A brief introduction of dkNET (NIDDK Information Network; https://dknet.org) and the services and resources that are available, including Resource Reports, Authentication Reports, FAIR Data Services, Discovery Portal and Hypothesis Center.
Research Data Alliance (RDA) Webinar: What do you really know about that anti...dkNET
What do you really know about that antibody? Ask dkNET
Research resources-defined here as the tools researchers use in their scientific studies-are a foundation of the biomedical enterprise. It is critical for researchers to be able to select the proper tools for their research, but also be aware of any issues that may arise in their application. Software tools and datasets may have bugs, cell lines get contaminated, knock outs may be incomplete and antibodies may have specificity problems. Such problematic resources can continue to be used in scientific studies, even after problems are detected. Many factors, including the inability to easily retrieve alerts about problematic resources, results in their continued use, wasting both time and money. To make it easy to find information about research resources and how they perform, dkNET (NIDDK Information Network, https://dknet.org), an on-line portal supported by the US National Institute of Diabetes, Digestive and Kidney diseases (NIDDK), has developed a resource information network that utilize Research Resource Identifiers (RRIDs) and natural language processes to aggregate information about individual antibodies, cell lines, organisms, digital tools, plasmids and biosamples. This information is presented in a Resource Report that provides information such as which papers have been published using these resources, who is using them and whether issues have been reported. Using this information, dkNET also provides tools to create authentication reports in support of the NIH rigor and reproducibility guidelines. The dkNET portal includes additional information to enable researchers to easily use and navigate large amounts of data and information about research resources in support of reproducible science.
By the end of this webinar, participants will be familiar with the services and tools provided at dkNET and will be able to create a detailed research resource report and produce an authentication report in support of NIH mandates and policies.
Presenter: Maryann Martone, PhD, FAIR Data Informatics Lab (FDI Lab), University of California, San Diego
dkNET Webinar: dkNET Hypothesis Center Live Demo 09/24/2021dkNET
Abstract
dkNET is creating a hub for big data and hypothesis generation, bringing together a collection of online tools that will allow researchers to explore different datasets and utilize analytics and visualization tools. The dkNET Hypothesis Center phenotype-genotype analytics module is currently performed utilizing data from the Signaling Pathways Project (SPP), and the Mouse Metabolic Phenotyping Centers (MMPC). Upcoming resources include the Human Islet Research Network Resource Browser, Appyters, Type 1 Diabetes Knowledge Portal,...and more. Through detailed tutorials and integrating different resources, the power of the dkNET Hypothesis Center can help answer the questions of immediate relevance to your research.
What you will learn:
- Introduction of the dkNET Hypothesis Center
- How to navigate and access tutorials that will teach you how to use FAIR data and bioinformatics tool(s)
- How the dkNET Hypothesis Center can assist in answering your research questions and generating hypotheses
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar - Vivli: A Global Clinical Trials Data Sharing Platform 12/11/2020dkNET
Abstract
Vivli (https://vivli.org/) is an independent, non-profit organization that has developed a global data-sharing and analytics platform. Our focus is on sharing individual participant-level data from completed clinical trials to serve the entire the scientific community and a diverse group of stakeholders including industry, academic institutions, government and non-profits. The Vivli platform includes an independent data repository, in-depth search engine and a secure research environment. This session will explore when it is appropriate to share your data using a managed access platform such as Vivli and will show how the Vivli team can support you in this process. We will also explore what studies are available that may be of interest to the dkNET community on the platform.
The top 3 key questions that Vivli can answer:
1. Why should I share data from my completed clinical studies?
2. How can Vivli help me share my clinical study data?
3. How can I request data from other completed studies?
Presenter: Ida Sim, MD, PhD, Professor of Medicine, University of California San Francisco and Co-Founder, Vivli
dkNET Webinars Information: https://dknet.org/about/webinar
Introduction to the Research Integrity Advisor Data Management Workshop, Bris...ARDC
Dr Jacobs' introduction to the RIA Data Management Workshop in Brisbane on 31 March 2017. The RIA Data Management Workshop series is a joint collaboration of the Australian Research Council, the National Health and Medical Research Council, the Australasian Research Management Society and the Australian National Data Service.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
Research Data Alliance (RDA) Webinar: What do you really know about that anti...dkNET
What do you really know about that antibody? Ask dkNET
Research resources-defined here as the tools researchers use in their scientific studies-are a foundation of the biomedical enterprise. It is critical for researchers to be able to select the proper tools for their research, but also be aware of any issues that may arise in their application. Software tools and datasets may have bugs, cell lines get contaminated, knock outs may be incomplete and antibodies may have specificity problems. Such problematic resources can continue to be used in scientific studies, even after problems are detected. Many factors, including the inability to easily retrieve alerts about problematic resources, results in their continued use, wasting both time and money. To make it easy to find information about research resources and how they perform, dkNET (NIDDK Information Network, https://dknet.org), an on-line portal supported by the US National Institute of Diabetes, Digestive and Kidney diseases (NIDDK), has developed a resource information network that utilize Research Resource Identifiers (RRIDs) and natural language processes to aggregate information about individual antibodies, cell lines, organisms, digital tools, plasmids and biosamples. This information is presented in a Resource Report that provides information such as which papers have been published using these resources, who is using them and whether issues have been reported. Using this information, dkNET also provides tools to create authentication reports in support of the NIH rigor and reproducibility guidelines. The dkNET portal includes additional information to enable researchers to easily use and navigate large amounts of data and information about research resources in support of reproducible science.
By the end of this webinar, participants will be familiar with the services and tools provided at dkNET and will be able to create a detailed research resource report and produce an authentication report in support of NIH mandates and policies.
Presenter: Maryann Martone, PhD, FAIR Data Informatics Lab (FDI Lab), University of California, San Diego
dkNET Webinar: dkNET Hypothesis Center Live Demo 09/24/2021dkNET
Abstract
dkNET is creating a hub for big data and hypothesis generation, bringing together a collection of online tools that will allow researchers to explore different datasets and utilize analytics and visualization tools. The dkNET Hypothesis Center phenotype-genotype analytics module is currently performed utilizing data from the Signaling Pathways Project (SPP), and the Mouse Metabolic Phenotyping Centers (MMPC). Upcoming resources include the Human Islet Research Network Resource Browser, Appyters, Type 1 Diabetes Knowledge Portal,...and more. Through detailed tutorials and integrating different resources, the power of the dkNET Hypothesis Center can help answer the questions of immediate relevance to your research.
What you will learn:
- Introduction of the dkNET Hypothesis Center
- How to navigate and access tutorials that will teach you how to use FAIR data and bioinformatics tool(s)
- How the dkNET Hypothesis Center can assist in answering your research questions and generating hypotheses
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar - Vivli: A Global Clinical Trials Data Sharing Platform 12/11/2020dkNET
Abstract
Vivli (https://vivli.org/) is an independent, non-profit organization that has developed a global data-sharing and analytics platform. Our focus is on sharing individual participant-level data from completed clinical trials to serve the entire the scientific community and a diverse group of stakeholders including industry, academic institutions, government and non-profits. The Vivli platform includes an independent data repository, in-depth search engine and a secure research environment. This session will explore when it is appropriate to share your data using a managed access platform such as Vivli and will show how the Vivli team can support you in this process. We will also explore what studies are available that may be of interest to the dkNET community on the platform.
The top 3 key questions that Vivli can answer:
1. Why should I share data from my completed clinical studies?
2. How can Vivli help me share my clinical study data?
3. How can I request data from other completed studies?
Presenter: Ida Sim, MD, PhD, Professor of Medicine, University of California San Francisco and Co-Founder, Vivli
dkNET Webinars Information: https://dknet.org/about/webinar
Introduction to the Research Integrity Advisor Data Management Workshop, Bris...ARDC
Dr Jacobs' introduction to the RIA Data Management Workshop in Brisbane on 31 March 2017. The RIA Data Management Workshop series is a joint collaboration of the Australian Research Council, the National Health and Medical Research Council, the Australasian Research Management Society and the Australian National Data Service.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Birgit Schmidt: RDA for Libraries from an International Perspectivedri_ireland
From "A National Approach to Open Research Data in Ireland", a workshop held on 8 September 2017 in National Library of Ireland, organised by The National Library of Ireland, the Digital Repository of Ireland, the Research Data Alliance and Open Research Ireland.
