.wustl.edu/research-data-testing/dss_speaker/dss_altman.html) initiated by the Libraries at the Washington University in St. Louis Libraries -- dedicated to the topics of data reproducibility, citation, sharing, privacy, and management.
In the presentation embedded below, I provide an overview of the major categories of new initiatives to promote research reproducibility, reliability, and reuse and related state of the art in informatics methods for managing data.
Prof George Alter, UMich, ICPSR, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17.
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
2) Prof George Alter, (Research Professor, ICPSR and Visiting Professor, ANU) George will share the benefit of over 50 years of experience in managing sensitive social science data in the ICPSR: https://www.icpsr.umich.edu/icpsrweb/
More about ICPSR: -- ICPSR (USA) maintains a data archive of more than 250,000 files of research in the social and behavioral sciences. It hosts 21 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields. -- ICPSR collaborates with a number of funders, including U.S. statistical agencies and foundations, to create thematic collections: see https://www.icpsr.umich.edu/icpsrweb/content/about/thematic-collections.html
Service and Support for Science IT-Peter Kunzst, University of ZurichMind the Byte
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.
Managing sensitive data at the Australian Data ArchiveARDC
Dr Steven McEachern, Director, Australian Data Archive, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
1) Dr Steve McEachern (Director, Aust Data Archive) Stevediscussed how the Australian Data Archive manages and publishes sensitive social science data.
More about ADA: -- The Australian Data Archive (ADA) provides a national service for the collection and preservation of digital research data and to make these data available for secondary analysis by academic researchers and other users. -- The ADA is comprised of seven sub-archives - Social Science, HIstorical, Indigenous, Longitudinal, Qualitative, Crime & Justice and International. -- ADA data is free of charge to all users -- The archive is managed by the ADA central office based in the ANU Centre for Social Research and Methods at the Australian National University (ANU).https://www.ada.edu.au/
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 document discusses the FAIR data principles and increasing adoption of FAIR. It begins by explaining the 15 FAIR principles for findable, accessible, interoperable and reusable data. It then discusses how adoption is increasing through funder requirements, the role of FAIR within EOSC, and related projects. However, it notes that most data is still not managed or shared according to FAIR principles due to barriers like time and effort required as well as lack of incentives and rewards. The document argues that both cultural and technical aspects must be addressed to fully implement FAIR.
What is sensitive data?
Can sensitive data be shared? 23 (research data) Things - Thing 10 Sharing Sensitive Data
Top five tips for sharing sensitive data
Lancaster University's IT Security Manager outlines key aspects of the university's security overview in May 2017. It discusses external requirements like Cyber Essentials Plus certification, meeting the standards of the Information Governance Toolkit, and working towards ISO 27001. The document also covers how the university classifies information, including personal and sensitive personal data. Guidelines are provided around securely transferring, storing, and disposing of information to protect data.
State of the Art Informatics for Research Reproducibility, Reliability, and...Micah Altman
In March, I had the pleasure of being the inaugural speaker in a new lecture series (http://library.wustl.edu/research-data-testing/dss_speaker/dss_altman.html) initiated by the Libraries at the Washington University in St. Louis Libraries -- dedicated to the topics of data reproducibility, citation, sharing, privacy, and management.
In the presentation embedded below, I provide an overview of the major categories of new initiatives to promote research reproducibility, reliability, and reuse and related state of the art in informatics methods for managing data.
Prof George Alter, UMich, ICPSR, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17.
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
2) Prof George Alter, (Research Professor, ICPSR and Visiting Professor, ANU) George will share the benefit of over 50 years of experience in managing sensitive social science data in the ICPSR: https://www.icpsr.umich.edu/icpsrweb/
More about ICPSR: -- ICPSR (USA) maintains a data archive of more than 250,000 files of research in the social and behavioral sciences. It hosts 21 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields. -- ICPSR collaborates with a number of funders, including U.S. statistical agencies and foundations, to create thematic collections: see https://www.icpsr.umich.edu/icpsrweb/content/about/thematic-collections.html
Service and Support for Science IT-Peter Kunzst, University of ZurichMind the Byte
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.
Managing sensitive data at the Australian Data ArchiveARDC
Dr Steven McEachern, Director, Australian Data Archive, presenting at the Managing and publishing sensitive data in the Social Sciences webinar on 29/3/17
FULL webinar recording: https://youtu.be/7wxfeHNfKiQ
Webinar description:
1) Dr Steve McEachern (Director, Aust Data Archive) Stevediscussed how the Australian Data Archive manages and publishes sensitive social science data.
