Collaborative Data Analysis with Taverna Workflows

•Transferir como PPT, PDF•

2 gostaram•651 visualizações

Microsoft eScience Workshop 2009 presentation on collaborative development of Taverna workflows for data analysis in FLOSS research.

Tecnologia Educação

Collaborative Data Analysis with Taverna Workflows Andrea Wiggins, Kevin Crowston & James Howison 16 October 2009

FLOSS Phenomenon ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

eScience Proof-of-Concept ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Research Replication Classifies projects based on metrics for success and stage of project growth English & Schweik, 2007 Examines dynamics of social networks of project communications over time Howison et al., 2006 Examines distribution of project sizes for evidence of preferential attachment theory of growth in networks Conklin, 2004 Description Study

FLOSS Research Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

RoRs: FLOSSmole ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

RoRs: SRDA ,[object Object],[object Object],[object Object]

Analysis Tool Requirements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Integrating Diverse Skills ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Taverna ,[object Object],[object Object],[object Object],[object Object],[object Object]

Work Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

“ Identifying success and tragedy of FLOSS Commons” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Classification Workflow

Key Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Important Details ,[object Object],[object Object],[object Object],[object Object],[object Object]

Extending Analyses ,[object Object],[object Object],[object Object],[object Object]

Workflow Re-use ,[object Object],[object Object],[object Object],[object Object],[object Object]

Challenges with Using Workflows ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Barriers to Uptake ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],Example: Recent Research

“Normal” Download-Release Patterns ,[object Object],[object Object]

[object Object],Taverna’s Download-Release Patterns 1.3.2-RC1 +2 presentations 1.5.0 ? ?

Interpretation ,[object Object],[object Object]

Questions? ,[object Object],[object Object],[object Object],[object Object]

Mais conteúdo relacionado

Mais procurados

Citation and reproducibility in software

Citation and reproducibility in software

Citation and reproducibility in software

Software Citation: Principles, Implementation, and Impact

Software Citation: Principles, Implementation, and Impact

Software Citation: Principles, Implementation, and Impact

Micropublication WormBase Workshop International Worm Meeting 2015

Micropublication WormBase Workshop International Worm Meeting 2015

Micropublication WormBase Workshop International Worm Meeting 2015

THOR Workshop - Services PANGAEA

THOR Workshop - Services PANGAEA

THOR Workshop - Services PANGAEA

Modern tools for sharing and synthesizing neuroimaging results

Modern tools for sharing and synthesizing neuroimaging results

Modern tools for sharing and synthesizing neuroimaging results

Krzysztof Gorgolewski

20171003 lancaster data conversations Chue-Hong

20171003 lancaster data conversations Chue-Hong

20171003 lancaster data conversations Chue-Hong

Lancaster University Library

HotSoS16 Tutorial "Text Analytics for Security" by Tao Xie and William Enck

HotSoS16 Tutorial "Text Analytics for Security" by Tao Xie and William Enck

HotSoS16 Tutorial "Text Analytics for Security" by Tao Xie and William Enck

User Expectations in Mobile App Security

User Expectations in Mobile App Security

User Expectations in Mobile App Security

Lecture 1: Being FAIR: FAIR data and model management In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation. Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects. Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester. In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face. I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects. [1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18

Being FAIR: FAIR data and model management SSBSS 2017 Summer School

Being FAIR: FAIR data and model management SSBSS 2017 Summer School

Being FAIR: FAIR data and model management SSBSS 2017 Summer School

Getting (and giving) credit for all that we do

Getting (and giving) credit for all that we do

Getting (and giving) credit for all that we do

Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation. The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions. http://www.fair-dom.org http://www.fairdomhub.org http://www.seek4science.org Presented at COMBINE 2016, Newcastle, 19 September. http://co.mbine.org/events/COMBINE_2016

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...

Intro to Reproducible Research

Intro to Reproducible Research

Intro to Reproducible Research

ROHub

What is Reproducibility? The R* brouhaha (and how Research Objects can help)

What is Reproducibility? The R* brouhaha (and how Research Objects can help)

What is Reproducibility? The R* brouhaha (and how Research Objects can help)

Avoiding the tower of babel - The Role of Data Description Standards in Biome...

Avoiding the tower of babel - The Role of Data Description Standards in Biome...

Avoiding the tower of babel - The Role of Data Description Standards in Biome...

