Biocuration 2014 - The Resource Identification Initiative

•Descargar como PPTX, PDF•

1 recomendación•1,236 vistas

The document discusses the Resource Identification Initiative (RII) which aims to improve reproducibility in scientific research by promoting the use of Research Resource Identifiers (RRIDs) in published literature. An experiment analyzing over 800 biological resources across multiple journals found that only about 50% were identifiable. The RII is developing standards and tools to integrate RRIDs into the publishing workflow so that resources are consistently and uniquely identified in a machine-readable way. This could help credit data contributions, enable data reanalysis, and improve reproducibility overall. A pilot project is underway to test the inclusion of RRIDs in publications.

Tecnología Empresariales

Melissa Haendel, OHSU Library
April 9, 2014
The Resource Identification
Initiative:
What are we curating anyway?
@ontowonka #RII

Journal guidelines for methods are often poor and
space is limited
“All companies from which materials were obtained should
be listed.” - A well-known journal
Reproducibility is dependent at a minimum, on
using the same resources. But…

How identifiable are resources in the
published literature?

An experiment in reproducibility
Domains:
Immunology
Cell biology
Neuroscience
Developmental
biology
General biology
Impact factors:
High
Medium
Low
84 Journals
248 papers
707 antibodies
104 cell lines
258 constructs
210 knockdown
reagents
437 model
organisms
Reporting Guidelines:
Stringent
Satisfactory
Loose

Only ~50% of resources were identifiable
Vasilevsky et al, 2013, PeerJ

There is no correlation between impact factor and
resource identification
Journal Impact Factor
0 10 20 30 40
Fractionofresourcesidentified
0.0
0.2
0.4
0.6
0.8
1.0 Antibodies
Cell Lines
Constructs
Knockdown reagents
Organisms

Resources are not more identifiable in journals
with stricter reporting requirements

http://validation.scienceexchange.com/#/cancer-biology
Attempting to reproduce 50 prominent cancer studies

A+ organization!
The Urban lab antibodies

Of 9 antibodies published in 5 articles, only
44% were identifiable
Percentidentifiable
0%
25%
50%
75%
100%
Commerical Ab
identifiable
Catalog number
reported
Source organism
reported
Target uniquely
identifiable

Resource information is not adequately
getting into the literature, EVEN
THOUGH IT IS READILY AVAILABLE
The problem is a lack of standards,
review, and tools

http://www.force11.org/Resource_Identification_Initiative
Numerous endorsers https://www.force11.org/RII/SignUp
Implementation of the new standard http://biosharing.org/bsg-000532

Promoting use of Research Resource
IDs (RRIDs) in the published literature
Antibodies
Software & Tools
Model Organisms
Pilot project runs from February – April
RRIDs should be:
Machine Readable
Consistent across publishers and
journals
Free to generate and access
Resources:

Sample citation:
Polyclonal rabbit anti-
MAPK3
antibody, Abgent, Cat#
AP7251E,
RRID:AB_2140114
1.
Research
er
submits a
manuscri
pt for
publicatio
n
2. Editor or
Publisher
asks for
inclusion of
RRID
3. Author goes to
Research
Identification
Portal to locate
RRID
4. RRID is
included
in
Methods
section
and
as
Keyword
Publishing Workflow

Potential outcomes
 Better reporting of materials and methods
 Making the literature machine readable
outside the paywall
 Reduce the curation load
 Change how we utilize the literature –
observations span journals and formats
 Credit and citation tracking for contributions
of resources
 Better retrospective reanalysis of data
 Ability for authors to subscribe to resources
and semantically similar entities

Be a beta tester!
http://www.antibodies-
online.com/resource-
identification-initiative/
Free gift!

Questions and issues
• Should the RRIDs be: DOIs? URIs? Nanopubs?
– Most sources do not create these
• Part of a new bibliographic type?
– For pilot, going into keywords to be outside paywall
• What is the best way to incorporate the RIID portal
into the publishers workflow?
– Provide text-mined checklist when author submits?
• How to best synchronize and coordinate
nomenclature with authoritative sources aggregated
in the RII Portal?
• How to utilize the RII Portal in dataset contribution to
data repositories?