More about ADA: -- The Australian Data Archive (ADA) provides a national service for the collection and preservation of digital research data and to make these data available for secondary analysis by academic researchers and other users. -- The ADA is comprised of seven sub-archives - Social Science, HIstorical, Indigenous, Longitudinal, Qualitative, Crime & Justice and International. -- ADA data is free of charge to all users -- The archive is managed by the ADA central office based in the ANU Centre for Social Research and Methods at the Australian National University (ANU).https://www.ada.edu.au/
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 document discusses the FAIR data principles and increasing adoption of FAIR. It begins by explaining the 15 FAIR principles for findable, accessible, interoperable and reusable data. It then discusses how adoption is increasing through funder requirements, the role of FAIR within EOSC, and related projects. However, it notes that most data is still not managed or shared according to FAIR principles due to barriers like time and effort required as well as lack of incentives and rewards. The document argues that both cultural and technical aspects must be addressed to fully implement FAIR.
What is sensitive data?
Can sensitive data be shared? 23 (research data) Things - Thing 10 Sharing Sensitive Data
Top five tips for sharing sensitive data
Cette conférence - e-santé : évolution ou révolution? - a pour but de présenter quelques projets développés par les hautes écoles et entreprises de pointe dans le domaine. A cette occasion, Prof. Dr. Henning Müller a fait un exposé intitulé: La e-santé en général et quelques projets de la HES-SO Valais.
This document summarizes a webinar on managing information and communication technology (ICT) in organizations. It discusses establishing an ICT strategy aligned with organizational goals, conducting an ICT health check, managing ICT risks, investing in ICT using a run-grow-transform model, developing policies for issues like BYOD, taking advantage of discounted or donated ICT products and services, sources of ICT help and support, and positioning ICT as an enabler to support organizational service delivery rather than a goal in itself. Upcoming webinars on data protection and social media are also announced.
The document discusses open data and data sharing, including defining open data, the benefits of open data, overcoming barriers to opening data such as concerns about scooping and sensitive data, best practices for making data open through formats, licensing and description, and the role of research databases and data citation in promoting open data.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
This document summarizes a presentation about open data and science in Africa. It discusses the benefits of open data, such as enabling more informed decisions and driving development. It also addresses challenges like researchers' fears of having errors or incomplete data exposed. The presentation promotes the African Open Science Platform, which aims to establish open data policies and build capacity through workshops on data skills. The platform connects stakeholders to advance open data and science across Africa.
This document discusses licensing research data for reuse. It begins by providing a scenario where a user has downloaded a dataset but is unsure what they can do with the data due to licensing. It then discusses that licensing is critical to enabling data reuse and citation. It provides information on AusGOAL, the Australian open access and licensing framework, and notes it is recommended for data publishing by ANDS partners. It also includes links to licensing guides and FAQs. In summary, the document emphasizes the importance of data licensing for enabling reuse and outlines Australia's recommended licensing system.
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.
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.
NIH Data Policy or: How I Learned to Stop Worrying and Love the Data Manageme...Kristin Briney
This document summarizes the key points from a presentation about NIH data management and sharing plan requirements. It discusses why these plans are now required for grants over $500,000, how to write an effective plan including what data to share, when, where, who will access it, and how it will be prepared. It also provides tips for effective long-term data management practices like file organization, documentation, backup plans, and security. Resources for creating data management plans and getting help from librarians and tools are also mentioned.
The document outlines a 23 Things program for research data management training, which releases weekly activities and has monthly webinars, and provides a calendar of events and list of coordinators for the program at UWA.
Slides from Wednesday 1st August - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
This document discusses sharing research data. It describes the Data Services Center, which provides data services including finding and providing access to datasets. It notes that funders and publishers require data sharing, and that shared data receives more citations. It recommends sharing the minimum data needed to reproduce results, and considering timing, usability and granularity of data sharing. For sharing methods, it recommends using disciplinary or general repositories like UR Research, Dryad and REACTUR, which provide long-term preservation and access. Workshops and help are available for data management and sharing.
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.
Big Data Infrastructure for Translational Research discusses challenges in building big data infrastructure for translational research. It defines big data as large and complex data difficult to process with typical tools. Big data comes from various sources like mobile devices, sensors, clinical monitors. Scaling data acquisition from patient bed to institution is discussed. Tools used include databases, scripting languages, statistical packages and visualization. Challenges include data capture, curation, storage, sharing and analysis. A multidisciplinary team approach is advocated to tackle big data challenges in translational medicine.