Krzysztof Gorgolewski

Presentation at Data Innovation Summit 2019 in Stockholm, Sweden. ABSTRACT Microscopes are capable of producing vast amounts of data, and when used in automated laboratories both the number and size of images present many challenges for storing, categorizing, analyzing, annotating, and transforming the data into actionable information that can used for decision making; either by humans or machines. In this presentation I will describe the informatics system we have established at the Department of Pharmaceutical Biosciences at Uppsala University, which consists of computational hardware (CPUs, GPUs, storage), middleware (Kubernetes), imaging database (OMERO), and workflow system (Pachyderm) to perform online prioritization of new data, as well as the continuous analytics system to automate the process from captured images to continuously updated and deployed AI models. The AI methodologies include Deep Learning models trained on image data, and conventional machine learning models trained on features extracted from images or chemical structures. Due to the microservice architecture the system is scalable and can be expanded using hybrid-architectures with cloud computing resources. The informatics system serves a robotized cell profiling setup with incubators, liquid handling and high-content microscopy. The lab is quite young and is targeting applications primarily in drug screening and toxicity assessment, with the aim to improve research using AI and intelligent design of experiments.

Automating the process of continuously prioritising data, updating and deploy...

Automating the process of continuously prioritising data, updating and deploy...

Automating the process of continuously prioritising data, updating and deploy...

BigData Testing by Shreya Pal

BigData Testing by Shreya Pal

BigData Testing by Shreya Pal

Agile Testing Alliance

Lab Notebooks as Data Management (SLA Winter Virtual Conference 2012)

Lab Notebooks as Data Management (SLA Winter Virtual Conference 2012)

Lab Notebooks as Data Management (SLA Winter Virtual Conference 2012)

Research Data (and Software) Management at Imperial: (Everything you need to ...

Research Data (and Software) Management at Imperial: (Everything you need to ...

Research Data (and Software) Management at Imperial: (Everything you need to ...

Sarah Anna Stewart

Data Visualization for Big Data: Experience from the Front Line

Data Visualization for Big Data: Experience from the Front Line

Data Visualization for Big Data: Experience from the Front Line

Rosa Romero Gómez, PhD

Mais procurados (20)

Citation and reproducibility in software

Citation and reproducibility in software

Citation and reproducibility in software

Software Citation: Principles, Implementation, and Impact

Software Citation: Principles, Implementation, and Impact

Software Citation: Principles, Implementation, and Impact

Micropublication WormBase Workshop International Worm Meeting 2015

Micropublication WormBase Workshop International Worm Meeting 2015

Micropublication WormBase Workshop International Worm Meeting 2015

THOR Workshop - Services PANGAEA

THOR Workshop - Services PANGAEA

THOR Workshop - Services PANGAEA

Modern tools for sharing and synthesizing neuroimaging results

Modern tools for sharing and synthesizing neuroimaging results

Modern tools for sharing and synthesizing neuroimaging results

20171003 lancaster data conversations Chue-Hong

20171003 lancaster data conversations Chue-Hong

20171003 lancaster data conversations Chue-Hong

HotSoS16 Tutorial "Text Analytics for Security" by Tao Xie and William Enck

HotSoS16 Tutorial "Text Analytics for Security" by Tao Xie and William Enck

HotSoS16 Tutorial "Text Analytics for Security" by Tao Xie and William Enck

User Expectations in Mobile App Security

User Expectations in Mobile App Security

User Expectations in Mobile App Security

Being FAIR: FAIR data and model management SSBSS 2017 Summer School

Being FAIR: FAIR data and model management SSBSS 2017 Summer School

Being FAIR: FAIR data and model management SSBSS 2017 Summer School

Getting (and giving) credit for all that we do

Getting (and giving) credit for all that we do

Getting (and giving) credit for all that we do

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...

Intro to Reproducible Research

Intro to Reproducible Research

Intro to Reproducible Research

ROHub

What is Reproducibility? The R* brouhaha (and how Research Objects can help)

What is Reproducibility? The R* brouhaha (and how Research Objects can help)

What is Reproducibility? The R* brouhaha (and how Research Objects can help)

Avoiding the tower of babel - The Role of Data Description Standards in Biome...

Avoiding the tower of babel - The Role of Data Description Standards in Biome...

Avoiding the tower of babel - The Role of Data Description Standards in Biome...

Automating the process of continuously prioritising data, updating and deploy...

Automating the process of continuously prioritising data, updating and deploy...

Automating the process of continuously prioritising data, updating and deploy...

