Repository: single-cell-data/TileDB-SOMA (language: python)

Issue:
[python/r/c++] Revisit `shape` for component arrays
## PRs

**Merged PRs:**

<details>

* #2909 `kerl/schevo-timestamp-methodize`
* #2913 `kerl/name-neaten`
* #2908 `kerl/ut-soma-exc-simplify`
* #2910 `kerl/test-common-parameterize`
* #2918 `kerl/cpp-test-deadstrip`
* #2919 `kerl/minor-unit-test-helper-mod`
* #2936 `kerl/cpp-ut-helper-neaten`
* #2938 `kerl/more-cur-dom-parameterize`
* #2915 `kerl/cpp-strict-int64-shape`
* #2911 `kerl/arrow-util-current-domain-optional`
* #2939 `kerl/step-two-temp`
* #2947 `kerl/cpp-ndarray-resize-testing`
* #2945 `kerl/dataframe-test-fixture`
* #2944 `kerl/cpp-variant-indexed-dataframes`
* #2916 `kerl/sdf-shape`
* #2917 `kerl/cpp-resizes`
* #2948 `kerl/upgrade-shape-int64`
* #2951 `kerl/sdf-test-accessors`
* #2953 `kerl/py-r-accessor-plumbing`
* #2957 `kerl/sdf-domain-accessors`
* #2960 `kerl/dense-link`
* #2963 `kerl/nightly-fix`
* #2970 `kerl/dense-writeable-after-create`
* #2968 `kerl/minor-trim`
* #2969 `kerl/more-py-domain-name-neaten`
* #2972 `kerl/libtiledbsoma-env-logging-level`
* #2962 `kerl/py-r-creation-paths`
* #2950 `kerl/py-r-test-2`
* #2994 `kerl/nanoarrow-helpers`
* #3011 `kerl/polydom3`
* #3017 `kerl/polydom5`
* #3018 `kerl/polydom6`
* #2990 `kerl/variant-nnz-bug`
* #3019 `kerl/index-swap`
* #3020 `kerl/ut-max-shape`
* #3012 `kerl/polydom4`
* #3025 `kerl/fix-3020-merge`
* #3026 `kerl/one-more-rename`
* #3028 `kerl/ff-not`
* #3029 `kerl/ut-vg`
* #3030 `kerl/table-utils-memory`
* #3067 `kerl/improve-sdf-test-field-names`
* #3068 `kerl/ut-generate`
* #3069 `kerl/cpp-sdf-domain-at-create`
* #3027 `kerl/hll-domainish`
* #3088 `kerl/max-domain-int64`
* #3090 `kerl/maybe-resize-soma-joinid-cpp-tweak`
* #3032 `kerl/sdf-domain-at-create` -- fixes #2967
* #3091 `kerl/maybe-resize-soma-joinid-py-r`
* #3095 `kerl/cpp-exp-resize-prep`
* #3089 `kerl/r-dataframe-shapeable`
* https://github.com/single-cell-data/SOMA/pull/233
* #3125 `kerl/cpp-ut-name-shortens`
* #3127 `kerl/helper-rename`
* #3130 `kerl/cpp-can-resizers-names`
* #3132 `kerl/cpp-dataframe-sizing-helpers`
* #3139 `kerl/cpp-dataframe-upgrade-test`
* #3140 `kerl/py-resizer-connects`
* #3151 `kerl/py-can-upgrade-shape`
* #3152 `kerl/registration-shape-acceessors`
* #3156 `kerl/py-exp-shaping`
* #3157 `kerl/py-exp-shaping2`
* #3148 `kerl/py-exp-resize`
* #3191 `kerl/py-domain-at-create-ut-1`
* #3190 `kerl/py-domain-at-create-ut-2`
* #3192 `kerl/py-domain-at-create-ut-3`
* #3193 `kerl/py-domain-at-create-ut-4`
* #3194 `kerl/py-domain-at-create-ut-5`
* #3203 `kerl/min-size-2`
* #3208 `kerl/r-min-sizing`
* #3211 `kerl/cpp-ugr-dom`
* #3232 `kerl/ff-interop`
* #3230 `kerl/ffon`
* #3234 `kerl/docstring-prune`
* #3236 `kerl/prefixing`
* #3241 `kerl/fix-bad-merge`
* #3235 `kerl/py-r-ugr-dom`
* #3237 `kerl/py-r-ugr-dom-2`
* #3238 `kerl/py-r-ugr-dom-3`
* #3253 `kerl/set-coords-rename`
* #3261 `kerl/pybind11-nda-sizing`
* #3265 `kerl/dense-227-a`
* #3263 `kerl/dense-range-trim`
* #3268 `kerl/dim-explosion`
* #3269 `kerl/python-227-dense-ned-read`
* #3270 `kerl/r-227-dense-fixes`
* #3280 `kerl/r-dense-227-more`
* #3286 `kerl/more-fn4m`
* #3283 `kerl/readthedocs-pre-1.15`
* #3288 `kerl/dense-ugrsh`
* #3289 `kerl/notebook-shape-upgrade`
* #3285 `kerl/new-shape-doc-updates`
* #3295 `kerl/notebook-data-refresh`
* #3290 `kerl/notebook-new-shape-refresh`
* #3301 `kerl/ffena`
* #3303 `kerl/r-data-refresh`
* #3300 `kerl/sdf-sjid-lower-zero`
* #3296 `kerl/dense-example-data-refresh`
* #3294 `kerl/new-shape-notebook-and-vignette`
* #3308 `kerl/upgrade-experiment-resources`
* #3309 `kerl/fix-notebook-merge`
* #3321 `kerl/more-use-shape`
* #3358 `kerl/revert-3300`
* #3368 `kerl/227a`
* #3369 `kerl/ucd1`
* #3370 `kerl/ucd2`
* #3371 `kerl/ucd3`
* #3372 `kerl/ucd4`
* #3396 `kerl/domain-at-create-docstrings`
* #3302 `kerl/new-shape-vignette`
* https://github.com/single-cell-data/SOMA/pull/250
* #3399 `kerl/new-shape-more-docstrings`
* #3400 `kerl/check-only-r`
* https://github.com/TileDB-Inc/somacore-feedstock/pull/27

</details>

**Closed/abandoned PRs:**

<details>

* #2785 -- This was only dogfooding for the core 2.25 release -- not to be merged
* #2952 `kerl/feature-flag-temp` -- folded into 2962
* #2995 `kerl/polydom`
* #2964 `kerl/tiledbsoma-io-test`
* #3189 `kerl/min-size`
* #3220 `kerl/cpp-ugr-dom-2`
* #3244 `kerl/dense-227-fixes`
* #3276 `kerl/dataframe-shape`

</details>

**Issues which are related but non-blocking:**

* https://github.com/single-cell-data/SOMA/pull/216
* https://github.com/TileDB-Inc/TileDB/pull/5303
* #2966
* #3081
* Note: R append mode does not exist yet -- see #1630 -- so an experiment-level resizer is not a priority in R
* #3271 
* #3272
* #3273

**See also:** [[sc-51048]](https://app.shortcut.com/tiledb-inc/story/51048).

## Problem to be solved

Users want to know the `shape` of an array, in the SciPy sense:

* Reads and writes are bounds-checked against the shape
* This retains its value regardless of which values of a sparse array are or are not actually occupied
* Users can `resize`.
  * Some users need the ability to grow their datasets later, using either `tiledbsoma.io`'s append mode, or subsequent writes using the `tiledbsoma` API.
  * Note that the cellxgene census doesn't need this: eact week's published census has fixed shape, and any updates will happen in new storage, on a new week.

Using TileDB-SOMA up until the present:

* The TIleDB `domain` is immutable after array creation
  * This does bounds-checking for reads and writes, which is good
  * To leverage this to function as a `shape`, users would need to set the `domain` at array-creation time. However, users lose the ability to grow their datasets later.
* There is a `non_empty_domain` accessor
  * This only indicates min/max coordinates _at which data exists_. Consider an `X` array for 100 cells and 200 genes. If non-zero expression counts exist only for cell join IDs 2-17, then the `non_empty_domain` will indicate `(2,17)` along `soma_dim_0`.
  * Consider an `obms["X_pca"]` within the same experiment. This may be 100 cells by 50 PCA components: we need a placd to store the number 50.
  * Therefore users cannot leverage this to function as a `shape` accessor.
* We have offered a `used_shape` accessor since TileDB-SOMA 1.5.
  * This functions as a `shape` accessor, in the SciPy sense, but it is not multi-writer safe.

New feature for TileDB-SOMA 1.15:

* Arrays will have a  `shape`
* Reads and writes are bounds-checked against the shape
* This retains its value regardless of which values of a sparse array are or are not actually occupied
* Users can `resize`
* The `used_shape` accessor will be deprecated in TileDB-SOMA 1.13, and slated for removal in TileDB-SOMA 1.14.

Compatiblity:

This will now require users to do an explicit `resize` before appending/growing TileDB-SOMA Experiments. Guidance in the form of example notebooks will be provided.

##  Tracking

See also: [[sc-41074]](https://app.shortcut.com/tiledb-inc/story/41074) and [[sc-51048]](https://app.shortcut.com/tiledb-inc/story/51048).

## Scheduling

Support arrives in TileDB Core 2.25. Deprecations for TileDB-SOMA will be released with 1.13. Full support within TileDB-SOMA will be release in 1.14.

## Details

SOMA API mods as we've discussed in a Google doc are as follows.

### `SOMADataFrame`

* `create`: Retain the `domain` argument
  * Issue:
    * Core has a `(lo, hi)` tuple per dim, e.g. `(0,99)` or `(10,19)`
    * SOMA has count per dim, with 0 implicit: e.g. 100 or 20
    * For `SparseNDArray` and `DenseNDArray` core can have `(lo, hi)` and SOMA can have `count`
    * For `DataFrame` there can be multiple dims --- default is a single `soma_joinid`
    * That could be treated either in `(lo, hi)` fashion or `count` fashion
    * However additional dims (e.g. `cell_type`) can be on any type, including strings, floats, etc. where there is no implicit lo=0
    * Therefore we need to keep the current SOMA API wherein `DataFrame` takes a `domain` argument (in `(lo, hi)` fashion) and not a `shape` argument (in `count` fashion)

### `SparseNDArray and DenseNDArray`

* `create`
  * Have an optional shape argument which is of type `Tuple[Int,...]` where each element is the cell count of the corresponding dimension
    * If unsupplied, or if supplied but None in any slot: use the minimum 0 in each slot – nothing larger makes sense since we will not support downsize
  * User guidance should make clear that it will not be possible to create an ‘old’ style array with the ‘new style’ API. (See also the upgrade logic below.)

