Promoting an ethical and GDPR-compliant approach to learning analyticsJisc
This document summarizes a presentation on promoting an ethical and GDPR-compliant approach to learning analytics. It discusses three key issues: whether allowing students to opt out of data collection could negatively impact their academic progress, what students should do if analytic suggestions conflict with their study goals, and how institutions can avoid overly simplistic metrics. The presentation also provides an overview of potential issues with learning analytics like invasion of privacy and examines students' rights under the new GDPR regulations.
RDMkit, a Research Data Management Toolkit. Built by the Community for the ...Carole Goble
https://datascience.nih.gov/news/march-data-sharing-and-reuse-seminar 11 March 2022
Starting in 2023, the US National Institutes of Health (NIH) will require institutes and researchers receiving funding to include a Data Management Plan (DMP) in their grant applications, including the making their data publicly available. Similar mandates are already in place in Europe, for example a DMP is mandatory in Horizon Europe projects involving data.
Policy is one thing - practice is quite another. How do we provide the necessary information, guidance and advice for our bioscientists, researchers, data stewards and project managers? There are numerous repositories and standards. Which is best? What are the challenges at each step of the data lifecycle? How should different types of data? What tools are available? Research Data Management advice is often too general to be useful and specific information is fragmented and hard to find.
ELIXIR, the pan-national European Research Infrastructure for Life Science data, aims to enable research projects to operate “FAIR data first”. ELIXIR supports researchers across their whole RDM lifecycle, navigating the complexity of a data ecosystem that bridges from local cyberinfrastructures to pan-national archives and across bio-domains.
The ELIXIR RDMkit (https://rdmkit.elixir-europe.org (link is external)) is a toolkit built by the biosciences community, for the biosciences community to provide the RDM information they need. It is a framework for advice and best practice for RDM and acts as a hub of RDM information, with links to tool registries, training materials, standards, and databases, and to services that offer deeper knowledge for DMP planning and FAIR-ification practices.
Launched in March 2021, over 120 contributors have provided nearly 100 pages of content and links to more than 300 tools. Content covers the data lifecycle and specialized domains in biology, national considerations and examples of “tool assemblies” developed to support RDM. It has been accessed by over 123 countries, and the top of the access list is … the United States.
The RDMkit is already a recommended resource of the European Commission. The platform, editorial, and contributor methods helped build a specialized sister toolkit for infectious diseases as part of the recently launched BY-COVID project. The toolkit’s platform is the simplest we could manage - built on plain GitHub - and the whole development and contribution approach tailored to be as lightweight and sustainable as possible.
In this talk, Carole and Frederik will present the RDMkit; aims and context, content, community management, how folks can contribute, and our future plans and potential prospects for trans-Atlantic cooperation.
Data policy must be partnered with data practice. Our researchers need to be the best informed in order to meet these new data management and data sharing mandates.
EC Open Access Co-ordination workshop - 4th May 2011Jisc
This document discusses open scholarship and the value of open access to scholarly works. It notes that opening up the scholarly record through open access, open bibliography, open citation, and open data can help researchers. It discusses ensuring quality in open scholarship through peer review, citations, and other measures. The document also highlights studies that demonstrate the cost-benefits of open access. Finally, it discusses how open scholarship can help power the knowledge economy and support areas like health care and science policy.
UK and US positions on open access – Steven Hill, HEFCE and Sarah Thomas, Harvard University
University of California and university digital library costing models – MacKenzie Smith, UC Davis
Total cost of ownership and flipped OA – Liam Earney, Jisc
Jisc and CNI conference, 6 July 2016
Data and information governance: getting this right to support an information...Jisc
This document discusses establishing data and information governance to support an information security program. It outlines establishing frameworks for information security and data management with defined roles, policies, procedures and tools. This includes classifying data, establishing data management principles, oversight groups and governance bodies to define strategies, manage risks and ensure compliance. The goal is to understand and promote the value of data assets while protecting confidentiality, integrity and availability. It also describes applying these frameworks and changing roles and responsibilities to better manage information assets.
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Strand 1: Connecting research and researchers: An introduction to ORCID by Ed...OAbooks
ORCID is an open, non-profit organization that provides a registry of unique researcher identifiers and aims to link researchers to their professional activities such as publications, datasets, and more. The presentation discusses the problems ORCID aims to address like linking researchers across databases and improving discoverability. It outlines ORCID's mission, benefits to the research community, how the ORCID registry works, privacy considerations, integration opportunities, growth since launch, international usage, members, support available, and how to join ORCID.
Promoting an ethical and GDPR-compliant approach to learning analyticsJisc
This document summarizes a presentation on promoting an ethical and GDPR-compliant approach to learning analytics. It discusses three key issues: whether allowing students to opt out of data collection could negatively impact their academic progress, what students should do if analytic suggestions conflict with their study goals, and how institutions can avoid overly simplistic metrics. The presentation also provides an overview of potential issues with learning analytics like invasion of privacy and examines students' rights under the new GDPR regulations.
RDMkit, a Research Data Management Toolkit. Built by the Community for the ...Carole Goble
https://datascience.nih.gov/news/march-data-sharing-and-reuse-seminar 11 March 2022
Starting in 2023, the US National Institutes of Health (NIH) will require institutes and researchers receiving funding to include a Data Management Plan (DMP) in their grant applications, including the making their data publicly available. Similar mandates are already in place in Europe, for example a DMP is mandatory in Horizon Europe projects involving data.
Policy is one thing - practice is quite another. How do we provide the necessary information, guidance and advice for our bioscientists, researchers, data stewards and project managers? There are numerous repositories and standards. Which is best? What are the challenges at each step of the data lifecycle? How should different types of data? What tools are available? Research Data Management advice is often too general to be useful and specific information is fragmented and hard to find.
