From d42d50b7668d24a0b8776c7013cec41b379bad7d Mon Sep 17 00:00:00 2001
From: Eduardo Hirata-Miyasaki
 <eduardo.hirata@tenant-ac-biohub-login-eduardo-hirata-57d4880-0.tenant-ac-biohub-login-all-users.tenant-ac-biohub.svc.cluster.local>
Date: Tue, 21 Jul 2026 23:13:06 +0000
Subject: [PATCH 1/4] perf(dynaclr): capture TensorStore I/O metrics in
 data-path profilers

profile_stages.py records file bytes_read/read counts and chunk-cache reads
after warmup so read amplification is measured directly rather than inferred
from tensor shapes; profile_num_workers.py defaults to the Stage-1 self-
positive recipe for a matched sweep.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../scripts/profiling/profile_num_workers.py  | 104 ++++++----
 .../scripts/profiling/profile_stages.py       | 179 +++++++++++++-----
 2 files changed, 197 insertions(+), 86 deletions(-)

diff --git a/applications/dynaclr/scripts/profiling/profile_num_workers.py b/applications/dynaclr/scripts/profiling/profile_num_workers.py
index e57279021..f72470549 100644
--- a/applications/dynaclr/scripts/profiling/profile_num_workers.py
+++ b/applications/dynaclr/scripts/profiling/profile_num_workers.py
@@ -1,19 +1,22 @@
 """Sweep num_workers to find optimal dataloader parallelism.
 
-Holds all other parameters constant and measures end-to-end ThreadDataLoader
-throughput (samples/sec and inter-batch latency) for num_workers in [1, 2, 4, 8].
+Holds all other parameters constant and measures ThreadDataLoader-only
+throughput (samples/sec and inter-batch latency). Defaults match the Reef
+Stage-1 self-positive recipe and sweep num_workers in [4, 8, 12, 16].
 
 Unlike profile_stages.py (which isolates individual pipeline stages) or
 profile_dataloaders.py (which compares two dataloader implementations), this
-script answers: does adding more CPU workers reduce GPU starvation?
+script answers whether adding thread workers improves loader service rate. It
+does not measure GPU utilization; validate the winner in a real ``fit`` run.
 
 Usage
 -----
-    uv run python applications/dynaclr/scripts/dataloader_inspection/profile_num_workers.py
+    uv run python applications/dynaclr/scripts/profiling/profile_num_workers.py
 """
 
 from __future__ import annotations
 
+import os
 import time
 
 import numpy as np
@@ -21,22 +24,32 @@
 from dynaclr.data.datamodule import MultiExperimentDataModule
 
 # ---------------------------------------------------------------------------
-# Config
+# Config — overridable via env; geometry/loader defaults match Reef Stage 1.
 # ---------------------------------------------------------------------------
 
-CELL_INDEX_PARQUET = "applications/dynaclr/configs/cell_index/benchmark_2exp.parquet"
+CELL_INDEX_PARQUET = os.environ.get(
+    "PROFILE_PARQUET", "applications/dynaclr/configs/cell_index/benchmark_2exp.parquet"
+)
 
-BATCH_SIZE = 128
-N_BATCHES = 30
-WARMUP = 5
-CACHE_POOL_BYTES = 500_000_000  # 500 MB
+BATCH_SIZE = int(os.environ.get("PROFILE_BATCH_SIZE", "256"))
+N_BATCHES = int(os.environ.get("PROFILE_N_BATCHES", "30"))
+WARMUP = int(os.environ.get("PROFILE_WARMUP", "5"))
+CACHE_POOL_BYTES = int(os.environ.get("PROFILE_CACHE_POOL_BYTES", "0"))
 
-Z_WINDOW = 16
-Z_EXTRACTION_WINDOW = 45
-YX_PATCH = (192, 192)
-FINAL_YX_PATCH = (160, 160)
+FILE_IO_CONCURRENCY = int(os.environ.get("PROFILE_FILE_IO_CONCURRENCY", "32"))
+Z_WINDOW = int(os.environ.get("PROFILE_Z_WINDOW", "1"))
+Z_EXTRACTION_WINDOW = int(os.environ.get("PROFILE_Z_EXTRACTION_WINDOW", "16"))
+_YX = int(os.environ.get("PROFILE_YX_PATCH", "256"))
+_FINAL_YX = int(os.environ.get("PROFILE_FINAL_YX_PATCH", "160"))
+YX_PATCH = (_YX, _YX)
+FINAL_YX_PATCH = (_FINAL_YX, _FINAL_YX)
+POSITIVE_CELL_SOURCE = os.environ.get("PROFILE_POSITIVE_CELL_SOURCE", "self")
+PREFETCH_FACTOR = int(os.environ.get("PROFILE_PREFETCH_FACTOR", "1"))
+BUFFER_SIZE = int(os.environ.get("PROFILE_BUFFER_SIZE", "1"))
 
-NUM_WORKERS_SWEEP = [1, 2, 4, 8]
+FOCUS_CHANNEL = os.environ.get("PROFILE_FOCUS_CHANNEL", "Phase3D")
+REFERENCE_PIXEL_SIZE_XY_UM = float(os.environ.get("PROFILE_REFERENCE_PIXEL_SIZE_XY_UM", "0.1494"))
+NUM_WORKERS_SWEEP = [int(x) for x in os.environ.get("PROFILE_NUM_WORKERS_SWEEP", "4,8,12,16").split(",")]
 
 
 # ---------------------------------------------------------------------------
@@ -51,20 +64,26 @@ def setup_dm(num_workers: int) -> MultiExperimentDataModule:
         z_window=Z_WINDOW,
         z_extraction_window=Z_EXTRACTION_WINDOW,
         z_focus_offset=0.3,
+        focus_channel=FOCUS_CHANNEL,
+        reference_pixel_size_xy_um=REFERENCE_PIXEL_SIZE_XY_UM,
         yx_patch_size=YX_PATCH,
         final_yx_patch_size=FINAL_YX_PATCH,
         channels_per_sample=1,
-        positive_cell_source="lookup",
-        positive_match_columns=["lineage_id"],
+        positive_cell_source=POSITIVE_CELL_SOURCE,
+        positive_match_columns=None if POSITIVE_CELL_SOURCE == "self" else ["lineage_id"],
         tau_range=(0.5, 2.0),
         tau_decay_rate=2.0,
-        stratify_by=["perturbation"],
+        batch_group_by="marker",
+        stratify_by="experiment",
         split_ratio=0.8,
         batch_size=BATCH_SIZE,
         num_workers=num_workers,
+        prefetch_factor=PREFETCH_FACTOR,
+        buffer_size=BUFFER_SIZE,
         seed=42,
         cache_pool_bytes=CACHE_POOL_BYTES,
         normalizations=[],
+        file_io_concurrency=FILE_IO_CONCURRENCY,
         augmentations=[],
     )
     dm.setup("fit")
@@ -86,11 +105,12 @@ def benchmark_dataloader(dataloader, n_batches: int = N_BATCHES, warmup: int = W
     Returns
     -------
     dict
-        Inter-batch timing stats, throughput in samples/sec, and VAST bandwidth in MB/s.
+        Inter-batch timing stats, throughput, logical tensor rate, and queued batch size.
     """
     timestamps = []
-    total_samples = 0
-    read_mb_per_batch = None
+    measured_batch_size = None
+    logical_mb_per_batch = None
+    output_mb_per_batch = None
 
     for i, batch in enumerate(dataloader):
         if i >= warmup + n_batches:
@@ -99,26 +119,32 @@ def benchmark_dataloader(dataloader, n_batches: int = N_BATCHES, warmup: int = W
         if i >= warmup:
             timestamps.append(now)
             if isinstance(batch, dict) and "anchor" in batch:
-                total_samples += batch["anchor"].shape[0]
-                if read_mb_per_batch is None:
-                    # anchor + positive (fit mode). Lower bound — ignores chunk alignment overhead.
-                    n_tensors = 2 if "positive" in batch else 1
-                    read_mb_per_batch = batch["anchor"].nelement() * batch["anchor"].element_size() * n_tensors / 1e6
+                measured_batch_size = batch["anchor"].shape[0]
+                if logical_mb_per_batch is None:
+                    anchor_bytes = batch["anchor"].nelement() * batch["anchor"].element_size()
+                    storage_sourced_tensors = 1 if POSITIVE_CELL_SOURCE == "self" else 2
+                    logical_mb_per_batch = anchor_bytes * storage_sourced_tensors / 1e6
+                    output_mb_per_batch = sum(
+                        batch[key].nelement() * batch[key].element_size()
+                        for key in ("anchor", "positive")
+                        if key in batch
+                    ) / 1e6
 
     if len(timestamps) < 2:
         return {"note": "not enough batches"}
 
     inter_batch = np.diff(timestamps)
     mean_s = inter_batch.mean()
-    bandwidth_mb_s = read_mb_per_batch / mean_s if read_mb_per_batch else 0.0
+    logical_mb_s = logical_mb_per_batch / mean_s if logical_mb_per_batch else 0.0
     return {
         "mean_ms": mean_s * 1000,
         "std_ms": inter_batch.std() * 1000,
         "median_ms": float(np.median(inter_batch) * 1000),
         "p95_ms": float(np.percentile(inter_batch, 95) * 1000),
-        "throughput_samples_per_sec": total_samples / inter_batch.sum(),
-        "read_mb_per_batch": read_mb_per_batch or 0.0,
-        "bandwidth_mb_s": bandwidth_mb_s,
+        "throughput_samples_per_sec": (measured_batch_size or 0) / mean_s,
+        "logical_mb_per_batch": logical_mb_per_batch or 0.0,
+        "logical_mb_s": logical_mb_s,
+        "output_mb_per_batch": output_mb_per_batch or 0.0,
     }
 
 
@@ -135,6 +161,7 @@ def main():
     print(f"batch_size={BATCH_SIZE}, z={Z_EXTRACTION_WINDOW}→{Z_WINDOW}")
     print(f"patch={YX_PATCH}→{FINAL_YX_PATCH}, channels_per_sample=1")
     print(f"warmup={WARMUP} batches, measured over {N_BATCHES} batches")
+    print(f"positive_source={POSITIVE_CELL_SOURCE}, prefetch_factor={PREFETCH_FACTOR}, buffer_size={BUFFER_SIZE}")
     print()
 
     # Setup is shared across runs — only the dataloader changes.
@@ -146,12 +173,15 @@ def main():
         dl = dm.train_dataloader()
         stats = benchmark_dataloader(dl)
         stats["num_workers"] = nw
+        queued_batches = nw * PREFETCH_FACTOR + BUFFER_SIZE
+        stats["queued_host_gib"] = stats["output_mb_per_batch"] * queued_batches / 1024
         results.append(stats)
         print(
             f"   {stats['mean_ms']:.1f} ± {stats['std_ms']:.1f} ms/batch"
             f"  |  p95={stats['p95_ms']:.1f} ms"
             f"  |  {stats['throughput_samples_per_sec']:.0f} samples/sec"
-            f"  |  {stats['bandwidth_mb_s']:.0f} MB/s"
+            f"  |  {stats['logical_mb_s']:.0f} logical MB/s"
+            f"  |  ≤{stats['queued_host_gib']:.1f} GiB queued tensors"
         )
         print()
 
@@ -159,15 +189,17 @@ def main():
     print("SUMMARY")
     print("=" * 60)
     print()
-    read_mb = results[0]["read_mb_per_batch"] if results else 0.0
-    print(f"Read volume per batch (lower bound): {read_mb:.0f} MB")
+    logical_mb = results[0]["logical_mb_per_batch"] if results else 0.0
+    print(f"Storage-sourced logical tensor bytes per batch: {logical_mb:.0f} MB")
+    print("Queued-host-memory values are upper-bound tensor payloads; pinning and metadata add overhead.")
     print()
-    print("| num_workers | mean ms/batch | p95 ms | samples/sec | MB/s (VAST) |")
-    print("|-------------|---------------|--------|-------------|-------------|")
+    print("| num_workers | mean ms/batch | p95 ms | samples/sec | logical MB/s | queued GiB upper bound |")
+    print("|-------------|---------------|--------|-------------|--------------|------------------------|")
     for r in results:
         print(
             f"| {r['num_workers']:11d} | {r['mean_ms']:13.1f} | {r['p95_ms']:6.1f}"
-            f" | {r['throughput_samples_per_sec']:11.0f} | {r['bandwidth_mb_s']:11.0f} |"
+            f" | {r['throughput_samples_per_sec']:11.0f} | {r['logical_mb_s']:12.0f}"
+            f" | {r['queued_host_gib']:22.1f} |"
         )
 
 
diff --git a/applications/dynaclr/scripts/profiling/profile_stages.py b/applications/dynaclr/scripts/profiling/profile_stages.py
index e00adc294..f7c35c533 100644
--- a/applications/dynaclr/scripts/profiling/profile_stages.py
+++ b/applications/dynaclr/scripts/profiling/profile_stages.py
@@ -1,30 +1,36 @@
 """Profile per-stage breakdown: I/O vs normalization vs augmentation vs crop.
 
 Isolates each stage of the training batch pipeline to find the bottleneck:
-1. I/O:            __getitems__ (tensorstore read + positive sampling)
+1. Data fetch:     __getitems__ (storage read + decompression + interpolation,
+                    tensor construction, and positive sampling)
 2. CPU→GPU:        .to(device) transfer
 3. Normalization:  NormalizeSampled (fov/timepoint stats)
 4. Augmentation:   affine + flip + contrast + scale + smooth + noise
 5. Final crop:     BatchedRandSpatialCropd (z_extraction → z_window)
 
-Uses the new MultiExperimentDataModule with the benchmark_2exp collection.
-Requires GPU.
+TensorStore counters report compressed file bytes and inner-chunk reads.
+
+Uses MultiExperimentDataModule with environment-overridable Reef defaults.
+Requires a GPU unless ``PROFILE_DEVICE=cpu`` is set.
 
