This document discusses the agINFRA project's efforts to enhance interoperability between agricultural data sources by developing a linked data framework for germplasm data. The agINFRA Germplasm Working Group aims to identify relevant standards, analyze existing schemas and vocabularies, and propose recommendations for exposing germplasm resources as linked open data. Key outcomes include a dossier of germplasm information and engagement with stakeholders. The proposed methodology involves defining a base schema, publishing local classifications as linked data, and linking data from different sources using common vocabularies. Implementation plans include publishing germplasm vocabularies and phenotypic data in 2014.
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
Presentation about the agINFRA Germplasm Working Group (http://wiki.aginfra.eu/index.php/Germplasm_Working_Group). Presented during Session 1 of the 1st International e-Conference on Germplasm Data Interoperability (https://sites.google.com/site/germplasminteroperability/)
re3data.org – Registry of Research Data RepositoriesHeinz Pampel
Heinz Pampel | GFZ German Research Centre for Geosciences, LIS
Maxi Kindling | Humboldt-Universität zu Berlin, Berlin School of Library and Information Science Frank Scholze | Karlsruhe Institute of Technology, KIT Library
RDA-Deutschland-Treffen 2015| Potsdam, November 26, 2015
Open Source Tools Facilitating Sharing/Protecting Privacy: Dataverse and Data...Merce Crosas
Presentation for the NFAIS Webinar series: Open Data Fostering Open Science: Meeting Researchers' Needs
http://www.nfais.org/index.php?option=com_mc&view=mc&mcid=72&eventId=508850&orgId=nfais
FAIR Workflows and Research Objects get a Workout Carole Goble
So, you want to build a pan-national digital space for bioscience data and methods? That works with a bunch of pre-existing data repositories and processing platforms? So you can share FAIR workflows and move them between services? Package them up with data and other stuff (or just package up data for that matter)? How? WorkflowHub (https://workflowhub.eu) and RO-Crate Research Objects (https://www.researchobject.org/ro-crate) that’s how! A step towards FAIR Digital Objects gets a workout.
Presented at DataVerse Community Meeting 2021
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
Presentation about the agINFRA Germplasm Working Group (http://wiki.aginfra.eu/index.php/Germplasm_Working_Group). Presented during Session 1 of the 1st International e-Conference on Germplasm Data Interoperability (https://sites.google.com/site/germplasminteroperability/)
re3data.org – Registry of Research Data RepositoriesHeinz Pampel
Heinz Pampel | GFZ German Research Centre for Geosciences, LIS
Maxi Kindling | Humboldt-Universität zu Berlin, Berlin School of Library and Information Science Frank Scholze | Karlsruhe Institute of Technology, KIT Library
RDA-Deutschland-Treffen 2015| Potsdam, November 26, 2015
Open Source Tools Facilitating Sharing/Protecting Privacy: Dataverse and Data...Merce Crosas
Presentation for the NFAIS Webinar series: Open Data Fostering Open Science: Meeting Researchers' Needs
http://www.nfais.org/index.php?option=com_mc&view=mc&mcid=72&eventId=508850&orgId=nfais
FAIR Workflows and Research Objects get a Workout Carole Goble
So, you want to build a pan-national digital space for bioscience data and methods? That works with a bunch of pre-existing data repositories and processing platforms? So you can share FAIR workflows and move them between services? Package them up with data and other stuff (or just package up data for that matter)? How? WorkflowHub (https://workflowhub.eu) and RO-Crate Research Objects (https://www.researchobject.org/ro-crate) that’s how! A step towards FAIR Digital Objects gets a workout.