Más contenido relacionado

La actualidad más candente

Dr Julie Stahlhut - Barcode Data Life-cycle

Consortium for the Barcode of Life (CBOL)

Collaborative Research: Scopus & RefWorks

Robin Featherstone

Pepe "Enriching Preprints with Provenance, Reproducibility, and Trustworthiness"

National Information Standards Organization (NISO)

eScience Institute presentation on eagle-i

mhaendel

Beacon: A Protocol for Federated Discovery and Sharing of Genomic Data

Miro Cupak

Charleston 2012: Altmetrics: Analyzing the Value in Scholarly Content

William Gunn

Beacon Network: A System for Global Genomic Data Sharing

Miro Cupak

Open Helix

David Cunningham

Beacon Network: A System for Global Genomic Data Sharing

Miro Cupak

How many medline platforms on the web?

Basset Hervé

S17 biot6838 santiago

Katherine Magner

IRIDA: Canada’s federated platform for genomic epidemiology, ABPHM 2015 WHsiao

IRIDA_community

eSouce Data for Clinical Trials

ClinCapture

Wimmics seminar--drug interaction knowledge base, micropublication, open anno...

jodischneider

On 30 April 2019, the OECD organised a webinar on the Adverse Outcome Pathway (AOP) framework. The AOP framework is a collaborative tool that applies an innovative approach for collecting mechanistic knowledge from various sources that can eventually support chemical safety assessment. The following questions were addressed: What is the AOP framework and why should you care? Why are we developing AOPs? Why collaborations are encouraged and why should scientific societies be brought in? What are the opportunities for collaboration in AOP development?

Adverse outcome pathways collaboration, Jason O’Brien from the Environment an...

OECD Environment

NCBO Tools and Web services

Trish Whetzel

GWA studies are perhaps most often used for studying the genetic basis of human diseases, but this technology also has great utility for studying the natural variation of other organisms. In this webcast, Ashley Hintz, Field Application Scientist, will discuss the utility of SVS for analyzing plant GWA data, using publicly available SNP data for Arabidopsis thaliana as a case study. Along the way, Ashley will demonstrate how SVS can be used to manage data, analyze population structure, perform genotype QA and ultimately replicate a published genetic association in A. thaliana using EMMAX regression. She will also address the flexibility of SVS for analyzing the genomes of other plant and animal species.

GWAS in a model organism: Arabidopsis thaliana

Golden Helix Inc

Scouting_activity

kamwela oscar

Sample Tracker: A web-based application for tracking and managing environment...

Gerard Devine

La actualidad más candente (19)

Dr Julie Stahlhut - Barcode Data Life-cycle

Collaborative Research: Scopus & RefWorks

Pepe "Enriching Preprints with Provenance, Reproducibility, and Trustworthiness"

eScience Institute presentation on eagle-i

Beacon: A Protocol for Federated Discovery and Sharing of Genomic Data

Charleston 2012: Altmetrics: Analyzing the Value in Scholarly Content

Beacon Network: A System for Global Genomic Data Sharing

Open Helix

Beacon Network: A System for Global Genomic Data Sharing

How many medline platforms on the web?

S17 biot6838 santiago

IRIDA: Canada’s federated platform for genomic epidemiology, ABPHM 2015 WHsiao

eSouce Data for Clinical Trials

Wimmics seminar--drug interaction knowledge base, micropublication, open anno...

Adverse outcome pathways collaboration, Jason O’Brien from the Environment an...

NCBO Tools and Web services

GWAS in a model organism: Arabidopsis thaliana

Scouting_activity

Sample Tracker: A web-based application for tracking and managing environment...

Similar a Biocuration 2014 - The Resource Identification Initiative

Resource Identification Initiative_RDA_March2014

Nicole Vasilevsky

On the Reproducibility of Science: Unique Identification of Research Resourc...