ELIXIR, the pan-national European Research Infrastructure for Life Science data, aims to enable research projects to operate “FAIR data first”. ELIXIR supports researchers across their whole RDM lifecycle, navigating the complexity of a data ecosystem that bridges from local cyberinfrastructures to pan-national archives and across bio-domains.
The ELIXIR RDMkit (https://rdmkit.elixir-europe.org (link is external)) is a toolkit built by the biosciences community, for the biosciences community to provide the RDM information they need. It is a framework for advice and best practice for RDM and acts as a hub of RDM information, with links to tool registries, training materials, standards, and databases, and to services that offer deeper knowledge for DMP planning and FAIR-ification practices.
Launched in March 2021, over 120 contributors have provided nearly 100 pages of content and links to more than 300 tools. Content covers the data lifecycle and specialized domains in biology, national considerations and examples of “tool assemblies” developed to support RDM. It has been accessed by over 123 countries, and the top of the access list is … the United States.
The RDMkit is already a recommended resource of the European Commission. The platform, editorial, and contributor methods helped build a specialized sister toolkit for infectious diseases as part of the recently launched BY-COVID project. The toolkit’s platform is the simplest we could manage - built on plain GitHub - and the whole development and contribution approach tailored to be as lightweight and sustainable as possible.
In this talk, Carole and Frederik will present the RDMkit; aims and context, content, community management, how folks can contribute, and our future plans and potential prospects for trans-Atlantic cooperation.
Data policy must be partnered with data practice. Our researchers need to be the best informed in order to meet these new data management and data sharing mandates.
EC Open Access Co-ordination workshop - 4th May 2011Jisc
This document discusses open scholarship and the value of open access to scholarly works. It notes that opening up the scholarly record through open access, open bibliography, open citation, and open data can help researchers. It discusses ensuring quality in open scholarship through peer review, citations, and other measures. The document also highlights studies that demonstrate the cost-benefits of open access. Finally, it discusses how open scholarship can help power the knowledge economy and support areas like health care and science policy.
UK and US positions on open access – Steven Hill, HEFCE and Sarah Thomas, Harvard University
University of California and university digital library costing models – MacKenzie Smith, UC Davis
Total cost of ownership and flipped OA – Liam Earney, Jisc
Jisc and CNI conference, 6 July 2016
Data and information governance: getting this right to support an information...Jisc
This document discusses establishing data and information governance to support an information security program. It outlines establishing frameworks for information security and data management with defined roles, policies, procedures and tools. This includes classifying data, establishing data management principles, oversight groups and governance bodies to define strategies, manage risks and ensure compliance. The goal is to understand and promote the value of data assets while protecting confidentiality, integrity and availability. It also describes applying these frameworks and changing roles and responsibilities to better manage information assets.
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Strand 1: Connecting research and researchers: An introduction to ORCID by Ed...OAbooks
ORCID is an open, non-profit organization that provides a registry of unique researcher identifiers and aims to link researchers to their professional activities such as publications, datasets, and more. The presentation discusses the problems ORCID aims to address like linking researchers across databases and improving discoverability. It outlines ORCID's mission, benefits to the research community, how the ORCID registry works, privacy considerations, integration opportunities, growth since launch, international usage, members, support available, and how to join ORCID.
UCL’s research IT management systems architecture review aligned with Open Sc...Jisc
The document summarizes a project to review UCL's research IT applications and architecture in alignment with open science principles. It provides background on the project scope and inputs, including academic consultation and open science workshops. Key outputs are identified as a high-level design, gap analysis, and mapping of systems against open science pillars. User feedback revealed desires like centralized access to researcher profiles and outputs, automated metadata processes, and support for a diversity of research outputs. The overview outlines future capabilities aimed towards an integrated solution supporting open science practices. Recommendations include further utilizing the current CRIS capabilities and continuing alignment with other programs through an agile delivery approach.
Certifying and Securing a Trusted Environment for Health Informatics Research...Jisc
The document discusses the certification and securing of a trusted environment for health informatics research data at the University of Dundee. It provides an overview of the Health Informatics Centre, its research data management platform, safe haven architecture, and ISO27001 certification. The platform standardizes data extraction and release, adds metadata and quality checks. A safe haven uses pseudonymized data and virtual environments prevent data from leaving. ISO27001 certification provides governance and reduces documentation through standardized information security practices.
NHS-HE forum information governance working groupJisc
The document summarizes discussions from an NHS-HE Forum Information Governance Working Group meeting. The working group deals with governance issues like the Information Governance Toolkit, NHS Digital Framework Contract, and data sharing agreements. It represents 36 organizations and meets quarterly. The group interacts with other advisory boards and works on standards around data security, patient consent, and ongoing topics like comparing governance toolkits and training. Newcastle University's implementation of these standards is also discussed.
1. The document discusses creating learning health systems (LHS) that use data to continually improve healthcare delivery and establish a social contract to share data for public benefit.
2. It proposes connected health cities (CHC) pilots in four regions of Northern England to test LHS approaches and share knowledge between regions.
3. The goals are to optimize care delivery using data, engage the public on data sharing, and accelerate digital health business growth in Northern England.
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.
Infrastructure requirements for open scholarship – Jisc and CNI conference 10...Jisc
1. The document discusses the infrastructure requirements for open scholarship and open access. It outlines various stakeholders and events in the research process like authorship, publishing, and accessing published works.
2. It also maps the various systems, standards, and services needed to support open scholarship like repositories, identifiers, licenses, and usage statistics. Ensuring the sustainability of critical infrastructure services is an ongoing challenge given differences between regional and national organizations.
3. Coordination between services may help address sustainability by consolidating functionality and presenting funders with coordinated offers based on common use cases.
The document summarizes findings from a survey on research data management practices. Some key findings include:
- 17% of researchers had lost data due to issues like hardware failure and human error.
- 68% of researchers currently share or plan to share their data. Main motivations for sharing include funder requirements and increasing citation/impact.
- Only 16% of researchers currently use university research data management support services, indicating a need to improve outreach and support.