Presented at DataVerse Community Meeting 2021
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
FAIRy stories: tales from building the FAIR Research CommonsCarole Goble
Plenary Lecture Presented at INCF Neuroinformatics 2019 https://www.neuroinformatics2019.org
Title: FAIRy stories: tales from building the FAIR Research Commons
Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any kind of Research Object is a mantra; a method; a meme; a myth; a mystery. For the past 15 years I have been working on FAIR in a range of projects and initiatives in the Life Sciences as we try to build the FAIR Research Commons. Some are top-down like the European Research Infrastructures ELIXIR, ISBE and IBISBA, and the NIH Data Commons. Some are bottom-up, supporting FAIR for investigator-led projects (FAIRDOM), biodiversity analytics (BioVel), and FAIR drug discovery (Open PHACTS, FAIRplus). Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. There are villains and heroes. Some have happy endings; all have morals.
Toward Semantic Representation of Science in Electronic Laboratory Notebooks ...Stuart Chalk
An electronic laboratory Notebook (ELN) can be characterized as a system that allows scientists to capture the data and resources used in performing scientific experiments. This allows users to easily organize and find their data however, little information about the scientific process is recorded.
In this paper we highlight the current status of progress toward semantic representation of science in ELNs.
CEDAR is one of the NIH-BD2K centres, more here: http://metadatacenter.org/
This presentation is part of a BoF on Biomedical Data I co-organized at the RDA Plenary 5; see the presentations on NIH BD2K, bioCADDIE (other NIH BD2K centre I am in), ELIXIR and Force11 here: https://rd-alliance.org/biomedical-data-p5-bof-session.html
PMR metabolomics and transcriptomics database and its RESTful web APIs: A dat...Araport
PMR database is a community resource for deposition and analysis of metabolomics data and related transcriptomics data. PMR currently houses metabolomics data from over 25 species of eukaryotes. In this talk, we introduce PMRs RESTful web APIs for data sharing, and demonstrate its applications in research using Araport to provide Arabidopsis metabolomics data.
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.
Software development should build on the successful work of others. The DMPTool helps researchers with data management planning, but what about other phases of the data life cycle? In this webinar, we will discuss what software integration with the DMPTool might look like, and why it is important. Topics include:
1. Background: why tools integration is important; why we are talking about this in terms of the DMPTool.
2. Details and plans for DMPTool2 regarding software integration and compatibility.
3. Future possibilities for software integration for DMPTool2
4. Example of successful integration of tools: work at the Center for Open Science.
bioCADDIE Webinar: The NIDDK Information Network (dkNET) - A Community Resear...dkNET
The NIDDK Information Network (dkNET; http://dknet.org) is a open community resource for basic and clinical investigators in metabolic, digestive and kidney disease. dkNET’s portal facilitates access to a collection of diverse research resources (i.e. the multitude of data, software tools, materials, services, projects and organizations available to researchers in the public domain) that advance the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This webinar was presented by dkNET principle investigator Dr. Jeffrey Grethe.
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.
Agro-Know & the European agricultural research information ecosystemNikos Manouselis
Slides of my talk to members of the Agricultural Information Institute (AII) of the Chinese Academy of Agricultural Sciences (CAAS), on September 19th, 2014.
FAIRy stories: the FAIR Data principles in theory and in practiceCarole Goble
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
FAIRy stories: tales from building the FAIR Research CommonsCarole Goble
Plenary Lecture Presented at INCF Neuroinformatics 2019 https://www.neuroinformatics2019.org
Title: FAIRy stories: tales from building the FAIR Research Commons
Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any kind of Research Object is a mantra; a method; a meme; a myth; a mystery. For the past 15 years I have been working on FAIR in a range of projects and initiatives in the Life Sciences as we try to build the FAIR Research Commons. Some are top-down like the European Research Infrastructures ELIXIR, ISBE and IBISBA, and the NIH Data Commons. Some are bottom-up, supporting FAIR for investigator-led projects (FAIRDOM), biodiversity analytics (BioVel), and FAIR drug discovery (Open PHACTS, FAIRplus). Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. There are villains and heroes. Some have happy endings; all have morals.
Toward Semantic Representation of Science in Electronic Laboratory Notebooks ...Stuart Chalk
An electronic laboratory Notebook (ELN) can be characterized as a system that allows scientists to capture the data and resources used in performing scientific experiments. This allows users to easily organize and find their data however, little information about the scientific process is recorded.
