MAINT: Update old classifier urls (#587)

qiime2 · Jun 26, 2024 · 42d57b6 · 42d57b6
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diff --git a/source/data-resources.rst b/source/data-resources.rst
@@ -79,5 +79,5 @@ The following databases are intended for use with q2-fragment-insertion, and
 are constructed directly from the
 `SEPP-Refs project <https://github.com/smirarab/sepp-refs/>`_.
 
-- `Silva 128 SEPP reference database <https://data.qiime2.org/2024.5/common/sepp-refs-silva-128.qza>`_ (MD5: ``7879792a6f42c5325531de9866f5c4de``)
-- `Greengenes 13_8 SEPP reference database <https://data.qiime2.org/2024.5/common/sepp-refs-gg-13-8.qza>`_ (MD5: ``9ed215415b52c362e25cb0a8a46e1076``)
+- `Silva 128 SEPP reference database <https://data.qiime2.org/classifiers/sklearn-0.21.2/sepp-ref-dbs/sepp-refs-silva-128.qza>`_ (MD5: ``7879792a6f42c5325531de9866f5c4de``)
+- `Greengenes 13_8 SEPP reference database <https://data.qiime2.org/classifiers/sklearn-0.21.2/sepp-ref-dbs/sepp-refs-gg-13-8.qza>`_ (MD5: ``9ed215415b52c362e25cb0a8a46e1076``)
diff --git a/source/tutorials/moving-pictures-usage.rst b/source/tutorials/moving-pictures-usage.rst
@@ -798,7 +798,7 @@ from sequence to taxonomy.
        from urllib import request
        from qiime2 import Artifact
        fp, _ = request.urlretrieve(
-           'https://data.qiime2.org/2024.5/common/gg-13-8-99-515-806-nb-classifier.qza',
+           'https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes/gg-13-8-99-515-806-nb-classifier.qza',
        )
 
        return Artifact.load(fp)

diff --git a/source/tutorials/moving-pictures.rst b/source/tutorials/moving-pictures.rst
@@ -383,7 +383,7 @@ In the next sections we'll begin to explore the taxonomic composition of the sam
 
 
 .. download::
-   :url: https://data.qiime2.org/2024.5/common/gg-13-8-99-515-806-nb-classifier.qza
+   :url: https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes/gg-13-8-99-515-806-nb-classifier.qza
    :saveas: gg-13-8-99-515-806-nb-classifier.qza
 
 .. command-block::

diff --git a/source/tutorials/pd-mice.rst b/source/tutorials/pd-mice.rst
@@ -528,7 +528,7 @@ Up until now we have been performing diversity analyses directly on ASVs; in oth
 For this analysis, we'll use a pre-trained naive Bayes machine-learning classifier that was trained to differentiate taxa present in the 99% Greengenes 13_8 reference set trimmed to 250 bp of the V4 hypervariable region (corresponding to the 515F-806R primers). `This classifier works`_ by identifying k-mers that are diagnostic for particular taxonomic groups, and using that information to predict the taxonomic affiliation of each ASV. We can download the pre-trained classifier here:
 
 .. download::
-   :url: https://data.qiime2.org/2024.5/common/gg-13-8-99-515-806-nb-classifier.qza
+   :url: https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes/gg-13-8-99-515-806-nb-classifier.qza
    :saveas: gg-13-8-99-515-806-nb-classifier.qza
 
 It's worth noting that Naive Bayes classifiers perform best when they're trained for the specific hypervariable region amplified. You can train a classifier specific for your dataset based on the :doc:`training classifiers tutorial <feature-classifier>` or download classifiers for other datasets from the :doc:`QIIME 2 resource page <../data-resources>`. Classifiers can be re-used for consistent versions of the underlying packages, database, and region of interest.