OpenData Public Research
Open Access Events: The Case for Open Data, Why you should Care
Map & Data Library - 5th Floor Robarts Library, University of Toronto
Thursday, Oct. 25 from 10:00-12:00
Organized by Data and Map Librarians, Marcel Fortin and Berenica Vejvoda
FAIRy stories: the FAIR Data principles in theory and in practiceCarole Goble
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
A Big Picture in Research Data ManagementCarole Goble
A personal view of the big picture in Research Data Management, given at GFBio - de.NBI Summer School 2018 Riding the Data Life Cycle! Braunschweig Integrated Centre of Systems Biology (BRICS), 03 - 07 September 2018
How are we Faring with FAIR? (and what FAIR is not)Carole Goble
Keynote presented at the workshop FAIRe Data Infrastructures, 15 October 2020
https://www.gmds.de/aktivitaeten/medizinische-informatik/projektgruppenseiten/faire-dateninfrastrukturen-fuer-die-biomedizinische-informatik/workshop-2020/
Remarkably it was only in 2016 that the ‘FAIR Guiding Principles for scientific data management and stewardship’ appeared in Scientific Data. The paper was intended to launch a dialogue within the research and policy communities: to start a journey to wider accessibility and reusability of data and prepare for automation-readiness by supporting findability, accessibility, interoperability and reusability for machines. Many of the authors (including myself) came from biomedical and associated communities. The paper succeeded in its aim, at least at the policy, enterprise and professional data infrastructure level. Whether FAIR has impacted the researcher at the bench or bedside is open to doubt. It certainly inspired a great deal of activity, many projects, a lot of positioning of interests and raised awareness. COVID has injected impetus and urgency to the FAIR cause (good) and also highlighted its politicisation (not so good).
In this talk I’ll make some personal reflections on how we are faring with FAIR: as one of the original principles authors; as a participant in many current FAIR initiatives (particularly in the biomedical sector and for research objects other than data) and as a veteran of FAIR before we had the principles.
A keynote given on the FAIR Data Principles at the FAIRplus Innovation and SME Forum, Hinxton Genome Campus, Cambridge, UK, 29 January 2020. The history of the principles, issues about the principles and speculations about the future
RO-Crate: A framework for packaging research products into FAIR Research ObjectsCarole Goble
RO-Crate: A framework for packaging research products into FAIR Research Objects presented to Research Data Alliance RDA Data Fabric/GEDE FAIR Digital Object meeting. 2021-02-25
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
FAIRy stories: the FAIR Data principles in theory and in practiceCarole Goble
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
A Big Picture in Research Data ManagementCarole Goble
A personal view of the big picture in Research Data Management, given at GFBio - de.NBI Summer School 2018 Riding the Data Life Cycle! Braunschweig Integrated Centre of Systems Biology (BRICS), 03 - 07 September 2018
How are we Faring with FAIR? (and what FAIR is not)Carole Goble
Keynote presented at the workshop FAIRe Data Infrastructures, 15 October 2020
https://www.gmds.de/aktivitaeten/medizinische-informatik/projektgruppenseiten/faire-dateninfrastrukturen-fuer-die-biomedizinische-informatik/workshop-2020/
Remarkably it was only in 2016 that the ‘FAIR Guiding Principles for scientific data management and stewardship’ appeared in Scientific Data. The paper was intended to launch a dialogue within the research and policy communities: to start a journey to wider accessibility and reusability of data and prepare for automation-readiness by supporting findability, accessibility, interoperability and reusability for machines. Many of the authors (including myself) came from biomedical and associated communities. The paper succeeded in its aim, at least at the policy, enterprise and professional data infrastructure level. Whether FAIR has impacted the researcher at the bench or bedside is open to doubt. It certainly inspired a great deal of activity, many projects, a lot of positioning of interests and raised awareness. COVID has injected impetus and urgency to the FAIR cause (good) and also highlighted its politicisation (not so good).
In this talk I’ll make some personal reflections on how we are faring with FAIR: as one of the original principles authors; as a participant in many current FAIR initiatives (particularly in the biomedical sector and for research objects other than data) and as a veteran of FAIR before we had the principles.
A keynote given on the FAIR Data Principles at the FAIRplus Innovation and SME Forum, Hinxton Genome Campus, Cambridge, UK, 29 January 2020. The history of the principles, issues about the principles and speculations about the future
RO-Crate: A framework for packaging research products into FAIR Research ObjectsCarole Goble
RO-Crate: A framework for packaging research products into FAIR Research Objects presented to Research Data Alliance RDA Data Fabric/GEDE FAIR Digital Object meeting. 2021-02-25
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
FAIR Workflows and Research Objects get a Workout Carole Goble
So, you want to build a pan-national digital space for bioscience data and methods? That works with a bunch of pre-existing data repositories and processing platforms? So you can share FAIR workflows and move them between services? Package them up with data and other stuff (or just package up data for that matter)? How? WorkflowHub (https://workflowhub.eu) and RO-Crate Research Objects (https://www.researchobject.org/ro-crate) that’s how! A step towards FAIR Digital Objects gets a workout.
Presented at DataVerse Community Meeting 2021
FAIRy stories: tales from building the FAIR Research CommonsCarole Goble
Plenary Lecture Presented at INCF Neuroinformatics 2019 https://www.neuroinformatics2019.org
Title: FAIRy stories: tales from building the FAIR Research Commons
Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any kind of Research Object is a mantra; a method; a meme; a myth; a mystery. For the past 15 years I have been working on FAIR in a range of projects and initiatives in the Life Sciences as we try to build the FAIR Research Commons. Some are top-down like the European Research Infrastructures ELIXIR, ISBE and IBISBA, and the NIH Data Commons. Some are bottom-up, supporting FAIR for investigator-led projects (FAIRDOM), biodiversity analytics (BioVel), and FAIR drug discovery (Open PHACTS, FAIRplus). Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. There are villains and heroes. Some have happy endings; all have morals.
presented at WORKS 2021
https://works-workshop.org/
16th Workshop on Workflows in Support of Large-Scale Science
November 15, 2021
Held in conjunction with SC21: The International Conference for High Performance Computing, Networking, Storage and Analysis
Building the FAIR Research Commons: A Data Driven Society of ScientistsCarole Goble
Science is knowledge work. The scientific method and scholarly communication are about facilitating “knowledge turns” – that is, the turning of observation and hypothesis through experimentation, comparison, and analysis into new, pooled knowledge. Turns depend on the FAIR flow and availability of data, methods for automated processing, reproducible results and on a society of scientists coordinating and collaborating. We need to build a new form of Research Commons and I will present my steps towards this.
