David Van Enckevort - FAIR sample and data access DataSciSIG
David van Enckevort from the University of Groningen describes FAIR Sample and Data Access in Biobanking and Biorepositories.
This talk was sponsored by the NIH Data Science Special Interest Group and part of a webinar panel on June 23, 2017 on Global Biobanking and Access to Specimens.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
FAIR Data Management and FAIR Data SharingMerce Crosas
Presentation at the Critical Perspective on the Practice of Digiral Archeology symposium: http://archaeology.harvard.edu/critical-perspectives-practice-digital-archaeology
The first workshop of the series "Services to support FAIR data" took place in Prague during the EOSC-hub week (on April 12, 2019).
Speaker: Kostas Repanas (EC DG RTD)
David Van Enckevort - FAIR sample and data access DataSciSIG
David van Enckevort from the University of Groningen describes FAIR Sample and Data Access in Biobanking and Biorepositories.
This talk was sponsored by the NIH Data Science Special Interest Group and part of a webinar panel on June 23, 2017 on Global Biobanking and Access to Specimens.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
FAIR Data Management and FAIR Data SharingMerce Crosas
Presentation at the Critical Perspective on the Practice of Digiral Archeology symposium: http://archaeology.harvard.edu/critical-perspectives-practice-digital-archaeology
The first workshop of the series "Services to support FAIR data" took place in Prague during the EOSC-hub week (on April 12, 2019).
Speaker: Kostas Repanas (EC DG RTD)
Presentation by Dr Steve McEachern, ADA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Creating impact with accessible data in agriculture and nutrition: sharing da...godanSec
Richard Finkers (Wageningen UR) presented at the 2nd International Workshop: Creating Impact with Open Data in Agriculture and Nutrition in The Hague, 11 September 2015.
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.
Managing and Sharing Research Data - Workshop at UiO - December 04, 2017Michel Heeremans
These slides were presented during a workshop on Research Data Management, given at the University of Oslo, Department of Geosciences on December 04, 2017
This presentation gives an overview of the key things that we need to consider before publishing data from the repository. It briefly discusses research data management, research data lifecycle, FAIR principles of research data management and then move on to key elements that should be considered while preparing datasets for publishing through repository.
Tools for improving data publication and usegodanSec
Fiona Smith (Open Data Institute) presented at the 2nd International Workshop: Creating Impact with Open Data in Agriculture and Nutrition in The Hague, 11 September 2015.
This presentation gives an overview of the key things that we need to consider before deciding to set up a data repository. It briefly talks about data repository, the software behind data repository and their limitations and merits. Additionally, the presenters shared IFPRI's experiences with Harvard Dataverse.
This presentation introduces the basics of the Dataverse including preparing the submission to the Dataverse, creating an account and logging in, adding datasets to the Dataverse account, and metadata.
‘Good, better, best’? Examining the range and rationales of institutional dat...Robin Rice
Introduction to panel presentations from Universities of Edinburgh, Southampton, Yale, Cornell at IPRES 2015 conference, Chapel Hill, North Carolina, 3 Nov 2015
Presentation by Kelly Hart, ONDC in PM&C, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Data sharing promotes many goals of the NIH research endeavor. It is particularly important for unique data that cannot be readily replicated. Data sharing allows scientists to expedite the translation of research results into knowledge, products, and procedures to improve human health. Do you know what a data sharing plan should include? Are you aware of common practices and standards for data sharing? Do you know what services are available to help share your data responsibly? This workshop will begin to address these questions. Q&A will follow the presentation. Anyone interested in or planning to apply for NIH funding should attend. Note: The NIH data-sharing policy applies to applicants seeking $500,000 or more in direct costs in any year of the proposed research.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
Presentation by Dr Steve McEachern, ADA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Creating impact with accessible data in agriculture and nutrition: sharing da...godanSec
Richard Finkers (Wageningen UR) presented at the 2nd International Workshop: Creating Impact with Open Data in Agriculture and Nutrition in The Hague, 11 September 2015.
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.
Managing and Sharing Research Data - Workshop at UiO - December 04, 2017Michel Heeremans
These slides were presented during a workshop on Research Data Management, given at the University of Oslo, Department of Geosciences on December 04, 2017
This presentation gives an overview of the key things that we need to consider before publishing data from the repository. It briefly discusses research data management, research data lifecycle, FAIR principles of research data management and then move on to key elements that should be considered while preparing datasets for publishing through repository.
