Semantics for integrated laboratory analytical processes - The Allotrope Perspective
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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.
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Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
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.
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• 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
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Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
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In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
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REDCap is a secure web application for building and managing online surveys and databases. It allows users to rapidly develop projects using either an online designer or by uploading a data dictionary template from Excel. Projects can include both surveys and databases. REDCap provides tools like branching logic, file uploading, scheduling, and exporting data to statistical software. The presentation provides examples of how REDCap has been used at Vanderbilt for projects involving training program tracking, appointment scheduling, and participant data collection. Users are encouraged to consider how REDCap could help with tasks requiring regular information gathering or projects with multiple steps and users.
Alan Cope (De Montfort University) – EXPLORER (create workflows and processes...
The survey of researchers at De Montfort University provided useful feedback on how to improve the university's open access research archive DORA. Technical work is underway to expand DORA's capabilities to accommodate a wider variety of research outputs, as informed by the survey results. Integration with the university's new website and research profiling system is in process to further embed DORA within the research environment. The EXPLORER project aims to enhance DORA and increase faculty engagement with the repository.
The UK National Chemical Database Service – an integration of commercial and ...
The UK National Chemical Database Service – an integration of commercial and ...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
At a time when the data explosion has simply been redefined as “Big”, the hurdles associated with building a subject-specific data repository for chemistry are daunting. Combining a multitude of non-standard data formats for chemicals, related properties, reactions, spectra etc., together with the confusion of licensing and embargoing, and providing for data exchange and integration with services and platforms external to the repository, the challenge is significant. This all at a time when semantic technologies are touted as the fundamental technology to enhance integration and discoverability. Funding agencies are demanding change, especially a change towards access to open data to parallel their expectations around Open Access publishing. The Royal Society of Chemistry has been funded by the Engineering and Physical Science Research of the UK to deliver a “chemical database service” for UK scientists. This presentation will provide an overview of the challenges associated with this project and our progress in delivering a chemistry repository capable of handling the complex data types associated with chemistry. The benefits of such a repository in terms of providing data to develop prediction models to further enable scientific discovery will be discussed and the potential impact on the future of scientific publishing will also be examined. What's hot (20)
Fairification experience clarifying the semantics of data matrices
Fairification experience clarifying the semantics of data matrices
Application of recently developed FAIR metrics to the ELIXIR Core Data Resources
Application of recently developed FAIR metrics to the ELIXIR Core Data Resources
ICIC 2013 Conference Proceedings Ricardo Eito Brun Uni Madrid
ICIC 2013 Conference Proceedings Ricardo Eito Brun Uni Madrid
Why ICT Fails in Healthcare: Software Maintenance and Maintainability
Why ICT Fails in Healthcare: Software Maintenance and Maintainability
ICIC 2014 Increasing the efficiency of pharmaceutical research through data i...
ICIC 2014 Increasing the efficiency of pharmaceutical research through data i...
Royal society of chemistry activities to develop a data repository for chemis...
Royal society of chemistry activities to develop a data repository for chemis...
Introduction to using REDCap for multi-site longitudinal research in medicine
Introduction to using REDCap for multi-site longitudinal research in medicine
Open interoperability standards, tools and services at EMBL-EBI
Open interoperability standards, tools and services at EMBL-EBI
Alan Cope (De Montfort University) – EXPLORER (create workflows and processes...
Alan Cope (De Montfort University) – EXPLORER (create workflows and processes...
The UK National Chemical Database Service – an integration of commercial and ...
The UK National Chemical Database Service – an integration of commercial and ...
Similar to Semantics for integrated laboratory analytical processes - The Allotrope Perspective
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
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.
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
UDM (Unified Data Model) - Enabling Exchange of Comprehensive Reaction Inform...
UDM (Unified Data Model) - Enabling Exchange of Comprehensive Reaction Inform...Frederik van den Broek
Slides from my talk at the ACS CINF Symposium on Chemical Nomenclature & Representation on 26 August 2019 in San Diego.
Abstract:
The first edition of the Beilstein Handbook of Organic Chemistry was published nearly 140 years ago. Electronic laboratory notebooks have been in use in chemistry for almost 20 years. And the life science industry still doesn't have a well-defined way of capturing and exchanging information about chemical reactions and relies on imprecise or vendor-specific data formats. Without a common language and structure to describe experiments, data integration is unnecessarily expensive and a significant part of published data has not been readily available for processing or analysis.
