Presentation by Luiz Olavo Bonino about the current state of the developments on FAIR Data supporting tools at the Dutch Techcentre for Life Sciences Partners Event on November 3-4 2016.
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
An overview on FAIR Data and FAIR Data stewardship, and the roadmap for FAIR Data solutions coordinated by the Dutch Techcentre for Life Sciences. This presentation was given at the Netherlands eScience Center's "Essential skills in data-intensive research" course week.
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.
LIBER Webinar: Are the FAIR Data Principles really fair?LIBER Europe
The FAIR Data Principles are a hot topic in research data managment. Their adoption within the H2020 funding programme means researchers now have to pay much more attention to how their share, publish and archive their data.
In this light, how can libraries help their research communities implement the FAIR principles? And write better data management plans?
This questions were addressed in a LIBER webinar containing some guidance and reflections on the principles themselves. Presented by Alastair Dunning, Head Research Data Services at the TU Delft (hosts of the 4TU.Centre for Research Data), it is based on a study of 37 data repositories (from subject specific repositories, to generic data archives, to national infrastructures), seeing how far they comply with each of the individual facets of the Data principles.
FAIR Data in Trustworthy Data Repositories Webinar - 12-13 December 2016| www...EUDAT
| www.eudat.eu | This webinar was co-organised by DANS, EUDAT and OpenAIRE and was held on 12th and 13th December 2016.
Everybody wants to play FAIR, but how do we put the principles into practice?
There is a growing demand for quality criteria for research datasets. In this webinar we will argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgements about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository.
In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders.
The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets.
In this webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.
Presentation by Luiz Olavo Bonino about the current state of the developments on FAIR Data supporting tools at the Dutch Techcentre for Life Sciences Partners Event on November 3-4 2016.
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
An overview on FAIR Data and FAIR Data stewardship, and the roadmap for FAIR Data solutions coordinated by the Dutch Techcentre for Life Sciences. This presentation was given at the Netherlands eScience Center's "Essential skills in data-intensive research" course week.
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.
LIBER Webinar: Are the FAIR Data Principles really fair?LIBER Europe
The FAIR Data Principles are a hot topic in research data managment. Their adoption within the H2020 funding programme means researchers now have to pay much more attention to how their share, publish and archive their data.
In this light, how can libraries help their research communities implement the FAIR principles? And write better data management plans?
This questions were addressed in a LIBER webinar containing some guidance and reflections on the principles themselves. Presented by Alastair Dunning, Head Research Data Services at the TU Delft (hosts of the 4TU.Centre for Research Data), it is based on a study of 37 data repositories (from subject specific repositories, to generic data archives, to national infrastructures), seeing how far they comply with each of the individual facets of the Data principles.
FAIR Data in Trustworthy Data Repositories Webinar - 12-13 December 2016| www...EUDAT
| www.eudat.eu | This webinar was co-organised by DANS, EUDAT and OpenAIRE and was held on 12th and 13th December 2016.
Everybody wants to play FAIR, but how do we put the principles into practice?
There is a growing demand for quality criteria for research datasets. In this webinar we will argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgements about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository.
In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders.
The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets.
In this webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.
Dataset Catalogs as a Foundation for FAIR* DataTom Plasterer
BioPharma and the broader research community is faced with the challenge of simply finding the appropriate internal and external datasets for downstream analytics, knowledge-generation and collaboration. With datasets as the core asset, we wanted to promote both human and machine exploitability, using web-centric data cataloguing principles as described in the W3C Data on the Web Best Practices. To do so, we adopted DCAT (Data CATalog Vocabulary) and VoID (Vocabulary of Interlinked Datasets) for both RDF and non-RDF datasets at summary, version and distribution levels. Further, we’ve described datasets using a limited set of well-vetted public vocabularies, focused on cross-omics analytes and clinical features of the catalogued datasets.
FAIR Data Knowledge Graphs–from Theory to PracticeTom Plasterer
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. Our processes enable simple creation of dataset records and linking to source data, providing a seamless federated knowledge graph for novice and advanced users alike.
Presented May 7th, 2019 at the Knowledge Graph Conference, Columbia University.