In this paper we highlight the current status of progress toward semantic representation of science in ELNs.
CEDAR is one of the NIH-BD2K centres, more here: http://metadatacenter.org/
This presentation is part of a BoF on Biomedical Data I co-organized at the RDA Plenary 5; see the presentations on NIH BD2K, bioCADDIE (other NIH BD2K centre I am in), ELIXIR and Force11 here: https://rd-alliance.org/biomedical-data-p5-bof-session.html
PMR metabolomics and transcriptomics database and its RESTful web APIs: A dat...Araport
PMR database is a community resource for deposition and analysis of metabolomics data and related transcriptomics data. PMR currently houses metabolomics data from over 25 species of eukaryotes. In this talk, we introduce PMRs RESTful web APIs for data sharing, and demonstrate its applications in research using Araport to provide Arabidopsis metabolomics data.
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.
Software development should build on the successful work of others. The DMPTool helps researchers with data management planning, but what about other phases of the data life cycle? In this webinar, we will discuss what software integration with the DMPTool might look like, and why it is important. Topics include:
1. Background: why tools integration is important; why we are talking about this in terms of the DMPTool.
2. Details and plans for DMPTool2 regarding software integration and compatibility.
3. Future possibilities for software integration for DMPTool2
4. Example of successful integration of tools: work at the Center for Open Science.
bioCADDIE Webinar: The NIDDK Information Network (dkNET) - A Community Resear...dkNET
The NIDDK Information Network (dkNET; http://dknet.org) is a open community resource for basic and clinical investigators in metabolic, digestive and kidney disease. dkNET’s portal facilitates access to a collection of diverse research resources (i.e. the multitude of data, software tools, materials, services, projects and organizations available to researchers in the public domain) that advance the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This webinar was presented by dkNET principle investigator Dr. Jeffrey Grethe.
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.
Agro-Know & the European agricultural research information ecosystemNikos Manouselis
Slides of my talk to members of the Agricultural Information Institute (AII) of the Chinese Academy of Agricultural Sciences (CAAS), on September 19th, 2014.
FAIRy stories: the FAIR Data principles in theory and in practiceCarole Goble
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
The swings and roundabouts of a decade of fun and games with Research Objects Carole Goble
Research Objects and their instantiation as RO-Crate: motivation, explanation, examples, history and lessons, and opportunities for scholarly communications, delivered virtually to 17th Italian Research Conference on Digital Libraries
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
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
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/
FAIRsharing presentation at the Japan Science and Technology AgencyPeter McQuilton
A 30 minute seminar presented at the National Bioscience Database Center, part of the Japanese Science and Technology Agency, based in Tokyo, Japan. This presentation covers the FAIR Principles, the aims, methodology and use of FAIRsharing, related projects such as Bioschemas, and international initiatives such as ELIXIR and EOSC.
Researchers require infrastructures that ensure a maximum of accessibility, stability and reliability to facilitate working with and sharing of research data. Such infrastructures are being increasingly summarised under the term Research Data Repositories (RDR). The project re3data.org – Registry of Research Data Repositories – began to index research data repositories in 2012 and offers researchers, funding organisations, libraries and publishers an overview of the heterogeneous research data repository landscape. In December 2014 re3data.org listed more than 1,030 research data repositories, which are described in detail using the re3data.org schema (http://dx.doi.org/10.2312/re3.003). Information icons help researchers to identify easily an adequate repository for the storage and reuse of their data. This talk describes the heterogeneous RDR landscape and presents a typology of institutional, disciplinary, multidisciplinary and project-specific RDR. Further, it outlines the features of re3data. org and it shows current developments for integration into data management planning tools and other services.
By the end of 2015 re3data.org and Databib (Purdue University, USA) will merge their services, which will then be managed under the auspices of DataCite. The aim of this merger is to reduce duplication of effort and to serve the research community better with a single, sustainable registry of research data repositories. The talk will present this organisational development as a best practice example for the development of international research information services.