Nicole Vasilevsky

CoBRA guideline : a tool to facilitate sharing, reuse, and reproducibility of...

Research Data Alliance

Identifying and tracking research resources using RRIDs: a practical approach

dkNET

Citations—often termed as intellectual transactions, acknowledgment of intellectual debts, and conceptual association—are a link between the author’s current study and already published work. It not only provides credibility to the author’s work but also helps funders evaluate the impact of the research study. Citation indexes are maintained for information retrieval of both cited and citing work, facilitating the literature search process. It also helps authors in identifying the number of citations that their papers have received. Citation data is considered as a legitimate measure to rank authors, journals, and publishers. Through this webinar, we aim to provide information about citation indexing and how authors and publishers can get indexed in established citation databases.

How to Get Indexed in International Citation Databases

SC CTSI at USC and CHLA

Polka "Emerging Vision"

National Information Standards Organization (NISO)

2015 12 ebi_ganley_final

Emma Ganley

Building on the success of the Molecular Libraries Program (MLP), the Broad Institute MLP team is co-leading with the National Center for Advancing Translational Sciences (NCATS) an NIH-sponsored project across 7 institutions to augment the data in PubChem with the creation of the Bioassay Research Database (BARD). The BARD platform standardizes the representation of bioassays in a next-generation repository and provides a user-friendly interface that supports sophisticated queries and data mining. Data originating from publicly-funded chemical biology research efforts will be presented with appropriate context including structured assay and result annotations. These annotations use relevant ontologies including, for example, the BioAssay Ontology, Gene Ontology, and the Unit Ontology. We simplified the representation of ontologies into a hierarchical data dictionary to enable data producers to more easily create and upload projects, assays, and results, while creating two separate user interfaces for data consumers. The BARD WebQuery Interface leverages a Google-like interface with auto-suggest functionality for complex queries, such as retrieval of all assays, and results for biological pathways such as “DNA repair” or “oxidative stress”; presentation of this information in a rich-user interface that includes spreadsheet support for structure-activity relationship analyses. Compounds, projects, and assays can be exported into an Amazon-like query cart for refining queries, and additional computations can be executed on datasets via community-developed plug-ins including promiscuity analyses via the BioActivity Data Associative Promiscuity Pattern Learning Engine (BADAPPLE) and a CYP450 metabolism site prediction plugin (hgp://www.farma.ku.dk/smartcyp/) using 2D structure fingerprints. Integration between the WebQuery and Desktop clients enables power users to initiate analyses in WebQuery and gain more insight via the Desktop client. Lastly, as industry and academia work together to innovate in small-molecule therapeutics, we have created an initial specification for the Assay Definition Standard. This standard through the Assay Definition Format has been used as the medium of data file transfer for data upload. We expect that the Chemical Biology community now has an opportunity to leverage this standard to routinely transfer assay and result data within and between information systems and organizations. This presentation will highlight the BARD platform with a focus on representing the cumulative body of work that exploits the ChemAxon toolkit.

EUGM 2013 - Andrea de Souza (Broad Institute): Setting the stage for the “SD”...

ChemAxon

BioAssay Research Database Presentation at the Chem Axon UGM 2013

Andrea de Souza

Aries systems eemug 2021 manuscript eval services panel sci score v2_edits

Anita Bandrowski

What Faculty Need to Know About Open Access & Increasing Their Publishing Im...

Charles Lyons

Rii stock centerdir_aug9_2016

Anita Bandrowski

GHGH FGHGH GHGHGH VHGH This work aims to provide a practical guide to assist students of Computer Science courses and related fields to conduct a systematic literature review. The steps proposed in this paper to conduct a systematic review were extracted from a technical report published by the researcher Bárbara Kitchenham [1] and arranged in a more objective format, in order to make information more accessible and practical, especially for those who are having their first contact with this technique.

