The document discusses the use of linked open data for academia. It introduces linked data and its key principles of using URIs to identify objects and including links between data from different sources. This allows data to be interconnected in a web of data rather than separate silos. Examples are given of applying these principles through projects like Bio2RDF that link life sciences data and LODAC that link academic data about species, museums and locations. Benefits include decentralized data sharing and integration across domains. Requirements for research data are that it be accessible, reusable and sustainable.
Profiling systems have achieved notable adoption by research institutions.1 Multi-site search of research profiling systems has substantially evolved since the first deployment of systems such as DIRECT2Experts.2 CTSAsearch is a federated search engine using VIVO-compliant Linked Open Data (LOD) published by members of the NIH-funded Clinical and Translational Science (CTSA) consortium and other interested parties. Sixty-four institutions are currently included, spanning six distinct platforms and three continents (North America, Europe and Australia). In aggregate, CTSAsearch has data on 150-300 thousand unique researchers and their 10 million publications. The public interface is available at http://research.icts.uiowa.edu/polyglot.
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
Profiling systems have achieved notable adoption by research institutions.1 Multi-site search of research profiling systems has substantially evolved since the first deployment of systems such as DIRECT2Experts.2 CTSAsearch is a federated search engine using VIVO-compliant Linked Open Data (LOD) published by members of the NIH-funded Clinical and Translational Science (CTSA) consortium and other interested parties. Sixty-four institutions are currently included, spanning six distinct platforms and three continents (North America, Europe and Australia). In aggregate, CTSAsearch has data on 150-300 thousand unique researchers and their 10 million publications. The public interface is available at http://research.icts.uiowa.edu/polyglot.
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
In this talk we describe how the Fourth Paradigm for Data-Intensive Research is providing a framework for us to develop tools, technologies and platforms to support actionable science. We discuss applications that take advantage of cloud computing, particularly Microsoft Azure, to realise the potential for turning data into decisions, knowledge and understanding. http://www.fourthpardigm.org and http://www.azure4research.com
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.
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.
Research Data Sharing: A Basic FrameworkPaul Groth
Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/
Scientific Units in the Electronic AgeStuart Chalk
Scientists have standardized on the SI unit system since the late 1700’s. While much work has been done over the years to refine and redefine the system, little has formally done to standardize the representation of the SI units in electronic systems.
This paper will present a summary of current efforts toward electronic representation of scientific units in text, XML, and RDF, an analysis of needs for current computer/network systems, and an outline of future work.
Short talk on Research Object and their use for reproducibility and publishing in the Systems Biology Commons Platform FAIRDOMHub, and the underlying software SEEK.
Rule-based Capture/Storage of Scientific Data from PDF Files and Export using...Stuart Chalk
Recently, the US government has mandated that publicly funded scientific research data be freely made available in a useable form, allowing integration of data in other systems. While this mandate has been articulated, existing publications and new papers (PDF) still do not provide accessible data, meaning that the usefulness is limited without human intervention.
This presentation outlines our efforts to extract scientific data from PDF files, using the PDFToText software and regular expressions (regex), and process it into a form that structures the data and its context (metadata). Extracted data is processed (cleaned, normalized), organized, and inserted into a contextually developed MySQL database. The data and metadata can then be output using a generic JSON-LD based scientific data model (SDM) under development in our laboratory.
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.
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
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
Written and presented by Carole Goble (University of Manchester) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Data integration is intrinsic to how modern research is undertaken in areas such as genomics, drug development and personalised medicine. To better enable this integration a large number of biomedical ontologies have been developed to provide standard semantics for describing metadata. There are now several hundred biomedical ontologies in widespread use that describe concepts such as genes, molecules, drugs and diseases. This amounts to millions of terms that are interconnected via relationships that naturally form a graph of biomedical terminology.
