This document discusses building FAIR data knowledge graphs from theory to practice. It begins by outlining what R&D researchers want to do with data, such as understanding disease mechanisms and using patient data, but that currently data is fragmented across systems. It then introduces the FAIR data principles and describes building a knowledge graph that incorporates data from multiple sources using standards like the Data Catalog vocabulary. The key challenges discussed are determining canonical representations for entities and linking data to public vocabularies through an enrichment process.
BioPharma and FAIR Data, a Collaborative AdvantageTom Plasterer
The concept of FAIR (Findable, Accessible, Interoperable and Reusable) data is becoming a reality as stakeholders from industry, academia, funding agencies and publishers are embracing this approach. For BioPharma being able to effectively share and reuse data is a tremendous competitive advantage, within a company, with peer organizations, key opinion leaders and regulatory agencies. A few key drivers, success stories and preliminary results of an industry data stewardship survey are presented.
FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world’s data. We started with data catalogues (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen.
This talk was presented at The Molecular Medicine Tri-Conference/Bio-IT West on March 11, 2019.
Dataset Catalogs as a Foundation for FAIR* DataTom Plasterer
BioPharma and the broader research community is faced with the challenge of simply finding the appropriate internal and external datasets for downstream analytics, knowledge-generation and collaboration. With datasets as the core asset, we wanted to promote both human and machine exploitability, using web-centric data cataloguing principles as described in the W3C Data on the Web Best Practices. To do so, we adopted DCAT (Data CATalog Vocabulary) and VoID (Vocabulary of Interlinked Datasets) for both RDF and non-RDF datasets at summary, version and distribution levels. Further, we’ve described datasets using a limited set of well-vetted public vocabularies, focused on cross-omics analytes and clinical features of the catalogued datasets.
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
As BioPharma adapts to incorporate nimble networks of suppliers, collaborators, and regulators the ability to link data is critical for dynamic interoperability. Adoption of linked data paradigm allows BioPharma to focus on core business: delivering valuable therapeutics in a timely manner.
Harnessing Edge Informatics to Accelerate Collaboration in BioPharma (Bio-IT ...Tom Plasterer
As scientists in the life sciences we are trained to pursue singular goals around a publication or a validated target or a drug submission. Our failure rates are exceedingly high especially as we move closer to patients in the attempt to collect sufficient clinical evidence to demonstrate the value of novel therapeutics. This wastes resources as well as time for patients depending upon us for the next breakthrough.
Edge Informatics is an approach to ameliorate these failures. Using both technical and social solutions together knowledge can be shared and leveraged across the drug development process. This is accomplished by making data assets discoverable, accessible, self-described, reusable and annotatable. The Open PHACTS project pioneered this approach and has provided a number of the technical and social solutions to enable Edge Informatics. A number of pre-competitive consortia and some content providers have also embraced this approach, facilitating networks of collaborators within and outside a given organization. When taken together more accurate, timely and inclusive decision-making is fostered.
Edge Informatics and FAIR (Findable, Accessible, Interoperable and Reusable) ...Tom Plasterer
Edge Informatics is an approach to accelerate collaboration in the BioPharma pipeline. By combining technical and social solutions knowledge can be shared and leveraged across the multiple internal and external silos participating in the drug development process. This is accomplished by making data assets findable, accessible, interoperable and reusable (FAIR). Public consortia and internal efforts embracing FAIR data and Edge Informatics are highlighted, in both preclinical and clinical domains.
This talk was presented at the Molecular Medicine Tri-Conference in San Francisco, CA on February 20, 2017
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues.
Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
BioPharma and FAIR Data, a Collaborative AdvantageTom Plasterer
The concept of FAIR (Findable, Accessible, Interoperable and Reusable) data is becoming a reality as stakeholders from industry, academia, funding agencies and publishers are embracing this approach. For BioPharma being able to effectively share and reuse data is a tremendous competitive advantage, within a company, with peer organizations, key opinion leaders and regulatory agencies. A few key drivers, success stories and preliminary results of an industry data stewardship survey are presented.
FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world’s data. We started with data catalogues (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen.
This talk was presented at The Molecular Medicine Tri-Conference/Bio-IT West on March 11, 2019.
Dataset Catalogs as a Foundation for FAIR* DataTom Plasterer
BioPharma and the broader research community is faced with the challenge of simply finding the appropriate internal and external datasets for downstream analytics, knowledge-generation and collaboration. With datasets as the core asset, we wanted to promote both human and machine exploitability, using web-centric data cataloguing principles as described in the W3C Data on the Web Best Practices. To do so, we adopted DCAT (Data CATalog Vocabulary) and VoID (Vocabulary of Interlinked Datasets) for both RDF and non-RDF datasets at summary, version and distribution levels. Further, we’ve described datasets using a limited set of well-vetted public vocabularies, focused on cross-omics analytes and clinical features of the catalogued datasets.
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
As BioPharma adapts to incorporate nimble networks of suppliers, collaborators, and regulators the ability to link data is critical for dynamic interoperability. Adoption of linked data paradigm allows BioPharma to focus on core business: delivering valuable therapeutics in a timely manner.
Harnessing Edge Informatics to Accelerate Collaboration in BioPharma (Bio-IT ...Tom Plasterer
As scientists in the life sciences we are trained to pursue singular goals around a publication or a validated target or a drug submission. Our failure rates are exceedingly high especially as we move closer to patients in the attempt to collect sufficient clinical evidence to demonstrate the value of novel therapeutics. This wastes resources as well as time for patients depending upon us for the next breakthrough.
Edge Informatics is an approach to ameliorate these failures. Using both technical and social solutions together knowledge can be shared and leveraged across the drug development process. This is accomplished by making data assets discoverable, accessible, self-described, reusable and annotatable. The Open PHACTS project pioneered this approach and has provided a number of the technical and social solutions to enable Edge Informatics. A number of pre-competitive consortia and some content providers have also embraced this approach, facilitating networks of collaborators within and outside a given organization. When taken together more accurate, timely and inclusive decision-making is fostered.
Edge Informatics and FAIR (Findable, Accessible, Interoperable and Reusable) ...Tom Plasterer
Edge Informatics is an approach to accelerate collaboration in the BioPharma pipeline. By combining technical and social solutions knowledge can be shared and leveraged across the multiple internal and external silos participating in the drug development process. This is accomplished by making data assets findable, accessible, interoperable and reusable (FAIR). Public consortia and internal efforts embracing FAIR data and Edge Informatics are highlighted, in both preclinical and clinical domains.
This talk was presented at the Molecular Medicine Tri-Conference in San Francisco, CA on February 20, 2017
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues.
Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
An overview on FAIR Data and FAIR Data stewardship, and the roadmap for FAIR Data solutions coordinated by the Dutch Techcentre for Life Sciences. This presentation was given at the Netherlands eScience Center's "Essential skills in data-intensive research" course week.
FAIR Data Management and FAIR Data SharingMerce Crosas
Presentation at the Critical Perspective on the Practice of Digiral Archeology symposium: http://archaeology.harvard.edu/critical-perspectives-practice-digital-archaeology
Presentation by Luiz Olavo Bonino about the current state of the developments on FAIR Data supporting tools at the Dutch Techcentre for Life Sciences Partners Event on November 3-4 2016.
LIBER Webinar: Are the FAIR Data Principles really fair?LIBER Europe
The FAIR Data Principles are a hot topic in research data managment. Their adoption within the H2020 funding programme means researchers now have to pay much more attention to how their share, publish and archive their data.
In this light, how can libraries help their research communities implement the FAIR principles? And write better data management plans?
This questions were addressed in a LIBER webinar containing some guidance and reflections on the principles themselves. Presented by Alastair Dunning, Head Research Data Services at the TU Delft (hosts of the 4TU.Centre for Research Data), it is based on a study of 37 data repositories (from subject specific repositories, to generic data archives, to national infrastructures), seeing how far they comply with each of the individual facets of the Data principles.