Presented at Symposium: The Future of a Data-Driven Society, Maastricht University, 25 Jan 2018 that accompanied the 42nd Dies Natalis where I was awarded an honorary doctorate
Personal video:
https://www.youtube.com/watch?v=k5WN6KDDatU&index=4&list=PLzi-FBaZlOOagma5dCW7WSA5lv22tmNMD
Video of the symposium:
https://www.youtube.com/watch?v=JN9eMMtCHf8&t=19s&index=6&list=PLzi-FBaZlOOagma5dCW7WSA5lv22tmNMD
A keynote given on experiences in curating workflows and web services.
3rd International Digital Curation Conference: "Curating our Digital Scientific Heritage: a Global Collaborative Challenge"
11-13 December 2007
Renaissance Hotel
Washington DC, USA
Publishing your research: Research Data Management (Introduction) Jamie Bisset
Publishing your research: Research Data Management (Introduction) (November 2013) slides. Delivered as part of the Durham University Researcher Development Programme. Further Training available at https://www.dur.ac.uk/library/research/training/
What is Reproducibility? The R* brouhaha (and how Research Objects can help)Carole Goble
presented at 1st First International Workshop on Reproducible Open Science @ TPDL, 9 Sept 2016, Hannover, Germany
http://repscience2016.research-infrastructures.eu/
Open Science: how to serve the needs of the researcher? Carole Goble
Open science Jisc CNI roundtable 2018
Lightning talk
What should the future look like?
What are the essential characteristics we desire in a relatively near future system to support scholarly communication across the full research life cycle?
What are the key areas requiring attention, action, or investment today to reach the future that we want to reach?
What are the best opportunities to build upon existing practices, investments and infrastructure, both
open and commercially provided?
Where must alternatives be developed?
What areas are already on good trajectories and can be left to evolve without additional intervention
Better software, better service, better research: The Software Sustainabilit...Carole Goble
Ever spotted some great looking software only to discover you can’t get it, it doesn’t work, there is no documentation to help fix it and the developers don’t have the time or incentive to help? Ever produced some software that you want to be widely used or have folks contribute? What’s the sustainability of that key platform/library/tool /database your lab uses day in and day out? Are you helping the providers? The same issues stand for Data (or as we now say “FAIR” Findable, Accessible, Interoperable, Reusable Data) and its metadata. Is anyone looking out for Europe’s data services– the datasets and analysis systems you use and you make – the standards they use and the curators and developers who make them? Or is FAIR just a FAIRy story? I’ll tell how two organisations with quite different structures and approaches - the UK’s Software Sustainability Institute and the ELIXIR European Research Infrastructure for Life Science Data – are working for the common goal of better software, better service, and better research.
https://www.rothamsted.ac.uk/events/14th-international-symposium-integrative-bioinformatics
presentation at https://researchsoft.github.io/FAIReScience/, FAIReScience 2021 online workshop
virtually co-located with the 17th IEEE International Conference on eScience (eScience 2021)
The swings and roundabouts of a decade of fun and games with Research Objects Carole Goble
Research Objects and their instantiation as RO-Crate: motivation, explanation, examples, history and lessons, and opportunities for scholarly communications, delivered virtually to 17th Italian Research Conference on Digital Libraries
Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble
Presented at Digital Life 2018, Bergen, March 2018. In the Trust and Accountability session.
In recent years we have seen a change in expectations for the management and availability of all the outcomes of research (models, data, SOPs, software etc) and for greater transparency and reproduciblity in the method of research. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for stewardship [1] have proved to be an effective rallying-cry for community groups and for policy makers.
The FAIRDOM Initiative (FAIR Data Models Operations, http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards and sensitivity to asset sharing and credit anxiety. Our aim is a FAIR Research Commons that blends together the doing of research with the communication of research. The Platform has been installed by over 30 labs/projects and our public, centrally hosted FAIRDOMHub [2] supports the outcomes of 90+ projects. We are proud to support projects in Norway’s Digital Life programme.
2018 is our 10th anniversary. Over the past decade we learned a lot about trust between researchers, between researchers and platform developers and curators and between both these groups and funders. We have experienced the Tragedy of the Commons but also seen shifts in attitudes.
In this talk we will use our experiences in FAIRDOM to explore the political, economic, social and technical, social practicalities of Trust.
[1] Wilkinson et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
[2] Wolstencroft, et al (2016) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research Nucleic Acids Research, 45(D1): D404-D407. DOI: 10.1093/nar/gkw1032
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
FAIR Computational Workflows
Computational workflows capture precise descriptions of the steps and data dependencies needed to carry out computational data pipelines, analysis and simulations in many areas of Science, including the Life Sciences. The use of computational workflows to manage these multi-step computational processes has accelerated in the past few years driven by the need for scalable data processing, the exchange of processing know-how, and the desire for more reproducible (or at least transparent) and quality assured processing methods. The SARS-CoV-2 pandemic has significantly highlighted the value of workflows.
This increased interest in workflows has been matched by the number of workflow management systems available to scientists (Galaxy, Snakemake, Nextflow and 270+ more) and the number of workflow services like registries and monitors. There is also recognition that workflows are first class, publishable Research Objects just as data are. They deserve their own FAIR (Findable, Accessible, Interoperable, Reusable) principles and services that cater for their dual roles as explicit method description and software method execution [1]. To promote long-term usability and uptake by the scientific community, workflows (as well as the tools that integrate them) should become FAIR+R(eproducible), and citable so that author’s credit is attributed fairly and accurately.
The work on improving the FAIRness of workflows has already started and a whole ecosystem of tools, guidelines and best practices has been under development to reduce the time needed to adapt, reuse and extend existing scientific workflows. An example is the EOSC-Life Cluster of 13 European Biomedical Research Infrastructures which is developing a FAIR Workflow Collaboratory based on the ELIXIR Research Infrastructure for Life Science Data Tools ecosystem. While there are many tools for addressing different aspects of FAIR workflows, many challenges remain for describing, annotating, and exposing scientific workflows so that they can be found, understood and reused by other scientists.
This keynote will explore the FAIR principles for computational workflows in the Life Science using the EOSC-Life Workflow Collaboratory as an example.
[1] Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes,Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, and Daniel Schober FAIR Computational Workflows Data Intelligence 2020 2:1-2, 108-121 https://doi.org/10.1162/dint_a_00033.
