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Simple Syntax
Like Markdown, but more powerful. No parentheses needed.
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Prompt: website
Agent: deepseek
Model: deepseek-chat
The *P-Value Integrity Hub is a single-page CRUD (Create, Read, Update, Delete) application designed to help researchers manage and analyze their datasets, focusing on addressing the misuse of p-values in scientific research. Here's a step-by-step description of how users interact with the application: 1. User Access and Authentication: - Users begin by accessing the application through a web browser. - They log in or sign up to create an account, ensuring secure access to their datasets and analyses. 2. Creating a New Dataset: - Upload Data: Users can create a new dataset by uploading a file (e.g., CSV, Excel) through a drag-and-drop interface or by manually entering data into a form. - Data Validation: The application validates the data format and structure upon upload, ensuring compatibility with the analysis tools. - Storage: Once validated, the dataset is sent to the backend and stored in the MongoDB database. 3. Reading and Exploring Datasets: - Data View: Users can view their uploaded datasets in a tabular format. The interface allows for dynamic filtering, sorting, and searching to explore specific data points. - Visualization: Users can generate graphs and charts to visualize data distributions and trends. Visualizations are interactive, allowing users to customize views and highlight specific data points. 4. Updating Datasets: - Edit Data: Users can update existing datasets by editing individual data points or adding new entries. - Version Control: The application tracks changes using version control, allowing users to revert to previous versions if needed. - Real-Time Sync: Changes are synchronized in real-time, ensuring that all collaborators have access to the most up-to-date information. 5. Analyzing Data for P-Value Misuse: - Automated Analysis: Upon dataset upload or update, the backend automatically analyzes the data for potential p-value misuses, such as p-hacking and uncorrected multiple comparisons. - Feedback: The analysis results are displayed in the interface, providing users with real-time feedback and recommendations for improving their statistical practices. 6. Deleting Datasets: - Remove Data: Users can delete entire datasets or specific data points. The application includes safeguards to prevent accidental deletions, such as confirmation prompts. - Audit Trail: Deletions are logged in an audit trail, maintaining a transparent record of all data interactions. 7. Collaboration and Sharing: - Invite Collaborators: Users can invite colleagues to collaborate on datasets and analyses. Collaborators can view and edit data in real-time. - Export Results: Users can export visualizations and analysis reports in publication-ready formats, ensuring consistency and professionalism in research communications. 8. Pre-Registration and Transparency: - Register Study Designs: Users can pre-register study designs and analysis plans before data collection begins, promoting transparency and reducing the risk of p-hacking. - Audit Trails: The application maintains a complete audit trail of all data interactions, providing a transparent record of changes and analyses performed. 9. Educational Support: - Access Resources: Users can access a library of tutorials and guides directly within the application, providing them with the knowledge needed to understand and correctly use p-values. - Contextual Guidance*: Tooltips and hints offer real-time guidance, helping users navigate complex statistical concepts with ease.
You are an expert web developer. Create a website based on this request: "The *P-Value Integrity Hub is a single-page CRUD (Create, Read, Update, Delete) application designed to help researchers manage and analyze their datasets, focusing on addressing the misuse of p-values in scientific research. Here's a step-by-step description of how users interact with the application: 1. User Access and Authentication: - Users begin by accessing the application through a web browser. - They log in or sign up to create an account, ensuring secure access to their datasets and analyses. 2. Creating a New Dataset: - Upload Data: Users can create a new dataset by uploading a file (e.g., CSV, Excel) through a drag-and-drop interface or by manually entering data into a form. - Data Validation: The application validates the data format and structure upon upload, ensuring compatibility with the analysis tools. - Storage: Once validated, the dataset is sent to the backend and stored in the MongoDB database. 3. Reading and Exploring Datasets: - Data View: Users can view their uploaded datasets in a tabular format. The interface allows for dynamic filtering, sorting, and searching to explore specific data points. - Visualization: Users can generate graphs and charts to visualize data distributions and trends. Visualizations are interactive, allowing users to customize views and highlight specific data points. 4. Updating Datasets: - Edit Data: Users can update existing datasets by editing individual data points or adding new entries. - Version Control: The application tracks changes using version control, allowing users to revert to previous versions if needed. - Real-Time Sync: Changes are synchronized in real-time, ensuring that all collaborators have access to the most up-to-date information. 5. Analyzing Data for P-Value Misuse: - Automated Analysis: Upon dataset upload or update, the backend automatically analyzes the data for potential p-value misuses, such as p-hacking and uncorrected multiple comparisons. - Feedback: The analysis results are displayed in the interface, providing users with real-time feedback and recommendations for improving their statistical practices. 6. Deleting Datasets: - Remove Data: Users can delete entire datasets or specific data points. The application includes safeguards to prevent accidental deletions, such as confirmation prompts. - Audit Trail: Deletions are logged in an audit trail, maintaining a transparent record of all data interactions. 7. Collaboration and Sharing: - Invite Collaborators: Users can invite colleagues to collaborate on datasets and analyses. Collaborators can view and edit data in real-time. - Export Results: Users can export visualizations and analysis reports in publication-ready formats, ensuring consistency and professionalism in research communications. 8. Pre-Registration and Transparency: - Register Study Designs: Users can pre-register study designs and analysis plans before data collection begins, promoting transparency and reducing the risk of p-hacking. - Audit Trails: The application maintains a complete audit trail of all data interactions, providing a transparent record of changes and analyses performed. 9. Educational Support: - Access Resources: Users can access a library of tutorials and guides directly within the application, providing them with the knowledge needed to understand and correctly use p-values. - Contextual Guidance*: Tooltips and hints offer real-time guidance, helping users navigate complex statistical concepts with ease."
Requirements:
As a refresher, for doing the html body, Scroll is a whitespace based language that uses a single indented space to mark a line (aka particle) as a subparticle of a parent line.
For example:
The extendible markup language that makes source beautiful and compiles to anything
Get StartedLike Markdown, but more powerful. No parentheses needed.
Build your own custom parsers.
Create stunning documents with minimal effort.
Built on the efficient PPS Stack.
First suggest a short, memorable domain name ending in scroll.pub that represents this website. Then provide the website files. Use this exact format:
---domain---
(domainscroll.pub here)
---index.scroll---
(body content here. no blank lines please.)
---style.css---
(CSS content here)
---script.js---
(JavaScript content here)
---end---