The Unified Data Model (UDM) project team aims to improve the situation. UDM is a collective effort of vendors and life science organizations to create an open, extendable and freely available reference model and data format for exchange of experimental information about compound synthesis and testing. Run under the umbrella of the Pistoia Alliance, the project team has published two releases of the UDM data format and it is expected that the model will continue to be improved as demand stipulates working with the Pistoia FAIR data implementation by industry community.Integrated Analysis of Toxicology Data supported by ToxBank
The SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing-1) research cluster is comprised of seven EU FP7 Health projects and is co-financed by Cosmetics Europe. The aim is to generate a proof-of-concept to show how the latest in vitro and in silico technologies, systems toxicology and toxicogenomics can be combined to deliver a test replacement for repeated dose systemic toxicity testing on animals. The SEURAT-1 strategy is to adopt a mode-of-action framework to describe repeated dose toxicity to derive predictions of in vivo toxicity responses. ToxBank is the cross-cluster infrastructure project whose activities include the development of a data warehouse to provide a web-accessible shared repository of research data and protocols. Experiments are generating dose response data over multiple timepoints using different omics platforms including transcriptomics, proteomics, metabolomics, epigenetics over a variety of different cell lines and a common set of reference compounds. Experimental data is also being generated from functional assays and bioreactors, and supplemented with in silico approaches including kinetic information. This complex and heterogeneous data is being consolidated and harmonized through the ToxBank data warehouse. It is being organized in order to perform an integrated data analysis and ultimately predict repeated dose systemic toxicity. Core technologies used include the ISA-Tab universal data exchange format, Representational State Transfer (REST) web services, the W3C Resource Description Framework (RDF) and the OpenTox standards. We describe the design of the data warehouse based on cluster requirements and the implementation based on open standards.
Medical science
This document presents a probabilistic model to increase the reliability of data analysis from multiplex sequencing performed on the SOLiD sequencing platform. The model characterizes multiplex runs and filters low-quality data by considering the intrinsic characteristics of each sequencing. It aims to identify faults in the sequencing process, guide filtering without discarding useful sequences, and assign confidence levels to the generated data. The model analyzes the SOLiD barcoding system used to tag multiple samples, as this reflects the overall sequencing quality and has lower processing costs than analyzing sequences of interest.
Chemistry Data Delivery from the US-EPA Center for Computational Toxicology a...
Chemistry Data Delivery from the US-EPA Center for Computational Toxicology a...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
In recent years, the growth of scientific data and the increasing need for data sharing and collaboration in the field of environmental chemistry has led to the creation of various software and databases that facilitate research and development into the safety and toxicity of chemicals. The US-EPA Center for Computational Toxicology and Exposure has been developing software and databases that serve the chemistry community for many years. This presentation will focus on several web-based software applications which have been developed at the USEPA and made available to the community. While the primary software application from the Center is the CompTox Chemicals Dashboard almost a dozen proof-of-concept applications have been built serving various capabilities. The publicly accessible Cheminformatics Modules (https://www.epa.gov/chemicalresearch/cheminformatics) provides access to six individual modules to allow for hazard comparison for sets of chemicals, structure-substructure-similarity searching, structure alerts and batch QSAR prediction of both physicochemical and toxicity endpoints. A number of other applications in development include a chemical transformations database (ChET) and a database of analytical methods and open mass spectral data (AMOS). Each of these depends on the underlying DSSTox chemicals database, a rich source of chemistry data for over 1.2 million chemical substances. I will provide an overview of all tools in development and the integrated nature of the applications based on the underlying chemistry data. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Accessing Environmental Chemistry Data via Data Dashboards
Accessing Environmental Chemistry Data via Data Dashboards US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
As part of its mission the Center for Computational Toxicology and Exposure (CCTE) delivers access to chemicals related data via online Dashboards. The CompTox Chemicals Dashboard (available at https://comptox.epa.gov/dashboard) provides access to >900,000 chemicals and associated data including experimental and predicted property data, in vivo hazard data, in vitro bioactivity data, exposure data, and various other data types. The application provides a set of flexible searches allowing for search, visualization and downloads of the data to the desktop for further interrogation. This presentation will provide an overview of the Dashboard and other proof-of-concept applications that are now publicly available (https://www.epa.gov/chemical-research/cheminformatics). For example, the Hazard Comparison module (shown in the figure below) allows profiling of chemicals based on toxicity types (https://doi.org/10.1007/s10098-019-01795-w). This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Enabling Clinical Data Reuse with openEHR Data Warehouse Environments
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Enabling Clinical Data Reuse with openEHR Data Warehouse Environments
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
Towards automated phenotypic cell profiling with high-content imaging
Presentation by Ola Spjuth (Uppsala University and Scaleout) at the Chemical Biology Seminar Series, February 6th, at Karolinska Institutet and Science for Life Laboratory, Stockholm, Sweden.