Open science can contribute to AI trustworthiness. This talk is a categorization of scientific data platforms, and a framing of AI trustworthiness with pointers to open science contributions.
German Conference on Bioinformatics 2021
https://gcb2021.de/
FAIR Computational Workflows
Computational workflows capture precise descriptions of the steps and data dependencies needed to carry out computational data pipelines, analysis and simulations in many areas of Science, including the Life Sciences. The use of computational workflows to manage these multi-step computational processes has accelerated in the past few years driven by the need for scalable data processing, the exchange of processing know-how, and the desire for more reproducible (or at least transparent) and quality assured processing methods. The SARS-CoV-2 pandemic has significantly highlighted the value of workflows.
This increased interest in workflows has been matched by the number of workflow management systems available to scientists (Galaxy, Snakemake, Nextflow and 270+ more) and the number of workflow services like registries and monitors. There is also recognition that workflows are first class, publishable Research Objects just as data are. They deserve their own FAIR (Findable, Accessible, Interoperable, Reusable) principles and services that cater for their dual roles as explicit method description and software method execution [1]. To promote long-term usability and uptake by the scientific community, workflows (as well as the tools that integrate them) should become FAIR+R(eproducible), and citable so that author’s credit is attributed fairly and accurately.
The work on improving the FAIRness of workflows has already started and a whole ecosystem of tools, guidelines and best practices has been under development to reduce the time needed to adapt, reuse and extend existing scientific workflows. An example is the EOSC-Life Cluster of 13 European Biomedical Research Infrastructures which is developing a FAIR Workflow Collaboratory based on the ELIXIR Research Infrastructure for Life Science Data Tools ecosystem. While there are many tools for addressing different aspects of FAIR workflows, many challenges remain for describing, annotating, and exposing scientific workflows so that they can be found, understood and reused by other scientists.
This keynote will explore the FAIR principles for computational workflows in the Life Science using the EOSC-Life Workflow Collaboratory as an example.
[1] Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes,Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, and Daniel Schober FAIR Computational Workflows Data Intelligence 2020 2:1-2, 108-121 https://doi.org/10.1162/dint_a_00033.
FAIR Workflows and Research Objects get a Workout Carole Goble
So, you want to build a pan-national digital space for bioscience data and methods? That works with a bunch of pre-existing data repositories and processing platforms? So you can share FAIR workflows and move them between services? Package them up with data and other stuff (or just package up data for that matter)? How? WorkflowHub (https://workflowhub.eu) and RO-Crate Research Objects (https://www.researchobject.org/ro-crate) that’s how! A step towards FAIR Digital Objects gets a workout.
Presented at DataVerse Community Meeting 2021
FAIRy stories: tales from building the FAIR Research CommonsCarole Goble
Plenary Lecture Presented at INCF Neuroinformatics 2019 https://www.neuroinformatics2019.org
Title: FAIRy stories: tales from building the FAIR Research Commons
Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any kind of Research Object is a mantra; a method; a meme; a myth; a mystery. For the past 15 years I have been working on FAIR in a range of projects and initiatives in the Life Sciences as we try to build the FAIR Research Commons. Some are top-down like the European Research Infrastructures ELIXIR, ISBE and IBISBA, and the NIH Data Commons. Some are bottom-up, supporting FAIR for investigator-led projects (FAIRDOM), biodiversity analytics (BioVel), and FAIR drug discovery (Open PHACTS, FAIRplus). Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. There are villains and heroes. Some have happy endings; all have morals.
presented at WORKS 2021
https://works-workshop.org/
16th Workshop on Workflows in Support of Large-Scale Science
November 15, 2021
Held in conjunction with SC21: The International Conference for High Performance Computing, Networking, Storage and Analysis
Building the FAIR Research Commons: A Data Driven Society of ScientistsCarole Goble
Science is knowledge work. The scientific method and scholarly communication are about facilitating “knowledge turns” – that is, the turning of observation and hypothesis through experimentation, comparison, and analysis into new, pooled knowledge. Turns depend on the FAIR flow and availability of data, methods for automated processing, reproducible results and on a society of scientists coordinating and collaborating. We need to build a new form of Research Commons and I will present my steps towards this.
Presented at Symposium: The Future of a Data-Driven Society, Maastricht University, 25 Jan 2018 that accompanied the 42nd Dies Natalis where I was awarded an honorary doctorate
Personal video:
https://www.youtube.com/watch?v=k5WN6KDDatU&index=4&list=PLzi-FBaZlOOagma5dCW7WSA5lv22tmNMD
Video of the symposium:
https://www.youtube.com/watch?v=JN9eMMtCHf8&t=19s&index=6&list=PLzi-FBaZlOOagma5dCW7WSA5lv22tmNMD
A keynote given on experiences in curating workflows and web services.
3rd International Digital Curation Conference: "Curating our Digital Scientific Heritage: a Global Collaborative Challenge"
11-13 December 2007
Renaissance Hotel
Washington DC, USA
Publishing your research: Research Data Management (Introduction) Jamie Bisset
Publishing your research: Research Data Management (Introduction) (November 2013) slides. Delivered as part of the Durham University Researcher Development Programme. Further Training available at https://www.dur.ac.uk/library/research/training/
What is Reproducibility? The R* brouhaha (and how Research Objects can help)Carole Goble
presented at 1st First International Workshop on Reproducible Open Science @ TPDL, 9 Sept 2016, Hannover, Germany
http://repscience2016.research-infrastructures.eu/
Open Science: how to serve the needs of the researcher? Carole Goble
Open science Jisc CNI roundtable 2018
Lightning talk
What should the future look like?
What are the essential characteristics we desire in a relatively near future system to support scholarly communication across the full research life cycle?
What are the key areas requiring attention, action, or investment today to reach the future that we want to reach?
What are the best opportunities to build upon existing practices, investments and infrastructure, both
open and commercially provided?
Where must alternatives be developed?