### All three of `SOMADataFrame`, `SparseNDArray`, `DenseNDArray`

* `write`
  * For new arrays, created with the new shape feature:
    * Core will bounds-check that coordinates provided at `write` time are within the current shape
    * Core will raise `tiledb.cc.TileDBError` to TileDB-SOMA, which will catch and raise `IndexError`, and R-standard behavior on the R side
  * For old arrays created before this feature:
    * Core will not bounds-check that coordinates provided at write time are within the current shape
* Existing `used_shape` accessor
  * TileDB-SOMA will deprecate this over a release cycle.
  * For new arrays: raise `NotImplementedError`
  * For old arrays: return what’s currently returned, with a deprecation warning.
  * Mechanism for determining old vs. new: `array.schema.version` (the core storage version).
* Existing `shape` accessor
  * For new arrays:
    * Have this return the new shape as proposed by core, no longer returning the TileDB domain.
  * For old arrays created before this feature:
    * Return the TileDB domain as now.
* Existing `non_empty_domain` accessor
  * Same behavior for old and new arrays (unaffected by this proposal).
  * Keep this accessor supported, but, with user notes that it’s generally non-useful
  * This should return None (or R equivalent) when there is a schema but no data have been written.
* New `maxshape` accessor
  * Maps the core-level `(lo, hi)` accessor for domain to count-style accessor hi+1. E.g. if the core domain is either `(0,99)` or `(50,99)` then TileDB-SOMA `maxshape` will say 100.
  * Same behavior for old and new arrays.
  * Let users query for what the TileDB domain is, with user notes that it’s the maximum that users can reshape to.
  * Issac suggests: maybe `domain` or `maxshape` (see h5py).
* New `resize` mutator
  * Note: `reshape` means something else in the community (numpy, zarr, h5py), e.g. a 5x20 (total 100 cells) being reinterpreted as 4x25 (still 100 cells). The standard name for changing cell-count is `resize`.
  * For old arrays created before this feature: raise `NotImplementedError`.
  * For new arrays:
    * Will raise `ValueError` if the new shape is smaller on any dim than currently in storage
    * Regardless of whether any data have been written whatsoever
    * Will raise `ValueError` if the new shape exceeds the TileDB domain from create time, which will serve TileDB-SOMA in a role of “max possible shape the user can reshape to”
    * Otherwise, any calls to write from this point will bounds-check writes within this new shape
    * **We don’t expect resize to be multi-writer safe with regard to write ; user notes must be clear on this point**
* New `tiledbsoma_upgrade_shape` method for SparseNDArray and DenseNDArray
  * This will leverage `array.schema.version` to see if an upgrade is needed
  * Leverage core support for storage-version updates
  * This will take a shape argument as in `create`
  * For arrays created with “just-right” size: this will succeed
  * For arrays created with “room-for-growth” / “two billion-ish” size: this will succeed
  * If the user passes a shape which exceeds the current TileDB domain: this will fail
* New `tiledbsoma_upgrade_domain method` for `DataFrame`
  * Same as for `SparseNDArray`/`DenseNDArray` except it will take a domain at the SOMA-API level just as `DataFrame`'s create method

### `tiledbsoma.io`

* The user-facing API has no shape arguments and thus won’t need changing.
* Internally to `tiledbsoma.io`, we’ll still ask the tiledbsoma API for the “big domain” (2 billionish)
* Append mode:
  * Will need a new `resize` method at the `Experiment` level
  * Users will need to:
    * Register as now
    * Call the experiment-level `resize`
      * Could be `exp.resize(...)`, or (better) this could be `tiledbsoma.io.reshape_experiment`
  * In either case: this method will take the new `obs` and `var` counts as inputs:
    * `exp.obs.reshape` to new `obs` count
    * `exp.ms[name].var.reshape` to new `var` count
    * `exp.ms[name].X[name].reshape` to new `obs` count x `var` count
    * `exp.ms[name].obsm[name].reshape` to new `obs` count x same width
    * `exp.ms[name].obsp[name].reshape` to new `obs` count x `obs` count
    * `exp.ms[name].varm[name].reshape` to new `var` count x same width
    * `exp.ms[name].varp[name].reshape` to new `var` count x `var` count
  * Do the individual append-mode writes as now

[python/r/c++] Revisit `shape` for component arrays
## PRs

**Merged PRs:**

<details>

* #2909 `kerl/schevo-timestamp-methodize`
* #2913 `kerl/name-neaten`
* #2908 `kerl/ut-soma-exc-simplify`
* #2910 `kerl/test-common-parameterize`
* #2918 `kerl/cpp-test-deadstrip`
* #2919 `kerl/minor-unit-test-helper-mod`
* #2936 `kerl/cpp-ut-helper-neaten`
* #2938 `kerl/more-cur-dom-parameterize`
* #2915 `kerl/cpp-strict-int64-shape`
* #2911 `kerl/arrow-util-current-domain-optional`
* #2939 `kerl/step-two-temp`
* #2947 `kerl/cpp-ndarray-resize-testing`
* #2945 `kerl/dataframe-test-fixture`
* #2944 `kerl/cpp-variant-indexed-dataframes`
* #2916 `kerl/sdf-shape`
* #2917 `kerl/cpp-resizes`
* #2948 `kerl/upgrade-shape-int64`
* #2951 `kerl/sdf-test-accessors`
* #2953 `kerl/py-r-accessor-plumbing`
* #2957 `kerl/sdf-domain-accessors`
* #2960 `kerl/dense-link`
* #2963 `kerl/nightly-fix`
* #2970 `kerl/dense-writeable-after-create`
* #2968 `kerl/minor-trim`
* #2969 `kerl/more-py-domain-name-neaten`
* #2972 `kerl/libtiledbsoma-env-logging-level`
* #2962 `kerl/py-r-creation-paths`
* #2950 `kerl/py-r-test-2`
* #2994 `kerl/nanoarrow-helpers`
* #3011 `kerl/polydom3`
* #3017 `kerl/polydom5`
* #3018 `kerl/polydom6`
* #2990 `kerl/variant-nnz-bug`
* #3019 `kerl/index-swap`
* #3020 `kerl/ut-max-shape`
* #3012 `kerl/polydom4`
* #3025 `kerl/fix-3020-merge`
* #3026 `kerl/one-more-rename`
* #3028 `kerl/ff-not`
* #3029 `kerl/ut-vg`
* #3030 `kerl/table-utils-memory`
* #3067 `kerl/improve-sdf-test-field-names`
* #3068 `kerl/ut-generate`
* #3069 `kerl/cpp-sdf-domain-at-create`
* #3027 `kerl/hll-domainish`
* #3088 `kerl/max-domain-int64`
* #3090 `kerl/maybe-resize-soma-joinid-cpp-tweak`
* #3032 `kerl/sdf-domain-at-create` -- fixes #2967
* #3091 `kerl/maybe-resize-soma-joinid-py-r`
* #3095 `kerl/cpp-exp-resize-prep`
* #3089 `kerl/r-dataframe-shapeable`
* https://github.com/single-cell-data/SOMA/pull/233
* #3125 `kerl/cpp-ut-name-shortens`
* #3127 `kerl/helper-rename`
* #3130 `kerl/cpp-can-resizers-names`
* #3132 `kerl/cpp-dataframe-sizing-helpers`
* #3139 `kerl/cpp-dataframe-upgrade-test`
* #3140 `kerl/py-resizer-connects`
* #3151 `kerl/py-can-upgrade-shape`
* #3152 `kerl/registration-shape-acceessors`
* #3156 `kerl/py-exp-shaping`
* #3157 `kerl/py-exp-shaping2`
* #3148 `kerl/py-exp-resize`
* #3191 `kerl/py-domain-at-create-ut-1`
* #3190 `kerl/py-domain-at-create-ut-2`
* #3192 `kerl/py-domain-at-create-ut-3`
* #3193 `kerl/py-domain-at-create-ut-4`
* #3194 `kerl/py-domain-at-create-ut-5`
* #3203 `kerl/min-size-2`
* #3208 `kerl/r-min-sizing`
* #3211 `kerl/cpp-ugr-dom`
* #3232 `kerl/ff-interop`
* #3230 `kerl/ffon`
* #3234 `kerl/docstring-prune`
* #3236 `kerl/prefixing`
* #3241 `kerl/fix-bad-merge`
* #3235 `kerl/py-r-ugr-dom`
* #3237 `kerl/py-r-ugr-dom-2`
* #3238 `kerl/py-r-ugr-dom-3`
* #3253 `kerl/set-coords-rename`
* #3261 `kerl/pybind11-nda-sizing`
* #3265 `kerl/dense-227-a`
* #3263 `kerl/dense-range-trim`
* #3268 `kerl/dim-explosion`
* #3269 `kerl/python-227-dense-ned-read`
* #3270 `kerl/r-227-dense-fixes`
* #3280 `kerl/r-dense-227-more`
* #3286 `kerl/more-fn4m`
* #3283 `kerl/readthedocs-pre-1.15`
* #3288 `kerl/dense-ugrsh`
* #3289 `kerl/notebook-shape-upgrade`
* #3285 `kerl/new-shape-doc-updates`
* #3295 `kerl/notebook-data-refresh`
* #3290 `kerl/notebook-new-shape-refresh`
* #3301 `kerl/ffena`
* #3303 `kerl/r-data-refresh`
* #3300 `kerl/sdf-sjid-lower-zero`
* #3296 `kerl/dense-example-data-refresh`
* #3294 `kerl/new-shape-notebook-and-vignette`
* #3308 `kerl/upgrade-experiment-resources`
* #3309 `kerl/fix-notebook-merge`
* #3321 `kerl/more-use-shape`
* #3358 `kerl/revert-3300`
* #3368 `kerl/227a`
* #3369 `kerl/ucd1`
* #3370 `kerl/ucd2`
* #3371 `kerl/ucd3`
* #3372 `kerl/ucd4`
* #3396 `kerl/domain-at-create-docstrings`
* #3302 `kerl/new-shape-vignette`
* https://github.com/single-cell-data/SOMA/pull/250
* #3399 `kerl/new-shape-more-docstrings`
* #3400 `kerl/check-only-r`
* https://github.com/TileDB-Inc/somacore-feedstock/pull/27