Data Citation Implementation Guidelines By Tim Clarkdatascienceiqss
This talk presents a set of detailed technical recommendations for operationalizing the Joint Declaration of Data Citation Principles (JDDCP) - the most widely agreed set of principle-based recommendations for direct scholarly data citation.
We will provide initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data.
We hope that these recommendations along with the new NISO JATS document schema revision, developed in parallel, will help accelerate the wide adoption of data citation in scholarly literature. We believe their adoption will enable open data transparency for validation, reuse and extension of scientific results; and will significantly counteract the problem of false positives in the literature.
Ingrid Dillo - Trustworthy repositories for open research datadri_ireland
From "A National Approach to Open Research Data in Ireland", a workshop held on 8 September 2017 in National Library of Ireland, organised by The National Library of Ireland, the Digital Repository of Ireland, the Research Data Alliance and Open Research Ireland.
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023dkNET
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Abstract
The dkNET (NIDDK Information Network) team is announcing an exciting new service - Biomed Resource Watch (BRW, https://scicrunch.org/ResourceWatch), a knowledge base for aggregating and disseminating known problems and performance information about research resources such as antibodies, cell lines, and tools. We aggregate trustworthy information from authorized sources such as Cellosaurus, Antibody Registry, Human Protein Atlas, ENCODE, and many more. In addition, BRW includes antibody specificity text mining information extracted from the literature via natural language processing. BRW provides researchers and curators an easy-to-use interface to report their claims about a specific resource. Researchers can check information about a resource before planning their experiments via BRW-enhanced Resource Reports. This new service aims to help improve efficiency in selecting appropriate resources, enhancing scientific rigor and reproducibility, and promoting a FAIR (Findable, Accessible, Interoperable, Reusable) research resource ecosystem in the biomedical research community.
Join us for a webinar to introduce the following resources & topics:
1. An overview of dkNET
2. How Resource Reports benefit you
3. Biomed Resource Watch
3.1 Navigating Biomed Resource Watch
3.2 How to Submit a Claim
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Discovering and Evaluating Antibodies, Cell Lines, Software To...dkNET
Abstract
dkNET’s Resource Reports (https://dknet.org/rin/rrids) enable researchers to discover research resources that would be useful for their research. The resource report integrated data set and analytics platform combines Research Resource Identifiers (RRIDs), text mining and data aggregation to help you identify key biomedical resources, track these resources, and compare their performance. Resource Reports offer a detailed overview of each resource along with citation metrics from the biomedical literature and even information about what resources have been used together. You'll gain insights about who is using particular resources and how the community views those resources, including usage in published protocols.
The dkNET Co-PI, Dr Jeffrey Grethe, will give you live demos during this webinar, including:
- How to find and select a research resource such as an antibody or a cell line
- How to find Research Resource Identifiers (RRIDs) and proper citation of your resources
- How to register resources to obtain RRIDs if the resources do not exist in the system
We hope this short webinar will provide an opportunity to use this tool to shape your research activities.
Presenter: Jeffrey Grethe, PhD, dkNET Co-Principal Investigator, University of California San Diego
Upcoming webinars schedule: https://dknet.org/about/webinar
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Birgit Schmidt: RDA for Libraries from an International Perspectivedri_ireland
From "A National Approach to Open Research Data in Ireland", a workshop held on 8 September 2017 in National Library of Ireland, organised by The National Library of Ireland, the Digital Repository of Ireland, the Research Data Alliance and Open Research Ireland.
Data Citation Implementation Guidelines By Tim Clarkdatascienceiqss
This talk presents a set of detailed technical recommendations for operationalizing the Joint Declaration of Data Citation Principles (JDDCP) - the most widely agreed set of principle-based recommendations for direct scholarly data citation.
We will provide initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data.
We hope that these recommendations along with the new NISO JATS document schema revision, developed in parallel, will help accelerate the wide adoption of data citation in scholarly literature. We believe their adoption will enable open data transparency for validation, reuse and extension of scientific results; and will significantly counteract the problem of false positives in the literature.
Ingrid Dillo - Trustworthy repositories for open research datadri_ireland
From "A National Approach to Open Research Data in Ireland", a workshop held on 8 September 2017 in National Library of Ireland, organised by The National Library of Ireland, the Digital Repository of Ireland, the Research Data Alliance and Open Research Ireland.
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023dkNET
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Abstract
The dkNET (NIDDK Information Network) team is announcing an exciting new service - Biomed Resource Watch (BRW, https://scicrunch.org/ResourceWatch), a knowledge base for aggregating and disseminating known problems and performance information about research resources such as antibodies, cell lines, and tools. We aggregate trustworthy information from authorized sources such as Cellosaurus, Antibody Registry, Human Protein Atlas, ENCODE, and many more. In addition, BRW includes antibody specificity text mining information extracted from the literature via natural language processing. BRW provides researchers and curators an easy-to-use interface to report their claims about a specific resource. Researchers can check information about a resource before planning their experiments via BRW-enhanced Resource Reports. This new service aims to help improve efficiency in selecting appropriate resources, enhancing scientific rigor and reproducibility, and promoting a FAIR (Findable, Accessible, Interoperable, Reusable) research resource ecosystem in the biomedical research community.
Join us for a webinar to introduce the following resources & topics:
1. An overview of dkNET
2. How Resource Reports benefit you
3. Biomed Resource Watch
3.1 Navigating Biomed Resource Watch
3.2 How to Submit a Claim
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Discovering and Evaluating Antibodies, Cell Lines, Software To...dkNET
Abstract
dkNET’s Resource Reports (https://dknet.org/rin/rrids) enable researchers to discover research resources that would be useful for their research. The resource report integrated data set and analytics platform combines Research Resource Identifiers (RRIDs), text mining and data aggregation to help you identify key biomedical resources, track these resources, and compare their performance. Resource Reports offer a detailed overview of each resource along with citation metrics from the biomedical literature and even information about what resources have been used together. You'll gain insights about who is using particular resources and how the community views those resources, including usage in published protocols.
The dkNET Co-PI, Dr Jeffrey Grethe, will give you live demos during this webinar, including:
- How to find and select a research resource such as an antibody or a cell line
- How to find Research Resource Identifiers (RRIDs) and proper citation of your resources
- How to register resources to obtain RRIDs if the resources do not exist in the system
We hope this short webinar will provide an opportunity to use this tool to shape your research activities.
Presenter: Jeffrey Grethe, PhD, dkNET Co-Principal Investigator, University of California San Diego
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours: NIH Data Management and Sharing Mandate 05/03/2024dkNET
Presenter: Jeffrey Grethe, PhD, Principal Investigator of NIDDK Information Network (dkNET), Center for Research in Biological Systems, University of California San Diego
For all proposals submitted on/after January 25 2023, NIH requires the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...dkNET
For all proposals submitted on/after January 25 2023, NIH requires data sharing from all NIH-funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and available resources that could help.
In our upcoming session on March 3, 2023, we are pleased to invite Dr. Jeffrey Grethe, dkNET co-PI and expert on Data Management and Sharing, Dr. Rebecca Rodriguez, Repository Program Director at NIDDK, Ms. Reaya Reuss, Chief of Staff to the Deputy Director at NIDDK, and the support team members from the NIDDK Central Repository. They will be available to answer any questions you may have.
*Previous Office Hours Slides and Recording: https://dknet.org/about/blog/2535
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar: Creating and Sustaining a FAIR Biomedical Data Ecosystem 10/09...dkNET
Abstract
In this presentation, Susan Gregurick, Ph.D., Associate Director of Data Science and Director, Office of Data Science Strategy at the National Institutes of Health, will share the NIH’s vision for a modernized, integrated FAIR biomedical data ecosystem and the strategic roadmap that NIH is following to achieve this vision. Dr. Gregurick will highlight projects being implemented by team members across the NIH’s 27 institutes and centers and will ways that industry, academia, and other communities can help NIH enable a FAIR data ecosystem. Finally, she will weave in how this strategy is being leveraged to address the COVID-19 pandemic.