What areas are already on good trajectories and can be left to evolve without additional intervention
Better software, better service, better research: The Software Sustainabilit...Carole Goble
Ever spotted some great looking software only to discover you can’t get it, it doesn’t work, there is no documentation to help fix it and the developers don’t have the time or incentive to help? Ever produced some software that you want to be widely used or have folks contribute? What’s the sustainability of that key platform/library/tool /database your lab uses day in and day out? Are you helping the providers? The same issues stand for Data (or as we now say “FAIR” Findable, Accessible, Interoperable, Reusable Data) and its metadata. Is anyone looking out for Europe’s data services– the datasets and analysis systems you use and you make – the standards they use and the curators and developers who make them? Or is FAIR just a FAIRy story? I’ll tell how two organisations with quite different structures and approaches - the UK’s Software Sustainability Institute and the ELIXIR European Research Infrastructure for Life Science Data – are working for the common goal of better software, better service, and better research.
https://www.rothamsted.ac.uk/events/14th-international-symposium-integrative-bioinformatics
presentation at https://researchsoft.github.io/FAIReScience/, FAIReScience 2021 online workshop
virtually co-located with the 17th IEEE International Conference on eScience (eScience 2021)
The swings and roundabouts of a decade of fun and games with Research Objects Carole Goble
Research Objects and their instantiation as RO-Crate: motivation, explanation, examples, history and lessons, and opportunities for scholarly communications, delivered virtually to 17th Italian Research Conference on Digital Libraries
Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble
Presented at Digital Life 2018, Bergen, March 2018. In the Trust and Accountability session.
In recent years we have seen a change in expectations for the management and availability of all the outcomes of research (models, data, SOPs, software etc) and for greater transparency and reproduciblity in the method of research. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for stewardship [1] have proved to be an effective rallying-cry for community groups and for policy makers.
The FAIRDOM Initiative (FAIR Data Models Operations, http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards and sensitivity to asset sharing and credit anxiety. Our aim is a FAIR Research Commons that blends together the doing of research with the communication of research. The Platform has been installed by over 30 labs/projects and our public, centrally hosted FAIRDOMHub [2] supports the outcomes of 90+ projects. We are proud to support projects in Norway’s Digital Life programme.
2018 is our 10th anniversary. Over the past decade we learned a lot about trust between researchers, between researchers and platform developers and curators and between both these groups and funders. We have experienced the Tragedy of the Commons but also seen shifts in attitudes.
In this talk we will use our experiences in FAIRDOM to explore the political, economic, social and technical, social practicalities of Trust.
[1] Wilkinson et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
[2] Wolstencroft, et al (2016) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research Nucleic Acids Research, 45(D1): D404-D407. DOI: 10.1093/nar/gkw1032
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
FAIR Computational Workflows
Computational workflows capture precise descriptions of the steps and data dependencies needed to carry out computational data pipelines, analysis and simulations in many areas of Science, including the Life Sciences. The use of computational workflows to manage these multi-step computational processes has accelerated in the past few years driven by the need for scalable data processing, the exchange of processing know-how, and the desire for more reproducible (or at least transparent) and quality assured processing methods. The SARS-CoV-2 pandemic has significantly highlighted the value of workflows.
This increased interest in workflows has been matched by the number of workflow management systems available to scientists (Galaxy, Snakemake, Nextflow and 270+ more) and the number of workflow services like registries and monitors. There is also recognition that workflows are first class, publishable Research Objects just as data are. They deserve their own FAIR (Findable, Accessible, Interoperable, Reusable) principles and services that cater for their dual roles as explicit method description and software method execution [1]. To promote long-term usability and uptake by the scientific community, workflows (as well as the tools that integrate them) should become FAIR+R(eproducible), and citable so that author’s credit is attributed fairly and accurately.
The work on improving the FAIRness of workflows has already started and a whole ecosystem of tools, guidelines and best practices has been under development to reduce the time needed to adapt, reuse and extend existing scientific workflows. An example is the EOSC-Life Cluster of 13 European Biomedical Research Infrastructures which is developing a FAIR Workflow Collaboratory based on the ELIXIR Research Infrastructure for Life Science Data Tools ecosystem. While there are many tools for addressing different aspects of FAIR workflows, many challenges remain for describing, annotating, and exposing scientific workflows so that they can be found, understood and reused by other scientists.
This keynote will explore the FAIR principles for computational workflows in the Life Science using the EOSC-Life Workflow Collaboratory as an example.
[1] Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes,Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, and Daniel Schober FAIR Computational Workflows Data Intelligence 2020 2:1-2, 108-121 https://doi.org/10.1162/dint_a_00033.
Open science can contribute to AI trustworthiness. This talk is a categorization of scientific data platforms, and a framing of AI trustworthiness with pointers to open science contributions.
German Conference on Bioinformatics 2021
https://gcb2021.de/
FAIR Computational Workflows
Computational workflows capture precise descriptions of the steps and data dependencies needed to carry out computational data pipelines, analysis and simulations in many areas of Science, including the Life Sciences. The use of computational workflows to manage these multi-step computational processes has accelerated in the past few years driven by the need for scalable data processing, the exchange of processing know-how, and the desire for more reproducible (or at least transparent) and quality assured processing methods. The SARS-CoV-2 pandemic has significantly highlighted the value of workflows.
This increased interest in workflows has been matched by the number of workflow management systems available to scientists (Galaxy, Snakemake, Nextflow and 270+ more) and the number of workflow services like registries and monitors. There is also recognition that workflows are first class, publishable Research Objects just as data are. They deserve their own FAIR (Findable, Accessible, Interoperable, Reusable) principles and services that cater for their dual roles as explicit method description and software method execution [1]. To promote long-term usability and uptake by the scientific community, workflows (as well as the tools that integrate them) should become FAIR+R(eproducible), and citable so that author’s credit is attributed fairly and accurately.
The work on improving the FAIRness of workflows has already started and a whole ecosystem of tools, guidelines and best practices has been under development to reduce the time needed to adapt, reuse and extend existing scientific workflows. An example is the EOSC-Life Cluster of 13 European Biomedical Research Infrastructures which is developing a FAIR Workflow Collaboratory based on the ELIXIR Research Infrastructure for Life Science Data Tools ecosystem. While there are many tools for addressing different aspects of FAIR workflows, many challenges remain for describing, annotating, and exposing scientific workflows so that they can be found, understood and reused by other scientists.
This keynote will explore the FAIR principles for computational workflows in the Life Science using the EOSC-Life Workflow Collaboratory as an example.
[1] Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes,Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, and Daniel Schober FAIR Computational Workflows Data Intelligence 2020 2:1-2, 108-121 https://doi.org/10.1162/dint_a_00033.