The Diversity of Biomedical Data, Databases and Standards (Research Data Alli...Peter McQuilton
A 10 minute presentation given in Denver (CO) on the 15th September as part of the IG Elixir Bridging Force, WG Biosharing Registry,WG Data Type Registries,WG Metadata Standards Catalog joint session of the Research Data Alliance 8th Plenary (part of International Data Week).
This presentation covers the proliferation of data, databases, and data standards in biomedicine, and how BioSharing can help inform and educate users on this landscape and relationships between data, databases and data standards.
Presentation delivered during the MEDHackathon 2016 Conference at Patras, Greece (13/7/2016). The presentation provides an overview of open data in agriculture and presents the use case of NEUROPUBLIC as a SME making use of open data for its commercial smart farming services.
Agricultural Data Interest Group & Wheat Data Working Group of RDAVassilis Protonotarios
Presentation delivered during the "Engagement in RDA from Southern-Eastern Europe, Mediterranean and Caucasus region" Workshop. 25/6/2015, Athens, Greece
Presentation delivered during the Introductory Course: "Introduction to agricultural & food safety datasets and semantic technologies" (http://irss.iit.demokritos.gr/2014/hackathon/introductory_course) of the SemaGrow 2nd Hackathon (http://wiki.agroknow.gr/agroknow/index.php/SemaGrow_Hackathon)
4/7/2014, NCSR Demokritos, Athens, Greece
Seeding organic agriculture courses on Moodle: the agriMoodle CaseVassilis Protonotarios
Presentation on agriMoodle delivered at the "Life for Agriculture - Agriculture for Life" international Conference.
6/6/2014, USAMVB, Bucharest, Romania. More info at http://agricultureforlife.usamv.ro/index.php/en/
Presentation made in the context of the FAO AIMS Webinar titled “Knowledge Organization Systems (KOS): Management of Classification Systems in the case of Organic.Edunet” (http://aims.fao.org/community/blogs/new-webinaraims-knowledge-organization-systems-kos-management-classification-systems)
21/2/2014
Presentation of some of the major germplasm data sources, including aggregators, networks and individual data providers. Information based on the agINFRA Dossier on Germplasm Data sources (available at http://wiki.aginfra.eu/index.php/Germplasm_Working_Group)
Presented during Session 3 of the 1st International e-Conference on Germplasm Data Interoperability (https://sites.google.com/site/germplasminteroperability/)
Presentation of the two agINFRA Germplasm data sources (CGRIS, China and CRA, Italy) and the metadata used for the description of their germplasm accessions. Presented during Session 2 of the 1st International e-Conference on Germplasm Data Interoperability (https://sites.google.com/site/germplasminteroperability/)
Presentation delivered during the Introductory Course to Big Data in Agriculture. 29/11/2013, NCSR Demokritos, Athens, Greece.
The presentation is heavily based on the report titled “Big Data Now: 2012 Edition", by O’Reilly Media, Inc.
More info about the event: http://wiki.agroknow.gr/agroknow/index.php/Athens_Green_Hackathon_2013
Presentation delivered during the Workshop on Agricultural Education, Methods, Practices and Technologies" (AgEdWS12). Pollenzo, Bra, Italy, 25/10/2012
Introducing a content integration process for a federation of agricultural in...Vassilis Protonotarios
Presentation titled "Introducing a content integration process for a federation of agricultural institutional repositories". MTSR 2011, Izmir, Turkey, 12/10/2011
Presentation titled "Designing a Training Session for Public Authorities". Rural Inclusion Workshop / EFITA 2011 Conference. Prague, Czech Republic 11-14/7/2011.
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.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
How libraries can support authors with open access requirements for UKRI fund...
