Introduction to FAIR principles in the context of computational biology models. Presented at a Workshop at the Basel Conference of Computational Biology. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
This talk was part of the 2020 Disease Map Modeling Community meeting, covering the steps towards publishing reproducible simulation studies (based on a reused model). Links to different COMBINE guidelines, tutorials and efforts. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
This document discusses challenges to reproducibility in systems biology and potential solutions. It notes a lack of data standards, quality, availability, and transparency make it difficult for researchers to reproduce results. Tools and initiatives discussed that aim to improve reproducibility include the COMBINE archive to bundle necessary files, graph databases to integrate model-related data, and version control systems to track model evolution over time. The overall goal is to better support scientists in sharing reproducible model-based studies.
This document discusses data and model management in systems biology. It covers topics such as data ownership, metadata, ontologies, standards for encoding models and analyses, and tools for working with systems biology models and data. Standards like SBML, SBGN, SED-ML and COMBINE Archive allow for structured representation, visualization, simulation, and sharing of models and data. Resources like SEEK enable curation, simulation and publication of models in a findable, accessible, interoperable and reusable (FAIR) manner.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
Introduction to the hands on session on "Standards and tools for model management" at the ICSB 2015.
Focus on COMBINE standards, tools for search, version control and archiving. Used management platform is SEEK.
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
This talk was part of the 2020 Disease Map Modeling Community meeting, covering the steps towards publishing reproducible simulation studies (based on a reused model). Links to different COMBINE guidelines, tutorials and efforts. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
This document discusses challenges to reproducibility in systems biology and potential solutions. It notes a lack of data standards, quality, availability, and transparency make it difficult for researchers to reproduce results. Tools and initiatives discussed that aim to improve reproducibility include the COMBINE archive to bundle necessary files, graph databases to integrate model-related data, and version control systems to track model evolution over time. The overall goal is to better support scientists in sharing reproducible model-based studies.
This document discusses data and model management in systems biology. It covers topics such as data ownership, metadata, ontologies, standards for encoding models and analyses, and tools for working with systems biology models and data. Standards like SBML, SBGN, SED-ML and COMBINE Archive allow for structured representation, visualization, simulation, and sharing of models and data. Resources like SEEK enable curation, simulation and publication of models in a findable, accessible, interoperable and reusable (FAIR) manner.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
Introduction to the hands on session on "Standards and tools for model management" at the ICSB 2015.
Focus on COMBINE standards, tools for search, version control and archiving. Used management platform is SEEK.
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
This document discusses SED-ML (Simulation Experiment Description Markup Language), a standard for describing computational simulations. SED-ML files contain information like the models, data, simulation settings and algorithms used in an experiment. Using SED-ML allows experiments to be reproduced and shared. The document encourages adopting SED-ML to make research more reproducible and help curation of models in repositories. It also provides an overview of tools that support SED-ML and ways to get involved in its development.
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.
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.
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
This document describes research on transforming a Wiki system into a relational database by adding search extension capabilities. As a proof of concept, the researchers created a database of 6902 flavonoid molecular structures from over 1687 plant species implemented on the MediaWiki platform. The system allows users to freely enter information while also enabling structured text searches, realizing relational database operations. This approach benefits from both the flexible Wiki style and query abilities of relational databases.
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.
Citing data in research articles: principles, implementation, challenges - an...FAIRDOM
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.
The Seven Deadly Sins of BioinformaticsDuncan Hull
Keynote talk at Bioinformatics Open Source Conference (BOSC) Special Interest Group at the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2007) in Vienna, July 2007 by Carole Goble, University of Manchester.
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/
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.
Started in 2004 (under ASTM Committee E13.15) the Analytical Information Markup Language (AnIML) is an XML based standard for capturing, sharing, viewing, and archiving analytical instrument data from any analytical technique.
This paper discusses the AnIML standard in terms of philosophy, structure, usage, and the resources available to work with the standard. Examples will be given for different techniques as well as strategies for migration of legacy data. Finally, the current status of the standard and time frame for promulgation through ASTM will be reported.
2015-02-10 The Open PHACTS Discovery Platform: Semantic Data Integration for ...open_phacts
The Open PHACTS Discovery Platform integrates multiple biomedical data resources into a single open access point using semantic web technology. It is guided by business questions from pharmaceutical companies to integrate data from sources like ChEMBL, DrugBank, UniProt, and more. The platform is run as a public-private partnership through 2021 to support drug discovery.
