Prepared and presented by Jo McEntyre (EMBL_EBI) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble
MultiScale Biology Network Springboard meeting, Nottingham, UK, 1 June 2015
FAIR Data and model management for Systems Biology
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. Yes, 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. And 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.
Data and model management for the Systems Biology community is a multi-faceted one including: the development and adoption appropriate community standards (and the navigation of the standards maze); the sustaining of international public archives capable of servicing quantitative biology; and the development of the necessary tools and know-how for researchers within their own institutes so that they can steward their assets in a sustainable, coherent and credited manner while minimizing burden and maximising personal benefit.
The FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has grown out of several efforts in European programmes (SysMO and EraSysAPP ERANets and the ISBE ESRFI) and national initiatives (de.NBI, German Virtual Liver Network, SystemsX, UK SynBio centres). It aims to support Systems Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges multi-scale biology presents.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
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.
Reproducible and citable data and models: an introduction.FAIRDOM
Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
This document introduces FAIRDOM, a consortium that provides a platform and services to help researchers organize, manage, share, and preserve research outputs according to FAIR principles. FAIRDOM has been in operation for 10 years and has over 50 installations supporting over 118 projects. It provides tools and services to help researchers collaborate better and integrate their data, models, publications and other research objects. FAIRDOM also works with other organizations and infrastructure providers to support broader research initiatives.
FAIR data and model management for systems biology.FAIRDOM
Written and presented by Carole Goble (University of Manchester) as part of Intelligent Systems for Molecular Biology (ISMB), Dublin. July 10th - 14th 2015.
Written and presented by Tom Ingraham (F1000), at the Reproducible and Citable Data and Model Workshop, in Warnemünde, Germany. September 14th -16th 2015.
Written and presented by Wolfgang Müller (HITS) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble
MultiScale Biology Network Springboard meeting, Nottingham, UK, 1 June 2015
FAIR Data and model management for Systems Biology
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. Yes, 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. And 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.
Data and model management for the Systems Biology community is a multi-faceted one including: the development and adoption appropriate community standards (and the navigation of the standards maze); the sustaining of international public archives capable of servicing quantitative biology; and the development of the necessary tools and know-how for researchers within their own institutes so that they can steward their assets in a sustainable, coherent and credited manner while minimizing burden and maximising personal benefit.
The FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has grown out of several efforts in European programmes (SysMO and EraSysAPP ERANets and the ISBE ESRFI) and national initiatives (de.NBI, German Virtual Liver Network, SystemsX, UK SynBio centres). It aims to support Systems Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges multi-scale biology presents.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
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.
Reproducible and citable data and models: an introduction.FAIRDOM
Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
This document introduces FAIRDOM, a consortium that provides a platform and services to help researchers organize, manage, share, and preserve research outputs according to FAIR principles. FAIRDOM has been in operation for 10 years and has over 50 installations supporting over 118 projects. It provides tools and services to help researchers collaborate better and integrate their data, models, publications and other research objects. FAIRDOM also works with other organizations and infrastructure providers to support broader research initiatives.
FAIR data and model management for systems biology.FAIRDOM
Written and presented by Carole Goble (University of Manchester) as part of Intelligent Systems for Molecular Biology (ISMB), Dublin. July 10th - 14th 2015.
Written and presented by Tom Ingraham (F1000), at the Reproducible and Citable Data and Model Workshop, in Warnemünde, Germany. September 14th -16th 2015.
Written and presented by Wolfgang Müller (HITS) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
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
Reproducible Research: how could Research Objects helpCarole Goble
Reproducible Research: how could Research Objects help, given at 21st Genomic Standards Consortium Meeting
Dates: May 20-23, 2019
https://press3.mcs.anl.gov/gensc/meetings/gsc21/
FAIR Data, Operations and Model management for Systems Biology and Systems Me...Carole Goble
This document discusses the FAIRDOM consortium's efforts to promote FAIR (Findable, Accessible, Interoperable, Reusable) principles for managing data, operations, and models from systems biology and systems medicine projects. It outlines challenges in asset management for multi-partner, multi-disciplinary projects using multiple formats and repositories. FAIRDOM provides pillars of support including community actions, platforms/tools, and a public project commons to help address these challenges and better enable sharing, reuse, and reproducibility of research assets according to FAIR principles.