The Ontology Lookup Service (OLS) (http://www.ebi.ac.uk/ols) integrates over 160 ontologies and provide a central point for the biomedical community to query and visualise ontologies. OLS also provide a RESTful API over the ontologies that is used in high-throughput data annotation pipelines. OLS is built on top of a Neo4j database that provides efficient indexes for extracting ontological relationships. We have developed generic tools for loading RDF/OWL ontologies into Neo4j where the indexes are optimised for serving common ontology queries. We are now moving to adopt graph database more widely in applications relating to ontology mapping prediction and recommendation systems for data annotation.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
In this talk we describe how the Fourth Paradigm for Data-Intensive Research is providing a framework for us to develop tools, technologies and platforms to support actionable science. We discuss applications that take advantage of cloud computing, particularly Microsoft Azure, to realise the potential for turning data into decisions, knowledge and understanding. http://www.fourthpardigm.org and http://www.azure4research.com
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.
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.
Research Data Sharing: A Basic FrameworkPaul Groth
Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/
Scientific Units in the Electronic AgeStuart Chalk
Scientists have standardized on the SI unit system since the late 1700’s. While much work has been done over the years to refine and redefine the system, little has formally done to standardize the representation of the SI units in electronic systems.
This paper will present a summary of current efforts toward electronic representation of scientific units in text, XML, and RDF, an analysis of needs for current computer/network systems, and an outline of future work.
Short talk on Research Object and their use for reproducibility and publishing in the Systems Biology Commons Platform FAIRDOMHub, and the underlying software SEEK.
Rule-based Capture/Storage of Scientific Data from PDF Files and Export using...Stuart Chalk
Recently, the US government has mandated that publicly funded scientific research data be freely made available in a useable form, allowing integration of data in other systems. While this mandate has been articulated, existing publications and new papers (PDF) still do not provide accessible data, meaning that the usefulness is limited without human intervention.
This presentation outlines our efforts to extract scientific data from PDF files, using the PDFToText software and regular expressions (regex), and process it into a form that structures the data and its context (metadata). Extracted data is processed (cleaned, normalized), organized, and inserted into a contextually developed MySQL database. The data and metadata can then be output using a generic JSON-LD based scientific data model (SDM) under development in our laboratory.
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.
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
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
Written and presented by Carole Goble (University of Manchester) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Data integration is intrinsic to how modern research is undertaken in areas such as genomics, drug development and personalised medicine. To better enable this integration a large number of biomedical ontologies have been developed to provide standard semantics for describing metadata. There are now several hundred biomedical ontologies in widespread use that describe concepts such as genes, molecules, drugs and diseases. This amounts to millions of terms that are interconnected via relationships that naturally form a graph of biomedical terminology.
The Ontology Lookup Service (OLS) (http://www.ebi.ac.uk/ols) integrates over 160 ontologies and provide a central point for the biomedical community to query and visualise ontologies. OLS also provide a RESTful API over the ontologies that is used in high-throughput data annotation pipelines. OLS is built on top of a Neo4j database that provides efficient indexes for extracting ontological relationships. We have developed generic tools for loading RDF/OWL ontologies into Neo4j where the indexes are optimised for serving common ontology queries. We are now moving to adopt graph database more widely in applications relating to ontology mapping prediction and recommendation systems for data annotation.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
Transparencias de las clases sobre Linked Data en el Máster de Bioinformática de la Universidad de Murcia. Para un mejor efecto, http://biordf.org:8080/UM_LSLD/Clases/UM_Bioinformatics_LD.html
1h SPARQL tutorial given at the "Practical Cross-Dataset Queries on the Web of Data" tutorial at WWW2012. Supported by the LATC FP7 Project. http://latc-project.eu/
Semantic web technologies offer a potential mechanism for the representation and integration of thousands of biomedical databases. Many of these databases offer cross-references to other data sources, but these are generally incomplete and prone to error. In this paper, we conduct an empirical analysis of the link structure of life science Linked Data, obtained from the Bio2RDF project. Three different link graphs for datasets, entities and terms are characterized by degree, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we utilize the symmetry and transitivity of entity links to build a benchmark and evaluate several popular entity matching approaches. Our findings indicate that the life science data network can help find hidden links, can be used to validate links, and may offer a mechanism to integrate a wider set of resources to support biomedical knowledge discovery.
