Portal is a SwiftUI package for element transitions across navigation contexts, scroll-based flowing headers, and advanced view mirroring capabilities.

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.

Render React DOM into a new context (aka "Portal")

An end to end tool from general public to govt portal & an awareness tool with CoronaVirus Facts, Mythbusters & Support to eradicate the language & context barrier of misinformation that's been spreading on social media & thus creating mass panic.

A framework-agnostic portal system for micro-frontend & streaming contexts.

Render React element into new context ("aka" Portal)

An MCP (Model Context Protocol) server that provides access to the official German Federal Legal Information Portal (rechtsinformationen.bund.de). **Claude Desktop will automatically use this server for ANY German legal question** to provide authoritative, fact-based answers with proper legal citations from official sources. NeuRIS

This extension adds an AI-powered chat to the DNN PersonaBar and exposes DNN as an MCP (Model Context Protocol) server so external AI clients can use your portal's tools.

SHARIAsource is a project of the Program in Islamic Law at Harvard Law School, that aims to provide comprehensive content and context on Islamic law in a way that is accessible and useful. It is a Portal into the digital world of Islamic legal studies and related tools from data science and AI.

This is an MCP (Model Context Protocol) server that provides access to data from [DATA.GOV.HK](https://data.gov.hk), the official open data portal of the Hong Kong government.

a centralized interface for obtaining API keys to perform various tasks with variable input formats. The API portal includes historical data tracking, triggers, and API invocation counts to provide users with the ability to generate unique results and gain valuable context.

Plug-and-play React (portal, hooks and context API) based very elegant, fast and minimal modal.

Context Rules: The Awareness sample app introduces you to the contextual rule features of the ContextHub Developer Portal

Collection of tools to utilize openEHR data in a research context using EHRbase, NUM-Portal and Docusaurus for comprehensive documentation.

A Job portal website built to handle the needs of both the recruiter as well as the applicants. Implemented using the MERN stack and React's Context API and Hooks

The project is to generate the report of SpecFlow test run and send the report via email. The report can show the total pass ratio, feature-based pass ratio and each test scenario context. If it is a portal UI testing and the test case fails, a screenshot will be captured in the test scenario context for that failed test step.

Context From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people. So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community. Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here. Edited: Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community. Content 2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number. The data is available from 22 Jan, 2020. Column Description Main file in this dataset is covid_19_data.csv and the detailed descriptions are below. covid_19_data.csv Sno - Serial number ObservationDate - Date of the observation in MM/DD/YYYY Province/State - Province or state of the observation (Could be empty when missing) Country/Region - Country of observation Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it) Confirmed - Cumulative number of confirmed cases till that date Deaths - Cumulative number of of deaths till that date Recovered - Cumulative number of recovered cases till that date 2019_ncov_data.csv This is older file and is not being updated now. Please use the covid_19_data.csv file Added two new files with individual level information COVID_open_line_list_data.csv This file is obtained from this link COVID19_line_list_data.csv This files is obtained from this link Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: India - https://www.kaggle.com/sudalairajkumar/covid19-in-india South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases Acknowledgements Johns Hopkins University for making the data available for educational and academic research purposes MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus Picture courtesy : Johns Hopkins University dashboard Inspiration Some insights could be Changes in number of affected cases over time Change in cases over time at country level Latest number of affected cases

Портальный архетип приложения с контекстной зависимостью

React Context API, Pose Animations, and React Portal

React learning with Hooks, Context, Portal, Parcel Js, Babel

A web portal to check uploaded documents for SIH-2022 . Uses NLP to extract the keywords from the documents to browse and scrape the web and to find the similar contexts.

I built a dummy food order app with React by using its core features, such as Components, React Portal, Modal State, Context API, Reducer, Refs & Forward Refs, Side Effect and so on...

🎓 School Admission Portal A responsive frontend-only React.js application that allows students to submit their admission forms and view their profiles. Includes an admin panel to manage (view, edit, delete) all student data using Context API and useReducer. Styled with Bootstrap 5 for a clean and modern UI.

React18的教程，教程涵盖React基础到项目的实战，已发布的内容中包括React相关的JS基础（变量、解构、展开、箭头函数、类、模块化、数组方法等）、React基础（React元素、JSX、虚拟DOM、列表的渲染）、React项目创建、组件、事件、Props、State、Ref、Portal、React中的CSS样式、Fragment、Context以及《学习记录器》和《汉堡到家》两个练习。

In this task, a data-set is used that comprise of different jobs posted on a job portal. The data-set was downloaded from Kaggle. It had the following basic properties: It was provided in .csv format. The data-set simulated the real life scenario of jobs posted on a job portal and comprised of Job's title, Job's description along with its category As the data was labeled so in the context of machine learning, it was a Supervised Machine learning problem i.e. I had access to the data that was already correctly labeled and I had to train a model using this historical data. The main goal was to build a model that could accurately classify new and unseen data when it was input to it i.e. to assign proper label to a job posting when its input to the model. As the nature of the data was "text" so this project also involved extensive usage of text mining techniques as well. Text in its basic form is unstructured and to develop predictive models, the data needs to be thoroughly pre-processed. So the pipeline of developing models that I followed was: Data Profiling Data Cleansing Exploratory Analysis Data Preprocessing Feature Extraction and Selection Model Development Model Evaluation When text data is pre-processed, the issue of curse of dimensionality usually appears i.e. data becomes highly multi-dimensional with lots of features ranging in thousands. Not all of those features are helpful and also it adversely affects the peformance of classifiers as well so following the best practices, I opted for best-in-class feature extraction methods and also applied feature selection techniques so as to compile only those features that will contribute in this prediction problem. For model development, I used and compared the following set of machine learning algorithms: Bernoulli Naive Bayes Multinomial Naive Bayes Random Forests Linear SVM and compared these algorithms on different metrics like accuracy, training and testing time. As per my analysis, SVM outshines all of the other models when it comes to accuracy. Random Forests accuracy score was also quite good but took considerable time during training phase. For implementation, I used Python. Specifically, I used the following libraries/modules of Python for different set of tasks: pandas, numpy sklearn nltk matplotlib To run the code, please make sure that the latest version of Python, Jupyter and aforementioned libraries are installed in your system.

React Portal / Context sandbox

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Modify a arcgis portal jsonld context to confirm to flemish standards

🎥Netflix clone using React, Styled-Components💅, Portal, Firebase & Context Api.