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Slides from a presentation given at the Pistoia Alliance European Conference 2015, in Zurich, Switzerland.
Pistoia Alliance European Conference 2015 - Susanne Bauerschmidt / Osthus
Pistoia Alliance European Conference 2015 - Susanne Bauerschmidt / Osthus
Pistoia Alliance
OSTHUS representative Michael Engels presented "Best Practice Reference Architecture for Data Curation" at BioIT World 2015.
Best Practice Reference Architecture for Data Curation
Best Practice Reference Architecture for Data Curation
OSTHUS
Current challenges facing the implementation of NoSQL-type databases involve how to use advanced rule-based analytics on large tables and key value stores, where metadata is often sparse. Graph databases or triple stores are great for utilizing one’s metadata, but are often computationally inefficient compared to NoSQL stores. To combat this problem, Modus Operandi will showcase a Predicate Store inside of its MOVIA product that can run advanced, first-order level, logical rule sets and queries against large tables or column stores directly to provide a scalable, rapid and advanced data analytics for cloud applications. This provides graph complexity in terms of content with the performance and scalability of NoSQL data approaches. The system also allows for both statistical algorithms as well as logic-based rule sets to be run concurrently, meaning that a host of parallel analytics can be run at once, providing deep analysis over a multitude of important pattern types.
Reasoning over big data
Reasoning over big data
OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, the Allotrope Foundation develops a comprehensive and innovative Framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its lifecycle. The talk describes how laboratory data and their semantic metadata descriptions are brought together to ease the management of vast amount of data that underpin almost every aspect of drug discovery and development.
Semantics for integrated laboratory analytical processes - The Allotrope Pers...
Semantics for integrated laboratory analytical processes - The Allotrope Pers...
OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, Allotrope Foundation is developing a comprehensive and innovative framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its life cycle. In this talk we describe how laboratory data and semantic metadata descriptions are brought together to ease the management of a vast amount of data that underpins almost every aspect of drug discovery and development.
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
OSTHUS
Common data issues in legacy data
Data Quality- How to clean up your legacy data?
Data Quality- How to clean up your legacy data?
OSTHUS
Building your laboratory informatics strategy: The benefit of reference architectures & data standardization. Presented by: Wolfgang Colsman, OSTHUS Dana Vanderwall, Bristol-Myers Squibb
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015
OSTHUS
Allotrope Foundation is building a framework (a software toolkit) to embed a set of federated, public, non-proprietary standards for analytical data in software utilized throughout the entire analytical chemistry data lifecycle, and serves as a basis for providing controlled vocabularies and taxonomies for a variety of pharmaceutical and biotech R&D applications. This framework provides extended capabilities to build in business rules and other analytics on top of the standardized vocabularies allowing companies enhanced abilities to classify and manage their data. Legacy systems can be maintained more easily and new technologies including cloud databases, Big Data Analytics, or reasoning engines can be employed to allow researchers unprecedented access to important contextualized data, because the foundational class structure is common and highly extensible to new and expanding domains. We will briefly describe some of the current data integration and management challenges facing the industry, e.g., utilization of legacy data warehouses, the creation of new data lakes, integration of existing semantic models, cloud-scale applications and how the Allotrope Framework provides a semantic basis for improved metadata and master data management through the use of modularized semantic models that capture the most pertinent entities, attributes and relationships needed to capture the plethora of laboratory data. We will provide an update on the rapid progress of development and the release of the Allotrope Framework 1.0, including: the Allotrope Data Format (for data and semantically-described metadata), Allotrope Taxonomies, and the first release of APIs (application programming interfaces), and how Allotrope Member companies have begun to integrate these into their internal environments. We will then discuss some of the potential extensions of this framework, which in the future, could enable state-of-the-art data integration and analytics capabilities for various applications.
Allotrope foundation vanderwall_and_little_bio_it_world_2016
Allotrope foundation vanderwall_and_little_bio_it_world_2016
OSTHUS
Recommandé
Slides from a presentation given at the Pistoia Alliance European Conference 2015, in Zurich, Switzerland.