Presented by Richard Kidd at "The Future Information Needs of Pharmaceutical & Medicinal Chemistry", Monday 28 November 2011 at The Linnean Society, Burlington Square, London run by the RSC CICAG group.
This document summarizes Professor Carole Goble's presentation on making research more reproducible and FAIR (Findable, Accessible, Interoperable, Reusable) through the use of research objects and related standards and infrastructure. It discusses challenges to reproducibility in computational research and proposes bundling datasets, workflows, software and other research products into standardized research objects that can be cited and shared to help address these challenges.
This document discusses using the T-BioInfo platform to provide practical education in bioinformatics. It describes how the platform can integrate different types of omics data and analysis into intuitive, visual pipelines. This allows non-experts to analyze and interpret complex datasets. Example projects are provided, such as using RNA-seq data to identify genes involved in a disease. The goal is to teach bioinformatics through collaborative, project-based learning without requiring programming skills. Learners would reconstruct simulated biological processes and contribute to ongoing analysis of real scientific datasets.
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.
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
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.
The document discusses making data FAIR (Findable, Accessible, Interoperable, and Reusable) through a novel combination of web technologies. It describes the core FAIR principles for each component - findable, accessible, interoperable, and reusable. It then discusses how applying these principles through an "internet-inspired" approach using existing standards and protocols could help make large, heterogeneous and complex data more actionable for various applications and users. The presentation provides examples of how this could work through a layered architecture similar to the internet, with shared standards and specifications at each layer.
A Generic Scientific Data Model and Ontology for Representation of Chemical DataStuart Chalk
The current movement toward openness and sharing of data is likely to have a profound effect on the speed of scientific research and the complexity of questions we can answer. However, a fundamental problem with currently available datasets (and their metadata) is heterogeneity in terms of implementation, organization, and representation.
To address this issue we have developed a generic scientific data model (SDM) to organize and annotate raw and processed data, and the associated metadata. This paper will present the current status of the SDM, implementation of the SDM in JSON-LD, and the associated scientific data model ontology (SDMO). Example usage of the SDM to store data from a variety of sources with be discussed along with future plans for the work.
FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world’s data. We started with data catalogues (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen.
This talk was presented at The Molecular Medicine Tri-Conference/Bio-IT West on March 11, 2019.
This document discusses SED-ML (Simulation Experiment Description Markup Language), a standard for describing computational simulations. SED-ML files contain information like the models, data, simulation settings and algorithms used in an experiment. Using SED-ML allows experiments to be reproduced and shared. The document encourages adopting SED-ML to make research more reproducible and help curation of models in repositories. It also provides an overview of tools that support SED-ML and ways to get involved in its development.
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.
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.
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
This document describes research on transforming a Wiki system into a relational database by adding search extension capabilities. As a proof of concept, the researchers created a database of 6902 flavonoid molecular structures from over 1687 plant species implemented on the MediaWiki platform. The system allows users to freely enter information while also enabling structured text searches, realizing relational database operations. This approach benefits from both the flexible Wiki style and query abilities of relational databases.
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.
Citing data in research articles: principles, implementation, challenges - an...FAIRDOM
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.
The Seven Deadly Sins of BioinformaticsDuncan Hull
Keynote talk at Bioinformatics Open Source Conference (BOSC) Special Interest Group at the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2007) in Vienna, July 2007 by Carole Goble, University of Manchester.
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/
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.
Started in 2004 (under ASTM Committee E13.15) the Analytical Information Markup Language (AnIML) is an XML based standard for capturing, sharing, viewing, and archiving analytical instrument data from any analytical technique.
This paper discusses the AnIML standard in terms of philosophy, structure, usage, and the resources available to work with the standard. Examples will be given for different techniques as well as strategies for migration of legacy data. Finally, the current status of the standard and time frame for promulgation through ASTM will be reported.
2015-02-10 The Open PHACTS Discovery Platform: Semantic Data Integration for ...open_phacts
The Open PHACTS Discovery Platform integrates multiple biomedical data resources into a single open access point using semantic web technology. It is guided by business questions from pharmaceutical companies to integrate data from sources like ChEMBL, DrugBank, UniProt, and more. The platform is run as a public-private partnership through 2021 to support drug discovery.