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: 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
Laboratories around the world continue to generate immense amounts of data that are non-proprietary and of value to the community. If available these data could dramatically reduce costs by minimizing rework and ultimately facilitating faster research. High quality reference data collections of chemical compound dictionaries, properties and spectra have been generated over many decades. With the advent of social networking tools and platforms such as Wikipedia, the community has an opportunity to contribute. The ChemSpider platform hosted by the Royal Society of Chemistry is a compound centric database with associated data. Already populated with almost 25 million unique compounds the community can deposit and host their own data, and curate and annotate existing data including those generated in Open Notebook Science Efforts. This presentation will provide an overview of progress to date and outline the vision of this community platform for chemistry and ensuring the longevity of chemistry reference data.
The two-day Systems Biology Data Management Foundry Workshop brought together 35 participants from 5 countries to improve collaboration among data management practitioners and explore opportunities in systems biology, synthetic biology, and systems medicine. Participants gained a better understanding of different systems through show-and-tell sessions, generated ideas for cross-integration, and discussed establishing a foundry to support developers. Outcomes included forming collaborations and planning for future meetings to continue developing solutions for open, interoperable, and reusable data management.
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.
Acs collaborative computational technologies for biomedical research an enabl...Sean Ekins
This document discusses enabling more open and collaborative approaches to drug discovery through computational technologies. It argues that pre-competitive data sharing could help integrate historical knowledge and deliver high value. Open drug discovery may be a better approach than the traditional closed model. Tools and open interfaces could facilitate more open collaboration between different sectors involved in biomedical research. Mobile apps may help scientists access and share data more easily. Crowdsourcing approaches could engage more contributors to knowledge bases.
OSFair2017 Workshop | How FAIR friendly is the FAIRDOM Hub? Exposing metadata...Open Science Fair
Carole Goble presents the FAIRDOM | OSFair2017 Workshop
Workshop title: How FAIR friendly is your data catalogue?
Workshop overview:
This workshop will build upon the work planned by the EOSCpilot data interoperability task and the BlueBridge workshop held on April 3 at the RDA meeting. We will investigate common mechanisms for interoperation of data catalogues that preserve established community standards, norms and resources, while simplifying the process of being/becoming FAIR. Can we have a simple interoperability architecture based on a common set of metadata types? What are the minimum metadata requirements to expose FAIR data to EOSC services and EOSC users?
DAY 3 - PARALLEL SESSION 6 & 7
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
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
Improving the Management of Computational Models -- Invited talk at the EBIMartin Scharm
Improving the Management of Computational Models:
storage – retrieval & ranking – version control
More information and slides to download at http://sems.uni-rostock.de/2013/12/martin-visits-the-ebi/
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
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.
This document discusses Research Objects (RO), which provide a framework for bundling, exchanging, and linking resources related to experiments in order to improve reproducibility. The RO framework uses unique identifiers, aggregation, and metadata to group related resources. Real-world examples of ROs include reviewed scientific papers, workflow runs, and Docker images. ROs can help make research fully FAIR (Findable, Accessible, Interoperable, Reusable). Tools and platforms like FAIRDOM, SEEK, and Figshare support the use of ROs.
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
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.
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.
Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM
Written and presented by Dagmar Waltemath (University of Rostock) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
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
Reproducible Research: how could Research Objects helpCarole Goble
Reproducible Research: how could Research Objects help, given at 21st Genomic Standards Consortium Meeting
Dates: May 20-23, 2019
https://press3.mcs.anl.gov/gensc/meetings/gsc21/
FAIR Data, Operations and Model management for Systems Biology and Systems Me...Carole Goble
This document discusses the FAIRDOM consortium's efforts to promote FAIR (Findable, Accessible, Interoperable, Reusable) principles for managing data, operations, and models from systems biology and systems medicine projects. It outlines challenges in asset management for multi-partner, multi-disciplinary projects using multiple formats and repositories. FAIRDOM provides pillars of support including community actions, platforms/tools, and a public project commons to help address these challenges and better enable sharing, reuse, and reproducibility of research assets according to FAIR principles.