Pistoia Alliance European Conference 2015 - Susanne Bauerschmidt / Osthus
Pistoia Alliance European Conference 2015 - Susanne Bauerschmidt / Osthus
Pistoia Alliance
OSTHUS representative Michael Engels presented "Best Practice Reference Architecture for Data Curation" at BioIT World 2015.
Best Practice Reference Architecture for Data Curation
Best Practice Reference Architecture for Data Curation
OSTHUS
Current challenges facing the implementation of NoSQL-type databases involve how to use advanced rule-based analytics on large tables and key value stores, where metadata is often sparse. Graph databases or triple stores are great for utilizing one’s metadata, but are often computationally inefficient compared to NoSQL stores. To combat this problem, Modus Operandi will showcase a Predicate Store inside of its MOVIA product that can run advanced, first-order level, logical rule sets and queries against large tables or column stores directly to provide a scalable, rapid and advanced data analytics for cloud applications. This provides graph complexity in terms of content with the performance and scalability of NoSQL data approaches. The system also allows for both statistical algorithms as well as logic-based rule sets to be run concurrently, meaning that a host of parallel analytics can be run at once, providing deep analysis over a multitude of important pattern types.
Reasoning over big data
Reasoning over big data
OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, the Allotrope Foundation develops a comprehensive and innovative Framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its lifecycle. The talk describes how laboratory data and their semantic metadata descriptions are brought together to ease the management of vast amount of data that underpin almost every aspect of drug discovery and development.
Semantics for integrated laboratory analytical processes - The Allotrope Pers...
Semantics for integrated laboratory analytical processes - The Allotrope Pers...
OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, Allotrope Foundation is developing a comprehensive and innovative framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its life cycle. In this talk we describe how laboratory data and semantic metadata descriptions are brought together to ease the management of a vast amount of data that underpins almost every aspect of drug discovery and development.
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
OSTHUS
Common data issues in legacy data
Data Quality- How to clean up your legacy data?
Data Quality- How to clean up your legacy data?
OSTHUS
Building your laboratory informatics strategy: The benefit of reference architectures & data standardization. Presented by: Wolfgang Colsman, OSTHUS Dana Vanderwall, Bristol-Myers Squibb
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015
OSTHUS
Allotrope Foundation is building a framework (a software toolkit) to embed a set of federated, public, non-proprietary standards for analytical data in software utilized throughout the entire analytical chemistry data lifecycle, and serves as a basis for providing controlled vocabularies and taxonomies for a variety of pharmaceutical and biotech R&D applications. This framework provides extended capabilities to build in business rules and other analytics on top of the standardized vocabularies allowing companies enhanced abilities to classify and manage their data. Legacy systems can be maintained more easily and new technologies including cloud databases, Big Data Analytics, or reasoning engines can be employed to allow researchers unprecedented access to important contextualized data, because the foundational class structure is common and highly extensible to new and expanding domains. We will briefly describe some of the current data integration and management challenges facing the industry, e.g., utilization of legacy data warehouses, the creation of new data lakes, integration of existing semantic models, cloud-scale applications and how the Allotrope Framework provides a semantic basis for improved metadata and master data management through the use of modularized semantic models that capture the most pertinent entities, attributes and relationships needed to capture the plethora of laboratory data. We will provide an update on the rapid progress of development and the release of the Allotrope Framework 1.0, including: the Allotrope Data Format (for data and semantically-described metadata), Allotrope Taxonomies, and the first release of APIs (application programming interfaces), and how Allotrope Member companies have begun to integrate these into their internal environments. We will then discuss some of the potential extensions of this framework, which in the future, could enable state-of-the-art data integration and analytics capabilities for various applications.
Allotrope foundation vanderwall_and_little_bio_it_world_2016
Allotrope foundation vanderwall_and_little_bio_it_world_2016
OSTHUS
Current labs can greatly benefit from a digital transformation. FAIR data principles are crucial in this process. Laying a solid data governance foundation is an invaluable long-term move.
