Jupyter Notebook good first issues

This issue proposes a comprehensive quality review of all existing RAG (Retrieval Augmented Generation) questions and answers within a growing repository. The goal is to ensure technical accuracy, clarity, and up-to-date information across all 10 sections, addressing potential factual errors, outdated content, misleading wording, incorrect difficulty tags, missing context, formatting issues, and inconsistencies in terminology.

This issue proposes expanding the repository's coverage of Retrieval Augmented Generation (RAG) architectures. The goal is to include more RAG types beyond the current ten to create a more comprehensive collection reflecting the evolving RAG landscape.

Optimize the existing NVD data fetcher (NVDDataFetch-V1.ipynb) to only fetch and update new or modified CVEs instead of re-downloading the entire dataset each time. This involves leveraging the NVD API's modification timestamps to efficiently update the existing pandas DataFrame.

This issue proposes moving the project's eligibility criteria from a Google Sheet to an in-repo Markdown file named `criteria.md`. This change aims to reduce reliance on external links and improve maintainability by keeping documentation alongside the code. All existing links to the Google Sheet will need to be updated to point to the new Markdown file.

This issue identifies a typo in two `Make.package` files within the `src_deankow` directory, where `CEXE_headers` is misspelled as `CEXE_heaeders`. This prevents the specified header files from being registered with the build system, meaning changes to these headers will not trigger recompilation, potentially leading to outdated code being used.

This issue identifies a syntax error in preprocessor directives within the `src_deankow` directory. A bare `elseif` token is used instead of the correct `#elif` directive, which will cause a compilation error if the `#else` branch is activated. The proposed fix involves replacing the nested `#if` statements with a single `#define` directive to control the selection of external potential forms.

This issue requests the Python implementation of various mathematical concepts and algorithms. It includes calculating volumes of geometric shapes, number theory algorithms like Zeller's Congruence and prime number generation, numerical methods such as Simpson's Rule and Newton-Raphson, and matrix operations like QR-decomposition. Some items are already marked as completed or crossed out.

This GitHub issue is a survey for students to report which LLM applications they use most frequently. Participants are instructed to create a new Markdown file in a specific directory within the course repository, fill in their GitHub username, and mark up to two LLM apps they use regularly. They then need to submit this file via a pull request to the main branch of the course repository.

This issue proposes adding checksum verification (SHA256 or MD5) for downloaded files, excluding Git FileObjects, to ensure data integrity. The implementation should include corresponding unit tests to validate the new functionality.

This issue proposes enhancing the reinsurance Gamma parameter to support multiyear and projection scenarios. Currently a single value, it needs to be changed to a double array to accommodate scenarios where contracts span multiple periods and cannot be broken down.

This issue is a general call for community engagement, inviting users to share opinions, ideas, ask questions, report bugs, and contribute solutions. It serves as an open forum for discussion and encourages pull requests for contributions.

The `AbsoluteQuantileRegressionScore` class in the library can be instantiated with a `sym=True` parameter, which leads to incorrect computations. The suggested fix is to raise an error when `sym=True` is used during instantiation to prevent this bug.

This issue proposes to optimize the DAG visualization by avoiding redundant recomputation of the Highest Work Path (HWP). The current approach on the frontend manually tracks and updates the HWP incrementally via WebSocket, which is more efficient than repeatedly calling an RPC that recomputes it from scratch.

This issue requires a comprehensive audit of all SQL lookups and ORM coverage within the `Municipio` schema. The goal is to map database objects to Django models, identify data access patterns, and document any gaps in ORM coverage, without making production changes.

The current anomaly detector tests pin specific AUC values, but one detector is pinned below chance (0.42), suggesting a potential inversion in its scoring. Additionally, the tests lack a minimum AUC assertion, meaning detectors that perform poorly or don't discriminate at all could pass indefinitely.

The `non_interacting.solve` function in the iDEA library unnecessarily constructs the initial Hamiltonian twice with identical arguments. The first construction results in variables that are never used, leading to redundant computation, particularly for the exchange operator in Hartree-Fock and hybrid methods.

This issue proposes creating an exercise to teach users about Support Vector Machines (SVMs). The goal is to cover the fundamentals of SVMs and how to optimize their hyperparameters. It's marked as a 'good first issue' and 'help wanted', suggesting it's suitable for newcomers and actively seeking contributions.

This issue proposes implementing hybrid vertical coordinates in the Dinosaur dycore. This change would allow the dycore to run on native ECMWF/UFS grids and potentially improve performance in mountainous regions. The implementation is described as relatively straightforward, with mathematical descriptions available in standard textbooks, and external contributions are welcomed.

This issue proposes implementing version-based documentation for the project using MkDocs, leveraging its built-in versioning capabilities. The goal is to allow users to access documentation for different project versions, as demonstrated by the provided MkDocs-Material link.

The current recalibration mode in the DePSI library does not produce output values for data points preceding the initialization period. This makes early data unusable, and the request is to modify the recalibration process to include these earlier data points.

This issue proposes replacing all `print` statements within the `arc_estimation`, `network`, and `network_adjustment` modules with calls to the Python `logging` module. This change aims to improve the ability to monitor code execution, particularly in batch processing environments (sbatch).

This issue proposes to standardize variable names within the DePSI system to improve consistency and simplify step parsing. The process involves first defining these unified names in a provided document and then incorporating this information into the official documentation.

This issue requests the implementation of local temporal coherence for network points, mirroring an existing implementation for densification. It appears to be an enhancement to extend a feature to a new context within the project.

This issue requests the addition of unit tests for functions within the `transformation.py` file that currently lack them. The goal is to ensure these functions produce correct outputs against known true values, which is particularly important for maintaining precision, especially when using `np.datetime64`.

A RuntimeWarning is occurring in the `fsolve` function within `depsi/stats.py` when calculating lambda0. This warning indicates that the iterative process is not making sufficient progress. The goal is to identify the root cause of this warning, implement a fix, and ensure that existing unit tests continue to pass.

This issue requests the creation of a new Python module, `visualization.py`, to visually represent point and arc data from DePSI. The visualizations should be specifically tailored for an MRM plot, with an example provided for plotting network arcs colored by their 'temp_coh' values.

This issue requires updating the documentation for the internal historical alert API. The existing documentation is incomplete and needs to include details on authentication, all available parameters with their types and constraints, supported aliases, and response structures. Additionally, an authenticated curl example and error response examples need to be added.

This issue requires updating the API documentation for the public v1 alert-city endpoint. Specifically, it needs to detail the request parameters, provide an executable example, and document expected success and error responses, ensuring clarity on date formatting and supported values.

This issue requires documenting a legacy API endpoint, `/api/alertcity`, which is currently referenced but lacks proper usage documentation. The documentation should cover its compatibility contract, parameter details (including date formats), supported values, example usage with `curl`, and highlight its legacy status compared to the newer v1 endpoint.

This issue requires a comprehensive audit of the `Dengue_global` SQL schema to identify and classify database objects. The goal is to determine which objects should be managed by the Django ORM and which should remain as explicit SQL boundaries, documenting the rationale for each decision.