- 41% of researchers hold some type of sensitive data like patient or personal information, underscoring the need for secure data storage and sharing policies.
1) The document discusses three paradigmatic positions that institutions may take regarding ethics and privacy in learning analytics: proceed with caution and respect existing policies, proceed with caution while still trying to be respectful, or adopt a data-driven approach and adapt policies accordingly.
2) Technical infrastructure is a major concern, as it can constrain or determine an institution's data policies. Systems developed by commercial platforms may not prioritize privacy and individual control.
3) The discussion activity prompts reflection on an institution's current position, any conflicts between stakeholder views, the technical systems that influence policy, and open questions about technology and privacy.
This document summarizes a presentation by Stephen Docherty, the Chief Information Officer of South London & Maudsley NHS Foundation Trust. It discusses how the IT department has transformed from a "command and control culture" with poor service to becoming more innovative and service-focused. It highlights projects like migrating staff to Office 365 and replacing aging PCs. It also discusses the vision to create an environment bringing together different groups to solve mental health problems through prototypes. Examples of digital prototypes discussed include applications to search patient records and remotely monitor sleep. The presentation ends by discussing sustainability transformation plans and how the King's Health Partnership will support local digital roadmap requirements.
Sitations are the way that researchers communicate how
their work builds on and relates to the work of others and
they can be used to trace how a discovery spreads and is
used by researchers in different disciplines and countries.
Creating a truly comprehensive map of scholarship,
however, relies on having a curated machine-readable
database of citation information, where the provenance of
every citation is clear and reusable. The Initiative for Open
Citations (I4OC), a campaign launched on 6 April 2017,
sought to make publisher members of Crossref aware that
they could open up the citation metadata they already give
to Crossref simply by asking them. With the support of
major publishers and the endorsement of funders and other
organisations, more than 50% of citation data in Crossref
is now freely available, up from less than 1% before the
campaign. This provides the foundation of a well-structured,
open database of literally millions of datapoints that anyone
can query, mine, consume and explore. The presenter will
discuss the aims of the campaign, the new innovative
services that are already using the data, what more still
needs to be done and how you can support the initiative.
Catriona J MacCallum, Hindawi
Lessons from Journal Research Data Policy Registry PilotJisc RDM
Linda Naughton presenting on the lessons learnt from the Journal Research Data Policy Registry pilot at a workshop organised by National Institutes of Health and SPARC held at the World Bank on the 7th of October in Washington, DC.
Lessons from the UK: Data access, patient trust & real-world impact with heal...Varsha Khodiyar
HDR UK is facilitating health data access in the UK for researchers through The Innovation Gateway. This allows researchers to discover and access de-identified health data from various custodians. HDR UK has emphasized transparency and patient/public involvement. During the COVID-19 pandemic, HDR UK coordinated data-driven research efforts and accelerated data access to support priority studies. This included enabling a clinical trial to more rapidly recruit participants using daily COVID test results. HDR UK is also laying the foundations for an international health data alliance to support open COVID-19 research globally.
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
Academy of Social Sciences chief execs - April 2011Jisc
This document provides a guide for academic societies on tendering for journal publishing services. It addresses issues societies may face in deciding whether to renegotiate with their current publisher, change business models, or use an external publisher. The guide covers preparing for the process, including considering the journal's role and society's disposition. It also discusses practical considerations around open access publishing and procuring a new agreement. Finally, it provides templates for requests for proposals and scoring proposals to help societies through the selection process.
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.
Grampian safe haven, research data networkJisc RDM
Safe havens" should be developed as an environment for population-based research where the risk of identifying individuals is minimized. Researchers in safe havens are bound by strict confidentiality codes preventing disclosure of personally identifying information and providing sanctions for breaches of confidentiality.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
The document summarizes a workshop hosted by the NIH Associate Director for Data Science to discuss charting the future of data science at NIH. The workshop goals were to get input from all stakeholders, identify strategic directions, policies, and funding initiatives, and have participants leave as advocates and supporters. The agenda included providing background, open discussion, identifying topics for breakout groups, subgroup discussions, and reporting back. The document provides context on current NIH data science efforts and examples of collaborators in building a biomedical research digital enterprise.
RDAP 16: Sustainability of data infrastructure: The history of science scienc...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of Panel 2, Sustainability
Presenter:
Kristin Eschenfelder, University of Wisconsin-Madison
Panel Leads:
Kristin Briney, University of Wisconsin-Milwaukee & Erica Johns, Cornell University
Aim analytics panel (2017 Fall): Privacy & ethicsSungjin Nam
U-M is exploring the use of learning analytics to improve student outcomes but must address privacy and ethical concerns. The document outlines potential issues like using data beyond its original purpose, re-identification of anonymized data, and lack of transparency. It proposes guiding principles of respect, transparency, accountability, empowerment, and continuous consideration. Next steps include finalizing principles, updating policies, and ensuring data protection. The principles aim to balance student privacy with using data to enhance learning while maintaining student awareness, control, and oversight.
The document discusses a proposed model for data privacy in eLearning. It summarizes research on privacy issues in different domains like healthcare, ecommerce, and eGovernment. It then discusses privacy risks specifically in eLearning, including issues with MOOCs and cloud computing. The model aims to balance data privacy to protect learners with using some private data to improve learning. It suggests students should be able to choose what data they share, and educational software should support anonymity, minimal data sharing, and educating about privacy. A student survey on these issues is also summarized.
UCL’s research IT management systems architecture review aligned with Open Sc...Jisc
The document summarizes a project to review UCL's research IT applications and architecture in alignment with open science principles. It provides background on the project scope and inputs, including academic consultation and open science workshops. Key outputs are identified as a high-level design, gap analysis, and mapping of systems against open science pillars. User feedback revealed desires like centralized access to researcher profiles and outputs, automated metadata processes, and support for a diversity of research outputs. The overview outlines future capabilities aimed towards an integrated solution supporting open science practices. Recommendations include further utilizing the current CRIS capabilities and continuing alignment with other programs through an agile delivery approach.