BioPharma and FAIR Data, a Collaborative AdvantageTom Plasterer
The concept of FAIR (Findable, Accessible, Interoperable and Reusable) data is becoming a reality as stakeholders from industry, academia, funding agencies and publishers are embracing this approach. For BioPharma being able to effectively share and reuse data is a tremendous competitive advantage, within a company, with peer organizations, key opinion leaders and regulatory agencies. A few key drivers, success stories and preliminary results of an industry data stewardship survey are presented.
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.
As BioPharma adapts to incorporate nimble networks of suppliers, collaborators, and regulators the ability to link data is critical for dynamic interoperability. Adoption of linked data paradigm allows BioPharma to focus on core business: delivering valuable therapeutics in a timely manner.
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
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016|...EUDAT
| www.eudat.eu | 1st Session: July 7, 2016.
In this webinar, Sarah Jones (DCC) and Marjan Grootveld (DANS) talked through the aspects that Horizon 2020 requires from a DMP. They discussed examples from real DMPs and also touched upon the Software Management Plan, which for some projects can be a sensible addition
Edge Informatics and FAIR (Findable, Accessible, Interoperable and Reusable) ...Tom Plasterer
Edge Informatics is an approach to accelerate collaboration in the BioPharma pipeline. By combining technical and social solutions knowledge can be shared and leveraged across the multiple internal and external silos participating in the drug development process. This is accomplished by making data assets findable, accessible, interoperable and reusable (FAIR). Public consortia and internal efforts embracing FAIR data and Edge Informatics are highlighted, in both preclinical and clinical domains.
This talk was presented at the Molecular Medicine Tri-Conference in San Francisco, CA on February 20, 2017
The presentation gives an overview of what metadata is and why it is important. It also addresses the benefits that metadata can bring and offers advice and tips on how to produce good quality metadata and, to close, how EUDAT uses metadata in the B2FIND service.
November 2016
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.
How to describe a dataset. Interoperability issuesValeria Pesce
Presented by Valeria Pesce during the pre-meeting of the Agricultural Data Interoperability Interest Group (IGAD) of the Research Data Alliance (RDA), held on 21 and 22 September 2015 in Paris at INRA.
Presentation by Luiz Olavo Bonino, Dutch Techcentre & Vrije University Amsterdam.
As one of the organisations present at the Lorentz workshop in January 2014 where the concept of FAIR Data has been created, the Dutch Techcentre for Life Sciences has, since then, worked on a number of solutions to support the adoption and dissemination of the FAIR Data Principles. This presentation presents the ecosystem on how to support FAIR data.
Dataset Catalogs as a Foundation for FAIR* DataTom Plasterer
BioPharma and the broader research community is faced with the challenge of simply finding the appropriate internal and external datasets for downstream analytics, knowledge-generation and collaboration. With datasets as the core asset, we wanted to promote both human and machine exploitability, using web-centric data cataloguing principles as described in the W3C Data on the Web Best Practices. To do so, we adopted DCAT (Data CATalog Vocabulary) and VoID (Vocabulary of Interlinked Datasets) for both RDF and non-RDF datasets at summary, version and distribution levels. Further, we’ve described datasets using a limited set of well-vetted public vocabularies, focused on cross-omics analytes and clinical features of the catalogued datasets.
FAIR Data Knowledge Graphs–from Theory to PracticeTom Plasterer
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. Our processes enable simple creation of dataset records and linking to source data, providing a seamless federated knowledge graph for novice and advanced users alike.
Presented May 7th, 2019 at the Knowledge Graph Conference, Columbia University.
BioPharma and FAIR Data, a Collaborative AdvantageTom Plasterer
The concept of FAIR (Findable, Accessible, Interoperable and Reusable) data is becoming a reality as stakeholders from industry, academia, funding agencies and publishers are embracing this approach. For BioPharma being able to effectively share and reuse data is a tremendous competitive advantage, within a company, with peer organizations, key opinion leaders and regulatory agencies. A few key drivers, success stories and preliminary results of an industry data stewardship survey are presented.
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.
As BioPharma adapts to incorporate nimble networks of suppliers, collaborators, and regulators the ability to link data is critical for dynamic interoperability. Adoption of linked data paradigm allows BioPharma to focus on core business: delivering valuable therapeutics in a timely manner.