Presenter: Susan Gregurick, Ph.D., Associate Director of Data Science and Director, Office of Data Science Strategy at the National Institutes of Health
dkNET Webinar Information: https://dknet.org/about/webinar
bioCADDIE Webinar: The NIDDK Information Network (dkNET) - A Community Resear...dkNET
The NIDDK Information Network (dkNET; http://dknet.org) is a open community resource for basic and clinical investigators in metabolic, digestive and kidney disease. dkNET’s portal facilitates access to a collection of diverse research resources (i.e. the multitude of data, software tools, materials, services, projects and organizations available to researchers in the public domain) that advance the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This webinar was presented by dkNET principle investigator Dr. Jeffrey Grethe.
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...dkNET
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023? New NIH Data Management and Sha...dkNET
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources (https://dknet.org/rin/research-data-management) that could help.
Upcoming Webinars Schedule: https://dknet.org/about/webinar
This presentation was provided by Maria Praetzellis of California Digital Library, during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
FAIRness Assessment of the Library of Integrated Network-based Cellular Signa...Kathleen Jagodnik
The FAIR Guiding Principles facilitate the Findability, Accessibility, Interoperability, and Reusability of digital resources. The Library of Integrated Network-based Cellular Signatures (LINCS) Project has sought to implement the FAIR principles in the provision of its resources in order to optimize usability. We have surveyed the FAIR principles and are implementing specific facets within the LINCS resources. Subsequently, with reference to the literature and other efforts to measure FAIRness, we are developing quantitative metrics to assess the FAIRness of each dataset and resource in order to provide users with objective measures of the characteristics of the LINCS project. Assessing and improving the FAIRness of LINCS is an ongoing effort by our team that will benefit from community input to ensure that all LINCS users are optimally engaged with this resource.
Research Data Management in GLAM: Managing Data for Cultural HeritageSarah Anna Stewart
Presentation given at the 'Open Science Infrastructures for Big Cultural Data' - Advanced International Masterclass in Plovdiv, Bulgaria. Dec. 13-15, 2018
Identifying and tracking research resources using RRIDs: a practical approachdkNET
At this presentation, you will learn (1) Why you need to use Research Resource identifier (RRID) (2) What is Resource Identification Initiative (3) How dkNET.org supports RRID (4) What can you do with RRID
Identification of Early Career Researchers: How Universities and Funding Orga...ORCID, Inc
Funding agencies, universities, and research institutes all face challenges of reliably identifying their researchers and monitoring outcomes over time. All researchers—and especially early career researchers seeking to establish their careers—need to be reliably connected to their research outputs, without the confusion common, changeable names creates. Graduate students and postdoctoral researchers supported by grants also have specific challenges: if they are not the PI, they are not included in grant information; they may not even know which grant(s) they are supported by; and as a result, the existing challenges of reliably tying publications to grant funding are even more problematic. The use of the unique, persistent ORCID identifier can help support outcomes tracking and evaluation.
In 2012, the U.S. National Institutes of Health Biomedical Research Workforce Working Group made recommendations that the NIH should take to support a sustainable biomedical research workforce in the U.S. In the course of its study, working group members were “frustrated and sometimes stymied” by the lack of quality, comprehensive data about biomedical researchers. In response, NIH has recommended the development of a simple, comprehensive tracking system for trainees, implemented a shared, voluntary researcher profile system called the Science Experts Network Curriculum Vitae (SciENcv), and encouraged the adoption of unique, persistent ORCID identifiers for researchers. Additionally, NIH has begun collecting data about individuals in graduate and undergraduate student project roles who are supported by NIH grants.
Research universities like Texas A&M are also responding by incorporating the ORCID identifier into their systems, enabling the improved identification, data collection, and career outcome tracking of students and postdoctoral researchers--and educating these early career researchers about the benefits they will receive from a unique, persistent research identifier. They are also beginning to link Electronic Theses and Dissertations (ETDs) to early career researchers' ORCID records.
ORCID is an independent, non-profit organization that provides an open registry of unique and persistent identifiers for researchers and scholars. ORCID collaborates with the community to integrate ORCID identifiers into research systems and workflows, improving data management and accuracy across systems. ORCID enables interoperability between research systems worldwide, ensuring that researchers are correctly and automatically linked to their contributions. Since its launch in October 2012, ORCID has seen rapid adoption by more than 670,000 researchers and 130+ member organizations.
From Webinar 4/23/14, https://orcid.org/content/identification-early-career-researchers-how-universities-and-funding-organizations-are-using
Lecture for a course at NTNU, 27th January 2021
CC-BY 4.0 Dag Endresen https://orcid.org/0000-0002-2352-5497
See also http://bit.ly/biodiversityinformatics
https://www.gbif.no/events/2021/lecture-ntnu-gbif.html
dkNET Webinar: The 4DN Data Portal - Data, Resources and Tools to Help Elucid...dkNET
Presenter: Andrew Schroeder, PhD. Project Manager & Senior Data Curator, 4D Nucleome Data Coordination and Integration Center (4DN-DCIC), Park Lab, Department of Biomedical Informatics, Harvard Medical School
Abstract
The Common Fund 4D Nucleome program, currently in its 9th year, is a consortium of researchers that aims to understand the principles behind the three-dimensional organization of the nucleus and how this organization can change over time to affect a variety of cellular processes. The 4DN Data Portal (data.4dnucleome.org) is an expanding resource hosting data generated by the 4DN Network and other reference nucleomics data sets. The portal provides tools for search, exploration, visualization, and download. An overview of the data portal, highlighting available data, how it can be found, visualized and used for analyses will be presented.
The top 3 key questions that the 4DN data portal can answer:
1. Are there significant sites of long-range chromatin contacts near my gene or region of interest?
2. What omics datasets are available for my tissue of interest?
3. Are there imaging datasets available that are relevant to my tissue of interest?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...dkNET
Presenter: Chen Li, PhD. Professor, Department of Computer Science, University of California Irvine
Abstract
Many data analytics projects have collaborators with complementary backgrounds, including biologists, bioinformaticians, computer scientists, and AI/ML experts. Many of them have limited experience to code, set up a computing infrastructure, and use MLmodels. Existing tools and services, such as email attachments, GitHub, and Google Drive are inefficient for sharing data and analyses. In this talk, we present an open source system called Texera that provides a cloud computing platform for collaborators to share data and analyses as workflows. After seven years of development, the system has a rich set of powerful features, such as shared editing, shared execution, version control, commenting, debugging, user-defined functions in multiple languages (e.g., Python, R, Java), and support of state-of-the-art AI/ML techniques. Its backend parallel engine enables scalable computation on large data sets using computing clusters. We will show a demo of the system, and present our vision supported by a recent NIH award, dkNET(NIDDK Information Network, https://dknet.org), to serve the diabetes, endocrinology, and metabolic diseases research communities through the FAIR sharing of data and knowledge.
Resource link: https://github.com/Texera/texera
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Unlocking the Power of FAIR Data Sharing with ImmPort 04/12/2024dkNET
Presenter: Sanchita Bhattacharya, ImmPort Science Program Lead, Bakar Computational Health Sciences Institute UCSF
Abstract
The Immunology Database and Analysis Portal (ImmPort, https://www.immport.org/home) is a domain-specific data repository for immunology-related data which is funded by the National Institutes of Health, National Institute of Allergy and Infectious Diseases, and Division of Allergy, Immunology, and Transplantation. ImmPort has been making scientific data Findable, Accessible, Interoperable, and Reusable (FAIR) for over 20 years. ImmPort data sets encompass over 7 million experimental results across 160 diseases and conditions, including data related to diabetes, kidney and liver transplantation, celiac disease, and many more conditions. In this webinar, participants will learn about data management and sharing through ImmPort, as well as finding and leveraging data sets of interest for research.