Application of recently developed FAIR metrics to the ELIXIR Core Data ResourcesPistoia Alliance
The FAIR (Findable, Accessible, Interoperable and Reusable) principles aim to maximize the discovery and reuse of digital resources. Using recently developed software and metrics to assess FAIRness and supported through an ELIXIR Implementation Study, Michel worked with a subset of ELIXIR Core Data Resources to apply these technologies. In this webinar, he will discuss their approach, findings, and lessons learned towards the understanding and promotion of the FAIR principles.
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
Lesson 8 in a set of 10 created by DataONE on Best Practices for Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Building a Network of Interoperable and Independently Produced Linked and Ope...Michel Dumontier
Over 15 years ago, Sir Tim Berners Lee proclaimed the founding of an exciting new future involving intelligent agents operating over smarter data in order to perform complex tasks at the behest of their human controllers. At the heart of this vision lies an uneasy alliance between tedious formal knowledge representations and powerful analytics over big, but often messy data. Bio2RDF, our decade old open source project to create Linked Data for the life sciences, has weaved emergent Semantic Web technologies such as ontologies and Linked Data to generate FAIR - Findable, Accessible, Interoperable, and Reusable - data in the form of billions of machine accessible statements for use in downstream biomedical discovery.
This revolution in data publication has been strengthened by action from global bioinformatics institutions such as the NCBI, NCBO, EBI, and DBCLS. Notably, NCBI's PubChem has successfully coupled large scale data integration with community-based standards to offer a remakable biochemical knowledge resource amenable to data hungry discovery tools. Yet, in the face of increasing pressure from researchers, funders, and publishers, will these approaches be sufficient for growing and maintaining a comprehensive knowledge graph that is inclusive of all biomedical research?
Towards metrics to assess and encourage FAIRnessMichel Dumontier
With an increased interest in the FAIR metrics, there is need to develop tools and appraoches that can assess the FAIRness of a digital resource. This talk begins to explore some ideas in this space, and invites people to participate in a working group focused on the development, application, and evaluation of FAIR metric efforts.
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
Some Frameworks for Improving Analytic Operations at Your CompanyRobert Grossman
I review three frameworks for analytic operations that are designed to improve the value obtained when deploying analytic models into products, services and internal operations.
Dataverse, Cloud Dataverse, and DataTagsMerce Crosas
Talk given at Two Sigma:
The Dataverse project, developed at Harvard's Institute for Quantitative Social Science since 2006, is a widely used software platform to share and archive data for research. There are currently more than 20 Dataverse repository installations worldwide, with the Harvard Dataverse repository alone hosting more than 60,000 datasets. Dataverse provides incentives to researchers to share their data, giving them credit through data citation and control over terms of use and access. In this talk, I'll discuss the Dataverse project, as well as related projects such as DataTags to share sensitive data and Cloud Dataverse to share Big Data.
An overview on FAIR Data and FAIR Data stewardship, and the roadmap for FAIR Data solutions coordinated by the Dutch Techcentre for Life Sciences. This presentation was given at the Netherlands eScience Center's "Essential skills in data-intensive research" course week.
FAIR Data Management and FAIR Data SharingMerce Crosas
Presentation at the Critical Perspective on the Practice of Digiral Archeology symposium: http://archaeology.harvard.edu/critical-perspectives-practice-digital-archaeology
Presentation by Luiz Olavo Bonino about the current state of the developments on FAIR Data supporting tools at the Dutch Techcentre for Life Sciences Partners Event on November 3-4 2016.
LIBER Webinar: Are the FAIR Data Principles really fair?LIBER Europe
The FAIR Data Principles are a hot topic in research data managment. Their adoption within the H2020 funding programme means researchers now have to pay much more attention to how their share, publish and archive their data.
In this light, how can libraries help their research communities implement the FAIR principles? And write better data management plans?
This questions were addressed in a LIBER webinar containing some guidance and reflections on the principles themselves. Presented by Alastair Dunning, Head Research Data Services at the TU Delft (hosts of the 4TU.Centre for Research Data), it is based on a study of 37 data repositories (from subject specific repositories, to generic data archives, to national infrastructures), seeing how far they comply with each of the individual facets of the Data principles.