The Fast Track to Fair Lab Data
The Fast Track to Fair Lab Data
OSTHUS
Speak in common terms – identify Business Outcomes (value) as well as technology Don’t say “semantics”, “FAIR”, “ontologies”, etc. – talk about outcomes and results Drive projects through results – QUICK WINS Identify the right data – build off of that (evolution not revolution) Think about legacy systems, provenance, governance, stewardship, etc. – have answers to the nay-sayers. Be honest what this will do and what it won’t ROI – have this in mind (Business Value not Tech Value) Cost savings (reduced hours, faster search, accurate reporting, better visibility, etc.) Risk Mitigation (improved regulatory, corporate knowledge vs. indivual, M&A, etc.) Innovation (what is the value to being a thought leader?)
Challenges & Opportunities of Implementation FAIR in Life Sciences
Challenges & Opportunities of Implementation FAIR in Life Sciences
OSTHUS
This talk will provide a means to discuss the capture, integration and dissemination of data across large enterprises. We will show how data variety is continuing to grow, meaning new data sources are steadily becoming available for use in analysis. Data veracity is also of importance since a large amount of data is fuzzy (uncertain) in nature. The ability to integrate these various data sources and provide improved capabilities to understand and use it is of increasing importance in today’s pharma climate. We call this Reference Master Data Management (RMDM). This talk will span an arc of data lifecycle management, beginning with instrument data, moving across to clinical studies, production, regulatory affairs and finally e-archiving (see Fig. 1). I will show how these systems can use a common semantics for modeling of important metadata, which can apply the FAIR principles of Findability, Accessibility, Interoperability and Reusability to a common “semantic hub” that can connect data sources of different varieties across the enterprise. ADF files, for example, use their Data Description layer to provide semantic metadata about file contents. Similarly, semantics can be used to describe clinical trials data, regulatory data, etc., through to archiving, for improved storage and search over long periods of time.
Data lifecycle mgt across the enterprise
Data lifecycle mgt across the enterprise
OSTHUS
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
From allotrope to reference master data management
From allotrope to reference master data management
OSTHUS
• Improve Data Management with Semantic Data Integration • Discuss the issues of data variety and data uncertainty • Moving from Big Data to Big Analysis • How to apply Analysis to Big Data (Big Analysis) • Benefits of Advanced Analytics in Life Science
Big Data becomes Big Analysis
Big Data becomes Big Analysis
OSTHUS
Eric Little, CDO at OSTHUS about "Early AI Adoption Via Advanced Analytics"
Early AI Adoption Via Advanced Analytics
Early AI Adoption Via Advanced Analytics
OSTHUS
The world is changing at a rapid pace. New varieties of data continue to spring up and be made available for integration and knowledge improvement across many domains. Knowledge engineering, using advanced techniques in data science, is therefore moving to the forefront of technology and IT concerns at many companies. We see this in the expansion of cloud technologies, semantic technologies, data analytics, and the construction of Data Lakes. Understanding one’s data and being able to derive complex patterns of interest from across a multitude of different data sources (public and private) should be of paramount concern for companies in the pharmaceutical, crop science and life science industries. Companies who embrace knowledge engineering practices will possess a distinct advantage in the coming years due to their ability to integrate and use data to their advantage. This talk will discuss recent trends in data science and will highlight some of the main points to consider for taking advantage of these new technologies and approaches. We will also cover certain lessons learned from real-world industry use cases to highlight how people are using these technologies for improved business benefits.
Why Data is Becoming the Most Valuable Asset Companies Posses
Why Data is Becoming the Most Valuable Asset Companies Posses
OSTHUS
• Big Data technologies, especially Data Lakes are spreading across many industries at the moment with the hopes that they will provide unprecedented capabilities for data integration and data analytics • In spite of the popularity and promise of these technology approaches, many early adopters are not seeking immediate solutions to their complex problems. Answers are not simply appearing – this talk will explore this issue more thoroughly • Of the 4 V’s of Big Data, Data Variety and Data Veracity (uncertainty) are of increasing importance. These can cause barriers to successful integration strategies , which, in turn, can lead to poorly performing analytics. • The problems of Variety and Veracity can be tackled using a new form of Data Science which combines formal ontologies with statistical heuristics. This talk will explore some key features of these approaches and how they can be developed together in symbiosis – leading to complex models that allow for improved analytics – or as we call it Big Analysis. • The end result is improved capture of data types/sources, from laboratory instrument data, to clinical data, to regulatory rules & submissions, all the way to business drivers for the enterprise. In the end providing advanced analytics capabilities that can be built as modules and expand across an enterprise.
