RARE and FAIR Science: Reproducibility and Research ObjectsCarole Goble
Keynote at JISC Digifest 2015 on Reproducibility and Research Objects in Scholarly Communication
Includes hidden slides
All material except maybe the IT Crowd screengrab reusable
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksCarole Goble
Keynote presentation at the iConference 2015, Newport Beach, Los Angeles, 26 March 2015.
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
http://ischools.org/the-iconference/
BEWARE: presentation includes hidden slides AND in situ build animations - best viewed by downloading.
Findable Accessable Interoperable Reusable < data |models | SOPs | samples | articles| * >. FAIR is a mantra; a meme; a myth; a mystery; a moan. For the past 15 years I have been working on FAIR in a bunch of projects and initiatives in Life Science projects. Some are top-down like Life Science European Research Infrastructures ELIXIR and ISBE, and some are bottom-up, supporting research projects in Systems and Synthetic Biology (FAIRDOM), Biodiversity (BioVel), and Pharmacology (open PHACTS), for example. 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. Some have happy endings. Who are the villains and who are the heroes? What are the morals we can draw from these stories?
Research Objects: more than the sum of the partsCarole Goble
Workshop on Managing Digital Research Objects in an Expanding Science Ecosystem, 15 Nov 2017, Bethesda, USA
https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem
Research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
A first step is to think of Digital Research Objects as a broadening out to embrace these artefacts or assets of research. The next is to recognise that investigations use multiple, interlinked, evolving artefacts. Multiple datasets and multiple models support a study; each model is associated with datasets for construction, validation and prediction; an analytic pipeline has multiple codes and may be made up of nested sub-pipelines, and so on. Research Objects (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described.
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
RARE and FAIR Science: Reproducibility and Research ObjectsCarole Goble
Keynote at JISC Digifest 2015 on Reproducibility and Research Objects in Scholarly Communication
Includes hidden slides
All material except maybe the IT Crowd screengrab reusable
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksCarole Goble
Keynote presentation at the iConference 2015, Newport Beach, Los Angeles, 26 March 2015.
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
http://ischools.org/the-iconference/
BEWARE: presentation includes hidden slides AND in situ build animations - best viewed by downloading.
Findable Accessable Interoperable Reusable < data |models | SOPs | samples | articles| * >. FAIR is a mantra; a meme; a myth; a mystery; a moan. For the past 15 years I have been working on FAIR in a bunch of projects and initiatives in Life Science projects. Some are top-down like Life Science European Research Infrastructures ELIXIR and ISBE, and some are bottom-up, supporting research projects in Systems and Synthetic Biology (FAIRDOM), Biodiversity (BioVel), and Pharmacology (open PHACTS), for example. 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. Some have happy endings. Who are the villains and who are the heroes? What are the morals we can draw from these stories?
Research Objects: more than the sum of the partsCarole Goble
Workshop on Managing Digital Research Objects in an Expanding Science Ecosystem, 15 Nov 2017, Bethesda, USA
https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem
Research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
A first step is to think of Digital Research Objects as a broadening out to embrace these artefacts or assets of research. The next is to recognise that investigations use multiple, interlinked, evolving artefacts. Multiple datasets and multiple models support a study; each model is associated with datasets for construction, validation and prediction; an analytic pipeline has multiple codes and may be made up of nested sub-pipelines, and so on. Research Objects (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described.
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
Being Reproducible: SSBSS Summer School 2017Carole Goble
Lecture 2:
Being Reproducible: Models, Research Objects and R* Brouhaha
Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices.
In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange.
In this talk I will explore these issues in more depth using the FAIRDOM Platform and its support for reproducible modelling. The talk will cover initiatives and technical issues, and raise social and cultural challenges.
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Reproducibility, Research Objects and Reality, Leiden 2016Carole Goble
Presented at the Leiden Bioscience Lecture, 24 November 2016, Reproducibility, Research Objects and Reality
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. It all sounds very laudable and straightforward. BUT…..
Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices.
In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange
In this talk I will explore these issues in data-driven computational life sciences through the examples and stories from initiatives I am involved, and Leiden is involved in too including:
· FAIRDOM which has built a Commons for Systems and Synthetic Biology projects, with an emphasis on standards smuggled in by stealth and efforts to affecting sharing practices using behavioural interventions
· ELIXIR, the EU Research Data Infrastructure, and its efforts to exchange workflows
· Bioschemas.org, an ELIXIR-NIH-Google effort to support the finding of assets.
Jean-Claude Bradley presents on "Peer Review and Science2.0: blogs, wikis and social networking sites" as a guest lecturer for the “Peer Review Culture in Scholarly Publication and Grantmaking” course at Drexel University. The main thrust of the presentation is that peer review alone is not capable of coping with the increasing flood of scientific information being generated and shared. Arguments are made to show that providing sufficient proof for scientific findings does scale and weakens the tragedy of the trusted source cascade.