 Usage
 -----
-    uv run python applications/dynaclr/scripts/dataloader_inspection/profile_stages.py
+    uv run python applications/dynaclr/scripts/profiling/profile_stages.py
 """
 
 from __future__ import annotations
 
+import os
 import time
 
 import numpy as np
+import tensorstore as ts
 import torch
 from monai.transforms import Compose
 
 from dynaclr.data.datamodule import MultiExperimentDataModule
 from viscy_transforms import (
+    BatchedChannelWiseZReductiond,
     BatchedRandAdjustContrastd,
     BatchedRandAffined,
     BatchedRandFlipd,
@@ -39,21 +45,52 @@
 # Config
 # ---------------------------------------------------------------------------
 
+# All knobs are environment-overridable. Geometry and data-path defaults match
+# the Reef Stage-1 recipe; PROFILE_PARQUET selects the collection.
 COLLECTION_YAML = "applications/dynaclr/configs/collections/benchmark_2exp.yml"
-CELL_INDEX_PARQUET = "applications/dynaclr/configs/cell_index/benchmark_2exp.parquet"
-
-BATCH_SIZE = 256
-N_BATCHES = 15
-WARMUP = 3
-CACHE_POOL_BYTES = 500_000_000
+CELL_INDEX_PARQUET = os.environ.get(
+    "PROFILE_PARQUET", "applications/dynaclr/configs/cell_index/benchmark_2exp.parquet"
+)
 
-Z_WINDOW = 1
-Z_EXTRACTION_WINDOW = 11
-YX_PATCH = (192, 192)
-FINAL_YX_PATCH = (160, 160)
+BATCH_SIZE = int(os.environ.get("PROFILE_BATCH_SIZE", "256"))
+N_BATCHES = int(os.environ.get("PROFILE_N_BATCHES", "15"))
+WARMUP = int(os.environ.get("PROFILE_WARMUP", "3"))
+FILE_IO_CONCURRENCY = int(os.environ.get("PROFILE_FILE_IO_CONCURRENCY", "32"))
+CACHE_POOL_BYTES = int(os.environ.get("PROFILE_CACHE_POOL_BYTES", "0"))
+
+Z_WINDOW = int(os.environ.get("PROFILE_Z_WINDOW", "1"))
+Z_EXTRACTION_WINDOW = int(os.environ.get("PROFILE_Z_EXTRACTION_WINDOW", "16"))
+_YX = int(os.environ.get("PROFILE_YX_PATCH", "256"))
+_FINAL_YX = int(os.environ.get("PROFILE_FINAL_YX_PATCH", "160"))
+YX_PATCH = (_YX, _YX)
+FINAL_YX_PATCH = (_FINAL_YX, _FINAL_YX)
+# None = all-channels mode; 1 = bag-of-channels (single "channel_0"). The
+# transforms below key on "channel_0", so 1 matches production BoC configs.
+_CPS = os.environ.get("PROFILE_CHANNELS_PER_SAMPLE", "1")
+FOCUS_CHANNEL = os.environ.get("PROFILE_FOCUS_CHANNEL", "Phase3D")
+REFERENCE_PIXEL_SIZE_XY_UM = float(os.environ.get("PROFILE_REFERENCE_PIXEL_SIZE_XY_UM", "0.1494"))
+CHANNELS_PER_SAMPLE = None if _CPS.lower() in ("none", "null", "") else int(_CPS)
+POSITIVE_CELL_SOURCE = os.environ.get("PROFILE_POSITIVE_CELL_SOURCE", "self")
 
 CHANNEL_KEY = "channel_0"
-DEVICE = "cuda"
+DEVICE = os.environ.get("PROFILE_DEVICE", "cuda")
+
+
+_TS_METRICS = {
+    "bytes_read": "/tensorstore/kvstore/file/bytes_read",
+    "file_reads": "/tensorstore/kvstore/file/read",
+    "chunk_reads": "/tensorstore/cache/chunk_cache/reads",
+}
+
+
+def _tensorstore_metrics() -> dict[str, int]:
+    """Return cumulative TensorStore counters used by this benchmark."""
+    collected = {metric["name"]: metric for metric in ts.experimental_collect_matching_metrics()}
+    snapshot = {}
+    for label, name in _TS_METRICS.items():
+        values = collected.get(name, {}).get("values", [])
+        snapshot[label] = int(sum(value.get("value", 0) for value in values))
+    return snapshot
 
 
 def _fmt(seconds: float) -> str:
@@ -69,16 +106,19 @@ def setup():
         z_window=Z_WINDOW,
         z_extraction_window=Z_EXTRACTION_WINDOW,
         z_focus_offset=0.3,
+        focus_channel=FOCUS_CHANNEL,
+        reference_pixel_size_xy_um=REFERENCE_PIXEL_SIZE_XY_UM,
         yx_patch_size=YX_PATCH,
         final_yx_patch_size=FINAL_YX_PATCH,
-        channels_per_sample=None,
-        positive_cell_source="lookup",
-        positive_match_columns=["lineage_id"],
+        channels_per_sample=CHANNELS_PER_SAMPLE,
+        positive_cell_source=POSITIVE_CELL_SOURCE,
+        positive_match_columns=None if POSITIVE_CELL_SOURCE == "self" else ["lineage_id"],
         tau_range=(0.5, 2.0),
         tau_decay_rate=2.0,
         stratify_by=["perturbation"],
         split_ratio=0.8,
         batch_size=BATCH_SIZE,
+        file_io_concurrency=FILE_IO_CONCURRENCY,
         num_workers=1,
         seed=42,
         cache_pool_bytes=CACHE_POOL_BYTES,
@@ -90,10 +130,10 @@ def setup():
 
 
 def build_transforms():
-    """Build the individual transform stages matching DynaCLR-3D-BagOfChannels-v2."""
+    """Build transform stages matching the Reef Stage-1 2D-MIP recipe."""
     normalization = NormalizeSampled(
         keys=[CHANNEL_KEY],
-        level="fov_statistics",
+        level="timepoint_statistics",
         subtrahend="mean",
         divisor="std",
     )
@@ -102,7 +142,7 @@ def build_transforms():
         BatchedRandAffined(
             keys=[CHANNEL_KEY],
             prob=0.8,
-            scale_range=[[0.9, 1.1], [0.9, 1.1], [0.9, 1.1]],
+            scale_range=[[0.8, 1.3], [0.8, 1.3], [0.8, 1.3]],
             rotate_range=[3.14, 0.0, 0.0],
             shear_range=[0.05, 0.05, 0.0, 0.05, 0.0, 0.05],
         ),
@@ -126,7 +166,7 @@ def build_transforms():
             prob=0.5,
             sigma_x=[0.25, 0.50],
             sigma_y=[0.25, 0.50],
-            sigma_z=[0.0, 0.2],
+            sigma_z=[0.0, 0.0],
         ),
         BatchedRandGaussianNoised(
             keys=[CHANNEL_KEY],
@@ -134,6 +174,8 @@ def build_transforms():
             mean=0.0,
             std=0.1,
         ),
+        BatchedRandSpatialCropd(keys=[CHANNEL_KEY], roi_size=(10, 192, 192)),
+        BatchedChannelWiseZReductiond(keys=[CHANNEL_KEY], allow_missing_keys=True),
     ]
 
     final_crop = BatchedRandSpatialCropd(
@@ -144,7 +186,7 @@ def build_transforms():
     return normalization, augmentations, final_crop
 
 
-def time_stage(fn, n_batches=N_BATCHES, warmup=WARMUP):
+def time_stage(fn, n_batches=N_BATCHES, warmup=WARMUP, on_measure_start=None):
     """Time a callable over multiple iterations, return stats.
 
     Parameters
@@ -155,6 +197,8 @@ def time_stage(fn, n_batches=N_BATCHES, warmup=WARMUP):
         Iterations to time after warmup.
     warmup : int
         Iterations to discard.
+    on_measure_start : callable or None
+        Called after warmup and immediately before the first measured iteration.
 
     Returns
     -------
@@ -163,6 +207,8 @@ def time_stage(fn, n_batches=N_BATCHES, warmup=WARMUP):
     """
     times = []
     for i in range(warmup + n_batches):
+        if i == warmup and on_measure_start is not None:
+            on_measure_start()
         if DEVICE == "cuda":
             torch.cuda.synchronize()
         t0 = time.perf_counter()
@@ -183,7 +229,7 @@ def time_stage(fn, n_batches=N_BATCHES, warmup=WARMUP):
 def main():
     """Profile individual dataloader pipeline stages."""
     print("=" * 70)
-    print("STAGE BREAKDOWN: I/O → Transfer → Normalize → Augment → Crop")
+    print("STAGE BREAKDOWN: Data fetch → Transfer → Normalize → Augment → Crop")
     print("=" * 70)
     print(f"batch_size={BATCH_SIZE}, z_extraction={Z_EXTRACTION_WINDOW}→z_window={Z_WINDOW}")
     print(f"patch={YX_PATCH}→{FINAL_YX_PATCH}, device={DEVICE}")
@@ -203,46 +249,62 @@ def random_indices():
     all_indices = [random_indices() for _ in range(WARMUP + N_BATCHES + 5)]
     idx_iter = iter(all_indices)
 
-    # ── Stage 1: I/O (__getitems__) ──
-    print("## Stage 1: I/O (__getitems__)")
-    batches = []
+    # ── Stage 1: Data fetch (__getitems__) ──
+    print("## Stage 1: Data fetch (__getitems__)")
+    sample_batch = None
 
     def io_step():
+        nonlocal sample_batch
         indices = next(idx_iter)
-        batch = dataset.__getitems__(indices)
-        batches.append(batch)
-        return batch
+        sample_batch = dataset.__getitems__(indices)
+        return sample_batch
 
-    io_stats, _ = time_stage(io_step)
+    metrics_before = {}
+    io_stats, _ = time_stage(io_step, on_measure_start=lambda: metrics_before.update(_tensorstore_metrics()))
+    metrics_after = _tensorstore_metrics()
+    metric_delta = {name: metrics_after[name] - metrics_before[name] for name in metrics_before}
 
     # Use the last batch for subsequent stages
-    sample_batch = batches[-1]
+    assert sample_batch is not None
     anchor = sample_batch["anchor"]
     positive = sample_batch.get("positive")
 
-    # Read volume: what was actually fetched from VAST (z_extraction_window, not z_window).
-    # anchor + positive (fit mode reads both). Lower bound — chunk alignment may add overhead.
-    n_tensors = 2 if positive is not None else 1
-    read_bytes = anchor.nelement() * anchor.element_size() * n_tensors
+    # Self positives clone the anchor and therefore perform one storage read
+    # even though the returned batch contains two tensors.
+    storage_sourced_tensors = 1 if POSITIVE_CELL_SOURCE == "self" else 2
+    logical_bytes = anchor.nelement() * anchor.element_size() * storage_sourced_tensors
+    read_bytes = metric_delta["bytes_read"] / N_BATCHES
     read_mb = read_bytes / 1e6
     bandwidth_mb_s = read_mb / (io_stats["mean_ms"] / 1000)
     io_stats["read_mb"] = read_mb
     io_stats["bandwidth_mb_s"] = bandwidth_mb_s
+    io_stats["logical_mb"] = logical_bytes / 1e6
+    io_stats["storage_amplification"] = read_bytes / logical_bytes
 
     print(f"   {io_stats['mean_ms']:.1f} ± {io_stats['std_ms']:.1f} ms")
-    pos_label = "+ positive" if positive is not None else ""
-    print(f"   read volume: {read_mb:.0f} MB (anchor{pos_label}) | bandwidth: {bandwidth_mb_s:.0f} MB/s")
+    print(
+        f"   TensorStore file bytes: {read_mb:.0f} MB/batch | bandwidth: {bandwidth_mb_s:.0f} MB/s | "
+        f"storage/logical: {io_stats['storage_amplification']:.2f}x"
+    )
+    print(
+        f"   measured reads: {metric_delta['chunk_reads'] / N_BATCHES:.1f} chunks/batch, "
+        f"{metric_delta['file_reads'] / N_BATCHES:.1f} file ranges/batch; "
+        f"storage-sourced tensor bytes: {io_stats['logical_mb']:.0f} MB/batch"
+    )
     print(f"   anchor shape: {anchor.shape}, dtype: {anchor.dtype}")
 
     # ── Stage 2: CPU→GPU transfer ──
     print("\n## Stage 2: CPU → GPU transfer")
 
     def transfer_step():
-        return anchor.to(DEVICE, non_blocking=True)
+        gpu_positive = positive.to(DEVICE, non_blocking=True) if positive is not None else None
+        return anchor.to(DEVICE, non_blocking=True), gpu_positive
 
-    transfer_stats, gpu_anchor = time_stage(transfer_step)
+    transfer_stats, (gpu_anchor, gpu_positive) = time_stage(transfer_step)
     print(f"   {transfer_stats['mean_ms']:.1f} ± {transfer_stats['std_ms']:.1f} ms")
-    print(f"   tensor size: {anchor.nelement() * anchor.element_size() / 1e6:.1f} MB")
+    output_bytes = anchor.nelement() * anchor.element_size()
+    output_bytes += positive.nelement() * positive.element_size() if positive is not None else 0
+    print(f"   output tensor size: {output_bytes / 1e6:.1f} MB")
 
     # ── Stage 3: Normalization ──
     print("\n## Stage 3: Normalization (subtract mean, divide std — manual)")
@@ -250,13 +312,17 @@ def transfer_step():
     # alignment that depends on the full DataModule context. Time the raw
     # arithmetic instead: this is what NormalizeSampled does per channel.
 