ABSTRACT:
Most university based research is publicly funded and researchers use government data in their work, the data derived from the research of others, and also produce data as part of the research process. The Geomatics and Cartographic Research Centre (GCRC) at Carleton University does this and also adheres to the principle that publicly funded research results should be created in such a way that they can be re-disseminated back to the public. I will therefore discuss how the GCRC collaboratively collects, uses, maps and re-disseminates its data and will highlight some of the open data issues it encounters while doing so. Also, it will be argued that even though the GCRC adheres to access principles, a lack of a national digital data archive and data preservation and management support from granting councils impedes the GCRC and others from sharing their data more broadly while open data strategies have yet to take research data into consideration. Most notably, Canada does not have a research data archive, preservation policy nor a network of university based data repositories.
Researchers use government data in their work, the data derived from the research of others, and also produce data as part of their research processes. Generally, but not always, university based research is publicly funded; however there are few opportunities to re-disseminate these publicly funded data back to the public and to other researchers. Publicly funded research data are not managed nor preserved during the course of academic research and not once a research project is completed, furthermore there is uneven access to government produced data. This conversation session will therefore explore some of these issues by examining research practices at the GCRC and other community based organizations.
Researchers use OpenData to inform their work, and are also producers of data and software that can be re-shared to the public. In Canada, much university research is supported by public funds and an argument can be made that the results of that research should be made accessible to the public. The research at the Geomatics and Cartographic Research Centre will be featured as will community based social policy research in Ottawa. In Canada some data are accessible, but mostly data are not, and if they are, cost recovery policies and regressive licensing impede their use. The talk will feature examples where data are open and where opportunities for evidence based decision making are restricted.
UNESCO Conference
The Memory of the World in the Digital age: Digitization and Preservation
26-28 September 2012, Vancouver, British Columbia, Canada
Tracey P. Lauriault, D. R. Fraser Taylor
Geomatics and Cartographic Research Centre
Carleton University, Canada
http://gcrc.carleton.ca
ABSTRACT The central argument of the paper is that maps and spatial information have been fundamental facet of the memory of societies from all over the world for millennia and their preservation should be an integral part of government digital data strategies. The digital era in map making is a relatively recent phenomenon and the first digital maps date from the 1960s. Digital mapping has accelerated very rapidly over the last decade. Such mapping is now ubiquitous with an increasing amount of spatially referenced information being created by non-governmental organizations, academia, the private sector and government as well by social networks and citizen scientists. Unfortunately despite this explosion of digital mapping little or no attention is being paid to their preservation and, as a result, what has been a fundamental source of scientific and cultural information, maps, are very much at risk. Already we are losing map information faster than it is being created and the loss of this central part of the cultural heritage of societies all over the world is a serious concern. There has already been a serious loss of maps such as the Canada Land Inventory and the 1986 BBC Domesday Project of 1986 and mapping agencies all over the world are struggling to preserve maps in the new digital era. It is somewhat paradoxical that it is easier to get maps that are hundreds, and in some cases thousands, of years old than maps of the late 20th and early 21 centuries. This paper examines the challenges and opportunities of preserving and accessing Canadian digital maps, atlases and geospatial information, which are cultural and scientific knowledge assets.
Geo The Big 5
Challenges and Opportunities Rising from
Open Geospatial
Association for Geographic Information (AGI)
Belfast, 13 May 2014
Tracey P. Lauriault
National Institute for Regional and Spatial Analysis (NIRSA)
National University of Ireland at Maynooth (NUIM)
AAG Session
4204 Data-based living: peopling and placing ‘big data
Tampa, Florida, April 11 2014
Tracey P. Lauriault and Rob Kitchin
National Institute for Regional and Spatial Analysis (NIRSA)
National University of Ireland at Maynooth (NUIM)
Biodiversity Information Networks: Dataflows for interdisciplinary sciencesGBIF_NPT
Danis and Parsons, presentation given at the World Conference on Marine Biodiversity, Aberdeen, September 2011.
ANSTRACT: In this paper, we present SCAR’s Marine Biodiversity Information Network (SCAR-MarBIN, www.scarmarbin.be), introduce the new Antarctic Biodiversity Information Facility (ANTABIF, www.biodiversity.aq) and argue that it has become vital and practicable to support an international mechanism for the exchange of scientific data. This approach allows to integrate large data volumes, and helps modern biologists to face a “data deluge” using new techniques and technologies currently developed in the field of biodiversity informatics. Biodiversity is an example of data-intensive science, and certainly requires an interdisciplinary, scalable approach to address complex systemic problems such as environmental change and its impact on marine ecosystems. This paper discusses the experience of data scientists seeking to collect, curate, and provide data during the timeframe of the International Polar Year. The data content of the SCAR-MarBIN and ANTABIF holdings has been explored, and recent published analyses are used to illustrate concrete examples. We find that while technology is a critical factor to address this dimension, the greater challenges are more socio-cultural than technical. We describe a vision of discoverable, open, linked, useful, and safe data and suggest the need for a rapid socio-technical evolution in the overall science data ecosystem.
Biodiversity Information Networks: dataflows for interdisciplinary scienceBruno Danis
In this paper, we present SCAR’s Marine Biodiversity Information Network (SCAR-MarBIN, www.scarmarbin.be), introduce the new Antarctic Biodiversity Information Facility (ANTABIF, HYPERLINK "http://www.biodiversity.aq" www.biodiversity.aq) and argue that it has become vital and practicable to support an international mechanism for the exchange of scientific data. This approach allows to integrate large data volumes, and helps modern biologists to face a “data deluge” using new techniques and technologies currently developed in the field of biodiversity informatics. Biodiversity is an example of data-intensive science, and certainly requires an interdisciplinary, scalable approach to address complex systemic problems such as environmental change and its impact on marine ecosystems. This paper discusses the experience of data scientists seeking to collect, curate, and provide data during the timeframe of the International Polar Year. The data content of the SCAR-MarBIN and ANTABIF holdings has been explored, and recent published analyses are used to illustrate concrete examples. We find that while technology is a critical factor to address this dimension, the greater challenges are more socio-cultural than technical. We describe a vision of discoverable, open, linked, useful, and safe data and suggest the need for a rapid socio-technical evolution in the overall science data ecosystem.
Hawke's Bay Open Data Conference - 2 May 2019enotsluap
Hawke's Bay Open Data Conference - 2 May 2019. Presentation on open data Policy, data available and innovative ways it is being reused. Also why the private sector could/should release data.