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering science data, medical data and ethics, and the FAIR data principles.
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
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/
Being FAIR: Enabling Reproducible Data ScienceCarole Goble
Talk presented at Early Detection of Cancer Conference, OHSU, Portland, Oregon USA, 2-4 Oct 2018, http://earlydetectionresearch.com/ in the Data Science session
Metadata and Semantics Research Conference, Manchester, UK 2015
Research Objects: why, what and how,
In practice the exchange, reuse and reproduction of scientific experiments is hard, dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: codes fork, data is updated, algorithms are revised, workflows break, service updates are released. Neither should they be viewed just as second-class artifacts tethered to publications, but the focus of research outcomes in their own right: articles clustered around datasets, methods with citation profiles. Many funders and publishers have come to acknowledge this, moving to data sharing policies and provisioning e-infrastructure platforms. Many researchers recognise the importance of working with Research Objects. The term has become widespread. However. What is a Research Object? How do you mint one, exchange one, build a platform to support one, curate one? How do we introduce them in a lightweight way that platform developers can migrate to? What is the practical impact of a Research Object Commons on training, stewardship, scholarship, sharing? How do we address the scholarly and technological debt of making and maintaining Research Objects? Are there any examples
I’ll present our practical experiences of the why, what and how of Research Objects.
Results may vary: Collaborations Workshop, Oxford 2014Carole Goble
Thoughts on computational science reproducibility with a focus on software. Given at the Software Sustainability Institute's 2014 Collaborations Workshop
NSF Workshop Data and Software Citation, 6-7 June 2016, Boston USA, Software Panel
FIndable, Accessible, Interoperable, Reusable Software and Data Citation: Europe, Research Objects, and BioSchemas.org
Scott Edmunds: GigaScience - a journal or a database? Lessons learned from th...GigaScience, BGI Hong Kong
Scott Edmunds talk at the HUPO congress in Geneva, September 6th 2011 on GigaScience - a journal or a database? Lessons learned from the Genomics Tsunami.
Jean-Claude Bradley presents on "Open Education in Chemistry Research and Classroom" at the Philadelphia University of Sciences on January 11, 2011. The talk covers screencasting, wikis, chemical information validation, Open Notebook Science and smartphones.
Jean-Claude Bradley presents on "Technology and Students - Mix, Match or Miss?" at the Villanova Teaching and Learning Strategies Symposium on May 13, 2010. Topics covered include screencasting, wikis, games and Second Life, with a particular focus on student response to these technologies.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
Keynote presentation delivered at ELAG 2013 in Gent, Belgium, on May 29 2013. Discusses Research Objects and the relationship to work my team has been involved in during the past couple of years: OAI-ORE, Open Annotation, Memento.
Being Reproducible: SSBSS Summer School 2017Carole Goble
Lecture 2:
Being Reproducible: Models, Research Objects and R* Brouhaha
Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices.
In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange.
In this talk I will explore these issues in more depth using the FAIRDOM Platform and its support for reproducible modelling. The talk will cover initiatives and technical issues, and raise social and cultural challenges.
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Reproducibility, Research Objects and Reality, Leiden 2016Carole Goble
Presented at the Leiden Bioscience Lecture, 24 November 2016, Reproducibility, Research Objects and Reality
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. It all sounds very laudable and straightforward. BUT…..
Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices.
In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange
In this talk I will explore these issues in data-driven computational life sciences through the examples and stories from initiatives I am involved, and Leiden is involved in too including:
· FAIRDOM which has built a Commons for Systems and Synthetic Biology projects, with an emphasis on standards smuggled in by stealth and efforts to affecting sharing practices using behavioural interventions
· ELIXIR, the EU Research Data Infrastructure, and its efforts to exchange workflows
· Bioschemas.org, an ELIXIR-NIH-Google effort to support the finding of assets.
Jean-Claude Bradley presents on "Peer Review and Science2.0: blogs, wikis and social networking sites" as a guest lecturer for the “Peer Review Culture in Scholarly Publication and Grantmaking” course at Drexel University. The main thrust of the presentation is that peer review alone is not capable of coping with the increasing flood of scientific information being generated and shared. Arguments are made to show that providing sufficient proof for scientific findings does scale and weakens the tragedy of the trusted source cascade.
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering science data, medical data and ethics, and the FAIR data principles.