The top 3 key questions that the ImmPort can answer:
1. How can researchers share data through ImmPort to comply with the NIH Data Management and Sharing policy?
2. How does ImmPort support FAIR data and why is this powerful for research?
3. What scientific data does ImmPort house that would be of interest to NIDDK researchers?
Upcoming webinars schedule: https://dknet.org/about/webinar
Presenter: Angela Oliveira Pisco , PhD
Abstract
Although the genome is often called the blueprint of an organism, it is perhaps more accurate to describe it as a parts list composed of the various genes that may or may not be used in the different cell types of a multicellular organism. While nearly every cell in the body has essentially the same genome, each cell type makes different use of that genome and expresses a subset of all possible genes. This has motivated efforts to characterize the molecular composition of various cell types within humans and multiple model organisms, both by transcriptional and proteomic approaches. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. One caveat to current approaches to make cell atlases is that individual organs are often collected at different locations, collected from different donors, and processed using different protocols. Controlled comparisons of cell types between different tissues and organs are especially difficult when donors differ in genetic background, age, environmental exposure, and epigenetic effects. To address this, we developed an approach to analyzing large numbers of organs from the same individual. We collected multiple tissues from individual human donors and performed coordinated single-cell transcriptome analyses on live cells. The donors come from a range of ethnicities, are balanced by gender, have a mean age of 51 years, and have a variety of medical backgrounds. Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues, leading to a total of 475 distinct cell types with reference transcriptome profiles. The Tabula Sapiens also provided an opportunity to densely and directly sample the human microbiome throughout the gastrointestinal tract. The Tabula Sapiens has revealed discoveries relating to shared behavior and subtle, organ-specific differences across cell types. We found T cell clones shared between organs and characterized organ-dependent hypermutation rates among B cells. Endothelial cells and macrophages are shared across tissues, often showing subtle but clear differences in gene expression. We found an unexpectedly large and diverse amount of cell type–specific RNA splice variant usage and discovered and validated many previously undefined splices. The intestinal microbiome was revealed to have nonuniform species distributions down to the 3-inch (7.62-cm) length scale. These are but a few examples of how the Tabula Sapiens represents a broadly useful reference...Full abstract: https://dknet.org/about/blog/2726
Resource link: https://tabula-sapiens-portal.ds.czbiohub.org
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The Multi-Omic Response to Exercise Training Across Rat Tissue...dkNET
Presenter: Malene Lindholm, PhD, Instructor, Department of Medicine, Stanford University
Abstract
The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to map the molecular responses to exercise and training to elucidate how exercise improves health and prevents disease. The first MoTrPAC data provides an extensive temporal map of the dynamic multi-omic response to endurance training across multiple rat tissues. All results can be viewed, interrogated, and downloaded in a user-friendly, publicly accessible data portal (https://motrpac-data.org). The MoTrPAC data compendium includes transcriptomics, proteomics, metabolomics, phosphoproteomics, acetylproteomics, ubiquitylproteomics, DNA methylation, chromatin accessibility, and multiplexed immunoassay data. This compilation constitutes of 211 datasets across 19 tissues, 25 molecular assays, and 4 training time points in adult male and female rats. Over 35,000 analytes were found to be differentially regulated in response to endurance training, with many displaying sexual dimorphism. We observed a male-specific recruitment of immune cells to adipose tissues and an anticorrelated transcriptional response in the adrenal gland related to the stress response. Temporal multi-omic and multi-tissue integration demonstrated similar temporal responses in the heart and skeletal muscle, reflecting a concerted adaptation of mitochondrial biogenesis and metabolism. Integrative multi-omic network analysis revealed connections between the heat shock-mediated stress response and mitochondrial biogenesis. Training increased phospholipids and decreased triacylglycerols in the liver, and there were extensive changes to mitochondrial protein acetylation. Many changes were relevant for human health conditions, such as non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular wellness, and tissue damage and repair. Altogether, this MoTrPAC resource provides an unprecedented view of the effects of exercise across an organism, revealing mechanistic details of how exercise impacts mammalian health. The MoTrPAC data hub is the primary online resource to disseminate this large-scale multi-omics data.
The top 3 questions that the MoTrPAC resource can answer:
1. What is the multi-omic response to endurance exercise across different tissues?
2. What are the top signaling pathways affected in response to exercise and do they differ between males and females?
3. How can the MoTrPAC data hub be utilized to interrogate all the MoTrPAC findings?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...dkNET
Presenter: Pieter Dorrestein, PhD, Professor, Skaggs School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology and Pediatrics, University of California San Diego
Abstract
In the analysis of organs, volatilome, or biofluids, the microbiome influences 15-70% of detectable mass spectrometry molecules. Typically, only 10% of human untargeted metabolomics data can be assigned a molecular structure, with merely 1-2% traceable to microbial origins. Human microbiomes contribute metabolites through the microbial metabolism of host-derived substances, digestion of food and beverage molecules, and de novo assembly using proteins encoded by genetic elements. Despite the significance of microbiome-derived metabolites to human health, there is no centralized knowledge base for community access. To address this, the "Collaborative Microbial Metabolite Center" (CMMC) leverages expertise in mass spectrometry, microbiome innovation, and the GNPS ecosystem to built a knowledgebase. It aims to create a user-accessible microbiome resource, enrich bioactivity knowledge, and facilitate data deposition. The CMMC includes the construction of a knowledge base, MicrobeMASST tool, and health phenotype enrichment workflows, the construction and use will be discussed in this presentation. The use of this ecosystem will be exemplified by the discovery of 20,000 bile acids, many of which were shown to be of microbial origin and linked to diet and IBD.
The top 3 key questions that this resource can answer:
1. How can we leverage the 1000’s of public metabolomics studies to discover microbial metabolites and their organ distributions as well as their phenotypic, including health, associations?
2. If one has an unknown molecule, how can one assess what microbes make a molecule without known structure?
3. How can one contribute to the expansion of the knowledgebase on microbial metabolites?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: An Encyclopedia of the Adipose Tissue Secretome to Identify Me...dkNET
Presenter: Paul Cohen, MD, PhD, Albert Resnick, M.D. Associate Professor, Rockefeller University
Abstract
White and brown adipocytes not only play a central role in energy storage and combustion but are also dynamic secretory cells that secrete signaling molecules linking levels of energy stores to vital physiological systems. Disruption of the signaling properties of adipocytes, as occurs in obesity, contributes to insulin resistance, type 2 diabetes, and other metabolic disorders. Fat cells have been estimated to secrete over 1,000 polypeptides and microproteins and an even larger number of small molecule metabolites. The great majority of the adipocyte secretome has not been defined or characterized. A major obstacle has been the lack of suitable technologies to quantitatively identify circulating proteins and metabolites, determine their cellular origin, and elucidate their function. Building on key innovations in chemical biology and mass spectrometry, our team is generating an encyclopedia of the white and brown adipocyte secretome in mouse models and humans. Our work has the potential to identify new secreted mediators with roles in obesity, type 2 diabetes, and metabolic diseases, provide a crucial resource for researchers and clinicians, and lead to new biomarkers and therapies.
The top 3 key questions that this resource can answer:
1. What techniques can be used to characterize the secretome of a cell type in vitro and in vivo?
2. What is the full complement of proteins and metabolites secreted by different kinds of adipocytes?
3. How should one prioritize uncharacterized secreted mediators for functional study?
Resource link: https://secrepedia.org/
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: A Single Cell Atlas of Human and Mouse White Adipose Tissue 11...dkNET
Presenter: Margo Emont, PhD. Instructor, Beth Israel Deaconess Medical Center/Harvard Medical School
Abstract
White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defense. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.
The top 3 key questions that this resource can answer:
1. How specific is my gene of interest to a particular cell type in adipose tissue?
2. Is the gene/pathway that I am studying in mouse adipose tissue also present in human adipose tissue (and is it regulated similarly in low vs high body weight)?