ABSTRACT
Phenotypic profiling of cells with high-content imaging is emerging as an important methodology with high predictive power. The true power of these methods comes when integrated into automated, robotized systems that can be run continuously and not restricted to batch analysis. One of the main challenges then becomes how to manage and continuously analyze the large amounts of data produced. In this talk I will present our efforts to establish an automated lab for cell profiling of drugs using multiplexed fluorescence imaging (Cell Painting). I will describe our computational and lab infrastructure as well as the systems, tools an methods we are developing to sustain continuous profiling of cells and continuous AI modeling. A key objective in the group is on improving screening and toxicity assessment, but also to explore predictions of mechanisms and pathways. The long-term goal is to build a closed-loop system where results from analyses are used by an AI system to design the next round of experiments and iteratively improve the confidence in predictions. Research website: https://pharmb.io
Towards Automated AI-guided Drug Discovery Labs
This document discusses using AI, automation, and intelligent experimental design to accelerate drug discovery. Key points include:
- AI and automation will be increasingly important in biological labs for experimental design, hypothesis generation and testing.
- Data velocity from labs will increase, requiring automated, continuous analytics and AI.
- The goal is to close the loop between data generation, analysis, and experimental design using AI to intelligently design new experiments.
- This would accelerate drug discovery by enabling prediction of safety concerns, explanation of drug mechanisms, and screening for new drugs.
ACS 248th Paper 71 ChAMP Project
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
The importance of data curation on QSAR Modeling: PHYSPROP open data as a cas...
This presentation highlighted how data curation impacts the reliability of QSAR models. We examined key datasets related to environmental endpoints to validate across chemical structure representations (e.g., mol file and SMILES) and identifiers (chemical names and registry numbers), and approaches to standardize data into QSAR-ready formats prior to modeling procedures. This allowed us to quantify and segregate data into quality categories. This improved our ability to evaluate the resulting models that can be developed from these data slices, and to quantify to what extent efforts developing high-quality datasets have the expected pay-off in terms of predicting performance. The most accurate models that we build will be accessible via our public-facing platform and will be used for screening and prioritizing chemicals for further testing.
Metabolomics Society meeting 2011 - presentatie Kees
This document summarizes three challenges for metabolomics study databases: 1) representing the biological context and complex study designs of samples through metadata, 2) implementing data preprocessing, identification, and quantification methods, and 3) embedding metabolomics data with other 'omics' data from the same samples. It provides an overview of the Netherlands Metabolomics Centre's open-source Data Support Platform, which allows flexible representation of study metadata and metabolomics data from various assays to address these challenges.
BioAssay Express: Creating and exploiting assay metadata
The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches.
This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
ISA-Tab Standards at Metabolomics Society Meeting, Tsuruoka 2014, Japan
Introducing ISA-Tab Format, key features, patterns, curation issues, supporting software and RDF conversion
Delivering The Benefits of Chemical-Biological Integration in Computational T...
Delivering The Benefits of Chemical-Biological Integration in Computational T...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Researchers at EPA’s National Center for Computational Toxicology integrate advances in biology, chemistry, and computer science to examine the toxicity of chemicals and help prioritize chemicals for further research based on potential human health risks. The goal of this research program is to quickly evaluate thousands of chemicals, but at a much reduced cost and shorter time frame relative to traditional approaches. The data generated by the Center includes characterization of thousands of chemicals across hundreds of high-throughput screening assays, consumer use and production information, pharmacokinetic properties, literature data, physical-chemical properties as well as the predictive computational modeling of toxicity and exposure. We have developed a number of databases and applications to deliver the data to the public, academic community, industry stakeholders, and regulators. This presentation will provide an overview of our work to develop an architecture that integrates diverse large-scale data from the chemical and biological domains, our approaches to disseminate these data, and the delivery of models supporting predictive computational toxicology. In particular, this presentation will review our new publicly-accessible CompTox Dashboard as the first application built on our newly developed architecture. This abstract does not reflect U.S. EPA policy. Metabolic Profiling_techniques and approaches.ppt
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in areas like instrumentation variability, data treatment, identification, and lack of standardization. Their work seeks to advance the field and provide insights into biochemistry, biomarkers, disease, and treatment responses through holistic analysis of small molecules.
Metabolic Profiling: Limitations, Challenges.ppt
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in analytical procedures, data treatment, and lack of standardization. Their work seeks to advance metabolomics as an expanding field that provides insights into biochemistry and discovers biomarkers for health and disease.