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: 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
Laboratories around the world continue to generate immense amounts of data that are non-proprietary and of value to the community. If available these data could dramatically reduce costs by minimizing rework and ultimately facilitating faster research. High quality reference data collections of chemical compound dictionaries, properties and spectra have been generated over many decades. With the advent of social networking tools and platforms such as Wikipedia, the community has an opportunity to contribute. The ChemSpider platform hosted by the Royal Society of Chemistry is a compound centric database with associated data. Already populated with almost 25 million unique compounds the community can deposit and host their own data, and curate and annotate existing data including those generated in Open Notebook Science Efforts. This presentation will provide an overview of progress to date and outline the vision of this community platform for chemistry and ensuring the longevity of chemistry reference data.
The two-day Systems Biology Data Management Foundry Workshop brought together 35 participants from 5 countries to improve collaboration among data management practitioners and explore opportunities in systems biology, synthetic biology, and systems medicine. Participants gained a better understanding of different systems through show-and-tell sessions, generated ideas for cross-integration, and discussed establishing a foundry to support developers. Outcomes included forming collaborations and planning for future meetings to continue developing solutions for open, interoperable, and reusable data management.
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.
Acs collaborative computational technologies for biomedical research an enabl...Sean Ekins
This document discusses enabling more open and collaborative approaches to drug discovery through computational technologies. It argues that pre-competitive data sharing could help integrate historical knowledge and deliver high value. Open drug discovery may be a better approach than the traditional closed model. Tools and open interfaces could facilitate more open collaboration between different sectors involved in biomedical research. Mobile apps may help scientists access and share data more easily. Crowdsourcing approaches could engage more contributors to knowledge bases.
OSFair2017 Workshop | How FAIR friendly is the FAIRDOM Hub? Exposing metadata...Open Science Fair
Carole Goble presents the FAIRDOM | OSFair2017 Workshop
Workshop title: How FAIR friendly is your data catalogue?
Workshop overview:
This workshop will build upon the work planned by the EOSCpilot data interoperability task and the BlueBridge workshop held on April 3 at the RDA meeting. We will investigate common mechanisms for interoperation of data catalogues that preserve established community standards, norms and resources, while simplifying the process of being/becoming FAIR. Can we have a simple interoperability architecture based on a common set of metadata types? What are the minimum metadata requirements to expose FAIR data to EOSC services and EOSC users?
DAY 3 - PARALLEL SESSION 6 & 7
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
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
Improving the Management of Computational Models -- Invited talk at the EBIMartin Scharm
Improving the Management of Computational Models:
storage – retrieval & ranking – version control
More information and slides to download at http://sems.uni-rostock.de/2013/12/martin-visits-the-ebi/
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
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.
This document discusses Research Objects (RO), which provide a framework for bundling, exchanging, and linking resources related to experiments in order to improve reproducibility. The RO framework uses unique identifiers, aggregation, and metadata to group related resources. Real-world examples of ROs include reviewed scientific papers, workflow runs, and Docker images. ROs can help make research fully FAIR (Findable, Accessible, Interoperable, Reusable). Tools and platforms like FAIRDOM, SEEK, and Figshare support the use of ROs.
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
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.
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.
Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM
Written and presented by Dagmar Waltemath (University of Rostock) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Improving the management of computational models.FAIRDOM
Written by Martin Scharm (University of Rostock), Ron Henkel (University of Rostock), Dagmar Waltemath (University of Rostock), Olaf Wolkenhauer (University of Rostock, Stellenbosch University), and presented by Martin Scharm (University of Rostock) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
The document discusses licensing, citation, and sustainability of intellectual property. It covers different types of licenses for software and data including open source, proprietary, and Creative Commons licenses. It provides resources for choosing an appropriate license, ensuring works are properly cited and credited to help sustain them, and guidelines for repositories, audits, and certifications.
Short talk on Research Object and their use for reproducibility and publishing in the Systems Biology Commons Platform FAIRDOMHub, and the underlying software SEEK.