Global RDF Descriptors for Germplasm Data
1. Global RDF Descriptors for
Germplasm Data
Vassilis Protonotarios
Agricultural Biotechnologist, PhD
Agro-Know, Greece
RDA 3° Plenary Meeting, Dublin, Ireland
Agricultural Data Interoperability Group Meeting
3. Connecting the pieces
agINFRA Germplasm
Working Group
Agricultural Data
Interoperability IG
Germplasm Data
Analysis
Agricultural linked
data layer
4. The agINFRA project
• A project funded under the FP7 program of EC
• Consortium with expertise on
– Technology / infrastructures
– Data / data management
Combined to facilitate agricultural data sharing
More info at:
www.aginfra.eu
5. The agINFRA project
• Aims to enhance the interoperability between
the agricultural data sources
– Data sharing by
• Metadata aggregation & linking data
• Design and deploy the linked ag-data framework
– Methodology for linking data
– Provide the infrastructure needed
• Both cloud- and grid-based services
• Tools, APIs etc.
6. agINFRA major data types
agINFRA
Bibliographic
Agri Statistics
& Economics
Educational
Germplasm
Soil data
Profiles
Raw data
Other?
9. The issue ?
• Heterogeneity!
– Data types
– Data formats
– Data management workflows
– Standards used
– Metadata exposure options
– ….
• Lack of connectivity with other data sources
11. The Germplasm Working Group
• Created in the context of the agINFRA project
• Initially included agINFRA stakeholders
– now expanded to host all stakeholders
• The group is NOT a group of experts on
germplasm data!
12. The scope of the agINFRA
Germplasm WG
• Enable/enhance interoperability between
germplasm databases
– By developing the services for
• exchanging their data and
• delivering their data to other partners
• Focusing on three actions:
1. Identify
2. Organize & Reuse
3. Propose
13. agINFRA Germplasm WG objectives
• IDENTIFY: collect all information related to germplasm
data
– People/groups
– Namespaces (metadata, KOS)
– Standards
– Workflows
– Events
• ORGANIZE & REUSE: engage all stakeholders & available
resources, analyze existing standards , facilitate
collaboration
• PROPOSE: linked data framework to connect data
sources
• facilitate data sharing between germplasm data sources
16. The Germplasm WG wiki
• Central point of reference
• Freely accessible (no login required)
http://wiki.aginfra.eu/index.php/Germplasm_Working_Group
17. Key outcomes of the group (1)
Dossier on Germplasm Information:
– Major programs
– Major information systems and services
– agINFRA germplasm data sources (CGRIS & CRA)
– Core standards for germplasm information
– Plant nomenclature, taxonomies and ontologies
– Plant genomic resources
– Related references and links
• Freely available from the Germplasm Group wiki
19. Key outcomes of the group (3)
• Speakers from key players in the biodiversity
data field
– GBIF, EURISCO, GENESYS, CGIAR, EGFAR, CRA etc.
• Aimed to provide the basis for the linked
germplasm data framework
21. DwC-G KOSs
• Germplasm Term Vocabulary
• A vocabulary of terms for describing and annotating
germplasm information resources
– http://purl.org/germplasm/germplasmTerm#TERM
• Germplasm Type vocabulary
• List of controlled values for some of the germplasm terms
– http://purl.org/germplasm/germplasmType#TYPE
• Germplasm ontology
• to digitize and provide persistent identifiers for the terms
contained within the PGR Descriptors publications
– http://purl.org/germplasm/ontology
23. DwC-SW
• An ontology using Darwin Core terms to make it possible to
describe biodiversity resources in the Semantic Web.
https://code.google.com/p/darwin-sw
24. Bioversity Crop Descriptors
• Crop Descriptors
– Provide an international format and a universally understood
language for plant genetic resources data.
– They are targeted at farmers, curators, breeders, scientists
and users and facilitate the exchange and use of resources.
– Information includes such details as the plant's height,
flowering patterns and ancestral history.
• FAO/Bioversity Multi-crop Passport Descriptors (MCPD)
– Originally published in 2001
– widely used as the international standard to facilitate
germplasm passport information exchange.