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
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016|...EUDAT
| www.eudat.eu | 1st Session: July 7, 2016.
In this webinar, Sarah Jones (DCC) and Marjan Grootveld (DANS) talked through the aspects that Horizon 2020 requires from a DMP. They discussed examples from real DMPs and also touched upon the Software Management Plan, which for some projects can be a sensible addition
Edge Informatics and FAIR (Findable, Accessible, Interoperable and Reusable) ...Tom Plasterer
Edge Informatics is an approach to accelerate collaboration in the BioPharma pipeline. By combining technical and social solutions knowledge can be shared and leveraged across the multiple internal and external silos participating in the drug development process. This is accomplished by making data assets findable, accessible, interoperable and reusable (FAIR). Public consortia and internal efforts embracing FAIR data and Edge Informatics are highlighted, in both preclinical and clinical domains.
This talk was presented at the Molecular Medicine Tri-Conference in San Francisco, CA on February 20, 2017
The presentation gives an overview of what metadata is and why it is important. It also addresses the benefits that metadata can bring and offers advice and tips on how to produce good quality metadata and, to close, how EUDAT uses metadata in the B2FIND service.
November 2016
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.
How to describe a dataset. Interoperability issuesValeria Pesce
Presented by Valeria Pesce during the pre-meeting of the Agricultural Data Interoperability Interest Group (IGAD) of the Research Data Alliance (RDA), held on 21 and 22 September 2015 in Paris at INRA.
Presentation by Luiz Olavo Bonino, Dutch Techcentre & Vrije University Amsterdam.
As one of the organisations present at the Lorentz workshop in January 2014 where the concept of FAIR Data has been created, the Dutch Techcentre for Life Sciences has, since then, worked on a number of solutions to support the adoption and dissemination of the FAIR Data Principles. This presentation presents the ecosystem on how to support FAIR data.
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/
In an expert webinar on April 15th 2020 we discussed (in Finnish) how the FAIR data principles affect service development in RDM services. I presented some relevant outputs from the FAIRsFAIR project. These are the slides (in English). The webinar will be published on the fairdata.fi service site https://www.fairdata.fi/koulutus/koulutuksen-tallenteet/
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
Presentation of the early prototype of "FAIR Profiles" - an example of the proposed DCAT Profile, proposed by the DCAT working group (but AFAIK never implemented). This prototype emerged from the activity of the "Skunkworks" group, from the Data FAIRport project.
Using Fedora Commons To Create A Persistent ArchivePhil Cryer
With the increasing amount of digital data and demand for open access to view and reuse such data continually increasing, the adoption of open source digital repository software is critical for long term storage and management of digital objects. By utilizing the open source Fedora Commons software, the Missouri Botanical Garden has created a stable, persistent archive for Tropicos digital objects, including specimen images, plant photos, and other digital media. Metadata, organized in standard Dublin Core extracted from Tropicos, are stored alongside the digital objects providing search and sharing of data via open standards such as REST and OAI, opening the door for mash-ups and alternative uses. The presentation will cover initial discovery, required hardware and software, and an overview of our experience implementing Fedora Commons. Lessons learned, pros and cons, and other options will also be covered.
Eu gdpr technical workflow and productionalization neccessary w privacy ass...Steven Meister
GDPR = General Data Protection Regulations or GDPR = Get Demand Payment Ready when your hacked or audited.
A Realistic project plan for GDPR Compliance. Another reality is the 95% not ready and even the 5% that say they are, will not like what they see in this plan in the hopes of becoming GDPR compliant.
There is just not enough time or people to get it done in the next 8 months and even if you had
2 years. This is a harsh reality and without the use of software technology and strict yet flexible, repeatable methodologies, it just won’t happen. Look at this Project plan of what needs to be done, do the math, see the complexity of data movement and code and programs needed then give us a call.
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.
Logical Data Fabric: Architectural ComponentsDenodo
Watch full webinar here: https://bit.ly/39MWm7L
Is the Logical Data Fabric one monolithic technology or does it comprise of various components? If so, what are they? In this presentation, Denodo CTO Alberto Pan will elucidate what components make up the logical data fabric.
