Node.js overtook React.js to become the most used web framework among software developers worldwide, as of 2024. According to the survey, 40.8 percent of respondents reported to be using Node.js, while around 39.5 percent were using React.js. Web framework A web framework, or what may sometimes be referred to as a web application framework is a software framework that supports the overall software development of web applications. Web frameworks are utilized to automate menial activities typically performed within web development. In other words, it simplifies the web development process and, therefore, makes it easier to build a website. JavaScript As aforementioned, before being overtaken by React.js, jQuery had been the most used web framework out of the millions of software developers throughout the world. Simply put, jQuery is a cross-platform JavaScript library, that simplifies the usage of JavaScript on websites. While jQuery may be losing popularity, JavaScript still remains one of the most popular programming languages used by software developers worldwide. Throughout the world, programming languages are associated with different salaries. JavaScript programmers are associated with a global salary of 65,580 U.S. dollars, while the salary associated with JavaScript programmers in the United States is significantly more, at 112,000 U.S. dollars.

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the JavaScript tech stack in 2024 was React, chosen by nearly ** percent of respondents. React Hooks ranked second, being preferred by over **** percent of respondents.

This dataset contains the predicted prices of the asset React over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Flutter is the most popular cross-platform mobile framework used by global developers, according to a 2023 developer survey. Based on the survey, 46 percent of software developers used Flutter. On the whole, roughly one third of mobile developers use cross-platform technologies or frameworks; the rest of mobile developers use native tools. Cross-platform mobile frameworks Simply put, cross-platform mobile frameworks are used to generate an app that is accessible through a large number of various end devices. A single code is used across multiple platforms for easy portability, and in the end the cost of software development is kept low. With cross-platform mobile frameworks, there is the possibility of maximum exposure to the target audience. For example, a single app can target both iOS and Android platforms, which maximizes the app’s reach. App developers In 2022, there were 26.4 million software developers worldwide. The number of developers is expected to grow in the coming years, especially due to the rise of app and web development in the growing digital age. Of the various kinds of software developers, mobile developers are among the lowest on the developer pay scale. Mobile developers on average make an annual salary of over 124,000 U.S. dollars, while engineering managers are the highest-paid software developers with an annual average salary of 96,000 U.S. dollars.

Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we contribute the REACT database, a collection of two datasets of human-robot interactions that display users’ natural reactions to robots during a collaborative game and a photography scenario. Further, we analyze the datasets to show that interaction history is an important factor that can influence human reactions to robots. As a result, we believe that future models for interpreting implicit feedback in HRI should explicitly account for this history. REACT opens up doors to this possibility in the future. We contribute the Reactions and EvaluAtive feedbaCk over Time (REACT) database. REACT consists of two datasets that contain observations of humans, robots and task-related data during human-robot interactions.

REal-time Assessment of Community Transmission (REACT-1) is one of the largest population surveillance studies in the world. It started in April 2020 to measure the prevalence of SARS-CoV-2 in the general population in England. Each month around 150,000 people completed a questionnaire and returned a PCR test.

The study tracked the progress of infection across England. It was commissioned by the Department of Health and Social Care and was carried out between April 2020 and April 2022 in partnership with Imperial College Healthcare NHS Trust and Ipsos MORI.

The partner study, REal-time Assessment of Community Transmission (REACT-2), started in May 2020 to determine the prevalence of and trends in antibodies levels in study participants. This study is also described in the Innovation Gateway.

For further information and the study questionnaires, please see the REACT study website.

We present the full meta-data catalogue in the Innovation Gateway. Researchers external to Imperial College London can apply to access subsets of anonymised data from REACT participants. To access the full REACT dataset on Imperial's TRE, researchers require an affiliation or collaboration with Imperial College London.