-    def norm_step():
-        x = gpu_anchor.clone()
+    def normalize_view(view):
+        x = view.clone()
         mean = x.mean(dim=(-3, -2, -1), keepdim=True)
         std = x.std(dim=(-3, -2, -1), keepdim=True)
         return (x - mean) / (std + 1e-8)
 
-    norm_stats, normed = time_stage(norm_step)
+    def norm_step():
+        normed_positive = normalize_view(gpu_positive) if gpu_positive is not None else None
+        return normalize_view(gpu_anchor), normed_positive
+
+    norm_stats, (normed, normed_positive) = time_stage(norm_step)
     print(f"   {norm_stats['mean_ms']:.1f} ± {norm_stats['std_ms']:.1f} ms")
 
     # ── Stage 4: Augmentations (individually) ──
@@ -268,19 +334,27 @@ def norm_step():
         "RandScaleIntensityd",
         "RandGaussianSmoothd",
         "RandGaussianNoised",
+        "RandSpatialCropd",
+        "ChannelWiseZReductiond",
     ]
     aug_total = 0.0
     current_input = normed
+    current_positive = normed_positive
 
     for aug_name, aug_transform in zip(aug_names, augmentations):
         t = Compose([aug_transform])
         inp = current_input
+        inp_positive = current_positive
 
-        def aug_step(transform=t, data=inp):
+        def aug_step(transform=t, data=inp, positive_data=inp_positive):
             d = {CHANNEL_KEY: data.clone()}
-            return transform(d)[CHANNEL_KEY]
+            transformed = transform(d)[CHANNEL_KEY]
+            if positive_data is not None:
+                positive = transform({CHANNEL_KEY: positive_data.clone()})[CHANNEL_KEY]
+                return transformed, positive
+            return transformed, None
 
-        stats, current_input = time_stage(aug_step)
+        stats, (current_input, current_positive) = time_stage(aug_step)
         aug_total += stats["mean_ms"]
         print(f"   {aug_name:30s} {stats['mean_ms']:8.1f} ± {stats['std_ms']:.1f} ms")
 
@@ -289,10 +363,15 @@ def aug_step(transform=t, data=inp):
     # ── Stage 5: Final crop ──
     print("\n## Stage 5: Final crop (BatchedRandSpatialCropd)")
     crop_input = current_input
+    crop_positive = current_positive
 
     def crop_step():
         d = {CHANNEL_KEY: crop_input.clone()}
-        return final_crop(d)[CHANNEL_KEY]
+        cropped = final_crop(d)[CHANNEL_KEY]
+        if crop_positive is not None:
+            positive = final_crop({CHANNEL_KEY: crop_positive.clone()})[CHANNEL_KEY]
+            return cropped, positive
+        return cropped, None
 
     crop_stats, _ = time_stage(crop_step)
     print(f"   {crop_stats['mean_ms']:.1f} ± {crop_stats['std_ms']:.1f} ms")
@@ -303,7 +382,7 @@ def crop_step():
     print("=" * 70)
 
     stages = {
-        "I/O (__getitems__)": io_stats["mean_ms"],
+        "Data fetch (__getitems__)": io_stats["mean_ms"],
         "CPU→GPU transfer": transfer_stats["mean_ms"],
         "Normalization": norm_stats["mean_ms"],
         "Augmentations (total)": aug_total,
@@ -314,7 +393,7 @@ def crop_step():
     print("\n| Stage | Time (ms) | % of total | Bandwidth |")
     print("|-------|-----------|------------|-----------|")
     for name, ms in stages.items():
-        if name == "I/O (__getitems__)":
+        if name == "Data fetch (__getitems__)":
             bw = f"{io_stats['bandwidth_mb_s']:.0f} MB/s ({io_stats['read_mb']:.0f} MB read)"
         else:
             bw = "—"

From e3457d234ef1b7e2792be274ef5ec3d5471ba738 Mon Sep 17 00:00:00 2001
From: Eduardo Hirata-Miyasaki
 <eduardo.hirata@tenant-ac-biohub-login-eduardo-hirata-57d4880-0.tenant-ac-biohub-login-all-users.tenant-ac-biohub.svc.cluster.local>
Date: Tue, 21 Jul 2026 23:13:06 +0000
Subject: [PATCH 2/4] fix(dynaclr): stabilize DDP logging and hparam
 checkpointing in engine

save_hyperparameters(ignore=...) so ContrastiveModule/BetaVaeModule
checkpoints carry their config; add sync_dist to hparam logging and skip
empty sample grids to avoid DDP logging desync.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 applications/dynaclr/src/dynaclr/engine.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/applications/dynaclr/src/dynaclr/engine.py b/applications/dynaclr/src/dynaclr/engine.py
index 924ee9b5c..1e87b1586 100644
--- a/applications/dynaclr/src/dynaclr/engine.py
+++ b/applications/dynaclr/src/dynaclr/engine.py
@@ -53,6 +53,7 @@ def __init__(
         auxiliary_heads: dict[str, BaseHead] | None = None,
     ) -> None:
         super().__init__()
+        self.save_hyperparameters(ignore=["encoder", "loss_function", "projection", "auxiliary_heads"])
         self.model = encoder
         if projection is not None:
             self.model.projection = projection
@@ -89,10 +90,10 @@ def on_train_epoch_start(self) -> None:  # noqa: D102
         if hasattr(self.loss_function, "step"):
             self.loss_function.step(self.current_epoch)
         if hasattr(self.loss_function, "temperature"):
-            self.log("hparams/temperature", self.loss_function.temperature)
+            self.log("hparams/temperature", self.loss_function.temperature, sync_dist=True)
         for head in self.auxiliary_heads.values():
             head.step(self.current_epoch)
-            self.log(f"hparams/loss_weight/{head.head_name}", head.get_weight())
+            self.log(f"hparams/loss_weight/{head.head_name}", head.get_weight(), sync_dist=True)
 
     def on_fit_start(self) -> None:  # noqa: D102
         if self.freeze_backbone:
@@ -189,8 +190,9 @@ def _log_metrics(self, loss, anchor, positive, stage: Literal["train", "val"], n
         )
 
     def _log_samples(self, key: str, imgs: Sequence[Sequence[np.ndarray]]):
-        if self.trainer.is_global_zero and self.logger is not None:
-            log_image_grid(self.logger, key, imgs, self.current_epoch, cmaps=["gray"] * 3)
+        if not imgs or not self.trainer.is_global_zero or self.logger is None:
+            return
+        log_image_grid(self.logger, key, imgs, self.current_epoch, cmaps=["gray"] * 3)
 
     def _log_step_samples(self, batch_idx, samples: tuple, stage: Literal["train", "val"]):
         if batch_idx < self.log_batches_per_epoch:
@@ -365,6 +367,7 @@ def __init__(
         log_enhanced_visualizations_frequency: int = 30,
     ):
         super().__init__()
+        self.save_hyperparameters(ignore=["vae", "loss_function"])
         self.model = vae
         self.loss_function = loss_function
         self.beta = beta

From a44d5a0fafb36babe0082014f1497e7d7932d832 Mon Sep 17 00:00:00 2001
From: Eduardo Hirata-Miyasaki
 <eduardo.hirata@tenant-ac-biohub-login-eduardo-hirata-57d4880-0.tenant-ac-biohub-login-all-users.tenant-ac-biohub.svc.cluster.local>
Date: Tue, 21 Jul 2026 23:13:06 +0000
Subject: [PATCH 3/4] docs: note Reef/Kelp vs Bruno cluster differences in
 CLAUDE.md

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 CLAUDE.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CLAUDE.md b/CLAUDE.md
index 6801f8eba..e6a33331f 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -55,6 +55,10 @@ Invariant: `#SBATCH --ntasks-per-node=N` must equal `trainer.devices` in the YAM
 
 The dynacell launcher (`applications/dynacell/tools/submit_benchmark_job.py`) already emits `--ntasks-per-node` correctly; this note is for hand-written scripts (e.g., `applications/cytoland/examples/configs/*/run_*.slurm`).
 
+### Running jobs on Reef/Kelp (CoreWeave) vs Bruno
+
+Our SLURM scripts above target Bruno (home institution, not preemptible). Reef/Kelp is a separate, preemptible CoreWeave cluster with different partitions, QOS, filesystem paths, and job-launch tooling (`slurm_run`) — see [docs/clusters/reef.md](./docs/clusters/reef.md) before adapting or writing a `.slurm`/`sbatch` script for Reef.
+
 ### Job monitoring and inspection
 
 **Process state ≠ training completeness.** Wandb's `state: finished` only means `wandb.finish()` was called — Lightning calls it on clean SIGTERM teardown via `SLURMEnvironment`, so a `scancel`'d run shows `finished` identically to one that hit `max_epochs`. Always cross-check.

From ba2746c9c1463090797e1c12d4176ed69940c3e4 Mon Sep 17 00:00:00 2001
From: Eduardo Hirata-Miyasaki
 <eduardo.hirata@tenant-ac-biohub-login-eduardo-hirata-57d4880-0.tenant-ac-biohub-login-all-users.tenant-ac-biohub.svc.cluster.local>
Date: Tue, 21 Jul 2026 23:13:17 +0000
Subject: [PATCH 4/4] feat(viscy-data): CSV metadata sidecar for
 write-protected datasets

Add viscy_data.meta_csv (build_provenance_fields, metadata_to_rows,
write_meta_rows_csv, read_meta_rows_csv) and thread a csv_dir option through
the preprocessing path so normalization/QC statistics can be written to and
read from per-store CSV sidecars instead of the zarr .zattrs, opening the
store read-only. Datasets mounted without write access can then be prepared
and trained on.

Wires csv_dir through VisCyTrainer.preprocess, generate_normalization_metadata,
the QC metrics run + config, the dynaclr cell-index preprocessing (CLI +
Nextflow module + workflow) and its parquet reader, and airtable prepare
(check_preprocessed + batch discovery). qc gains a viscy-data dependency for
the shared module; uv.lock updated accordingly.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../airtable/src/airtable_utils/prepare.py    |   73 +-
 .../src/airtable_utils/prepare_cli.py         |   99 +-
 applications/airtable/tests/test_prepare.py   |  153 ++
 .../dynaclr/docs/DAGs/ai_ready_datasets.md    |   88 +-
 applications/dynaclr/docs/DAGs/training.md    |   15 +-
 applications/dynaclr/nextflow/README.md       |    4 +
 applications/dynaclr/nextflow/main.nf         |    4 +-
 .../preprocessing/preprocess_cell_index.nf    |    8 +-
 applications/dynaclr/nextflow/nextflow.config |    1 +
 .../workflows/training_preprocessing.nf       |    6 +-
 .../src/dynaclr/data/preprocess_cell_index.py |   17 +-
 applications/qc/pyproject.toml                |    1 +
 applications/qc/src/qc/cli.py                 |    1 +
 applications/qc/src/qc/config.py              |    6 +
 applications/qc/src/qc/qc_metrics.py          |   48 +-
 applications/qc/tests/conftest.py             |   19 +
 applications/qc/tests/test_focus.py           |   56 +
 .../viscy-data/src/viscy_data/cell_index.py   |   52 +-
 .../viscy-data/src/viscy_data/meta_csv.py     |  225 ++
 packages/viscy-data/tests/test_meta_csv.py    |  199 ++
 .../tests/test_preprocess_cell_index.py       |  124 +
 .../viscy-utils/src/viscy_utils/meta_utils.py |   63 +-
 .../viscy-utils/src/viscy_utils/trainer.py    |   10 +
 packages/viscy-utils/tests/test_meta_utils.py |   60 +
 uv.lock                                       | 2396 ++++++++---------
 25 files changed, 2351 insertions(+), 1377 deletions(-)
 create mode 100644 applications/airtable/tests/test_prepare.py
 create mode 100644 packages/viscy-data/src/viscy_data/meta_csv.py
 create mode 100644 packages/viscy-data/tests/test_meta_csv.py
 create mode 100644 packages/viscy-data/tests/test_preprocess_cell_index.py

diff --git a/applications/airtable/src/airtable_utils/prepare.py b/applications/airtable/src/airtable_utils/prepare.py
index d35333a11..edadf0083 100644
--- a/applications/airtable/src/airtable_utils/prepare.py
+++ b/applications/airtable/src/airtable_utils/prepare.py
@@ -11,6 +11,8 @@
 from iohub import open_ome_zarr
 from pydantic import BaseModel, Field
 
+from viscy_data.meta_csv import read_meta_rows_csv
+
 logger = logging.getLogger(__name__)
 
 # ---------------------------------------------------------------------------
@@ -58,6 +60,7 @@ class SlurmStageConfig(BaseModel):
     time: str = "06:00:00"
     gres: str | None = None
     constraint: str | None = None
+    qos: str | None = None
 
 
 class SlurmConfig(BaseModel):
@@ -92,6 +95,8 @@ class PrepareConfig(BaseModel):
     nfs_root: Path = Path("/hpc/projects/intracellular_dashboard/organelle_dynamics")
     vast_root: Path = Path("/hpc/projects/organelle_phenotyping/datasets")
     workspace_dir: Path = Path("/hpc/mydata/eduardo.hirata/repos/viscy")
+    csv_dir: Path | None = None
+    jobs_dir: Path | None = None
     concatenate: ConcatenateConfig = Field(default_factory=ConcatenateConfig)
     qc: QCParams = Field(default_factory=QCParams)
     preprocess: PreprocessParams = Field(default_factory=PreprocessParams)
@@ -195,19 +200,28 @@ def check_zarr_version(zarr_path: Path) -> dict[str, int | str | None]:
     return result
 
 
-def check_preprocessed(zarr_path: Path) -> bool:
-    """Check if normalization metadata has been written to the zarr store.
+def check_preprocessed(zarr_path: Path, csv_dir: Path | None = None) -> bool:
+    """Check if normalization metadata has been written for the zarr store.
 
     Parameters
     ----------
     zarr_path : Path
         Path to the zarr store root.
+    csv_dir : Path or None
+        If given, check the per-store CSV sidecar under this directory
+        (written by ``viscy preprocess --csv_dir ...``) instead of the
+        store's ``.zattrs``/``zarr.json`` — for datasets prepared without
+        write access.
 