Talk delivered at YOW! Developer Conferences in Melbourne, Brisbane and Sydney Australia on 1-9 December 2016.
Abstract: Governments collect a lot of data. Data on air quality, toxic chemicals, laws and regulations, public health, and the census are intended to be widely distributed. Some data is not for public consumption. This talk focuses on open government data — the information that is meant to be made available for benefit of policy makers, researchers, scientists, industry, community organisers, journalists and members of civil society.
We’ll cover the evolution of Linked Data, which is now being used by Google, Apple, IBM Watson, federal governments worldwide, non-profits including CSIRO and OpenPHACTS, and thousands of others worldwide.
Next we’ll delve into the evolution of the U.S. Environmental Protection Agency’s Open Data service that we implemented using Linked Data and an Open Source Data Platform. Highlights include how we connected to hundreds of billions of open data facts in the world’s largest, open chemical molecules database PubChem and DBpedia.
WHO SHOULD ATTEND
Data scientists, software engineers, data analysts, DBAs, technical leaders and anyone interested in utilising linked data and open government data.
Google's recent announcement that it will support the use of microformats in their search opens up new possibilities for librarians and library technologists to support the goals of the semantic web; namely to provide better access, reuse and recombinations of library resources and services on the open web. This lightning talk introduces the semantic web and semantic markup technologies.
The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published
on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of
scientists and organizations to support the reusability of digital assets. It has since been
adopted by research institutions worldwide. The guidelines are timely as we see
unprecedented volume, complexity, and creation speed of data.
Apresentação na mesa de conversa com pesquisadores sobre acesso aberto, diretrizes e elaboração de planos de gestão de dados da UNIRIO no dia 14 de junho de 2018.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
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4. WHAT IS FAIR DATA?
Findable:
F1. (meta)data are assigned a globally
unique and persistent identifier;
F2. data are described with rich metadata;
F3. metadata clearly and explicitly include
the identifier of the data it describes;
F4. (meta)data are registered or indexed in
a searchable resource;
http://www.nature.com/articles/sdata201618
6. WHAT IS FAIR DATA?
Accessible:
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;
■ http://www.nature.com/articles/sdata201618
8. WHAT IS FAIR DATA?
Interoperable:
I1. (meta)data use a formal, accessible,
shared, and broadly applicable
language for knowledge
representation.
I2. (meta)data use vocabularies that
follow FAIR principles;
I3. (meta)data include qualified
references to other (meta)data;
■ http://www.nature.com/articles/sdata201618
10. WHAT IS FAIR DATA?
Reusable:
R1. meta(data) are richly described
with 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
detailed provenance;
R1.3. (meta)data meet domain-relevant
community standards;
■ http://www.nature.com/articles/sdata201618
15. FAIR DATA POINT
A particular class of FAIR Data System that provides access to
datasets in a FAIR manner. The datasets can be external or
internal to the FAIR Data Point. Also, the source data can be a
non-FAIR dataset or a FAIR Data Resource. If the source data is
non-FAIR, the FAIR Data Point needs to made the necessary FAIR
transformations on the fly.
FAIR Data Resource
non-FAIR Data Resource
28. FAIR DATA POINT - ARCHITECTURE
FAIR API / GUI
Metadata
Provider
FAIR Accessor
Metrics Gatherer Security Enforcer
FAIR Metadata FAIR Data
29.
