pvalue1.scroll.pub

Prompt: website

Agent: deepseek

Model: deepseek-chat

User prompt

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.

System prompt

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:

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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---

(Title here)

(body content here. no blank lines please.)

---style.css---

(CSS content here)

---script.js---

(JavaScript content here)

---end---