Reinventing Laboratory Data To Be Bigger, Smarter & Faster
Reinventing Laboratory Data To Be Bigger, Smarter & Faster
OSTHUS
The Allotrope Foundation is a consortium of major pharmaceutical companies and a partner network whose goal is to address challenges in the pharmaceutical industry by providing a set of public, non-proprietary standards for using and integrating analytical laboratory data. Current challenges in data management within the pharmaceutical industry often center around inconsistent or incomplete data and metadata and proprietary data formats. Because of a lack of standardization, several operations (e.g. integration of instruments/applications, transfer of methods or results, archiving for regulatory purposes) require unnecessary efforts. Further, higher level aggregation of data, e.g. regulatory filings, that are derived from multiple sources of laboratory data are costly to create. These unnecessary costs impact operations within a company’s laboratories, between partnering companies, and between a company and contract research organizations (CROs). Finally, the accelerating transition of laboratories from hybrid (paper + electronic) to purely electronic data streams, coupled with an ever-increasing regulatory scrutiny of electronic data management practices, further require a comprehensive solution. This talk will discuss how The Allotrope Foundation is providing a new framework for data standards through collaboration between numerous stakeholders.
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
OSTHUS
In our webinar on Jan 17th, 2017, Eric and Heiner gave attendees insights on the following: 1. What semantics are (model/data separation, graphs, apply better meaning to data, etc.) 2. Why you should consider using these technologies (real world examples of benefits our customers are seeing) 3. How to pick the right tech for your needs (provide a description of the types of graph/RDF stores out there – we have a matrix based on features – and show how various SPARQL queries work against legacy data.)
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
OSTHUS
Life science’s main asset is its data. Data forms the basis of scientific decision making and its availability via electronic systems is a prerequisite for collaborative work and successful innovation. While more data is published as linked (open) data, huge amounts of data remain unused in internal data silos, such as various ELN’s, because of substantial integration efforts and data quality issues. Since the overwhelming amount of data is unstructured, information extraction and corresponding classification and semantic labeling of content is required. To generate value from your ELN data, a solid informatics strategy is needed to ensure data quality and streamline analytics. Semantic technologies are key enabler to overcome existing limitations.
Why paperless lab is just the first step towards a smart lab
Why paperless lab is just the first step towards a smart lab
OSTHUS
OSTHUS VP Data Science Dr. Eric Little talked about Smart Data for Smart Labs at this years SmartLab EU in Munich.
Smart Data for Smart Labs
Smart Data for Smart Labs
OSTHUS
During SmartLab Exchange 2015, Allotrope Foundation and OSTHUS presented the latest update on the Allotrope Framework. To learn more, please view the slides below. Presented by: Dana Vanderwall, BMS Research IT & Automation Patrick Chin, Merck Research Laboratories IT Wolfgang Colsman, OSTHUS
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
OSTHUS
TEMPLATE FOR RESEARCH DEFENSE
RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx
RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx
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Current labs can greatly benefit from a digital transformation. FAIR data principles are crucial in this process. Laying a solid data governance foundation is an invaluable long-term move.
The Fast Track to Fair Lab Data
The Fast Track to Fair Lab Data
OSTHUS
Speak in common terms – identify Business Outcomes (value) as well as technology Don’t say “semantics”, “FAIR”, “ontologies”, etc. – talk about outcomes and results Drive projects through results – QUICK WINS Identify the right data – build off of that (evolution not revolution) Think about legacy systems, provenance, governance, stewardship, etc. – have answers to the nay-sayers. Be honest what this will do and what it won’t ROI – have this in mind (Business Value not Tech Value) Cost savings (reduced hours, faster search, accurate reporting, better visibility, etc.) Risk Mitigation (improved regulatory, corporate knowledge vs. indivual, M&A, etc.) Innovation (what is the value to being a thought leader?)