Certifying and Securing a Trusted Environment for Health Informatics Research...Jisc
The document discusses the certification and securing of a trusted environment for health informatics research data at the University of Dundee. It provides an overview of the Health Informatics Centre, its research data management platform, safe haven architecture, and ISO27001 certification. The platform standardizes data extraction and release, adds metadata and quality checks. A safe haven uses pseudonymized data and virtual environments prevent data from leaving. ISO27001 certification provides governance and reduces documentation through standardized information security practices.
NHS-HE forum information governance working groupJisc
The document summarizes discussions from an NHS-HE Forum Information Governance Working Group meeting. The working group deals with governance issues like the Information Governance Toolkit, NHS Digital Framework Contract, and data sharing agreements. It represents 36 organizations and meets quarterly. The group interacts with other advisory boards and works on standards around data security, patient consent, and ongoing topics like comparing governance toolkits and training. Newcastle University's implementation of these standards is also discussed.
1. The document discusses creating learning health systems (LHS) that use data to continually improve healthcare delivery and establish a social contract to share data for public benefit.
2. It proposes connected health cities (CHC) pilots in four regions of Northern England to test LHS approaches and share knowledge between regions.
3. The goals are to optimize care delivery using data, engage the public on data sharing, and accelerate digital health business growth in Northern England.
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.
Infrastructure requirements for open scholarship – Jisc and CNI conference 10...Jisc
1. The document discusses the infrastructure requirements for open scholarship and open access. It outlines various stakeholders and events in the research process like authorship, publishing, and accessing published works.
2. It also maps the various systems, standards, and services needed to support open scholarship like repositories, identifiers, licenses, and usage statistics. Ensuring the sustainability of critical infrastructure services is an ongoing challenge given differences between regional and national organizations.
3. Coordination between services may help address sustainability by consolidating functionality and presenting funders with coordinated offers based on common use cases.
The document summarizes findings from a survey on research data management practices. Some key findings include:
- 17% of researchers had lost data due to issues like hardware failure and human error.
- 68% of researchers currently share or plan to share their data. Main motivations for sharing include funder requirements and increasing citation/impact.
- Only 16% of researchers currently use university research data management support services, indicating a need to improve outreach and support.
- 41% of researchers hold some type of sensitive data like patient or personal information, underscoring the need for secure data storage and sharing policies.
1) The document discusses three paradigmatic positions that institutions may take regarding ethics and privacy in learning analytics: proceed with caution and respect existing policies, proceed with caution while still trying to be respectful, or adopt a data-driven approach and adapt policies accordingly.
2) Technical infrastructure is a major concern, as it can constrain or determine an institution's data policies. Systems developed by commercial platforms may not prioritize privacy and individual control.
3) The discussion activity prompts reflection on an institution's current position, any conflicts between stakeholder views, the technical systems that influence policy, and open questions about technology and privacy.
This document summarizes a presentation by Stephen Docherty, the Chief Information Officer of South London & Maudsley NHS Foundation Trust. It discusses how the IT department has transformed from a "command and control culture" with poor service to becoming more innovative and service-focused. It highlights projects like migrating staff to Office 365 and replacing aging PCs. It also discusses the vision to create an environment bringing together different groups to solve mental health problems through prototypes. Examples of digital prototypes discussed include applications to search patient records and remotely monitor sleep. The presentation ends by discussing sustainability transformation plans and how the King's Health Partnership will support local digital roadmap requirements.
Sitations are the way that researchers communicate how
their work builds on and relates to the work of others and
they can be used to trace how a discovery spreads and is
used by researchers in different disciplines and countries.
Creating a truly comprehensive map of scholarship,
however, relies on having a curated machine-readable
database of citation information, where the provenance of
every citation is clear and reusable. The Initiative for Open
Citations (I4OC), a campaign launched on 6 April 2017,
sought to make publisher members of Crossref aware that
they could open up the citation metadata they already give
to Crossref simply by asking them. With the support of
major publishers and the endorsement of funders and other
organisations, more than 50% of citation data in Crossref
is now freely available, up from less than 1% before the
campaign. This provides the foundation of a well-structured,
open database of literally millions of datapoints that anyone
can query, mine, consume and explore. The presenter will
discuss the aims of the campaign, the new innovative
services that are already using the data, what more still
needs to be done and how you can support the initiative.
Catriona J MacCallum, Hindawi
Lessons from Journal Research Data Policy Registry PilotJisc RDM
Linda Naughton presenting on the lessons learnt from the Journal Research Data Policy Registry pilot at a workshop organised by National Institutes of Health and SPARC held at the World Bank on the 7th of October in Washington, DC.
Lessons from the UK: Data access, patient trust & real-world impact with heal...Varsha Khodiyar
HDR UK is facilitating health data access in the UK for researchers through The Innovation Gateway. This allows researchers to discover and access de-identified health data from various custodians. HDR UK has emphasized transparency and patient/public involvement. During the COVID-19 pandemic, HDR UK coordinated data-driven research efforts and accelerated data access to support priority studies. This included enabling a clinical trial to more rapidly recruit participants using daily COVID test results. HDR UK is also laying the foundations for an international health data alliance to support open COVID-19 research globally.
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
Academy of Social Sciences chief execs - April 2011Jisc
This document provides a guide for academic societies on tendering for journal publishing services. It addresses issues societies may face in deciding whether to renegotiate with their current publisher, change business models, or use an external publisher. The guide covers preparing for the process, including considering the journal's role and society's disposition. It also discusses practical considerations around open access publishing and procuring a new agreement. Finally, it provides templates for requests for proposals and scoring proposals to help societies through the selection process.
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.