30. FAIR Data Point metadata
Title
Responsible institution(s)
Contact
FAIR API version
License
…
31. FDP METADATA
<http://dev-vm.fair-dtls.surf-hosted.nl:8082/fdp> dct:alternative "DTL FDP"@en ;
dct:description "The DTL FAIR Data Point hosts the FAIR Data versions of datasets that
have been made FAIR during BYODs as well as other relevant life sciences datasets"@en ;
dct:subject "FAIR Data" , "Life Sciences" ;
dct:title "DTL FAIR Data Point"@en ;
<http://www.re3data.org/schema/3-0#api> <http://dtls.nl/fdp#api=1> ;
<http://www.re3data.org/schema/3-0#catalog> <http://dev-vm.fair-dtls.surf-hosted.nl:
8082/fdp/biobank> , <http://dev-vm.fair-dtls.surf-hosted.nl:8082/fdp/comparativeGenomics> ,
<http://dev-vm.fair-dtls.surf-hosted.nl:8082/fdp/patient-registry> , <http://dev-vm.fair-
dtls.surf-hosted.nl:8082/fdp/textmining> , <http://dev-vm.fair-dtls.surf-hosted.nl:8082/fdp/
transcriptomics> ;
<http://www.re3data.org/schema/3-0#institution> <http://dtls.nl> ;
<http://www.re3data.org/schema/3-0#institutionCountry> <http://lexvo.org/id/iso3166/NL>
;
<http://www.re3data.org/schema/3-0#lastUpdate> "2016-10-27"^^xsd:date ;
<http://www.re3data.org/schema/3-0#software> "FAIR Data Point" ;
<http://www.re3data.org/schema/3-0#startDate> "2016-10-27"^^xsd:date ;
a <http://www.re3data.org/schema/3-0#Repository> ;
rdfs:label "DTL FAIR Data Point"@en ;
<http://xmlns.com/foaf/0.1/landingpage> <http://dev-vm.fair-dtls.surf-hosted.nl:8082/
fdp/swagger-ui.html> .
32. FAIR Data Point metadata
Catalog metadata
Title
Theme taxonomy
Issued date
…
40. FAIR Data Point metadata
Catalog 2
metadata
Catalog 1 metadata
Dataset 1
metadata
Distribution
1.a metadata
Data record
metadata
Distribution
1.b metadata
Dataset 2
metadata
Distribution
2.a metadata
Data record
metadata
Distribution
2.b metadata
Dataset 3
metadata
Distribution
3.a metadata
Data record
metadata
42. METADATA LAYERS
Layer Description Example Standard
FDP (Data
repository)
Information about the FDP as
a data repository
PID, title, description,
license, owner, API
version, etc.
RE3Data
Catalog Information about the
catalog of datasets offered
PID, title, description,
publisher, etc.
W3C DCAT
#Catalog
Dataset Information about each of
the offered datasets
Publisher, issue date,
theme, etc.
W3C DCAT
#Dataset,
Distribution Information about how the
dataset is distributed
AccessURL,
downloadURL, format,
mediaType, etc.
W3C DCAT
#Distribution
Data record Information about the actual
data, types, identifiers, etc.
Data items types,
identifiers, domain,
range, etc.
RML
OAI-PMH
43. DEMO FAIR DATA POINT
http://dev-vm.fair-dtls.surf-hosted.nl:8082/fdp/swagger-ui.html
http://dev-vm.fair-dtls.surf-hosted.nl:8082/fdp/
API
GUI
52. FAIRIFICATION PROCESS
■ Retrieve original data
■ Dataset identification and analysis
■ Definition of the semantic model
■ Data transformation
■ License assignment
■ Metadata definition
■ FAIR Data resource (data, metadata, license)
deployment
55. FAIRIFIER
■ Transform non-FAIR datasets into FAIR Data Resources
(dataset in FAIR format, license and metadata)
■ Data munging
■ Semantic modeling
■ License definition
■ Metadata definition and extraction
■ Data publication
59. FAIRIFICATION - NEW DATASET TYPE
Original dataset
FAIR Data Resource
FAIR Format
Metadata Licensesubmit generate
FAIR Data
Model Registry
store
Non-FAIR
- FAIR
mapping
60. FAIRIFICATION - RECURRING DATASET TYPE
Original dataset
FAIR Data Resource
FAIR Format
Metadata Licensesubmit generate
FAIR Data
Model Registry
query
Non-FAIR
- FAIR
mapping
retrieve
61. ■ A particular class of FAIR Data System to provide
support for data interoperability;
■ Supports publication and access to FAIR data.