Challenges & Opportunities of Implementation FAIR in Life Sciences
Challenges & Opportunities of Implementation FAIR in Life Sciences
OSTHUS
This talk will provide a means to discuss the capture, integration and dissemination of data across large enterprises. We will show how data variety is continuing to grow, meaning new data sources are steadily becoming available for use in analysis. Data veracity is also of importance since a large amount of data is fuzzy (uncertain) in nature. The ability to integrate these various data sources and provide improved capabilities to understand and use it is of increasing importance in today’s pharma climate. We call this Reference Master Data Management (RMDM). This talk will span an arc of data lifecycle management, beginning with instrument data, moving across to clinical studies, production, regulatory affairs and finally e-archiving (see Fig. 1). I will show how these systems can use a common semantics for modeling of important metadata, which can apply the FAIR principles of Findability, Accessibility, Interoperability and Reusability to a common “semantic hub” that can connect data sources of different varieties across the enterprise. ADF files, for example, use their Data Description layer to provide semantic metadata about file contents. Similarly, semantics can be used to describe clinical trials data, regulatory data, etc., through to archiving, for improved storage and search over long periods of time.
Data lifecycle mgt across the enterprise
Data lifecycle mgt across the enterprise
OSTHUS
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
From allotrope to reference master data management
From allotrope to reference master data management
OSTHUS
• Improve Data Management with Semantic Data Integration • Discuss the issues of data variety and data uncertainty • Moving from Big Data to Big Analysis • How to apply Analysis to Big Data (Big Analysis) • Benefits of Advanced Analytics in Life Science
Big Data becomes Big Analysis
Big Data becomes Big Analysis
OSTHUS
Eric Little, CDO at OSTHUS about "Early AI Adoption Via Advanced Analytics"
Early AI Adoption Via Advanced Analytics
Early AI Adoption Via Advanced Analytics
OSTHUS
The world is changing at a rapid pace. New varieties of data continue to spring up and be made available for integration and knowledge improvement across many domains. Knowledge engineering, using advanced techniques in data science, is therefore moving to the forefront of technology and IT concerns at many companies. We see this in the expansion of cloud technologies, semantic technologies, data analytics, and the construction of Data Lakes. Understanding one’s data and being able to derive complex patterns of interest from across a multitude of different data sources (public and private) should be of paramount concern for companies in the pharmaceutical, crop science and life science industries. Companies who embrace knowledge engineering practices will possess a distinct advantage in the coming years due to their ability to integrate and use data to their advantage. This talk will discuss recent trends in data science and will highlight some of the main points to consider for taking advantage of these new technologies and approaches. We will also cover certain lessons learned from real-world industry use cases to highlight how people are using these technologies for improved business benefits.
Why Data is Becoming the Most Valuable Asset Companies Posses
Why Data is Becoming the Most Valuable Asset Companies Posses
OSTHUS
• Big Data technologies, especially Data Lakes are spreading across many industries at the moment with the hopes that they will provide unprecedented capabilities for data integration and data analytics • In spite of the popularity and promise of these technology approaches, many early adopters are not seeking immediate solutions to their complex problems. Answers are not simply appearing – this talk will explore this issue more thoroughly • Of the 4 V’s of Big Data, Data Variety and Data Veracity (uncertainty) are of increasing importance. These can cause barriers to successful integration strategies , which, in turn, can lead to poorly performing analytics. • The problems of Variety and Veracity can be tackled using a new form of Data Science which combines formal ontologies with statistical heuristics. This talk will explore some key features of these approaches and how they can be developed together in symbiosis – leading to complex models that allow for improved analytics – or as we call it Big Analysis. • The end result is improved capture of data types/sources, from laboratory instrument data, to clinical data, to regulatory rules & submissions, all the way to business drivers for the enterprise. In the end providing advanced analytics capabilities that can be built as modules and expand across an enterprise.