The webinar discussed FAIRDOM services that can help applicants to the ERACoBioTech call with their data management plans and requirements. FAIRDOM offers webinars on developing data management plans, and their platform and tools can help with organizing, storing, sharing, and publishing research data and models in a FAIR manner by utilizing metadata standards. Different levels of support are available, from general community resources through their hub, to premium customized support for individual projects. Consortia can include FAIRDOM as a subcontractor within the guidelines of the ERACoBioTech call.
How to make your published data findable, accessible, interoperable and reusablePhoenix Bioinformatics
Seminar Presentation for PMB Department, UC Berkeley for Love Data Week. Subject is how to prepare publications and associated data sets for maximum reuse.
Scientific Data overview of Data Descriptors - WT Data-Literature integration...Susanna-Assunta Sansone
This document introduces Scientific Data, a new peer-reviewed journal for publishing data descriptors from Nature Publishing Group. It will provide structured metadata and narrative articles to describe datasets for reuse. The journal is now open for submissions and will launch in May 2014, featuring an advisory panel and sections for standardized data descriptor articles and experimental metadata. It aims to give proper credit for data sharing and promote open access, reuse and peer review of curated scientific datasets.
Data citationworkshop idcc_2014 AltmanMicah Altman
Sound, reproducible scholarship rests upon a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research.
A few days ago I was honored to officially announce the Data Citation Working Group's Joint Declaration of Data Citation Principles at IDCC 2014, from which the above quote is taken.
This Joint Data Citation Principles identifies guiding principles for the scholarly citation of data. This recommendation is a s collaborative work with CODATA, FORCE 11, DataCite and many other individuals and organizations. And in the week since it has been released, it has already garnered over twenty institutional endorsements.
Some slides introducing the principles are here:
[slideshare id=31957135&doc=datacitationworkshopidcc20142altmandraft-140305141032-phpapp01]
To summarize, from 1977 through 2009 there were three phases of development in the area of data citation.
The first phase of development focused on the role of citation to facilitate description and information retrieval. This phase introduced the principles that data in archives should be described as works rather than media, using author, title, and version.
The second phase of development extended citations to support data access and persistence. This phase introduced the principles that research data used by publication should be cited, that those citations should include persistent identifiers, and that the citations should be directly actionable on the web.
The third phase of development focused on using citations for verification and reproducibility. Although verification and reproducibility had always been one of the motivations for data archiving – it had not been a focus of citation practice. This phase introduced the principles that citations should support verifiable linkage of data and published claims, and it started the trend towards wider integration with the publishing ecosystem
And over the last five years the importance and urgency of scientific data management and access has been recognized more broadly. The culmination of this trend toward increasing recognition, thus far, is an increasingly widespread consensus by researchers and funders of research that data is a fundamental product of research and therefore a citable product. The fourth and current phase of data development work focuses on integration with the scholarly research and publishing ecosystem. This includes integration of data citation in standardized ways within publication, catalogs, tool chains, and larger systems of attribution.
Read the full recommendation here, along with examples, references and endorsements:
Joint Declaration of Data Citation Principles
1. The document discusses the EOSC Dataset Minimum Information (EDMI) approach for exposing research data in the European Open Science Cloud (EOSC).
2. EDMI defines a set of 12 minimum metadata properties to facilitate finding and accessing datasets without being overly descriptive.
3. The approach was developed by engaging EOSC demonstrator data repositories and repositories to propose methods for exposing metadata in a simple and sustainable way.
NIH iDASH meeting on data sharing - BioSharing, ISA and Scientific DataSusanna-Assunta Sansone
1) The document discusses Susanna-Assunta Sansone's roles and work related to promoting FAIR data standards and practices.
2) It highlights some of her leadership positions with organizations like BioSharing that work to map and promote standards.
3) The document also discusses Scientific Data, a peer-reviewed journal launched by Nature Publishing Group to publish detailed descriptions of scientifically valuable datasets to facilitate reuse.
2013 CrossRef Workshops Citing Data Ed PentzCrossref
The document summarizes a joint statement from a data citation synthesis group on the principles of linkability and citability of research data. The group was made up of 36 members from around 20 organizations. They compared existing data citation principles and merged them into a consensus set of 8 principles. The principles state that data should be considered citable research products, attribution should be given to all data contributors, data citations should be provided when claims rely on data, citations should include persistent unique identifiers, citations should facilitate access to data and metadata, metadata and identifiers should persist beyond data lifespan, citations should include version/subset details, and citation methods should be flexible enough within communities but not differ so much as to compromise interoperability.