Presented by Richard Kidd at "The Future Information Needs of Pharmaceutical & Medicinal Chemistry", Monday 28 November 2011 at The Linnean Society, Burlington Square, London run by the RSC CICAG group.
This document summarizes Professor Carole Goble's presentation on making research more reproducible and FAIR (Findable, Accessible, Interoperable, Reusable) through the use of research objects and related standards and infrastructure. It discusses challenges to reproducibility in computational research and proposes bundling datasets, workflows, software and other research products into standardized research objects that can be cited and shared to help address these challenges.
This document discusses using the T-BioInfo platform to provide practical education in bioinformatics. It describes how the platform can integrate different types of omics data and analysis into intuitive, visual pipelines. This allows non-experts to analyze and interpret complex datasets. Example projects are provided, such as using RNA-seq data to identify genes involved in a disease. The goal is to teach bioinformatics through collaborative, project-based learning without requiring programming skills. Learners would reconstruct simulated biological processes and contribute to ongoing analysis of real scientific datasets.
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.
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
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.
The document discusses making data FAIR (Findable, Accessible, Interoperable, and Reusable) through a novel combination of web technologies. It describes the core FAIR principles for each component - findable, accessible, interoperable, and reusable. It then discusses how applying these principles through an "internet-inspired" approach using existing standards and protocols could help make large, heterogeneous and complex data more actionable for various applications and users. The presentation provides examples of how this could work through a layered architecture similar to the internet, with shared standards and specifications at each layer.
A Generic Scientific Data Model and Ontology for Representation of Chemical DataStuart Chalk
The current movement toward openness and sharing of data is likely to have a profound effect on the speed of scientific research and the complexity of questions we can answer. However, a fundamental problem with currently available datasets (and their metadata) is heterogeneity in terms of implementation, organization, and representation.
To address this issue we have developed a generic scientific data model (SDM) to organize and annotate raw and processed data, and the associated metadata. This paper will present the current status of the SDM, implementation of the SDM in JSON-LD, and the associated scientific data model ontology (SDMO). Example usage of the SDM to store data from a variety of sources with be discussed along with future plans for the work.
FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world’s data. We started with data catalogues (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen.
This talk was presented at The Molecular Medicine Tri-Conference/Bio-IT West on March 11, 2019.
FAIR Data Knowledge Graphs–from Theory to PracticeTom Plasterer
This document discusses building FAIR data knowledge graphs from theory to practice. It begins by outlining what R&D researchers want to do with data, such as understanding disease mechanisms and using patient data, but that currently data is fragmented across systems. It then introduces the FAIR data principles and describes building a knowledge graph that incorporates data from multiple sources using standards like the Data Catalog vocabulary. The key challenges discussed are determining canonical representations for entities and linking data to public vocabularies through an enrichment process.
FAIR Ddata in trustworthy repositories: the basicsOpenAIRE
This video illustrates how certified digital repositories contribute to making and keeping research data findable, accessible, interoperable and reusable (FAIR). Trustworthy repositories support Open Access to data, as well as Restricted Access when necessary, and they offer support for metadata, sustainable and interoperable file formats, and persistent identifiers for future citation. Presented by Marjan Grootveld (DANS, OpenAIRE).
Main references
• Core Trust Seal for trustworthy digital repositories: https://www.coretrustseal.org/
• EUDAT FAIR checklist: https://doi.org/10.5281/zenodo.1065991
• European Commission’s Guidelines on FAIR data management: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
• FAIR data principles: www.force11.org/group/fairgroup/fairprinciples
• Overview of metadata standards and tools: https://rdamsc.dcc.ac.uk/
https://www.youtube.com/watch?v=5YqAH3f9LiU
Digital Transformation is a key goal of many large and small companies, as well as of most research institutes today. However, a key prerequisite and enabler of digital transformation is computational accessibility and interoperability of data, as laid out in the FAIR Data principles. The Hyve has been involved in the FAIR Data movement since the start, and for this webinar, our CEO Kees van Bochove will be talking to a very special guest, Ruben Kok, director of DTL. DTL and its predecessor NBIC, as well as ‘spinoff’ GO-FAIR have spent an enormous amount of effort in the past years on outreach, training, tools and community building around the FAIR Data Principles. Where do we stand today? What can we expect to see in the coming years for FAIR and FAIR biomedical data (e.g. Personal Health Train) in particular?