Reinventing Laboratory Data To Be Bigger, Smarter & Faster
Reinventing Laboratory Data To Be Bigger, Smarter & Faster
OSTHUS
The Allotrope Foundation is a consortium of major pharmaceutical companies and a partner network whose goal is to address challenges in the pharmaceutical industry by providing a set of public, non-proprietary standards for using and integrating analytical laboratory data. Current challenges in data management within the pharmaceutical industry often center around inconsistent or incomplete data and metadata and proprietary data formats. Because of a lack of standardization, several operations (e.g. integration of instruments/applications, transfer of methods or results, archiving for regulatory purposes) require unnecessary efforts. Further, higher level aggregation of data, e.g. regulatory filings, that are derived from multiple sources of laboratory data are costly to create. These unnecessary costs impact operations within a company’s laboratories, between partnering companies, and between a company and contract research organizations (CROs). Finally, the accelerating transition of laboratories from hybrid (paper + electronic) to purely electronic data streams, coupled with an ever-increasing regulatory scrutiny of electronic data management practices, further require a comprehensive solution. This talk will discuss how The Allotrope Foundation is providing a new framework for data standards through collaboration between numerous stakeholders.
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
OSTHUS
In our webinar on Jan 17th, 2017, Eric and Heiner gave attendees insights on the following: 1. What semantics are (model/data separation, graphs, apply better meaning to data, etc.) 2. Why you should consider using these technologies (real world examples of benefits our customers are seeing) 3. How to pick the right tech for your needs (provide a description of the types of graph/RDF stores out there – we have a matrix based on features – and show how various SPARQL queries work against legacy data.)
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
OSTHUS
Life science’s main asset is its data. Data forms the basis of scientific decision making and its availability via electronic systems is a prerequisite for collaborative work and successful innovation. While more data is published as linked (open) data, huge amounts of data remain unused in internal data silos, such as various ELN’s, because of substantial integration efforts and data quality issues. Since the overwhelming amount of data is unstructured, information extraction and corresponding classification and semantic labeling of content is required. To generate value from your ELN data, a solid informatics strategy is needed to ensure data quality and streamline analytics. Semantic technologies are key enabler to overcome existing limitations.
Why paperless lab is just the first step towards a smart lab
Why paperless lab is just the first step towards a smart lab
OSTHUS
OSTHUS VP Data Science Dr. Eric Little talked about Smart Data for Smart Labs at this years SmartLab EU in Munich.
Smart Data for Smart Labs
Smart Data for Smart Labs
OSTHUS
During SmartLab Exchange 2015, Allotrope Foundation and OSTHUS presented the latest update on the Allotrope Framework. To learn more, please view the slides below. Presented by: Dana Vanderwall, BMS Research IT & Automation Patrick Chin, Merck Research Laboratories IT Wolfgang Colsman, OSTHUS
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
OSTHUS
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(13)
The Fast Track to Fair Lab Data
The Fast Track to Fair Lab Data
Challenges & Opportunities of Implementation FAIR in Life Sciences
Challenges & Opportunities of Implementation FAIR in Life Sciences
Data lifecycle mgt across the enterprise
Data lifecycle mgt across the enterprise
From allotrope to reference master data management
From allotrope to reference master data management
Big Data becomes Big Analysis
Big Data becomes Big Analysis
Early AI Adoption Via Advanced Analytics
Early AI Adoption Via Advanced Analytics
Why Data is Becoming the Most Valuable Asset Companies Posses
Why Data is Becoming the Most Valuable Asset Companies Posses
Reinventing Laboratory Data To Be Bigger, Smarter & Faster
Reinventing Laboratory Data To Be Bigger, Smarter & Faster
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
Why paperless lab is just the first step towards a smart lab
Why paperless lab is just the first step towards a smart lab
Smart Data for Smart Labs
Smart Data for Smart Labs
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
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TEMPLATE FOR RESEARCH DEFENSE
RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx
RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx
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When engaging in research or community-based studies, the utilization of various tools within Participatory Rural Appraisal (PRA) becomes integral. Among these tools, the ranking and scoring exercise stands out as a methodological approach.
Ranking and Scoring Exercises for Research
Ranking and Scoring Exercises for Research
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Statistics -Merged notes from unit 1 to 5
Statistics notes ,it includes mean to index numbers
Statistics notes ,it includes mean to index numbers
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Lecture_2_Deep_Learning_Overview-newone1
Lecture_2_Deep_Learning_Overview-newone1
Ranking and Scoring Exercises for Research
Ranking and Scoring Exercises for Research
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Statistics notes ,it includes mean to index numbers
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