The document summarizes the African Open Science Platform (AOSP), an initiative to create an open digital ecosystem in Africa. It discusses AOSP's goals of building capacities, policies, shared computing resources, and tools to support open science and interaction with societal stakeholders. It also outlines AOSP's governance structure, initial activities, key supporting communities, the current African open science landscape, and a framework for future policy, infrastructure, capacity building, and incentives to further open science on the continent.
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.
This document discusses energy transitions. It was written by Dénes Csala, a lecturer in energy storage systems dynamics. The document contains random words and does not provide any clear information that can be summarized in 3 sentences or less.
This document discusses using GitLab for revision control and managing code. It notes some common barriers to using Git like not wanting to share code or feeling code is not ready to share. It then compares GitLab to GitHub and discusses how to set up a GitLab instance on campus for private code repositories with total privacy and control. Installation of GitLab was described as easy and it provides benefits like backups of work and adding collaborators.
Cette conférence - e-santé : évolution ou révolution? - a pour but de présenter quelques projets développés par les hautes écoles et entreprises de pointe dans le domaine. A cette occasion, Prof. Dr. Henning Müller a fait un exposé intitulé: La e-santé en général et quelques projets de la HES-SO Valais.
This document summarizes a webinar on managing information and communication technology (ICT) in organizations. It discusses establishing an ICT strategy aligned with organizational goals, conducting an ICT health check, managing ICT risks, investing in ICT using a run-grow-transform model, developing policies for issues like BYOD, taking advantage of discounted or donated ICT products and services, sources of ICT help and support, and positioning ICT as an enabler to support organizational service delivery rather than a goal in itself. Upcoming webinars on data protection and social media are also announced.
The document discusses open data and data sharing, including defining open data, the benefits of open data, overcoming barriers to opening data such as concerns about scooping and sensitive data, best practices for making data open through formats, licensing and description, and the role of research databases and data citation in promoting open data.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
This document summarizes a presentation about open data and science in Africa. It discusses the benefits of open data, such as enabling more informed decisions and driving development. It also addresses challenges like researchers' fears of having errors or incomplete data exposed. The presentation promotes the African Open Science Platform, which aims to establish open data policies and build capacity through workshops on data skills. The platform connects stakeholders to advance open data and science across Africa.
This document discusses licensing research data for reuse. It begins by providing a scenario where a user has downloaded a dataset but is unsure what they can do with the data due to licensing. It then discusses that licensing is critical to enabling data reuse and citation. It provides information on AusGOAL, the Australian open access and licensing framework, and notes it is recommended for data publishing by ANDS partners. It also includes links to licensing guides and FAQs. In summary, the document emphasizes the importance of data licensing for enabling reuse and outlines Australia's recommended licensing system.
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.
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.
NIH Data Policy or: How I Learned to Stop Worrying and Love the Data Manageme...Kristin Briney
This document summarizes the key points from a presentation about NIH data management and sharing plan requirements. It discusses why these plans are now required for grants over $500,000, how to write an effective plan including what data to share, when, where, who will access it, and how it will be prepared. It also provides tips for effective long-term data management practices like file organization, documentation, backup plans, and security. Resources for creating data management plans and getting help from librarians and tools are also mentioned.
The document outlines a 23 Things program for research data management training, which releases weekly activities and has monthly webinars, and provides a calendar of events and list of coordinators for the program at UWA.
Slides from Wednesday 1st August - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
This document discusses sharing research data. It describes the Data Services Center, which provides data services including finding and providing access to datasets. It notes that funders and publishers require data sharing, and that shared data receives more citations. It recommends sharing the minimum data needed to reproduce results, and considering timing, usability and granularity of data sharing. For sharing methods, it recommends using disciplinary or general repositories like UR Research, Dryad and REACTUR, which provide long-term preservation and access. Workshops and help are available for data management and sharing.
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.
Big Data Infrastructure for Translational Research discusses challenges in building big data infrastructure for translational research. It defines big data as large and complex data difficult to process with typical tools. Big data comes from various sources like mobile devices, sensors, clinical monitors. Scaling data acquisition from patient bed to institution is discussed. Tools used include databases, scripting languages, statistical packages and visualization. Challenges include data capture, curation, storage, sharing and analysis. A multidisciplinary team approach is advocated to tackle big data challenges in translational medicine.
The document summarizes the African Open Science Platform (AOSP), an initiative to create an open digital ecosystem in Africa. It discusses AOSP's goals of building capacities, policies, shared computing resources, and tools to support open science and interaction with societal stakeholders. It also outlines AOSP's governance structure, initial activities, key supporting communities, the current African open science landscape, and a framework for future policy, infrastructure, capacity building, and incentives to further open science on the continent.