3. What are the changes in gene expression in a specific cell type at low vs high body weight?
Resource link:
https://singlecell.broadinstitute.org/single_cell/study/SCP1376/a-single-cell-atlas-of-human-and-mouse-white-adipose-tissue#study-summary
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The National Sleep Research Resource (NSRR) - Opportunities fo...dkNET
Presenter: Susan Redline, MD, MPH, Peter C. Farrell Professor of Sleep Medicine, Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Abstract
Experimental, clinical and epidemiological studies have identified multiple inter-relationships of sleep with glucose regulation and metabolic disease. In one meta-analysis, after overweight and family history of diabetes, the next 7 top risk factors for incident diabetes were measures of sleep health. These included poor sleep quality, insomnia, short or extremely long sleep duration, and sleep apnea; each sleep problem was associated with incident diabetes with relative risks ranging from 1.38 to 1.74. A mechanism linking sleep apnea with diabetes is through the effects of intermittent hypoxemia on insulin sensitivity. However, studies using neurophysiological markers of sleep in healthy adults showed that selective reduction of slow wave sleep reduced glucose tolerance by 23%, thus additionally suggesting the importance neurophysiological mechanisms during sleep in glucose regulation. In support of this, longitudinal epidemiological studies demonstrated that higher proportions of slow wave sleep (N3) were protective for the development of type 2 diabetes. Recent animal and human studies also point to the effects of sleep micro-architecture—specifically the coupling of slow waves and spindles- on short-term and long-term glucose regulation, possibly through the effects on signaling between the hippocampus and hypothalamus, and changes in autonomic nervous system output. Experimental data also demonstrate a prominent role of the circadian system in regulating glucose and lipid levels. In support of those studies, epidemiological associations have identified significant associations between actigraphy-based measures of sleep irregularity (a marker of circadian disruption) with incident metabolic dysfunction and hypertension. This rich data implicating sleep disturbances as drivers of metabolic disease, coupled with data indicating a high prevalence of sleep and circadian disorders in the population, suggest novel opportunities to target sleep and circadian pathways for preventing or treating metabolic dysfunction, as well as key knowledge gaps.
The National Sleep Research Resource (NSRR; sleepdata.org) provides a large and growing repository of well-annotated polysomnograms (PSGs), actigraphy studies, and questionnaires, some associated with clinical and biochemical data relevant to understanding the links between sleep and circadian disorders with metabolic disease. Notably, the NSRR includes over 50,000 PSGs, which concurrently include multiple physiological signals with high temporal resolution, allowing generation of thousands of variables summarizing dynamic physiological changes and “cross-talk” between physiological systems...(Please see https://dknet.org/about/blog/2674 for full abstract)
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Leveraging Computational Strategies to Identify Type 1 Diabete...dkNET
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Wenting Wu, PhD. Research Assistant Professor, Center for Diabetes and Metabolic Diseases, Department of Medical and Molecular Genetics, Associate Director of Data and Analytics Core for Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine
Abstract
Type 1 diabetes (T1D) is an immune-mediated disease that results in insulin insufficiency and affects 0.3% of the population, including both children and adults. To support clinical trial efforts, there is an urgent need to develop reliable biomarkers capable of predicting T1D risk and guiding therapeutic interventions. Recently, whole blood bulk RNA sequencing has been used to guide T1D clinical trial design and assess response to disease modifying interventions. While the use of bulk RNA sequencing is cost-effective, these datasets provide limited information about cell specific gene expression changes. Here, we aimed to apply computational strategies to deconvolute cell type composition using cell specific gene expression references. Single-cell RNA sequencing (scRNA-seq) was conducted to profile peripheral blood mononuclear cells obtained from youth within recent T1D onset and age- and sex-matched controls and identified 31 distinct cell clusters. Using this pre-defined reference dataset, we ran computational algorithms CIBERSORTx and other deconvolution methods simultaneously to deconvolute cell proportions using public clinical trial data. We focused our initial analysis on data from the TN-20 Rituximab trial, which tested the anti-CD20 monoclonal antibody rituximab vs placebo in recent onset T1D. This talk will introduce recent advances of scRNA-seq techniques and computational deconvolution methods and demonstrate that how we apply different deconvolution approaches for secondary analysis of existing clinical trial data, in the purpose of linking cell specific immune signatures associated with drug responder status.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Estimating Relative Beta-Cell Function During Continuous Gluco...dkNET
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Joon Ha, PhD. Associate Professor, Department of Mathematics, Howard University, Washington DC.
Abstract
The most common form of diabetes, type 2 diabetes (T2D) is a failure of insulin-secreting pancreatic beta-cells to increase insulin to the level required to maintain normal blood glucose. Thus, identifying beta-cell function and insulin sensitivity in those who are at high risk is crucial to preventing and delaying the disease. Hyper-glycemic clamp and euglycemic hyper- insulinemic clamp are considered to be gold standard measures for these quantities. However, these two methods demand highly skilled labor and thus are cost-prohibitive. Glucose challenge tests have been used to estimate beta-cell function and insulin sensitivity. The product of beta-cell function and insulin sensitivity, termed the disposition index (DI), is of great value because it measures beta-cell function relative to insulin requirements. However, glucose challenge tests are expensive and time-consuming and therefore impractical to implement in large-scale clinical studies. To address this challenge, we developed a model disposition index (mDI estimated without insulin) that does not require insulin measurements during an oral glucose tolerance test (OGTT) (Ha et al., Diabetes 2021 (70) suppl. 1). mDI outperforms the conventional oral disposition index (oDI) at predicting progression to diabetes.
To further increase access and refine the assessments of beta-cell function, we are adapting our model to calculate a model disposition index using continuous glucose monitoring (CGM). CGM has been in the spotlight of diabetes management and has revolutionized the field of medicine as they are approved for glucose monitoring and clinical decision-making in patients with diabetes. CGM devices are relatively inexpensive compared to oral glucose challenge tests, accessible, and simple to use, especially in remote or free-living environments. The CGM device continuously measures interstitial glucose every 5 minutes and provides glucose profiles for 7-14 days. Thus, there are numerous data points compared to glucose challenge tests, but the abundant data points have not previously been used for estimating metabolic parameters. We compared mDI to two widely used CGM-derived metabolic parameters for assessing metabolic status and risk, mean glucose and glycemic excursion. Both mean glucose and glycemic excursion correlated strongly with mDI. The new approach promises to be cost- effective and easy to perform and therefore implementable in large-scale clinical studies. As for specific clinical applications, estimated model parameters during OGTTs identified ethnic differences in common pathways to T2D between Pima Indians and Koreans.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Postpartum Glucose Screening Among Homeless Women with Gestati...dkNET
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Rie Sakai-Bizmark, PhD. Assistant Professor, The Lundquist Institute at Harbor-UCLA Medical Center, David Geffen School of Medicine at UCLA
Abstract
Women with gestational diabetes mellitus (GDM) are at high risk of developing glucose intolerance after delivery. In the long term, women with GDM have a nearly 10-fold higher risk of developing type 2 diabetes mellitus (T2D) than women without GDM. The American Diabetes Association (ADA) and the American College of Obstetrics and Gynecology (ACOG) recommend that women with GDM undergo a 75-g oral glucose tolerance test (OGTT) between four and 12 weeks postpartum, and periodically thereafter. However, postpartum glucose screening (PGS) rate is historically low despite of various interventions to improve such rate. We hypothesized that PGS rate is lower among postpartum homeless women than their housed counterparts, and that interventions to improve PGS rate among postpartum homeless women with GDM should be tailored to their unique circumstances. The Japanese Society of Diabetes and Pregnancy (JSDP) modified the method to perform PGS with random plasma glucose (RPG) and glycated hemoglobin (HbA1c), which are simple and less invasive, to reduce the risk of COVID-19 infection by shortening the time spent in the hospital. RPG or HbA1c test do not require fasting. Therefore, homeless women who utilized care for other reasons could have the test as PGS. Given the barriers faced by homeless individuals, we hypothesize that RPG and HbA1c at healthcare utilizations during the postpartum period could be one of the strategies to identify high-risk individuals early because 1] healthcare utilizations are an opportunity for healthcare providers and social workers to educate homeless patients on GDM and their insurance eligibility and coverage for the screening, and 2] the physical barriers to health care access, which are often cited as a reason for the low PGS rate, are removed.