The agenda outlines an introductory meeting to discuss FAIR technology and tools. It includes:
- Welcome and goal setting from 13:00-13:10
- Short introductions from 13:10-13:45
- A presentation on FAIR technology and tools from 13:45-14:30
- A question and answer session from 14:30-15:00
- A wrap up from 15:00
The meeting aims to introduce various organizations to FAIR principles and related technologies through a presentation and discussion.
AFAIR in Astronomy Research - Slides. In this webinar ARDC is partnering with the ADACS project to explore the FAIR data principles in the context of Astronomy research and the ASVO and IVOA as a community exemplars of the implementation of the FAIR data principles.
These slides from: Keith Russell (ARDC): Looking at FAIR
In this talk Keith will provide an overview of the FAIR principles and how it was used in astronomy before it became official. He will conclude the talk by discussing what other disciplines can learn from their approach.
OpenTox - an open community and framework supporting predictive toxicology an...Barry Hardy
This document discusses OpenTox, an open source framework for predictive toxicology. OpenTox aims to address challenges of data integration by providing a common framework, standards, and tools to integrate diverse data sources and predictive models. It discusses knowledge sharing approaches and applications developed within OpenTox, including for chemical structure curation, (Q)SAR model building and validation, and integrated analysis of experimental data. The document outlines ongoing work to develop ToxBank, a data warehouse to support predictive toxicology research through unified data access and integrated analysis.
The document discusses FAIR (Findable, Accessible, Interoperable, Reusable) principles for data and their implementation. It describes the origins of FAIR, defines what makes data FAIR, and discusses tools for evaluating FAIRness like FAIRsharing and FAIR metrics. It also outlines a strategy for implementing FAIR metrics in the ASIO project, including developing a bridge between ASIO and FAIRmetrics and using it to evaluate resources and ASIO's ontology network for FAIR compliance.
This document discusses challenges around big data and the need for cross-domain interoperability in the Internet of Fair Data and Services (IFDS). It introduces the FAIR data principles which aim to make data findable, accessible, interoperable and reusable. The principles address metadata, identifiers, vocabularies and licensing. Adopting FAIR could reduce costs associated with data preparation and management. The IFDS builds on these principles to enable control and negotiation over digital resources across heterogeneous systems through an "hourglass" design. Communities are encouraged to define shared standards and services to improve interoperability according to FAIR.
DataCite and its Members: Connecting Research and Identifying KnowledgeETH-Bibliothek
PIDs and their metadata support scholarly research and its increasing amounts and
variety of scholarly output. DataCite provides services which enable the research community to identify, connect, cite and track these outputs, making content FAIR. New
services include data level metrics and the use of identifiers for organizations and new
types of content, e.g. software, repositories and instruments. As an open, collaborative
and community driven membership organization we rely on our members for their
input and experience to build services that are beneficial for the research community
as a whole. DataCite services as well as current and future initiatives will be described
and it will be shown how members can contribute and benefit. Over the course of the
years, our membership has grown and diversified and we are therefore refreshing and
clarifying our member model. The new member model will be presented and described.
Access to biomedical data is increasingly important to enable data driven science in the research community.
The Linked Open Data (LOD) principles (by Tim Berner-Lee) have been suggested to judge the quality of data by its accessibility (open data access), by its format and structures, and by its interoperability with other data sources.
The objective is to use interoperable data sources across the Web with ease.
The FAIR (findable, accessible, interoperable, reusable) data principles have been introduced for similar reasons with a stronger emphasis on achieving reusability.
In this presentation we assess the FAIR principles against the LOD principles to determine, to which degree, the FAIR principles reuse LOD principles, and to which degree they extend the LOD principles.
This assessment helps to clarify the relationship between both schemes and gives a better understanding, what extension FAIR represents in comparison to LOD.
We conclude, that LOD gives a clear mandate to the openness of data, whereas FAIR asks for a stated license for access and thus includes the concept of reusability under consideration of the license agreement.
Furthermore, FAIR makes strong reference to the contextual information required to improve reuse of the data, e.g., provenance information.
