Visualizing Primary Data form Taxonomic Literaturemillerjeremya
Visualizing Primary Data form Taxonomic Literature
Jeremy Miller, Donat Agosti, Lyubomir Penev, Guido Sautter, Teodor Georgiev, Terry Catapano, David Patterson, David King, Serrano Pereira, Rutger Aldo Vos, Soraya Sierra
EU BON General Meeting, 1-4 June 2015, Cambridge, United Kingdom
The document discusses the ISA (Investigation/Study/Assay) framework for enabling data reuse and reproducibility in bioscience research. The ISA framework provides a generic format for rich experimental descriptions and an infrastructure of open source software tools. It aims to minimize the burden of reporting, curating, sharing data and metadata from bioscience experiments to enable comprehension, reuse of data, and reproducibility. The framework promotes community engagement to develop community standards and document use cases.
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksCarole Goble
Keynote presentation at the iConference 2015, Newport Beach, Los Angeles, 26 March 2015.
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
http://ischools.org/the-iconference/
BEWARE: presentation includes hidden slides AND in situ build animations - best viewed by downloading.
Current advances to bridge the usability-expressivity gap in biomedical seman...Maulik Kamdar
I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search
A keynote given on experiences in curating workflows and web services.
3rd International Digital Curation Conference: "Curating our Digital Scientific Heritage: a Global Collaborative Challenge"
11-13 December 2007
Renaissance Hotel
Washington DC, USA
Visualizing Primary Data form Taxonomic Literaturemillerjeremya
Visualizing Primary Data form Taxonomic Literature
Jeremy Miller, Donat Agosti, Lyubomir Penev, Guido Sautter, Teodor Georgiev, Terry Catapano, David Patterson, David King, Serrano Pereira, Rutger Aldo Vos, Soraya Sierra
EU BON General Meeting, 1-4 June 2015, Cambridge, United Kingdom
The document discusses the ISA (Investigation/Study/Assay) framework for enabling data reuse and reproducibility in bioscience research. The ISA framework provides a generic format for rich experimental descriptions and an infrastructure of open source software tools. It aims to minimize the burden of reporting, curating, sharing data and metadata from bioscience experiments to enable comprehension, reuse of data, and reproducibility. The framework promotes community engagement to develop community standards and document use cases.
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksCarole Goble
Keynote presentation at the iConference 2015, Newport Beach, Los Angeles, 26 March 2015.
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
http://ischools.org/the-iconference/
BEWARE: presentation includes hidden slides AND in situ build animations - best viewed by downloading.
Current advances to bridge the usability-expressivity gap in biomedical seman...Maulik Kamdar
I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search
A keynote given on experiences in curating workflows and web services.
3rd International Digital Curation Conference: "Curating our Digital Scientific Heritage: a Global Collaborative Challenge"
11-13 December 2007
Renaissance Hotel
Washington DC, USA
Bio-GraphIIn is a graph-based, integrative and semantically enabled repository for life science experimental data. It addresses the need for a system that supports retrospective data submissions, handles heterogeneous experimental data, and overcomes the fragmentation of existing data formats and databases. Bio-GraphIIn uses the Investigation/Study/Assay (ISA) framework and ontologies to semantically represent experimental metadata and enable rich queries across studies, with the goal of facilitating integrative data analysis.
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.
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
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.
Metagenomic Data Provenance and Management using the ISA infrastructure --- o...Alejandra Gonzalez-Beltran
Metagenomic Data Provenance and Management using the ISA infrastructure - overview, implementation patterns & software tools
Slides presented at EBI Metagenomics Bioinformatics course: http://www.ebi.ac.uk/training/course/metagenomics2014
The document discusses the ISA infrastructure, which provides a framework for tracking metadata in bioscience experiments from data collection to sharing in linked data clouds. The infrastructure includes a metadata syntax, open source software tools, and a user community. It allows annotation of experimental metadata, materials, and processes using ontologies to make semantics explicit and enable integration and knowledge discovery. The infrastructure is growing with over 30 public and private resources adopting it to facilitate standards-compliant sharing of investigations across life science domains.
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
RARE and FAIR Science: Reproducibility and Research ObjectsCarole Goble
Keynote at JISC Digifest 2015 on Reproducibility and Research Objects in Scholarly Communication
Includes hidden slides
All material except maybe the IT Crowd screengrab reusable
The document discusses the ISA infrastructure, which provides a standardized format (ISA-TAB) for experimental metadata and data exchange. It can be used across various domains like toxicology, systems biology, and nanotechnology. The Risa R package integrates experimental metadata with analysis and allows updating metadata. Nature Scientific Data is a new publication for describing valuable datasets. The ISA framework has been adopted by over 30 public and private resources and is growing in use for facilitating reuse of investigations in various life science domains. Toxicity examples include EU projects on predictive toxicology and a rat study of drug candidates. Questions can be directed to the ISA tools group.