Providing open data is of interest for its societal and commercial value, for transparency, and because more people can do fun things with data. There is a growing number of initiatives to provide open data, from, for example, the UK government and the World Bank. However, much of this data is provided in formats such as Excel files, or even PDF files. This raises the question of
- How best to provide access to data so it can be most easily reused?
- How to enable the discovery of relevant data within the multitude of available data sets?
- How to enable applications to integrate data from large numbers of formerly unknown data sources?
One way to address these issues to to use the design principles of linked data (http://www.w3.org/DesignIssues/LinkedData.html), which suggest best practices for how to publish and connect structured data on the Web. This presentation gives an overview of linked data technologies (such as RDF and SPARQL), examples of how they can be used, as well as some starting points for people who want to provide and use linked data.
The presentation was given on August 8, at the Hacknight event (http://hacknight.se/) of Forskningsavdelningen (http://forskningsavd.se/) (Swedish: “Research Department”) a hackerspace in Malmö.
Presentation delivered in the context of the Agricultural Data Interoperability WG meeeting, during the RDA 3rd Plenary Meeting in Dublin, Ireland. 26/3/2014.
The presentation is mostly focused on the work done by the agINFRA project towards proposing a methodology for the definition of Germplasm descriptors as RDF, based on the existing work of experts in the field and making use of the existing effort in this direction.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
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
Data Publishing at Harvard's Research Data Access SymposiumMerce Crosas
Data Publishing: The research community needs reliable, standard ways to make the data produced by scientific research available to the community, while giving credit to data authors. As a result, a new form of scholarly publication is emerging: data publishing. Data publishing - or making data reusable, citable, and accessible for long periods - is more than simply providing a link to a data file or posting the data to the researcher’s web site. We will discuss best practices, including the use of persistent identifiers and full data citations, the importance of metadata, the choice between public data and restricted data with terms of use, the workflows for collaboration and review before data release, and the role of trusted archival repositories. The Harvard Dataverse repository (and the Dataverse open-source software) provides a solution for data publishing, making it easy for researchers to follow these best practices, while satisfying data management requirements and incentivizing the sharing of research data.
Data Publishing Workflows with DataverseMicah Altman
By: Mercè Crosas, Director of Data Science at the Institute for Quantitative Social Science (IQSS) at Harvard University
The Dataverse software provides multiple workflows for data publishing to support a wide range of data policies and practices established by journals, as well as data sharing needs from various research communities. This talk will describe these workflows from the user experience and from the system's technical implementation.
This talk was presented as part of the Information Science Brown Bag talks, hosted by the Program on Information Science. (See http://drmaltman.wordpress.com)
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
Similar to Introduction of Linked Data for Science (20)
Presented at Journal Paper Track, The Web Conference, Lyon, France, April 15, 2018
https://doi.org/10.1145/3184558.3186234
Abstract: Linked Open Data (LOD) technology enables web of data and exchangeable knowledge graphs through the Internet. However, the change in knowledge is happened everywhere and every time, and it becomes a challenging issue of linking data precisely because the misinterpretation and misunderstanding of some terms and concepts may be dissimilar under different context of time and different community knowledge. To solve this issue, we introduce an approach to the preservation of knowledge graph, and we select the biodiversity domain to be our case studies because knowledge of this domain is commonly changed and all changes are clearly documented. Our work produces an ontology, transformation rules, and an application to demonstrate that it is feasible to present and preserve knowledge graphs and provides open and accurate access to linked data. It covers changes in names and their relationships from different time and communities as can be seen in the cases of taxonomic knowledge.