</details>

**Closed/abandoned PRs:**

<details>

* #2785 -- This was only dogfooding for the core 2.25 release -- not to be merged
* #2952 `kerl/feature-flag-temp` -- folded into 2962
* #2995 `kerl/polydom`
* #2964 `kerl/tiledbsoma-io-test`
* #3189 `kerl/min-size`
* #3220 `kerl/cpp-ugr-dom-2`
* #3244 `kerl/dense-227-fixes`
* #3276 `kerl/dataframe-shape`

</details>

**Issues which are related but non-blocking:**

* https://github.com/single-cell-data/SOMA/pull/216
* https://github.com/TileDB-Inc/TileDB/pull/5303
* #2966
* #3081
* Note: R append mode does not exist yet -- see #1630 -- so an experiment-level resizer is not a priority in R
* #3271 
* #3272
* #3273

**See also:** [[sc-51048]](https://app.shortcut.com/tiledb-inc/story/51048).

## Problem to be solved

Users want to know the `shape` of an array, in the SciPy sense:

* Reads and writes are bounds-checked against the shape
* This retains its value regardless of which values of a sparse array are or are not actually occupied
* Users can `resize`.
  * Some users need the ability to grow their datasets later, using either `tiledbsoma.io`'s append mode, or subsequent writes using the `tiledbsoma` API.
  * Note that the cellxgene census doesn't need this: eact week's published census has fixed shape, and any updates will happen in new storage, on a new week.

Using TileDB-SOMA up until the present:

* The TIleDB `domain` is immutable after array creation
  * This does bounds-checking for reads and writes, which is good
  * To leverage this to function as a `shape`, users would need to set the `domain` at array-creation time. However, users lose the ability to grow their datasets later.
* There is a `non_empty_domain` accessor
  * This only indicates min/max coordinates _at which data exists_. Consider an `X` array for 100 cells and 200 genes. If non-zero expression counts exist only for cell join IDs 2-17, then the `non_empty_domain` will indicate `(2,17)` along `soma_dim_0`.
  * Consider an `obms["X_pca"]` within the same experiment. This may be 100 cells by 50 PCA components: we need a placd to store the number 50.
  * Therefore users cannot leverage this to function as a `shape` accessor.
* We have offered a `used_shape` accessor since TileDB-SOMA 1.5.
  * This functions as a `shape` accessor, in the SciPy sense, but it is not multi-writer safe.

New feature for TileDB-SOMA 1.15:

* Arrays will have a  `shape`
* Reads and writes are bounds-checked against the shape
* This retains its value regardless of which values of a sparse array are or are not actually occupied
* Users can `resize`
* The `used_shape` accessor will be deprecated in TileDB-SOMA 1.13, and slated for removal in TileDB-SOMA 1.14.

Compatiblity:

This will now require users to do an explicit `resize` before appending/growing TileDB-SOMA Experiments. Guidance in the form of example notebooks will be provided.

##  Tracking

See also: [[sc-41074]](https://app.shortcut.com/tiledb-inc/story/41074) and [[sc-51048]](https://app.shortcut.com/tiledb-inc/story/51048).

## Scheduling

Support arrives in TileDB Core 2.25. Deprecations for TileDB-SOMA will be released with 1.13. Full support within TileDB-SOMA will be release in 1.14.

## Details

SOMA API mods as we've discussed in a Google doc are as follows.

### `SOMADataFrame`

* `create`: Retain the `domain` argument
  * Issue:
    * Core has a `(lo, hi)` tuple per dim, e.g. `(0,99)` or `(10,19)`
    * SOMA has count per dim, with 0 implicit: e.g. 100 or 20
    * For `SparseNDArray` and `DenseNDArray` core can have `(lo, hi)` and SOMA can have `count`
    * For `DataFrame` there can be multiple dims --- default is a single `soma_joinid`
    * That could be treated either in `(lo, hi)` fashion or `count` fashion
    * However additional dims (e.g. `cell_type`) can be on any type, including strings, floats, etc. where there is no implicit lo=0
    * Therefore we need to keep the current SOMA API wherein `DataFrame` takes a `domain` argument (in `(lo, hi)` fashion) and not a `shape` argument (in `count` fashion)

### `SparseNDArray and DenseNDArray`

* `create`
  * Have an optional shape argument which is of type `Tuple[Int,...]` where each element is the cell count of the corresponding dimension
    * If unsupplied, or if supplied but None in any slot: use the minimum 0 in each slot – nothing larger makes sense since we will not support downsize
  * User guidance should make clear that it will not be possible to create an ‘old’ style array with the ‘new style’ API. (See also the upgrade logic below.)

### All three of `SOMADataFrame`, `SparseNDArray`, `DenseNDArray`

* `write`
  * For new arrays, created with the new shape feature:
    * Core will bounds-check that coordinates provided at `write` time are within the current shape
    * Core will raise `tiledb.cc.TileDBError` to TileDB-SOMA, which will catch and raise `IndexError`, and R-standard behavior on the R side
  * For old arrays created before this feature:
    * Core will not bounds-check that coordinates provided at write time are within the current shape
* Existing `used_shape` accessor
  * TileDB-SOMA will deprecate this over a release cycle.
  * For new arrays: raise `NotImplementedError`
  * For old arrays: return what’s currently returned, with a deprecation warning.
  * Mechanism for determining old vs. new: `array.schema.version` (the core storage version).
* Existing `shape` accessor
  * For new arrays:
    * Have this return the new shape as proposed by core, no longer returning the TileDB domain.
  * For old arrays created before this feature:
    * Return the TileDB domain as now.
* Existing `non_empty_domain` accessor
  * Same behavior for old and new arrays (unaffected by this proposal).
  * Keep this accessor supported, but, with user notes that it’s generally non-useful
  * This should return None (or R equivalent) when there is a schema but no data have been written.
* New `maxshape` accessor
  * Maps the core-level `(lo, hi)` accessor for domain to count-style accessor hi+1. E.g. if the core domain is either `(0,99)` or `(50,99)` then TileDB-SOMA `maxshape` will say 100.
  * Same behavior for old and new arrays.
  * Let users query for what the TileDB domain is, with user notes that it’s the maximum that users can reshape to.
  * Issac suggests: maybe `domain` or `maxshape` (see h5py).
* New `resize` mutator
  * Note: `reshape` means something else in the community (numpy, zarr, h5py), e.g. a 5x20 (total 100 cells) being reinterpreted as 4x25 (still 100 cells). The standard name for changing cell-count is `resize`.
  * For old arrays created before this feature: raise `NotImplementedError`.
  * For new arrays:
    * Will raise `ValueError` if the new shape is smaller on any dim than currently in storage
    * Regardless of whether any data have been written whatsoever
    * Will raise `ValueError` if the new shape exceeds the TileDB domain from create time, which will serve TileDB-SOMA in a role of “max possible shape the user can reshape to”
    * Otherwise, any calls to write from this point will bounds-check writes within this new shape
    * **We don’t expect resize to be multi-writer safe with regard to write ; user notes must be clear on this point**
* New `tiledbsoma_upgrade_shape` method for SparseNDArray and DenseNDArray
  * This will leverage `array.schema.version` to see if an upgrade is needed
  * Leverage core support for storage-version updates
  * This will take a shape argument as in `create`
  * For arrays created with “just-right” size: this will succeed
  * For arrays created with “room-for-growth” / “two billion-ish” size: this will succeed
  * If the user passes a shape which exceeds the current TileDB domain: this will fail
* New `tiledbsoma_upgrade_domain method` for `DataFrame`
  * Same as for `SparseNDArray`/`DenseNDArray` except it will take a domain at the SOMA-API level just as `DataFrame`'s create method

### `tiledbsoma.io`

* The user-facing API has no shape arguments and thus won’t need changing.
* Internally to `tiledbsoma.io`, we’ll still ask the tiledbsoma API for the “big domain” (2 billionish)
* Append mode:
  * Will need a new `resize` method at the `Experiment` level
  * Users will need to:
    * Register as now
    * Call the experiment-level `resize`
      * Could be `exp.resize(...)`, or (better) this could be `tiledbsoma.io.reshape_experiment`
  * In either case: this method will take the new `obs` and `var` counts as inputs:
    * `exp.obs.reshape` to new `obs` count
    * `exp.ms[name].var.reshape` to new `var` count
    * `exp.ms[name].X[name].reshape` to new `obs` count x `var` count
    * `exp.ms[name].obsm[name].reshape` to new `obs` count x same width
    * `exp.ms[name].obsp[name].reshape` to new `obs` count x `obs` count
    * `exp.ms[name].varm[name].reshape` to new `var` count x same width
    * `exp.ms[name].varp[name].reshape` to new `var` count x `var` count
  * Do the individual append-mode writes as now

[python] Tutorial notebook for the new-shape feature
**Issue and/or context:** As tracked on issue #2407 / [[sc-51048]](https://app.shortcut.com/tiledb-inc/story/51048).

Note that the intended Python and R API changes are all agreed on and finalized as described in #2407.

**Changes:**

* Notebook material for Python. (R material will be a separate PR.)
**Notes for Reviewer:**

* The Python material is ready for review.
* Handy link for reviewers:
  * https://github.com/single-cell-data/TileDB-SOMA/blob/kerl/new-shape-notebook-and-vignette/apis/python/notebooks/tutorial_soma_shape.ipynb
* I'm working on the R material.


Discussion / hints:
#2785 is a quick-and-dirty concept-prover -- its sole function is to flush out any API misunderstandings we might have, in prep for 2.25.0 core release.
#2785 is a quick-and-dirty concept-prover -- its sole function is to flush out any API misunderstandings we might have, in prep for 2.25.0 core release.