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.
This document discusses energy transitions. It was written by Dénes Csala, a lecturer in energy storage systems dynamics. The document contains random words and does not provide any clear information that can be summarized in 3 sentences or less.
This document discusses using GitLab for revision control and managing code. It notes some common barriers to using Git like not wanting to share code or feeling code is not ready to share. It then compares GitLab to GitHub and discusses how to set up a GitLab instance on campus for private code repositories with total privacy and control. Installation of GitLab was described as easy and it provides benefits like backups of work and adding collaborators.
This document discusses cloud computing and the challenges of complete data deletion. It notes that cloud storage allows for infinite storage and rapid resource provisioning, but deleted data may still remain accessible due to technical challenges. Specifically, it outlines that data is often stored in multiple copies and locations, making full deletion difficult. The document argues that complete and verifiable deletion is important for sensitive data and privacy rights, but remains an unsolved problem due to the complex and distributed nature of cloud infrastructure.
This document discusses software as a research object and the importance of research software. Some key points:
- Many researchers rely on software for their work but few have formal software training. Software is integral to modern research.
- Studies have found low reproducibility in scientific publications due to issues with unavailable software and code. Proper documentation and sharing of research software is needed.
- The Software Sustainability Institute aims to cultivate better, more sustainable research software to enable world-class research. They provide training, community support, and advocate for improved software practices and policies.
- Culture change is needed to incentivize sharing of research software and code. Mechanisms are emerging to properly credit software contributions and cite
This document discusses using smartphone data to gain psychologically important insights. It summarizes past research that achieved 85% accuracy in predicting bipolar symptoms and 92% accuracy in detecting deception using location and usage data. The document then describes two of the author's own apps, ParkinsonEaston and Getting Log, which analyze location, movement patterns, and usage logs. It notes that recent changes in Android software now limit background access to location data, but discusses ways researchers can still gain insights while respecting users' privacy, such as focusing on places visited rather than movements.
The document discusses the author's past, present, and future approach to releasing software code accompanying research publications. In the past, the author published a paper on sentiment analysis without adequately documenting or testing the accompanying code. Currently, the author aims to improve by adding unit tests, documentation, and code repositories to validate results and ease accessibility. Going forward, the author hopes more researchers will release code to allow others to build upon their work more quickly and advocates for support like research software engineers to help with this process. Releasing code, even imperfect code, allows others to help improve it and leads to greater research impacts.
This document discusses challenges and options for sharing qualitative research data while protecting confidentiality. It outlines strategies such as obtaining informed consent across the data lifecycle, anonymizing data through techniques like replacing names with pseudonyms, and regulating access to sensitive data. The document provides examples of consent forms that address data sharing and future reuse. It also describes the UK Data Service's repository ReShare, which allows researchers to deposit and publish metadata about their data collections while applying access controls or embargoes as needed.
Making Qualitative Data Open - Libby Bishop, UK Data ServiceThordis Sveinsdottir
This document discusses challenges and options for sharing qualitative research data while protecting confidentiality. It outlines strategies such as obtaining informed consent across the data lifecycle, anonymizing data through techniques like replacing identifying information, and regulating access to sensitive data. The document provides examples of consent forms that address data sharing and archiving. It also describes the UK Data Service's repository ReShare, which allows researchers to deposit and publish metadata about their data collections while applying access controls or embargoes as needed.
Data Sharing with ICPSR: Fueling the Cycle of Science through Discovery, Acce...ICPSR
Data Sharing with ICPSR was presented at IASSIST 2015 in Minneapolis, MN.
The learning objectives and content cover:
- Federal data sharing requirements and
other good reasons to share data
• Options for sharing data
• Protection of confidentiality when
sharing data
• Data discovery tools
• Online data exploration tools from ICPSR
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
Data sharing promotes many goals of the NIH research endeavor. It is particularly important for unique data that cannot be readily replicated. Data sharing allows scientists to expedite the translation of research results into knowledge, products, and procedures to improve human health. Do you know what a data sharing plan should include? Are you aware of common practices and standards for data sharing? Do you know what services are available to help share your data responsibly? This workshop will begin to address these questions. Q&A will follow the presentation. Anyone interested in or planning to apply for NIH funding should attend. Note: The NIH data-sharing policy applies to applicants seeking $500,000 or more in direct costs in any year of the proposed research.