BigData Testing by Shreya Pal

BigData Testing by Shreya Pal

BigData Testing by Shreya Pal

Lab Notebooks as Data Management (SLA Winter Virtual Conference 2012)

Lab Notebooks as Data Management (SLA Winter Virtual Conference 2012)

Lab Notebooks as Data Management (SLA Winter Virtual Conference 2012)

Research Data (and Software) Management at Imperial: (Everything you need to ...

Research Data (and Software) Management at Imperial: (Everything you need to ...

Research Data (and Software) Management at Imperial: (Everything you need to ...

Data Visualization for Big Data: Experience from the Front Line

Data Visualization for Big Data: Experience from the Front Line

Data Visualization for Big Data: Experience from the Front Line

Semelhante a Collaborative Data Analysis with Taverna Workflows

Replicating FLOSS Research as eResearch

Replicating FLOSS Research as eResearch

Replicating FLOSS Research as eResearch

eResearch workflows for studying free and open source software development

eResearch workflows for studying free and open source software development

eResearch workflows for studying free and open source software development

Dynamic Social Network Analysis (and more!) with eResearch Tools

Dynamic Social Network Analysis (and more!) with eResearch Tools

Dynamic Social Network Analysis (and more!) with eResearch Tools

Software Mining and Software Datasets

Software Mining and Software Datasets

Software Mining and Software Datasets

Software Analytics - Achievements and Challenges

Software Analytics - Achievements and Challenges

Software Analytics - Achievements and Challenges

Workshop on Managing Digital Research Objects in an Expanding Science Ecosystem, 15 Nov 2017, Bethesda, USA https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem Research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge. A first step is to think of Digital Research Objects as a broadening out to embrace these artefacts or assets of research. The next is to recognise that investigations use multiple, interlinked, evolving artefacts. Multiple datasets and multiple models support a study; each model is associated with datasets for construction, validation and prediction; an analytic pipeline has multiple codes and may be made up of nested sub-pipelines, and so on. Research Objects (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described.

Research Objects: more than the sum of the parts

Research Objects: more than the sum of the parts

Research Objects: more than the sum of the parts

Lviv Data Science Club (Sergiy Lunyakin)

Lviv Data Science Club (Sergiy Lunyakin)

Lviv Data Science Club (Sergiy Lunyakin)

Lviv Startup Club

The Center for Open Science (COS) was founded as a non-profit technology start-up in 2013 with the goal of improving transparency and reproducibility by connecting the scholarly workflow. COS achieves this goal through the development of a free, open source web application called the Open Science Framework (OSF), providing features like file sharing and citing, persistent urls, provenance tracking, and automated versioning. Initial workflow API connections focused on storage services and included Figshare, GitHub, Amazon S3, Dropbox, and Dataverse. The team is now working to connect other parts of the workflow with services like DMPTool, Databib/re3data, and Databrary. This session will introduce the core architecture and the problems that it solves, and illustrate how connecting services can benefit everyone involved in supporting the research ecosystem. COS is funded through the generosity of grants from the Laura and John Arnold Foundation, the John Templeton Foundation, the Alfred P. Sloan Foundation, the Association of Research Libraries, and others. Presented at CNI Fall 2014, Washington, DC.

Improving Integrity, Transparency, and Reproducibility Through Connection of ...

Improving Integrity, Transparency, and Reproducibility Through Connection of ...

Improving Integrity, Transparency, and Reproducibility Through Connection of ...

Research Solutions for Education

Research Solutions for Education

Research Solutions for Education

By: Mercè Crosas, Director of Data Science at the Institute for Quantitative Social Science (IQSS) at Harvard University The Dataverse software provides multiple workflows for data publishing to support a wide range of data policies and practices established by journals, as well as data sharing needs from various research communities. This talk will describe these workflows from the user experience and from the system's technical implementation. This talk was presented as part of the Information Science Brown Bag talks, hosted by the Program on Information Science. (See http://drmaltman.wordpress.com)

Data Publishing Workflows with Dataverse

Data Publishing Workflows with Dataverse

Data Publishing Workflows with Dataverse

Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum

Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum

Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum

Metadata and Semantics Research Conference, Manchester, UK 2015 Research Objects: why, what and how, In practice the exchange, reuse and reproduction of scientific experiments is hard, dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: codes fork, data is updated, algorithms are revised, workflows break, service updates are released. Neither should they be viewed just as second-class artifacts tethered to publications, but the focus of research outcomes in their own right: articles clustered around datasets, methods with citation profiles. Many funders and publishers have come to acknowledge this, moving to data sharing policies and provisioning e-infrastructure platforms. Many researchers recognise the importance of working with Research Objects. The term has become widespread. However. What is a Research Object? How do you mint one, exchange one, build a platform to support one, curate one? How do we introduce them in a lightweight way that platform developers can migrate to? What is the practical impact of a Research Object Commons on training, stewardship, scholarship, sharing? How do we address the scholarly and technological debt of making and maintaining Research Objects? Are there any examples I’ll present our practical experiences of the why, what and how of Research Objects.