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
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/
Being FAIR: Enabling Reproducible Data ScienceCarole Goble
Talk presented at Early Detection of Cancer Conference, OHSU, Portland, Oregon USA, 2-4 Oct 2018, http://earlydetectionresearch.com/ in the Data Science session
Metadata and Semantics Research Conference, Manchester, UK 2015
Research Objects: why, what and how,
In practice the exchange, reuse and reproduction of scientific experiments is hard, dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: codes fork, data is updated, algorithms are revised, workflows break, service updates are released. Neither should they be viewed just as second-class artifacts tethered to publications, but the focus of research outcomes in their own right: articles clustered around datasets, methods with citation profiles. Many funders and publishers have come to acknowledge this, moving to data sharing policies and provisioning e-infrastructure platforms. Many researchers recognise the importance of working with Research Objects. The term has become widespread. However. What is a Research Object? How do you mint one, exchange one, build a platform to support one, curate one? How do we introduce them in a lightweight way that platform developers can migrate to? What is the practical impact of a Research Object Commons on training, stewardship, scholarship, sharing? How do we address the scholarly and technological debt of making and maintaining Research Objects? Are there any examples
I’ll present our practical experiences of the why, what and how of Research Objects.
Results may vary: Collaborations Workshop, Oxford 2014Carole Goble
Thoughts on computational science reproducibility with a focus on software. Given at the Software Sustainability Institute's 2014 Collaborations Workshop
NSF Workshop Data and Software Citation, 6-7 June 2016, Boston USA, Software Panel
FIndable, Accessible, Interoperable, Reusable Software and Data Citation: Europe, Research Objects, and BioSchemas.org
Scott Edmunds: GigaScience - a journal or a database? Lessons learned from th...GigaScience, BGI Hong Kong
Scott Edmunds talk at the HUPO congress in Geneva, September 6th 2011 on GigaScience - a journal or a database? Lessons learned from the Genomics Tsunami.
Jean-Claude Bradley presents on "Open Education in Chemistry Research and Classroom" at the Philadelphia University of Sciences on January 11, 2011. The talk covers screencasting, wikis, chemical information validation, Open Notebook Science and smartphones.
Jean-Claude Bradley presents on "Technology and Students - Mix, Match or Miss?" at the Villanova Teaching and Learning Strategies Symposium on May 13, 2010. Topics covered include screencasting, wikis, games and Second Life, with a particular focus on student response to these technologies.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
Keynote presentation delivered at ELAG 2013 in Gent, Belgium, on May 29 2013. Discusses Research Objects and the relationship to work my team has been involved in during the past couple of years: OAI-ORE, Open Annotation, Memento.
Keynote speech - Carole Goble - Jisc Digital Festival 2015Jisc
Carole Goble is a professor in the school of computer science at the University of Manchester.
In this keynote, Carole offered her insights into research data management and data centres.
Spark Summit Europe: Share and analyse genomic data at scaleAndy Petrella
Share and analyse genomic data
at scale with Spark, Adam, Tachyon & the Spark Notebook
Sharp intro to Genomics data
What are the Challenges
Distributed Machine Learning to the rescue
Projects: Distributed teams
Research: Long process
Towards Maximum Share for efficiency
Slides describing Force11 Work and background of several of the speakers, used for talks to University of Lethbridge, Carnegie Mellon and to Elsevier internally
Recomendations for infrastructure and incentives for open science, presented to the Research Data Alliance 6th Plenary. Presenter: William Gunn, Director of Scholarly Communications for Mendeley.
Data Communities - reusable data in and outside your organization.Paul Groth
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
Written and presented by Carole Goble (University of Manchester) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
myExperiment and the Rise of Social MachinesDavid De Roure
Talk at hubbub 2012, Indianapolis, 25 September 2012. The talk introduces myExperiment and Wf4Ever, discusses the future of research communication including FORCE11, and introduces the SOCIAM project (Theory and Practice of Social Machines) which launches in October 2012.
"Towards a Science of Reproducible Science?" DPRMA Workshop talk at JCDL 2013, Indianapolis, 25th July 2013. Workshop website is http://dprma.oerc.ox.ac.uk/
Paper is
David De Roure. 2013. Towards computational research objects. In Proceedings of the 1st International Workshop on Digital Preservation of Research Methods and Artefacts (DPRMA '13). ACM, New York, NY, USA, 16-19. DOI=10.1145/2499583.2499590 http://doi.acm.org/10.1145/2499583.2499590
Computational Reproducibility vs. Transparency: Is It FAIR Enough?Bertram Ludäscher
Keynote at CLIR Workshop (Webinar): Torward Open, Reproducible, and Reusable Research. February 10, 2021. https://reusableresearch.com/
ABSTRACT. The “reproducibility crisis” has resulted in much interest in methods and tools to improve computational reproducibility. FAIR data principles (data should be findable, accessible, interoperable, and reusable) are also being adapted and evolved to apply to other artifacts, notably computational analyses (scientific workflows, Jupyter notebooks, etc.). The current focus on computational reproducibility of scripts and other computational workflows sometimes overshadows a somewhat neglected and arguably more important issue: transparency of data analysis, including data wrangling and cleaning. In this talk I will ask the question: What information is gained by conducting a reproducibility experiment? This leads to a simple model (PRIMAD) that aims to answer this question by sorting out different scenarios. Finally, I will present some features of Whole-Tale, a computational platform for reproducible and transparent computational experiments.