Application of recently developed FAIR metrics to the ELIXIR Core Data ResourcesPistoia Alliance
The FAIR (Findable, Accessible, Interoperable and Reusable) principles aim to maximize the discovery and reuse of digital resources. Using recently developed software and metrics to assess FAIRness and supported through an ELIXIR Implementation Study, Michel worked with a subset of ELIXIR Core Data Resources to apply these technologies. In this webinar, he will discuss their approach, findings, and lessons learned towards the understanding and promotion of the FAIR principles.
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
Lesson 8 in a set of 10 created by DataONE on Best Practices for Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Building a Network of Interoperable and Independently Produced Linked and Ope...Michel Dumontier
Over 15 years ago, Sir Tim Berners Lee proclaimed the founding of an exciting new future involving intelligent agents operating over smarter data in order to perform complex tasks at the behest of their human controllers. At the heart of this vision lies an uneasy alliance between tedious formal knowledge representations and powerful analytics over big, but often messy data. Bio2RDF, our decade old open source project to create Linked Data for the life sciences, has weaved emergent Semantic Web technologies such as ontologies and Linked Data to generate FAIR - Findable, Accessible, Interoperable, and Reusable - data in the form of billions of machine accessible statements for use in downstream biomedical discovery.
This revolution in data publication has been strengthened by action from global bioinformatics institutions such as the NCBI, NCBO, EBI, and DBCLS. Notably, NCBI's PubChem has successfully coupled large scale data integration with community-based standards to offer a remakable biochemical knowledge resource amenable to data hungry discovery tools. Yet, in the face of increasing pressure from researchers, funders, and publishers, will these approaches be sufficient for growing and maintaining a comprehensive knowledge graph that is inclusive of all biomedical research?
Towards metrics to assess and encourage FAIRnessMichel Dumontier
With an increased interest in the FAIR metrics, there is need to develop tools and appraoches that can assess the FAIRness of a digital resource. This talk begins to explore some ideas in this space, and invites people to participate in a working group focused on the development, application, and evaluation of FAIR metric efforts.
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
Some Frameworks for Improving Analytic Operations at Your CompanyRobert Grossman
I review three frameworks for analytic operations that are designed to improve the value obtained when deploying analytic models into products, services and internal operations.
Dataverse, Cloud Dataverse, and DataTagsMerce Crosas
Talk given at Two Sigma:
The Dataverse project, developed at Harvard's Institute for Quantitative Social Science since 2006, is a widely used software platform to share and archive data for research. There are currently more than 20 Dataverse repository installations worldwide, with the Harvard Dataverse repository alone hosting more than 60,000 datasets. Dataverse provides incentives to researchers to share their data, giving them credit through data citation and control over terms of use and access. In this talk, I'll discuss the Dataverse project, as well as related projects such as DataTags to share sensitive data and Cloud Dataverse to share Big Data.
The FAIR data movement and 22 Feb 2023.pdfAlan Morrison
To realize the promise of FAIR data, companies must be data mature. They must adopt data-centric architecture and the #FAIR (findable, accessible, interoperable and reusable) principles. When they do, the data they need will be linked and self-describing. The data when queried will tell you where it is.
A desiloed, #semantic graph data abstraction--the only feasible means behind creating FAIR data at this point--is not only the means to data discovery, but also a path to model-driven development and data sharing at scale, both of which will break an organization's habit of duplicating data and logic.
This webinar highlights fresh enterprise case studies that are starting to realize the dream of #FAIRdata, as well as how these companies are succeeding:
- Zero copy integration: How to think about eliminating #dataduplication and stop the application buying binge that only exacerbates the problem.
- Dynamic, unified data model: Standard graphs provide a means of modeling once, use anywhere, for conceptual, logical and physical purposes all at once.
- Persuasion and teamwork: The #graph approach provides an ideal way to loop business units and domain experts in and empower them to recommend model changes that are easily implemented.