Mtsr2015 goble-keynote

Mtsr2015 goble-keynote

Mtsr2015 goble-keynote

06 styles and_greenfield_design

06 styles and_greenfield_design

06 styles and_greenfield_design

2013 06-24 Wf4Ever: Annotating research objects (PDF)

2013 06-24 Wf4Ever: Annotating research objects (PDF)

2013 06-24 Wf4Ever: Annotating research objects (PDF)

Stian Soiland-Reyes

2013 06-24 Wf4Ever: Annotating research objects (PPTX)

2013 06-24 Wf4Ever: Annotating research objects (PPTX)

2013 06-24 Wf4Ever: Annotating research objects (PPTX)

Stian Soiland-Reyes

Presentation for Harvard's ABCD Technology in Education group: The Institute for Quantitative Social Science (IQSS) is a unique entity at Harvard - it combines research, software development, and specialized services to provide innovative solutions to research and scholarship problems at Harvard and beyond. I will talk about the software projects that IQSS is currently working on (Dataverse, Zelig, Consilience, and OpenScholar), including the research and development processes, the benefits provided to the Harvard community, and the impacts on research and scholarship.

Abcd iqs ssoftware-projects-mercecrosas

Abcd iqs ssoftware-projects-mercecrosas

Abcd iqs ssoftware-projects-mercecrosas

Sharing massive data analysis: from provenance to linked experiment reports

Sharing massive data analysis: from provenance to linked experiment reports

Sharing massive data analysis: from provenance to linked experiment reports

Reproducibility: 10 Simple Rules

Reproducibility: 10 Simple Rules

Reproducibility: 10 Simple Rules

Annika Eriksson

Crediting informatics and data folks in life science teams

Crediting informatics and data folks in life science teams

Crediting informatics and data folks in life science teams

Software Analytics: Data Analytics for Software Engineering

Software Analytics: Data Analytics for Software Engineering

Software Analytics: Data Analytics for Software Engineering

Semelhante a Collaborative Data Analysis with Taverna Workflows (20)

Replicating FLOSS Research as eResearch

Replicating FLOSS Research as eResearch

Replicating FLOSS Research as eResearch

eResearch workflows for studying free and open source software development

eResearch workflows for studying free and open source software development

eResearch workflows for studying free and open source software development

Dynamic Social Network Analysis (and more!) with eResearch Tools

Dynamic Social Network Analysis (and more!) with eResearch Tools

Dynamic Social Network Analysis (and more!) with eResearch Tools

Software Mining and Software Datasets

Software Mining and Software Datasets

Software Mining and Software Datasets

Software Analytics - Achievements and Challenges

Software Analytics - Achievements and Challenges

Software Analytics - Achievements and Challenges

Research Objects: more than the sum of the parts

Research Objects: more than the sum of the parts

Research Objects: more than the sum of the parts

Lviv Data Science Club (Sergiy Lunyakin)

Lviv Data Science Club (Sergiy Lunyakin)

Lviv Data Science Club (Sergiy Lunyakin)

Improving Integrity, Transparency, and Reproducibility Through Connection of ...

Improving Integrity, Transparency, and Reproducibility Through Connection of ...

Improving Integrity, Transparency, and Reproducibility Through Connection of ...