We propose Crop Vocabulary(CVO) as a basis of the core vocabulary of crop names that becomes the guidelines for data interoperability between agricultural ICT systems on the food chain. Since a single species is treated in different ways, there are many different types of crop names. So, we organize the crop name discriminated by properties such as scientific name, planting method, edible part and registered cultivar information. Also, Crop Vocabulary is also linked to existing vocabularies issued by Japanese government agency and international organization such as AGROVOC. It is expected to use in the data format in the agricultural ICT system.
Presented in 45th Asia Pacific Advanced Network (APAN45) Meeting, Singapore (2018)
Presented as the invited talk at International Workshop on kNowledge eXplication for Industry (kNeXI2017). In this talk, I explain the experience and lesson learnt how to build ontologies. I am currently building the agriculture activity ontology (AAO). It describes classification and properties of various activities in the agriculture domain. It is formalized with Description Logics.
Presented at the Interest Group on Agricultural Data (IGAD) ,3 April, 2017, Barcelona, Spain
Abstract: n this talk, we present the current status of our agriculture ontologies that are developed to accelerate the data use in agriculture.
The agriculture activity ontology formalizes the activities in agriculture. We have developed it for three years. Now we are developing its applications. One application is to exchange formats between different farmer management systems. Another ontology is the crop ontology that standardizes the names of crops. The structure is simple but has links to many other standards in distribution industry, food industry and so on.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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Charlie Greenberg, Host
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Introduction of Linked Data for Science
1. Linked Open Data for ACademia
Introduction of Linked Data
for Science
Hideaki Takeda
takeda@nii.ac.jp / ORCID:0000-0002-2909-7163
Professor, National Institute of Informatics
2013 International Conference on Open Data in Biodiversity and Ecological Research, 20 November, 2013
2. Linked Open Data for ACademia
Researchers in 1983
Survey, Research, and Writing
Printed Articles
Survey
Article Writing
Data
Data
Real World
Object
3. Linked Open Data for ACademia
Researchers in 2013distribution of articles
Digital
More articles ever!
Sharing and re-use of data
Digital Articles
Printed Articles
Real and Digital objects as target
Survey
Article Writing
Digital Information
Data
Acquiring Data
Publishing Data
Data
Real World
Object
4. Linked Open Data for ACademia
Trends of Research and Data
• Rapid Growth
– Increase of article publications
– Big data and many (small) databases
• Open and Share
– Open access
– Data sharing
• Integration
– Among different types of data
– Across domains
5. Linked Open Data for ACademia
Key Requirements
• Accessibility
– Research results must be shared
• Reusability
– Research results are expected to be re-used by
other research
• Sustainability
– Research results must be preserved
6. Linked Open Data for ACademia
Key Requirements
• Accessibility
– Research results must be shared
• Reusability
– Research results are expected to be re-used by
other research
• Sustainability
– Research results must be preserved
7. Linked Open Data for ACademia
Open Data
• Open Data is not just “data which is
open”, rather …
• “A piece of data or content is open if anyone is
free to use, reuse, and redistribute it —
subject only, at most, to the requirement to
attribute and/or share-alike.” http://opendefinition.org/
• Use, re-use, redistribute
• Open license
8. Linked Open Data for ACademia
5 ★ Open Data
- link your data to
other data to
provide context
- use URIs to denote things, so that
people can point at your stuff
- use non-proprietary formats (e.g., CSV
instead of Excel)
- make it available as structured data (e.g., Excel instead
of image scan of a table)
- make your stuff available on the Web (whatever format)
under an open license
http://5stardata.info/
9. Linked Open Data for ACademia
Linked Data/Linked Open Data (LOD)
- link your data to
other data to
provide context
- use URIs to denote things, so that
people can point at your stuff
11. Linked Open Data for ACademia
Web of Data
Another data to
the observation
Data identical
to this
What’s the
meaning of
the data?