Fix patch:
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diff --git a/apis/python/notebooks/tutorial_exp_query.ipynb b/apis/python/notebooks/tutorial_exp_query.ipynb
index 5dc8804677..8e773fd033 100644
--- a/apis/python/notebooks/tutorial_exp_query.ipynb
+++ b/apis/python/notebooks/tutorial_exp_query.ipynb
@@ -32,16 +32,31 @@
     "In this notebook, we'll take a quick look at the SOMA experiment-query API. The dataset used is from Peripheral Blood Mononuclear Cells (PBMC), which is freely available from 10X Genomics.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2472cd1a-2d49-4268-9b9b-1bed49ccfa1b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "First we'll unpack and open the experiment:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9b8851d9-27f1-437b-a070-b41a65a5609e",
+   "id": "c70b2d82-2012-481c-a7a6-5b574de69241",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "exp = soma.Experiment.open('data/sparse/pbmc3k')"
+    "import tarfile\n",
+    "import tempfile\n",
+    "sparse_uri = tempfile.mktemp()\n",
+    "with tarfile.open(\"data/pbmc3k-sparse.tgz\") as handle:\n",
+    "    handle.extractall(sparse_uri)\n",
+    "exp = soma.Experiment.open(sparse_uri)"
    ]
   },
   {
@@ -204,6 +219,14 @@
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/ipykernel_32661/1931588187.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  p.groupby('louvain').size().sort_values()\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -506,6 +529,14 @@
    "source": [
     "adata"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ddd6e682-5c54-47cb-8c54-f0d03a5b6567",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/apis/python/notebooks/tutorial_soma_append_mode.ipynb b/apis/python/notebooks/tutorial_soma_append_mode.ipynb
index a69a40a0f6..01e5e07db8 100644
--- a/apis/python/notebooks/tutorial_soma_append_mode.ipynb
+++ b/apis/python/notebooks/tutorial_soma_append_mode.ipynb
@@ -81,7 +81,7 @@
     {
      "data": {
       "text/plain": [
-       "'/tmp/append-example-20241103-094551'"
+       "'/tmp/append-example-20241105-170144'"
       ]
      },
      "execution_count": 2,
@@ -140,15 +140,7 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████| 5.58M/5.58M [00:01<00:00, 4.18MB/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ad1 = sc.datasets.pbmc3k()\n",
     "sc.pp.calculate_qc_metrics(ad1, inplace=True)\n",
@@ -190,53 +182,17 @@
      "output_type": "stream",
      "text": [
       "Registration: registering isolated AnnData object.\n",
-      "Wrote   /tmp/append-example-20241103-094551/obs\n",
-      "Wrote   /tmp/append-example-20241103-094551/ms/RNA/var\n",
-      "Writing /tmp/append-example-20241103-094551/ms/RNA/X/data\n",
-      "Wrote   /tmp/append-example-20241103-094551/ms/RNA/X/data\n",
-      "Wrote   /tmp/append-example-20241103-094551\n"
+      "Wrote   /tmp/append-example-20241105-170144/obs\n",
+      "Wrote   /tmp/append-example-20241105-170144/ms/RNA/var\n",
+      "Writing /tmp/append-example-20241105-170144/ms/RNA/X/data\n",
+      "Wrote   /tmp/append-example-20241105-170144/ms/RNA/X/data\n",
+      "Wrote   /tmp/append-example-20241105-170144\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'/tmp/append-example-20241103-094551'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<frozen abc>:119: FutureWarning: SparseDataset is deprecated and will be removed in late 2024. It has been replaced by the public classes CSRDataset and CSCDataset.\n",
-      "\n",
-      "For instance checks, use `isinstance(X, (anndata.experimental.CSRDataset, anndata.experimental.CSCDataset))` instead.\n",
-      "\n",
-      "For creation, use `anndata.experimental.sparse_dataset(X)` instead.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145833/ms/RNA/X/data\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145833\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'/tmp/append-example-20240521-145833'"
+       "'/tmp/append-example-20241105-170144'"
       ]
      },
      "execution_count": 5,
@@ -368,12 +324,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "69ba087b-cb5f-4851-8ee6-3a4d828d70a6",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         soma_dim_0  soma_dim_1  soma_data\n",
+      "0                 0          70        1.0\n",
+      "1                 0         166        1.0\n",
+      "2                 0         178        2.0\n",
+      "3                 0         326        1.0\n",
+      "4                 0         363        1.0\n",
+      "...             ...         ...        ...\n",
+      "2286879        2699       32697        1.0\n",
+      "2286880        2699       32698        7.0\n",
+      "2286881        2699       32702        1.0\n",
+      "2286882        2699       32705        1.0\n",
+      "2286883        2699       32708        3.0\n",
+      "\n",
+      "[2286884 rows x 3 columns]\n",
+      "\n",
+      "((0, 2699), (0, 32732))\n"
+     ]
+    }
+   ],
    "source": [
     "with tiledbsoma.Experiment.open(experiment_uri) as exp:\n",
     "    X = exp.ms[\"RNA\"].X[\"data\"]\n",
@@ -382,10 +361,22 @@
     "    print(X.used_shape())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "880b17b9-dc1b-4b29-8a4e-f9a983b27785",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "While you can ask all dataframes and arrays in the experiment for their `.domain` or `.shape`, respectively, one at a time, there's also the handy `show_experiment_shape` which traverses the experiment for you.\n",
+    "\n",
+    "The dataframe domains and array shapes are soft limits on what values can be read from, or written to, them."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "bc5b5740-79a4-4831-8549-f06ac079fb02",
+   "execution_count": 28,
+   "id": "851fb064-2f40-43ff-b6a5-402c194ba177",
    "metadata": {
     "tags": []
    },
@@ -396,23 +387,23 @@
      "text": [
       "\n",
       "[DataFrame] obs \n",
-      "  URI file:///tmp/append-example-20241103-094551/obs\n",
-      "  count                2700\n",
-      "  domain               ((0, 2699),)\n",
+      "  URI file:///tmp/append-example-20241105-170146/obs\n",
+      "  count                8100\n",
+      "  domain               ((0, 8099),)\n",
       "  maxdomain            ((0, 9223372036854773758),)\n",
       "  upgraded             True\n",
       "\n",
       "[DataFrame] ms/RNA/var \n",
-      "  URI file:///tmp/append-example-20241103-094551/ms/RNA/var\n",
+      "  URI file:///tmp/append-example-20241105-170146/ms/RNA/var\n",
       "  count                32738\n",
       "  domain               ((0, 32737),)\n",
       "  maxdomain            ((0, 9223372036854773758),)\n",
       "  upgraded             True\n",
       "\n",
       "[SparseNDArray] ms/RNA/X/data \n",
-      "  URI file:///tmp/append-example-20241103-094551/ms/RNA/X/data\n",
-      "  used_shape           ((0, 2699), (0, 32732))\n",
-      "  shape                (2700, 32738)\n",
+      "  URI file:///tmp/append-example-20241105-170146/ms/RNA/X/data\n",
+      "  used_shape           ((0, 8099), (0, 32732))\n",
+      "  shape                (8100, 32738)\n",
       "  maxshape             (9223372036854773759, 9223372036854773759)\n",
       "  upgraded             True\n"
      ]
@@ -423,7 +414,7 @@
        "True"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 29,
    "id": "81ca4031",
    "metadata": {
     "tags": []
@@ -468,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 30,
    "id": "d703ebb7",
    "metadata": {
     "tags": []
@@ -492,21 +483,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "dd0cf6c9-cd3c-4faf-84f5-8659cac7c4f5",
+   "execution_count": 31,
+   "id": "38f78883-6af5-43f9-8661-add028e3dee1",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Registration: starting with experiment /tmp/append-example-20241105-170146\n",
+      "Registration: found nobs=8100 nvar=32738 from experiment.\n",
+      "Registration: registering AnnData object.\n",
+      "Registration: accumulated to nobs=10800 nvar=32738.\n",
+      "Registration: complete.\n"
+     ]
+    }
+   ],
    "source": [
-    "# XXX TEMP\n",
-    "tiledbsoma.io.resize_experiment(exp.uri, nobs=rd.get_obs_shape(), nvars=rd.get_var_shapes())"
+    "rd = tiledbsoma.io.register_anndatas(\n",
+    "    experiment_uri,\n",
+    "    [ad2],\n",
+    "    measurement_name=measurement_name,\n",
+    "    obs_field_name=\"obs_id\",\n",
+    "    var_field_name=\"var_id\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e880517d-b738-4d8e-a508-e84703eec5be",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "As described on in the tutorial on the TileDB-SOMA shape feature, the `domain` of dataframes and the `shape` of N-dimensional arrays are soft limits on what values can be read from or written to. In order to ingest more data, we'll need to increase those soft limits.\n",
+    "\n",
+    "First let's look at what they currently are:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "f183088d-b428-47ce-99f6-c12157867357",
+   "execution_count": 32,
+   "id": "68a236ff-15fc-49f5-9285-bbd128aeae6b",
    "metadata": {
     "tags": []
    },
@@ -517,23 +537,23 @@
      "text": [
       "\n",
       "[DataFrame] obs \n",
-      "  URI file:///tmp/append-example-20241103-094551/obs\n",
-      "  count                5400\n",
-      "  domain               ((0, 5399),)\n",
+      "  URI file:///tmp/append-example-20241105-170146/obs\n",
+      "  count                8100\n",
+      "  domain               ((0, 8099),)\n",
       "  maxdomain            ((0, 9223372036854773758),)\n",
       "  upgraded             True\n",
       "\n",
       "[DataFrame] ms/RNA/var \n",
-      "  URI file:///tmp/append-example-20241103-094551/ms/RNA/var\n",
+      "  URI file:///tmp/append-example-20241105-170146/ms/RNA/var\n",
       "  count                32738\n",
       "  domain               ((0, 32737),)\n",
       "  maxdomain            ((0, 9223372036854773758),)\n",
       "  upgraded             True\n",
       "\n",
       "[SparseNDArray] ms/RNA/X/data \n",
-      "  URI file:///tmp/append-example-20241103-094551/ms/RNA/X/data\n",
-      "  used_shape           ((0, 5399), (0, 32732))\n",
-      "  shape                (5400, 32738)\n",
+      "  URI file:///tmp/append-example-20241105-170146/ms/RNA/X/data\n",
+      "  used_shape           ((0, 8099), (0, 32732))\n",
+      "  shape                (8100, 32738)\n",
       "  maxshape             (9223372036854773759, 9223372036854773759)\n",
       "  upgraded             True\n"
      ]
@@ -544,84 +564,139 @@
        "True"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# XXX TEMP\n",
     "tiledbsoma.io.show_experiment_shapes(exp.uri)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "be11a416-c2e3-4aed-8edf-ce5f1efc777d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Then we apply the resize, and look at the domains and shapes again:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "2ee7c0e4-eb36-482e-a37e-7c786607b76a",
+   "execution_count": 33,
+   "id": "09e8a10f-90c7-493c-9e91-26f09d96e527",
    "metadata": {
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Registration: starting with experiment /tmp/append-example-20241103-094551\n",
-      "Registration: found nobs=2700 nvar=32738 from experiment.\n",
-      "Registration: registering AnnData object.\n",
-      "Registration: accumulated to nobs=5400 nvar=32738.\n",
-      "Registration: complete.\n",
-      "Wrote   /tmp/append-example-20241103-094551/obs\n",
-      "Wrote   /tmp/append-example-20241103-094551/ms/RNA/var\n",
-      "Writing /tmp/append-example-20241103-094551/ms/RNA/X/data\n",
-      "Wrote   /tmp/append-example-20241103-094551/ms/RNA/X/data\n",
-      "Wrote   file:///tmp/append-example-20241103-094551\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "'file:///tmp/append-example-20241103-094551'"
