Merge pull request #169 from Caltech-IPAC/raen/improve-credentials-info

Clarify that credentials are not required

Author: troyraen

tutorials/cloud_access/cloud-access-intro.md

@@ -27,8 +27,6 @@ Learning Goals:
 
 ## 1. Cloud basics
 
-### 1.1 Terminology
-
 AWS S3 is an [object store](https://en.wikipedia.org/wiki/Object_storage) where the fundamental entities are "buckets" and "objects".
 Buckets are containers for objects, and objects are blobs of data.
 Users may be more familiar with [filesystem](https://en.wikipedia.org/wiki/File_system) "files" and "directories".
@@ -43,15 +41,17 @@ The following S3 terms are also used in this notebook:
 
 +++
 
-### 1.2 General access
+### 1.1 General access
 
 Most of the common python methods used to read images and catalogs from a local disk can also be pointed at cloud storage buckets.
 This includes methods like Astropy `fits.open` and Pandas `read_parquet`.
 The cloud connection is handled by a separate library, usually [s3fs](https://s3fs.readthedocs.io), [fsspec](https://filesystem-spec.readthedocs.io), or [pyarrow.fs](https://arrow.apache.org/docs/python/api/filesystems.html).
 
 The IRSA buckets are public and access is free.
 Credentials are not required.
-Anonymous connections can be made, often by setting a keyword argument like `anon=True`.
+However, most tools will look for credentials by default and raise an error when none are found.
+To access without credentials, users can make an anonymous connection, usually with a keyword argument such as `anon=True`.
+This notebook demonstrates with the `s3fs`, `astropy`, and `pyarrow` libraries.
 
 +++
 

tutorials/parquet-catalog-demos/wise-allwise-catalog-demo.md

@@ -31,7 +31,8 @@ kernelspec:
 ## Introduction
 
 This notebook demonstrates access to the [HEALPix](https://ui.adsabs.harvard.edu/abs/2005ApJ...622..759G/abstract)-partitioned (order 5), [Apache Parquet](https://parquet.apache.org/) version of the [AllWISE Source Catalog](https://wise2.ipac.caltech.edu/docs/release/allwise/expsup/sec1_3.html#src_cat).
-The catalog is available through the [AWS Open Data](https://aws.amazon.com/opendata) program, as part of the [NASA Open-Source Science Initiative](https://science.nasa.gov/open-science-overview).
+The catalog is available through the [AWS Open Data](https://registry.opendata.aws/wise-allwise/) program, as part of the [NASA Open-Source Science Initiative](https://science.nasa.gov/open-science-overview).
+Access is free and no special permissions or credentials are required.
 
 Parquet is convenient for large astronomical catalogs in part because the storage format supports efficient database-style queries on the files themselves, without having to load the catalog into a database (or into memory) first.
 The AllWISE catalog is fairly large at 340 GB.
@@ -65,13 +66,13 @@ from pyarrow.fs import S3FileSystem
 
 +++
 
-This AllWISE catalog is stored in an [AWS S3](https://aws.amazon.com/s3/) bucket.
-To connect to an S3 bucket we just need to point the reader at S3 instead of the local filesystem, and pass in AWS credentials.
+This AllWISE catalog is stored in an [AWS S3](https://aws.amazon.com/s3/) cloud storage bucket.
+To connect to an S3 bucket we just need to point the reader at S3 instead of the local filesystem.
 (Here, a "reader" is a python library that reads parquet files.)
 We'll use [pyarrow.fs.S3FileSystem](https://arrow.apache.org/docs/python/generated/pyarrow.fs.S3FileSystem.html) for this because it is recognized by every reader in examples below, and we're already using pyarrow.
-[s3fs](https://s3fs.readthedocs.io/en/latest/index.html) is another common option.
-The call to `S3FileSystem` will look for AWS credentials in environment variables and/or the file ~/.aws/credentials.
-Credentials can also be passed as keyword arguments.
+([s3fs](https://s3fs.readthedocs.io/en/latest/index.html) is another common option.)
+To access without credentials, we'll use the keyword argument `anonymous=True`.
+More information about accessing S3 buckets can be found at [](#cloud-access-intro).
 
 ```{code-cell} ipython3
 bucket = "nasa-irsa-wise"