The whole process is bringing #enterprises like Walmart, Uber, Goldman Sachs and Nokia into the age of #contextualcomputing. Learn how to be a fast follower by thinking big, but starting small.
The global need to securely derive (instant) insights, have motivated data architectures from distributed storage, to data lakes, data warehouses and lake-houses. In this talk we describe Tag.bio, a next generation data mesh platform that embeds vital elements such as domain centricity/ownership, Data as Products, Self-serve architecture, with a federated computational layer. Tag.bio data products combine data sets, smart APIs, statistical and machine learning algorithms into decentralized data products for users to discover insights using FAIR Principles. Researchers can use its point and click (no-code) system to instantly perform analysis and share versioned, reproducible results. The platform combines a dynamic cohort builder with analysis protocols and applications (low-code) to drive complex analysis workflows. Applications within data products are fully customizable via R and Python plugins (pro-code), and the platform supports notebook based developer environments with individual workspaces.
Join us for a talk/demo session on Tag.bio data mesh platform and learn how major pharma industries and university health systems are using this technology to promote value based healthcare, precision healthcare, find cures for disease, and promote collaboration (without explicitly moving data around). The talk also outlines Tag.bio secure data exchange features for real world evidence datasets, privacy centric data products (confidential computing) as well as integration with cloud services
A Generic Scientific Data Model and Ontology for Representation of Chemical DataStuart Chalk
The current movement toward openness and sharing of data is likely to have a profound effect on the speed of scientific research and the complexity of questions we can answer. However, a fundamental problem with currently available datasets (and their metadata) is heterogeneity in terms of implementation, organization, and representation.
To address this issue we have developed a generic scientific data model (SDM) to organize and annotate raw and processed data, and the associated metadata. This paper will present the current status of the SDM, implementation of the SDM in JSON-LD, and the associated scientific data model ontology (SDMO). Example usage of the SDM to store data from a variety of sources with be discussed along with future plans for the work.
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons Learned in Academic and Life Science Settings
Dan Housman, Recombinant by Deloitte
The Recombinant by Deloitte team has worked with organizations such as Kimmel Cancer Center as a model to adapt existing mature i2b2 implementations to meet business and scientific needs. Other organizations are increasingly focused on how to use cloud and high performance computing models to achieve different performance levels. Advanced initiatives are progressing to link commercial tools such as Qlikview to explore tranSMART data and to solve for key gaps in scientific pipelines. Dan will present recent lessons learned, new capabilities, and some of the impact on the path forwards for future tranSMART updates.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
This is a talk that I gave at the Molecular Medicine Tri Conference on data commons and data sharing to accelerate research discoveries and improve patient outcomes. It also covers how your organization can build a data commons using the Open Commons Consortium's Data Commons Framework and the University of Chicago's Gen3 data commons platform.
A talk prepared for Workshop Working on data stewardship? Meet your peers!
Datum: 03 OKT 2017
https://www.surf.nl/agenda/2017/10/workshop-working-on-data-stewardship-meet-your-peers/index.html
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Jack DiGiovanna
Making data and analytics FAIR has transformative potential within organizations to build on existing knowledge. FAIR resources also democratize access to information and tools in underserved communities. Global standards and analysis platforms provide strong foundational elements. However, FAIRness across time and different sectors of the biomedical workforce presents challenges. Here we summarize how platforms make data and analysis FAIR today and what we see as key areas of future focus.
BioIT 2024 invited talk.
