beta.uniprot.org - 5 Lessons Learned

•Descargar como PPT, PDF•

2 recomendaciones•661 vistas

Eric Jain

Lightning Talk at BOSC 2007.

Tecnología Empleo

[object Object],[object Object],Solution

[object Object],data set identifier format http://beta.uniprot.org/uniprot /?query=insulin data set query +format, sort, offset, limit, compress...

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

Más contenido relacionado

Destacado

Democracy Thai2907

1 Pa S2 Sociedades

Paul Mc Cartney

Computadora

To Epos%20 Toy 1940

Manual Actualizado

Decálogo Etico Para Bloggers

3D-DRESD Polaris

Mellon Proposal

Desistir

Llevar Durar Tardar

λίγα λόγια για την άννα

xryandreoy

In every industry, real-time network monitoring and rapid troubleshooting have become mission-critical. Network disruptions are now business disruptions, with financial and sometimes even legal consequences. From the desktop to the datacenter, from wireless LANs to Gigabit backbones, on local segments and across distributed networks, WildPackets products enable IT organizations to quickly find and fix problems affecting mission-critical network services. WildPackets products are sold in over 60 countries through a broad network of channel and strategic partners. More than 5,000 customers across all industrial sectors use WildPackets products daily to troubleshoot networks and maximize network uptime. For more information go to www.wildpackets.com.

WildPackets

gigamon

Destacado (13)

Democracy Thai2907

1 Pa S2 Sociedades

Paul Mc Cartney

Computadora

To Epos%20 Toy 1940

Manual Actualizado

Decálogo Etico Para Bloggers

3D-DRESD Polaris

Mellon Proposal

Desistir

Llevar Durar Tardar

λίγα λόγια για την άννα

WildPackets

Similar a beta.uniprot.org - 5 Lessons Learned

Easy and affordable user testing - Front Trends 2017

Ida Aalen

Complex, black-box algorithms may be appropriate when you work with clients who want plug-and-play predictions but for clients in fields like healthcare, education, nonprofits, or management, your will typically need to use approaches that make sense to them and that they can modify or replicate on their own. Those needs rule out many common data science tools, such as Python or R, because they overwhelming for non-specialists, and even common statistical applications such as SPSS or SAS, because they are expensive and not built for replication. The open-source application jamovi, which resembles SPSS but is based on R, represents an ideal compromise for consulting work: it is free, it is powerful, it is exceptionally easy to use, and it designed to facilitate iterative analysis and reproducibility. In this session, you’ll learn the basics of jamovi and how you can best use it for consulting work in data analysis. Free online videos and downloadable resources are also provided via https://datalab.cc/tools/jamovi.

Hands-on data consulting with jamovi

Barton Poulson

Rapid prototyping 2.0

Devbridge Group

Practical Usability

Karolina Coates

Hci final presentation

Tarun Chakravorty

Hci final presentation

Tarun Chakravorty

1. introduction to data science —

swethaT16

Hci final presentation

Tarun Chakravorty

Bruck business training with the knowledge pulse_bei group__slideshare_100222

Peter A. Bruck

"Open" includes users - Leverage their input

Randy Earl

BAQMaR 2007

BAQMaR

The Scientific Method of Experimentation by Google PM

Product School

Never show a design you haven't tested

Ida Aalen

Blenderbot

taeseon ryu

Advanced Python Skills for Data Scientists

Serhii Kushchenko

An Introduction to Video Production for Digital Media (2012)

Eddie Vega

A common level of understanding is the key to collaboration and the production of good quality. Any discussions about and the modelling of business processes and the respectively manipulated data can be only as good as the underlying comprehension of the matter itself. Based on his experiences, the author claims that the absence of or a flawed understanding is the main cause for bad software and failed IT-projects. IT people must understand what users need (and not only what they want or ask for). Business and management people in turn must understand what information they need in order to do their business, how they can best use this information, and what they must demand of IT-people to get real value out of their software. This book describes and documents a method and procedure for achieving mutual understanding between all stakeholders. Furthermore, it discusses how experiences and chances are shared and how difficulties in the application of methods occur. More information: https://vdf.ch/information-modelling.html