+       "True"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "tiledbsoma.io.resize_experiment(exp.uri, nobs=rd.get_obs_shape(), nvars=rd.get_var_shapes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f183088d-b428-47ce-99f6-c12157867357",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wrote   /tmp/append-example-20240521-145833/ms/RNA/X/data\n"
+      "\n",
+      "[DataFrame] obs \n",
+      "  URI file:///tmp/append-example-20241105-170146/obs\n",
+      "  count                8100\n",
+      "  domain               ((0, 10799),)\n",
+      "  maxdomain            ((0, 9223372036854773758),)\n",
+      "  upgraded             True\n",
+      "\n",
+      "[DataFrame] ms/RNA/var \n",
+      "  URI file:///tmp/append-example-20241105-170146/ms/RNA/var\n",
+      "  count                32738\n",
+      "  domain               ((0, 32737),)\n",
+      "  maxdomain            ((0, 9223372036854773758),)\n",
+      "  upgraded             True\n",
+      "\n",
+      "[SparseNDArray] ms/RNA/X/data \n",
+      "  URI file:///tmp/append-example-20241105-170146/ms/RNA/X/data\n",
+      "  used_shape           ((0, 8099), (0, 32732))\n",
+      "  shape                (10800, 32738)\n",
+      "  maxshape             (9223372036854773759, 9223372036854773759)\n",
+      "  upgraded             True\n"
      ]
     },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tiledbsoma.io.show_experiment_shapes(exp.uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcd2c6bc-b7ec-41a3-933a-c477ee3ebf4b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Now we can ingest the new data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "2f500c43-1e44-4a56-bf87-3ba936b973f7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Wrote   /tmp/append-example-20240521-145833\n"
+      "Wrote   /tmp/append-example-20241105-170146/obs\n",
+      "Wrote   /tmp/append-example-20241105-170146/ms/RNA/var\n",
+      "Writing /tmp/append-example-20241105-170146/ms/RNA/X/data\n",
+      "Wrote   /tmp/append-example-20241105-170146/ms/RNA/X/data\n",
+      "Wrote   file:///tmp/append-example-20241105-170146\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'/tmp/append-example-20240521-145833'"
+       "'file:///tmp/append-example-20241105-170146'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rd = tiledbsoma.io.register_anndatas(\n",
-    "    experiment_uri,\n",
-    "    [ad2],\n",
-    "    measurement_name=measurement_name,\n",
-    "    obs_field_name=\"obs_id\",\n",
-    "    var_field_name=\"var_id\",\n",
-    ")\n",
-    "\n",
     "tiledbsoma.io.from_anndata(\n",
     "    experiment_uri,\n",
     "    ad2,\n",
@@ -644,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 36,
    "id": "a7b2aebe",
    "metadata": {
     "tags": []
@@ -654,20 +729,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                obs_id  n_genes_by_counts     when\n",
-      "0     AAACATACAACCAC-1                781   Monday\n",
-      "1     AAACATTGAGCTAC-1               1352   Monday\n",
-      "2     AAACATTGATCAGC-1               1131   Monday\n",
-      "3     AAACCGTGCTTCCG-1                960   Monday\n",
-      "4     AAACCGTGTATGCG-1                522   Monday\n",
-      "...                ...                ...      ...\n",
-      "5395  TTTCGAACTCTCAT-2               1155  Tuesday\n",
-      "5396  TTTCTACTGAGGCA-2               1227  Tuesday\n",
-      "5397  TTTCTACTTCCTCG-2                622  Tuesday\n",
-      "5398  TTTGCATGAGAGGC-2                454  Tuesday\n",
-      "5399  TTTGCATGCCTCAC-2                724  Tuesday\n",
+      "                 obs_id  n_genes_by_counts       when\n",
+      "0      AAACATACAACCAC-3                781  Wednesday\n",
+      "1      AAACATTGAGCTAC-3               1352  Wednesday\n",
+      "2      AAACATTGATCAGC-3               1131  Wednesday\n",
+      "3      AAACCGTGCTTCCG-3                960  Wednesday\n",
+      "4      AAACCGTGTATGCG-3                522  Wednesday\n",
+      "...                 ...                ...        ...\n",
+      "10795  TTTCGAACTCTCAT-2               1155    Tuesday\n",
+      "10796  TTTCTACTGAGGCA-2               1227    Tuesday\n",
+      "10797  TTTCTACTTCCTCG-2                622    Tuesday\n",
+      "10798  TTTGCATGAGAGGC-2                454    Tuesday\n",
+      "10799  TTTGCATGCCTCAC-2                724    Tuesday\n",
       "\n",
-      "[5400 rows x 3 columns]\n"
+      "[10800 rows x 3 columns]\n"
      ]
     }
    ],
@@ -694,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 37,
    "id": "4a1cc20e",
    "metadata": {
     "tags": []
@@ -743,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 38,
    "id": "d640bde0",
    "metadata": {
     "tags": []
@@ -754,21 +829,21 @@
      "output_type": "stream",
      "text": [
       "         soma_dim_0  soma_dim_1  soma_data\n",
-      "0                 0          70        1.0\n",
-      "1                 0         166        1.0\n",
-      "2                 0         178        2.0\n",
-      "3                 0         326        1.0\n",
-      "4                 0         363        1.0\n",
+      "0                 0          70       20.0\n",
+      "1                 0         166       20.0\n",
+      "2                 0         178       40.0\n",
+      "3                 0         326       20.0\n",
+      "4                 0         363       20.0\n",
       "...             ...         ...        ...\n",
-      "4573763        5399       32697       10.0\n",
-      "4573764        5399       32698       70.0\n",
-      "4573765        5399       32702       10.0\n",
-      "4573766        5399       32705       10.0\n",
-      "4573767        5399       32708       30.0\n",
+      "9147531       10799       32697       10.0\n",
+      "9147532       10799       32698       70.0\n",
+      "9147533       10799       32702       10.0\n",
+      "9147534       10799       32705       10.0\n",
+      "9147535       10799       32708       30.0\n",
       "\n",
-      "[4573768 rows x 3 columns]\n",
+      "[9147536 rows x 3 columns]\n",
       "\n",
-      "((0, 5399), (0, 32732))\n"
+      "((0, 10799), (0, 32732))\n"
      ]
     }
    ],
@@ -809,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 39,
    "id": "c3c185fb",
    "metadata": {
     "tags": []
@@ -846,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 40,
    "id": "ae2d62ae",
    "metadata": {
     "tags": []
@@ -855,10 +930,10 @@
     {
      "data": {
       "text/plain": [
-       "'/tmp/append-example-20241103-095132'"
+       "'/tmp/append-example-20241105-170622'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -881,65 +956,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "ac21dd19-2fd5-41ec-98e5-2596e0795f0d",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
-   "source": [
-    "rd2 = tiledbsoma.io.register_anndatas(\n",
-    "    experiment_uri=None,  # new Experiment, from scratch\n",
-    "    adatas=ads,\n",
-    "    measurement_name=measurement_name,\n",
-    "    obs_field_name=\"obs_id\",\n",
-    "    var_field_name=\"var_id\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d6cff24a-1de8-46e1-b502-e69a0e92dc92",
-   "metadata": {
-    "tags": []
-   },
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Registration: registering AnnData object.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Registration: accumulated to nobs=8100 nvar=32738.\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "Registration: registering AnnData object.\n",
+      "Registration: accumulated to nobs=2700 nvar=32738.\n",
+      "Registration: registering AnnData object.\n",
+      "Registration: accumulated to nobs=5400 nvar=32738.\n",
+      "Registration: registering AnnData object.\n",
+      "Registration: accumulated to nobs=8100 nvar=32738.\n",
       "Registration: complete.\n"
      ]
     }
    ],
    "source": [
-    "# XXX TEMP\n",
-    "tiledbsoma.io.resize_experiment(exp.uri, nobs=rd2.get_obs_shape(), nvars=rd2.get_var_shapes())"
+    "rd2 = tiledbsoma.io.register_anndatas(\n",
+    "    experiment_uri=None,  # new Experiment, from scratch\n",
+    "    adatas=ads,\n",
+    "    measurement_name=measurement_name,\n",
+    "    obs_field_name=\"obs_id\",\n",
+    "    var_field_name=\"var_id\",\n",
+    ")"
    ]
   },
   {
@@ -960,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 42,
    "id": "27ed22b2",
    "metadata": {
     "tags": []
@@ -970,70 +1014,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Wrote   /tmp/append-example-20241103-095132/obs\n",
-      "Wrote   /tmp/append-example-20241103-095132/ms/RNA/var\n",
-      "Writing /tmp/append-example-20241103-095132/ms/RNA/X/data\n",
-      "Wrote   /tmp/append-example-20241103-095132/ms/RNA/X/data\n",
-      "Wrote   file:///tmp/append-example-20241103-095132\n",
-      "Wrote   /tmp/append-example-20241103-095132/obs\n",
-      "Wrote   /tmp/append-example-20241103-095132/ms/RNA/var\n",
-      "Writing /tmp/append-example-20241103-095132/ms/RNA/X/data\n",
-      "Wrote   /tmp/append-example-20241103-095132/ms/RNA/X/data\n",
-      "Wrote   file:///tmp/append-example-20241103-095132\n",
-      "Wrote   /tmp/append-example-20241103-095132/obs\n",
-      "Wrote   /tmp/append-example-20241103-095132/ms/RNA/var\n",
-      "Writing /tmp/append-example-20241103-095132/ms/RNA/X/data\n",
-      "Wrote   /tmp/append-example-20241103-095132/ms/RNA/X/data\n",
-      "Wrote   file:///tmp/append-example-20241103-095132\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145839/ms/RNA/X/data\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145839\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145839/obs\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145839/ms/RNA/var\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Writing /tmp/append-example-20240521-145839/ms/RNA/X/data\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145839/ms/RNA/X/data\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Wrote   /tmp/append-example-20240521-145839\n"
+      "Wrote   /tmp/append-example-20241105-170622/obs\n",
+      "Wrote   /tmp/append-example-20241105-170622/ms/RNA/var\n",
+      "Writing /tmp/append-example-20241105-170622/ms/RNA/X/data\n",
+      "Wrote   /tmp/append-example-20241105-170622/ms/RNA/X/data\n",
+      "Wrote   /tmp/append-example-20241105-170622\n",
+      "Wrote   /tmp/append-example-20241105-170622/obs\n",
+      "Wrote   /tmp/append-example-20241105-170622/ms/RNA/var\n",
+      "Writing /tmp/append-example-20241105-170622/ms/RNA/X/data\n",
+      "Wrote   /tmp/append-example-20241105-170622/ms/RNA/X/data\n",
+      "Wrote   file:///tmp/append-example-20241105-170622\n",
+      "Wrote   /tmp/append-example-20241105-170622/obs\n",
+      "Wrote   /tmp/append-example-20241105-170622/ms/RNA/var\n",
+      "Writing /tmp/append-example-20241105-170622/ms/RNA/X/data\n",
+      "Wrote   /tmp/append-example-20241105-170622/ms/RNA/X/data\n",
+      "Wrote   file:///tmp/append-example-20241105-170622\n"
      ]
     },
     {
@@ -1074,6 +1069,13 @@
    ],
    "source": [
     "for ad in ads:\n",
+    "    if tiledbsoma.Experiment.exists(experiment_uri):\n",
+    "        tiledbsoma.io.resize_experiment(\n",
+    "            experiment_uri,\n",
+    "            nobs=rd2.get_obs_shape(),\n",
+    "            nvars=rd2.get_var_shapes()\n",
+    "        )\n",
+    "\n",
     "    tiledbsoma.io.from_anndata(\n",
     "        experiment_uri,\n",
     "        ad,\n",
@@ -1094,7 +1096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 43,
    "id": "8f86fd3d",
    "metadata": {
     "tags": []
@@ -1111,13 +1113,13 @@
       "3     AAACCGTGCTTCCG-3                960  Wednesday\n",
       "4     AAACCGTGTATGCG-3                522  Wednesday\n",