ROHub is a digital library and management system for research objects (ROs). It enables scientists to create, manage, and share ROs, which are semantic aggregations of related scientific resources, annotations, and research context. ROHub provides APIs and a web portal for scientists to use throughout the research lifecycle. It stores ROs long-term to support reproducibility and allows for monitoring changes to assess quality.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
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.
An overview of Text and Data Mining (ContentMining) including live demonstrations. The fundamentals: discover, scrape, normalize , facet/index, analyze, publish are exemplified using the recent Zika outbreak. Mining covers textual and non-textual content and examples of chemistry and phylogenetic tress are given.
BioSharing.org - mapping the landscape of community standards, databases, dat...Alejandra Gonzalez-Beltran
This document summarizes Alejandra González-Beltrán's presentation on BioSharing.org, which maps the landscape of community standards, databases, and data policies. It discusses how BioSharing aims to help stakeholders make informed decisions for data interoperability through curating crowdsourcing information on existing standards and policies. It also describes how BioSharing integrates information from the MIBBI Project and is working with the MICheckout tool to help users create and use modular standards components.
Research Objects: more than the sum of the partsCarole Goble
Workshop on Managing Digital Research Objects in an Expanding Science Ecosystem, 15 Nov 2017, Bethesda, USA
https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem
Research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
A first step is to think of Digital Research Objects as a broadening out to embrace these artefacts or assets of research. The next is to recognise that investigations use multiple, interlinked, evolving artefacts. Multiple datasets and multiple models support a study; each model is associated with datasets for construction, validation and prediction; an analytic pipeline has multiple codes and may be made up of nested sub-pipelines, and so on. Research Objects (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described.
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.
Use of ContentMine tools on the Open Access subset of EuropePubMedCentral to discover new knowledge about the Zika virus.
Three slides have embedded movies - these do not show in slideshare and a first pass of this can be seen as a single file at https://vimeo.com/154705161
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.
The Human Phenotype Ontology (HPO) was developed to describe phenotypic abnormalities, aka, “deep phenotyping”, whereby symptoms and characteristic phenotypic findings (a phenotypic profile) are captured. The HPO has been utilized to great success for assisting computational phenotype comparison against known diseases, other patients, and model organisms to support diagnosis of rare disease patients. Clinicians and geneticists create phenotypic profiles based on clinical evaluation, but this is time consuming and can miss important phenotypic features. Patients are sometimes the best source of information about their symptoms that might otherwise be missed in a clinical encounter. However, HPO primarily use medical terminology, which can be difficult for patients and their families to understand. To make the HPO accessible to patients, we systematically added non-expert terminology (i.e., layperson terms) synonyms. Using semantic similarity, patient-recorded phenotypic profiles can be evaluated against those created clinically for undiagnosed patients to determine the improvement gained from the patient-driven phenotyping, as well as how much the patient phenotyping narrows the diagnosis. This patient-centric HPO can be utilized by all: in patient-centered rare disease websites, in patient community platforms and registries, or even to post one’s hard-to-diagnosed phenotypic profile on the Web.
Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanis...mhaendel
Presented at the IRDiRC 2017 conference in Paris, Feb 9th, 2017 (http://irdirc-conference.org/). This talk reviews use of the Human Phenotype Ontology for phenotype comparisons against other patients, known diseases, and animal models for diagnostic discovery. It also discusses the new Phenopackets Exchange mechanism for open phenotypic data sharing.
www.monarchinitiative.org
www.phenopackets.org
www.human-phenotype-ontology.org
Bio-GraphIIn is a graph-based, integrative and semantically enabled repository for life science experimental data. It addresses the need for a system that supports retrospective data submissions, handles heterogeneous experimental data, and overcomes the fragmentation of existing data formats and databases. Bio-GraphIIn uses the Investigation/Study/Assay (ISA) framework and ontologies to semantically represent experimental metadata and enable rich queries across studies, with the goal of facilitating integrative data analysis.
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.
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
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.