Writing-a-Systematic-Literature-Review.pdf

ssuser50a5ec

Writing-a-Systematic-Literature-Review.pdf

ssuser50a5ec

Evidence-Based Health Care: A Tutorial Part 3

chasbandy

Force11: Enabling transparency and efficiency in the research landscape

mhaendel

NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...

National Information Standards Organization (NISO)

Information seeking

menglesakis

RDA Scholarly Infrastructure 2015

William Gunn

FAIRer Research

Carole Goble

Similar a Biocuration 2014 - The Resource Identification Initiative (20)

Resource Identification Initiative_RDA_March2014

On the Reproducibility of Science: Unique Identification of Research Resourc...

CoBRA guideline : a tool to facilitate sharing, reuse, and reproducibility of...

Identifying and tracking research resources using RRIDs: a practical approach

How to Get Indexed in International Citation Databases

Polka "Emerging Vision"

2015 12 ebi_ganley_final

EUGM 2013 - Andrea de Souza (Broad Institute): Setting the stage for the “SD”...

BioAssay Research Database Presentation at the Chem Axon UGM 2013

Aries systems eemug 2021 manuscript eval services panel sci score v2_edits

What Faculty Need to Know About Open Access & Increasing Their Publishing Im...

Rii stock centerdir_aug9_2016

Writing-a-Systematic-Literature-Review.pdf

Evidence-Based Health Care: A Tutorial Part 3

Force11: Enabling transparency and efficiency in the research landscape

NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...

Information seeking

RDA Scholarly Infrastructure 2015

FAIRer Research

Más de mhaendel

Presented at AMIA TBI CRI 2018. Rare disease patients are expert in their medical history and these patients not only are some of the most engaged, but also they can themselves provision data for use in clinical evaluation. We therefore created a lay-person version of our clinical deep phenotyping instrument, the Human Phenotype Ontology. Here, we evaluate the diagnostic utility of this lay-HPO, and debut a new software tool for patient-led deep phenotyping.

Patient-led deep phenotyping using a lay-friendly version of the Human Phenot...

mhaendel

Semantics for rare disease phenotyping, diagnostics, and discovery

mhaendel

Presented at the Association of University Technology Managers (AUTM) Annual Conference 2018 Moderator: Arvin Paranjpe, Oregon Health & Science University Speakers: Frank Curci, Ater Wynne LLP Melissa Haendel, Oregon Health & Science University Charles Williams, University of Oregon Big data is an open frontier, and it’s quickly expanding. However, transaction costs and legal barriers stand squarely in the way of meaningful, far-reaching data integration. We’ll grapple with the issues regarding a large-scale data integration project across humans, model and non-model organisms. Without pointing fingers, we’ll also share a few highlights from the (Re)usable Data Project, which outlined a five-part rubric to evaluate data licenses with respect to clarity and the reuse and redistribution of data. In addition, the topic raises the question: How well-suited are off-the-shelf software and data licenses for universities? Data scientists and software programmers are all too quick to pick one when they release their technology on GitHub. What should technology transfer professionals recommend? We’ll discuss the usefulness and attributes of a uniform software and data license for university researchers and software programmers.

The Software and Data Licensing Solution: Not Your Dad’s UBMTA

mhaendel

Presented at PIDapalooza 2018. https://pidapalooza.org/ Determining identifier equivalency is key to data integration and to realizing the scientific discoveries that can only be made by collating our vast disconnected data stores. There are two key problems in determining equivalency - conceptual and syntactic alignment. Conceptual alignment often relies on Xrefs and string-matching against synonyms. There is indeed a better way! Algorithmic determination of identifier equivalency across different sources can use a combination of Xrefs, priors rules, existing semantic relations, and synonyms to create equivalency cliques than can highlight the discrepancies in conceptual definitions for manual review. This is especially useful for data sources annotated with concept drift and differences, such as diseases. Syntactic issues are that there are so many variations of the same identifier, making data joins difficult. We present a framework to reconcile and provide authoritative and integration-ready prefixed identifiers (CURIES), to capture and consolidate prefixes and to build links across key resource registries. The combination of JSON-LD context technology with a prefix metadata repository provides the basis for the infrastructure to handle identifiers in a consistent fashion. Finally, this architecture also allows resources to be self describing "beacons" with respect to their identifiers.