       "...                ...                ...        ...\n",
-      "2695  TTTCGAACTCTCAT-3               1155  Wednesday\n",
-      "2696  TTTCTACTGAGGCA-3               1227  Wednesday\n",
-      "2697  TTTCTACTTCCTCG-3                622  Wednesday\n",
-      "2698  TTTGCATGAGAGGC-3                454  Wednesday\n",
-      "2699  TTTGCATGCCTCAC-3                724  Wednesday\n",
+      "8095  TTTCGAACTCTCAT-5               1155     Friday\n",
+      "8096  TTTCTACTGAGGCA-5               1227     Friday\n",
+      "8097  TTTCTACTTCCTCG-5                622     Friday\n",
+      "8098  TTTGCATGAGAGGC-5                454     Friday\n",
+      "8099  TTTGCATGCCTCAC-5                724     Friday\n",
       "\n",
-      "[2700 rows x 3 columns]\n"
+      "[8100 rows x 3 columns]\n"
      ]
     }
    ],
@@ -1142,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 44,
    "id": "bffce533",
    "metadata": {
     "tags": []
@@ -1191,7 +1193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 45,
    "id": "05cf63a0",
    "metadata": {
     "tags": []
diff --git a/apis/python/notebooks/tutorial_soma_objects.ipynb b/apis/python/notebooks/tutorial_soma_objects.ipynb
index a4bb0215b3..00ece015c9 100644
--- a/apis/python/notebooks/tutorial_soma_objects.ipynb
+++ b/apis/python/notebooks/tutorial_soma_objects.ipynb
@@ -55,23 +55,14 @@
    "execution_count": 2,
    "id": "c3af1793-e2be-45e1-8128-bb64536673f7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Experiment 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k' (open for 'r') (2 items)\n",
-       "    'ms': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms' (unopened)\n",
-       "    'obs': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/obs' (unopened)>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "experiment = tiledbsoma.open(\"data/dense/pbmc3k\")\n",
-    "experiment"
+    "import tarfile\n",
+    "import tempfile\n",
+    "dense_uri = tempfile.mktemp()\n",
+    "with tarfile.open(\"data/pbmc3k-dense.tgz\") as handle:\n",
+    "    handle.extractall(dense_uri)\n",
+    "experiment = tiledbsoma.Experiment.open(dense_uri)"
    ]
   },
   {
@@ -95,9 +86,9 @@
     {
      "data": {
       "text/plain": [
-       "<Collection 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms' (open for 'r') (2 items)\n",
-       "    'RNA': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/RNA' (unopened)\n",
-       "    'raw': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/raw' (unopened)>"
+       "<Collection 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms' (open for 'r') (2 items)\n",
+       "    'RNA': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/RNA' (unopened)\n",
+       "    'raw': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/raw' (unopened)>"
       ]
      },
      "execution_count": 3,
@@ -120,7 +111,7 @@
     {
      "data": {
       "text/plain": [
-       "<DataFrame 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/obs' (open for 'r')>"
+       "<DataFrame 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/obs' (open for 'r')>"
       ]
      },
      "execution_count": 4,
@@ -188,11 +179,11 @@
      "data": {
       "text/plain": [
        "soma_joinid: int64 not null\n",
-       "obs_id: large_string not null\n",
-       "n_genes: int64 not null\n",
-       "percent_mito: float not null\n",
-       "n_counts: float not null\n",
-       "louvain: large_string not null"
+       "obs_id: large_string\n",
+       "n_genes: int64\n",
+       "percent_mito: float\n",
+       "n_counts: float\n",
+       "louvain: dictionary<values=string, indices=int32, ordered=0>"
       ]
      },
      "execution_count": 5,
@@ -957,9 +948,9 @@
     {
      "data": {
       "text/plain": [
-       "<Collection 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms' (open for 'r') (2 items)\n",
-       "    'RNA': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/RNA' (unopened)\n",
-       "    'raw': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/raw' (unopened)>"
+       "<Collection 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms' (open for 'r') (2 items)\n",
+       "    'RNA': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/RNA' (unopened)\n",
+       "    'raw': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/raw' (unopened)>"
       ]
      },
      "execution_count": 10,
@@ -992,9 +983,9 @@
     {
      "data": {
       "text/plain": [
-       "<Measurement 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/raw' (open for 'r') (2 items)\n",
-       "    'X': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/raw/X' (unopened)\n",
-       "    'var': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/raw/var' (unopened)>"
+       "<Measurement 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/raw' (open for 'r') (2 items)\n",
+       "    'X': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/raw/X' (unopened)\n",
+       "    'var': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/raw/var' (unopened)>"
       ]
      },
      "execution_count": 11,
@@ -1047,8 +1038,8 @@
     {
      "data": {
       "text/plain": [
-       "<Collection 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/RNA/X' (open for 'r') (1 item)\n",
-       "    'data': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/RNA/X/data' (unopened)>"
+       "<Collection 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/RNA/X' (open for 'r') (1 item)\n",
+       "    'data': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/RNA/X/data' (unopened)>"
       ]
      },
      "execution_count": 12,
@@ -1080,7 +1071,7 @@
     {
      "data": {
       "text/plain": [
-       "<DenseNDArray 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/dense/pbmc3k/ms/RNA/X/data' (open for 'r')>"
+       "<DenseNDArray 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmpy12r_o6f/ms/RNA/X/data' (open for 'r')>"
       ]
      },
      "execution_count": 13,
@@ -1412,22 +1403,14 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<SparseNDArray 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/sparse/pbmc3k/ms/RNA/X/data' (open for 'r')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "experiment = tiledbsoma.open(\"data/sparse/pbmc3k\")\n",
-    "X = experiment.ms[\"RNA\"].X[\"data\"]\n",
-    "X"
+    "import tarfile\n",
+    "import tempfile\n",
+    "sparse_uri = tempfile.mktemp()\n",
+    "with tarfile.open(\"data/pbmc3k-sparse.tgz\") as handle:\n",
+    "    handle.extractall(sparse_uri)\n",
+    "experiment = tiledbsoma.Experiment.open(sparse_uri)"
    ]
   },
   {
@@ -1460,6 +1443,7 @@
     }
    ],
    "source": [
+    "X = experiment.ms[\"RNA\"].X[\"data\"]\n",
     "X.schema"
    ]
   },
@@ -1484,7 +1468,7 @@
     {
      "data": {
       "text/plain": [
-       "(2147483646, 2147483646)"
+       "(2638, 1838)"
       ]
      },
      "execution_count": 23,
@@ -1560,7 +1544,7 @@
     {
      "data": {
       "text/plain": [
-       "<tiledbsoma._sparse_nd_array.SparseNDArrayRead at 0x150e1ddd0>"
+       "<tiledbsoma._sparse_nd_array.SparseNDArrayRead at 0x16ac624d0>"
       ]
      },
      "execution_count": 25,
@@ -1604,7 +1588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 27,
    "id": "f62472c6-e67c-44f0-8ed2-df9bec3ae3e8",
    "metadata": {
     "tags": []
@@ -1613,11 +1597,11 @@
     {
      "data": {
       "text/plain": [
-       "<9223372036854773760x9223372036854773760 sparse matrix of type '<class 'numpy.float32'>'\n",
-       "\twith 4848644 stored elements in COOrdinate format>"
+       "<COOrdinate sparse matrix of dtype 'float32'\n",
+       "\twith 4848644 stored elements and shape (2638, 1838)>"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1648,7 +1632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 28,
    "id": "d5d9ca87-58cc-44bf-ba48-7e2bf3b6c5a7",
    "metadata": {
     "tags": []
@@ -1657,11 +1641,11 @@
     {
      "data": {
       "text/plain": [
-       "<9223372036854773760x9223372036854773760 sparse matrix of type '<class 'numpy.float32'>'\n",
-       "\twith 18380 stored elements in COOrdinate format>"
+       "<COOrdinate sparse matrix of dtype 'float32'\n",
+       "\twith 18380 stored elements and shape (2638, 1838)>"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1683,7 +1667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 29,
    "id": "d542f63e-7ca8-4e68-8933-cb15f17bc8cb",
    "metadata": {
     "tags": []
@@ -1692,10 +1676,10 @@
     {
      "data": {
       "text/plain": [
-       "array([0, 1, 2, ..., 7, 8, 9])"
+       "array([0, 0, 0, ..., 9, 9, 9], dtype=int32)"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1703,6 +1687,14 @@
    "source": [
     "sliced_X.nonzero()[0]"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f812d75-bd42-417c-8337-0111cd648a85",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/apis/python/notebooks/tutorial_soma_reading.ipynb b/apis/python/notebooks/tutorial_soma_reading.ipynb
index 03a83168d5..fafbb442d7 100644
--- a/apis/python/notebooks/tutorial_soma_reading.ipynb
+++ b/apis/python/notebooks/tutorial_soma_reading.ipynb
@@ -88,22 +88,14 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<DataFrame 'file:///opt/TileDB-SOMA/apis/python/notebooks/data/sparse/pbmc3k/obs' (open for 'r')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "experiment = tiledbsoma.open(\"data/sparse/pbmc3k\")\n",
-    "obs = experiment.obs\n",
-    "obs"
+    "import tarfile\n",
+    "import tempfile\n",
+    "sparse_uri = tempfile.mktemp()\n",
+    "with tarfile.open(\"data/pbmc3k-sparse.tgz\") as handle:\n",
+    "    handle.extractall(sparse_uri)\n",
+    "experiment = tiledbsoma.Experiment.open(sparse_uri)"
    ]
   },
   {
@@ -133,14 +125,16 @@
        "n_genes: int64\n",
        "percent_mito: float\n",
        "n_counts: float\n",
-       "louvain: large_string\n",
+       "louvain: dictionary<values=string, indices=int32, ordered=0>\n",
        "----\n",
        "soma_joinid: [[0,1,2,3,4,...,2633,2634,2635,2636,2637]]\n",
        "obs_id: [[\"AAACATACAACCAC-1\",\"AAACATTGAGCTAC-1\",\"AAACATTGATCAGC-1\",\"AAACCGTGCTTCCG-1\",\"AAACCGTGTATGCG-1\",...,\"TTTCGAACTCTCAT-1\",\"TTTCTACTGAGGCA-1\",\"TTTCTACTTCCTCG-1\",\"TTTGCATGAGAGGC-1\",\"TTTGCATGCCTCAC-1\"]]\n",
        "n_genes: [[781,1352,1131,960,522,...,1155,1227,622,454,724]]\n",
        "percent_mito: [[0.030177759,0.037935957,0.008897362,0.017430846,0.012244898,...,0.021104366,0.00929422,0.021971496,0.020547945,0.008064516]]\n",
        "n_counts: [[2419,4903,3147,2639,980,...,3459,3443,1684,1022,1984]]\n",
-       "louvain: [[\"CD4 T cells\",\"B cells\",\"CD4 T cells\",\"CD14+ Monocytes\",\"NK cells\",...,\"CD14+ Monocytes\",\"B cells\",\"B cells\",\"B cells\",\"CD4 T cells\"]]"
+       "louvain: [  -- dictionary:\n",
+       "[\"CD4 T cells\",\"CD14+ Monocytes\",\"B cells\",\"CD8 T cells\",\"NK cells\",\"FCGR3A+ Monocytes\",\"Dendritic cells\",\"Megakaryocytes\"]  -- indices:\n",
+       "[0,2,0,1,4,...,1,2,2,2,0]]"
       ]
      },
      "execution_count": 3,
@@ -149,6 +143,7 @@
     }
    ],
    "source": [
+    "obs = experiment.obs\n",
     "table = obs.read().concat()\n",
     "table"
    ]
@@ -180,14 +175,16 @@
        "n_genes: int64\n",
        "percent_mito: float\n",
        "n_counts: float\n",
-       "louvain: large_string\n",
+       "louvain: dictionary<values=string, indices=int32, ordered=0>\n",
        "----\n",
        "soma_joinid: [[270,1163,1891,926,277,...,2186,1522,662,1288,1840]]\n",
        "obs_id: [[\"ACGAACTGGCTATG-1\",\"CGATACGACAGGAG-1\",\"GGGCCAACCTTGGA-1\",\"CAGGTTGAGGATCT-1\",\"ACGAGGGACAGGAG-1\",...,\"TAGTCTTGGCTGTA-1\",\"GACGCTCTCTCTCG-1\",\"ATCTCAACCTCGAA-1\",\"CTAATAGAGCTATG-1\",\"GGCATATGGGGAGT-1\"]]\n",
        "n_genes: [[2455,2033,2020,2000,1997,...,270,267,246,239,212]]\n",