According to the LOD principles, such meta-data would be considered interoperable data as well, however, the requirement of extending of data with meta-data does indicate that FAIR is an extension of the LOD (in contrast to the inverse).
The document outlines plans for the VODAN Africa FAIR data project. It discusses the FAIR principles of findability, accessibility, interoperability, and reusability and how they will guide the project. The architecture will include tools like CEDAR for machine-readable data production and a triple store for exposing metadata. An initial minimal viable product will integrate clinical data from DHIS2 to validate the approach before full deployment.
Open Insights Harvard DBMI - Personal Health Train - Kees van Bochove - The HyveKees van Bochove
In this talk, the Personal Health Train concept will be introduced, which enables running personalized medicine workflows as trains visiting data stations (e.g. hospital records, primary care records, clinical studies and registries, patient-held data from e.g. wearable sensors etc.) The Personal Health Train is a very powerful concept, which is however dependent on source medical data to be coded with appropriate metadata on consent, license, scope etc. of the data, and the data itself to be encoded using biomedical data standards, which is an ever growing field in biomedical informatics. In order to realize the Personal Health Train biomedical data will need to be FAIR, i.e. adopt the FAIR Guiding Principles. This talk will cover the emerging GO-FAIR international movement, and provide examples of how several European health data networks currently are adopting open standards based stacks, to enable routine health care data to be come accessible for research.
The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published
on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of
scientists and organizations to support the reusability of digital assets. It has since been
adopted by research institutions worldwide. The guidelines are timely as we see
unprecedented volume, complexity, and creation speed of data.
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
CINECA webinar slides: Open science through fair health data networks dream o...CINECAProject
Since the FAIR data principles were published in 2016, many organizations including science funders and governments have adopted these principles to promote and foster true open science collaborations. However, to define a vision and create a video of a Personal Health Train that leverages worldwide FAIR health data in a federated manner is one step. To actually make this happen at scale and be able to show new scientific and medical insights for it is quite another!
In this webinar, we will dive into the basics of FAIR health data, but also take stock of the current situation in health data networks: after a year of frantic research and collaborations and many open datasets and hackathons on COVID-19, has the situation actually improved? Are we sharing health data on a global scale to improve medical practice, or is quality medical data still only accessible to researchers with the right credentials and deep pockets?
This webinar is part of the “How FAIR are you” webinar series and hackathon, which aim at increasing and facilitating the uptake of FAIR approaches into software, training materials and cohort data, to facilitate responsible and ethical data and resource sharing and implementation of federated applications for data analysis.
The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions.
This webinar took place on 21st January 2021 and is part of the CINECA webinar series.
For previous and upcoming CINECA webinars see:
https://www.cineca-project.eu/webinars
Similar to When is a model FAIR – and why should we care? (20)
This document provides an overview of the Computational Modeling in Biology Network (COMBINE) which coordinates the standardization of data and models in computational biology. It describes COMBINE's role in developing standards for encoding models (SBML), visualizing models (SBGN), and simulating models (SED-ML). The document also discusses COMBINE's guidance on publishing models according to FAIR principles, developing software tools and libraries to support the standards, and establishing best practices through documentation and training resources.
This document provides an overview of standards and best practices for making computational models reusable through the use of model repositories and standard formats. It discusses the COMBINE initiative for standardizing the encoding of models and simulations. The document encourages authors to make their models and data FAIR (Findable, Accessible, Interoperable, Reusable) by using community standards for publishing, exchanging, and archiving models. Examples of open model repositories and standards-compliant tools and libraries are provided to demonstrate how authors can improve sharing and reuse of their models.
Presentation on how to enable model reuse in systems biology. Presented as part of the series "Führende Köpfe in der IT - Wissenschaftlerinnen im Dialog" (ZB Med, Bonn, Germany)
This document summarizes work using Neo4j graph databases for computational systems biology models. It discusses:
1) Projects using Neo4j to integrate storage of models and simulation studies, enable ranked retrieval, and identify frequent patterns in models.
2) Tools developed including MASYMOS for linking models, simulations, annotations via graph structures, and STON for converting SBGN maps to Neo4j.
3) Applications including model repositories, analysis tools, and identifying common reaction motifs in models.