Similar to Research Objects for FAIRer Science (20)
The ELIXIR FAIR Knowledge Ecosystem for practical know-how: RDMkit and FAIRCo...Carole Goble
Presented at the FAIR Data in Practice Symposium, 16 may 2023 at BioITWorld Boston. https://www.bio-itworldexpo.com/fair-data. The ELIXIR European research Infrastructure for life science data is an inter-governmental organizations coordinating, integrating and sustaining FAIR data and software resources across its 23 nations. To help advise users, data stewards, project managers and service providers, ELIXIR has developed complementary community-driven, open knowledge resources for guiding FAIR Research Data Management (RDMkit) and providing FAIRification recipes (FAIRCookbook). 150+ people have contributed content so far, including representatives of the pharmaceutical industry.
Can’t Pay, Won’t Pay, Don’t Pay: Delivering open science, a Digital Research...Carole Goble
Invited talk, PHIL_OS, March 30-31 2023, Exeter
https://opensciencestudies.eu/whither-open-science. Includes hidden slides.
FAIR and Open Science needs Digital Research Infrastructure, which is a federated system of systems and needs funding models that are fit for purpose
Culture change needed for paying for Open Science’s infrastructure and funding support for data driven research needs more reality and less rhetoric
RO-Crate: packaging metadata love notes into FAIR Digital ObjectsCarole Goble
Abstract
slides available at: https://zenodo.org/record/7147703#.Y7agoxXP2F4
The Helmholtz Metadata Collaboration aims to make the research data [and software] produced by Helmholtz Centres FAIR for their own and the wider science community by means of metadata enrichment [1]. Why metadata enrichment and why FAIR? Because the whole scientific enterprise depends on a cycle of finding, exchanging, understanding, validating, reproducing), integrating and reusing research entities across a dispersed community of researchers.
Metadata is not just “a love note to the future” [2], it is a love note to today’s collaborators and peers. Moreover, a FAIR Commons must cater for the metadata of all the entities of research – data, software, workflows, protocols, instruments, geo-spatial locations, specimens, samples, people (well as traditional articles) – and their interconnectivity. That is a lot of metadata love notes to manage, bundle up and move around. Notes written in different languages at different times by different folks, produced and hosted by different platforms, yet referring to each other, and building an integrated picture of a multi-part and multi-party investigation. We need a crate!
RO-Crate [3] is an open, community-driven, and lightweight approach to packaging research entities along with their metadata in a machine-readable manner. Following key principles - “just enough” and “developer and legacy friendliness - RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility and citability. As a self-describing and unbounded “metadata middleware” framework RO-Crate shows that a little bit of packaging goes a long way to realise the goals of FAIR Digital Objects (FDO)[4], and to not just overcome platform diversity but celebrate it while retaining investigation contextual integrity.
In this talk I will present the why, and how Research Object packaging eases Metadata Collaboration using examples in big data and mixed object exchange, mixed object archiving and publishing, mass citation, and reproducibility. Some examples come from the HMC, others from EOSC, USA and Australia, and from different disciplines.
Metadata is a love note to the future, RO-Crate is the delivery package.
[1] https://helmholtz-metadaten.de/en
[2] Scott, Jason The Metadata Mania, http://ascii.textfiles.com/archives/3181, June 2011
[3] Soiland-Reyes, Stian et al. “Packaging Research Artefacts with RO-Crate”. Data Science, 2022; 5(2):97-138, DOI: 10.3233/DS-210053
[4] De Smedt K, Koureas D, Wittenburg P. “FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units”. Publications. 2020; 8(2):21. https://doi.org/10.3390/publications8020021
Research Software Sustainability takes a VillageCarole Goble
The Research Software Alliance (ReSA) and the Netherlands eScience Center hosted a two-day international workshop to set the future agenda for national and international funders to support sustainable research software.
As the importance of software in research has become increasingly apparent, so has the urgent need to sustain it. Funders can play a crucial role in this respect by ensuring structural support. Over the past few years, a variety of methods for sustaining research software have been explored, including improving and extending funding policies and instruments. During the workshop, funding organizations joined forces to explore how they can effectively contribute to making research software sustainable.