Metagenomic Data Provenance and Management using the ISA infrastructure --- o...Alejandra Gonzalez-Beltran
Metagenomic Data Provenance and Management using the ISA infrastructure - overview, implementation patterns & software tools
Slides presented at EBI Metagenomics Bioinformatics course: http://www.ebi.ac.uk/training/course/metagenomics2014
The document discusses the ISA infrastructure, which provides a framework for tracking metadata in bioscience experiments from data collection to sharing in linked data clouds. The infrastructure includes a metadata syntax, open source software tools, and a user community. It allows annotation of experimental metadata, materials, and processes using ontologies to make semantics explicit and enable integration and knowledge discovery. The infrastructure is growing with over 30 public and private resources adopting it to facilitate standards-compliant sharing of investigations across life science domains.
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
RARE and FAIR Science: Reproducibility and Research ObjectsCarole Goble
Keynote at JISC Digifest 2015 on Reproducibility and Research Objects in Scholarly Communication
Includes hidden slides
All material except maybe the IT Crowd screengrab reusable
The document discusses the ISA infrastructure, which provides a standardized format (ISA-TAB) for experimental metadata and data exchange. It can be used across various domains like toxicology, systems biology, and nanotechnology. The Risa R package integrates experimental metadata with analysis and allows updating metadata. Nature Scientific Data is a new publication for describing valuable datasets. The ISA framework has been adopted by over 30 public and private resources and is growing in use for facilitating reuse of investigations in various life science domains. Toxicity examples include EU projects on predictive toxicology and a rat study of drug candidates. Questions can be directed to the ISA tools group.
ROHub is a digital library and management system for research objects (ROs). It enables scientists to create, manage, and share ROs, which are semantic aggregations of related scientific resources, annotations, and research context. ROHub provides APIs and a web portal for scientists to use throughout the research lifecycle. It stores ROs long-term to support reproducibility and allows for monitoring changes to assess quality.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
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.
An overview of Text and Data Mining (ContentMining) including live demonstrations. The fundamentals: discover, scrape, normalize , facet/index, analyze, publish are exemplified using the recent Zika outbreak. Mining covers textual and non-textual content and examples of chemistry and phylogenetic tress are given.
BioSharing.org - mapping the landscape of community standards, databases, dat...Alejandra Gonzalez-Beltran
This document summarizes Alejandra González-Beltrán's presentation on BioSharing.org, which maps the landscape of community standards, databases, and data policies. It discusses how BioSharing aims to help stakeholders make informed decisions for data interoperability through curating crowdsourcing information on existing standards and policies. It also describes how BioSharing integrates information from the MIBBI Project and is working with the MICheckout tool to help users create and use modular standards components.
Research Objects: more than the sum of the partsCarole Goble
Workshop on Managing Digital Research Objects in an Expanding Science Ecosystem, 15 Nov 2017, Bethesda, USA
https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem
Research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
A first step is to think of Digital Research Objects as a broadening out to embrace these artefacts or assets of research. The next is to recognise that investigations use multiple, interlinked, evolving artefacts. Multiple datasets and multiple models support a study; each model is associated with datasets for construction, validation and prediction; an analytic pipeline has multiple codes and may be made up of nested sub-pipelines, and so on. Research Objects (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described.
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.
Use of ContentMine tools on the Open Access subset of EuropePubMedCentral to discover new knowledge about the Zika virus.
Three slides have embedded movies - these do not show in slideshare and a first pass of this can be seen as a single file at https://vimeo.com/154705161
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.
The Human Phenotype Ontology (HPO) was developed to describe phenotypic abnormalities, aka, “deep phenotyping”, whereby symptoms and characteristic phenotypic findings (a phenotypic profile) are captured. The HPO has been utilized to great success for assisting computational phenotype comparison against known diseases, other patients, and model organisms to support diagnosis of rare disease patients. Clinicians and geneticists create phenotypic profiles based on clinical evaluation, but this is time consuming and can miss important phenotypic features. Patients are sometimes the best source of information about their symptoms that might otherwise be missed in a clinical encounter. However, HPO primarily use medical terminology, which can be difficult for patients and their families to understand. To make the HPO accessible to patients, we systematically added non-expert terminology (i.e., layperson terms) synonyms. Using semantic similarity, patient-recorded phenotypic profiles can be evaluated against those created clinically for undiagnosed patients to determine the improvement gained from the patient-driven phenotyping, as well as how much the patient phenotyping narrows the diagnosis. This patient-centric HPO can be utilized by all: in patient-centered rare disease websites, in patient community platforms and registries, or even to post one’s hard-to-diagnosed phenotypic profile on the Web.
Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanis...mhaendel
Presented at the IRDiRC 2017 conference in Paris, Feb 9th, 2017 (http://irdirc-conference.org/). This talk reviews use of the Human Phenotype Ontology for phenotype comparisons against other patients, known diseases, and animal models for diagnostic discovery. It also discusses the new Phenopackets Exchange mechanism for open phenotypic data sharing.
www.monarchinitiative.org
www.phenopackets.org
www.human-phenotype-ontology.org
Empowering patients by increasing accessibility to clinical terminologyNicole Vasilevsky
Flash talk at Medical Library Association Pacific Northwest Chapter meeting in Portland, OR on October 18, 2016.
http://pnc-mla.cloverpad.org/annual2016
Authors: Erin Foster, Mark Engelstad, Chris Mungall, Peter Robinson, Sebastian Kohler, Melissa Haendel and Nicole Vasilevsky
The Monarch Initiative: From Model Organism to Precision Medicinemhaendel
NIH BD2K all-hands meeting poster November 12, 2015.
Attempts at correlating phenotypic aspects of disease with causal genetic influences are often confounded by the challenges of interpreting diverse data distributed across numerous resources. New approaches to data modeling, integration, tooling, and community practices are needed to make efficient use of these data. The Monarch Initiative is an international consortium working on the development of shared data, tools, and standards to enable direct translation of integrated genotype, phenotype, and environmental data from human and model organisms to enhance our understanding of human disease. We utilize sophisticated semantic mapping techniques across a diverse set of standardized ontologies to deeply integrate data across species, sources, and modalities. Using phenotype similarity matching algorithms across these data enables disorder prediction, variant prioritization, and patient matching against known diseases and model organisms. These similarity algorithms form the core of several innovative tools. The Exomiser, which enables exome variant prioritization by combining pathogenicity, frequency, inheritance, protein interaction, and cross-species phenotype data. Our Phenotype Sufficiency tool provides clinicians the ability to compare patient phenotypic profiles using the Human Phenotype Ontology to determine uniqueness and specificity in support of variant prioritization. The PhenoGrid visualization widget illustrates phenotype similarity between patients, known diseases, and model organisms. Monarch develops models in collaboration with the community in support of the burgeoning genotype-phenotype disease research community. We have successfully used Exomiser to solve a number of undiagnosed patient cases in collaboration with the NIH Undiagnosed Disease Program. Ongoing development in coordination with the Global Alliance for Genetic Health (GA4GH) and other groups will catalyze the realization of our goal of a vital translational community focused on the collaborative application of integrated genotype, phenotype, and environmental data to human disease.
The Application of the Human Phenotype Ontology mhaendel
Presented at the II International Summer School for Rare Disease and Orphan Drug Registries, September 15-19, 2014, Organized by the National Centre for Rare Diseases
Istituto Superiore di Sanità (ISS), Rome, Italy.
Note the extensive contribution by many consortium members and partners listed in the acknowledgements slide.
Credit where credit is due: acknowledging all types of contributionsmhaendel
This is an update for COASP (http://oaspa.org/conference/) on the representation of attribution beyond authorship of a publication. Publications are proxies for the projects and people that area actually engaged in the work, and represent the dissemination aspect. How can we better understand the individual contributions and their impact? The openRIF, openVIVO and FORCE11 Attribution WG efforts aim to represent scholarship in a computationally tractable manner so as to enable credit and evaluation of all types of scholarly contributions.
On the Reproducibility of Science: Unique Identification of Research Resourc...Nicole Vasilevsky
Poster presentation at the Data Information Literacy Symposium at Purdue University in Indiana, Sept. 2013. This study is published here: https://peerj.com/articles/148/
The document summarizes data science education resources developed by researchers at Oregon Health & Science University. It describes the challenges of managing vast amounts of biomedical data and the goal of providing training to address this issue. The team developed skills courses and open educational resources (OERs) on topics across the data science life cycle. Courses included introductory, advanced, and targeted workshops. OER modules covered a range of data science topics and mapped to competencies for health sciences librarians. The team seeks to disseminate the resources broadly while addressing challenges around customization for different users and protection of intellectual property.
Couture Curricula - BD2K Data Science Tailored to Your NeedsNicole Vasilevsky
Poster presentation at Force2016 (https://www.force11.org/meetings/force2016) describing Big Data to Science (BD2K) efforts at Oregon Health & Science University.