Inter-connection between data in difference
data sources is enabled
12. Linked Open Data for ACademia
Linked Data Principles
• The four rules for Linked Data
– Use URIs as names for things
• Give a URI to every object in the world!
– Use HTTP URIs so that people can look up those names.
• Don’t use URN
– When someone looks up a URI, provide useful information, using the
standards (RDF, SPARQL)
• Provide machine-readable data for URI
– Include links to other URIs. so that they can discover more things.
• Make data linked together just like Web
Linked Data, TBL, http://www.w3.org/DesignIssues/LinkedData.html
13. Linked Open Data for ACademia
How to express data in Linked Data
• Use RDF(+RDFS, OWL)
– Very simple:<Subject> <predicate> <object> .
<http://www-kasm.nii.ac.jp/~takeda#me> rdfs:type foaf:Person .
<http://www-kasm.nii.ac.jp/~takeda#me> foaf:name “Hideaki Takeda” .
<http://www-kasm.nii.ac.jp/~takeda#me> foaf:gender “male” .
<http://www-kasm.nii.ac.jp/~takeda#me> foaf:knows
<http://southampton.rkbexplorer.com/id/person07113> .
foaf:Person
rdfs:type
http://www-kasm.nii.ac.jp/
~takeda#me
foaf:knows
foaf:name
“Hideaki Takeda”
foaf:gender
“male”
http://southampton.rkbexplorer.com
/id/person07113
14. Linked Open Data for ACademia
Linked Dataの記述
foaf:Person
rdfs:type
http://www-kasm.nii.ac.jp/
~takeda#me
foaf:knows
foaf:name
foaf:gender
“Hideaki Takeda”
“male”
http://southampton.rkbexplorer.com/
id/person-07113
owl:sameAs
dbpprop:occupation
dbpedia:Computer_scientist
<http://dbpedia.org/resource/Tim_Berners-Lee>
dbpprop:name
“Sir Tim Berners-Lee”
dbpprop:birthPlace
“London, England”
dbpprop:birthDate
“1955-06-08”
15. Linked Open Data for ACademia
Linking Open Data (LOD)
•
•
•
•
•
The project to collect published Linked Data
Major Linked Data
(Translated from the original resources)
– Dbpedia (Wikipedia) 270 Million Triples
– Geonames:Geo names and their latitudes and longitudes, 93 Million
Triples
– MusicBrainz:Music
– WordNet:Dictionary
– DBLP bibliography:Bibliography for technical papers. 28 Million Triples
– US Census Data: 1 Billion Triples
(Crawling)
– FOAF (Friend Of A Friend)
(Wrapper)
– Flickr Wrapper
19. Linked Open Data for ACademia
Benefits of LOD for Science
• Truly de-centralized database
– No need for central database
– Everyone can create one and join the cloud!
• Truly open and sharable data and schemata
– Easy for re-use and mash-up
– Easy for cross-domain/discipline use and connection
• A single format for all kind of data
– Easy for data processing
20. Linked Open Data for ACademia
Bio2RDF
At the heart of Linked Data for the Life Sciences
• Bio2RDF is an open source framework to produce
and provide biological linked data that uses
simple conventions on the emerging semantic
web
• Bio2RDF reduces the time and
effort involved in data
integration so that you can get
to doing science
• 19 datasets;
1,010,758,291 triples
http://bio2rdf.org/
21. Linked Open Data for ACademia
Alison Callahan, José Cruz-Toledo, Peter Ansell, Michel Dumontier: Bio2RDF Release 2: Improved Coverage, Interoperability
and Provenance of Life Science Linked Data, The Semantic Web: Semantics and Big Data, Lecture Notes in Computer Science
Volume 7882, 2013, pp 200-212
23. Linked Open Data for ACademia
LODAC Location:
Integration of location information
LODAC Project
- connecting academic data LODAC SPECIES: Connecting species data by name
Specimen
DB
Species
Info. DB
App. for query expansion
DBPedia Japanese
Research
GBIF
Taxon
Name DB
DB
BioSci.