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Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
FAIR Data Knowledge Graphs–from Theory to Practice
1. FAIR* Data Knowledge Graphs–from Theory to Practice
Tom Plasterer, PhD
Director, Bioinformatics, Research Bioinformatics 7-8 May 2019
* Findable, Accessible, Interoperable and Reusable
2. What do R&D Researchers want the ability to do?
3
• Gain a greater understanding of
the biology of the molecular
mechanisms of diseases
• Use the human as a model
organism to a greater degree
• Discover how the microbiome is
involved with human
pathogenesis
• Understanding molecular
mechanisms of drug failures
• Use patient-level clinical data to
identify subphenotypes of
diseases
Integrative Informatics: A hybrid approach to
integrating data for Drug Discovery
@Mathew Woodwark;
Pharma 2020: March 28, 2018
3. Can R&D researchers do these things today?
4
• Currently, data exists in file shares, on
laptops, eLN, in silos of managed
systems and unknown places
• The level of data integration is
immature and fragmented
• Using systems biology approaches
requires considerable time and effort
• Bioinformatics groups become a
bottleneck to analyzing data
• Research scientists not empowered
to use information and knowledge to
answer complex questions
Integrative Informatics: A hybrid approach to
integrating data for Drug Discovery
@Mathew Woodwark;
Pharma 2020: March 28, 2018
5. 6
FAIR Principles: One-Slide Overview
Findable:
• F1 (meta)data are assigned a globally
unique and persistent identifier
• F2 data are described with rich metadata
• F3 metadata clearly and explicitly include
the identifier of the data it describes
• F4 (meta)data are registered or indexed in a
searchable resource
The FAIR Guiding Principles for scientific data management and stewardship
Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016)
Accessible:
• A1 (meta)data are retrievable by their identifier
using a standardized communications protocol
• A1.1 the protocol is open, free, and universally
implementable
• A1.2 the protocol allows for an authentication and
authorization procedure, where necessary;
• A2 metadata are accessible, even when the data
are no longer available;
Interoperable:
• I1 (meta)data use a formal, accessible,
shared, and broadly applicable language for
knowledge representation
• I2 (meta)data use vocabularies that follow
FAIR principles
• I3 (meta)data include qualified references to
other (meta)data
Reusable:
• R1 meta(data) are richly described with a plurality
of accurate and relevant attributes
• R1.1 (meta)data are released with a clear and
accessible data usage license
• R1.2 (meta)data are associated with detailed
provenance
• R1.3 (meta)data meet domain-relevant
community standards
7. 8
Knowledge Graph: Innovation Trigger
Gartner Identifies Five Emerging Technology
Trends That Will Blur the Lines Between
Human and Machine
8. 9
Knowledge Graph: Key Features and Differentiators
Federation:
• Leave Data in place or ETL pipeline?
• URIs, indices really important
Standards Support (Syntactic and Semantic)
• Universal structure or bespoke?
• Universal query language or bespoke?
Analytics Enablement
• Reasoning, inferencing, graph methodologies
Hybrid
• Underlying data in multiple shapes and
repositories
For Machines (and occasionally people)
Cypher
9. 10
Starting Point: Modeling Business Questions
core:Study
core:Project
core:Target
core:Subject
core:Drug
core:Indication core:TherapeuticArea
core:BiologicalSample
core:Measurement core:Technologycore:Visit
bdm:Cohort
core:hasSubject
core:hasProject
core:hasDrug
core:hasIndication
bdm:hasArm
bdm:participatesIn
core:hasTA
core:hasTarget
core:hasMeasurement
core:hasSample
core:hasVisit
core:measuredBy
Find all subjects
diagnosed with SLE
with a disease activity
score > 5
Find all studies evaluating
the target PD-L1 with
RNA Seq Datasets
bnav:measuredInStudy
10. 11
Challenge is determining the “stickiest”
representation for a given instance
• Studies all have a ‘D’-code and then a
number of other internal and external
identifiers