Open Source Tools Facilitating Sharing/Protecting Privacy: Dataverse and Data...Merce Crosas
Presentation for the NFAIS Webinar series: Open Data Fostering Open Science: Meeting Researchers' Needs
http://www.nfais.org/index.php?option=com_mc&view=mc&mcid=72&eventId=508850&orgId=nfais
Oxford DTP - Sansone - Data publications and Scientific Data - Dec 2014Susanna-Assunta Sansone
- The document discusses the need for open and accessible data in research. It notes that over 50% of studies are not published due to selective reporting of results.
- There is a movement for "FAIR data" in life and medical sciences, where data is findable, accessible, interoperable, and reusable. However, not much data currently meets these standards.
- Publishers can play a role in incentivizing data sharing by implementing policies requiring data availability and format standards for publishing research. This includes supporting data citations and data journals.
Lesson 8 in a set of 10 created by DataONE on Best Practices for Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
This document summarizes a SHARE membership meeting about the SHARE Notification Service. It discusses how the service will gather research release events from multiple providers, normalize the data, and notify consumers like funders and repositories. The Center for Open Science is helping to build the modular, scalable infrastructure. Challenges include inconsistent metadata across sources and a lack of identifiers. Future phases aim to reconcile records and provide more comprehensive researcher profiles.
A presentation on the SageCite project given at the JISC MRD International Workshop in March 2011. Describes the application domain and citation challenges in SageCite.
BROWN BAG TALK WITH MICAH ALTMAN INTEGRATING OPEN DATA INTO OPEN ACCESS JOURNALSMicah Altman
This talk, is part of the MIT Program on Information Science brown bag series (http://informatics.mit.edu)
This talk discusses findings from an analysis of data sharing and citation policies in Open Access journals and describes a set of novel tools for open data publication in open access journal workflows. Bring your lunch and enjoy a discussion fit for scholars, Open Access fans, and students alike.
Dr Micah Altman is Director of Research and Head/Scientist, Program on Information Science for the MIT Libraries, at the Massachusetts Institute of Technology.
Linking Data to Publications through Citation and Virtual ArchivesMicah Altman
This document discusses linking data to publications through citation and virtual archives. It argues that data citation and sharing infrastructure are necessary for scientific reproducibility and open data. It outlines elements of data management plans and requirements for data sharing infrastructure, including persistence, provenance, access control and incentives. The document advocates for data citations as first-class objects and emerging practices like assigning DOIs to datasets. It presents several use cases for the Dataverse network, a virtual archive designed for research data sharing through federated and organizational models.
This document discusses data citation and how to implement it for publishers and data repositories. It covers how publishers can include data citations in their Crossref metadata and how repositories can link datasets to publications. It also introduces the Crossref Event Data service, which captures these data citations and other relationships between DOIs and makes them openly available via APIs. This allows data citations to be more widely discovered and adopted.
Presentation slides from a talk by Gareth Knight which discussed the need to consider data sharing activities in academic citizenship, different approaches that may be taken to publish data associated with publications, and the opportunities presented by data journals
Enriching Scholarship 2014 Beyond the Journal Article: Publishing and Citing ...Natsuko Nicholls
The document discusses data sharing policies and mandates from various organizations including federal funding agencies in the US and internationally, journals, and a paradigm shift toward more transparent and collaborative research that integrates publications and data. Key points include requirements for data management plans from NIH and NSF, expectations of funding agencies in other countries to maximize access to research data, a journal policy requiring data to be made available, and challenges around measuring the impact of shared data given the lack of common practices and standards for citing data.
Research Data Sharing and Re-Use: Practical Implications for Data Citation Pr...SC CTSI at USC and CHLA
Date: Apr 4, 2018
Speaker: Hyoungjoo Park, PhD candidate, School of Information Studies, University of Wisconsin-Milwaukee, and Dietmar Wolfram, PhD
Overview: It is increasingly common for researchers to make their data freely available. This is often a requirement of funding agencies but also consistent with the principles of open science, according to which all research data should be shared and made available for reuse. Once data is reused, the researchers who have provided access to it should be acknowledged for their contributions, much as authors are recognised for their publications through citation. Hyoungjoo Park and Dietmar Wolfram have studied characteristics of data sharing, reuse, and citation and found that current data citation practices do not yet benefit data sharers, with little or no consistency in their format. More formalised citation practices might encourage more authors to make their data available for reuse.