Equivalence is in the (ID) of the beholder

mhaendel

The NIH Data Commons must treat the data it will contain not unlike the mortar and stones of a road. To help our fellow scientists travelers use the road, we must engineer for heavy traffic and diverse destinations. There are many steps to architecting a robust and persistent road. First, the data must be sourced and manipulated into common data models. This requires versioned access to the data, equivalency determination of identifiers within the data or minting of new ones for the data and/or within it, manipulating the data according to common data models (e.g. a genotype-to-pehnotype association in one source may relate a variant to a disease, where in another it may be a set of alleles associated with a set of phenotypes, each source models the data differently). Inclusion of the data in the Commons must meet all licensing restrictions, which are varied and usually poorly declared, as well as security, HIPAA, and ethics requirements. Software tools are needed to perform the Enhance-Transform-Load (ETL) process on a regular cycle to keep the data current, and to assess changes and quality assurance over time. For records that disappear, there needs to be a way to keep an archive of them. Once in the Commons, the data requires a map to navigate the roads: where do you want to go? Indexing and search across the data requires having the data be self-reporting - loading ontologies used in the data for indexing and providing faceted query over these and other attributes, sophisticated text mining tools, relevance ranking, and equivalency and similarity determination from amongst different providers. Once found, the users need vehicles to drive upon the road. These are their workspaces, the place where they design and implement the operations they need in order to get where they want to go. Unimaginable scientific emeralds are to be found at the end of the road, as the sum of all the data, if well integrated and made computationally reusable, has proven to be well beyond the sum of its parts in getting us where we want to go.

Building (and traveling) the data-brick road: A report from the front lines ...

mhaendel

GA4GH Monarch Driver Project Introduction

mhaendel

GA4GH Phenotype Ontologies Task team update

mhaendel

Biomedical data integrators grapple with a fundamental blocker in research today: licensing for data use and redistribution. Complex licensing and data reuse restrictions hinder most publicly-funded, seemingly “open” biomedical data from being put to its full potential. Such issues include missing licenses, non-standard licenses, and restrictive provisions. The sheer diversity of licenses are particularly thorny for those that aim to redistribute data. Redistributors are often required to contact each sub-source to obtain permissions, and this is complicated by the fact that on each side of the agreement there may be multiple legal entities involved and some sub-sources may themselves already be aggregating data from other sub-sources. Furthermore, interpreting legal compliance with source data licensing and use agreements is complicated, as data is often manipulated, shared, and redistributed by many types of research groups and users in various and subtle ways. Here, we debut a new effort, the (Re)usable Data Project, where we have created a five-part rubric to evaluate biomedical data sources and their licensing information to determine the degree to which unnegotiated and unrestricted reuse and redistribution are provided. We have tested the (Re)usable Data rubric against various biomedical data sources, ranking each source on a scale of zero to five stars, and have found that approximately half of the resources rank poorly, getting 2.5 stars or less. Our goal is to help biomedical informaticians and other users navigate the plethora of issues in reusing and redistributing biomedical data. The (Re)usable Data project aims to promote standardization and ease of reuse licensing practices by data providers.