This keynote helped frame the discussion from the perspective of community involvement in research software sustainability.
https://future-of-research-software.org/
this talk is available at Goble, Carole. (2022, November 8). Research Software Sustainability takes a Village. International funders workshop, The Future of Research Software, Amsterdam, The Netherlands. Zenodo. https://doi.org/10.5281/zenodo.7304596
“Bioscience has emerged as a data-rich discipline, in a transformation that is spreading as widely now as molecular biology in the twentieth century. We look forward to supporting new research careers, where data are valued and shared widely, where new software is a natural part of Biology, and where re-analysis and modelling are as creative as experimentation in understanding the rules of life and their applications.” Prof Andrew Millar FRS, chair Expert Group UKRI-BBSRC Review of data-intensive bioscience 2020.
Indeed - biomedical science is knowledge work and knowledge turning - the turning of observation and hypothesis through experimentation, comparison, and analysis into new, pooled knowledge. Turns depend on the FAIR and Open flow and availability of data and methods for automated processing and reproducible results, and on a society of scientists coordinating and collaborating.
For the past 25 years I have worked on the social and technical challenges in digital infrastructure to support scientific collaboration, data and method sharing, and automate scientific processing. Big ideas I have been instrumental in – sharing and publishing high quality computational workflows, semantic web technologies in bioscience, ecosystems of Research Objects as the currency of scholarly knowledge, FAIR data principles - preached revolution to inspire but need nudges* to get traction.
I’ll talk about making good on Andrew’s quote: what I’m doing to nudge and where we need to do more. I’ll also talk about my experiences as a woman in a digital infrastructure and computer science over the past 40 years – and some nudging is needed there too.
*Thaler RH, Sunstein CR (2008) Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press. ISBN 978-0-14-311526-7. OCLC 791403664.
https://www.bsc.es/research-and-development/research-seminars/hybrid-bsc-rslife-sessionbioinfo4women-seminar-love-money-fame-nudge-enabling-data-intensive
Open Research: Manchester leading and learningCarole Goble
Open and FAIR science has an international momentum. Large scale communities are striving to make and manage the digital infrastructure needed for scientists to be open as possible, closed as necessary, as expected by the NIH, OECD, UNESCO and the EC. ELIXIR is such a research infrastructure in Europe for Life Sciences. This talk will highlight two of ELIXIR's Open Science resources built by Open Science communities to enable life science researchers to be open, and led by Manchester. And how can we learn from these and bring these practices to Manchester?
Launch: Manchester Office for Open Research, 4th April 2022
https://www.openresearch.manchester.ac.uk/
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.
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
presentation at https://researchsoft.github.io/FAIReScience/, FAIReScience 2021 online workshop
virtually co-located with the 17th IEEE International Conference on eScience (eScience 2021)
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 Data Bridging from researcher data management to ELIXIR archives in the...Carole Goble
ISMB-ECCB 2021, NIH/ODSS Session, 27 July 2021
ELIXIR is the pan-national European Research Infrastructure for Life Science data, whose 23 national nodes and the EBI coordinate the development and long-term sustainability of domain public databases. FAIR services, policies and curation approaches aim to build a FAIR connected data ecosystem of trusted domain repositories, from ENA, HPA and EGA to specialised resources like CorkOakDB and PIPPA for plant phenotypes. But this is only one part of the data landscape and often the end of data’s journey. The nodes support research projects to operate “FAIR data first”, working with institutional and national platforms that are often generic or designed for project-based data management. We need to bridge between project-based and community-based, and support researchers across their whole RDM lifecycle, navigating the complexity this ecosystem. The ELIXIR-CONVERGE project and its flagship RDMkit toolkit (https://rdmkit.elixir-europe.org) aims to do just that.
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: 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
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
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
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.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Research Objects for FAIRer Science
1. Research Objects for
FAIRer Science
Professor Carole Goble CBE FREng FBCS
The University of Manchester, UK
carole.goble@manchester.ac.uk
VIVO/SciTS Conferences 6-8 August 2014, Austin,TX
2. Scientific publications have at least
two goals:
(i) to announce a result and
(ii) to convince readers that the
result is correct
…..
papers in experimental science
should describe the results and
provide a clear enough protocol to
allow successful repetition and
extension
Jill Mesirov
Accessible Reproducible Research
Science 22Jan 2010: 327(5964): 415-416
DOI: 10.1126/science.1179653
VirtualWitnessing*
*Leviathan and the Air-Pump: Hobbes, Boyle, and the
Experimental Life (1985) Shapin and Schaffer.
3. VirtualWitnessing*
*Leviathan and the Air-Pump: Hobbes, Boyle, and the
Experimental Life (1985) Shapin and Schaffer.