Dilemmas related to sharing research data were presented. We talked about fraud and misuse and examples of retracted journal articles because of proven fraud. Licences for research data were introduced and requests from journals about open access policies. Researchers need to check and verify journal in which they will published. They should use DOAJ for that. Unfortunately there are more and more hijacked journals. When making data available for secondary use researchers should confirm that distribution is in compliance with ethical norms and legal system.
Event was one of Foster Cessda training events for doctoral students.
Related link: https://www.fosteropenscience.eu/project/index.php?option=com_content&view=category&layout=blog&id=23&Itemid=104
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.
La comparsa, pochi decenni fa, di Internet e della connettività globale ha dato origine ad un fenomeno assolutamente nuovo: un accumulo di enormi quantità di dati conservati in banche digitali, la cui quantità raddoppia ogni pochi giorni e in prospettiva ogni poche ore. E’ la realtà dei Big Data, di cui molto si parla e discute, sovente con toni entusiastici. Ma Big Data vuol dire anche problemi di utilizzo, di interpretazione e rischi di distorsioni. Se questo è rilevante per i dati che hanno un valore economico, l’accumulo di informazione e il come viene trattata ha risvolti altrettanto rilevanti sulla formazione di conoscenza.
Per affrontare queste sfide, cruciali sono il rapporto fra etica e scienza, l’analisi critica su come i dati vengono prodotti e proposti, e il coinvolgimento di tutti i soggetti sociali chiamati in causa.
12 settembre 2019 | Torino, Polo del '900
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.
Sdal air health and social development (jan. 27, 2014) finalkimlyman
This document summarizes a workshop on health and social development analytics using big data. It discusses how data sources are becoming larger, more diverse and used for multiple purposes. This presents opportunities to better understand issues but also challenges around privacy, bias and data quality. The workshop aims to identify partnership opportunities and prototype projects using integrated data to address health and social issues. Case studies from various institutions are presented using combined data sources like medical records, surveys and environmental factors.
This document summarizes a presentation on proposed changes to the informed consent process. The key proposed changes include shortening consent forms to only include the most relevant details, publicly posting consent documents for clinical trials, and allowing for broad consent for secondary use of biospecimens including de-identified samples. The goals of the proposed changes are to build more trust in the consent process and make it more meaningful. However, it is unclear if the changes will fully achieve these goals given challenges such as the open-ended nature of consent agreements. The presentation also discusses empirical studies conducted on community perspectives and issues regarding public health biobanks and consent.
What are sensitive data and why might they be trickier to publish?ARDC
This document discusses sharing sensitive human data legally and ethically. It defines what constitutes sensitive data, including personal or identifiable information that could cause harm or discrimination. Sensitive data includes health, genetic, biometric, and other personal information. The document outlines privacy laws that prevent disclosure of sensitive data without consent. It advises modifying data to protect privacy if possible, obtaining ethics approval, and only sharing data in accordance with the conditions of informed consent. Funders and publishers have policies supporting responsible data sharing. The key takeaway is that sharing sensitive human data ethically is possible if proper plans and procedures are followed to de-identify data and protect participant privacy.
This document discusses health-related data, including types of data, legal constraints around sharing data, and best practices for managing data. It covers:
1) Different types of health data like medical records, generated data, and acquired data. It also defines personal, identifying, and anonymized data.
2) Legal requirements for sharing data, including various acts and regulations around privacy, confidentiality, and individuals' rights to their data.
3) Best practices for managing data sharing and security, including obtaining consent, implementing access agreements, having governance committees, and separating identifiable from non-identifiable data.
Finding and Accessing Human Genomics DatasetsManuel Corpas
This document summarizes a workshop about finding and accessing human genomic datasets. The workshop covered various data sources such as public repositories, case studies on accessing data from the University of Cambridge, and a demonstration of the Repositive platform which aims to simplify accessing genomic data through a single search. Hands-on sessions allowed participants to search for genomic data themes in small groups using Repositive and report their results. Overall the workshop aimed to educate researchers on challenges of accessing genomic data and introduce Repositive as a tool to help address fragmentation and simplify the workflow for discovering and accessing genomic datasets.
Presentation for Northwestern University's first Computational Research Day, April 22, 2014. http://www.it.northwestern.edu/research/about/campus-events/research-day/agenda.html . By Cunera Buys, e-Science Librarian, and Claire Stewart, Director, Center for Scholarly Communication and Digital Curation and Head, Digital Collections
Finding and Using Secondary Data and Resources for ResearchDr. Karen Whiteman
This document provides an overview of secondary data sources and how to find and use secondary data for research. It discusses what secondary data is, common myths about secondary data, pros and cons of using secondary data, questions to consider when choosing secondary data, examples of large data banks like ICPSR, and how to create a personalized dataset from secondary sources. The document aims to dispel myths and provide guidance on successfully utilizing large secondary datasets for research.