     Returns
     -------
     bool
         True if normalization stats are present.
     """
+    if csv_dir is not None:
+        sidecar = read_meta_rows_csv(csv_dir, zarr_path)
+        return sidecar is not None and bool((sidecar["field_name"] == "normalization").any())
+
     zarr_json = zarr_path / "zarr.json"
     zattrs = zarr_path / ".zattrs"
 
@@ -223,6 +237,34 @@ def check_preprocessed(zarr_path: Path) -> bool:
     return False
 
 
+# ---------------------------------------------------------------------------
+# Batch discovery (filesystem glob, no NFS/Airtable dependency)
+# ---------------------------------------------------------------------------
+
+
+def discover_zarr_stores(data_root: Path) -> list[Path]:
+    """Find dataset zarr stores under a root laid out like VAST's convention.
+
+    Matches ``{data_root}/{dataset_name}/{dataset_name}.zarr`` exactly (the
+    zarr's own basename must match its containing directory's name) — this
+    excludes sibling ``tracking.zarr`` dirs as well as any other variant
+    zarr stores (e.g. ``{dataset_name}_chunked.zarr``,
+    ``{dataset_name}_sharded.zarr``) that may sit alongside the canonical
+    store for rechunking/testing purposes.
+
+    Parameters
+    ----------
+    data_root : Path
+        Root directory containing one subdirectory per dataset.
+
+    Returns
+    -------
+    list[Path]
+        Sorted paths to each dataset's zarr store.
+    """
+    return sorted(p for p in Path(data_root).glob("*/*.zarr") if p.stem == p.parent.name)
+
+
 # ---------------------------------------------------------------------------
 # Discovery (reads NFS zarr via iohub)
 # ---------------------------------------------------------------------------
@@ -339,7 +381,7 @@ def generate_crop_concat_config(
     }
 
 
-def generate_qc_config(data_path: Path, qc_params: QCParams) -> dict:
+def generate_qc_config(data_path: Path, qc_params: QCParams, csv_dir: Path | None = None) -> dict:
     """Build a QC config dict compatible with ``qc run -c``.
 
     Parameters
@@ -348,6 +390,9 @@ def generate_qc_config(data_path: Path, qc_params: QCParams) -> dict:
         Path to the VAST zarr (target of QC).
     qc_params : QCParams
         Focus-slice QC parameters.
+    csv_dir : Path or None
+        If given, ``qc run`` writes results to a per-store CSV sidecar
+        under this directory (read-only store) instead of ``.zattrs``.
 
     Returns
     -------
@@ -357,6 +402,7 @@ def generate_qc_config(data_path: Path, qc_params: QCParams) -> dict:
     return {
         "data_path": str(data_path),
         "num_workers": qc_params.num_workers,
+        "csv_dir": str(csv_dir) if csv_dir else None,
         "focus_slice": {
             "channel_names": qc_params.channel_names,
             "NA_det": qc_params.NA_det,
@@ -412,6 +458,8 @@ def _slurm_header(job_name: str, output_dir: Path, cfg: SlurmStageConfig) -> str
         lines.append(f"#SBATCH --gres={cfg.gres}")
     if cfg.constraint:
         lines.append(f'#SBATCH --constraint="{cfg.constraint}"')
+    if cfg.qos:
+        lines.append(f"#SBATCH --qos={cfg.qos}")
     return "\n".join(lines)
 
 
@@ -532,7 +580,7 @@ def generate_qc_slurm(
         export PYTHONNOUSERSITE=1
 
         echo "=== QC: focus slice detection ==="
-        uv run --project "{workspace_dir}" --package qc \
+        uv run --project "{workspace_dir}" --package viscy-qc \
             qc run -c "{qc_config_path}"
         echo "QC complete."
     """)
@@ -546,6 +594,7 @@ def generate_preprocess_slurm(
     workspace_dir: Path,
     preprocess_params: PreprocessParams,
     slurm_cfg: SlurmStageConfig,
+    csv_dir: Path | None = None,
 ) -> str:
     """Generate SLURM script for normalization preprocessing (CPU only).
 
@@ -563,6 +612,10 @@ def generate_preprocess_slurm(
         Normalization preprocessing parameters.
     slurm_cfg : SlurmStageConfig
         SLURM resource parameters.
+    csv_dir : Path or None
+        If given, ``viscy preprocess`` writes results to a per-store CSV
+        sidecar under this directory (read-only store) instead of
+        ``.zattrs``.
 
     Returns
     -------
@@ -576,6 +629,7 @@ def generate_preprocess_slurm(
         ch_flag = f"--channel_names={ch_arg}"
     else:
         ch_flag = " ".join(f"--channel_names={c}" for c in ch_arg)
+    csv_dir_flag = f' --csv_dir "{csv_dir}"' if csv_dir else ""
 
     body = dedent(f"""\
 
@@ -586,7 +640,7 @@ def generate_preprocess_slurm(
         uv run --project "{workspace_dir}" --package dynaclr \
             viscy preprocess --data_path "{vast_zarr_path}" \
             {ch_flag} --num_workers {preprocess_params.num_workers} \
-            --block_size {preprocess_params.block_size}
+            --block_size {preprocess_params.block_size}{csv_dir_flag}
         echo "Preprocess complete."
     """)
     return header + "\n" + body
@@ -597,7 +651,9 @@ def generate_preprocess_slurm(
 # ---------------------------------------------------------------------------
 
 
-def check_dataset_status(dataset_name: str, nfs_root: Path, vast_root: Path) -> dict[str, str]:
+def check_dataset_status(
+    dataset_name: str, nfs_root: Path, vast_root: Path, csv_dir: Path | None = None
+) -> dict[str, str]:
     """Check existence and version info for a dataset across NFS and VAST.
 
     Parameters
@@ -608,6 +664,9 @@ def check_dataset_status(dataset_name: str, nfs_root: Path, vast_root: Path) ->
         NFS root directory.
     vast_root : Path
         VAST root directory.
+    csv_dir : Path or None
+        If given, check the ``preprocessed`` status via the CSV sidecar
+        under this directory instead of ``.zattrs`` (see ``check_preprocessed``).
 
     Returns
     -------
@@ -629,7 +688,7 @@ def check_dataset_status(dataset_name: str, nfs_root: Path, vast_root: Path) ->
         ver = check_zarr_version(vast["zarr"])
         zarr_fmt = str(ver["zarr_format"]) if ver["zarr_format"] else "?"
         ome_ver = str(ver["ome_version"]) if ver["ome_version"] else "?"
-        preprocessed = "yes" if check_preprocessed(vast["zarr"]) else "no"
+        preprocessed = "yes" if check_preprocessed(vast["zarr"], csv_dir=csv_dir) else "no"
 
     return {
         "dataset": dataset_name,
diff --git a/applications/airtable/src/airtable_utils/prepare_cli.py b/applications/airtable/src/airtable_utils/prepare_cli.py
index c4e9486bb..23fe904ac 100644
--- a/applications/airtable/src/airtable_utils/prepare_cli.py
+++ b/applications/airtable/src/airtable_utils/prepare_cli.py
@@ -7,6 +7,7 @@
 import subprocess
 
 import click
+from viscy_data.meta_csv import backfill_zattrs_to_csv
 
 from airtable_utils.prepare import (
     PrepareConfig,
@@ -15,6 +16,7 @@
     check_zarr_version,
     discover_channels,
     discover_wells,
+    discover_zarr_stores,
     filter_raw_channels,
     format_status_table,
     generate_concatenate_script,
@@ -100,7 +102,7 @@ def run(dataset_name: str, config_path: str, dry_run: bool, force: bool) -> None
         ver = check_zarr_version(vast["zarr"])
         is_v3 = ver["zarr_format"] == 3
         is_ome05 = ver["ome_version"] == "0.5"
-        is_preprocessed = check_preprocessed(vast["zarr"])
+        is_preprocessed = check_preprocessed(vast["zarr"], csv_dir=cfg.csv_dir)
 
         if is_v3 and is_ome05 and is_preprocessed:
             click.echo(
@@ -149,7 +151,7 @@ def run(dataset_name: str, config_path: str, dry_run: bool, force: bool) -> None
     click.echo(f"  Wrote: {crop_concat_path}")
 
     # 8. Generate qc_config.yml
-    qc_cfg = generate_qc_config(vast["zarr"], cfg.qc)
+    qc_cfg = generate_qc_config(vast["zarr"], cfg.qc, csv_dir=cfg.csv_dir)
     qc_config_path = vast["output_dir"] / "qc_config.yml"
     write_yaml(qc_cfg, qc_config_path)
     click.echo(f"  Wrote: {qc_config_path}")
@@ -192,6 +194,7 @@ def run(dataset_name: str, config_path: str, dry_run: bool, force: bool) -> None
         workspace_dir=cfg.workspace_dir,
         preprocess_params=cfg.preprocess,
         slurm_cfg=cfg.slurm.preprocess,
+        csv_dir=cfg.csv_dir,
     )
     preprocess_script_path = vast["output_dir"] / "03_preprocess.sh"
     preprocess_script_path.write_text(preprocess_script)
@@ -246,10 +249,100 @@ def status(dataset_names: tuple[str, ...], config_path: str) -> None:
     """Check NFS/VAST existence and version status for one or more datasets."""
     cfg = _load_prepare_config(config_path)
 
-    rows = [check_dataset_status(name, cfg.nfs_root, cfg.vast_root) for name in dataset_names]
+    rows = [check_dataset_status(name, cfg.nfs_root, cfg.vast_root, csv_dir=cfg.csv_dir) for name in dataset_names]
     click.echo(format_status_table(rows))
 
