docs: a descriptive page for every example dataset

docs/api/datasets.md

@@ -67,8 +67,18 @@
 
 ../datasets/overview
 ../datasets/bbbc021
+../datasets/rohban
+../datasets/pki
 ../datasets/jump_target2
-../datasets/jump_cells
+../datasets/jump_crispr
 ../datasets/jump_lite
+../datasets/chroma
+../datasets/neuropainting
 ../datasets/oasis_pilot
+../datasets/pooled_rare
+../datasets/jump_cells
+../datasets/jump_export
+../datasets/jump_plate
+../datasets/scallops_arv471
+../datasets/cp_posh
 ```

docs/datasets/jump_export.ipynb

@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3f1f3663",
+   "metadata": {},
+   "source": [
+    "# One CellProfiler export\n",
+    "\n",
+    "{func}`~mantispy.ds.jump_export` is a single, unmodified `ExportToSpreadsheet` directory, one field of view of a DMSO well of `BR00121438` {cite:p}`Chandrasekaran_2023`. It is the example input for {func}`~mantispy.io.read_profiles`: the raw `Image.csv` plus the `Cells`, `Cytoplasm` and `Nuclei` tables, exactly as CellProfiler wrote them. The images this was measured from are in {func}`~mantispy.ds.jump_plate`, and the well-level profiles of the same plate in {func}`~mantispy.ds.jump_target2`.\n",
+    "\n",
+    "**Use it if** you have your own CellProfiler `ExportToSpreadsheet` output and want to read it into cells. [From a CellProfiler run](../tutorials/data/cellprofiler.ipynb) walks through that on this directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "20cab890",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-29T02:53:31.787363Z",
+     "iopub.status.busy": "2026-09-29T02:53:31.786609Z",
+     "iopub.status.idle": "2026-09-29T02:53:35.950473Z",
+     "shell.execute_reply": "2026-09-29T02:53:35.949247Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import mantispy as mt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a19fb984",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-29T02:53:35.955131Z",
+     "iopub.status.busy": "2026-09-29T02:53:35.954517Z",
+     "iopub.status.idle": "2026-09-29T02:53:36.130313Z",
+     "shell.execute_reply": "2026-09-29T02:53:36.129500Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Cells.csv', 'Cytoplasm.csv', 'Experiment.csv', 'Image.csv', 'Nuclei.csv']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "directory = mt.ds.jump_export()\n",
+    "sorted(p.name for p in directory.iterdir())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5e2505",
+   "metadata": {},
+   "source": [
+    "## Reading it into cells\n",
+    "\n",
+    "`Cytoplasm` is the only object the JUMP pipeline gives a parent in both `Cells` and `Nuclei`, so it is the one that joins all three tables, and one cytoplasm is one cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "65755bcc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-29T02:53:36.136463Z",
+     "iopub.status.busy": "2026-09-29T02:53:36.136204Z",
+     "iopub.status.idle": "2026-09-29T02:53:40.091121Z",
+     "shell.execute_reply": "2026-09-29T02:53:40.089031Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 239 × 5839\n",
+       "    obs: 'Metadata_ImageNumber', 'Metadata_ObjectNumber', 'Metadata_AbsPositionZ', 'Metadata_AbsTime', 'Metadata_BinningX', 'Metadata_BinningY', 'Metadata_ChannelID', 'Metadata_ChannelName', 'Metadata_Col', 'Metadata_ExposureTime', 'Metadata_FieldID', 'Metadata_ImageResolutionX', 'Metadata_ImageResolutionY', 'Metadata_ImageSizeX', 'Metadata_ImageSizeY', 'Metadata_MainEmissionWavelength', 'Metadata_MainExcitationWavelength', 'Metadata_MaxIntensity', 'Metadata_ObjectiveMagnification', 'Metadata_ObjectiveNA', 'Metadata_PlaneID', 'Metadata_Plate', 'Metadata_PositionX', 'Metadata_PositionY', 'Metadata_PositionZ', 'Metadata_Row', 'Metadata_Site', 'Metadata_Well', 'Metadata_Center_X', 'Metadata_Center_Y'\n",
+       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature'\n",
+       "    uns: 'mantispy'\n",
+       "    layers: None (.X)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "with warnings.catch_warnings():\n",
+    "    warnings.simplefilter(\"ignore\")\n",
+    "    cells = mt.io.read_profiles(directory, primary_object=\"Cytoplasm\", resolution=\"cell\")\n",
+    "cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d32f3c2",
+   "metadata": {},
+   "source": [
+    "The metadata columns that came off the export name each cell by its plate, well, site and object number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "135ac6a8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-29T02:53:40.094853Z",
+     "iopub.status.busy": "2026-09-29T02:53:40.094280Z",
+     "iopub.status.idle": "2026-09-29T02:53:40.112523Z",
+     "shell.execute_reply": "2026-09-29T02:53:40.111506Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_Plate</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>Metadata_Site</th>\n",
+       "      <th>Metadata_ObjectNumber</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BR00121438</td>\n",
+       "      <td>J04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BR00121438</td>\n",
+       "      <td>J04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BR00121438</td>\n",
+       "      <td>J04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BR00121438</td>\n",
+       "      <td>J04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BR00121438</td>\n",
+       "      <td>J04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_Plate Metadata_Well  Metadata_Site  Metadata_ObjectNumber\n",
+       "0     BR00121438           J04              1                      1\n",
+       "1     BR00121438           J04              1                      2\n",
+       "2     BR00121438           J04              1                      3\n",
+       "3     BR00121438           J04              1                      4\n",
+       "4     BR00121438           J04              1                      5"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cells.obs[[\"Metadata_Plate\", \"Metadata_Well\", \"Metadata_Site\", \"Metadata_ObjectNumber\"]].head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}