Grampian safe haven, research data networkJisc RDM
Safe havens" should be developed as an environment for population-based research where the risk of identifying individuals is minimized. Researchers in safe havens are bound by strict confidentiality codes preventing disclosure of personally identifying information and providing sanctions for breaches of confidentiality.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
The document summarizes a workshop hosted by the NIH Associate Director for Data Science to discuss charting the future of data science at NIH. The workshop goals were to get input from all stakeholders, identify strategic directions, policies, and funding initiatives, and have participants leave as advocates and supporters. The agenda included providing background, open discussion, identifying topics for breakout groups, subgroup discussions, and reporting back. The document provides context on current NIH data science efforts and examples of collaborators in building a biomedical research digital enterprise.
RDAP 16: Sustainability of data infrastructure: The history of science scienc...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of Panel 2, Sustainability
Presenter:
Kristin Eschenfelder, University of Wisconsin-Madison
Panel Leads:
Kristin Briney, University of Wisconsin-Milwaukee & Erica Johns, Cornell University
Aim analytics panel (2017 Fall): Privacy & ethicsSungjin Nam
U-M is exploring the use of learning analytics to improve student outcomes but must address privacy and ethical concerns. The document outlines potential issues like using data beyond its original purpose, re-identification of anonymized data, and lack of transparency. It proposes guiding principles of respect, transparency, accountability, empowerment, and continuous consideration. Next steps include finalizing principles, updating policies, and ensuring data protection. The principles aim to balance student privacy with using data to enhance learning while maintaining student awareness, control, and oversight.
The document discusses a proposed model for data privacy in eLearning. It summarizes research on privacy issues in different domains like healthcare, ecommerce, and eGovernment. It then discusses privacy risks specifically in eLearning, including issues with MOOCs and cloud computing. The model aims to balance data privacy to protect learners with using some private data to improve learning. It suggests students should be able to choose what data they share, and educational software should support anonymity, minimal data sharing, and educating about privacy. A student survey on these issues is also summarized.
To develop a model of students’ data privacy in eLearning that protects student privacy while supporting educational goals. The model considers what types of data educators need to manage learning, what data students are willing to share, and technical privacy measures for educational software. A survey asked students about sharing data via educational software, what data educators require, the role of intelligent technologies, and student control over data sharing. The proposed model balances student and educator needs through techniques like pseudonymity, anonymity, and student-selected data sharing within secure systems.
Data security issues, ethical issues and challenges to privacy in knowledge-i...Tore Hoel
Presentation at “Finnish-Norwegian Workshop in Learning Analytics”, Helsinki, 21-22 May 2015. Organised by The Research Council of Norway and Academy of Finland
This document summarizes key issues around student privacy and data collection in learning analytics. It discusses how students may not know what data is being collected about them, how it will be used, and for how long. Terms and conditions documents from Coursera, edX, and FutureLearn were analyzed and found to be lengthy without clearly explaining data practices. Implications for learning analytics include ensuring transparency about data collection and use, giving students access and control over their personal data, and moving beyond just opt-in/opt-out models to empower student privacy self-management. Addressing these challenges is important for developing trust and reciprocal care between institutions and students.
University of Minnesota’s Lisa Johnston talks about five ways your library can support researchers when sharing their data. From the October 22, 2015 webinar, How to assist researchers in sharing their research data: http://libraryconnect.elsevier.com/library-connect-webinars?commid=175949
Presentation at LAK19, Tempe, Arizona. Text available at Proceedings of the 9th International Conference on Learning Analytics & Knowledge - https://dl.acm.org/citation.cfm?id=3303796
Pages 235-244
The document discusses learning analytics and the Jisc learning analytics service. It provides an overview of what learning analytics is, the goals of the Jisc service which include helping institutions get started with learning analytics and providing standard tools, and the components of the Jisc service including a code of practice, community resources, data collection and products like Data Explorer and Study Goal. It also discusses working with institutions, engagement activities, the on-boarding process, and engaging with solution providers.
This document discusses challenges for the higher education sector in implementing learning analytics at scale. It begins with an overview of learning analytics and its potential uses. Key challenges mentioned include developing a consensus approach for Norway, addressing privacy issues, establishing infrastructure for data handling and analysis, and developing standards and competencies. The document calls for establishing several resources to help institutions, including tools for assessing readiness, developing strategies, conducting research, and managing data and analytics processes according to privacy standards.
The Student Data Privacy Manifesto begins a reasonable conversation among parents, education leaders, and technology providers on the future of student data privacy protection and transparency.
Jisc learning analytics service core slidesPaul Bailey
The document summarizes Jisc's learning analytics service, which aims to help higher education institutions use student data and analytics to improve student outcomes. The service provides tools for predictive modeling, dashboards, and an app for students. It also offers guidance on legal and ethical issues, workshops on implementation, and connects institutions with analytics solution providers. The goal is to support 40 institutions by 2018 through the free core service and additional fee-based products and services.
The document discusses national learning analytics in the UK and Jisc's role in providing learning analytics services. It describes Jisc's learning analytics tools and products like the Data Explorer dashboards, Study Goal app, and Learning Data Hub. It outlines Jisc's onboarding process for institutions and examples of how they are working with universities and colleges to implement learning analytics.
Data Protection by Design and Default for Learning AnalyticsTore Hoel
The Principle of Data Protection by Design and Default as a lever for bringing Pedagogy into the Discourse on Learning Analytics. Workshop presentation at ICCE 2016 conference in Mumbai, India 29 November 2016
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.
A Study on Data Mining Techniques, Concepts and its Application in Higher Edu...IRJET Journal
This document discusses how data mining techniques can be applied in higher education to analyze educational data and improve various aspects of the student experience and institutional effectiveness. It provides an overview of common data mining methods like classification, clustering, association rule mining and their uses in higher education for applications such as student performance analysis, course recommendation systems, dropout prediction, and curriculum improvement. It also addresses potential issues around privacy, data security and ethics when using data mining in education.