 
+@prepare.command("batch-preprocess")
+@click.argument("data_root", type=click.Path(exists=True, file_okay=False))
+@click.option(
+    "-c",
+    "--config",
+    "config_path",
+    required=True,
+    type=click.Path(exists=True),
+    help="Path to prepare config YAML.",
+)
+@click.option("--dry-run", is_flag=True, help="Generate configs/scripts without submitting SLURM jobs.")
+def batch_preprocess(data_root: str, config_path: str, dry_run: bool) -> None:
+    """Preprocess every not-yet-preprocessed dataset zarr under DATA_ROOT.
+
+    Discovers `{name}/{name}.zarr` stores under DATA_ROOT, skips any already
+    marked preprocessed in the CSV sidecar, mirrors metadata that's already
+    sitting in a store's own `.zattrs` (e.g. computed elsewhere with write
+    access, then copied here) into the CSV sidecar for free, and only
+    generates + submits QC/normalization SLURM jobs for stores with neither.
+    Safe to re-run repeatedly as new datasets appear — anything already
+    covered (via CSV or zattrs) is always skipped, so this never redoes work.
+    """
+    cfg = _load_prepare_config(config_path)
+    if cfg.csv_dir is None:
+        raise click.ClickException(
+            "batch-preprocess requires 'csv_dir' in the prepare config — datasets under "
+            "DATA_ROOT may be owned by teammates without write access to their .zattrs."
+        )
+    jobs_root = cfg.jobs_dir or cfg.csv_dir
+
+    stores = discover_zarr_stores(data_root)
+    click.echo(f"Discovered {len(stores)} dataset(s) under {data_root}.")
+
+    pending = [store for store in stores if not check_preprocessed(store, csv_dir=cfg.csv_dir)]
+
+    backfilled = []
+    todo = []
+    for store in pending:
+        if backfill_zattrs_to_csv(store, cfg.csv_dir):
+            backfilled.append(store)
+        else:
+            todo.append(store)
+
+    click.echo(
+        f"  {len(stores) - len(pending)} already in CSV sidecar, "
+        f"{len(backfilled)} backfilled from existing zattrs, {len(todo)} need SLURM jobs."
+    )
+
+    for store in todo:
+        dataset_name = store.stem
+        job_dir = jobs_root / "_jobs" / dataset_name
+        job_dir.mkdir(parents=True, exist_ok=True)
+
+        qc_config_path = job_dir / "qc_config.yml"
+        write_yaml(generate_qc_config(store, cfg.qc, csv_dir=cfg.csv_dir), qc_config_path)
+
+        qc_script_path = job_dir / "02_qc.sh"
+        qc_script_path.write_text(
+            generate_qc_slurm(dataset_name, job_dir, qc_config_path, cfg.workspace_dir, cfg.slurm.qc)
+        )
+
+        preprocess_script_path = job_dir / "03_preprocess.sh"
+        preprocess_script_path.write_text(
+            generate_preprocess_slurm(
+                dataset_name,
+                job_dir,
+                store,
+                cfg.workspace_dir,
+                cfg.preprocess,
+                cfg.slurm.preprocess,
+                csv_dir=cfg.csv_dir,
+            )
+        )
+        click.echo(f"  Wrote: {job_dir}")
+
+        if dry_run:
+            continue
+
+        result_qc = subprocess.run(["sbatch", str(qc_script_path)], capture_output=True, text=True, check=True)
+        qc_job_id = _parse_slurm_job_id(result_qc.stdout)
+        result_pp = subprocess.run(["sbatch", str(preprocess_script_path)], capture_output=True, text=True, check=True)
+        pp_job_id = _parse_slurm_job_id(result_pp.stdout)
+        click.echo(f"  {dataset_name}: QC job {qc_job_id}, preprocess job {pp_job_id}")
+
+    if dry_run:
+        click.echo("\n--dry-run: configs and scripts generated, nothing submitted.")
+    else:
+        click.echo(f"\nSubmitted jobs for {len(todo)} dataset(s).")
+
+
 def main() -> None:
     """Entry point for the prepare CLI."""
     prepare()
diff --git a/applications/airtable/tests/test_prepare.py b/applications/airtable/tests/test_prepare.py
new file mode 100644
index 000000000..a4fb2b314
--- /dev/null
+++ b/applications/airtable/tests/test_prepare.py
@@ -0,0 +1,153 @@
+"""Tests for airtable_utils.prepare — CSV-sidecar (csv_dir) wiring."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from airtable_utils.prepare import (
+    PrepareConfig,
+    PreprocessParams,
+    QCParams,
+    SlurmStageConfig,
+    check_preprocessed,
+    discover_zarr_stores,
+    generate_preprocess_slurm,
+    generate_qc_config,
+    generate_qc_slurm,
+)
+from viscy_data.meta_csv import metadata_to_rows, write_meta_rows_csv
+
+
+def test_prepare_config_default_csv_dir_is_none():
+    """csv_dir defaults to None, so existing configs without it are unaffected."""
+    cfg = PrepareConfig()
+    assert cfg.csv_dir is None
+
+
+def test_prepare_config_accepts_csv_dir(tmp_path):
+    """csv_dir can be set to a path."""
+    cfg = PrepareConfig(csv_dir=tmp_path)
+    assert cfg.csv_dir == tmp_path
+
+
+def test_prepare_config_default_jobs_dir_is_none():
+    """jobs_dir defaults to None; batch-preprocess falls back to csv_dir."""
+    cfg = PrepareConfig()
+    assert cfg.jobs_dir is None
+
+
+def test_check_preprocessed_csv_dir_present(tmp_path):
+    """check_preprocessed reads the CSV sidecar when csv_dir is given."""
+    store_path = "/data/exp.zarr"
+    rows = metadata_to_rows(
+        {"fov_statistics": {"mean": 1.0, "std": 0.1, "median": 1.0, "iqr": 0.2, "max": 5.0, "min": 0.0}},
+        store_path=store_path,
+        position_path="A/1/0",
+        channel_name="Phase3D",
+        field_name="normalization",
+    )
+    write_meta_rows_csv(tmp_path, store_path, rows)
+
+    assert check_preprocessed(Path(store_path), csv_dir=tmp_path) is True
+
+
+def test_check_preprocessed_csv_dir_absent(tmp_path):
+    """check_preprocessed returns False (not an error) when no sidecar exists yet."""
+    assert check_preprocessed(Path("/data/never_run.zarr"), csv_dir=tmp_path) is False
+
+
+def test_check_preprocessed_csv_dir_ignores_other_field(tmp_path):
+    """A sidecar with only focus_slice rows (no normalization) is not 'preprocessed'."""
+    store_path = "/data/exp.zarr"
+    rows = metadata_to_rows(
+        {"fov_statistics": {"z_focus_mean": 4.0, "z_focus_std": 0.5}},
+        store_path=store_path,
+        position_path="A/1/0",
+        channel_name="Phase3D",
+        field_name="focus_slice",
+    )
+    write_meta_rows_csv(tmp_path, store_path, rows)
+
+    assert check_preprocessed(Path(store_path), csv_dir=tmp_path) is False
+
+
+def test_generate_qc_config_includes_csv_dir(tmp_path):
+    """csv_dir is written into the qc_config.yml dict when given."""
+    cfg = generate_qc_config(Path("/data/exp.zarr"), QCParams(), csv_dir=tmp_path)
+    assert cfg["csv_dir"] == str(tmp_path)
+
+
+def test_generate_qc_config_omits_csv_dir_by_default():
+    """csv_dir is None in the dict when not given, matching QCConfig's default."""
+    cfg = generate_qc_config(Path("/data/exp.zarr"), QCParams())
+    assert cfg["csv_dir"] is None
+
+
+def test_generate_preprocess_slurm_includes_csv_dir_flag(tmp_path):
+    """--csv_dir is appended to the viscy preprocess invocation when csv_dir is set."""
+    script = generate_preprocess_slurm(
+        dataset_name="exp",
+        vast_output_dir=tmp_path,
+        vast_zarr_path=tmp_path / "exp.zarr",
+        workspace_dir=Path("/workspace"),
+        preprocess_params=PreprocessParams(),
+        slurm_cfg=SlurmStageConfig(partition="cpu"),
+        csv_dir=tmp_path / "csvs",
+    )
+    assert f'--csv_dir "{tmp_path / "csvs"}"' in script
+    assert "viscy preprocess" in script
+
+
+def test_generate_preprocess_slurm_omits_csv_dir_flag_by_default(tmp_path):
+    """No --csv_dir flag when csv_dir is not given (existing behavior unchanged)."""
+    script = generate_preprocess_slurm(
+        dataset_name="exp",
+        vast_output_dir=tmp_path,
+        vast_zarr_path=tmp_path / "exp.zarr",
+        workspace_dir=Path("/workspace"),
+        preprocess_params=PreprocessParams(),
+        slurm_cfg=SlurmStageConfig(partition="cpu"),
+    )
+    assert "--csv_dir" not in script
+
+
+def test_discover_zarr_stores_finds_dataset_zarrs_and_skips_tracking(tmp_path):
+    """discover_zarr_stores finds {name}/{name}.zarr but skips sibling tracking.zarr."""
+    for name in ("dsB", "dsA"):
+        (tmp_path / name / f"{name}.zarr").mkdir(parents=True)
+        (tmp_path / name / "tracking.zarr").mkdir(parents=True)
+    (tmp_path / "not_a_dataset").mkdir()
+    (tmp_path / "not_a_dataset" / "notes.txt").write_text("")
+
+    stores = discover_zarr_stores(tmp_path)
+
+    assert stores == [tmp_path / "dsA" / "dsA.zarr", tmp_path / "dsB" / "dsB.zarr"]
+
+
+def test_discover_zarr_stores_empty_root(tmp_path):
+    """No dataset subdirectories -> empty list, not an error."""
+    assert discover_zarr_stores(tmp_path) == []
+
+
+def test_slurm_header_includes_qos_when_set(tmp_path):
+    """--qos is appended to the SBATCH header when the SlurmStageConfig sets it."""
+    script = generate_qc_slurm(
+        dataset_name="exp",
+        vast_output_dir=tmp_path,
+        qc_config_path=tmp_path / "qc_config.yml",
+        workspace_dir=Path("/workspace"),
+        slurm_cfg=SlurmStageConfig(partition="gpu", qos="mid"),
+    )
+    assert "#SBATCH --qos=mid" in script
+
+
+def test_slurm_header_omits_qos_by_default(tmp_path):
+    """No --qos line when qos is not set (existing Bruno configs unaffected)."""
+    script = generate_qc_slurm(
+        dataset_name="exp",
+        vast_output_dir=tmp_path,
+        qc_config_path=tmp_path / "qc_config.yml",
+        workspace_dir=Path("/workspace"),
+        slurm_cfg=SlurmStageConfig(partition="gpu"),
+    )
+    assert "--qos" not in script
diff --git a/applications/dynaclr/docs/DAGs/ai_ready_datasets.md b/applications/dynaclr/docs/DAGs/ai_ready_datasets.md
index 8e000769f..cfa93a11d 100644
--- a/applications/dynaclr/docs/DAGs/ai_ready_datasets.md
+++ b/applications/dynaclr/docs/DAGs/ai_ready_datasets.md
@@ -45,13 +45,21 @@ tracking.zarr   (cell tracking results)
   ├──► sbatch 02_qc.sh                   # GPU (~30 min)
   │       qc run -c qc_config.yml        # focus-slice detection on Phase3D channel
   │       → writes focus_slice metadata into {dataset}.zarr
+  │         (or a CSV sidecar under csv_dir, if set — read-only stores)
   │
   └──► sbatch 03_preprocess.sh           # CPU, preempted partition (~4 hrs)
           viscy preprocess               # computes per-channel normalization stats
               --data_path {dataset}.zarr
           → writes normalization metadata into {dataset}.zarr
+            (or a CSV sidecar under csv_dir, if set — read-only stores)
 ```
 
+If `prepare_config.yaml` sets `csv_dir`, both jobs open `{dataset}.zarr`
+read-only and write to a per-store CSV sidecar under `csv_dir` instead —
+use this when VAST datasets are mounted read+execute only. See
+[training.md](training.md) for the corresponding `--csv-dir` flag on
+`preprocess-cell-index`.
+
 ## Pipeline DAG (process dependency)
 
 ```
@@ -92,6 +100,8 @@ Both write metadata back to the same zarr; their outputs are checked by
 nfs_root: /hpc/projects/intracellular_dashboard/organelle_dynamics
 vast_root: /hpc/projects/organelle_phenotyping/datasets
 workspace_dir: /hpc/mydata/eduardo.hirata/repos/viscy
+csv_dir: null                  # optional: set to a path to preprocess read-only
+                                # datasets via CSV sidecars instead of zarr zattrs
 
 concatenate:
   channel_names: null          # null = auto-detect raw channels (Phase3D + "raw " prefix)
@@ -141,8 +151,11 @@ node or an interactive session; it blocks until biahub's internal SLURM jobs fin
 `01_concatenate.sh` completes; no need to wait for QC before running preprocess.
 - Channel auto-detection (`channel_names: null`) keeps channels with prefix `Phase3D` or `raw` .
 Virtual stains (`nuclei_prediction`, `membrane_prediction`) and deconvolved channels are excluded.
-- `check_preprocessed()` checks for `normalization` key in zarr metadata; used by `prepare status`
-and as a gate before evaluation.
+- `check_preprocessed()` checks for `normalization` key in zarr metadata (or, when `csv_dir` is set,
+for a `normalization` row in the per-store CSV sidecar); used by `prepare status` and as a gate
+before evaluation.
+- `csv_dir` is a single shared root for all datasets under one `prepare_config.yaml` — per-store
+CSVs are named from a slug of the store path, so multiple datasets can safely share one root.
 - Raw channel names written to `crop_concat.yml` are repeated once per well entry — this is a
 biahub concatenate requirement.
 
@@ -161,3 +174,74 @@ All AI-ready data lives under `/hpc/projects/organelle_phenotyping/`:
 
 Collection YAMLs use `datasets_root: /hpc/projects/organelle_phenotyping` and
 `${datasets_root}/datasets/...` placeholders — resolved at load time by `load_collection()`.
+
+## Batch preprocessing on Reef (many datasets, incremental)
+
+`prepare run` assumes Bruno's NFS-assemble → `biahub concatenate` → VAST
+pipeline. On Reef, `{name}.zarr` + `tracking.zarr` stores are usually
+Globus-copied from Bruno already assembled — there's nothing to
+concatenate, and per-file ownership varies by whoever ran the transfer
+(everyone else in the group has read-only access to those files). For this
+case, use `prepare batch-preprocess <data_root> -c prepare_config.yaml`
+instead of `prepare run` per dataset:
+
+```bash
+prepare batch-preprocess /bio/projects/compimaging/data/infectomics/imaging/datasets \
+    -c reef_prepare_config.yaml
+
+# Dry-run: generate configs/scripts without submitting
+prepare batch-preprocess /bio/projects/compimaging/data/infectomics/imaging/datasets \
+    -c reef_prepare_config.yaml \
+    --dry-run
+```
+
+It discovers every `{data_root}/{name}/{name}.zarr` store (skipping sibling
+`tracking.zarr` dirs), skips any already marked preprocessed in the CSV
+sidecar (via `check_preprocessed(..., csv_dir=...)`), and generates +
+submits `02_qc.sh`/`03_preprocess.sh`-equivalent SLURM jobs for the rest —
+**always** via `csv_dir` (required for this command), never `.zattrs`
+directly, so it works uniformly regardless of who owns a given dataset.
+Re-running it as new datasets land only processes the new ones; nothing
+already in the sidecar gets redone.
+
+Since the batch may not have write access into each dataset's own
+directory, generated `qc_config.yml`/`02_qc.sh`/`03_preprocess.sh` land
+under `{jobs_dir or csv_dir}/_jobs/{dataset_name}/` instead — a writable
+scratch location shared by everyone using the same `prepare_config.yaml`.
+
+Reef also needs an explicit `--qos` on every `sbatch` (see
+[docs/clusters/reef.md](../../../../docs/clusters/reef.md)) — set
+`slurm.qc.qos`/`slurm.preprocess.qos` in the config; unset on Bruno-style
+configs, which don't need it. Example Reef config:
+
+```yaml
+csv_dir: /bio/projects/compimaging/data/infectomics/metadata_csv
+jobs_dir: /bio/projects/compimaging/data/infectomics/metadata_csv   # optional; defaults to csv_dir
+workspace_dir: /mnt/main0/home/eduardo.hirata/repos/VisCy
+
+qc:
+  channel_names: [Phase3D]
+  NA_det: 1.35
+  lambda_ill: 0.450
+  pixel_size: 0.1494
+  device: cuda
+
+preprocess:
+  channel_names: -1
+  num_workers: 32
+  block_size: 32
+
+slurm:
+  qc:
+    partition: h100_reserved
+    gres: gpu:1
+    qos: mid
+    time: "00:30:00"
+  preprocess:
+    partition: cpu
+    qos: mid
+    time: "04:00:00"
+```
+
+`nfs_root`/`vast_root`/`concatenate` can be left at their defaults — this
+command never reads them.
diff --git a/applications/dynaclr/docs/DAGs/training.md b/applications/dynaclr/docs/DAGs/training.md
index ca448dd7c..eefb8ea4c 100644
--- a/applications/dynaclr/docs/DAGs/training.md
+++ b/applications/dynaclr/docs/DAGs/training.md
@@ -6,7 +6,8 @@ Datasets must be AI-ready before building a collection. See [ai_ready_datasets.m
 for the full data preparation pipeline (`prepare run` → concatenate → QC → preprocess).
 
 A dataset is ready when `prepare status` shows `preprocessed: yes` — meaning both
-`normalization` and `focus_slice` metadata exist in the zarr zattrs.
+`normalization` and `focus_slice` metadata exist in the zarr zattrs (or, for
+read-only stores, in the per-store CSV sidecar under `csv_dir`).
 