Building a Standard for Standards: The ChAMP Project
The document describes the ChAMP project, which aims to develop a standard set of metadata for representing and annotating chemical analysis information. It discusses the motivation being to facilitate searching and aggregation of analysis data across literature, publications and repositories. The standard would define important characteristics and metadata about analysis methodologies in a way that is easy for users to implement across disciplines. It outlines the key components including developing an ontology of chemical analysis terms and minimum metadata requirements for annotation.
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Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
UDM (Unified Data Model) - Enabling Exchange of Comprehensive Reaction Inform...
UDM (Unified Data Model) - Enabling Exchange of Comprehensive Reaction Inform...
Integrated Analysis of Toxicology Data supported by ToxBank
Integrated Analysis of Toxicology Data supported by ToxBank
Chemistry Data Delivery from the US-EPA Center for Computational Toxicology a...
Chemistry Data Delivery from the US-EPA Center for Computational Toxicology a...
Accessing Environmental Chemistry Data via Data Dashboards
Accessing Environmental Chemistry Data via Data Dashboards
Enabling Clinical Data Reuse with openEHR Data Warehouse Environments
Enabling Clinical Data Reuse with openEHR Data Warehouse Environments
Enabling Clinical Data Reuse with openEHR Data Warehouse Environments
Enabling Clinical Data Reuse with openEHR Data Warehouse Environments
Towards automated phenotypic cell profiling with high-content imaging
Towards automated phenotypic cell profiling with high-content imaging
The importance of data curation on QSAR Modeling: PHYSPROP open data as a cas...
The importance of data curation on QSAR Modeling: PHYSPROP open data as a cas...
Metabolomics Society meeting 2011 - presentatie Kees
Metabolomics Society meeting 2011 - presentatie Kees
BioAssay Express: Creating and exploiting assay metadata
BioAssay Express: Creating and exploiting assay metadata
ISA-Tab Standards at Metabolomics Society Meeting, Tsuruoka 2014, Japan
ISA-Tab Standards at Metabolomics Society Meeting, Tsuruoka 2014, Japan
Delivering The Benefits of Chemical-Biological Integration in Computational T...
Delivering The Benefits of Chemical-Biological Integration in Computational T...
Building a Standard for Standards: The ChAMP Project
Building a Standard for Standards: The ChAMP Project
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The Fast Track to Fair Lab Data
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.
Challenges & Opportunities of Implementation FAIR in Life Sciences
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?)
Data lifecycle mgt across the enterprise
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.
From allotrope to reference master data management
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.
Big Data becomes Big Analysis
• 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
Early AI Adoption Via Advanced Analytics
This document discusses the increasing complexity of data analytics and the need to integrate different approaches. It covers:
- The 4 V's of big data - volume, velocity, variety and veracity and how data complexity is rising.
- How semantic technologies can provide advantages over traditional mathematical clustering techniques.
- How the amount of data and need to utilize it effectively is growing exponentially, driven by factors like cloud computing and new data types.
- The idea of "Big Analysis" which combines semantic technologies with traditional data science methods to allow for deeper analysis of complex data patterns.
- How interdisciplinary thought across industries and education is needed to drive innovation with new paradigms and non-traditional approaches
Why Data is Becoming the Most Valuable Asset Companies Posses
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.
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
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.)
Why paperless lab is just the first step towards a smart lab
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.
Smart Data for Smart Labs
This document discusses how semantic technologies can help improve the integration and sharing of laboratory data. It outlines some of the current challenges laboratories face with data silos and incompatible systems. Semantic technologies provide interoperability, improved searching and browsing, and the ability to reuse data. They also allow for automated reasoning by adding formal representations and logic. Moving forward, big data analytics will be increasingly important, and semantic technologies provide advantages by adding metadata, understanding relationships and context in data, and enabling better data models and integration across complex information. Ultimately, smart laboratories of the future can provide integrated, sharable, scalable data and analytics capabilities using these approaches.
Reasoning over big data
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.
Best Practice Reference Architecture for Data Curation
OSTHUS representative Michael Engels presented "Best Practice Reference Architecture for Data Curation" at BioIT World 2015.