This proposed study will use administrative data from five states (AZ, CO, NC, NJ, and OR), which collectively include 9.3% of the US female homeless population. Each state will provide detailed, linked, multi-level, anonymized data for postpartum homeless women from four sources: 1] Medicaid claims; 2] Homeless Management Information System (HMIS); 3] birth records; and 4] the American Hospital Association (AHA) database to obtain hospital characteristics. With data from 2013 to 2020, an estimated sample size of 24,000 homeless women who delivered babies and 3,290 postpartum homeless women with GDM will be included.
First, we will estimate rates of GDM and PGS among homeless women. Second, we will estimate the cost-effectiveness of performing RPG and HbA1c tests...[Full abstract: https://dknet.org/about/blog/2581]
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies ...dkNET
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies Analyzed with Linear Mixed Models
Presenter:
Kylie K. Harrall, MS, Research Instructor, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus
Abstract
Planning a reproducible study requires selecting a sample size which will ensure appropriate statistical power. Free point-and-click software (Kreidler et al., Journal of Statistical Software, 2013, 10.18637/jss.v054.i10) makes it easy to select a sample size for clustered and longitudinal designs with linear mixed models. The software, a suite of training modules, and reference materials are freely available online (www.SampleSizeShop.org ). The software interface and training materials are aimed at biomedical scientists, included those funded by NIDDK. We give examples of study designs for which the software will compute power and sample size, including a study with clustering, a study with longitudinal repeated measures, and a study with multiple outcomes, where heterogeneity of response among subgroups is of interest.
The top 3 key questions that the Sample Size Shop can answer:
1. What free, online, point-and-click, wizard-style, NIH-funded, validated, published power and sample size software provides calculations for studies with clusters, longitudinal studies, and longitudinal studies with clusters?
2. Can GLIMMPSE (www.SampleSizeShop.org) compute power and sample size for randomized controlled clinical trials and observational studies funded by NIDDK?
3. Why use validated power and sample size software instead of writing simulations?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Se...dkNET
Abstract
AIRR-seq data (antibody/B-cell and T-cell receptor sequences from Adaptive Immune Receptor Repertoires) can describe the adaptive immune response in exquisite detail, and comparison and analysis of these data across studies and institutions can greatly contribute to the development of diagnostics and therapeutics, including the discovery of monoclonal antibodies for treatment of autoimmune diseases.
The AIRR community has developed protocols and standards for curating, analyzing and sharing AIRR-seq data (www.airr-community.org), and supports the AIRR Data Commons, a set of geographically distributed repositories that follows the AIRR Community’s metadata standards and the FAIR principles. The ADC currently comprises > 5 Bn receptor sequences from over 86 studies and ~9000 repertoires. The data model of the ADC has recently been expanded to include gene expression and cell phenotype data from single immune receptor cells, as well as MHC/HLA genotyping.
The iReceptor Gateway (ireceptor.org) queries this AIRR Data Commons for specific “metadata”, e.g. “find all repertoires from T1D studies” or for specific CDR3 sequences (e.g., find all repertoires from healthy individuals expressing this CDR3 sequence). Data from these federated repositories can then be analyzed through the Gateway by several sophisticated analysis tools, or downloaded for further analysis offline. The iReceptor Team at Simon Fraser University has recently initiated a collaboration to greatly expand the amount of bulk and single-cell immune profiling data from T1D studies in the AIRR Data Commons. For more information on obtaining or sharing AIRR-seq data contact support@ireceptor.org.
The top 3 key questions that the Adaptive Immune Receptor Repertoire (AIRR) can answer:
1. A researcher observes that many individuals with Type 1 Diabetes express a specific B-cell or T-cell receptor compared to controls (i.e., a “public” clonotype). To what degree is this receptor observed to be public across other T1D studies or other autoimmune disease populations?
2. Can Machine Learning be used to identify individuals who will respond well to a new cancer immunotherapy based on differences in their antibody/B-cell or T-cell receptor repertoires as curated in the AIRR Data Commons?
3. Is there an association between particular HLA, immunoglobulin (IG), or T-cell receptor (TR) germline gene polymorphisms and propensity toward specific infectious or autoimmune diseases?
Presenters:
Dr. Felix Breden, Scientific Director, iReceptor
Dr. Brian Corrie, Technical Director, iReceptor
Dr. Kira Neller, Bioinformatics Director, iReceptor
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The Mission and Progress of the(sugar)science: Helping Scienti...dkNET
Abstract
The(sugar)science was launched two years ago with the aim of helping scientists who study type 1 diabetes (T1D) and related interdisciplinary fields connect globally. We also wanted to create a digital space where trainees in the field can be supported, celebrated and connected to future positions. As part of our mission, our all volunteer team created the State of the Science series (2021. 2022), connecting global thought leaders around T1D research topics for discussion with a larger scientific audience. The second State of Science series was led by women scientists following the ADA publication which highlighted the paucity of women scientists in the leadership positions in the field.
To encourage the scientific community at large to dive into pre-existing data and pull out novel hypotheses that pertain to T1D, we created and together with dkNET, hosted D-Challenge 2021 and 2022. These competitions awarded $40K and $50K respectively to those who mined data and developed the most creative and testable hypothesis as judged by scientific experts in the field. These teams were also able to have an audience with the JDRFT1D Fund as part of a "pitch polish" which facilitated their interaction with venture capital.
To date, we have hosted over 200 interviews with T1D focused scientists in academia and industry and have an audience of 35K. Our reach on social media continues to grow and our metrics indicate a robust following. We share opportunities for positions in the field, engage and support trainees and together, our young scientific team published a paper, Similarities between bacterial GAD and human GAD65: Implications in gut mediated autoimmune type 1 diabetes, PLOS, February 2022.
We are currently engaged in the build of a T1D TCR Repository. We connected the AIRR data commons community with top TCR scientists in the field to begin this community based venture. It has the possibility to be incredibly instructive in defining the prodrome , which will further inform the field as it pertains to understanding the etiology of T1D.
Current team members that will join the discussion today will be Neha Mejety, Johns Hopkins University undergraduate and Tiffany Richardson, doctoral degree candidate at VUMC Diabetes.
The top 3 key questions that the(sugar)science can answer:
1. How can I find scientists to collaborate with in Type 1 diabetes research?
2. Where can I learn about Type 1 diabetes trending topics?
3. Where can I find forums to discuss novel ideas with scientists or key opinion leaders and find opportunities for Type 1 diabetes research ?
Presenters:
Monica Westley, PhD, Founder, the(sugar)science
Tiffany Richardson
Neha Majety
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: The Human BioMolecular Atlas Program (HuBMAP) 10/14/2022dkNET
Abstract
HuBMAP aims to catalyze the development of an open, global framework for comprehensively mapping the human body at cellular resolution. HuBMAP goals include: (1) Accelerate the development of the next generation of tools and techniques for constructing high resolution spatial tissue maps. (2) Generate foundational 3D tissue atlases. (3) Establish an open data platform. (4) Coordinate and collaborate with other funding agencies, programs, and the biomedical research community. (5) Support projects that demonstrate the value of the resources developed by the program. The HuBMAP Portal can be found at https://portal.hubmapconsortium.org and the Visible Human MOOC describes the compilation and coverage of HuBMAP data, demonstrates new single-cell analysis and mapping techniques, and introduces major features of the HuBMAP portal.