The document discusses security, data privacy, and learning performance in eLearning environments. It presents a predictive model created using machine learning to analyze the relationships between these factors. A survey was conducted of students to understand their perspectives on security of educational software, data privacy, and the value of intelligent learning environments. The results showed students believe software security has improved but cyberattacks remain a risk. Students are willing to share personal data to benefit from intelligent assistance and recommendations. Ensuring privacy through technical and policy approaches like GDPR is important to support learning performance. The predictive model accurately characterizes these relationships to help learning.
What is Student Information System_ Purpose & Features _ Proctur.pdfProctur
An educational institution's student information system (SIS) is designed to effectively manage and arrange student-related data. It functions as a thorough database that collects, maintains, and tracks numerous pieces of information about students throughout their academic careers.
1) The document discusses big data and learning analytics in education, including how it has been featured in the NMC Horizon Report from 2010-2013. It describes how big data can be used for educational research purposes such as modeling student knowledge, behavior, experiences, profiling student groups, and analyzing learning components and instructional principles.
2) Examples of learning analytics in practice are provided, including Purdue University's Signals project, Saddleback Community College's personalized learning system, and analytics tools used at other universities.
3) Potential applications of learning analytics discussed include using data to provide insights into student reading habits, facilitating anonymous peer feedback and grading in writing courses, and capturing data to engage students in interactive teaching situations.
The ethics of MOOC research: why we should involve learnersRebecca Ferguson
Presentation given by Rebecca Ferguson at the FutureLearn Academic Network (FLAN) meeting at the University of Southampton, UK, on 2 December 2015. #flnetwork
The document discusses the SpeakApps project which aims to develop tools and tasks for oral production and interaction using a learning analytics approach. It provides an overview of learning analytics and references a learning analytics reference model. The model describes analyzing data from the SpeakApps platform to evaluate claims about task design, specifically regarding time limitations for recordings. Data sources would include behavioral logs from the platform and user generated content to assess the engagement and experiences of students, teachers, and instructional designers.
Similar to Addressing the wicked problem of learning data privacy though principle and practice (20)
The document announces a community launch event for digital storytelling in January 2024. It discusses using digital storytelling in higher education to support learning and teaching. Examples include using digital stories for formative assessment, reflective exercises, and research dissemination across various disciplines. Feedback from students and staff who participated in digital storytelling workshops was very positive and found it to be transformative and help give voice to their experiences. The document also profiles speakers who will discuss using digital stories to explore difficult concepts, hear the student voice, and facilitate staff reflections. It emphasizes that digital storytelling can introduce humanity and creativity into pedagogy and help develop core skills. Attendees will participate in a Miro activity to discuss benefits, applications,
This document summarizes a Jisc strategy forum that took place in Northern Ireland on December 14, 2023. It outlines Jisc's planned services and initiatives for 2023-2024, including expanding network access and launching new cybersecurity, analytics, and equipment services. It discusses feedback received from further and higher education members on how Jisc can better deliver solutions, empower communities, and provide vision/strategy. Activities at the forum focused on understanding members' needs/challenges and discussing how Jisc can better support key priorities in Northern Ireland, such as affordable infrastructure, digital skills, and cybersecurity for FE and efficiency, student experience, and collaboration for HE.
This document summarizes a Jisc Scotland strategy forum that took place on December 12, 2023. It outlines Jisc's planned solutions and services for 2023-2024 including deploying resilient Janet access, IT health checks, online surveys, SD-WAN services, and more. The document discusses how Jisc engages stakeholders through relationship management, research, communities, training and events. It summarizes feedback from further education and higher education members on how Jisc can improve advocacy by delivering the right solutions, empowering communities, and having a clear vision and strategy. Finally, it outlines activities for the forum, including understanding members' needs and priorities and discussing how Jisc supports national priorities in Scotland.
The Jisc provided a strategic update to stakeholders. Key highlights included:
- Achievements from the last year like data collection and analysis following the HESA merger, digital transformation support, and cost savings from licensing deals.
- Customer testimonials from Bridgend College on extending eduroam and from the University of Northampton on curriculum design support from Jisc.
- Priorities for the coming year like connectivity upgrades, new cybersecurity services, and improved customer experience.
- A financial summary showing income sources like membership fees and expenditures on areas like connectivity and cybersecurity.
This document summarizes VirtualSpeech, a company that provides virtual reality (VR) and artificial intelligence (AI) powered professional development training. It offers over 150 online courses covering topics like public speaking, leadership, and sales. Users can practice skills in immersive VR scenarios and receive feedback from conversational AI. The training is used by over 450,000 individuals across 130 countries and 150 universities. VirtualSpeech aims to enhance traditional learning with interactive VR practice sessions and real-time feedback to boost skills retention.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
2. The Wicked
Problem of
Learning
Data Privacy
Jenn Stringer, MLIS
University of California, Berkeley
IMS Global: Samantha Birk, IMS Global, John Fritz, University of Maryland, Baltimore County, Oliver Heyer, University of California, Berkeley, Virginia Lacefield, University of Kentucky,
Virginia Lacefield, University of Kentucky, Adam Recktenwald, University of Kentucky, Marianne Schroeder, University of British Columbia
University of California (UC): Mary Ellen Kreher, UC Office of the President, Jim Phillips UC, Santa Cruz, James Williamson, UC, Los Angeles
3. Academic Data
Personally identifiable records, e.g,
transcripts (course work, GPA,
major), enrollments, academic plan,
SAT scores etc.