This document summarizes Dagmar Waltemath's presentation on model management for systems biology projects. It discusses the need for effective data management strategies due to the large, complex, and heterogeneous nature of systems biology data. It recommends using a data management plan, dedicated model management systems like FAIRDOMHub, standards for sharing data, publishing models in repositories, ensuring model quality, and tracking provenance. The goal is to make studies reproducible, valuable, and sustainable.
These are the slides from COMBINE 2015. In this talk, I presented the different approaches we take to determine the similarity between simulation models encoded in SBML or CeLLML -- namely: Information Retrieval based ranked model retrieval; annotation-based feature extraction for sets of models; and structure-based similarity search and clustering of model sets.
This document discusses improving reproducibility of simulation studies in computational biology through better management of simulation models and data. The SEMS project aims to develop standards and tools to link related data such as publications, models, simulations, results and more. This will be achieved by using graph databases and COMBINE standards to integrate data from various repositories. Tools will be created to search, compare, cluster and visualize models and their evolution over time to enable more reproducible and reusable simulation studies.
The document summarizes the work of the SBGN-ED+ project, which aims to further develop and integrate the Systems Biology Graphical Notation (SBGN) for modeling biological networks. Some key goals of the project include contributing to the SBGN specification and library, implementing SBGN support for model version control and merging in software tools like SBGN-ED, and using SBGN maps to display differences between model versions. The project also seeks to incorporate SBGN maps into model search, comparison and integration of model-related data. This would help address the need for standardized visual representations of biological networks to reduce ambiguity and enable sharing of computational models.
Some slides put together on analogies between biosamples and model samples. Prepared for the Biosamples workshop at The University of Manchester, 17th June 2015.
Talk in the research seminar of the Systems Biology group at the University of Rostock. The goal was to introduce the two new projects running in SEMS from summer 2015: The de.NBI-SYSBIO German Network for Bioinformatics infrastructure (focus: systems biology data management) and SBGN-ED (support and further development of SBGN-ED and libSBGN).
MaSyMoS is a tool for finding hidden treasures in model repositories by enabling semantic searches across models, annotations, and associated data. It addresses a common problem researchers face in difficulty managing and accessing their data. MaSyMoS allows users to query model repositories to find models associated with certain publications, genes, or behaviors. It also provides files needed to run simulations from retrieved models. The tool aims to help researchers better discover, organize, and leverage existing computational models.
This document discusses challenges in modeling reproducibility, dissemination, and management. It notes that researchers struggle with data management. Standards are needed for reproducible modeling results, including models, annotations, and protocols. Models should be disseminated through public repositories for higher visibility, long-term availability, and quality checks. Management of models and related data can be improved through integration into graph databases linked to ontologies, as well as version control systems. The SEMS projects aim to address these issues to foster dissemination, ensure reproducibility, and improve management of computational models.
The document presents work from the Department of Systems Biology and Bioinformatics at the University of Rostock on improving reproducibility in systems biology simulations. It discusses developing standards for representing simulations (SED-ML) and modeling provenance to better reproduce published results and enable model reuse. The goals are to specify simulation experiments, develop simulation management methods focusing on model provenance, establish links between model data, and promote reproducible science.
This document discusses three approaches to integrating model-related data in computational biology:
1) The COMBINE archive which bundles all model data into a single zip file for easy distribution.
2) Using a graph database (MORRE) to manage existing model data by representing it as a network of interrelated nodes that can be queried using information retrieval techniques.
3) Integrating model data into the semantic web and linked open data through BIO2RDF to enable automated reasoning and linking to other biological knowledge bases.
Ron Henkel's presentation of our Ranked Retrieval approach; 2012 PALs meeting of the Sysmo-SEEK project in Heidelberg, Germany. 28th-30th of November 2012.
A presentation on annotations for computational biological models. Second part is on SED-ML, a format for the storage of simulation experiment descriptions.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
Assessment and Planning in Educational technology.pptx
When is a model FAIR – and why should we care?
1. When is a model FAIR –
and why should we care?
Dagmar Waltemath
Basel Computational Biology Conference, Sep 13 2021 | https://www.bc2.ch/
CC BY-NC-ND 3.0
Department of Medical Informatics
University Medicine Greifswald (Germany)
22. A little FAIRness is easy to achieve.
Dagmar Waltemath | Department of Medical Informatics
https://twitter.com/waltelab
https://orcid.org/0000-0002-5886-5563