The js-tweaks extension for CKAN offers a collection of JavaScript scripts, macros, and helpers aimed at streamlining common tasks and user interactions within a CKAN instance. It primarily focuses on enriching the user interface through simple modifications that can improve the overall usability of the platform. By providing readily available tools to implement features such as tooltips, this extension facilitates a more interactive and informative environment for CKAN users. Key Features: Tooltip Implementation: Allows for the rapid addition of basic tooltips to elements on a CKAN page by simply adding the data-tooltip="text" attribute. Bootstrap Tooltip Compatibility: Supports the use of Bootstrap's tooltip functionality for more advanced tooltip implementations and customization using standard Bootstrap attributes (data-toggle="tooltip" data-placement="top" title="Tooltip on top"). Customizable UI: Provides a foundation for further UI enhancements through the inclusion of JavaScript scripts and macros, which is meant to allow for targeted tweaks to match specific user needs or preferences. Simplified Routing: The goal is to make daily routing easier. Technical Integration: The js-tweaks extension is enabled by adding js-tweaks to the ckan.plugins setting in the CKAN configuration file (/etc/ckan/default/ckan.ini by default). After modifying the configuration, restarting CKAN instance is necessary to apply the configurations to enable the modifications offered by the extension. Benefits & Impact: Implementing the js-tweaks extension enables CKAN administrators to quickly implement enhancements to the user interface and routing within the overall platform, such as by adding tooltips or building on the JS, improving user experience without extensive coding or modification to the core CKAN system. While the provided documentation is limited, it aims to reduce complexity and make CKAN interfaces intuitive.

Mapping of CSD model attribute values to JSON serialized values.

The description of the attributes from the DependentVariable class in version 1.0 of the CSD model.

Context

Data set containing Tweets captured during the Nintendo E3 2018 Conference.

Content

All Twitter APIs that return Tweets provide that data encoded using JavaScript Object Notation (JSON). JSON is based on key-value pairs, with named attributes and associated values. The JSON file include the following objects and attributes:

• Tweet - Tweets are the basic atomic building block of all things Twitter. The Tweet object has a long list of ‘root-level’ attributes, including fundamental attributes such as id, created_at, and text. Tweet child objects include user, entities, and extended_entities. Tweets that are geo-tagged will have a place child object.

  • User - Contains public Twitter account metadata and describes the author of the Tweet with attributes as name, description, followers_count, friends_count, etc.

  • Entities - Provide metadata and additional contextual information about content posted on Twitter. The entities section provides arrays of common things included in Tweets: hashtags, user mentions, links, stock tickers (symbols), Twitter polls, and attached media.

  • Extended Entities - All Tweets with attached photos, videos and animated GIFs will include an extended_entities JSON object.

  • Places - Tweets can be associated with a location, generating a Tweet that has been ‘geo-tagged.’

More information here.

Acknowledgements

I used the filterStream() function to open a connection to Twitter's Streaming API, using the keywords #NintendoE3 and #NintendoDirect. The capture started on Tuesday, June 12th 04:00 am UCT and finished on Tuesday, June 12th 05:00 am UCT.

Inspiration

• Time analysis
• Try text mining!
• Cross-language differences in Twitter
• Use this data to produce a sentiment analysis
• Twitter geolocation
• Network analysis: graph theory, metrics and properties of the network, community detection, network visualization, etc.

The datasets used for this manuscript were derived from multiple sources: Denver Public Health, Esri, Google, and SafeGraph. Any reuse or redistribution of the datasets are subjected to the restrictions of the data providers: Denver Public Health, Esri, Google, and SafeGraph and should consult relevant parties for permissions.1. COVID-19 case dataset were retrieved from Denver Public Health (Link: https://storymaps.arcgis.com/stories/50dbb5e7dfb6495292b71b7d8df56d0a )2. Point of Interests (POIs) data were retrieved from Esri and SafeGraph (Link: https://coronavirus-disasterresponse.hub.arcgis.com/datasets/6c8c635b1ea94001a52bf28179d1e32b/data?selectedAttribute=naics_code) and verified with Google Places Service (Link: https://developers.google.com/maps/documentation/javascript/reference/places-service)3. The activity risk information is accessible from Texas Medical Association (TMA) (Link: https://www.texmed.org/TexasMedicineDetail.aspx?id=54216 )The datasets for risk assessment and mapping are included in a geodatabase. Per SafeGraph data sharing guidelines, raw data cannot be shared publicly. To view the content of the geodatabase, users should have installed ArcGIS Pro 2.7. The geodatabase includes the following:1. POI. Major attributes are locations, name, and daily popularity.2. Denver neighborhood with weekly COVID-19 cases and computed regional risk levels.3. Simulated four travel logs with anchor points provided. Each is a separate point layer.

Context

Data set containing Tweets captured during the 2018 UEFA Champions League Final between Real Madrid and Liverpool.

Content

All Twitter APIs that return Tweets provide that data encoded using JavaScript Object Notation (JSON). JSON is based on key-value pairs, with named attributes and associated values. The JSON file include the following objects and attributes:

• Tweet - Tweets are the basic atomic building block of all things Twitter. The Tweet object has a long list of ‘root-level’ attributes, including fundamental attributes such as id, created_at, and text. Tweet child objects include user, entities, and extended_entities. Tweets that are geo-tagged will have a place child object.

  • User - Contains public Twitter account metadata and describes the author of the Tweet with attributes as name, description, followers_count, friends_count, etc.

  • Entities - Provide metadata and additional contextual information about content posted on Twitter. The entities section provides arrays of common things included in Tweets: hashtags, user mentions, links, stock tickers (symbols), Twitter polls, and attached media.

  • Extended Entities - All Tweets with attached photos, videos and animated GIFs will include an extended_entities JSON object.

  • Places - Tweets can be associated with a location, generating a Tweet that has been ‘geo-tagged.’

More information here.

Acknowledgements

I used the filterStream() function to open a connection to Twitter's Streaming API, using the keyword #UCLFinal. The capture started on Saturday, May 27th 6:45 pm UCT (beginning of the match) and finished on Saturday, May 27th 8:45 pm UCT.

Inspiration

• Time analysis
• Try text mining!
• Cross-language differences in Twitter
• Use this data to produce a sentiment analysis
• Twitter geolocation
• Network analysis: graph theory, metrics and properties of the network, community detection, network visualization, etc.

This is a dataset of GitHub repositories that were tagged with Jest, for JavaScript and TypeScript languages, that used Snapshot Testing. Information on all repositories is available in the file "0_Snapshot Testing Dataset.xlsx" (named to be the very first file). Most files represent the repository packed in targz format as "

In total there are 686 repositories. We collected only public repositories that were tagged with the Jest keyword, for JavaScript and TypeScript, had at least 1 star, and at least 1 snapshot file. The spreadsheet data was collected on July 13, 2022.

We also have all scripts used to gather this data. Here, "python_scripts.zip" has all python scripts to find repositories based on queries and save their attributes, and "node_and_shell_scripts.zip" contain the node and shell scripts to download a tarball of the repository. Therefore you should first use the python scripts to collect repositories & their attribute, and later use the node & shell to download a copy of the repositories. Moreover, inside each script folder/zip there is a Readme file with instructions and examples.

Our GitHub repository is an exact copy of this dataset <https://github.com/hscrocha/SnapshotTestingDataset>, but it is much better organized into folders and the README files for the scripts will be nicely displayed on it.