A Data Citation Roadmap for Scholarly Data RepositoriesLIBER Europe
This document summarizes a webinar about developing a roadmap for data citation in scholarly data repositories. The webinar discussed recommendations for repositories to support unique, persistent identifiers for datasets that resolve to landing pages containing human- and machine-readable metadata. It also covered tracking data citations between repositories and publishers, and next steps to publish an updated recommendations paper incorporating feedback from the community. The webinar was presented by experts from DataCite, Harvard University and other organizations to facilitate best practices for data citation.
The document discusses how a publisher can implement data citation principles in practice. It summarizes the 8 Data Citation Principles put forth by the Force11 group, which focus on issues like credit, identification, access, and persistence of data citations. The document then provides examples of how Elsevier, as a publisher, has worked to put these principles into practice through features that integrate datasets with research articles.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Citing data in research articles: principles, implementation, challenges - and the benefits of changing our ways.
1. Citing data in research articles:
principles, implementation, challenges
- and the benefits of changing our ways
Jo McEntyre
Europe PMC, EMBL-EBI
www.ebi.ac.uk
3. Familiar Complexity!
Article‘Package’ExternalResources
“Recognized” data repos:
file|structured record,
Accession|DOI|API+ Accession
Institutional repos:
file|structured record,
URL|DOI|API+Accession
Author database|‘website’:
file|struct record,
URL|DOI|API+Accession
Supp info tables/data:
file, URL|DOI
Cross-reference
Dataset list
Ref to external
resRef to external
res
Reference list
Fig Source data:
file, URL|DOI
Fig (caption + graphic)
Cross-reference
Ref to external
resource
Adapted from Thomas Lemberger, EMBO
4. Europe PMC literature database
Europe PMC
• Abstracts: 30 million
• Full-text articles: 3 million
• Article citation counts
• Grants
• ORCIDs
• Semantic annotation
• Data citations
• Data integration
Europe PMC is a member of the PMC
International Collaboration.
Funded by 28 European funders of life science research
5. About EMBL-EBI
• Part of the European
Molecular Biology
Laboratory
• International, non-profit
research institute
• Europe’s hub for
biological data services
and research
6. Making data discoverable
Labs around the
world deposit
data and we…
Archive it
Classify it
Share it with
other data
providers
Analyse, add
value and
integrate it
…provide
tools to help
researchers
use it
A collaborative
enterprise
8. Data Citation in Europe PMC full text
Literature*
Added-Value
Submitted
*OMIM, Clinical trials, GO
Submission statements
vs reuse?
260K
9. Data Citation Principals Engender Two
Big Ideas
"sound, reproducible scholarship rests upon a
foundation of robust, accessible data"
"data should be considered legitimate, citable
products of research"
These slides are adapted from:
http://www.slideshare.net/joanstarr/data-citation-a-joint-declaration-
10. 1 Importance
2 Credit and Attribution
3 Evidence
4 Unique Identification
5 Access
6 Persistence
7 Specificity and Verifiability
8 Interoperability and flexibility
Full Principles: https://www.force11.org/datacitation
Joint Declaration on Data Citation Principles
11. Joint Declaration
Data should be considered legitimate, citable
products of research. Data citations should be
accorded the same importance in the scholarly
record as citations of other research objects, such as
publications.
1. Importance
12. Data citations should facilitate giving scholarly credit
and normative and legal attribution to all contributors
to the data, recognizing that a single style or
mechanism of attribution may not be applicable to all
data.
2. Credit and Attribution
Joint Declaration
13. In scholarly literature, whenever and wherever
a claim relies upon data, the corresponding data
should be cited.
3. Evidence
Joint Declaration
14. A data citation should include a persistent method
for identification that is machine actionable, globally
unique, and widely used by a community.
4. Unique identification
etc.. !!!