This document discusses various methods of data collection. It describes primary data collection methods like personal interviews, questionnaires, and observation. It also discusses secondary data collection from published sources like government publications and commercial research, as well as unpublished sources. The key differences between primary and secondary data are described, such as primary data being real-time while secondary data is from the past. Popular data storage methods include databases, spreadsheets, and statistical programs. The document emphasizes that the best data collection method depends on the research problem and available resources.
Clinical research ethics and regulationRoger Watson
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Sharing Qualitative Data - Challenges and Opportunities
1. Sharing Qualitative Data:
Challenges and Opportunities
Libby Bishop
UK Data Service
UK Data Archive, University of Essex
Lancaster University
5 April 2017
2. Plan for the day
• Introductions
• Sharing qualitative data – pros and cons
• Ethical issues – consent for sharing
• Anonymising data
• Sharing practicalities
• Documentation for qualitative data
• Considerations about where to share data
3. How Britain Dies is a research project run by the think tank
Demos and funded by Help the Hospices. One focus of our
work is to look at the views of dying people and their families
around what makes a good death and where people are
dissatisfied with how they and their loved ones die.
I am interested in accessing Oral Interviews… to analyse the
responses of psychiatric nurses to changes in their profession …
I believe they will give me an unrivalled opportunity to bring the
voice of the nurse to the foreground.
This data will be used to pilot test an innovative method for
qualitative data analysis using crowd sourcing technology.
Research - reusers’ comments
4. “But no one reuses qualitative data…”
Health and Social Consequences of the Foot and Mouth
Disease Epidemic in North Cumbria, 2001-2003 (SN5407)
• secondary analysis to study families and food;
• policy briefing of the economic cost of animal health
diseases with aim of considering how UK may be better
prepared to deal with outbreaks like this;
• use transcripts from previous focus groups with farmers to
study biosecurity on dairy farms in the UK;
• the data will be used as teaching material for medical
students for interview skills;
• building a speech recognition engine to automatically
transcribe interviews for qualitative research.
5. Re-use purposes of qualitative data
downloaded from UK Data Service, 2002-2016
6. Sharing Data – pros and cons_________________________________________________________________________________________________________________
• Group One: generate reasons for why researchers
should/might want to share their data.
o What are the benefits of sharing data?
o Who does sharing data benefit? And how?
• Group Two: generate reasons for why researchers should
not/might not want to share their data.
o What are some of the concerns associated with sharing data?
o What are some of the impediments to sharing data?
• Take about 5 minutes in groups, then we will discuss.
7. Qualitative data – challenges for sharing
• Strong relationships of trust, commitments to
confidentiality
• Participant identity difficult to conceal
• Audio and visual data
• Research locations potentially identifiable
• Difficult to anonymise data without reducing research
value
• Research may investigate illegal activities
• But potential benefits of data sharing make it imperative
to face these challenges
8. Arguments for sharing and open data
• Duties to participants –
protect and
• Empower – give voice
• Avoid burdensome replication
• Duties to scholarly
community
• Transparency
• Research integrity
• Duties to public
• Use public funds wisely
9. Benefits to researchers of data sharing
• increases visibility of your scholarly work
• may enhance your reputation
• may increase citations of your publications
• provides long-term safe storage for data
• satisfies publishers’ data access policies
• enable collaborations on related themes and
new topics
10. Funder policies
• Largely based on the OECD Principles and Guidelines for
Access to Research Data from Public Funding
• UK: variety of models
• Research Councils UK: Publicly funded research
data…should be made openly available with as few
restrictions as possible
• Data management plans and recommendation only
• Dedicated data centres
• Europe (European Research Council/Horizon 2020)
• data management guidelines for Horizon 2020 (~ policies)
• http://www.dcc.ac.uk/resources/policy-and-legal/overview-
funders-data-policies
11. Ethics and sharing
• Common worry about onward sharing of data
covering ‘sensitive topics’, but all data are not
sensitive
• Consider both legal and ethical duties
• Deal with personal sensitive data properly
• Discuss consent status and implications
12. Data Protection Act, 1998
• Personal data:
• relate to a living individual
• individual can be identified from
those data or from those data and
other information
• include any expression of opinion
about the individual
• Only disclose personal data with
consent (and if legally required)
• Sensitive- race/ethnicity, pol.