No. of Names:
113118
No. of Triples:14,532,449 DB
LODAC Museum: LOD of data in museums
Raw Data for entities Minimum Data to identify entities Data for entities
Raw
Data from Source A
Integrated data
Data from Source B
Work
dc:references
dc:references
crm:P55_has_current_location
crm:P55_has_current_location dc:creator
dc:creator
dc:creator
Museum crm:P55_has_current_location
dc:references
dc:references
Creator
dc:references
dc:references
CKAN Japanese:
Catalog for Open Data
24. Linked Open Data for ACademia
LODAC SPECIES: Linking Species
Information with names
Museum
Specimen
DB
Species
Info. DB
Research
DB
GBIF
Taxon Name
LOD
BioSci.
DB
No. of Species Names:113118
No. of Triples:14,532,449
25. Linked Open Data for ACademia
Data model for intergration
TaxonName
rdfs:subClassOf
rdfs:subClassOf
CommonName
rdf:type
ScientificName
rdf:type
TaxonRank
rdf:type
rdf:type
rdf:type
hasTaxonRank
hasCommonName
hasScientificName
hasSuperTaxon
species
species
Butterfly
hasTaxonRank
BDLS
collectedDate
dcterms:source
crm:has_current_location
collectionLocality
institutionName
dcterms:publisher
rdf:type
Specimen
: owl:Class
: Named Graph
Bryophytes
26. Linked Open Data for ACademia
Search application
with LODAC SPECIES
http://lod.ac/apps/lsdcs
27. Linked Open Data for ACademia
LODAC Museum
• Integrated database for information on
museums in Japan
Type of Information
– Data
• No. of museums:114
• No. of triples:
40,059,131
RDF type
No. of items
Collections (total)
lodac:Specimen +
lodac:Work
ca. 1,770,000
Collections (specimen)
lodac:Specimen
ca. 1,690,000
Collections (creative and
historical work)
lodac:Work
ca. 130,000
Creators
foaf:Person
ca.
Institutes
Foaf:Organization
ca. 200,000
• Integration by creator, work and institute
• Data publication by RDF
• Some applications using the data
8,800
28. Linked Open Data for ACademia
Integrated data processing by RDF
Collect
Refine
Integrate
Publish
Use
Processed by RDF
•
•
•
•
•
Collect:RDF by converting RDB / by scraping Web
Refine: Define schema and covert data by schema
Integrate: Schema mapping, ID mapping
Publish: Dump data / SPARQL Endpoint
Use: Mash-up applications
29. Linked Open Data for ACademia
Collect
Extracting collection data from
museum websites
Extract
Property
Value
Property
Value
30. Linked Open Data for ACademia
Dataset
Collect
Type
Art work
(lodac:Work)
No.
Data source
ca.80,000 Catalog of the collections of 3 National Art
Museum (25,180), National Museum of
Western Art (4,373), Tokushima Pref. Art
Museum (18,482) … over 100 museums
Database for National Treasure & Important
Cultural Property of National Designated (915)
The Japanese Art Thesaurus (266)
Specimen
(lodac:Speciment)
Person (foaf:Person)
Facilities (icls.
Museum)
ca.1,690,000 (100+ Museum collections)
Science Net (National Science Museum)
ca. 8,800 The Japanese Art Thesaurus
ca. 200,000 The Japanese Art Thesaurus
Cultural Heritage Online
GIS data National and Regional Planning
Bureau
31. Linked Open Data for ACademia
Refine
Standardization of data
Re-organized common metadata.
dc:title
crm:P45_consistOf
skos:preflabel
Raw Data
....
lodac:era
Re-organized Metadata
Current organized policies
・Use existing metadata
・Define own metadata.