        "percent_mito: [[0.015774649,0.022166021,0.010576352,0.026962927,0.014631685,...,0,0.032258064,0,0.0016666667,0.012173913]]\n",
        "n_counts: [[8875,6722,8415,8011,7928,...,652,682,609,600,575]]\n",
-       "louvain: [[\"Megakaryocytes\",\"CD4 T cells\",\"Dendritic cells\",\"B cells\",\"Dendritic cells\",...,\"CD4 T cells\",\"Megakaryocytes\",\"CD4 T cells\",\"CD4 T cells\",\"Megakaryocytes\"]]"
+       "louvain: [  -- dictionary:\n",
+       "[\"CD4 T cells\",\"CD14+ Monocytes\",\"B cells\",\"CD8 T cells\",\"NK cells\",\"FCGR3A+ Monocytes\",\"Dendritic cells\",\"Megakaryocytes\"]  -- indices:\n",
+       "[7,0,6,2,6,...,0,7,0,0,7]]"
       ]
      },
      "execution_count": 4,
@@ -423,12 +420,12 @@
     {
      "data": {
       "text/plain": [
-       "soma_joinid: int64\n",
+       "soma_joinid: int64 not null\n",
        "obs_id: large_string\n",
        "n_genes: int64\n",
        "percent_mito: float\n",
        "n_counts: float\n",
-       "louvain: large_string"
+       "louvain: dictionary<values=string, indices=int32, ordered=0>"
       ]
      },
      "execution_count": 6,
@@ -1455,7 +1452,7 @@
     {
      "data": {
       "text/plain": [
-       "<SparseNDArray 'file:///opt/TileDB-SOMA/apis/python/notebooks/data/sparse/pbmc3k/ms/RNA/X/data' (open for 'r')>"
+       "<SparseNDArray 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmptdjti_ah/ms/RNA/X/data' (open for 'r')>"
       ]
      },
      "execution_count": 15,
@@ -1489,7 +1486,7 @@
     {
      "data": {
       "text/plain": [
-       "<tiledbsoma._sparse_nd_array.SparseNDArrayRead at 0x128b09c90>"
+       "<tiledbsoma._sparse_nd_array.SparseNDArrayRead at 0x13dc67dd0>"
       ]
      },
      "execution_count": 16,
@@ -1524,7 +1521,7 @@
       "text/plain": [
        "<pyarrow.SparseCOOTensor>\n",
        "type: float\n",
-       "shape: (9223372036854773760, 9223372036854773760)"
+       "shape: (2638, 1838)"
       ]
      },
      "execution_count": 17,
@@ -1551,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 18,
    "id": "cbeea976-f00c-47b6-b8d1-4ea1b31afd25",
    "metadata": {
     "tags": []
@@ -1565,7 +1562,7 @@
        "shape: (2638, 1838)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1588,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 19,
    "id": "28d04c96-01a3-429e-a5a8-84ec8bca0453",
    "metadata": {
     "tags": []
@@ -1597,11 +1594,11 @@
     {
      "data": {
       "text/plain": [
-       "<2638x1838 sparse matrix of type '<class 'numpy.float32'>'\n",
-       "\twith 4848644 stored elements in COOrdinate format>"
+       "<COOrdinate sparse matrix of dtype 'float32'\n",
+       "\twith 4848644 stored elements and shape (2638, 1838)>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1634,7 +1631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 20,
    "id": "6f757ce0-d9dc-44bb-99f6-3be709edff0e",
    "metadata": {
     "tags": []
@@ -1645,10 +1642,10 @@
       "text/plain": [
        "<pyarrow.SparseCOOTensor>\n",
        "type: float\n",
-       "shape: (9223372036854773760, 9223372036854773760)"
+       "shape: (2638, 1838)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1670,7 +1667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 21,
    "id": "32f21b63-83a7-45e3-a18b-f44f068b1697",
    "metadata": {
     "tags": []
@@ -1684,7 +1681,7 @@
        "shape: (2638, 1838)"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1706,7 +1703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 22,
    "id": "45874532-327a-46fa-ad27-7043bd80e8f9",
    "metadata": {
     "tags": []
@@ -1730,7 +1727,7 @@
        "          0.        ,  0.        ]], dtype=float32)"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1749,7 +1746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 23,
    "id": "972de6ca-19a7-49f0-8fe9-7ece199cad1d",
    "metadata": {
     "tags": []
@@ -1758,11 +1755,11 @@
     {
      "data": {
       "text/plain": [
-       "<2638x1838 sparse matrix of type '<class 'numpy.float32'>'\n",
-       "\twith 11028 stored elements in Compressed Sparse Row format>"
+       "<Compressed Sparse Row sparse matrix of dtype 'float32'\n",
+       "\twith 11028 stored elements and shape (2638, 1838)>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1774,7 +1771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 24,
    "id": "10c5f21e-5ec2-44ee-bae8-302d5f874a41",
    "metadata": {
     "tags": []
@@ -1783,11 +1780,11 @@
     {
      "data": {
       "text/plain": [
-       "<1x1838 sparse matrix of type '<class 'numpy.float32'>'\n",
-       "\twith 1838 stored elements in Compressed Sparse Row format>"
+       "<Compressed Sparse Row sparse matrix of dtype 'float32'\n",
+       "\twith 1838 stored elements and shape (1, 1838)>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1806,7 +1803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 25,
    "id": "10856361-4b6e-476b-9938-a52ef50e6db1",
    "metadata": {
     "tags": []
@@ -1830,7 +1827,7 @@
        "          0.        ,  0.        ]], dtype=float32)"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1849,7 +1846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 26,
    "id": "45f2aa69-238e-478f-8c3a-9d4c90f3e507",
    "metadata": {
     "tags": []
@@ -2019,7 +2016,7 @@
        "[2638 rows x 6 columns]"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2030,7 +2027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 27,
    "id": "74fb8a3e-9139-4711-ae12-6064222eeae0",
    "metadata": {
     "tags": []
@@ -2151,7 +2148,7 @@
        "[1838 rows x 3 columns]"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2185,7 +2182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 28,
    "id": "2bd9d50a-5a04-4e65-8ac5-352f0dd64065",
    "metadata": {
     "tags": []
@@ -2231,7 +2228,7 @@
        "0            2  ATAD3C"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2252,7 +2249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 29,
    "id": "879435ff-b38c-4da0-8459-1fa55e3a60ba",
    "metadata": {
     "tags": []
@@ -2261,13 +2258,12 @@
     {
      "data": {
       "text/plain": [
-       "<Measurement 'file:///opt/TileDB-SOMA/apis/python/notebooks/data/sparse/pbmc3k/ms/raw' (open for 'r') (2 items)\n",
-       "    'X': Collection 'file:///opt/TileDB-SOMA/apis/python/notebooks/data/sparse/pbmc3k/ms/raw/X' (open for 'r') (1 item)\n",
-       "        'data': SparseNDArray 'file:///opt/TileDB-SOMA/apis/python/notebooks/data/sparse/pbmc3k/ms/raw/X/data' (open for 'r')\n",
-       "    'var': DataFrame 'file:///opt/TileDB-SOMA/apis/python/notebooks/data/sparse/pbmc3k/ms/raw/var' (open for 'r')>"
+       "<Measurement 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmptdjti_ah/ms/raw' (open for 'r') (2 items)\n",
+       "    'X': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmptdjti_ah/ms/raw/X' (unopened)\n",
+       "    'var': 'file:///var/folders/ns/59wnqfl57ydcmpbsghk2w1_80000gn/T/tmptdjti_ah/ms/raw/var' (unopened)>"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2288,7 +2284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 30,
    "id": "f09b47e0-8501-4a1b-a734-6087197f9272",
    "metadata": {
     "tags": []
@@ -2334,7 +2330,7 @@
        "0           30  ATAD3C"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2354,7 +2350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 31,
    "id": "924f8aa7-e8b8-4fda-b513-34442d482dec",
    "metadata": {
     "tags": []
@@ -2366,7 +2362,7 @@
        "8"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2375,6 +2371,14 @@
     "X_raw = experiment[\"ms\"][\"raw\"].X[\"data\"]\n",
     "X_raw.read((slice(None), [30])).coos().concat().to_scipy().nnz"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a8f4119-6663-491e-aa2f-00aa37caebf1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -2393,7 +2397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/apis/python/notebooks/tutorial_spatial.ipynb b/apis/python/notebooks/tutorial_spatial.ipynb
index 3da57c05f7..22b57db6ce 100644
--- a/apis/python/notebooks/tutorial_spatial.ipynb
+++ b/apis/python/notebooks/tutorial_spatial.ipynb
@@ -225,8 +225,8 @@
     {
      "data": {
       "text/plain": [
-       "<Collection 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial' (open for 'r') (1 item)\n",
-       "    'scene1': 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1' (unopened)>"
+       "<Collection 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial' (open for 'r') (1 item)\n",
+       "    'scene1': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1' (unopened)>"
       ]
      },
      "execution_count": 8,
@@ -257,7 +257,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/jules/.pyenv/versions/3.11.9/envs/soma/lib/python3.11/site-packages/anndata/_core/anndata.py:402: FutureWarning: The dtype argument is deprecated and will be removed in late 2024.\n",
+      "/Users/kerl/.pyenv/versions/3.11.9/lib/python3.11/site-packages/anndata/_core/anndata.py:402: FutureWarning: The dtype argument is deprecated and will be removed in late 2024.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -331,10 +331,10 @@
     {
      "data": {
       "text/plain": [
-       "<Scene 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1' (open for 'r') (3 items)\n",
-       "    'img': 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/img' (unopened)\n",
-       "    'obsl': 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/obsl' (unopened)\n",
-       "    'varl': 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/varl' (unopened)>"
+       "<Scene 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1' (open for 'r') (3 items)\n",
+       "    'img': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/img' (unopened)\n",
+       "    'obsl': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/obsl' (unopened)\n",
+       "    'varl': 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/varl' (unopened)>"
       ]
      },
      "execution_count": 11,
@@ -408,7 +408,7 @@
     {
      "data": {
       "text/plain": [
-       "<MultiscaleImage 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/img/tissue' (open for 'r')>"
+       "<MultiscaleImage 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/img/tissue' (open for 'r')>"
       ]
      },
      "execution_count": 13,
@@ -516,7 +516,7 @@
     {
      "data": {
       "text/plain": [
-       "<DenseNDArray 'file:///home/jules/Software/TileDB-Inc/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/img/tissue/lowres' (open for 'r')>"
+       "<DenseNDArray 'file:///Users/kerl/git/single-cell-data/TileDB-SOMA/apis/python/notebooks/data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/soma/spatial/scene1/img/tissue/lowres' (open for 'r')>"
       ]
      },
      "execution_count": 17,
@@ -548,7 +548,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0xfeff3ae31890>"
+       "<matplotlib.image.AxesImage at 0x1482fe210>"
       ]
      },
      "execution_count": 19,
@@ -1181,7 +1181,7 @@
     {
      "data": {
       "text/plain": [
-       "SpatialRead(data=<tiledbsoma._read_iters.TableReadIter object at 0xfeff3aedd490>, data_coordinate_space=CoordinateSpace(axes=(Axis(name='x', unit='pixels'), Axis(name='y', unit='pixels'))), output_coordinate_space=CoordinateSpace(axes=(Axis(name='x', unit='pixels'), Axis(name='y', unit='pixels'))), coordinate_transform=<IdentityTransform\n",
+       "SpatialRead(data=<tiledbsoma._read_iters.TableReadIter object at 0x147e9b750>, data_coordinate_space=CoordinateSpace(axes=(Axis(name='x', unit='pixels'), Axis(name='y', unit='pixels'))), output_coordinate_space=CoordinateSpace(axes=(Axis(name='x', unit='pixels'), Axis(name='y', unit='pixels'))), coordinate_transform=<IdentityTransform\n",
        "  input axes: ('x', 'y')\n",
        "  output axes: ('x', 'y')>)"
       ]