Research Solutions for Education

Research Solutions for Education

Research Solutions for Education

Data Publishing Workflows with Dataverse

Data Publishing Workflows with Dataverse

Data Publishing Workflows with Dataverse

Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum

Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum

Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum

Mtsr2015 goble-keynote

Mtsr2015 goble-keynote

Mtsr2015 goble-keynote

06 styles and_greenfield_design

06 styles and_greenfield_design

06 styles and_greenfield_design

2013 06-24 Wf4Ever: Annotating research objects (PDF)

2013 06-24 Wf4Ever: Annotating research objects (PDF)

2013 06-24 Wf4Ever: Annotating research objects (PDF)

2013 06-24 Wf4Ever: Annotating research objects (PPTX)

2013 06-24 Wf4Ever: Annotating research objects (PPTX)

2013 06-24 Wf4Ever: Annotating research objects (PPTX)

Abcd iqs ssoftware-projects-mercecrosas

Abcd iqs ssoftware-projects-mercecrosas

Abcd iqs ssoftware-projects-mercecrosas

Sharing massive data analysis: from provenance to linked experiment reports

Sharing massive data analysis: from provenance to linked experiment reports

Sharing massive data analysis: from provenance to linked experiment reports

Reproducibility: 10 Simple Rules

Reproducibility: 10 Simple Rules

Reproducibility: 10 Simple Rules

Crediting informatics and data folks in life science teams

Crediting informatics and data folks in life science teams

Crediting informatics and data folks in life science teams

Software Analytics: Data Analytics for Software Engineering

Software Analytics: Data Analytics for Software Engineering

Software Analytics: Data Analytics for Software Engineering

Mais de Andrea Wiggins

Online Communities in Citizen Science & BirdCams

Online Communities in Citizen Science & BirdCams

Online Communities in Citizen Science & BirdCams

Free as in Puppies: Compensating for ICT Constraints in Citizen Science

Free as in Puppies: Compensating for ICT Constraints in Citizen Science

Free as in Puppies: Compensating for ICT Constraints in Citizen Science

Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Net...

Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Net...

Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Net...

Online Communities in Citizen Science

Online Communities in Citizen Science

Online Communities in Citizen Science

Citizen Science Phenotypes

Citizen Science Phenotypes

Citizen Science Phenotypes

The Evolving Landscape of Citizen Science

The Evolving Landscape of Citizen Science

The Evolving Landscape of Citizen Science

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

Data Management for Citizen Science

Data Management for Citizen Science

Data Management for Citizen Science

With Great Data Comes Great Responsibility

With Great Data Comes Great Responsibility

With Great Data Comes Great Responsibility

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Mechanisms for Data Quality and Validation in Citizen Science

Mechanisms for Data Quality and Validation in Citizen Science

Mechanisms for Data Quality and Validation in Citizen Science

Open Source & Citizen Science

Open Source & Citizen Science

Open Source & Citizen Science

From Conservation to Crowdsourcing: A Typology of Citizen Science

From Conservation to Crowdsourcing: A Typology of Citizen Science

From Conservation to Crowdsourcing: A Typology of Citizen Science

Motivation by Design: Technologies, Experiences, and Incentives

Motivation by Design: Technologies, Experiences, and Incentives

Motivation by Design: Technologies, Experiences, and Incentives

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Open Source, Open Science, & Citizen Science

Open Source, Open Science, & Citizen Science

Open Source, Open Science, & Citizen Science

Reclassifying Success and Tragedy in FLOSS Projects

Reclassifying Success and Tragedy in FLOSS Projects

Reclassifying Success and Tragedy in FLOSS Projects

Crowdsourcing Science

Crowdsourcing Science

Crowdsourcing Science

Intellectual Diversity in the iSchools: Past, Present and Future

Intellectual Diversity in the iSchools: Past, Present and Future

Intellectual Diversity in the iSchools: Past, Present and Future

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Mais de Andrea Wiggins (20)

Online Communities in Citizen Science & BirdCams

Online Communities in Citizen Science & BirdCams

Online Communities in Citizen Science & BirdCams

Free as in Puppies: Compensating for ICT Constraints in Citizen Science

Free as in Puppies: Compensating for ICT Constraints in Citizen Science

Free as in Puppies: Compensating for ICT Constraints in Citizen Science

Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Net...

Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Net...

Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Net...

Online Communities in Citizen Science

Online Communities in Citizen Science

Online Communities in Citizen Science

Citizen Science Phenotypes

Citizen Science Phenotypes

Citizen Science Phenotypes

The Evolving Landscape of Citizen Science

The Evolving Landscape of Citizen Science

The Evolving Landscape of Citizen Science

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

Data Management for Citizen Science

Data Management for Citizen Science

Data Management for Citizen Science

With Great Data Comes Great Responsibility

With Great Data Comes Great Responsibility

With Great Data Comes Great Responsibility

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Mechanisms for Data Quality and Validation in Citizen Science