Reusable data for biomedicine: A data licensing odyssey

mhaendel

Data Translator: an Open Science Data Platform for Mechanistic Disease Discovery

mhaendel

Global phenotypic data sharing standards to maximize diagnostic discovery

mhaendel

Presented at the 2017 International Biocuration Conference. Data relevant to any given scientific investigation is highly decentralized across thousands of specialized databases. Within the Biocuration community, we recognize that the value of open scientific knowledge bases is that they make scientific knowledge easier to find and compute, thereby maximizing impact and minimizing waste. The ever-increasing number of databases makes us necessarily question what are our priorities with respect to maintaining them, developing new ones, or senescing/subsuming ones that have completed in their mission. Therefore, open biomedical data repositories should be carefully evaluated according to quality, accessibility, and value of the database resources over time and across the translational divide. Traditional citation count and publication impact factors as a measure of success or value are known to be inadequate to assess the usefulness of a resource. This is especially true for integrative resources. For example, almost everyone in biomedicine relies on PubMed, but almost no one ever cites or mentions it in their publications. While the Nucleic Acids Research Database issues have increased citation of some databases, many still go unpublished or uncited; even novel derivations of methodology, applications, and workflows from biomedical knowledge bases are often “adapted” but never cited. There is a lack of citation best practices for widely used biomedical database resources (e.g. should a paper be cited? A URL? Is mention of the name and access date sufficient?). We have developed a draft evaluation rubric for evaluating open science databases according to the commonly cited FAIR principles -- Findable, Accessible, Interoperable, and Reusable, but with three additional principles: Traceable, Licensed, and Connected. These additions are largely overlooked and underappreciated, yet are critical to reuse of the knowledge contained within any given database. It is worth noting that FAIR principles apply not only to the resource as a whole, but also to their key components; this “fractal FAIRness” means that even the license, identifiers, vocabularies, APIs themselves must be Findable, Accessible, Interoperable, Reusable, etc. Here we report on initial testing of our evaluation rubric on the recent NIH/Wellcome Trust Open Science projects and seek community input for how to further advance this rubric as a Biocuration community resource.

How open is open? An evaluation rubric for public knowledgebases

mhaendel

Whole-exome sequencing has revolutionized disease research, but many cases remain unsolved because ~100-1000 candidates remain after removing common or non-pathogenic variants. We present Genomiser to prioritize coding and non-coding variants by leveraging phenotype data encoded with the Human Phenotype Ontology and a curated database of non-coding Mendelian variants. Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes.

Deep phenotyping to aid identification of coding & non-coding rare disease v...

mhaendel

Science in the open, what does it take?

mhaendel

Presented at the IRDiRC 2017 conference in Paris, Feb 9th, 2017 (http://irdirc-conference.org/). This talk reviews use of the Human Phenotype Ontology for phenotype comparisons against other patients, known diseases, and animal models for diagnostic discovery. It also discusses the new Phenopackets Exchange mechanism for open phenotypic data sharing. www.monarchinitiative.org www.phenopackets.org www.human-phenotype-ontology.org

Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanis...

mhaendel

Phenopackets as applied to variant interpretation

mhaendel

This is an update for COASP (http://oaspa.org/conference/) on the representation of attribution beyond authorship of a publication. Publications are proxies for the projects and people that area actually engaged in the work, and represent the dissemination aspect. How can we better understand the individual contributions and their impact? The openRIF, openVIVO and FORCE11 Attribution WG efforts aim to represent scholarship in a computationally tractable manner so as to enable credit and evaluation of all types of scholarly contributions.

Credit where credit is due: acknowledging all types of contributions

mhaendel

The Human Phenotype Ontology (HPO) was developed to describe phenotypic abnormalities, aka, “deep phenotyping”, whereby symptoms and characteristic phenotypic findings (a phenotypic profile) are captured. The HPO has been utilized to great success for assisting computational phenotype comparison against known diseases, other patients, and model organisms to support diagnosis of rare disease patients. Clinicians and geneticists create phenotypic profiles based on clinical evaluation, but this is time consuming and can miss important phenotypic features. Patients are sometimes the best source of information about their symptoms that might otherwise be missed in a clinical encounter. However, HPO primarily use medical terminology, which can be difficult for patients and their families to understand. To make the HPO accessible to patients, we systematically added non-expert terminology (i.e., layperson terms) synonyms. Using semantic similarity, patient-recorded phenotypic profiles can be evaluated against those created clinically for undiagnosed patients to determine the improvement gained from the patient-driven phenotyping, as well as how much the patient phenotyping narrows the diagnosis. This patient-centric HPO can be utilized by all: in patient-centered rare disease websites, in patient community platforms and registries, or even to post one’s hard-to-diagnosed phenotypic profile on the Web.