Test patch:
diff --git a/apis/python/notebooks/data/dense/pbmc3k/ms/RNA/var/__commits/__1688077630971_1688077630971_5184df9c698d4c13919275c4e2e3a524_18.con b/apis/python/notebooks/data/dense/pbmc3k/ms/RNA/var/__commits/__1688077630971_1688077630971_5184df9c698d4c13919275c4e2e3a524_18.con
deleted file mode 100644
index b2af36de60..0000000000
--- a/apis/python/notebooks/data/dense/pbmc3k/ms/RNA/var/__commits/__1688077630971_1688077630971_5184df9c698d4c13919275c4e2e3a524_18.con
+++ /dev/null
@@ -1,1 +0,0 @@
-__commits/__1688077630971_1688077630971_ea54e87c37fc4a13b186bb983738f422_18.wrt
diff --git a/apis/python/tests/test_notebook_sparse_dense.py b/apis/python/tests/test_notebook_sparse_dense.py
index 05db891cc1..67e7ea8361 100644
--- a/apis/python/tests/test_notebook_sparse_dense.py
+++ b/apis/python/tests/test_notebook_sparse_dense.py
@@ -1,3 +1,5 @@
+import tarfile
+
 import pytest
 
 import tiledbsoma
@@ -7,8 +9,12 @@
 
 
 @pytest.mark.parametrize("name", ["sparse", "dense"])
-def test_notebook_path_dense(name):
-    path = PY_ROOT / f"notebooks/data/{name}/pbmc3k"
+def test_notebook_path_dense(tmp_path, name):
+    tgz_path = PY_ROOT / f"notebooks/data/pbmc3k-{name}.tgz"
+    uri = tmp_path.as_posix()
+
+    with tarfile.open(tgz_path) as handle:
+        handle.extractall(uri)
 
-    with tiledbsoma.Experiment.open(path.as_posix()) as exp:
+    with tiledbsoma.Experiment.open(uri) as exp:
         assert len(exp.obs.read().concat()) == 2638
diff --git a/profiler/tests/objects.py b/profiler/tests/objects.py
index f934d6a973..86d91579a8 100644
--- a/profiler/tests/objects.py
+++ b/profiler/tests/objects.py
@@ -1,8 +1,19 @@
+import tarfile
+import tempfile
+
 import tiledbsoma
 
+sparse_uri = tempfile.mktemp()
+with tarfile.open("../apis/python/notebooks/data/pbmc3k-sparse.tgz") as handle:
+    handle.extractall(sparse_uri)
+
+dense_uri = tempfile.mktemp()
+with tarfile.open("../apis/python/notebooks/data/pbmc3k-dense.tgz") as handle:
+    handle.extractall(dense_uri)
+
 # This test is extracted from tutorial_soma_objects.ipynb notebook
 # To test the profile run: `python main.py python tests/objects.py` in the profile folder
-experiment = tiledbsoma.open("../apis/python/notebooks/data/dense/pbmc3k")
+experiment = tiledbsoma.Experiment.open(sparse_uri)
 
 obs = experiment.obs
 print(f"obs schema {obs.schema}")
@@ -28,7 +39,7 @@
 
 print(X.read((None, int(idx[0]))).to_numpy())
 
-experiment = tiledbsoma.open("../apis/python/notebooks/data/sparse/pbmc3k")
+experiment = tiledbsoma.Experiment.open(dense_uri)
 X = experiment.ms["RNA"].X["data"]
 
 print(X.schema)