Similar to OpenData Public Research, University of Toronto, Open Access Week, 25/11/2011 (20)
Série de webinaires sur le gouvernement ouvert du Canada
L'équipe du #GouvOuvert est de retour avec un nouveau webinaire le 28 novembre! Nous allons discuter au sujet des #coulisses des #donnéesouvertes au avec la professeure
@TraceyLauriault
de
@Carleton_U
et
@JaimieBoyd
. Inscrivez-vous maintenant: http://ow.ly/UQvu50xabIb
Week 13 (Apr. 8) – Assemblages, Genealogies and Dynamic Nominalism
Course description:
The emphasis is to learn to envision data genealogically, as a social and technical assemblages, as infrastructure and reframe them beyond technological conceptions. During the term we will explore data, facts and truth; the power of data both big and small; governmentality and biopolitics; risk, probability and the taming of chance; algorithmic culture, dynamic nominalism, categorization and ontologies; the translation of people, space and social phenomena into and by data and software and the role of data in the production of knowledge.
This class format is a graduate MA seminar and a collaborative workshop. We will work with Ottawa Police Services and critically examine the socio-technological data assemblage of that institution. This includes a fieldtrip to the Elgin street station; a tour of the 911 Communication Centre and we will meet with data experts.
April 4, 2019, 17:30-19:30
IOG's Policy Crunch
Disruptive Innovation and Public Policy in the Digital Age event series
The Global Race in Digital Governance
https://iog.ca/events/the-global-race-in-digital-governance/
March 25, 2019, 9:30 AM
International Meeting of NAICS code Experts
Statistics Canada
Simon Goldberg Room, RH Coats building
100 Tunney’s Pasture Driveway
With research contributions by Ben Wright, Carleton University and Dustin Moores, University of Ottawa
Presented at the:
Canadian Aviation Safety Collaboration Forum
International Civil Aviation Organization (ICAO)
Montreal, QC
January 23, 2019
This presentation was made in real-time while attending the Forum. The objective was to observe and listen, and share some examples outside of this community that may provide insight about data sharing models with a focus on governance.
From Aspiration to Reality: Open Smart Cities
Open smart cities might become a reality for Canada. Globally there are a number of initiatives, programs, and practices that are open smart city like which means that it is possible to have an open, responsive and engaged city that is both socio-technologically enabled, but also one where there is receptivity to and a willingness to grow a critically informed type of technological citizenship (Feenberg). For an open smart city to exist, public officials, the private sector, scholars, civil society and residents and citizens require a definition and a guide to start the exercise of imagining what an open smart city might look like. There is much critical scholarship about the smart city and there are many counter smart city narratives, but there are few depictions of what engagement, participatory design and technological leadership might be. The few examples that do exist are project based and few are systemic. An open smart city definition and guide was therefore created by a group of stakeholders in such a way that it can be used as the basis for the design of an open smart city from the ground up, or to help actors shape or steer the course of emerging or ongoing data and networked urbanist forms (Kitchin) of smart cities to lead them towards being open, engaged and receptive to technological citizenship.
This talk will discuss some of the successes resulting from this Open Smart Cities work, which might also be called a form or engaged scholarship. For example the language for the call for tender of the Infrastructure Canada Smart City Challenge was modified to include as a requisite that engagement and openness be part of the submissions from communities. Also, those involved with the guide have been writing policy articles that critique either AI or the smart city while also offering examples of what is possible. These articles are being read by proponents of Sidewalk Labs in Toronto. Also, the global Open Data Conference held in Argentina in September of 2018 hosted a full workshop on Open Smart Cities and finally Open North is working toward developing key performance indicators to assess those shortlisted by Infrastructure Canada and to help those communities develop an Open Smart Cities submission. The objective of the talk is to demonstrate that it is actually possible to shift public policy on large infrastructure projects, at least, in the short term.
This week we will learn about user generated content (UGC), citizen science, crowdsourcing & volunteered geographic information (VGI). We will also discuss divergent views on data humanitarianism.
Cottbus Brandenburg University of Technology Lecture series on Smart RegionsCritically Assembling Data, Processes & Things: Toward and Open Smart CityJune 5, 2018
This lecture will critically focus on smart cities from a data based socio-technological assemblage approach. It is a theoretical and methodological framework that allows for an empirical examination of how smart cities are socially and technically constructed, and to study them as discursive regimes and as a large technological infrastructural systems.
The lecture will refer to the research outcomes of the ERC funded Programmable City Project led by Rob Kitchin at Maynooth University and will feature examples of empirical research conducted in Dublin and other Irish cities.
In addition, the lecture will discuss the research outcomes of the Canadian Open Smart Cities project funded by the Government of Canada GeoConnections Program. Examples will be drawn from five case studies namely about the cities of Edmonton, Guelph, Ottawa and Montreal, and the Ontario Smart Grid as well as number of international best practices. The recent Infrastructure Canada Canadian Smart City Challenge and the controversial Sidewalk Lab Waterfront Toronto project will also be discussed.
It will be argued that no two smart cities are alike although the technological solutionist and networked urbanist approaches dominate and it is suggested that these kind of smart cities may not live up to the promise of being better places to live.
In this lecture, the ideals of an Open Smart City are offered instead and in this kind of city residents, civil society, academics, and the private sector collaborate with public officials to mobilize data and technologies when warranted in an ethical, accountable and transparent way in order to govern the city as a fair, viable and livable commons that balances economic development, social progress and environmental responsibility. Although an Open Smart City does not yet exist, it will be argued that it is possible.
Conference of Irish Geographies 2018
The Earth as Our Home
Automating Homelessness May 12, 2018
The research for these studies is funded by a European Research Council Advanced Investigator award ERC-2012-AdG-323636-SOFTCITY.
Presentation #2:Open/Big Urban DataLessons Learned from the Programmable City ProjectMansion House, Dublin, May 9th, 201810am-2pmhttp://progcity.maynoothuniversity.ie/2018/03/lessons-for-smart-cities-from-the-programmable-city-project/
Financé par : GéoConnexions
Dirigé par : Nord Ouvert
Le noyau de l’équipe :
Rachel Bloom et Jean-Noé Landry, Nord Ouvert
Dr Tracey P. Lauriault, Carleton University
David Fewer, Clinique d’intérêt public et de politique d’Internet du Canada (CIPPIC)
Dr Mark Fox, University of Toronto
Assistant et assistante de recherche, Carleton University
Carly Livingstone
Stephen Letts
Open Smart City in Canada Project
Funded by: GeoConnections
Lead by: OpenNorth
Project core team:
Rachel Bloom & Jean-Noe Landry, Open North
Dr. Tracey P. Lauriault, Carleton University
David Fewer, LL.M., Canadian Internet Policy and Public Interest Clinic (CIPPIC)
Dr. Mark Fox, University of Toronto
Research Assistants Carleton University
Carly Livingstone
Stephen Letts
Introductory remarks
- Jean-Noe Landry, Executive Director, Open North
Webinar 2 includes:
- Summary of Webinar 1: E-Scan and Assessment of Smart -
Cities in Canada (listen at: http://bit.ly/2yp7H8k )
- Situating smart cities amongst current digital practices
- Towards guiding principles for Open Smart Cities
- Examples of international best practices from international cities
- Observations & Next Steps
Webinar Presenters:
- Rachel Bloom, Open North
- Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
Content Contributors:
- David Fewer CIPPIC,
- Mark Fox U. of Toronto,
- Stephen Letts (RA Carleton U.)