Deep phenotyping for everyone

mhaendel

Keynote presented at the the Ninth International Biocuration Conference Geneva, Switzerland, April 10-14, 2016 The health of an individual organism results from complex interplay between its genes and environment. Although great strides have been made in standardizing the representation of genetic information for exchange, there are no comparable standards to represent phenotypes (e.g. patient disease features, variation across biodiversity) or environmental factors that may influence such phenotypic outcomes. Phenotypic features of individual organisms are currently described in diverse places and in diverse formats: publications, databases, health records, registries, clinical trials, museum collections, and even social media. In these contexts, biocuration has been pivotal to obtaining a computable representation, but is still deeply challenged by the lack of standardization, accessibility, persistence, and computability among these contexts. How can we help all phenotype data creators contribute to this biocuration effort when the data is so distributed across so many communities, sources, and scales? How can we track contributions and provide proper attribution? How can we leverage phenotypic data from the model organism or biodiversity communities to help diagnose disease or determine evolutionary relatedness? Biocurators unite in a new community effort to address these challenges.

Why the world needs phenopacketeers, and how to be one

mhaendel

On the frontier of genotype-2-phenotype data integration

mhaendel

The Monarch Initiative: A semantic phenomics approach to disease discovery

mhaendel

Más de mhaendel (20)

Patient-led deep phenotyping using a lay-friendly version of the Human Phenot...

Semantics for rare disease phenotyping, diagnostics, and discovery

The Software and Data Licensing Solution: Not Your Dad’s UBMTA

Equivalence is in the (ID) of the beholder

Building (and traveling) the data-brick road: A report from the front lines ...

GA4GH Monarch Driver Project Introduction

GA4GH Phenotype Ontologies Task team update

Reusable data for biomedicine: A data licensing odyssey

Data Translator: an Open Science Data Platform for Mechanistic Disease Discovery

Global phenotypic data sharing standards to maximize diagnostic discovery

How open is open? An evaluation rubric for public knowledgebases

Deep phenotyping to aid identification of coding & non-coding rare disease v...

Science in the open, what does it take?

Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanis...

Phenopackets as applied to variant interpretation

Credit where credit is due: acknowledging all types of contributions

Deep phenotyping for everyone

Why the world needs phenopacketeers, and how to be one

On the frontier of genotype-2-phenotype data integration

The Monarch Initiative: A semantic phenomics approach to disease discovery

Último

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

DianaGray10

2024: Domino Containers - The Next Step. News from the Domino Container commu...

Martijn de Jong

Manulife - Insurer Innovation Award 2024

The Digital Insurer

Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

UK Journal

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

Top 10 Most Downloaded Games on Play Store in 2024

SynarionITSolutions

A Principled Technologies deployment guide Conclusion Deploying VMware Cloud Foundation 5.1 on next gen Dell PowerEdge servers brings together critical virtualization capabilities and high-performing hardware infrastructure. Relying on our hands-on experience, this deployment guide offers a comprehensive roadmap that can guide your organization through the seamless integration of advanced VMware cloud solutions with the performance and reliability of Dell PowerEdge servers. In addition to the deployment efficiency, the Cloud Foundation 5.1 and PowerEdge solution delivered strong performance while running a MySQL database workload. By leveraging VMware Cloud Foundation 5.1 and PowerEdge servers, you could help your organization embrace cloud computing with confidence, potentially unlocking a new level of agility, scalability, and efficiency in your data center operations.

Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...