Joint Declaration
15. Data citations should facilitate access to the data
themselves and to such associated metadata,
documentation, code, and other materials, as are
necessary for both humans and machines to make
informed use of the referenced data.
5. Access
Joint Declaration
16. Unique identifiers, and metadata describing the
data, and its disposition, should persist -- even
beyond the lifespan of the data they describe.
6. Persistence
Joint Declaration
17. Data citations should facilitate identification of,
access to, and verification of the specific data that
support a claim. Citations or citation metadata
should include information about provenance and
fixity sufficient to facilitate verifying that the specific
timeslice, version and/or granular portion of data
retrieved subsequently is the same as was
originally cited.
7. Specificity and Verifiability
Joint Declaration
18. Data citation methods should be sufficiently flexible
to accommodate the variant practices among
communities, but should not differ so much that they
compromise interoperability of data citation practices
across communities.
8. Interoperability and flexibility
Joint Declaration
20. An implementation example
Principle 2:
Credit and
Attribution
Principle 4, 5,
6:
Unique ID
Access
Persistence
Principle 7:
Specificity
and
Verifiability
Principle 8: Interoperability and flexibility
Creators, Year, Dataset Title, DOI, Data Repository, version
(Resolves to landing page with
access to metadata, docs, and
data)
Slide from
Mercè Crosas, Ph.D.
Harvard University
32. JATS support for data citation
<mixed-citation publication-type='data'>
<name><surname>Heinz</surname><given-names>D.W.</given-
names></name>,
<name><surname>Baase</surname><given-names>W.A.</given-
names></name>,
<etal>et. al.</etal>
<data-title>How amino-acid insertions are allowed in an
alpha-helix of T4
lysozyme</data-title>.
<source>PDB Europe</source>,
accession <pub-id pub-id-type='accession' assigning-
authority='pdb'
xlink:href='http://www.ebi.ac.uk/pdbe/entry/search/index?te
xt:102L'>102l</pub-id>.
<pub-id pub-id-type='doi'
xlink:href='http://dx.doi.org/10.2210/pdb102l/pdb'>10.2210/
pdb102l/pdb</pub-id>
</mixed-citation>
33. Minimal, maximal & extensible citation
Resource
name
I
D
Resource
name
Resolution ‘template’ I
D
Author
list
Resource
name
Resolution
‘template’
I
D
Tim
e
? Author
list
Resource
name
Resolution
‘template’
I
D
Tim
e
?
For example:
new data vs pre-existing
data
For example:
version
Thomas Lemberger, EMBO
34. Integrated Research
Reused from: seier+seier,
Flickr
Reused from: Images
Money, Flickr
Articles
Data
People
Institutions
Funders
35. A data citation should include a persistent method
for identification that is machine actionable, globally
unique, and widely used by a community.
4. Unique identification
etc..
Joint Declaration
36. 1. Discoverability through accessibility
• Deposit in a public/open database
• Where possible, structured archive (e.g. PDB,
ENA) >> unstructured archive (e.g. Zenodo,
Figshare)
• Uniquely identify it: PID, Accession number, DOI,
ROI
• Give it context: metadata (and more)
• All of the above = citable =
37. 2. Discoverability through structured data
structured data is one of the true
enablers of life science
- Discovery of homology between genes across species
- Predicting function based on protein folds
• Structured data can be cross-analysed, compared by
algorithm, and encourages development of new products
and tools
38. Structured data is good value for money
Annual cost of generating new protein
structure data in labs around the world
Annual cost of
maintaining it
in a central
database
39. Degrees of Data
Unstructured/semi-
structured
Structured
Added Value
Metadata
A picture of a graph
A spreadsheet of my results
A record in a DNA
sequence
database
A graphical display of a genome
A narrative with
citations, pictures
and attachments
Article
40. Metadata – critical to discoverability
Generic: title, submitters, date, file format, version.
citation
basic search
Wagner F.F., 23-APR-2002, TPA: Homo sapiens SMP1
gene, RHD gene and RHCE gene, INSDC, 14-NOV-2006
(Rel. 89, Last updated, Version 7). BN000065
Specific: organism, tissue, assay, page number …
deep search
analysis
computation