 ## Step-by-step detail
 
@@ -30,8 +31,10 @@ dynaclr build-cell-index \
   ▼
 dynaclr preprocess-cell-index \
     /hpc/.../collections/<collection>.parquet \
-    --focus-channel Phase3D
-  │  opens each unique FOV once from zarr zattrs:
+    --focus-channel Phase3D \
+    [--csv-dir /path/to/csv_sidecars]
+  │  opens each unique FOV once from zarr zattrs (or CSV sidecars under
+  │  --csv-dir, for read-only stores):
   │    norm_mean/std/median/iqr/max/min  — per (cell, timepoint, channel)
   │    z_focus_mean                      — per FOV (mean across timepoints)
   │    z                                 — per timepoint focus slice index
@@ -71,7 +74,7 @@ viscy fit  (GPU, hours–days)
 | Step                  | Command                                                                   | Input                                  | Output                                                    |
 | --------------------- | ------------------------------------------------------------------------- | -------------------------------------- | --------------------------------------------------------- |
 | Build cell index      | `dynaclr build-cell-index <collection.yml> <out.parquet> --num-workers 8` | collection YAML + zarr + tracking CSVs | parquet with TCZYX shape columns                          |
-| Preprocess cell index | `dynaclr preprocess-cell-index <parquet> --focus-channel Phase3D`         | parquet + zarr zattrs                  | parquet with norm stats, per-timepoint z, empties removed |
+| Preprocess cell index | `dynaclr preprocess-cell-index <parquet> --focus-channel Phase3D [--csv-dir <dir>]` | parquet + zarr zattrs (or CSV sidecars) | parquet with norm stats, per-timepoint z, empties removed |
 | Train (interactive)   | `uv run viscy fit --config configs/training/<model>.yml`                  | training config + parquet              | checkpoints + logs                                        |
 | Train (SLURM)         | `sbatch configs/training/<model>.sh`                                      | training config + parquet              | checkpoints + logs                                        |
 | Resume (SLURM)        | `CKPT_PATH=.../last.ckpt sbatch configs/training/<model>.sh`              | checkpoint path env var                | resumed checkpoints                                       |
@@ -151,7 +154,8 @@ Version `collection.yml` in git. The parquet is derived deterministically from:
 
 1. The collection YAML (experiment definitions, channels, wells)
 2. Tracking zarrs (cell positions)
-3. Zarr zattrs (normalization + focus stats from `viscy preprocess` + `qc run`)
+3. Zarr zattrs, or CSV sidecars for read-only stores (normalization + focus
+   stats from `viscy preprocess` + `qc run`)
 
 To reproduce: `build-cell-index` → `preprocess-cell-index` from the same collection YAML.
 
@@ -159,6 +163,7 @@ To reproduce: `build-cell-index` → `preprocess-cell-index` from the same colle
 
 - `preprocess-cell-index` overwrites the parquet in-place by default. Pass `--output` to write elsewhere.
 - `--focus-channel Phase3D` selects which channel's `per_timepoint` focus indices are written to the `z` column. Use the channel that has the sharpest axial contrast (label-free Phase3D for most experiments).
+- `--csv-dir <dir>` reads norm/focus metadata from the per-store CSV sidecars under `<dir>` instead of opening each FOV's zarr zattrs. Use this when the source stores were preprocessed with `viscy preprocess`/`qc run --csv_dir <dir>` (read-only stores) — see [ai_ready_datasets.md](ai_ready_datasets.md).
 - At training time, `ExperimentRegistry.__post_init__` reads `plate.zattrs["focus_slice"][channel]["dataset_statistics"]["z_focus_mean"]` to compute per-experiment z_ranges for patch extraction. This is the only zarr metadata read at training startup; the parquet is self-contained for all per-cell data.
 - The `z` column in the parquet is carried through to embeddings obs during predict — downstream consumers (e.g., visualization) can use it to recover the in-focus plane for each cell at each timepoint.
 - For performance tuning (num_workers, pin_memory, batch_size, augmentation placement), see [profiling.md](profiling.md) — authored after the first validated profiling sweep.
diff --git a/applications/dynaclr/nextflow/README.md b/applications/dynaclr/nextflow/README.md
index f2d4f9184..ce3448dc3 100644
--- a/applications/dynaclr/nextflow/README.md
+++ b/applications/dynaclr/nextflow/README.md
@@ -42,7 +42,11 @@ nextflow run applications/dynaclr/nextflow/main.nf -entry training_preprocessing
     --parquet_out /hpc/projects/organelle_phenotyping/models/collections/<name>.parquet \
     --focus_channel Phase3D \
     --workspace_dir /hpc/mydata/eduardo.hirata/repos/viscy \
+    [--csv_dir /path/to/csv_sidecars] \
     -resume
+# --csv_dir is optional: pass it when the source stores were preprocessed
+# read-only (via `viscy preprocess`/`qc run --csv_dir ...`) and norm/focus
+# metadata lives in per-store CSV sidecars instead of zarr zattrs.
 
