Hi team, Based on the feedback, I’ve made a few updates to the design. In the datasets table, I removed the preview button—now users can view dataset details by clicking directly on the dataset name. I’ve also added an option to open datasets directly in platforms like VS Code or Jupyter Notebook. When clicked, a pop-up appears prompting the user to open the dataset in the selected environment.
If the Apache Airavata extension is not already installed, the pop-up will guide the user to install it first. Once installed, the extension opens in a new tab within VS Code and displays the selected dataset. Users can then choose a download path and click the download button to save the dataset to their local machine. Additionally, the extension tab shows a separate section listing previously downloaded datasets also. https://youtu.be/ZITvWY95nyw Let me know your thoughts! Best regards, Nipuna On Sat, Apr 5, 2025 at 1:37 AM Dimuthu Upeksha <dimuthu.upeks...@gmail.com> wrote: > Hi Nipuna, > > This is a good start. Can we explore how we can enable these data > availability through jupyter lab and vs code extensions? For example, if we > have a jupyter lab extension for Airavata to pick the data item on demand > and mount the data directly into the jupyter lab, that would be a great > addition. Disregarding how we can do it technically, you can focus on > drawing up designs from the ux perspective. > > Thanks > Dimuthu > > On Fri, Apr 4, 2025 at 1:48 PM Nipuna Bandara <ndesha...@gmail.com> wrote: > >> Dear Dev Team, >> >> I’m Nipuna Deshan Bandara, a final-year undergraduate at Uva Wellassa >> University of Sri Lanka, pursuing a degree in Computer Science and >> Technology. I'm deeply passionate about the UI/UX field, and I'm >> enthusiastic about contributing to the Apache Airavata project. >> >> I’ve been working on the data catalogue component and have designed the >> user flow for it. I’ve attached a YouTube link demonstrating the flow I >> created .(https://youtu.be/oXXU1dT1yYE) >> >> In first screen it allows users to search for datasets and also browse >> them using the category bar. The datasets are displayed in a table view, >> and each entry provides options to preview the dataset, download it as a >> ZIP file, or generate code snippets. When the download button is clicked, >> the dataset is downloaded manually. The code button opens a pop-up window >> containing ready-to-use code in multiple options of languages such as >> Python, Java, and JavaScript. This helps users fetch the dataset directly >> using code. The preview button navigates to a detailed view of the dataset, >> showcasing its description, key features, and a sample preview. >> >> Looking forward to your feedback and suggestions. >> >> Best regards, >> Nipuna Deshan Bandara >> www.linkedin.com/in/nipuna-bandara >> >