Tools for improving data publication and usegodanSec
Fiona Smith (Open Data Institute) presented at the 2nd International Workshop: Creating Impact with Open Data in Agriculture and Nutrition in The Hague, 11 September 2015.
This presentation gives an overview of the key things that we need to consider before deciding to set up a data repository. It briefly talks about data repository, the software behind data repository and their limitations and merits. Additionally, the presenters shared IFPRI's experiences with Harvard Dataverse.
This presentation introduces the basics of the Dataverse including preparing the submission to the Dataverse, creating an account and logging in, adding datasets to the Dataverse account, and metadata.
‘Good, better, best’? Examining the range and rationales of institutional dat...Robin Rice
Introduction to panel presentations from Universities of Edinburgh, Southampton, Yale, Cornell at IPRES 2015 conference, Chapel Hill, North Carolina, 3 Nov 2015
Presentation by Kelly Hart, ONDC in PM&C, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Data sharing promotes many goals of the NIH research endeavor. It is particularly important for unique data that cannot be readily replicated. Data sharing allows scientists to expedite the translation of research results into knowledge, products, and procedures to improve human health. Do you know what a data sharing plan should include? Are you aware of common practices and standards for data sharing? Do you know what services are available to help share your data responsibly? This workshop will begin to address these questions. Q&A will follow the presentation. Anyone interested in or planning to apply for NIH funding should attend. Note: The NIH data-sharing policy applies to applicants seeking $500,000 or more in direct costs in any year of the proposed research.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
Open Insights Harvard DBMI - Personal Health Train - Kees van Bochove - The HyveKees van Bochove
In this talk, the Personal Health Train concept will be introduced, which enables running personalized medicine workflows as trains visiting data stations (e.g. hospital records, primary care records, clinical studies and registries, patient-held data from e.g. wearable sensors etc.) The Personal Health Train is a very powerful concept, which is however dependent on source medical data to be coded with appropriate metadata on consent, license, scope etc. of the data, and the data itself to be encoded using biomedical data standards, which is an ever growing field in biomedical informatics. In order to realize the Personal Health Train biomedical data will need to be FAIR, i.e. adopt the FAIR Guiding Principles. This talk will cover the emerging GO-FAIR international movement, and provide examples of how several European health data networks currently are adopting open standards based stacks, to enable routine health care data to be come accessible for research.
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
OSFair2017 workshop | Monitoring the FAIRness of data sets - Introducing the ...Open Science Fair
Elly Dijk & Peter Doorn present the DANS approach to FAIR metrics
Workshop title: Open Science Monitor
Workshop overview:
Which are the measurable components of Open Science? How do we build a trustworthy, global open science monitor? This workshop will discuss a potential framework to measure Open Science, including the path from the publishing of an open policy (registries of policies and how these are represented or machine read), to the use of open methodologies, and the opening up of research results, their recording and measurement.
DAY 2 - PARALLEL SESSION 5
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues.
Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
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/
Towards metrics to assess and encourage FAIRnessMichel Dumontier
With an increased interest in the FAIR metrics, there is need to develop tools and appraoches that can assess the FAIRness of a digital resource. This talk begins to explore some ideas in this space, and invites people to participate in a working group focused on the development, application, and evaluation of FAIR metric efforts.
Lesson 2 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Access to biomedical data is increasingly important to enable data driven science in the research community.
The Linked Open Data (LOD) principles (by Tim Berner-Lee) have been suggested to judge the quality of data by its accessibility (open data access), by its format and structures, and by its interoperability with other data sources.
The objective is to use interoperable data sources across the Web with ease.
The FAIR (findable, accessible, interoperable, reusable) data principles have been introduced for similar reasons with a stronger emphasis on achieving reusability.
In this presentation we assess the FAIR principles against the LOD principles to determine, to which degree, the FAIR principles reuse LOD principles, and to which degree they extend the LOD principles.
This assessment helps to clarify the relationship between both schemes and gives a better understanding, what extension FAIR represents in comparison to LOD.
We conclude, that LOD gives a clear mandate to the openness of data, whereas FAIR asks for a stated license for access and thus includes the concept of reusability under consideration of the license agreement.
Furthermore, FAIR makes strong reference to the contextual information required to improve reuse of the data, e.g., provenance information.
According to the LOD principles, such meta-data would be considered interoperable data as well, however, the requirement of extending of data with meta-data does indicate that FAIR is an extension of the LOD (in contrast to the inverse).
FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world’s data. We started with data catalogues (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen.