The top 3 key questions that HuBMAP can answer:
1. What assay types are best to map the human body in 3D and across scales?
2. What Common Coordinate System (CCF) is best to construct the Human Reference Atlas?
3. How can others help construct and/or use the Human Reference Atlas?
Presenters:
Katy Börner, PhD, Victor H. Yngve Distinguished Professor of Engineering and Information Science, Department of Intelligent Systems Engineering and Information Science, Indiana University
Jeffrey Spraggins, PhD, Assistant Professor, Department of Cell and Developmental Biology, Vanderbilt University
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "Visualizing Organelle and Cell Longevity In Situ" 05/20/22dkNET
Presenter: Rafael Arrojo e Drigo, Ph.D. Assistant Professor, Department of Molecular Physiology and Biophysics, Vanderbilt University
Abstract
Cells in largely post-mitotic organs can be as old as their host organism. These long-lived cells (LLCs) face a lifelong demand for performance to maintain organ function and are constantly exposed to drivers of molecular and cellular damage. Accordingly, dysfunction of LLCs is associated with aging and age-associated disease processes. Understanding cellular longevity mechanisms requires the identity and distribution pattern of LLCs. We developed imaging tools to quantify the age of cells in situ, which led to the discovery of new LLC types throughout the mouse body. This includes different cell types in the pancreas, where most beta cells can be as old as neurons in the brain. In this presentation, I will show how we apply different microscopy tools to bridge spatial and temporal scales in biology to quantify protein complex, organelle, and cell age in tissues. Applicable to virtually any cell, this imaging platform can reveal the temporal dynamics and longevity of structural components in vivo and their contribution to cell and tissue organization and function.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "Integrative Artificial Intelligence Approach to Predict T1D" 0...dkNET
Presenter: Kenneth Young, Ph.D. Assistant Professor, Health Informatics Institute, University of South Florida
Abstract
Type 1 diabetes (T1D) is a complex and heterogenous autoimmune disease that is no longer considered a clear-cut clinically diagnosed disease. T1D is multifaceted and the efficacy of therapeutic interventions varies greatly. With the evidence of etiological differences in T1D and the availability of high-dimensional multi-omics data in combination with clinical and environmental data, this project aims to use an artificial intelligence (AI) exploratory approach that may aid in the identification of new markers to predict IA and T1D.
This project utilizes data from NIDDK funded by The Environmental Determinants of Diabetes in the Young (TEDDY) study. TEDDY has generated over 900TB of diverse data types including multi-omics data, deep phenotyping, and environmental factor measurements every three-six months for fifteen years. We utilize deep learning methods, such as convolutional neural networks (CNN) and recurrent neural networks (RNN) that apply bidirectional long short-term memory (LSTM), in combination with multi-layer perceptron (MLP), to evaluate the prediction of IA and T1D. To aid in T1D predication, this project uses innovative and transformative AI approaches that combine temporal and static data, which may ultimately provide insights into the complex heterogeneity, diversity, and pathogenesis of T1D. The knowledge gained from this project may not only help advance the T1D community, but may have a broad impact on a variety of autoimmune diseases such as celiac and thyroid diseases which frequently coexist and share genetic susceptibility to T1D.
dkNET-HIRN Webinar "T Cell Antigen Discovery: Experimental and Computational ...dkNET
dkNET New Investigator Pilot Program in Bioinformatics Awardee Seminar Series
Co-Hosted with Human Islet Research Network (HIRN)
Presenter: Alok V. Joglekar, Ph.D. Assistant Professor, Center for Systems Immunology and Department of Immunology, University of Pittsburgh School of Medicine
Abstract
T cells are key players in many autoimmune diseases including Type 1 Diabetes. T cell responses are highly antigen specific by virtue of their T cell receptors (TCRs), that recognize epitopes on target cells. The enormous diversity of TCRs in an immune response poses a challenge in studying them, particularly regarding their antigenic specificity. Several experimental approaches have been developed to identify T cell specificities, with a recent surge in cell-based assays. More recently, computational approaches to predict T cell specificity are being developed and show great promise. This webinar will provide an overview of the experimental and computational approaches to identify T cell antigens. Furthermore, we will highlight the research performed in the Joglekar lab towards applying these approaches for auto-antigen discovery in Type 1 Diabetes. Finally, we will project what the future of these approaches may be, particularly for studying autoimmune diseases.
dkNET Webinar: Machine Learning to Analyze Pancreas Imaging in Diabetes 04/22...dkNET
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Jack Virostko, Ph.D. Assistant Professor of Diagnostic Medicine, Dell Medical School, University of Texas at Austin; Appointments in Oncology, Oden Institute for Computational Engineering and Sciences, LIVESTRONG Cancer Institutes
Abstract
The pancreas is smaller in individuals with diabetes and those at risk for developing the disease. Furthermore, quantitative measures of pancreas morphology and composition are altered in individuals with diabetes and display longitudinal changes accompanying disease progression. This talk will introduce MRI techniques for interrogating the pancreas. I will also demonstrate how machine learning may improve our understanding of pancreas changes in individuals with diabetes.
Upcoming webinars schedule: https://dknet.org/about/webinar
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
ISI 2024: Application Form (Extended), Exam Date (Out), EligibilitySciAstra
The Indian Statistical Institute (ISI) has extended its application deadline for 2024 admissions to April 2. Known for its excellence in statistics and related fields, ISI offers a range of programs from Bachelor's to Junior Research Fellowships. The admission test is scheduled for May 12, 2024. Eligibility varies by program, generally requiring a background in Mathematics and English for undergraduate courses and specific degrees for postgraduate and research positions. Application fees are ₹1500 for male general category applicants and ₹1000 for females. Applications are open to Indian and OCI candidates.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
2. An NIDDK Resource dknet.org
About dkNET
• Research resource information portal for biomedical researchers
• Information network to connect DK researchers and NIH-funded
centers
• Funded by National Institute of Health (NIH) - National Institute of
Diabetes and Digestive and Kidney Diseases (NIDDK)
• Developed and maintained by the FAIR Data Informatics Laboratory
(fdilab.org) at UCSD (Supports major informatics projects in
neuroscience and biomedicine)
3. An NIDDK Resource dknet.org
• Basic and clinical researchers in diabetes, digestive, obesity, endocrine, metabolic,
kidney, urologic, nutrition, bone and blood diseases.
• Tools and services are relevant across all biomedical domains
Image credits: NIDDK, NLM
Diabetes Digestive System Endocrine System Urinary System
WhWHO IS dkNET’S TARGET AUDIENCE?
4. An NIDDK Resource dknet.org
5 AREAS IN THE PORTAL
● dkNET provides:
○ Resource Reports: helps researchers find and evaluate resources
○ Authentication Reports: assists researchers in preparing NIH authentication plans
○ FAIR Data Services: assists researchers in finding data repositories and standards
for preparing data management plans
○ Discovery Portal: connects researchers to more than 200 biomedical databases
○ Hypothesis Center: powerful tools for data mining and hypothesis generation of
FAIR ‘omics data
5. dknet.orgAn NIDDK Resource
How Can dkNET Help Researchers?
Construct a
hypothesis
Ask a question
Do background
research
Plan
experiments
Collect and
analyze
data
Publish
results
Discovery Portal Hypothesis Center Resource Reports
Authentication
Reports
Information
Material
Data
Tools
Funded grant and
funding opportunities
Literature
Tutorials
Resource Reports
Hypothesis Center
FAIR Data Resources
Data Management
Data Repositories
Resource Reports
Cite RRID
Track Resources
Resource Identification
Authentication
plansNIH Mandates on Rigor
and Reproducibility for
grant submission
7. An NIDDK Resource dknet.org
Resource Reports
Find and evaluate key research resources:
Antibodies, organisms, cell lines, tools and services, plasmids, biosamples
I’m planning my
experiments, and
would like to find and
evaluate key
resources.
I’m publishing a
paper, and would like
to find RRIDs for
citing key resources.
Construct a
hypothesis
Ask a question
Do background
research
Plan
experiments
Collect and
analyze data
Publish
results
8. dknet.orgAn NIDDK Resource
Resource Reports
Report includes:
● Citations of usage
and mentions
● Who is using them?
● Do they have issues?
● How do they
compare?
● Is anyone near me
familiar with them?
9. dknet.orgAn NIDDK Resource
The resource information network
Alerts
RRID
Sources and
metadata
Literature
Collaborators
Validation &
Ratings
Assembled a unique
data set that
aggregates
information about
key research
resources for
biomedical research
using Research
Resource Identifiers
(RRIDs)
10. dknet.orgAn NIDDK Resource
RRID:AB_1855196
Fothergill LJ et al. Cell and Tissue Research. 375 (2) 359-69, 2019.
WHAT ARE RRIDS?
● A persistent identifier for
research resources
● FDILabs was instrumental in the
development and adoption of
RRIDs
● Supplied by authors to identify
resources in the materials and
methods section
● Designed to answer two simple
questions:
○ What resources were used in a
study
○ Who else has published with this
resource?