Student Information & Advising
Systems
Learning Data
Personally identifiable user activity,
e.g., Page views, Discussion posts,
Quiz responses, Video views etc.
recorded in LMS’s and other 3rd party
learning applications
Learning Record Store
Institutional Data
Aggregate, often deidentified,
historical records
e.g. Graduation rates, yield,
application data, demographics,
race/ethnicity
Enterprise Data Warehouse
Learning Data in Context
3
“Learning data refers to data
generated by students,
faculty, and/or staff that
relates to and documents the
teaching and learning
experience and academic
achievement. It can be used
alone or combined with the
student record and other
data points to support
student success and
research.” 1
1 https://www.imsglobal.org/learning-data-analytics-key-principles
4. Learning Analytics Defined
“Learning analytics is the measurement, collection, analysis and reporting of data about
learners and their contexts, for purposes of understanding and optimising learning and the
environments in which it occurs.”2
21st International Conference of Learning Analytics & Knowledge, Banff, Alberta 2011
5. ● increase ability to make
institutional decisions
● impact student outcomes
● empower students to make
changes to their behavior that
positively affects their learning
● enable faculty to support students
and make changes to their
courses based on data
● support faculty teaching and
pedagogy
● support educational research
Why do we care?
6. Learning Data
● “Old Days”
○ local hosting meant local logs
○ ad-hoc reporting mainly for systems issues
● “Cloud SaaS”
○ logs not local and not accessible
○ vendors use to improve systems and troubleshoot issues
● Contracts
○ if we have them -- not always specific about ownership of this kind of data
○ they do have security and privacy language
● No Contract?
○ goodluck! - at the whim of the third party provider no security/privacy guarantees
7. ● What: “ Clickstream Data” “Logfile Data”
● Who: Vendors, the institution, libraries, publishers, other third-parties
7
Someone is Collecting A LOT of
data
10. “If it is free, you are not the
customer. You are the
product.”4
4http://blogs.harvard.edu/futureoftheinternet/2012/03/21/meme-patrol-when-something-online-is-free-youre-not-the-customer-youre-the-product/
17. Learning Data
● Just the data please
○ even if we have “ownership” we need “access”
○ logfile ‘dumps” for historic reporting (nightly, monthly, etc.)
○ realtime for early warning systems, advising, student alerts, etc.
● Standards
○ LTI - enables interoperability
○ Caliper and xAPI - defines learner activity to enable analysis across systems
18. The University of California should have
a say in how suppliers collect, use, and
manage our data.
19. ● 10 Campuses
● 150 Academic Disciplines
● 238,700 Students
● 44,517 Academic Staff
Who are “we”? The UC System
20. University of California: Learning Data Privacy
Principles
1. Ownership: The University of California (UC), its faculty, and students retain ownership of the data and subsequent
computational transformations of the data they produce. Individual data owners have the right to determine how their data will
be used. The UC acts as stewards of data on behalf of its faculty and students.
2. Ethical Use: Learning data collection, use, and computational transformation are governed by pedagogical and instructional
concerns, with an aim toward student success through prescriptive, descriptive, or predictive methodologies. As with grades
and other sensitive data, uses of learning analytics should be pursued on a “need to know” basis.
3. Transparency: Data owners have a right to understand the specific methods and purposes for which their data are collected,
used and transformed, including what data are being transmitted to third-party service providers (and their affiliated partners)
and the details of how algorithms are applied that shape summaries, particularly outputs and visualizations.
4. Freedom of Expression: Faculty and students retain the right to communicate and engage with each other in the learning
process without the concern that their data will be mined for unintended or unknown purposes.
5. Protection: Stewards, on behalf of data owners, will ensure learning data are secure and protected in alignment with all
federal, state, and university regulations regarding secure disposition.
6. Access and Control: Data owners have the right to access their data. Given that faculty and students own their learning data
and share in its disposition, access to and ultimate authority and control of the data rests with the faculty and student owners,
and the data stewards acting on their behalf. Data retention access and control practices will be governed under UC policies
and supplier contractual agreements.
20
http://bit.ly/UCLearningDataPrivacyPrinciples
21. University of California:Learning Data Recommended
Practices
1. Ownership: Service providers will recognize learning data ownership and access as a right of the faculty and students.
2. Usage Right: Through a user’s profile setting, service providers will enable users to control the use of their intellectual property. Thus,
it will be the user’s choice to grant terms such as, “a royalty-free, transferable, perpetual, irrevocable, non-exclusive, worldwide license
to reproduce, modify, publish, publicly display, make derivative works.”
3. Opt-in: Other than those data elements distinctly required for instruction, where appropriate, students will have a choice about the use
of learning data collected by faculty and service providers in an "opt in" rather than "opt out" approach.
4. Interoperable Data: Service providers will provide learning data to the institution in recognized standard interoperability format(s) to
minimize integration costs, support cross-platform and cross-application uses, and promote institutional and academic analysis and
research.
5. Data without Fees: Service providers will not charge the faculty, students, or other university learning data stewards for the right of
access, including the delivery of these data to the University.
6. Transparency: Service providers will inform the UC about the learning data they collect and how these data will be used, which in the
course of an academic term shall be based on pedagogical concerns and curricular improvement.
7. Service Provider Security: All service provider platforms on which student learning data are stored will conform with UC and state
mandated security procedures governing the reporting of unexpected incidents and corrections that may occur.
8. Campus Security: UC learning data stewards will ensure that all faculty and student data are stored securely in conformance with
University data security policy. Learning data stewards will report any learning data security incidents as appropriate to faculty and
students, and will provide information about their remedy.
21
22. IMS Global Learning Data & Analytics Key
Principles
1. Ownership: Faculty, staff, and students generate and own their learning data. As governed by institutional policies, individuals, being
owners of the data they generate, have the right to access, port, and control the disposition of their data stored by the institution, its
service providers, and their affiliated partners.
2. Stewardship: As stewards of learning data, institutions should have a data governance plan and governance policies that protect the
data and the interests of its owners. These should transcend, but encompass, existing protocols, such as IRB.
3. Governance: Learning data use and retention will be governed by institutional policies, and faculty and students retain the right of
data access and retrieval.
4. Access: Learning data, whether generated locally or in a vendor-supplied system, is strategic to an institution’s business and
mission and must be available to the institution.