Project Name:
- Open Smart Cities in Canada
Date:
- December 14, 2017
Canada is a data and technological society. There is no sector that is uninformed by data or unmediated by code, algorithms, software and infrastructure. Consider the Internet of Things (IoT), smart cities, and precision agriculture; or smart fisheries, forestry, and energy and of course governing. In a data based and technological society, leadership is the responsibility of all citizens, a parent, teacher, scholar, administrator, public servant, nurse and doctor, mayor and councillor, fisher, builder, business person, industrialist, MP, MLA, PM, and so on. In other words leadership is distributed and requires people power. This form of citizenship, according to Andrew Feenberg, Canada Research Chair in Philosophy of Technology, requires agency, knowledge and the capacity to act or power. In this GovMaker Keynote I will introduce the concept of technological citizenship, I will discuss what principled public interest governing might look like, and how we might go about critically applying philosophy in our daily practice. In terms of practice I will discuss innovative policy and regulation such as the right to repair movement, EU legislation such as the right to explanation, data subjects and the right to access and also data sovereignty from a globalization and an indigenous perspective.
More from Communication and Media Studies, Carleton University (20)
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Essentials of Automations: The Art of Triggers and Actions in FME
OpenData Public Research, University of Toronto, Open Access Week, 25/11/2011
1. OpenData & Public Research
Tracey P. Lauriault
Open Access Events: The Case for Open Data, Why you
should Care
Map & Data Library - 5th Floor Robarts Library, University of Toronto
Thursday, Oct. 25 from 10:00-12:00
3. Informed Decision Making
1. Citizen participation in decision-making is fundamental to
democracy
2. Good decisions are made by informed citizens
3. Quality civic data and information are key to keeping
citizens & public institutions informed; and
4. Citizen projects using civic data generate innovative
solutions to social, economic and environmental problems.
5. We all benefit from better and more open consultations
using public data.
4. Court Action - access to data
Mine tailings near the abandoned Little Bay copper mine in
north-central Newfoundland. (Photo courtesy DFO)
8. IPY – Research funding and data management
http://www.ipy-api.gc.ca/pg_IPYAPI_052-eng.html
9. Canadian Institute for Health Information
Policy on Access to Research Outputs
http://www.cihr-irsc.gc.ca/e/34846.html
http://www.cihr-irsc.gc.ca/e/35664.html
10. Open Government
http://www.infocom.gc.ca/fra/rp-pr-ori-ari_2010_1.aspx
11. Licenses
CIPPIC has a dual mission:
1) to fill voids in public policy debates on technology law issues, ensure balance in policy and law-making
processes, and provide legal assistance to under-represented organizations and individuals on matters
involving the intersection of law and technology; and
2) to provide a high quality and rewarding clinical legal education experience to students of law.
http://www.cippic.ca/open-licensing/
13. Global Map
ISCGM: http://www.iscgm.org/cgi-bin/fswiki/wiki.cgi
Data Use Agreement: http://www.iscgm.org/agreement.html
GCRC & Global Map: https://gcrc.carleton.ca/confluence/display/GCRCWEB/Global+Map
14. GEOSS – Global Earth Observation System of
Systems
GEOSS: http://www.earthobservations.org
17. GRCR Research Centre Funding
• SSHRC
• Initiative on the New Economy (INE)
• Government of Canada
Major Collaborative Initiative Grant HRSDC - Data Development Projects on
• Image, Text, Sound and Technology Homelessness Program
(ITST) Strategic Grant Heritage Canada - Gateway Fund
• INE Outreach Grant & Standard Statistics Canada, Geography Division
Research Grants Department of Foreign Affairs and
• International Polar Year International Trade Grant
Canada Natural Resources Canada
• NSERC
• Indian & Northern Affairs • Canada Foundation for
• Canadian International Polar Year Innovation (CFI)
Secretariat Office
• Government of Nunavut • Scientific Committee on
• Inuit Heritage Trust Antarctic Research
• Kitikmeot Heritage Society • Inukshuk Wireless
(NPO)
https://gcrc.carleton.ca/confluence/display/GCRCWEB/Overview
19. Atlas of the Risk of Homelessness
– Partnership Data Sharing
Data & Software
- Nunaliit
Cybercartographic Atlas
Framework ( BSD)
- Data Liberation Intiative
(DLI) Statistics Canada
(Restricted use)
- FCM QoLRS (Viewing only)
- City Neighbourhood
framework data files
(Viewing only)
- Toronto Community
Housing (Viewing only)
https://gcrc.carleton.ca/confluence/display/GCRCWEB/Atlases
20. ISIUOP – Participatory Data Collection
Data & Software
- Nunaliit Cybercartographic Atlas Framework (BSD)
- Geogratis Framework & Topographic Data (Unrestricted terms of use)
- Flow lines collected by different hunters (Shared rights)
- More sensitive data – e.g. Bear Dens, sacred sites, environmentally
sensitive data are for viewing & use by the community only
- Data will become part of IPY Canada
https://gcrc.carleton.ca/confluence/display/ISIUOP/Inuit+Sea+Ice+Use+and+Occupancy+Project+(ISIUOP)
21. Data – Uncertain & Restrictive
Cybercartographic atlases are created with:
• Restricted Data
Library and Archives Canada – Maps & Photographs
Statistics Canada, expensive due to Cost Recovery Policies also restrictive
licencing
• Uncertain Data Access
• atlascine.org Film Canada Year Book
• Alliance Atlantis
• Cinemaclock
https://gcrc.carleton.ca/confluence/display/GCRCWEB/Atlases
22. Data - Open and Semi Open
Cybercartographic atlases are created with:
• Data accessed from open access sources
• GeoGratis (NRCan) - Landsat Mosaic - Unrestricted User License
• GeoBase (NRCan) - Framework Data - Unrestricted User License
• Scientific Committee on Antarctic Research (SCAR) - Antarctic
Treaty System
• Data from semi open access sources
• Data Liberation Initiative, CU Library - Restricted to University
• Google Maps - Open APIs, useable but under terms
23. Atlases
Atlas of Arctic Bay
- Nunaliit
Cybercartographic
Atlas Framework (New
BSD)
- Google API (Semi
Open)
Atlas of Arctic Bay
- Nunaliit
Cybercartographic
Atlas Framework (New
BSD)
- Geogratis Framework
Data (Unrestricted terms of
use)
- Statistics Canada
Trade Division Data
(Restricted use and $$$$$)
https://gcrc.carleton.ca/confluence/display/GCRCWEB/Atlases
24. GCRC – Guiding Principles
• Products produced w/public funds belong to the public
Whenever possible open access comes first
BSD License
Use data from open access sources
Creative Commons
Share as much as possible
Publish in Open Access Journals
Create and use open source software, tools, widgets, etc.