opinion, religion, union, health,
sexual life, criminal offence
• processed fairly and
lawfully
• obtained and processed for
specified purpose
• adequate, relevant and not
excessive for purpose
• accurate
• not kept longer than
necessary
• processed in accordance
with the rights of data
subjects, e.g. right to be
informed about how data will
be used, stored, processed,
transferred, destroyed; right
to access info and data held
• kept secure
• not transferred abroad
without adequate protection
13. Three tools for sharing confidential data
• Obtain informed consent, also for data sharing
and preservation / curation
• Protect identities e.g. anonymisation, not
collecting personal data
• Regulate access where needed (all or part of
data) e.g. by group, use, time period
Use in different combinations depending on data
14. Consent needed across the data life cycle
• Engagement in the research process
• Explain the nature of research participation
• Dissemination in presentations, publications, the
web
• Agree who approves research outputs
• Data sharing and archiving –consent for sharing
• consider future uses of data
• balanced view of risks and benefits
• participants should be able to choose
Always dependent on the research context – special
cases for covert research, verbal consent, etc.
15. In practice: wording in consent form /
information sheet
Complete for all purposes: use, publishing, sharing
Examples of consent forms:
https://www.ukdataservice.ac.uk/manage-data/legal-ethical/consent-
data-sharing/consent-forms
16. In practice: consent form / information sheet
We expect to use your contributed information in various outputs,
including a report and content for a website. Extracts of interviews
and some photographs may both be used. We will get your
permission before using a quote from you or a photograph of you.
After the project has ended, we intend to archive the interviews at
…. Then the interview data can be disseminated for reuse by other
researchers, for research and learning purposes.
The interviews will be archived at ……. and disseminated so
other researchers can reuse this information for research and
learning purposes:
I agree for the audio recording of my interview to be
archived and disseminated for reuse
I agree for the transcript of my interview to be archived and
disseminated for reuse
I agree for any photographs of me taken during interview to
be archived and disseminated for reuse
17. Case study
SN 5407 Health and Social Consequences of the Foot and Mouth
Disease Epidemic in North Cumbria, 2001-2003 (SN5407 at UK
Data Archive) Maggie Mort, Lancaster University
Discover.ukdataservice.ac.uk/catalogue
• funded by Department of Health
• recruit panel of 54 local people in affected area at time of FM
crisis: farmers, agricultural professionals, small businesses,
health professionals, vets, residents
• weekly diaries for 18 months describing how their life was
affected by the crisis and process of recovery observed around
them (handwritten)
• in-depth interviews and group discussions (audio recordings,
transcripts)
• at start of research – researchers believed no data could or
should be archived
18. Consent – exercise – in groups
• Read handout – “Assessing statements in
consent forms”
• Use a project from your group, or the foot
and mouth case
• Write a draft of a consent statement
seeking permission for data sharing – just
the portion about data sharing….
19. Anonymising qualitative data
Plan or apply editing at time of transcription
except: longitudinal studies - anonymise when
data collection complete (linkages)
Avoid blanking out; use pseudonyms or replacements
Avoid over-anonymising - removing/aggregating
information in text can distort data
Consistency within research team and throughout
project
Identify replacements, e.g. with [brackets]
Keep separate anonymisation log of all replacements,
aggregations or removals made
20. Sample anonymisation log
Example: Anonymisation log interview transcripts
Interview / Page Original Changed to
Int1
p1 Spain European
country
p1 E-print Ltd Printing
company
p2 20th
June June
p2 Amy Moira
Int2
p1 Francis my friend
21. Anonymisation – exercise – in groups
• Read the interview extract from the foot
and mouth project
• Decide what elements you think need
anonymisation, and how would you make
the necessary changes
24. In practice: data with access conditions
Health and Social Consequences of the Foot and Mouth Disease
Epidemic in North Cumbria, 2001-2003 (study 5407 in UK Data Archive
collection) by M. Mort, Lancaster University, Institute for Health
Research.
• Interviews (audio + transcript) and written diaries with 54 people
• 40 interview and diary transcripts are archived and available for re-
use by registered users
• 3 interviews and 5 diaries were embargoed until 2015
• audio files archived and only available by permission from
researchers
discover.ukdataservice.ac.uk/catalogue/?sn=5407
doc.ukdataservice.ac.uk/doc/5407/mrdoc/pdf/q5407userguide.pdf
25. Documenting qualitative data
• Why (bother) documenting your research project?