Capturing, representing,
sharing the information
needed to understand how a
research result came about.
Context of results
• Inputs, outputs, process…
Context of resources
• Instruments, data, software,
people…
4. “An article about computational
science in a scientific publication
is not the scholarship itself, it is
merely advertising of the
scholarship. The actual
scholarship is the complete
software development
environment, [the complete
data] and the complete set of
instructions which generated the
figures.”
David Donoho, “Wavelab and Reproducible
Research,” 1995
datasets
data collections
standard operating
procedures
software
algorithms
configurations
tools and apps
codes
workflows
scripts
code libraries
services,
system software
infrastructure,
compilers
hardware
Morin et al Shining Light into Black Boxes
Science 13 April 2012: 336(6078) 159-160
Ince et alThe case for open computer programs
Nature 482, 2012
5. “I can’t immediately reproduce the research in
my own laboratory. It took an estimated 280
hours for an average user to approximately
reproduce the paper.”
Phil Bourne
NIH BigWig for Data Science
6. a reproducibility paradox
big, fast,
complicated,
multi-step,
multi-type
multi-field
greater
expectations
of
reproducibility
diy publishing
greater access
14. • Collaboration –
Complementarity correlation
• Modellers share more than
Experimentalists
• Experimentalists reuse models
more than Modellers
• Active enclave sharing
• Public sharing tricky even after
publication, bribery and threats
• Data Hugging, Flirting and
Voyerism
15. • Playground rules apply
• Fluid, transient collaborations >
membership mgt pain in a*se
• Shameless exploitation of PI
competitiveness & vanity
• PI & Funder leadership
• Pan project spawned
collaborations –YES!!!!
• But not necessarily visible to us.
16. Data discovery
Data assembly,
cleaning, and
refinement
Ecological Niche
Modeling
Statistical analysis
Data collection
Insights Scholarly Communication
& Reporting
Enclosed sea problem
(Ready et al., 2010)
Pilumnus hirtellus
Scientific
Workflows
17. BioSTIF
method
instruments and laboratory
materials
Data discovery
Data assembly,
cleaning, and
refinement
Ecological Niche
Modeling
Statistical analysis
Data collection
Insights Scholarly Communication
& Reporting
Method Matters!
19. "Mapping present and future predicted distribution patterns for a meso-grazer
guild in the Baltic Sea" by Sonja Leidenberger et al
20. 1st International Workshop on Social Object Networks (SocialObjects 2011), Boston, October 9th 2011.
Find, Click ‘n’ Go
File ‘n’ Forget
SpecialistCurators
21. 24
Properties What would you ask a publication if you could?
Identity and Description
Uniqueness
Authenticity
Who are you ?
Where and when were you born ?
Who were your parents (creators) ?
Review, Reuse, and Repurpose For which purpose were you conceived and have been used ?
Inspection
Visualization
Annotations
What do you have inside ?
Representation How is your content structured ?
Access Rights May I access all your parts ?
Adaptability Which parts can I replace ?
Evolution & Versioning
Provenance
What have they done to you ?
Who and When ?
Why did they do that ?
Quality Why are you relevant to me ?
Can I believe what you are saying or trust your results ?
Reproducibility Do you still produce the same results ?
Fitness Are you still working ?
How could I repair you ?
Credit and attribution How could I thank you ?
How could I talk about you ?
25. Howard Ratner, STM Innovations Seminar 2012
was: Chair STM Future Labs Committee, CEO EVP Nature PublishingGroup,
now: Director of Development for CHORUS (Clearinghouse for the Open Research of US)
http://www.youtube.com/watch?v=p-W4iLjLTrQ&list=PLC44A300051D052E5
http://www.myexperiment.org/packs/196.html
26.
27.
28. What The Commons* Is and Is Not
Is Not:
– A database
– Confined to one physical
location
– A new large
infrastructure
– Owned by any one group
Is:
– A conceptual framework
– Analogous to the Internet
– A collaboratory
– A few shared rules
• All research objects
have unique
identifiers
• All research objects
have limited
provenance
Philip E. Bourne Ph.D.
Associate Director for Data Science, National Institutes of Health
http://www.slideshare.net/pebourne
*The NIH BD2K Commons Framework $100million in 2015
30. http://www.researchobject.org/
A Framework to Bundle and Relate multi-hosted
(digital) resources of a scientific experiment or
investigation using standard mechanisms & uniform
access protocols. Carriers of Research Context
Outputs are first class
citizens to be managed,
credited and tracked:
data, software
Research Objects
31. Links
• Recording & linking
together the
components of an
experiment
• Linking across
experiments.