REACT has a data sharing agreement with NHS England, details of which can be found under agreement DARS-NIC-431352-G7F1M-v2.2 via https://digital.nhs.uk/services/data-access-request-service-dars/data-uses-register

This dataset contains the predicted prices of the asset REACT-DROPZONE github.com/react-dropzone/REACT-DROPZONE over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

PostgreSQL over Function Support: Highlights the dependency on the over function in PostgreSQL when using CKAN versions greater than v2.8.6 or v2.9.1 when utilizing the datastoresearchsql endpoint, ensuring advanced SQL features are available. Technical Integration: The extension is installed as a Python package within the CKAN virtual environment and activated by adding dataexplorer_view to the ckan.plugins setting in the CKAN configuration file. This process makes the React-based data explorer available as a data view option in CKAN resources. The extension uses the datastoresearchsql API to give the application the capability to filter data via SQL-like queries. Benefits & Impact: The dataexplorer-react extension offers improved data exploration capabilities within CKAN, leading to better user engagement and insights. Enhanced UI/UX of data preview and visualization will encourage higher user adoption. The SQL-like query builder gives CKAN users more enhanced control over the filtering and analysis of data.

This dataset contains the predicted prices of the asset REACT-SCAN github.com/aidenybai/REACT-SCAN over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset contains relevant information used in the study of collecting odometer images via WhatsApp to measure vehicle miles traveled (VMT) in California. In recent years, policymakers have increasingly suggested interventions for sustainable transportation, directly or indirectly aiming to reduce vehicle miles traveled. Researchers in transportation have used various ways to collect VMT data for their analysis, but there are pros and cons to each method. Self-reported VMTs from travel surveys are traditionally used to understand factors affecting VMT or estimate policy effects, yet such data suffer from recall error, rounding, and heaping biases in reporting numbers by which researchers derive VMT. Surveys with the latest technologies, such as a dedicated GPS device, can provide more accurate VMT but can be intrusive on participants’ privacy and too expensive to scale. Passively collected data, such as traffic counts or states’ odometer reading data, rarely capture travelers’ chara..., , # Dataset of "Data Collection of Odometer Images via WhatsApp to Measure Vehicle Miles Traveled"

Dataset DOI: 10.5061/dryad.g79cnp62m

Description of the data and file structure

This dataset contains relevant information used in the study "Data collection of Odometer images via WhatsApp to measure vehicle miles traveled", an NCST-supported project conducted by the Institute of Transportation Studies, UC Davis.

This repository contains three files:

1. posts_pii_removed.csv
2. survey_pii_removed.csv
3. users_pii_removed.csv

These files represent the odometer images collected during the study, data of the prescreening survey, and the user statistics, respectively, with all personal identifiable information removed.

Files and variables

File: posts_pii_removed.csv

Description:Â This data contains information about the posts (odometer images submitted during the study).

Variables

• id: ID. String.
• image_create_date: The datetime when..., For the "survey" subdataset, we applied a grouping of the age of the study participants. For the "users" subdataset, we removed the exact time of the day.

The dataexplorer-graphql extension enhances CKAN's capabilities by providing a user-friendly Data Explorer application. Built with React, the application fetches data via a GraphQL API, offering an interactive interface for exploring data stored within CKAN. To use this extension effectively, a Data-API instance with a Hasura backend needs to be running to expose data for querying through GraphQL, and the datastore tables also needs to be tracked by the Hasura instance to make the graphql API available. Key Features: React-based Data Explorer: Offers a modern, interactive user interface built with React for data exploration. GraphQL Data Fetching: Leverages a GraphQL API to retrieve data, enabling efficient and flexible querying of datasets. Multiview Visualization: Allows users to visualize data in multiple ways via the dataexplorer_view plugin. Table View Support: Includes dataexplorer_table_view plugin for displaying data in a tabular format. Technical Integration: The extension adds the dataexplorer_view and dataexplorer_table_view plugins to CKAN, enabling the Data Explorer interface. A crucial configuration step involves setting the ckanext.data_explorer_react.data_api_url parameter in CKAN's configuration file, pointing to the Data-API instance's URL. Proper setup of the Data-API, including a Hasura backend and tracking of datastore tables, is essential for the extension to function correctly. Datastore tables must have proper aliases without any numbers at the start. Benefits & Impact: By implementing the dataexplorer-graphql extension, CKAN users gain access to a modern data exploration interface that leverages the power of GraphQL. This improves the usability of CKAN for data analysis and visualization tasks.