Principled Technologies

presentation ICT roal in 21st century education

jfdjdjcjdnsjd

This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

HTML Injection Attacks: Impact and Mitigation Strategies

Boston Institute of Analytics

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Neo4j

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

Automating Google Workspace (GWS) & more with Apps Script

wesley chun

🐬 The future of MySQL is Postgres 🐘

RTylerCroy

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

Artificial Intelligence: Facts and Myths

Joaquim Jorge

Biocuration 2014 - The Resource Identification Initiative

1. Melissa Haendel, OHSU Library April 9, 2014 The Resource Identification Initiative: What are we curating anyway? @ontowonka #RII

2. Journal guidelines for methods are often poor and space is limited “All companies from which materials were obtained should be listed.” - A well-known journal Reproducibility is dependent at a minimum, on using the same resources. But…

3. How identifiable are resources in the published literature?

4. An experiment in reproducibility Domains: Immunology Cell biology Neuroscience Developmental biology General biology Impact factors: High Medium Low 84 Journals 248 papers 707 antibodies 104 cell lines 258 constructs 210 knockdown reagents 437 model organisms Reporting Guidelines: Stringent Satisfactory Loose

5. Only ~50% of resources were identifiable Vasilevsky et al, 2013, PeerJ

6. There is no correlation between impact factor and resource identification Journal Impact Factor 0 10 20 30 40 Fractionofresourcesidentified 0.0 0.2 0.4 0.6 0.8 1.0 Antibodies Cell Lines Constructs Knockdown reagents Organisms

7. Resources are not more identifiable in journals with stricter reporting requirements

8. http://validation.scienceexchange.com/#/cancer-biology Attempting to reproduce 50 prominent cancer studies

9. Even for some of the highest profile papers, we still have to go back to the authors to identify resources Resources reported in the 50 Reproducibility Initiative studies show similar results Vasilevsky et al., 2013, PeerJ Reproducibility Initiative http://shar.es/BSsab

10. Maybe labs are just disorganized?

11. Meet the Urban Lab Meet the Urban Lab

12. A+ organization! The Urban lab antibodies

13. Of 9 antibodies published in 5 articles, only 44% were identifiable Percentidentifiable 0% 25% 50% 75% 100% Commerical Ab identifiable Catalog number reported Source organism reported Target uniquely identifiable

14. Resource information is not adequately getting into the literature, EVEN THOUGH IT IS READILY AVAILABLE The problem is a lack of standards, review, and tools

15. http://www.force11.org/Resource_Identification_Initiative Numerous endorsers https://www.force11.org/RII/SignUp Implementation of the new standard http://biosharing.org/bsg-000532

16. Promoting use of Research Resource IDs (RRIDs) in the published literature Antibodies Software & Tools Model Organisms Pilot project runs from February – April RRIDs should be: Machine Readable Consistent across publishers and journals Free to generate and access Resources:

17. Sample citation: Polyclonal rabbit anti- MAPK3 antibody, Abgent, Cat# AP7251E, RRID:AB_2140114 1. Research er submits a manuscri pt for publicatio n 2. Editor or Publisher asks for inclusion of RRID 3. Author goes to Research Identification Portal to locate RRID 4. RRID is included in Methods section and as Keyword Publishing Workflow

18. Potential outcomes  Better reporting of materials and methods  Making the literature machine readable outside the paywall  Reduce the curation load  Change how we utilize the literature – observations span journals and formats  Credit and citation tracking for contributions of resources  Better retrospective reanalysis of data  Ability for authors to subscribe to resources and semantically similar entities

19. Be a beta tester! http://www.antibodies- online.com/resource- identification-initiative/ Free gift!

20. Questions and issues • Should the RRIDs be: DOIs? URIs? Nanopubs? – Most sources do not create these • Part of a new bibliographic type? – For pilot, going into keywords to be outside paywall • What is the best way to incorporate the RIID portal into the publishers workflow? – Provide text-mined checklist when author submits? • How to best synchronize and coordinate nomenclature with authoritative sources aggregated in the RII Portal? • How to utilize the RII Portal in dataset contribution to data repositories?

Biocuration 2014 - The Resource Identification Initiative

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (19)

Similar a Biocuration 2014 - The Resource Identification Initiative

Similar a Biocuration 2014 - The Resource Identification Initiative (20)

Más de mhaendel

Más de mhaendel (20)

Último

Último (20)

Biocuration 2014 - The Resource Identification Initiative