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From allotrope to reference master data management
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Why Data is Becoming the Most Valuable Asset Companies Posses
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Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
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Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
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在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
毕业原版【微信:41543339】【(Glasgow毕业证书)格拉斯哥大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(Chester毕业证书)切斯特大学毕业证如何办理
毕业原版【微信:41543339】【(Chester毕业证书)切斯特大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
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【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
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Semantics for integrated laboratory analytical processes - The Allotrope Perspective
- 1. LIDER workshop, Munich 13th of July 2015 Semantics for Integrated Laboratory Analytical Processes The Allotrope Perspective Heiner Oberkampf
- 2. slide 2 Agenda Initial Situation Allotrope Foundation Approach and IT-Solution Allotrope Data Format Domain Taxonomies Use Cases
- 3. slide 3 Laboratory Analytical Processes sample dataanalytical process
- 4. slide 4 Laboratory Analytical Processes Application 1 Application 2Application 3
- 5. slide 5 Common Problems It’s hard to find data based on intuitive starting points [e.g. study, project, analyst, technique] It’s hard to integrate data from different labs instruments, or online/offline because the file format is different It’s hard to mine a collection of data because the details and the context of the experiment is stored somewhere else Can’t interpret data later because the context is incomplete, inconsistent, often free text Instrument & software interoperability is limited…at best
- 6. slide 6 Allotrope Data Format
- 7. slide 7 Allotrope Foundation Member Companies: AbbVie, Amgen, Baxter, Bayer, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Genentech/Roche, GlaxoSmithKline, Merck & Co., Pfizer Secretariat: Drinker Biddle Project Management Legal & Logistics Support Professional Software Firm: OSTHUS Framework development Technical leadership Partner Network: ACD/Labs, Agilent Technologies, BIOVIA, BSSN Software, Erasmus MC, IDBS, Mestrelab Research, Mettler Toledo, Sartorius, Shimadzu, Thermo Scientific, University of Southampton, Waters
- 8. slide 8 Allotrope Data Format (ADF) ADF is based on Hierarchical Data Format (HDF 5), which is specificially designed to store and organize large amounts of numerical data.
- 9. slide 9 API Stack The Allotrope Framework provides APIs to read and write data contained in ADF Thus, developers do not have to concern themselves with RDF, SPARQL, semantics or complex graph patterns. Platform independent file format (HDF 5) Data Package API Data Cube API Data Description API (Apache Jena) Analytical Data API Taxonomies Triple Store API
- 10. slide 10 Allotrope Foundation Taxonomies (AFT)
- 11. slide 11 Scope and Current Status 13 analytical techniques are already implemented: small molecules: • gas chromatography • Karl Fischer • liquid chromatography • mass spectrometry • nuclear magnetic repulsion spectrometry • thermogravimetric analysis • ultra violet spectrometry large molecules: • capillary electrophoresis • cell counter • cell culture analyzer • blood gas analysis both: • balance • pH 530 140 2220 270 Number of Classes:
- 12. slide 12 Reused Vocabularies and Ontologies Directly imported: Simple Knowledge Organization System (SKOS) Quantities, Units, Dimensions and Data Types Ontologies (QUDT) The RDF Data Cube Vocabulary (QB) Partly reused definitions: Chemical Methods Ontology (CHMO) Proteomics Standards Initiative – Mass Spectrometry (PSI-MS) International Union of Pure and Applied Chemistry (IUPAC) …
- 14. slide 14 Analytical Workflow The basic analytical workflow and data flow gets standardized
- 15. slide 15 Liquid Chromatography Mass Spectrometry Data set of rank 2 Additional dimensions: - sample - retention time - device - … Only meta data is expressed in RDF, while the numeric data is natively represented in HDF 5. The ADF Data Cube Ontology provides the mapping between RDF meta data descriptions and physical storage in HDF 5. mass ioncount
- 16. slide 16 Imaging Mass Spectrometry Nature Reviews Cancer 10, 639-646 (September 2010) | doi:10.1038/nrc2917
- 17. slide 17 High Performance Liquid Chromatography <HPLCSystem1/QuaternarySolventManager> <HPLCSystem1/SampleManager> <HPLCSystem1/ColumnManager> <HPLCSystem1/PDADetector> <HPLCSystem1> http://registry.mycompany.com/systems/hplc/hplc-uv/ Linked Data Platform relative URLs under HPLC-UV Base URL in Registry af-e:has component
- 18. slide 18 Conclusion Initially: Experiments were performed to get approval for drugs. Today: Experiments generate data that can be used in many different contexts. Why Semantics? Good framework for standardized data descriptions and needed to realize the potential of the available data Linked Data allows to relate information stored in ADF with additional context: e.g. materials, devices, chemicals, processes, locations etc.
- 19. slide 19 Questions? Heiner Oberkampf heiner.oberkampf@osthus.com www.osthus.com Allotrope Foundation: www.allotrope.org