This document summarizes roles for libraries in providing research data management services. It describes data services at the University of Oregon Library including consultations, education workshops, and developing data management web pages. It discusses support for documentation provided by the University of Idaho Library through instruction sessions, research consultations, and emphasizing good documentation practices. It outlines data management trainings provided by Oregon Health & Science University Library including workshops with researchers, individual consultations, and developing new data services.
Enhancing the Human Phenotype Ontology for Use by the LaypersonNicole Vasilevsky
Presentation at the International Conference on Biological Ontology & BioCreative, August 1-4, 2016, Corvallis, Oregon, USA.
Abstract
In rare or undiagnosed diseases, physicians rely upon genotype and phenotype information in order to compare abnormalities to other known cases and to inform diagnoses. Patients are often the best sources of information about their symptoms and phenotypes. The Human Phenotype Ontology (HPO) contains over 12,000 terms describing abnormal human phenotypes. However, the labels and synonyms in the HPO primarily use medical terminology, which can be difficult for patients and their families to understand. In order to make the HPO more accessible to non-medical experts, we systematically added new synonyms using non-expert terminology (i.e., layperson terms) to the existing HPO classes or tagged existing synonyms as layperson. As a result, the HPO contains over 6,000 classes with layperson synonyms.
Creating Sustainable Communities in Open Data Resources: The eagle-i and VIVO...Robert H. McDonald
This is the slidedeck for my ACRL 2015 TechConnect Presentation with Nicole Vasilevsky (OHSU). For more on the program see - <a>http://bit.ly/1xcQbCr</a>.
The Role of Libraries in Data Management and CurationNicole Vasilevsky
The Role of Libraries in Data Management and Curation, presented at the American Library Association conference in Las Vegas, NV, 07/29/14.
Abstract:
As increasing amounts of data are being generated, applying best practices in handling data is important, and librarians are well poised to assist users. During this session, we will discuss the role of libraries in assisting with data management, application of metadata, ontologies, data standards, and the publication of data in repositories and on the Semantic Web. This talk will describe best data practices and engage the attendees in interactive activities to demonstrate these principles.
Why the world needs phenopacketeers, and how to be onemhaendel
Keynote presented at the the Ninth International Biocuration Conference Geneva, Switzerland, April 10-14, 2016
The health of an individual organism results from complex interplay between its genes and environment. Although great strides have been made in standardizing the representation of genetic information for exchange, there are no comparable standards to represent phenotypes (e.g. patient disease features, variation across biodiversity) or environmental factors that may influence such phenotypic outcomes. Phenotypic features of individual organisms are currently described in diverse places and in diverse formats: publications, databases, health records, registries, clinical trials, museum collections, and even social media. In these contexts, biocuration has been pivotal to obtaining a computable representation, but is still deeply challenged by the lack of standardization, accessibility, persistence, and computability among these contexts. How can we help all phenotype data creators contribute to this biocuration effort when the data is so distributed across so many communities, sources, and scales? How can we track contributions and provide proper attribution? How can we leverage phenotypic data from the model organism or biodiversity communities to help diagnose disease or determine evolutionary relatedness? Biocurators unite in a new community effort to address these challenges.
Open science curriculum for students, June 2019Dag Endresen
Living Norway seminar on Open Science in Trondheim 12th June 2019.
https://livingnorway.no/2019/04/26/living-norway-seminar-2019/
https://www.gbif.no/events/2019/living-norway-seminar.html
This document discusses open science and research. It defines open science as making research transparent and accessible at all stages of the research process through open access, open data, open source code and open notebooks. It outlines the key elements of open science like open access publishing, open data repositories, open source software, citizen science and more. It also discusses open science initiatives in Europe, Africa and South Africa and the need for urgent policy actions to promote open science.
The NIDDK Information Network (dkNET) portal facilitates access to research resources relevant to metabolic, digestive, and kidney diseases. dkNET functions as a search engine to discover resources across millions of records in hundreds of biomedical databases. It provides the ability to search across data sources, a resource registry, and the literature. dkNET aims to make it easy to find relevant research resources through its concept-based search interface.
The document discusses methodologies for sharing long-tail data and what has been learned. It notes that unique identifiers (PIDs) are important for identifying entities across contexts. Standards like MINI and common data elements (CDEs) help ensure data is findable, accessible, and reusable. The Neuroscience Information Framework (NIF) aggregates ontologies and searches over 200 data sources to organize information. What we have learned is that data should be in repositories, not personal servers; people are key to these efforts; and resources should be comprehensive and support each other to advance open data sharing.