Mechanisms for Data Quality and Validation in Citizen Science

Mechanisms for Data Quality and Validation in Citizen Science

Open Source & Citizen Science

Open Source & Citizen Science

Open Source & Citizen Science

From Conservation to Crowdsourcing: A Typology of Citizen Science

From Conservation to Crowdsourcing: A Typology of Citizen Science

From Conservation to Crowdsourcing: A Typology of Citizen Science

Motivation by Design: Technologies, Experiences, and Incentives

Motivation by Design: Technologies, Experiences, and Incentives

Motivation by Design: Technologies, Experiences, and Incentives

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Open Source, Open Science, & Citizen Science

Open Source, Open Science, & Citizen Science

Open Source, Open Science, & Citizen Science

Reclassifying Success and Tragedy in FLOSS Projects

Reclassifying Success and Tragedy in FLOSS Projects

Reclassifying Success and Tragedy in FLOSS Projects

Crowdsourcing Science

Crowdsourcing Science

Crowdsourcing Science

Intellectual Diversity in the iSchools: Past, Present and Future

Intellectual Diversity in the iSchools: Past, Present and Future

Intellectual Diversity in the iSchools: Past, Present and Future

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Último

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

Maria Levchenko

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

Automating Google Workspace (GWS) & more with Apps Script

Automating Google Workspace (GWS) & more with Apps Script

Automating Google Workspace (GWS) & more with Apps Script

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Enterprise Knowledge

Evaluating the top large language models.pdf

Evaluating the top large language models.pdf

Evaluating the top large language models.pdf

ChristopherTHyatt

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

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

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

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

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Tech Trends Report 2024 Future Today Institute.pdf

Tech Trends Report 2024 Future Today Institute.pdf

Tech Trends Report 2024 Future Today Institute.pdf

Data Cloud, More than a CDP by Matt Robison

Data Cloud, More than a CDP by Matt Robison

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Principled Technologies

The 7 Things I Know About Cyber Security After 25 Years | April 2024

The 7 Things I Know About Cyber Security After 25 Years | April 2024

The 7 Things I Know About Cyber Security After 25 Years | April 2024

CNv6 Instructor Chapter 6 Quality of Service

CNv6 Instructor Chapter 6 Quality of Service

CNv6 Instructor Chapter 6 Quality of Service

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

🐬 The future of MySQL is Postgres 🐘

🐬 The future of MySQL is Postgres 🐘

🐬 The future of MySQL is Postgres 🐘

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

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

Artificial Intelligence: Facts and Myths

Artificial Intelligence: Facts and Myths

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

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

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

Martijn de Jong

Último (20)

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

Automating Google Workspace (GWS) & more with Apps Script

Automating Google Workspace (GWS) & more with Apps Script

Automating Google Workspace (GWS) & more with Apps Script

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Evaluating the top large language models.pdf

Evaluating the top large language models.pdf

Evaluating the top large language models.pdf

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

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

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

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

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Tech Trends Report 2024 Future Today Institute.pdf

Tech Trends Report 2024 Future Today Institute.pdf

Tech Trends Report 2024 Future Today Institute.pdf

Data Cloud, More than a CDP by Matt Robison

Data Cloud, More than a CDP by Matt Robison

Data Cloud, More than a CDP by Matt Robison

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

The 7 Things I Know About Cyber Security After 25 Years | April 2024

The 7 Things I Know About Cyber Security After 25 Years | April 2024

The 7 Things I Know About Cyber Security After 25 Years | April 2024

CNv6 Instructor Chapter 6 Quality of Service

CNv6 Instructor Chapter 6 Quality of Service

CNv6 Instructor Chapter 6 Quality of Service

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

🐬 The future of MySQL is Postgres 🐘

🐬 The future of MySQL is Postgres 🐘

🐬 The future of MySQL is Postgres 🐘

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

Artificial Intelligence: Facts and Myths

Artificial Intelligence: Facts and Myths

Artificial Intelligence: Facts and Myths

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

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

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

Collaborative Data Analysis with Taverna Workflows

1. Collaborative Data Analysis with Taverna Workflows Andrea Wiggins, Kevin Crowston & James Howison 16 October 2009

2.

3.

4. Research Replication Classifies projects based on metrics for success and stage of project growth English & Schweik, 2007 Examines dynamics of social networks of project communications over time Howison et al., 2006 Examines distribution of project sizes for evidence of preferential attachment theory of growth in networks Conklin, 2004 Description Study

5.

6.

7.

8.

9.

10.

11.

12.

13. Classification Workflow

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.