11. dknet.orgAn NIDDK Resource
Growth of RRIDs
● RRIDs in use across
major publishers:
○ Cell Press
○ Elsevier
○ Wiley
○ Nature
● 2019: Part of JATS
standard (Journal Article
Tag Suite)
● Over 205,700 resources
have been identified
+1,000
Distinct
Journals
12. dknet.orgAn NIDDK Resource
Resources Rating and Alerts
RRIDs provide us with an
opportunity to alert
researchers to issues with a
particular resource at time
of publication
dkNET aggregates information from multiple sources about
known issues with these resources
13. The Resource Information Network is inserted into the Publication
Process
● RRIDs stand between
authors and publications
● Authors go to a single portal,
the Resource Information
Portal, to obtain RRIDs,
which are issued by over 25
distinct authorities for
different types of resources
● Powered by dkNET
14. dknet.orgAn NIDDK Resource
Impact of RRIDs: Use of RRIDs associated with a
lower reported use of problematic cell lines
Babic Z, et al. eLIFE 2019; 8:e41676 DOI: 10.7554/eLife.41676
8.66%
3.33%
16.11%
5.36%
61.55%
decrease
66.73%
decrease
15. dknet.orgAn NIDDK Resource
Authentication Reports
1. Alert researchers of any issues with cell lines and
antibodies before the study is performed
2. Compliance with the NIH requirements for grant
applications
I’m writing a proposal,
and would like to know
how to prepare
“authentication of key
biological resources”
section.
I’m planning my
experiments and
would like to know
how to identify and
validate resources.
Construct a
hypothesis
Ask a question
Do background
research
Plan
experiments
Collect and
analyze data
Publish
results
16. dknet.orgAn NIDDK Resource
NIH Rigor & Reproducibility Guidelines
https://grants.nih.gov/reproducibility/documents/grant-guideline.pdf
dkNET provides help to
researchers:
● Comply with new
mandates for
research rigor and
reproducibility
● Make it easier for
identifying resources
and preparing
authentication plans
● Manage and share
FAIR research data
17. dknet.orgAn NIDDK Resource
Custom Authentication Report - Cell lines and Antibodies
• NIH requires that
you create an
authentication plan
for key research
resources in your
grant application
• dkNET can help!
• Tool for evaluating
how your resources
perform and best
practices for
designing an
authentication plan
18. dknet.orgAn NIDDK Resource
FAIR data resources for
biomedicine
FDILabs: Co-authors of the FAIR data principles
I’m writing a proposal,
and would like to know
how to prepare data
management plan.
Construct a
hypothesis
Ask a question
Do background
research
Plan
experiments
Collect and
analyze data
Publish
results
I’m publishing a
paper, and would like
to find a data
repository to share my
data.
19. dknet.orgAn NIDDK Resource
FAIR Data Resources
We are assisting with the interpretation of FAIR
for DK domains and providing tools to help with
● Research data management to support FAIR
● Sharing data: Suggested data repositories
and the standards they use
20. dknet.orgAn NIDDK Resource
FAIR data principles
High level principles to make data:
• Findable
• Accessible
• Interoperable
• Re-usable
Mark D. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship,
Scientific Data (2016). DOI: 10.1038/sdata.2016.18
…for humans and machines
21. 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
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
15 attributes of FAIR
● 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
Re-usable
● 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
Interoperable
22. FAIR Partnership
Researchers Repositories and
Registries
Indexers
Aggregators
• Good data
management
• Good
documentation
• Rich metadata
• Prepare to share
• Submit to
repository
• Persistent identifier
• Machine based access
• Clear license
• Open, domain
specific standards
• Machine readable
metadata
• Persistent metadata
• Bidirectional links
• Data citation
• Index
• Effective Search
• Persistent
metadata
Community Organizations
23. dknet.orgAn NIDDK Resource
Research Data Management Overview
Provide information about:
• What is research data
management?
• Provide links to
resource, such as
California Digital Library
data management
planning tool
• Provide specificity for
DK applications
24. dknet.orgAn NIDDK Resource
Where Can I Deposit My Data?
• List of DK specific
repositories,
recommended by
NLM and various
journals
• Created in
conjunction with
NIDDK
25. dknet.orgAn NIDDK Resource
dkNET Summer of Data Student Internship Program
Project Duration: 6 weeks starting June 22 to July 31, 2020
Application Due Date: April 17, 2020
Goal:
• Provide students an opportunity to use the dkNET tools and resources for their research projects
• Enable students to provide feedback on their experience to the dkNET team
• Students will learn best practices to enhance rigor and reproducibility
• Students will learn the basics of good data management by following the FAIR data principles
Award: $1000 awards to a limited number of students who are working in a lab during summer 2020
https://dknet.org/about/Summer-Internship
26. dknet.orgAn NIDDK Resource
Discovery Portal
I would like to know
what research
resources such as
information, data or
materials are
available.
I’m looking for funding
opportunities or
funded grant
information.
Construct a
hypothesis
Ask a question
Do background
research
Plan
experiments
Collect and
analyze data
Publish
results
27. dknet.orgAn NIDDK Resource
Discovery Portal
• 200+ biomedical
databases
• Literature
• Centralized search
• DK specific content
• Funding
opportunities -
original databases
29. dknet.orgAn NIDDK Resource
Hypothesis Center
I would like to find top
genes that are
relevant to a signaling
pathway.
Construct a
hypothesis
Ask a question
Do background
research
Plan
experiments
Collect and
analyze data
Publish
results
I have a gene or a list
of genes and would
like to find relevant
signaling pathways.
30. dknet.orgAn NIDDK Resource
Hypothesis Center - Signaling Pathways Project
SPP simplifies data mining of ‘omics data,
connects bench researchers to FAIR data
to allow them to easily interrogate the data
to generate hypotheses
● Find genes with important roles in
receptors, enzymes, organs and
tissues
● Define signaling pathways relevant
to a single gene or a regulation
32. dknet.orgAn NIDDK Resource
Summary - How Can dkNET Help
Researchers?
• Resource Reports
• help researchers find resources for their research
• Keeps researchers updated on resources with warnings, ratings and validation information
• Find RRIDs and register resources
• Find who else used the resources
• Rigor and Reproducibility Support
• Authentication Reports: assists in creating authentication plans
• FAIR Data Services: assists in developing DMP by finding appropriate data repositories
• Discovery Portal connects researchers to the broader biomedical community (+200 databases)
• Hypothesis Center enables researchers to harness the power of FAIR big data
• Analyze omics data to develop research hypotheses
33. dknet.orgAn NIDDK Resource
Get involved in the dkNET Community
dkNET Homepage: dkNET.org
Sign up
Newsletter
Join Webinar
Join Summer Program
Follow us
@dknet_info
Check Out or Post
News and Funding
Opportunities
Blog, Calendar
34. dknet.orgAn NIDDK Resource
Links to Available Resources
General Information
• dkNET Information https://dknet.org/about/product_info
• Research Resource Identifier (RRID) Information https://dknet.org/about/rrid
• What is Resource Reports? https://dknet.org/about/resource_reports_info
• NIH Mandates on Rigor and Transparency https://dknet.org/about/NIH-Policy-Rigor-Reproducibility
Services
• Resource Reports https://dknet.org/rin/rrids
• Authentication Reports https://dknet.org/rin/rrid-report
• Discovery portal https://dknet.org/data/search#all
• Hypothesis Center https://dknet.org/about/hypothesis_center
• Pilot funding databases https://dknet.org/about/funding
Data management
• FAIR data/research data management https://dknet.org/rin/research-data-management
• Suggested data repositories https://dknet.org/about/Suggested-data-repositories-niddk
Newsletter (Funding opportunity,
events, news, new tools...etc.)
• https://dknet.org/about/maillist
Webinar
• Upcoming webinar https://dknet.org/about/webinar
• dkNET recorded webinar https://www.youtube.com/channel/UCwukSrB8L61Fhwjv3x20lOQ
Summer of Data Student
Internship
• https://dknet.org//about/Summer-Internship
• Download Flyer