ContentMining for France and Europe; Lessons from 2 years in UKpetermurrayrust
This document summarizes Peter Murray-Rust's presentation on two years of content mining in the UK and lessons for France and Europe. Some key points discussed include:
- Content mining can save lives by enabling researchers to search literature and find past warnings, as in the case of Ebola.
- However, publishers like Elsevier and Wiley have stopped researchers' content mining efforts, hampering their work.
- France, Europe and the UK must actively support content mining through funding, tools, training and protecting researchers from restrictive publishers.
- Examples are given of ContentMine fellows' projects mining literature on topics like weevil-plant associations, cell migration and depression in animals.
Institutional repositories play an important role in making scholarly research openly accessible and increasing its impact. They house research outputs like journal articles, theses, and other works. Repositories enhance the visibility of the hosting institution and facilitate further research by providing access to latest information. There are currently over 1,400 repositories worldwide adhering to interoperability standards so their contents can be indexed together. Repositories provide benefits like increased citations and downloads for authors, and give institutions metrics to assess their research programs. For these reasons, more Indian institutions are establishing their own open access repositories.
An open science introduction. Olinfer 18, La havana, Cuba 12-14 nov 2018pascal aventurier
Open Science is the practice of conducting science openly, where research data, lab notes and processes are freely available under terms of reuse. It promotes collaboration and contributions from others. The document discusses benefits like increased verification, reduced duplication and innovation. It also covers topics like open access, research data management, data repositories, and the FAIR principles. The goal of Open Science is greater efficiency, transparency and interdisciplinary work.
Leveraging the power of the web - Open Repositories 2015Kaitlin Thaney
This document discusses leveraging the power of the open web for science. It notes that current systems are creating friction despite original intentions of openness. It advocates for building capacity for open, web-enabled research through infrastructure, tools, standards, incentives and training to support reuse, collaboration and interoperability. The goal is to foster sustainable communities of practitioners doing open science.
Towards Open Methods: Using Scientific Workflows in LinguisticsRichard Littauer
The document discusses how scientific workflows can be used in linguistics research to automate processing, analysis, and management of linguistic data. Workflows make research more reproducible by documenting methods. They could allow accessing and downloading open linguistic databases. Hypothetical examples show workflows linking text characters to dictionary definitions. Workflows may help standardize part-of-speech tags. Tracking workflows early can help share methods and ensure reproducibility.
Open Access and Research Communication: The Perspective of Force11Maryann Martone
Presentation at the National Federation of Advanced Information Services Workshop: Open Access to Published Research: Current Status and Future Directions, Philadelphia, PA USA November 22, 2013
Museum collections as research data - October 2019Dag Endresen
This document discusses how natural history museums can embrace open science principles by making their collections openly available as research data. It provides context on initiatives like GBIF and DiSSCo that aim to publish biodiversity data according to common standards. While only around 5-10% of specimen records are currently digitized globally, the push for open access to publicly funded research means that museums need to develop new approaches to remain relevant providers of scientific resources. Open science practices like data sharing, citation and reuse can help address reproducibility issues and enable new discovery.
Reuse of Structured Data: Semantics, Linkage, and Realizationandrea huang
In order to increase the reuse value of existing datasets, it is now becoming a general practice to add semantic links among the records in a dataset, and to link these records to external resources. The enriched datasets are published on the web for both human and machine to consume and re‐purpose.
In this paper, we make use of publicly available structured records from a digital archive catalogue, and we demonstrate a principled approach to converting the records into semantically rich and interlinked resources for all to reuse. While exploring the various issues involved in the process of reusing and
re‐purposing existing datasets, we review the recent progress in the field of Linked Open Data (LOD), and examine twelve well‐known knowledge bases built with a Linked Data approach.
We also discuss the general issues of data quality, metadata vocabularies, and data provenance. The concrete outcome
of this research work is the following:
(1) a website data.odw.tw that hosts more than 840,000
semantically enriched catalogue records across multiple subject areas,
(2) a lightweight ontology voc4odw for describing data reuse and provenance, among others, and
(3) a set of open source software tools available to all to perform the kind of data conversion and enrichment we did in this research. We have used and extended CKAN (The Comprehensive Knowledge Archive Network) as a platform to host and publish Linked Data. Our extensions to CKAN is open sourced as well.
As the records we drawn from the originally catalogue are released under the Creative Commons licenses, the semantically enriched resources we now re‐publish on the Web are free for all to reuse as well.