 # Local test (no SLURM) — append `-profile local`
 ```
diff --git a/applications/dynaclr/nextflow/main.nf b/applications/dynaclr/nextflow/main.nf
index dfacbec75..097c95382 100644
--- a/applications/dynaclr/nextflow/main.nf
+++ b/applications/dynaclr/nextflow/main.nf
@@ -19,6 +19,7 @@
 //       --parquet_out /hpc/.../collections/<name>.parquet \
 //       --focus_channel Phase3D \
 //       --workspace_dir /hpc/mydata/eduardo.hirata/repos/viscy \
+//       [--csv_dir /path/to/csv_sidecars] \
 //       -resume
 //
 // Zarr/parquet files are read/written in place on VAST (no staging).
@@ -65,6 +66,7 @@ workflow training_preprocessing {
         params.parquet_out,
         params.focus_channel,
         params.num_workers,
-        params.workspace_dir
+        params.workspace_dir,
+        params.csv_dir
     )
 }
diff --git a/applications/dynaclr/nextflow/modules/preprocessing/preprocess_cell_index.nf b/applications/dynaclr/nextflow/modules/preprocessing/preprocess_cell_index.nf
index c619fe118..cb3f63e17 100644
--- a/applications/dynaclr/nextflow/modules/preprocessing/preprocess_cell_index.nf
+++ b/applications/dynaclr/nextflow/modules/preprocessing/preprocess_cell_index.nf
@@ -1,5 +1,6 @@
 // Enrich the cell index parquet with norm stats + per-timepoint focus slice z.
-// Opens each unique FOV once from zarr zattrs; overwrites parquet in place.
+// Opens each unique FOV once from zarr zattrs (or CSV sidecars if csv_dir is
+// set); overwrites parquet in place.
 
 process PREPROCESS_CELL_INDEX {
     label 'cpu'
@@ -7,16 +8,19 @@ process PREPROCESS_CELL_INDEX {
     input:
     val parquet_in
     val focus_channel
+    val csv_dir
     val workspace_dir
 
     output:
     val parquet_in, emit: parquet
 
     script:
+    def csvDirFlag = csv_dir ? "--csv-dir ${csv_dir}" : ""
     """
     uv run --project=${workspace_dir} --package=dynaclr \
         dynaclr preprocess-cell-index \
         ${parquet_in} \
-        --focus-channel ${focus_channel}
+        --focus-channel ${focus_channel} \
+        ${csvDirFlag}
     """
 }
diff --git a/applications/dynaclr/nextflow/nextflow.config b/applications/dynaclr/nextflow/nextflow.config
index 3920fa2ed..85cb678b0 100644
--- a/applications/dynaclr/nextflow/nextflow.config
+++ b/applications/dynaclr/nextflow/nextflow.config
@@ -24,6 +24,7 @@ params {
     parquet_out     = null        // Required for -entry training_preprocessing
     focus_channel   = "Phase3D"
     num_workers     = 8
+    csv_dir         = null        // Optional: read norm/focus metadata from CSV sidecars instead of zattrs
 }
 
 process {
diff --git a/applications/dynaclr/nextflow/workflows/training_preprocessing.nf b/applications/dynaclr/nextflow/workflows/training_preprocessing.nf
index 517acac3c..d9470b7f6 100644
--- a/applications/dynaclr/nextflow/workflows/training_preprocessing.nf
+++ b/applications/dynaclr/nextflow/workflows/training_preprocessing.nf
@@ -9,6 +9,9 @@
 //   --parquet_out       output parquet path
 //   --focus_channel     channel used for per-timepoint z (default: Phase3D)
 //   --num_workers       build-cell-index parallelism (default: 8)
+//   --csv_dir           optional: read norm/focus metadata from CSV sidecars
+//                        (written by `viscy preprocess`/`qc run --csv_dir`)
+//                        instead of zarr zattrs, for read-only stores
 
 include { BUILD_CELL_INDEX       } from '../modules/preprocessing/build_cell_index'
 include { PREPROCESS_CELL_INDEX  } from '../modules/preprocessing/preprocess_cell_index'
@@ -21,8 +24,9 @@ workflow TRAINING_PREPROCESSING {
         focus_channel
         num_workers
         workspace_dir
+        csv_dir
 
     main:
     BUILD_CELL_INDEX(collection_yaml, parquet_out, num_workers, workspace_dir)
-    PREPROCESS_CELL_INDEX(BUILD_CELL_INDEX.out.parquet, focus_channel, workspace_dir)
+    PREPROCESS_CELL_INDEX(BUILD_CELL_INDEX.out.parquet, focus_channel, csv_dir, workspace_dir)
 }
diff --git a/applications/dynaclr/src/dynaclr/data/preprocess_cell_index.py b/applications/dynaclr/src/dynaclr/data/preprocess_cell_index.py
index e49bc1c74..53ae77f48 100644
--- a/applications/dynaclr/src/dynaclr/data/preprocess_cell_index.py
+++ b/applications/dynaclr/src/dynaclr/data/preprocess_cell_index.py
@@ -17,14 +17,25 @@
     default=None,
     help="Channel name for focus_slice lookup (e.g. Phase3D). Default: first channel per FOV.",
 )
-def main(parquet_path, output, focus_channel):
+@click.option(
+    "--csv-dir",
+    default=None,
+    help=(
+        "Read normalization/focus_slice metadata from per-store CSV sidecars under this "
+        "directory instead of from zarr zattrs. Use when the zarr stores were preprocessed "
+        "with `viscy preprocess --csv_dir ...` / `qc run` configured with `csv_dir`."
+    ),
+)
+def main(parquet_path, output, focus_channel, csv_dir):
     """Preprocess a cell index parquet: add normalization stats, focus slice, remove empty frames.
 
-    Reads precomputed metadata from zarr zattrs and writes them as parquet
-    columns. Requires `viscy preprocess` to have been run on the zarr stores.
+    Reads precomputed metadata from zarr zattrs (or CSV sidecars, with
+    `--csv-dir`) and writes them as parquet columns. Requires `viscy
+    preprocess` to have been run on the zarr stores.
     """
     preprocess_cell_index(
         parquet_path=parquet_path,
         output_path=output,
         focus_channel=focus_channel,
+        csv_dir=csv_dir,
     )
diff --git a/applications/qc/pyproject.toml b/applications/qc/pyproject.toml
index a4878194c..4e7c98952 100644
--- a/applications/qc/pyproject.toml
+++ b/applications/qc/pyproject.toml
@@ -26,6 +26,7 @@ dependencies = [
   "airtable-utils",
   "click",
   "pydantic",
+  "viscy-data",
   "viscy-utils",
   "waveorder",
 ]
diff --git a/applications/qc/src/qc/cli.py b/applications/qc/src/qc/cli.py
index bb983faa4..d587615b1 100644
--- a/applications/qc/src/qc/cli.py
+++ b/applications/qc/src/qc/cli.py
@@ -59,6 +59,7 @@ def run(config_path: str):
             zarr_dir=cfg.data_path,
             metrics=metrics,
             num_workers=cfg.num_workers,
+            csv_dir=cfg.csv_dir,
         )
         click.echo("QC metrics complete.")
 
diff --git a/applications/qc/src/qc/config.py b/applications/qc/src/qc/config.py
index b574e844e..9e03fe6d6 100644
--- a/applications/qc/src/qc/config.py
+++ b/applications/qc/src/qc/config.py
@@ -69,9 +69,15 @@ class QCConfig(BaseModel):
         Configuration for focus slice detection. None to skip.
     annotation : AnnotationConfig or None
         Channel and experiment metadata annotation. None to skip.
+    csv_dir : str or None
+        If given, QC metric results are written to a per-store CSV sidecar
+        under this directory instead of into the store's ``.zattrs``, and
+        the store is opened read-only. Use for datasets mounted without
+        write access. By default None (write to ``.zattrs`` as usual).
     """
 
     data_path: str
     num_workers: int = 4
     focus_slice: FocusSliceConfig | None = None
     annotation: AnnotationConfig | None = None
+    csv_dir: str | None = None
diff --git a/applications/qc/src/qc/qc_metrics.py b/applications/qc/src/qc/qc_metrics.py
index 1c3f27d62..49e962f53 100644
--- a/applications/qc/src/qc/qc_metrics.py
+++ b/applications/qc/src/qc/qc_metrics.py
@@ -7,6 +7,7 @@
 from iohub.core.config import TensorStoreConfig
 from tqdm import tqdm
 
+from viscy_data.meta_csv import build_provenance_fields, metadata_to_rows, write_meta_rows_csv
 from viscy_utils.meta_utils import write_meta_field
 
 _logger = logging.getLogger(__name__)
@@ -68,6 +69,7 @@ def generate_qc_metadata(
     zarr_dir: str,
     metrics: list[QCMetric],
     num_workers: int = 4,
+    csv_dir: str | None = None,
 ) -> None:
     """Run composable QC metrics across an HCS dataset.
 
@@ -83,10 +85,15 @@ def generate_qc_metadata(
         List of QC metric instances to compute.
     num_workers : int
         Number of workers for data loading.
+    csv_dir : str or None, optional
+        If given, results are written to a per-store CSV sidecar under
+        this directory instead of into the store's ``.zattrs``, and the
+        store is opened read-only. Use for datasets mounted without write
+        access. By default None (write to ``.zattrs`` as usual).
     """
     with ngff.open_ome_zarr(
         zarr_dir,
-        mode="r+",
+        mode="r" if csv_dir else "r+",
         implementation="tensorstore",
         implementation_config=TensorStoreConfig(data_copy_concurrency=num_workers),
     ) as plate:
@@ -101,13 +108,44 @@ def generate_qc_metadata(
 
                 position_results = []
 
-                for _, pos in tqdm(position_map, desc="Positions"):
+                for pos_name, pos in tqdm(position_map, desc="Positions"):
                     result = metric(pos, channel_name, channel_index, num_workers)
-                    position_results.append((pos, result))
+                    position_results.append((pos_name, pos, result))
 
-                all_results = [r for _, r in position_results]
+                all_results = [r for _, _, r in position_results]
                 dataset_stats = metric.aggregate_dataset(all_results)
 
+                if csv_dir is not None:
+                    provenance = build_provenance_fields()
+                    rows = []
+                    if dataset_stats:
+                        rows.extend(
+                            metadata_to_rows(
+                                {"dataset_statistics": dataset_stats},
+                                store_path=zarr_dir,
+                                position_path=None,
+                                channel_name=channel_name,
+                                field_name=metric.field_name,
+                            )
+                        )
+                    for pos_name, _, result in position_results:
+                        metadata = {**result}
+                        if dataset_stats:
+                            metadata["dataset_statistics"] = dataset_stats
+                        rows.extend(
+                            metadata_to_rows(
+                                metadata,
+                                store_path=zarr_dir,
+                                position_path=pos_name,
+                                channel_name=channel_name,
+                                field_name=metric.field_name,
+                            )
+                        )
+                    for row in rows:
+                        row.update(provenance)
+                    write_meta_rows_csv(csv_dir, zarr_dir, rows)
+                    continue
+
                 if dataset_stats:
                     write_meta_field(
                         position=plate,
@@ -116,7 +154,7 @@ def generate_qc_metadata(
                         subfield_name=channel_name,
                     )
 
-                for pos, result in position_results:
+                for _, pos, result in position_results:
                     metadata = {**result}
                     if dataset_stats:
                         metadata["dataset_statistics"] = dataset_stats
diff --git a/applications/qc/tests/conftest.py b/applications/qc/tests/conftest.py
index 9f04decd2..ffab5182b 100644
--- a/applications/qc/tests/conftest.py
+++ b/applications/qc/tests/conftest.py
@@ -57,6 +57,25 @@ def temporal_hcs_dataset(tmp_path_factory: TempPathFactory) -> Path:
     return dataset_path
 
 
+@fixture(scope="function")
+def fresh_temporal_hcs_dataset(tmp_path_factory: TempPathFactory) -> Path:
+    """Function-scoped (unshared) counterpart to ``temporal_hcs_dataset``.
+
+    Use this for assertions about the *absence* of zattrs fields — other
+    tests write into the session-scoped ``temporal_hcs_dataset``, so it
+    can't be used to check that a code path left zattrs untouched.
+    """
+    dataset_path = tmp_path_factory.mktemp("fresh_temporal_qc.zarr")
+    _build_temporal_hcs(
+        dataset_path,
+        CHANNEL_NAMES,
+        NUM_TIMEPOINTS,
+        ZYX_SHAPE,
+        np.float32,
+    )
+    return dataset_path
+
+
 MULTI_WELL_CHANNELS = ["Phase", "Fluorescence_405"]
 
 
diff --git a/applications/qc/tests/test_focus.py b/applications/qc/tests/test_focus.py
index 040359b12..049fc3d83 100644
--- a/applications/qc/tests/test_focus.py
+++ b/applications/qc/tests/test_focus.py
@@ -6,6 +6,7 @@
 
 from qc.focus import FocusSliceMetric
 from qc.qc_metrics import generate_qc_metadata
+from viscy_data.meta_csv import read_meta_rows_csv
 
 
 @pytest.fixture
@@ -114,3 +115,58 @@ def test_generate_qc_metadata_all_channels(temporal_hcs_dataset, focus_metric_al
             assert ch in plate.zattrs["focus_slice"]
             for _, pos in plate.positions():
                 assert ch in pos.zattrs["focus_slice"]
+
+
+def test_csv_dir_does_not_write_zattrs(tmp_path, fresh_temporal_hcs_dataset, focus_metric):
+    """csv_dir opens the store read-only and never touches .zattrs."""
+    generate_qc_metadata(
+        zarr_dir=fresh_temporal_hcs_dataset,
+        metrics=[focus_metric],
+        num_workers=1,
+        csv_dir=tmp_path,
+    )
+
+    with open_ome_zarr(fresh_temporal_hcs_dataset, mode="r") as plate:
+        assert "focus_slice" not in plate.zattrs
+        for _, pos in plate.positions():
+            assert "focus_slice" not in pos.zattrs
+
+
+def test_csv_dir_matches_zattrs_values(tmp_path, temporal_hcs_dataset, focus_metric):
+    """CSV sidecar rows carry the same fov/per-timepoint focus values as the zattrs path."""
+    generate_qc_metadata(
+        zarr_dir=temporal_hcs_dataset,
+        metrics=[focus_metric],
+        num_workers=1,
+        csv_dir=tmp_path,
+    )
+    sidecar = read_meta_rows_csv(tmp_path, temporal_hcs_dataset)
+    assert sidecar is not None
+
+    generate_qc_metadata(
+        zarr_dir=temporal_hcs_dataset,
+        metrics=[focus_metric],
+        num_workers=1,
+    )
+    with open_ome_zarr(temporal_hcs_dataset, mode="r") as plate:
+        for fov_name, pos in plate.positions():
+            zattrs_meta = pos.zattrs["focus_slice"]["Phase"]
+            fov_row = sidecar[
+                (sidecar["position_path"] == fov_name)
+                & (sidecar["channel_name"] == "Phase")
+                & (sidecar["field_name"] == "focus_slice")
+                & (sidecar["scope"] == "fov")
+            ]
+            assert len(fov_row) == 1
+            assert fov_row.iloc[0]["z_focus_mean"] == pytest.approx(zattrs_meta["fov_statistics"]["z_focus_mean"])
+
+            for t_str, expected_idx in zattrs_meta["per_timepoint"].items():
+                tp_row = sidecar[
+                    (sidecar["position_path"] == fov_name)
+                    & (sidecar["channel_name"] == "Phase")
+                    & (sidecar["field_name"] == "focus_slice")
+                    & (sidecar["scope"] == "timepoint")
+                    & (sidecar["timepoint"] == int(t_str))
+                ]
+                assert len(tp_row) == 1
+                assert int(tp_row.iloc[0]["value"]) == expected_idx
diff --git a/packages/viscy-data/src/viscy_data/cell_index.py b/packages/viscy-data/src/viscy_data/cell_index.py
index 09a72f64f..fd0559613 100644
--- a/packages/viscy-data/src/viscy_data/cell_index.py
+++ b/packages/viscy-data/src/viscy_data/cell_index.py
@@ -31,6 +31,7 @@
     CELL_INDEX_OPS_COLUMNS,
     CELL_INDEX_TIMELAPSE_COLUMNS,
 )
+from viscy_data.meta_csv import read_meta_rows_csv
 
 _logger = logging.getLogger(__name__)
 
@@ -238,11 +239,14 @@ def preprocess_cell_index(
     parquet_path: str | Path,
     output_path: str | Path | None = None,
     focus_channel: str | None = None,
+    csv_dir: str | Path | None = None,
 ) -> None:
     """Add normalization stats, focus slice, and remove invalid rows.
 
     Reads precomputed metadata from each FOV's ``zattrs`` (written by
-    ``viscy preprocess``) and writes them as parquet columns:
+    ``viscy preprocess``) — or, if ``csv_dir`` is given, from the per-store
+    CSV sidecar written by the read-only equivalent of that step (see
+    ``viscy_data.meta_csv``) — and writes them as parquet columns:
 
     - ``norm_mean``, ``norm_std``, ``norm_median``, ``norm_iqr``,
       ``norm_max``, ``norm_min`` — per-timepoint, per-channel statistics
@@ -262,11 +266,17 @@ def preprocess_cell_index(
     focus_channel : str | None
         Channel name for ``focus_slice`` lookup (e.g. ``"Phase3D"``).
         When ``None``, uses the first channel_name in each FOV's group.
+    csv_dir : str | Path | None
+        If given, read normalization/focus_slice metadata from the
+        per-store CSV sidecar under this directory (as written by
+        ``generate_normalization_metadata``/``generate_qc_metadata`` with
+        ``csv_dir`` set) instead of opening each FOV's zarr store.
 
     Raises
     ------
     ValueError
-        If a FOV has no normalization metadata (run ``viscy preprocess`` first).
+        If a FOV has no normalization metadata (run ``viscy preprocess``,
+        or the ``csv_dir`` equivalent, first).
     """
     if output_path is None:
         output_path = parquet_path
@@ -275,14 +285,48 @@ def preprocess_cell_index(
     n_before = len(df)
 
     fov_col = "fov" if "fov" in df.columns else "fov_name"
+    stat_keys = ["mean", "std", "median", "iqr", "max", "min"]
 
-    # Build lookups from zarr zattrs (one open per unique FOV)
+    # Build lookups from zarr zattrs, or from CSV sidecars if csv_dir is set
+    # (one open/read per unique FOV or store).