• Enables you to understand/interpret data when you return to it
• Needed to make data reusable
• Helps avoid incorrect use/misinterpretation by others
• What kinds of materials count as documentation?
• Study level: research design, funding proposals, questionnaires,
methods sections, reports
• Transcription/translation protocols
• Anonymisation records
• Data level: characteristics of individuals, other units of analysis
Should not place unreasonable burden on primary researchers
(and funding is available…)
26. Preparing qualitative data
• Consider license and access conditions
• Assemble documentation – methods/publications
• Prepare study description catalogue record
• Prepare a data list
• Data
Convert formats?
Amend layout?
Check data-level documentation
Correct typos (do not remove, but use tags)
Disclosure review – anonymise? Potentially
libelous/ scandalous content
27. Useful documentation
• Interview schedule or topic guide
• Observation templates/grids
• Diary template
• Stimuli e.g. scenarios, photos, images
• Field notes
• Outputs e.g. reports
• Consent agreement
• Errata
28. What to keep from analysis software
• Discipline-specific customs/ planned data analyses
• Proprietary nature of software
• CAQDAS , e.g. NVIVO, Atlas-ti, Max-QDA
• What to keep?
Clean transcripts, anonymised where needed
Final coding frame – any open format
Data list – data items categorised (finding aid)
Export of any quantitative (coded) data tables
Data ordered time-stamped memos (research
notes), like a research diary
30. • A user guide could contain a variety of documents that provide
context: interview schedule, transcription notes, even photos
Qualitative study – user guide and doc
31. Qualitative study – data listing
• Data listing provides an at-a-glance summary of interview sets
32. You can publish data nearly anywhere, but…
• Web sites
• Generic repositories – Zenodo, figshare
• Institutional repositories – University of Lancaster
• PURE http://www.lancaster.ac.uk/library/information-
for/researchers/research-data-management/data-and-pure/
• Domain Data Centers
• UK Data Service https://www.ukdataservice.ac.uk/
• ReShare – self archiving for social science data
• Journals (not many take data but increasing)
• Cloud
• Jisc UK Research Data Discovery Service project
• Find a repository:
• Registry of Research Data Repositories-http://www.re3data.org/
• Registry of Open Access Repositories (ROAR)
• http://roar.eprints.org/
33. Consider trusted institutions (FAIR*)
• Discoverable: Publicly available and freely searchable
• Usable: Established machine-actionable digital formats
• Meaningful: Offer metadata and documentation to
facilitate data re-use
• Citable: Assign persistent, globally resolvable, machine-
actionable identifiers linked to specific versions of data
• Secure: Maintain policies and procedural controls to
protect confidentiality and personal privacy as required
by law and research ethics standards
• Durable: Ensure long-term preservation of and access
*Findable; Accessible; Inter-operable; Re-usable
34. One option for sharing - ReShare
• Self-deposit repository for social
research data-open to all
• Features:
• Embargo option for publication
• Set permissions at the file-level
• UKDS staff review data collections:
• Confidentiality and copyright
• Documentation and file formats
• reshare.ukdataservice.ac.uk
37. Data sharing outcome – Foot & Mouth
• sought advice from copyright specialist re. terms of agreement for archiving
• met with UK Data Service for advice data archiving
• developed separate consent forms for written and audio material, with opt in /
opt out and an embargo option
• piloted discussion on data archiving with 4 panel members to explore:
• feelings re. data anonymisation, confidentiality, copyright, ownership
• understanding of archiving by participants and information required
• user options of archived data - scholarly / educational purposes
• discussed archiving individually with each panel member
• 7 panel members declined archiving their data
• 40 interview and diary transcripts were made available for re-use by
registered users
• 3 interviews and 5 diaries were embargoed until 2015
• audio files archived and only available by permission from researchers
Detailed information: www.esds.ac.uk/findingData/snDescription.asp?sn=5407
38.
39. Our data management guidance
• Online best practice guidance: ukdataservice.ac.uk/manage-data.aspx
• Managing and Sharing Research Data – a Guide to Good Practice:
(Sage Publications Ltd)
• Anonymisation tool - http://data-archive.ac.uk/curate/standards-
tools/tools
• Helpdesk for queries: ukdataservice.ac.uk/help/get-in-touch.aspx
• Training: www.data-archive.ac.uk/create-manage/advice-training/events
40. …how do we design
systems that make use of
our data collectively to
benefit society as a whole,
while at the same time
protecting people
individually?…This is it:
this is the fundamental
issue of the information
age.”
Bruce Schneier 2015 Data and Goliath