• API calls to an internal clinical study API
and an external (licensed content) API to
obtain the exact matches
(skos:exactMatch)
• Process is abstracted in an Enrichment
Service
• New relationships (triples) are added to
the wrapped data model and pushed into
a knowledge graph
Enrichment: Core Ontology Classes & API mapping
core:Study
http://data.rd.astrazeneca.net/study/bdm/CP1103
http://clinicaltrials.astrazeneca.net/study/D4660C00001
http://identifiers.org/clinicaltrials/NCT01448850
http://trialtrove.citeline.com/ClinicalTrial/154466
skos:exactMatch
"azct:D4660C00001"
"ctg:NCT01448850"
"trialtrove:154466"
dct:identifier
11. 12
Now find “stickiest”
representation for a given
instance from a label
• Use system label for the
indication
• Send to Enrichment API
(augmented public disease
vocabularies) and generate the
preferred URI to obtain the close
matches (skos:closeMatch)
• Process is abstracted in an
Enrichment Service
• New relationships (triples) are
added to the wrapped data
model and pushed into a
knowledge graph
Enrichment: Core Ontology Classes & Label Matching
core:Indication
http://data.rd.astrazeneca.net/indication/bdm/Rheumatoid%20Arthritis
http://purl.obolibrary.org/obo/DOID_7148
http://identifiers.org/mesh/D001172
skos:closeMatch
"Rheumatoid Arthritis (D001172) "
bnav:diseaseNameSymbol
"Rheumatoid Arthritis"
skos:prefLabel
12. 13
Now find “stickiest” representation
for a given instance from a label
without a good vocabulary
• Aligned internal Technology
vocabulary with best public label
and URI
• Send to Enrichment API
(augmented BDM-technology
vocabulary) and generate the
preferred URI to obtain the close
matches (skos:exactMatch)
• Process is abstracted in an
Enrichment Service
• New relationships (triples) are
added to the wrapped data model
and pushed into a knowledge graph
Enrichment: Core Ontology Classes & Mixed Vocabs
core:Technology
http://data.rd.astrazeneca.net/technology/bdm/BDMTECH00005
"Blood Gas"
skos:prefLabel
http://identifiers.org/ncit/C71252
skos:exactMatch
"Arterial Blood Gas Measurement"
skos:prefLabel
14. 15
Dataset Catalogs: Find me Datasets about:
Projects
Study
Indication/
Disease
Technology
Targets
Cohort DatesAgent
Therapeutic
Area
Drugs
15. 16
Dataset Catalog is a collection of Dataset Records
• Catalogs are needed to supporting FAIR (Findable) data
• Catalogs can and should support Enterprise MDM strategies
• Consumers can be internal or external
Dataset Catalogs are needed so data consumers can find Datasets
• Dataset records need sufficient metadata to support discoverability
• Dataset terms are NOT the data instance
Dataset Catalogs surface dataset provenance and enable data access
Dataset Catalogs can provide datasets for multiple consumption patters
• Analytics readiness and fit
• ‘Walking’ across information models
Dataset Catalogs: Findability Starts Here
17. Data Discoverability: Multi-phase Filtering
Data Catalog Filter
Phase 1
Experiment Metadata Filter
Phase 2
Ad hoc Analyses Filtering
Phase 3
Outbound
to Data Analytics
Data Science
Tools
Statistical
Filtering
e.g., clinical trial with > 50
participants
Dataset
Catalog
Descriptions
19. R&D | RDI
Multi-Phase Filtering joins the Catalog and Domain Model
• Balance to what belongs in a catalog record vs. instance data
Public Domain Ontologies and Identifiers should be reused
• Consensus is emerging around best practices and cross-mapping
DCTERMS, DCAT, VoID are almost sufficient
• Extend for local needs
Lots of Activity to Learn and Shape Best Practices
• Didn’t reinvent a wheel
FAIR Knowledge Graph: Take-aways
20. R&D | RDI
Thanks
Key Influencers
David Wood
Tim Berners-Lee
Lee Harland
Jane Lomax
James Malone
Dean Allemang
Barend Mons
Carole Goble
Bernadette Hyland
Bob Stanley
Eric Little
Michel Dumontier
John Wilbanks
Hans Constandt
Filip Pattyn
Dan Crowther
Tim Hoctor
Ian Harrow
AstraZeneca/Pistoia FAIR
Data Community
Tom Plasterer
Rajan Desai
Nic Sinibaldi
Chia-Chien Chiang
Kerstin Forsberg
Ola Engkvist
Ian Dix
Colin Wood
Ted Slater
Martin Romacker
Eric Neumann
Jeff Saltzman
Kathy Reinold
Nirmal Keshava
Bryan Takasaki