Reddit: /r/science

Investigating Social Media Interactions and Popularity Metrics

By Reddit [source]

About this dataset

The Reddit Subreddit Science dataset offers an in-depth exploration of the science-related conversations and content taking place on the popular website, Reddit. This dataset provides valuable insights into user interactions, sentiment analysis and popularity trends across various types of science topics ranging from astrophysics to neuroscience. The data comprises key features such as post titles, post scores, comment counts, creation times and post URLs which will help us to understand the dynamics and sentiments of the scientific discussions within this popular forum. Utilizing this data set can empower us to analyze how a certain topic has changed over time in terms or relevance or what kind of posts are most successful at gaining attention from users. Ultimately we can leverage this analysis to better comprehend shifts in public opinion towards various aspects of current scientific knowledge

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How to use the dataset

Research Ideas

• Analyzing the topic trends within the subreddit over time, in order to understand which topics are most popular with readers.
• Identifying relationships between levels of interaction (comments and upvotes) and sentiment (through text analysis), to track how users react to certain topics.
• Tracking post and user metrics over time (such as average post length or number of comments per post), in order to monitor changes in outlook on the subreddit community as a whole

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: science.csv | Column name | Description | |:--------------|:-----------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL associated with the post. (String) | | comms_num | The number of comments associated with the post. (Integer) | | created | The date and time the post was created. (DateTime) | | body | The content of the post. (String) | | timestamp | The timestamp of the post. (DateTime) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

The statistic shows the most sought after and used framework by developers in the United States, as of early 2017. According to the survey, **** percent of respondents reported the usage of React, while **** percent reportedly wanted to use this tool.

This dataset contains the predicted prices of the asset REACT-TOASTIFY github.com/fkhadra/REACT-TOASTIFY over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset contains the predicted prices of the asset Reactive Network REACT over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

As of 2024, around 25.2 percent of software developers used .NET (5+) Framework, while around 21.2 percent of respondents used NumPy. In contrast, the least favored frameworks were JAX and MFC with only 2 percent of respondents utilizing them.

This dataset contains the predicted prices of the asset REACT-SLICK github.com/akiran/REACT-SLICK over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Marine mammals and diving birds face several physiological challenges under water, affecting their thermoregulation and locomotion as well as their sensory systems. Therefore, marine mammals have modified ears for improved underwater hearing. Underwater hearing in marine birds have been studied in a few species, but for the record-holding divers, such as penguins, there are no detailed data. We played underwater noise bursts to gentoo penguins (Pygoscelis papua) in a large tank at sound pressure levels between 100 and 120 dB re 1 µPa rms. The penguins showed a graded reaction to the noise bursts, ranging from no reactions at 100 dB to strong reactions in more than 62% of the playbacks at 120 dB re 1 µPa. The responses were always directed away from the sound source. The fact that penguins can detect and react to underwater stimuli may indicate that they make use of sound stimuli for orientation and prey detection during dives. Further, it suggests that penguins may be sensitive to anthr...

This dataset is a large-scale collection of Twitter messages used for sentiment analysis tasks. It consists of millions of tweets labeled with sentiments to understand public emotions expressed online. The dataset provides real-time insights into how users react to various events across domains like politics, healthcare, and entertainment. Tweets are drawn from diverse users, timestamps, and queries, offering a rich context for text-based emotion analysis. The sentiments are categorized into negative, neutral, and positive classes. It serves as a benchmark for evaluating natural language processing techniques on short and informal social media content.