31
32. Linked Open Data for ACademia
Refine
Metadata schema for works
lodac:Work
Genre
Type of cultural assets
Creator
Nationality
Title
Title Pronunciation (yomi)
Title in English
Inscription
Seal
No. of parts
Collection
Created year
Estimated starting year
Material
Property
lodac:genre
lodac:culturalAssets
dc:creator / dc11:creator
crm:P7_took_place_at
dc:title / skos:prefLabel
dc:title @ja-hrkt / skos:altLabel
dc:title @en / skos:altLabel
crm:P62I_is_depicted_by
crm:P65_shows_visual_item
crm:P57_has_number_of_parts
dc:isPartOf
dc:created
lodac:estimatedStartYear
dc:medium / crm:P45_consists_of
33. Linked Open Data for ACademia
Integrating Data
Integrate
Raw Data for entities
Minimum Data to identify entities
Raw Data for entities
Integrated data
Data from Source B
Data from Source A
Work
dc:references
dc:references
crm:P55_has_current_location
crm:P55_has_current_location
dc:creator
dc:creator
dc:creator
crm:P55_has_current_location
Museum
dc:references
dc:references
Creator
dc:references
dc:references
34. Linked Open Data for ACademia
Integrate
Integrate Item
Integrating Data
Source
A.Japanese Art Thesaurus
Amount
of Data
648
Facilities
77
B.Cultural Heritage Online
Title of important
cultural properties
Creator information
and Work Title
Integration
Data
A.Japanese Art Thesaurus (Art work)
915
3,800
74
B.DB for National Treasure (Art work)
10,115
A.Japanese Art Thesaurus (Creator)
1,332
15,020
B.All of art work (Work title string)
61,861
A.Japanese Art Thesaurus (Creator)
1,332
Creator name
615
B.All of art work title(using creator name)
61,861
34
35. Linked Open Data for ACademia
Publish
Publishing data as RDF
<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:lodac="http://lod.a
c/ns/lodac#" xmlns:dc="http://purl.org/dc/terms/"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:skos="http://www.w3.org
/2004/02/skos/core#">
<foaf:Person rdf:about="http://lod.ac/id/359">
<lodac:creates rdf:resource="http://lod.ac/id/20029"/>
ID-resource URI
(Own address)
http://lod.ac/id/359
Links to her/his
work URI
<lodac:creates rdf:resource="http://lod.ac/id/20128"/>
<lodac:creates rdf:resource="http://lod.ac/id/20755"/>
External link
DBpedia Japanese
<lodac:creates rdf:resource="http://lod.ac/id/24768"/>
<lodac:creates rdf:resource="http://lod.ac/id/26732"/>
……
<dc:references rdf:resource="http://ja.dbpedia.org/resource/下村観山"/>
<dc:references rdf:resource="http://lod.ac/ref/359"/>
<rdfs:label xml:lang="ja">下村観山</rdfs:label>
<skos:prefLabel xml:lang="ja">下村観山</skos:prefLabel>
<foaf:name xml:lang="ja">下村観山</foaf:name>
</foaf:Person>
Ref-resource URI
http://lod.ac/ref/359
36. Linked Open Data for ACademia
Use
Yokohama Art Spot
LODAC Museum × Yokohama Art LOD
– Application using
museum and local data
– Data related to art in
Yokohama
• Collections
• Events
• Q&A
http://lod.ac/apps/yas/
× PinQA
37. Linked Open Data for ACademia
System Architecture
Use
‣ Python + SPARQLWrapper
‣ Geolocation
Yokohama
Art LOD
PinQA
Question
User
JSON
SPARQL
Yokohama Art Spot
LODAC
Museum
Work
Event
Answer
Artist
Institution
Artist
Institution
38. Linked Open Data for ACademia
Conclusion
• Data and Web
– Great Potential!
• Linked Data - Exploit the power of Web –
– Simple Structure: URI and RDF
– Truly distributed data management
– Easy to link to each other
– Suitable for inter-disciplinary areas
• Left Issues
– Scalability
– Sustainability
• DOI: DataCite
• ORCID