Design for open source browsers
Participate in open access, open data, open source fora
Encourage these principles in public consultations
Education & Capacity building
Adhere to interoperability standards and specifications
27. EMIS Data Providers
Provider Type of data
AQPP Association québécoise des pharmaciens propriétaires
Système d'information pour la gestion des organismes communautaires (SIGOC)
Agence Inventaire des cliniques médicales
Référentiel établissement de l'Agence de Montréal
DSP Dossier client informatisé - Maladies infectieuses (DCIMI)
CJM-Batshaw Fichier du centre jeunesse de Montréal et du centre jeunesse Batshaw
CGTSIM Défavorisation scolaire (CGTSIM)
DGA Contours des dépenses
SRA Hébergement
ISQ Projection de la population
Recensement 2006 (Montréal) population totale selon l'âge et le sexe - territoires de
CLSC
Recensement 2006 (Qc) - profil semi-personnalisé de la population - RSS (Qc),
territoires de CLSC (Mtl) Échantillon 100%
Recensement 2006 (Qc) - profils semi-personnalisés cumulatifs - RSS (Qc),
StatCan
territoires de CLSC (Mtl), voisinage Échantillon 20%
Recensement 2006 (RMR de Montréal) - profils standards cumulatifs - AD, SR et
SDR de la RMR de Montréal
Stratégie d'accès communautaire aux statistiques sociales - Liste des tableaux
disponibles
Intégration-CLSC
RAMQ
Banque des données jumelées
Enquête sur la santé dans les collectivités canadiennes - Fichier de microdonnées à
ESCC
grande diffusion (FMGD)
NIS Nationwide inpatient sample
Ville de Montréal Géobase de la ville de Montréal
CMM Orthophotos de la Communauté métropolitaine de Montréal
Consommation et offre normalisées des services offerts par les médecins
Découpage géographique - M34
Données sur la clientèle hospitalière
Performance hospitalière
Personnel du réseau : salariés et cadres
Rapports financiers des établissements
Rapports statistiques annuels des centres jeunesse
Rapports statistiques des centres de réadaptation
MSSS Rapports statistiques des centres hospitaliers et de soins de longue durée
Registre de la salle d'urgence
Relevé quotidien de la situation à l'urgence et au centre hospitalier
Fichier des tumeurs
32. Social Planning Council of Ottawa
Data & Software
- CIMS Infrastructure (Open Source)
- Health Districts(Viewing only)
- Community Social Data Strategy Data (Viewing
only)
- Geogratis Data (unrestricted use)
- Ontario Ministry of Natural Resources (Viewing
only)
- Community created data sets (Viewing only)
- City of Ottawa Ward Boundaries (OpenData)
Using Population Health Data to Profile the Health and Well-
Being of Children and Youth in Eastern Ontario
http://www.cims-scic.ca/CYHNEO_atlas
Community Information and Mapping System (CIMS)
http://www.cims-scic.ca/
34. RM-Halton Geography Examples
Report Card Partners
• Halton Catholic District
School Board
• ROCK Reach Out Centre for
Kids
• Halton Children's Aid Society
• Halton District School Board
• Halton Region, Departments
of Health and Social &
Community Services
• Halton Regional Police
Services
• Transitions for Youth
Source:
Consortium Members – Halton Region - Our Kids Our Community Report Card
http://www.ourkidsnetwork.ca/about/partners.shtml
35. City of Hamilton
Source:
Consortium Member – Social Planning and Research Council of Hamilton
http://www.sprc.hamilton.on.ca/CommunityMappingService.php
36. RM-Halton Geography Examples
0
Source:
Consortium Member – Community Development Halton: Community Lens
http://www.cdhalton.ca/lens/index.htm
37. Sault Ste. Marie
Source:
Consortium Member Sault Ste. Marie Innovation Centre presentation entitled 2009 United Way
Donation and Socio-Demographic Maps
40. App de participation
publique
http://fixmystreet.ca/
http://apps4ottawa.ca/en/apps
http://howdtheyvote.ca also see
http://datalibre.ca/2011/03/29/tools-for-the-elections/
44. WeHub
The Water and Environmental Hub (WEHUB) project is an open source web platform that aggregates, federates, and connects
water data and information with users looking to download, analyze, model and interpret water and environmental-based
information. By combining water expertise with an open web development approach and an entrepreneurial foundation, the
project will spur economic diversification and benefit both public users and the private sector by improving the access to water
data and tools for academia, government, industry, NGOs and the general public.
http://www.waterenvironmentalhub.ca/
47. SEAWA
The SEAWA Watershed Planning and Advisory Council (WPAC) was built on a foundation set by the
Citizens Water Study Group in 2006. This led to the establishment of the watershed stewardship group
Prairie River Stewards, and then to the Initiator’s Group, and in turn to SEAWA. The South East Alberta
Watershed Alliance (SEAWA) was formed in 2007, and incorporated as a non-profit Society in 2008.
http://www.albertawater.com/seawa/
48. Openness
The more open, accessible, interoperable, free and discoverable
public data the more innovation we will see, the more stories
we can tell about Canada from multiple points of view. Public
policy can also become more intelligent as citizens,
researchers, NGOs, business and other levels of government
when they too have access to the data they need to inform it.
These are the public's data after all!
That is why open data and open government matters.