■ Fosters an ecosystems of applications and services;
■ Federated architecture: different FAIRports (and other
FAIR Data Systems) are interconnectable;
■ Supports citations of datasets and data items;
■ Provides metrics for data usage and citation;
DataFAIRport
62. FAIR Data Search
Engine
FAIRifier +
(Meta)Data
Publication
Metadata storage
Data storage
(optional)
Transformation
Services Registry
(optional)
FAIR Data Point
DataFAIRport
DTL
FAIR Data PointFAIR Data Point
F A I
R
63. FAIRPORT
DataFAIRportFind,&Access,&Interoperate&&&Re3use&Data
Stewardship API FAIR Data API
(Meta)Data Storage component
Metadata storage
Data storage
DataVerse EUDAT Data Repository
Semantic resolver Ontology storage
Data storage API / FAIR Data API
Data usage policy
Management component
GUI (Data publishing, search, mgmt)
Data Mgmt App
FAIR Data System
Metrics storage
Data Consumer
Data Producer
Data Consumer Apps
Ex. *APInatomy, BRAIN,
etc)
Data Consumer Apps
Ex. *APInatomy, BRAIN,
etc)
Data Consumer Apps
Ex. *APInatomy, BRAIN,
etc)
Data Consumer Apps
Ex. *APInatomy, BRAIN,
etc)Data Mgmt AppData Mgmt App
Data
Stewardship
Apps
64. ■ Allow third-party annotation on existing knowledge
bases
■ Capture the provenance of the annotator and the
original statement
Open RDF
Knowledge AnnotatorORKA
68. TOOLS ROADMAP
Dec 16 Jan 17 Feb 17 Mar 17
FAIR Data
Point
Version 1
Metadata editor,
release metadata,
POST, FAIR
accessor
Version 1.1
Reintroduce OAI-
PMH compliance
Version 1.2
Update
notification
FAIR Data
Search
Engine
Beta 1
Crawler,
metadata index
and search GUI
Beta 2
Improved search
GUI, search API
FAIRifier
Beta 1
OpenRefine + RDF
plugin, publication
to FAIR Data Point
Beta 2
Metadata
definition and
extraction (RML),
license picker
69. TOOLS ROADMAP
Dec 16 Jan 17 Feb 17 Mar 17
FAIR Data
Model
Registry
Alpha 1
Start of the
integration work
ORKA
Beta 1
Definition of 2-3
use cases
Beta 2
Extended with
features required
by the use cases
Data
FAIRport
Alpha 1
Start of the
integration work
71. EXTENDING EXISTING DATA REPOSITORIES
Metadata
Provider
FAIR Accessor
Metrics Gatherer Security Enforcer
+
Metadata
Provider
FAIR Data
Accessor
Metrics Gatherer Access Controller
EUDAT Current
Components
EUDAT Current
Components
EUDAT Current
Components
Current
Solution
Components
72. FAIR HACKATHON - GOALS
■ Align solutions with FAIR Data Point specifications.
■ Metadata content
■ API
■ Data
73. FAIR HACKATHON OUTCOME
■ FAIR data model for solutions content;
■ Architecture of the required adjustments/extensions;
■ Technical specification of the adjustments/extensions;
■ Proof-of-concept of the adjusted solution;
78. DTL’S FAIR HACKATHONS ROADMAP
■ EUDAT (pilot project ongoing)
■ EGA (July 6-8 2016)
■ Molgenis (Oct 19-20 2016)
■ Patient registry solution providers (Oct 25-27 2016)
■ Mendeley (Nov 18 2016)
■ Quaero Systems (Nov 24 2016)
■ tranSMART (TBD)
■ phenotypeDB (TBD)
■ Euretos Knowledge Platform (TBD)
■ NIH, Australian National Data Services, Brazilian open government
data, …
79. BRING YOUR OWN DATA - BYOD
■ Goals:
■ Learn how to make data linkable “hands-on” with experts
■ Create a “telling story” to demonstrate its use
■ Make FAIR Data at the source
■ Composition:
■ Data owners – specialists on given datasets
■ Data interoperability experts
■ Domain experts
Source: Marcos Roos
84. NETHERLANDS
BYOD Planning
Execution
Day One
Introduction
SW, LD, Ontology intro
Use case intro
Workgroups division
Working sessions
WWW/TTTALA
Day Two
Progress report
Working sessions
Groups reports
WWW/TTTALA
Day Three
Data integration
Answer driving question
Explore data
Demo improvement
Final report
WWW/TTTALA