34. repeat replicate
DrummondC Replicability is not Reproducibility: Nor is it Good Science, online
Peng RD, Reproducible Research in Computational Science Science 2 Dec 2011: 1226-1227.
Methods
(techniques, algorithms,
spec. of the steps)
Materials
(datasets, parameters,
algorithm seeds)
Experiment
Instruments
(codes, services, scripts,
underlying libraries)
Laboratory
(sw and hw infrastructure,
systems software,
integrative platforms)
Setup
reusereproduce
Executable Research Object
35. same experiment
same set up
same lab
same experiment
same set up
different lab
same experiment
different set up
different experiment
some of same
Validate
reusereproduce
repeat replicate
http://www.biomedcentral.com/biome/carole-goble-on-reproducible-
research-what-it-really-means-how-to-reach-it/
36. Design
Execution
Result Analysis
Collection
Publish /
Report
Peer
Review
Peer
Reuse
Modelling
Can I repeat &
defend my
method?
Can I review / reproduce
and compare my results /
method with your results /
method?
Can I review /
replicate and certify
your method?
Can I transfer your
results into my
research and reuse
this method?
* Adapted from Mesirov, J. Accessible Reproducible Research Science 327(5964), 415-416 (2010)
Research Report
Prediction
Monitoring
Cleaning
37. specialist codes
libraries, platforms, tools
services
(cloud)
hosted
services
commodity
platforms
data collections
catalogues software
repositories
my data
my process
my codes
integrative
frameworks
gateways
49. Identity
Annotation
Aggregation
FAIR RO Core Model
DOIs
URIs
Handles
ORCID
Aggregations
Resource maps
Proxies
Annotation first
class and stand-off
Identity persistence
and resolution
Citation
W3C
OAM
OAI-
ORE
54. • RO Management
– Transportation / Access / Citation
– Id location of RO “container”
– Provenance of RO & contents
– Behaviour/lifecycle of RO & contents
– Policies
• RO Interpretation
– What the RO and its content mean
– How they can be compared and validated
– How they can be used, executed, linked
• Interpretation variations
– Type (e.g.Workflows)
– Discipline (e.g. Biology)
– Task (e.g. Discovery, Execution)
– Activity (e.g. Experiment)
Progression Levels
Management and Interpretation for Integrated Applications
55. Progression Levels
Management and Interpretation for Integrated Applications
• RO Management
– Transportation / Access / Citation
– Id location of RO “container”
– Provenance of RO & contents
– Behaviour/lifecycle of RO & contents
– Policies
• RO Interpretation
– What the RO and its content mean
– How they can be compared and validated
– How they can be used, executed, linked
• Interpretation variations
– Type (e.g.Workflows)
– Discipline (e.g. Biology)
– Task (e.g. Discovery, Execution)
– Activity (e.g. Experiment)
57. Checklists
Versioning
Provenance
Dependencies
NISO-JATS
EXPO, ISA
JERM, OBI
MIAME, SBML
GIT
MIM Ontology
PROV
PAV
VoID
Puppet Docker
Make
PAV
RO Model roevowfprov
wfdesc
SysBio Workflows
DCAT
Annotation
Profiles
.
Depth: how deeply
described
Coverage: how
much is covered.
Progression levels
Semantic FrameworkExperiment
VIVO-ISF
DC
58. Checklists
aka Minimum Information Models
Safety, quality, consistency
Validation, monitoring
Common in experimental
science
Checklists defined in terms of
the RO model and its
annotations
Services execute against
model and an RO’s
annotations Zhao et. al. A Checklist-BasedApproach for QualityAssessment
of Scientific Information 3rd In.Workshop on LinkedScience, 2013
Minim Checklist Ontology to
describe checklists
Must, Should…
Cardinalities…
Rules…
http://purl.org/net/mim/ns
59. Towards Smart IntegratedApplications & Mediation
1. Id & Cite fluid things
2. First class citizenship &
uniform handling of artifacts
3. Compound
4. Mixed, leaky Containers
5. Span outcomes, evolve
outputs, emergence
6. Layered interpretation and
management profiles using
standards
7. Machine-processable
8. Technology Independent
Bechhofer,Why linked data is not enough for scientists,
DOI: 10.1016/j.future.2011.08.004
60. Towards Smart IntegratedApplications & Mediation
Bechhofer,Why linked data is not enough for scientists,
DOI: 10.1016/j.future.2011.08.004
1. Id & Cite fluid things
2. First class citizenship &
uniform handling of artifacts
3. Compound
4. Mixed, leaky Containers
5. Span outcomes, evolve
outputs, emergence
6. Layered interpretation and
management profiles using
standards
7. Machine-processable
8. Technology Independent
61. Research Objects Framework
a systematic approach to representing
a different unit of scholarship
“development” view“logical” view
“process” view “physical” view
SERVICESPOLICIES
LIFECYCLESMETADATA
PROFILES
63. ments as the access and live repositories, it could be implemented with slower (or offline) stora
tives.