5. Interoperability: The collection, use, and access to learning data requires institutional and supplier collaboration, which is
dependent upon interoperability standards, protocols, data formats, and content to achieve institutions goals.
6. Efficacy: Learning data collection, use, and computational transformation is aimed at student and instructor success and
instructional concerns through prescriptive, descriptive, or predictive methodologies.
7. Security & Privacy: Individuals’ security and privacy relating to collecting, using, and algorithmically transforming learning data is
fundamental and must not be treated as optional. It must also be balanced with the effective use of the data.
8. Transparency: Individuals have the right to understand the specific reasons, methods, and purposes for which their learning data is
collected, used, and transformed. This includes any learning data being shared with third-party service providers and other
institutional affiliates or partners. Individuals also have the right to know how their data is transformed and/or used thru processes
such as summative or algorithmic modifications, particular outputs, and visualizations.
22
https://www.imsglobal.org/learning-data-analytics-ke
principles
23. We Are Not Alone...
● [2011] - Asilomar Convention for Learning Research in Higher Education - http://asilomar-highered.info/
● [2012] - Asilomar II: Student Data and Records in the Digital Era - https://sites.stanford.edu/asilomar/
● Responsible Use of Student Data in Higher Education - http://ru.stanford.edu/ (Stanford CAROL & Ithaka
S+R)
● DELICATE Framework - http://www.laceproject.eu/blog/ethics-privacy-in-learning-analytics-a-delicate-issue/
(Learning Analytics Community Exchange)
● IMS Global Learning Data & Analytics Key Principles - http://www.imsglobal.org/learning-data-analytics-key-
principles
24. IMS Global Learning Data & Analytics Key
Principles
https://www.imsglobal.org/learning-data-analytics-key-principles
1. Ownership: Faculty, staff, and students generate and own their learning data. As governed by institutional policies, individuals,
being owners of the data they generate, have the right to access, port, and control the disposition of their data stored by the
institution, its service providers, and their affiliated partners.
2. Stewardship: As stewards of learning data, institutions should have a data governance plan and governance policies that
protect the data and the interests of its owners. These should transcend, but encompass, existing protocols, such as IRB.
3. Governance: Learning data use and retention will be governed by institutional policies, and faculty and students retain the right
of data access and retrieval.
4. Access: Learning data, whether generated locally or in a vendor-supplied system, is strategic to an institution’s business and
mission and must be available to the institution.
5. Interoperability: The collection, use, and access to learning data requires institutional and supplier collaboration, which is
dependent upon interoperability standards, protocols, data formats, and content to achieve institutions goals.
6. Efficacy: Learning data collection, use, and computational transformation is aimed at student and instructor success and
instructional concerns through prescriptive, descriptive, or predictive methodologies.
7. Security & Privacy: Individuals’ security and privacy relating to collecting, using, and algorithmically transforming learning data
is fundamental and must not be treated as optional. It must also be balanced with the effective use of the data.
8. Transparency: Individuals have the right to understand the specific reasons, methods, and purposes for which their learning
data is collected, used, and transformed. This includes any learning data being shared with third-party service providers and
other institutional affiliates or partners. Individuals also have the right to know how their data is transformed and/or used thru
processes such as summative or algorithmic modifications, particular outputs, and visualizations.
25. IMS Global Learning Data & Analytics Key
Principles
https://www.imsglobal.org/learning-data-analytics-key-principles
1. Ownership: Faculty, staff, and students generate and own their learning data. As governed by institutional policies, individuals,
being owners of the data they generate, have the right to access, port, and control the disposition of their data stored by the
institution, its service providers, and their affiliated partners.
2. Stewardship: As stewards of learning data, institutions should have a data governance plan and governance policies that
protect the data and the interests of its owners. These should transcend, but encompass, existing protocols, such as IRB.
3. Governance: Learning data use and retention will be governed by institutional policies, and faculty and students retain the right
of data access and retrieval.
4. Access: Learning data, whether generated locally or in a vendor-supplied system, is strategic to an institution’s business and
mission and must be available to the institution.
5. Interoperability: The collection, use, and access to learning data requires institutional and supplier collaboration, which is
dependent upon interoperability standards, protocols, data formats, and content to achieve institutions goals.
6. Efficacy: Learning data collection, use, and computational transformation is aimed at student and instructor success and
instructional concerns through prescriptive, descriptive, or predictive methodologies.
7. Security & Privacy: Individuals’ security and privacy relating to collecting, using, and algorithmically transforming learning data
is fundamental and must not be treated as optional. It must also be balanced with the effective use of the data.
8. Transparency: Individuals have the right to understand the specific reasons, methods, and purposes for which their learning
data is collected, used, and transformed. This includes any learning data being shared with third-party service providers and
other institutional affiliates or partners. Individuals also have the right to know how their data is transformed and/or used thru
processes such as summative or algorithmic modifications, particular outputs, and visualizations.
26. Libraries and Learning
Data/Analytics
“Though few academic libraries are encountering it just yet, it is only a matter of time before
higher education institutions integrate learning analytics at every level of the organization.”8
“Learning analytics initiatives pose a myriad of ethical questions. For example, are institutions
who possess learning data required to act on it? Might learning data be used to “profile”
students?”8
“At institutions that have committed to a learning analytics future, librarians can also ask
questions to clarify the library’s role as well as advocate for library inclusion in learning
analytics processes.”8
8 Bell, Steven. Keeping up with … Learning Analytics. ACRL Blog. http://www.ala.org/acrl/publications/keeping_up_with/learning_analytics
9 Oakleaf, Megan. “Getting Ready and Getting Started: Academic Librarian Involvement in Institutional Learning Analytics Initiatives.” Journal of Academic Librarianship. 42(4). 2016.
27. jisc.ac.uk
One Castlepark, Tower Hill, Bristol BS2 0JA
customerservices@jisc.ac.uk
T 020 3697 5800
Jenn Stringer, MLIS
University of California, Berkeley