Text (personal views position statement) to accompany presentation on what research infrastructures really need for data, XLDB-Europe, 8-10th June 2011, Edinburgh
ONTOLOGY SERVICE CENTER: A DATAHUB FOR ONTOLOGY APPLICATIONIJwest
With the growth of data-oriented research in humanities, a large number of research datasets have been
created and published through web services. However, how to discover, integrate and reuse these distributed
heterogeneous research datasets is a challenging task. Ontology is the soul between series digital humanities
resources, which provides a good way for people to discover and understand these datasets. With the release
of more and more linked open data and knowledge bases, a large number of ontologies have been produced
at the same time. These ontologies have different publishing formats, consumption patterns, and interactions
ways, which are not conductive to the user’s understanding of the datasets and the reuse of the ontologies.
The Ontology Service Center platform consists of Ontology Query Center and Ontology Validation Center,
mainly using linked data and ontology-based technologies. The Ontology Query Center realizes the functions
of ontology publishing, querying, data interaction and online browsing, while the Ontology Validation
Center can verify the status of using certain ontologies in the linked datasets. The empirical part of the paper
uses the Confucius portrait as an example of how OSC can be used in the semantic annotation of images. In
a word, the purpose of this paper is to construct the applied ecology of ontology to promote the development
of knowledge graphs and the spread of ontology.
ONTOLOGY SERVICE CENTER: A DATAHUB FOR ONTOLOGY APPLICATION dannyijwest
With the growth of data-oriented research in humanities, a large number of research datasets have been
created and published through web services. However, how to discover, integrate and reuse these distributed
heterogeneous research datasets is a challenging task. Ontology is the soul between series digital humanities
resources, which provides a good way for people to discover and understand these datasets. With the release
of more and more linked open data and knowledge bases, a large number of ontologies have been produced
at the same time
Metadata as Linked Data for Research Data Repositoriesandrea huang
“Every man has his own cosmology and who can say that his own is right.” said by Einstein. This is also true when we come to understand data semantics that one data may be different interpreted by different data creators, curators and re-users. Then, how do we build a better research data repository?
We start with the point made by Willis, C., Greenberg, J., & White, H. (2012) that the metadata of research data increases the access to and reuse of the data. And Stanford, Harvard, and Cornell believe the use of linked data technologies is a promising method to gather contextual information about research resources.
To look for inspiration tools that can meet the urgent needs of innovative solutions providing feature-rich services for helping data publishing such as visualization, validation & reuse in different applications by research repositories (Assante, et.al, 2016), the CKAN (Comprehensive Knowledge Archive Network) as a major solution that makes linked metadata available, citable, and validated becomes our first choice.
Original file: http://m.odw.tw/u/odw/m/metadata-as-linked-data-for-research-data-repositories/
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Research resources: curating the new eagle-i discovery system
1. Research
resources:
cura,ng
the
new
eagle-‐i
discovery
system
Nicole
Vasilevsky1,
Tenille
Johnson2,
Karen
Corday2,
Carlo
Torniai1,
Ma:hew
Brush1,
Sco:
Hoffmann1,
Erik
Segerdell1,
Melanie
L.
Wilson1,
Christopher
J.
Shaffer1,
David
Robinson1,
and
Melissa
A.
Haendel1**
1
Oregon
Health
&
Science
University,
Library,
Portland,
Oregon
2
Harvard
Medical
School,
Center
for
Biomedical
InformaTcs,
Cambridge,
Massachuse:s
The
Ideal
Scholarly
Research
Cycle
o Researchers
produce
data
and
resources
that
lead
to
publicaTons.
Resources
and
data
1
Public
repositories
• eagle-‐i
• MODs
• NIF
• Entrez
Gene...
Researcher
2
Professional
networking:
• VIVO
• Harvard
Profiles
• LinkedIn…
o Published
data
informs
researchers
of
new
experimental
designs.
Publica,ons
Public
repositories
• PubMed
• Google
Scholar
• Mendeley…
3
o InformaTon
about
researchers,
resources,
data,
and
published
papers
is
stored
in
various
public
repositories.
How
can
we
make
this
cycle
more
efficient?
Provide
scien,sts
with
the
tools
they
need
to
record
their
resources
during
the
course
of
research
During
the
course
of
collecTng
informaTon
about
research
resources,
which
many
laboratories
were
willing
to
share,
we
discovered
that
while
larger
core
faciliTes
rouTnely
have
resource
and
workflow
organizaTon
strategies,
primary
research
labs
very
rarely
do.
This
creates
barriers
to
reproducing
experiments
as
well
as
to
publishing
and
sharing
resources.
Giving
labs
organizaTonal
tools
can
help
address
these
issues.