Open Archival Information System Pilot
ROs are “Information Packages”
ROManager
RODL
64. • A single, transferable object
encapsulates description and
resources
– Download, transfer, publish
• ZIP-based format + manifest
describes aggregation and
annotations
– Unpack with standard tooling
• JSON-LD for manifest
– Lightweight linked-data format
– Use JSON tooling and services
Baking with off the
shelf platforms
OMEX archive
bundle
Adobe
UCF
OREPROVODF
65. • Work with local folder
structure.
– Version: github.
– Metadata: Local tooling
– Metadata about aggregation
and its resources: “hidden
folder”
• Zenodo/figshare pull
snapshot from github
– DOIs for aggregation
– new DOIs: release cycles
Baking with off the
shelf platforms
http://dx.doi.org/10.6084/m9.figshare.1031591
66. FARSITE
coded descriptions of
clinical study cohorts
an NHS tool to assess the
feasibility of gathering a cohort
packages codes,
study, and metadata
Home
Baking
68. integrated database and journal
http://www.gigasciencejournal.com
galaxy.cbiit.cuhk.edu.hk
[Peter Li]
69. Nanopub: represents structured
data along with its provenance in a
single publishable and citable entry
Galaxy workflows: re-enact the analysis
Research Object:
aggregates the
(digital) resources
contributing to
findings of
(computational)
research (results,
data and software)
as citable
compound digital
objects
http://isa-tools.github.io/soapdenovo2/
http://sandbox.wf4ever-project.org/portal/ro?ro=http://sandbox.wf4ever-project.org/rodl/ROs/SOAP2denovo2-Aureus/
[Alejandra Gonzalez-Beltran
Philippe Rocca-Serra]
70. what’s the least we can do?
how might ROs minted and used by science teams?
how might ROs be implemented and used by developer teams?
Standards
Models
Platforms
Id Schemes
Resolution
Light touch
Extensible
Infiltration
Mapping
Making,
Curating, Using
Nudging
Sharing
Linking
Infiltration
Embedding into
and changing
work practices
TOOLS
Citing
Technical Social
Reward
Mixed stewardship
Citation
Schemes
Fragility
73. Stealthy not Sneaky
to reduce the friction
instrument the world
Incremental
JIJIT not JIC
Focus on Personal
Productivity
not Public Good
Auto-magical
From made reproducible to born reproducible
What’s the least we can do?
74. KnowledgeTurns
Transportation & Mediation
Unit of Scholarly Currency
Context, Comparison
Distributed: Search, Discover, Index, Harvest, Port
Research Turns
Release model: Evolution, Emergence,
Discourse, Comparison, Historical review
Forks, Merges & Fixivity
Flow across groups, projects and articles
Anti-Salami, Threaded Publications
Schopf, Treating Data Like Software: A Case for Production Quality Data, JCDL 2012Goble, De Roure, Bechhofer, Accelerating Knowledge Turns, I3CK, 2013
Profile Focus
Body of knowledge around methods, workflows,
software, data, person, rather than publication.
First class citation, credit and respect
75. Open Research Practice is (increasingly) like
Open Source Software Practice.
(Which we know a lot about)
76. FAIR research practice benefits from a shared and
principled approach for identification, aggregation
and annotation of research components of all kinds.
– Using existing standards, vocabularies, frameworks,
platforms, infrastructures. Using linked data and
semantic interoperability
VIVO - to represent the
full context of
researchers’ work.
SciTS – to study the
research process and
research collaboration
78. • Barend Mons
• Sean Bechhofer
• Philip Bourne
• Matthew Gamble
• Raul Palma
• Jun Zhao
• AlanWilliams
• Stian Soiland-Reyes
• Paul Groth
• Tim Clark
• Juliana Freire
• Alejandra Gonzalez-Beltran
• Philippe Rocca-Serra
• Ian Cottam
All the members of the Wf4Ever team
iSOCO: Intelligent Software Components S.A.,
Spain
University of Manchester, School of Computer
Science, Manchester, United Kingdom
University of Oxford, Department of Zoology,
Oxford, UK
Poznan Supercomputing and Networking
Center. Poznan, Poland
IAA: Instituto de Astrofísica de Andalucía,
Granada, Spain
Leiden University Medical Centre, Centre for
Human and Clinical Genetics, The Netherlands
Colleagues in Manchester’s Information
Management Group
RO Advisory Board Members
http://www.researchobject.org
http://www.wf4ever-project.org