– Now expanded to include emerging documentation needs,
this new version resulted from consultation with more than
300 scientists from 187 institutions in 87 countries.
25. Wheat descriptors
• Descriptors for wheat and Aegilops (1978)
• Descriptors for wheat (Revised) (1985)
– Not available as Linked Data
27. Linked Data vocabularies
• Metadata vocabularies: Metadata sets, metadata element
sets
– they provide metadata elements to describe individual pieces of
information in the data sets.
– Example: Dublin Core is a vocabulary that prescribes the
property dc:date for the publishing date of a document.
• Value vocabularies (KOS): Controlled vocabularies,
authority data
– they provide sets of values for (some of) the metadata
elements.
– Example: AGROVOC provides a set of values for agricultural
topics that can be used as values for the dc:subject property.
28. LOD guidelines (Berners Lee, 2006)
1.“Use URIs as names for things”
– concepts / values in value vocabularies and classes and properties in description vocabularies, as well
as the vocabularies themselves, have to be identified by URIs.
2.“Use HTTP URIs so that people can look up those names”
– the URIs for concept / values, classes and properties, as well as vocabularies, have to be resolved as
HTTP URLs.
3.“When someone looks up a URI, provide useful information”
– the URLs for concepts, classes and properties, as well as vocabularies, have to return an HTML page
with useful information when requested by browsers, or RDF when requested by RDF software;
besides, vocabularies should be available for querying behind a SPARQL endpoint.
4.“Include links to other URIs, so that more things can be
discovered”
– the URIs of concepts, classes and properties should whenever possible be linked to URIs in other
vocabularies, for instance as close match of another concept or sub-class of another class.
29. Proposed methodology
1. Analyze metadata schemas & KOSs used to
describe germplasm resources
2. Define attributes & vocabularies that can be
used to expose germplasm resources in linked
data format.
3. Provide a set of recommendations for the
exposure of germplasm resources as linked data
4. Embed the recommendations in the data
infrastructure of agINFRA
– to allow the exposure of germplasm resources as
LOD.
31. Application of the linked agricultural
data framework in germplasm
1. Definition of base schema
– Darwin Core for Germplasm to be used as base
schema
• Already available in SKOS
• Vocabularies published as linked data
– Germplasm Vocabularies
• Germplasm Term Vocabulary
• Germplasm Type Vocabulary
– Germplasm ontology
2. Publication of local classifications / lists for
germplasm as LOD KOSs
– if possible use DwC Types directly
32. Application of the linked agricultural
data framework in germplasm
3. Linking of terms in new KOSs to terms in existing
KOSs
– e.g. DwC Types, AGROVOC
4. Link CAAS and CRA germplasm records using
scientific name > AGROVOC
5. Collaboration with technical partners
– technical specifications on how to write procedures that extract the
relevant data from the database and "triplify" them (i.e. both serialize
them as RDF and use URIs instead of just strings whenever possible, also
linking to AGROVOC URIs when possible).
33. …and more next steps (optional)
• Update the existing analysis with new data
• Collect new user requirements
• (re)define the mappings between metadata
schemas and KOSs (if needed)
• Fine-tune the linked data approach
35. Time plan
• June 2014: Germplasm vocabularies
– Metadata model: Darwin Core SW + DwC-G as the
reference
• Publish local classifications / lists for germplasm as LOD
KOSs (if possible use DwC Types directly)
• Link terms in new KOSs to terms in existing KOSs (e.g.
DwC Types, AGROVOC)
• Germplasm phenotypic values / classifications linked to
Phenotypic Ontology terms?
36. Time plan
• August 2014: Germplasm RDF
– Expose some RDF output and API access for
germplasm datasets (basic DwC RDF, essentially
basic passport descriptors).
– Mandatory data for interlinking: scientific name
OR AGROVOC term
37. Time plan
• October 2014: Consuming data from agINFRA
services and components
– Link CGRIS and CRA germplasm records using
scientific name > AGROVOC