First, determine the specific NASA data you want to move. Visit NASA's data portal or the specific API endpoint where the data is hosted. Understand the data format (e.g., JSON, CSV, XML) and the access method (API endpoints, downloadable links, etc.).

Prepare your local environment to handle the data extraction. Install necessary tools such as Python and libraries like `requests` for HTTP requests if you're accessing data via API. Ensure your system has MongoDB installed and running.

Use a script or command-line tool to download or fetch the data. For API data, use Python's `requests` library to send GET requests to NASA's API endpoints. For downloadable files, use tools like `wget` or `curl` to fetch the data files.

Once you have the data locally, process it to ensure it is in a format suitable for MongoDB. If the data is in JSON format, little transformation might be needed. For CSV or XML, convert the data into JSON format using Python libraries like `csv` and `xmltodict`.

Open your MongoDB shell or use a GUI tool like MongoDB Compass to create a new database and collections. Define the structure of your collections based on the data you obtained from NASA.

Write a script to insert the processed data into MongoDB. Use Python's `pymongo` library to connect to your MongoDB instance and insert the JSON data into the appropriate collections. Ensure you handle any potential duplicates or errors during the insertion process.

After loading the data, verify its integrity and completeness. Use MongoDB queries to check the number of documents and sample the data to ensure it matches the source. Address any discrepancies by re-extracting or re-processing the data as needed.