This talk was presented at The Molecular Medicine Tri-Conference/Bio-IT West on March 11, 2019.
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Presentation by Prof Chris Rowe, ADNet, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
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Presentation by Miranda Cumpston, ACTA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
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Presentation by Olivier Salvado, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
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FAIR for the future: embracing all things dataARDC
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Slides from the 26 Nov Make your data count webinar. The research community has long grappled with the problem of assessing and tracking the results of scholarship. Research data is no exception. The Make Data Count (MDC) project (https://makedatacount.org/), funded by the Sloan Foundation, has delivered a data usage metric standard (Code of Practice) and a workflow for the retrieval and display of standardised usage and citation metrics in your repository interface.
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Our international speaker line-up includes Daniella Lowenberg (California Digital Library) and Patricia Cruse (DataCite).
Recording available: https://youtu.be/Lkysz0Mc7fo
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.
A Strategic Approach: GenAI in EducationPeter Windle
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4. Australian Funders
Open access policies
Australian Code for the Responsible Conduct of Research
Data management statement
required for national competitive
grants
www.arc.gov.au/policies-strategies/strategy/research-
data-management
National Statement on Ethical Conduct
in Human Research
Human Research Ethics Application
(HREA)
5. Advantages for researchers
• Transparency and reproducibility
• Maximises value of investment
• Citations and impact
• Collaborations
• Secure ongoing storage
• Ethical obligation (clinical trials)
• Publications with data cited more often
6. Data sharing/publication isn’t all ”open data”
Five Safes risk management framework
Image CCBY
http://archive.stats.govt.nz/browse_for_stats/snapshots-of-
nz/integrated-data-infrastructure/keep-data-safe.aspx
7. F.A.I.R. data principles
• Drafted in a workshop in 2014
• Nature article* and support by FORCE11
• Received international recognition
• Technology agnostic
• Discipline independent
• Both the data and the metadata
• Human readable and machine readable
Image by Sanja Pundir CC-BY-SA
* Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management
and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016).
8. “FAIR principles provide ‘steps along a path’ toward machine-
actionability; adopting, in whole or in part, the FAIR principles, leads
the resource along the continuum towards this optimal state.”
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data
management and stewardship. Sci. Data 3:160018 doi:
10.1038/sdata.2016.18 (2016).
9. Findable
• Describe your data
• Give it a persistent globally unique
identifier
• Make it findable through discipline
specific search routes and generic ones
F1. (meta)data are assigned a globally
unique and eternally persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in
a searchable resource.
F4. metadata specify the data identifier.
10. Accessible
• Accessibility is a spectrum
• Deposit in repository
• If not open, provide information how the
researcher can get access to the data and
background information (e.g. codebooks,
methods, software, algorithms)
A1 (meta)data are retrievable by their
identifier using a standardized communications
protocol.
A1.1 the protocol is open, free, and universally
implementable.
A1.2 the protocol allows for an authentication
and authorization procedure, where necessary.
A2 metadata are accessible, even when the data
are no longer available.
Open Mediated Closed
11. Interoperable
• Use a standard file format
• Use a community agreed vocabulary
(MeSH, SNOMED CT, ICD-10…)
• Link to relevant information
I1. (meta)data use a formal, accessible,
shared, and broadly applicable
language for knowledge representation.
I2. (meta)data use vocabularies (and
ontologies) that follow FAIR principles.
I3. (meta)data include qualified
references to other (meta)data.
12. Reusable
Other aspects on top of F.A.I. :
• Discipline specific information about the
output
• Information on how the data was
created
• A machine readable licence
(Creative Commons recommended, see
our licensing guide
ands.org.au/guides/copyright-data-and-
licensing)
R1. meta(data) have a plurality of accurate
and relevant attributes.
R1.1. (meta)data are released with
a clear and accessible data usage license.
R1.2. (meta)data are associated with
their provenance.
R1.3. (meta)data meet domain-relevant
community standards.
13. F.A.I.R resources
Icons made by Freepik are licensed by CC BY 3.0
Entire FAIR resources graphic is licensed under a Creative Commons
Attribution 4.0 International License
ands.org.au/working-with-data/fairdata
Self assessment tool
15. With the exception of third party images or where otherwise indicated, this work is licensed under the Creative Commons Attribution 4.0 International Licence.
The ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure
Strategy Program
Kate LeMay Senior Research Data Specialist
kate.lemay@ardc.edu.au
@katelemayardc