The
eagle-‐i
workflow
Seman,c
Web
Entry
and
Edi,ng
Tool
Data
Cura,on
at
eagle-‐i
Data
collecTon
CuraTon
guidelines
Decision
trees
BiocuraTon
User
interface
design
SWEET
Search
applicaTon
Components
of
the
eagle-‐i
annotaTon
tool,
known
by
the
acronym
SWEET,
are
generated
directly
from
the
eagle-‐i
ontology.
The
SWEET
contains
both
annotaTon
fields
that
are
auto-‐populated
using
the
ontology
(purple
box)
and
free
text
(orange
box).
Entrez
Gene
ID
links
out
to
the
NCBI
database
(red
box).
Fields
in
the
SWEET
can
also
link
records
to
other
records
in
the
repository,
such
as
related
publicaTons
or
documentaTon
(blue
box).
Users
can
request
new
terms
be
added
to
the
ontology
using
the
Term
Request
field.
SPARQL
query
tool
for
QA
Ontology
development
Google
code
Ontology
Browser
Development
of
data
curaTon
pracTces
at
eagle-‐i
depended
on
the
Resource
NavigaTon
team
for
data
collecTon,
the
CuraTon
team
for
ontology
development
and
data
QA,
and
the
SoWware
team
for
user
interface
design
in
an
iteraTve
process.
Tools
and
documentaTon
were
developed
to
assist
users
and
team
members
with
each
of
these
processes.
Ontological
modeling
of
research
resources
Decision
trees
assist
with
data
entry
and
annota,on
of
resources
Denotes
quesTons
eliciTng
informaTon
for
annotaTon.
AnnotaTon
tool
InsTtuTonal
repositories
Denotes
required
annotaTons.
Denotes
redirecTon
to
a
different
decision
tree.
Denotes
drop
down
or
annotaTon
field
examples.
Denotes
higher
value/priority
annotaTons.
Denotes
medium
value/
priority
annotaTons.
Denotes
lower
value/priority
annotaTons.
erms
new
t
Biocurator
t
eques
R
Ontology
Request
resources
Researcher
Search
applicaTon
Lessons
Learned
eagle-‐i
parTcipaTng
lab
The
goal
of
eagle-‐i
is
to
make
scienTfic
research
resources
more
visible
via
a
federated
network
of
insTtuTonal
repositories.
Using
an
ontology-‐driven
approach
for
biomedical
resource
annotaTon
and
discovery,
the
Network
currently
includes
resources
from
23
insTtuTons.
www.eagle-‐i.net
Open
source
so;ware
available
at:
h=ps://open.med.harvard.edu/display/eaglei/So;ware
eagle-‐i
Ontology
GoogleCode:
h=p://code.google.com/p/eagle-‐i/
Major
eagle-‐i
resource
types
are
shown
as
dark
boxes.
Persons
and
laboratories
play
a
central
role
in
eagle-‐i.
Classes
and
properTes
are
reused
from
pre-‐exisTng
ontologies
or
created
de
novo.
Examples
of
some
of
the
relaTons
between
the
classes
are
indicated.
New
ini,a,ves
with
eagle-‐i
NCATS
has
funded
two
new
projects
that
leverage
eagle-‐i
to
further
translaTonal
science.
The
first
project
aims
to
expand
the
breadth,
quality,
and
discoverability
of
data
about
people
and
resources
by
harmonizing
the
ontologies
of
VIVO,
eagle-‐i,
and
ShareCenter
(www.ctsaconnect.org).
The
second
project
aims
to
expand
the
eagle-‐i
plakorm
to
new
CTSA
insTtuTons,
and
to
publish
resources
as
Linked
Open
Data.
• Balance
the
data
you
need
with
the
data
you
can
get
• Documenta,on
and
quality
assurance
are
itera,ve
• Tools
and
technology
choices
depend
on
the
above
Acknowledgements
**We,
the
authors,
represent
the
members
and
leaders
of
the
eagle-‐i
CuraTon
team,
and
describe
some
of
the
efforts
and
products
of
all
teams
involved
in
the
development
of
the
eagle-‐i
discovery
system.
We
would
like
to
thank
the
Resource
NavigaTon
team,
led
by
Richard
Pearse;
SoWware
Build
team,
led
by
Daniela
Bourges;
and
Project
Management
team,
led
by
Julie
McMurry.
We
would
also
like
to
thank
Jackie
Wirz.
We
gratefully
acknowlege
NIH
award
#U24RR029825.