Information modelling (Stefan Berner): Extract

vdf Hochschulverlag AG

Input for an Open Space Session at the Consensa Post Merger Integration Summit 25.-26.3.2010 in Hamburg. Main Hypothesis: internal social media makes expertise visible accross the organizational boundaries of the new units. Expertise istthe first step to mutal respect, which helps to build up trust. Trust is the basis for future collaboration. These bootom-up established trust can be stronger than all the political issues and can therefore contribute to a more effective post merger integration.

Is a post merger integration in an Enterprise 2.0 more effective?

Michael Heiss

In oratory, “brevity is the soul of wit.” In startups, this principle takes the form of the MVP – the “minimum viable product” – with just enough development and functionality to successfully launch a product or service. In data science, this takes the form of visualizations and analyses that are “minimally sufficient” or as simple as possible while still providing the critical insight into data. A data science MVP not only makes the analyst’s job faster, less complicated, and less prone to errors, it is also the ideal method for communicating insights with clients and colleagues. In this presentation we will discuss several of the most important elements of MVPs as applied to data science, such as methods for data wrangling, choosing tools for visualization and analysis, designing graphics, and principles of minimally sufficient collaboration and communication.

MVP of visualization and analytics

Barton Poulson

Two lectures on prototyping for the Knowledge-based Entrepreneurship M.Sc. course at UCPH Innovation Hub, the University of Copenhagen in December 2015. The contains a brief overview of our case study from an IT-research project aimed at improving coordination in elder care centers. As part of the project, we built a series of prototypes. It then continues with a systematic description of prototypes and their properties, along with concrete examples. Several simple pieces of advice, as well as common pitfalls, are presented.

Prototyping for knowledge based entrepreneurship

Vlad Manea

Similar a beta.uniprot.org - 5 Lessons Learned (20)

Easy and affordable user testing - Front Trends 2017

Hands-on data consulting with jamovi

Rapid prototyping 2.0

Practical Usability

Hci final presentation

1. introduction to data science —

Hci final presentation

Bruck business training with the knowledge pulse_bei group__slideshare_100222

"Open" includes users - Leverage their input

BAQMaR 2007

The Scientific Method of Experimentation by Google PM

Never show a design you haven't tested

Blenderbot

Advanced Python Skills for Data Scientists

An Introduction to Video Production for Digital Media (2012)

Information modelling (Stefan Berner): Extract

Is a post merger integration in an Enterprise 2.0 more effective?

MVP of visualization and analytics

Prototyping for knowledge based entrepreneurship

Más de Eric Jain

Image classification with Deeplearning4j

Eric Jain

Combining your data with Zenobase

Java Time Puzzlers

OSGi and Eclipse RCP

UniProt REST API

UniProt & Ontologies

Más de Eric Jain (6)

Image classification with Deeplearning4j

Combining your data with Zenobase

Java Time Puzzlers

OSGi and Eclipse RCP

UniProt REST API

UniProt & Ontologies

Último

What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

HampshireHUG

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

Martijn de Jong

BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men

Delhi Call girls

Handwritten Text Recognition for manuscripts and early printed texts

Maria Levchenko

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

Delhi Call girls

Finology Group – Insurtech Innovation Award 2024

The Digital Insurer

Evaluating the top large language models.pdf

ChristopherTHyatt

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024. Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights. Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer. Learn about: - The essence and purpose of taxonomies and ontologies in information and knowledge management; - Advantages of semantic layers leveraging organizational taxonomies; and - Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

Enterprise Knowledge

Axa Assurance Maroc - 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

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

[2024]Digital Global Overview Report 2024 Meltwater.pdf

hans926745

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

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

Neo4j

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

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

In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.

EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx

Earley Information Science

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

debabhi2

How to convert PDF to text with Nanonets

naman860154