     stat_lookup: dict[tuple[str, str, str, int], dict[str, float]] = {}
     focus_lookup: dict[tuple[str, str], float] = {}
     focus_per_t_lookup: dict[tuple[str, str], dict[int, int]] = {}
 
     for (store_path, fov), group in df.groupby(["store_path", fov_col]):
         fov_path = f"{group['well'].iloc[0]}/{fov}" if "/" not in str(fov) else str(fov)
+        fc = focus_channel or group["channel_name"].iloc[0]
+
+        if csv_dir is not None:
+            sidecar = read_meta_rows_csv(csv_dir, store_path)
+            if sidecar is None:
+                raise ValueError(
+                    f"Store '{store_path}' has no CSV sidecar under '{csv_dir}'. "
+                    "Run `viscy preprocess --csv_dir ...` on this dataset first."
+                )
+            fov_rows = sidecar[sidecar["position_path"] == fov_path]
+            norm_rows = fov_rows[(fov_rows["field_name"] == "normalization") & (fov_rows["scope"] == "timepoint")]
+            if norm_rows.empty:
+                raise ValueError(
+                    f"FOV '{fov_path}' in store '{store_path}' has no normalization metadata "
+                    f"in the CSV sidecar under '{csv_dir}'."
+                )
+            for _, row in norm_rows.iterrows():
+                stat_lookup[(str(store_path), str(fov), str(row["channel_name"]), int(row["timepoint"]))] = {
+                    stat: row[stat] for stat in stat_keys
+                }
+
+            focus_rows = fov_rows[(fov_rows["field_name"] == "focus_slice") & (fov_rows["channel_name"] == fc)]
+            fov_focus = focus_rows[focus_rows["scope"] == "fov"]
+            if not fov_focus.empty and pd.notna(fov_focus["z_focus_mean"].iloc[0]):
+                focus_lookup[(str(store_path), str(fov))] = float(fov_focus["z_focus_mean"].iloc[0])
+            per_t_focus = focus_rows[focus_rows["scope"] == "timepoint"]
+            if not per_t_focus.empty:
+                focus_per_t_lookup[(str(store_path), str(fov))] = {
+                    int(r["timepoint"]): int(r["value"]) for _, r in per_t_focus.iterrows()
+                }
+            continue
+
         with open_ome_zarr(f"{store_path}/{fov_path}", mode="r") as pos:
             norm_meta = pos.zattrs.get("normalization", None)
             focus_meta = pos.zattrs.get("focus_slice", {})
@@ -295,7 +339,6 @@ def preprocess_cell_index(
             for t_str, tp_stats in ch_stats.get("timepoint_statistics", {}).items():
                 stat_lookup[(str(store_path), str(fov), ch_name, int(t_str))] = tp_stats
 
-        fc = focus_channel or group["channel_name"].iloc[0]
         ch_focus = focus_meta.get(fc, {})
         fov_stats = ch_focus.get("fov_statistics", {})
         z_focus = fov_stats.get("z_focus_mean")
@@ -308,7 +351,6 @@ def preprocess_cell_index(
             }
 
     # Vectorized lookup: build norm + focus column arrays
-    stat_keys = ["mean", "std", "median", "iqr", "max", "min"]
     store_arr = df["store_path"].astype(str).to_numpy()
     fov_arr = df[fov_col].astype(str).to_numpy()
     ch_arr = df["channel_name"].astype(str).to_numpy()
diff --git a/packages/viscy-data/src/viscy_data/meta_csv.py b/packages/viscy-data/src/viscy_data/meta_csv.py
new file mode 100644
index 000000000..930860b2f
--- /dev/null
+++ b/packages/viscy-data/src/viscy_data/meta_csv.py
@@ -0,0 +1,225 @@
+"""CSV metadata sidecar for zarr stores that cannot be written to.
+
+Mirrors the ``.zattrs["normalization"]`` / ``.zattrs["focus_slice"]``
+metadata tree that ``viscy preprocess`` / ``qc run`` normally write in
+place. When a store is mounted read-only, callers pass a ``csv_dir``
+instead: one CSV file is written per zarr store, and the same statistics
+can later be read back by ``preprocess_cell_index`` in place of ``.zattrs``.
+"""
+
+from __future__ import annotations
+
+import subprocess
+import sys
+from datetime import UTC, datetime
+from pathlib import Path
+
+import pandas as pd
+from iohub import open_ome_zarr
+
+__all__ = [
+    "backfill_zattrs_to_csv",
+    "build_provenance_fields",
+    "csv_path_for_store",
+    "metadata_to_rows",
+    "read_meta_rows_csv",
+    "write_meta_rows_csv",
+]
+
+_FIELD_NAMES = ("normalization", "focus_slice")
+
+_KEY_COLUMNS = ["position_path", "channel_name", "field_name", "scope", "timepoint"]
+
+
+def csv_path_for_store(csv_dir: str | Path, store_path: str | Path) -> Path:
+    """Resolve the sidecar CSV path for a zarr store under ``csv_dir``.
+
+    The filename is the store's own name without the ``.zarr`` suffix
+    (e.g. ``2025_01_24_A549_G3BP1_DENV.zarr`` ->
+    ``2025_01_24_A549_G3BP1_DENV.csv``). The full store path is still
+    recorded in each row's ``store_path`` column, so provenance is
+    preserved even though the filename is short. Two distinct stores that
+    share the same name would collide onto one CSV; within a single
+    ``csv_dir`` fleet, store names are expected to be unique.
+    """
+    return Path(csv_dir) / f"{Path(store_path).stem}.csv"
+
+
+def build_provenance_fields() -> dict:
+    """Timestamp + best-effort git commit + CLI invocation for sidecar rows."""
+    try:
+        git_commit = (
+            subprocess.check_output(
+                ["git", "rev-parse", "HEAD"],
+                cwd=Path(__file__).resolve().parent,
+                stderr=subprocess.DEVNULL,
+            )
+            .decode()
+            .strip()
+        )
+    except (FileNotFoundError, subprocess.CalledProcessError):
+        git_commit = "unknown"
+    return {
+        "written_at": datetime.now(UTC).isoformat(),
+        "git_commit": git_commit,
+        "cli_invocation": " ".join(sys.argv),
+    }
+
+
+def metadata_to_rows(
+    metadata: dict,
+    store_path: str | Path,
+    position_path: str | None,
+    channel_name: str,
+    field_name: str,
+) -> list[dict]:
+    """Flatten a ``write_meta_field``-shaped metadata dict into sidecar rows.
+
+    One row per (scope, timepoint): ``dataset_statistics``/``fov_statistics``
+    each produce a single row with ``timepoint=None``; ``timepoint_statistics``
+    (dict of dicts, as used for normalization) or ``per_timepoint`` (dict of
+    scalars, as used for focus_slice) produce one row per timepoint, with the
+    scalar case stored under a ``value`` column.
+
+    Parameters
+    ----------
+    metadata : dict
+        Same shape as would be passed to ``write_meta_field``.
+    store_path : str or Path
+        Path to the zarr store this metadata belongs to.
+    position_path : str or None
+        FOV path (e.g. ``"A/1/0"``), or None for dataset/plate-level rows.
+    channel_name : str
+        Channel this metadata was computed for.
+    field_name : str
+        Metadata field, e.g. ``"normalization"`` or ``"focus_slice"``.
+
+    Returns
+    -------
+    list[dict]
+        Rows ready to be assembled into a DataFrame by ``write_meta_rows_csv``.
+    """
+    rows = []
+    for meta_key, scope in (("dataset_statistics", "dataset"), ("fov_statistics", "fov")):
+        stats = metadata.get(meta_key)
+        if stats:
+            rows.append(
+                {
+                    "store_path": str(store_path),
+                    "position_path": position_path,
+                    "channel_name": channel_name,
+                    "field_name": field_name,
+                    "scope": scope,
+                    "timepoint": None,
+                    **stats,
+                }
+            )
+    for meta_key in ("timepoint_statistics", "per_timepoint"):
+        per_timepoint = metadata.get(meta_key)
+        if per_timepoint:
+            for t_str, value in per_timepoint.items():
+                stats = value if isinstance(value, dict) else {"value": value}
+                rows.append(
+                    {
+                        "store_path": str(store_path),
+                        "position_path": position_path,
+                        "channel_name": channel_name,
+                        "field_name": field_name,
+                        "scope": "timepoint",
+                        "timepoint": int(t_str),
+                        **stats,
+                    }
+                )
+    return rows
+
+
+def write_meta_rows_csv(csv_dir: str | Path, store_path: str | Path, rows: list[dict]) -> None:
+    """Upsert ``rows`` into the per-store sidecar CSV under ``csv_dir``.
+
+    Rows are keyed on ``(position_path, channel_name, field_name, scope,
+    timepoint)``; a new row overwrites any existing row with the same key.
+    Written atomically (temp file + rename) so an interrupted write can't
+    truncate a previously valid sidecar.
+
+    Parameters
+    ----------
+    csv_dir : str or Path
+        Root directory for per-store sidecar CSVs.
+    store_path : str or Path
+        Path to the zarr store this metadata belongs to.
+    rows : list[dict]
+        Rows produced by ``metadata_to_rows``, with provenance fields
+        (``written_at``, ``git_commit``, ``cli_invocation``) merged in.
+    """
+    if not rows:
+        return
+    csv_dir = Path(csv_dir)
+    csv_dir.mkdir(parents=True, exist_ok=True)
+    csv_path = csv_path_for_store(csv_dir, store_path)
+
+    new_df = pd.DataFrame(rows)
+    if csv_path.exists():
+        existing_df = pd.read_csv(csv_path)
+        combined = pd.concat([existing_df, new_df], ignore_index=True)
+    else:
+        combined = new_df
+    combined = combined.drop_duplicates(subset=_KEY_COLUMNS, keep="last").reset_index(drop=True)
+
+    tmp_path = csv_path.with_suffix(".csv.tmp")
+    combined.to_csv(tmp_path, index=False)
+    tmp_path.replace(csv_path)
+
+
+def read_meta_rows_csv(csv_dir: str | Path, store_path: str | Path) -> pd.DataFrame | None:
+    """Read the per-store sidecar CSV, or None if it doesn't exist."""
+    csv_path = csv_path_for_store(csv_dir, store_path)
+    if not csv_path.exists():
+        return None
+    return pd.read_csv(csv_path)
+
+
+def backfill_zattrs_to_csv(zarr_dir: str | Path, csv_dir: str | Path, dry_run: bool = False) -> bool:
+    """Mirror existing ``.zattrs`` normalization/focus_slice metadata into a CSV sidecar.
+
+    For stores that were already preprocessed somewhere with write access
+    (e.g. on Bruno, then copied elsewhere read-only), this avoids
+    recomputing anything — it's a plain read of the already-computed
+    values, reshaped to match the CSV sidecar contract. Only reads the
+    store (``mode="r"``); never requires write access to it.
+
+    Parameters
+    ----------
+    zarr_dir : str or Path
+        Path to the zarr store to read from.
+    csv_dir : str or Path
+        Root directory for per-store sidecar CSVs.
+    dry_run : bool
+        If True, report whether there's anything to backfill without
+        actually writing to the sidecar.
+
+    Returns
+    -------
+    bool
+        True if ``.zattrs`` had any normalization/focus_slice metadata to
+        mirror (and, unless ``dry_run``, it was written to the sidecar);
+        False if there was nothing to backfill.
+    """
+    rows = []
+    with open_ome_zarr(zarr_dir, mode="r") as plate:
+        for field_name in _FIELD_NAMES:
+            for channel_name, metadata in plate.zattrs.get(field_name, {}).items():
+                rows.extend(metadata_to_rows(metadata, zarr_dir, None, channel_name, field_name))
+        for pos_name, pos in plate.positions():
+            for field_name in _FIELD_NAMES:
+                for channel_name, metadata in pos.zattrs.get(field_name, {}).items():
+                    rows.extend(metadata_to_rows(metadata, zarr_dir, pos_name, channel_name, field_name))
+
+    if not rows:
+        return False
+    if dry_run:
+        return True
+    provenance = build_provenance_fields()
+    for row in rows:
+        row.update(provenance)
+    write_meta_rows_csv(csv_dir, zarr_dir, rows)
+    return True
diff --git a/packages/viscy-data/tests/test_meta_csv.py b/packages/viscy-data/tests/test_meta_csv.py
new file mode 100644
index 000000000..410890026
--- /dev/null
+++ b/packages/viscy-data/tests/test_meta_csv.py
@@ -0,0 +1,199 @@
+"""Tests for viscy_data.meta_csv — CSV sidecar for metadata that would normally go to zattrs."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from viscy_data.meta_csv import (
+    build_provenance_fields,
+    csv_path_for_store,
+    metadata_to_rows,
+    read_meta_rows_csv,
+    write_meta_rows_csv,
+)
+
+
+def test_csv_path_for_store_uses_dataset_name():
+    """The CSV is named after the store (zarr stem), not the full path."""
+    p = csv_path_for_store("/csvs", "/bio/projects/x/datasets/exp_a/exp_a.zarr")
+    assert p.name == "exp_a.csv"
+    # trailing slash is handled the same way
+    assert csv_path_for_store("/csvs", "/data/exp_a.zarr/").name == "exp_a.csv"
+
+
+def test_csv_path_for_store_is_deterministic():
+    """Same store path always resolves to the same CSV file."""
+    p1 = csv_path_for_store("/csvs", "/data/exp_a.zarr")
+    p2 = csv_path_for_store("/csvs", "/data/exp_a.zarr")
+    assert p1 == p2
+    assert p1.suffix == ".csv"
+
+
+def test_build_provenance_fields_has_expected_keys():
+    """Provenance dict always has written_at, git_commit, cli_invocation."""
+    fields = build_provenance_fields()
+    assert set(fields) == {"written_at", "git_commit", "cli_invocation"}
+    assert isinstance(fields["written_at"], str)
+    assert isinstance(fields["git_commit"], str)
+
+
+def test_metadata_to_rows_normalization_shape():
+    """dataset/fov/timepoint_statistics each produce the expected rows."""
+    metadata = {
+        "dataset_statistics": {"mean": 1.0, "std": 0.1},
+        "fov_statistics": {"mean": 1.1, "std": 0.2},
+        "timepoint_statistics": {
+            "0": {"mean": 1.2, "std": 0.3},
+            "1": {"mean": 1.3, "std": 0.4},
+        },
+    }
+    rows = metadata_to_rows(
+        metadata, store_path="/data/exp.zarr", position_path="A/1/0", channel_name="Phase", field_name="normalization"
+    )
+    scopes = {(r["scope"], r["timepoint"]) for r in rows}
+    assert scopes == {("dataset", None), ("fov", None), ("timepoint", 0), ("timepoint", 1)}
+    tp0 = next(r for r in rows if r["scope"] == "timepoint" and r["timepoint"] == 0)
+    assert tp0["mean"] == 1.2
+    assert tp0["position_path"] == "A/1/0"
+    assert tp0["field_name"] == "normalization"
+
+
+def test_metadata_to_rows_scalar_per_timepoint_uses_value_column():
+    """focus_slice's per_timepoint (scalar ints) land under a 'value' column."""
+    metadata = {
+        "fov_statistics": {"z_focus_mean": 5.0, "z_focus_std": 0.5},
+        "per_timepoint": {"0": 4, "1": 6},
+    }
+    rows = metadata_to_rows(
+        metadata, store_path="/data/exp.zarr", position_path="A/1/0", channel_name="Phase", field_name="focus_slice"
+    )
+    tp_rows = {r["timepoint"]: r["value"] for r in rows if r["scope"] == "timepoint"}
+    assert tp_rows == {0: 4, 1: 6}
+    fov_row = next(r for r in rows if r["scope"] == "fov")
+    assert fov_row["z_focus_mean"] == 5.0
+
+
+def test_metadata_to_rows_skips_missing_keys():
+    """Missing scopes (e.g. no dataset_statistics) produce no row for that scope."""
+    rows = metadata_to_rows(
+        {"fov_statistics": {"mean": 1.0}},
+        store_path="/data/exp.zarr",
+        position_path="A/1/0",
+        channel_name="Phase",
+        field_name="normalization",
+    )
+    assert len(rows) == 1
+    assert rows[0]["scope"] == "fov"
+
+
+def test_write_then_read_round_trip(tmp_path):
+    """Rows written to the sidecar can be read back with the same values."""
+    rows = [
+        {
+            "store_path": "/data/exp.zarr",
+            "position_path": "A/1/0",
+            "channel_name": "Phase",
+            "field_name": "normalization",
+            "scope": "timepoint",
+            "timepoint": 0,
+            "mean": 1.0,
+            "std": 0.1,
+        }
+    ]
+    write_meta_rows_csv(tmp_path, "/data/exp.zarr", rows)
+    result = read_meta_rows_csv(tmp_path, "/data/exp.zarr")
+    assert result is not None
+    assert len(result) == 1
+    assert result.iloc[0]["mean"] == 1.0
+
+
+def test_read_missing_sidecar_returns_none(tmp_path):
+    """No sidecar exists yet for this store -> None, not an error."""
+    assert read_meta_rows_csv(tmp_path, "/data/never_written.zarr") is None
+
+
+def test_write_upserts_on_matching_key(tmp_path):
+    """A second write with the same key overwrites the row instead of duplicating it."""
+    store_path = "/data/exp.zarr"
+    row = {
+        "store_path": store_path,
+        "position_path": "A/1/0",
+        "channel_name": "Phase",
+        "field_name": "normalization",
+        "scope": "timepoint",
+        "timepoint": 0,
+        "mean": 1.0,
+    }
+    write_meta_rows_csv(tmp_path, store_path, [dict(row)])
+    updated_row = dict(row)
+    updated_row["mean"] = 2.0
+    write_meta_rows_csv(tmp_path, store_path, [updated_row])
+
+    result = read_meta_rows_csv(tmp_path, store_path)
+    assert len(result) == 1
+    assert result.iloc[0]["mean"] == 2.0
+
+
+def test_write_appends_new_key_without_dropping_existing(tmp_path):
+    """Writing a different field_name (e.g. focus_slice after normalization) keeps both."""
+    store_path = "/data/exp.zarr"
+    norm_row = {
+        "store_path": store_path,
+        "position_path": "A/1/0",
+        "channel_name": "Phase",
+        "field_name": "normalization",
+        "scope": "timepoint",
+        "timepoint": 0,
+        "mean": 1.0,
+    }
+    focus_row = {
+        "store_path": store_path,
+        "position_path": "A/1/0",
+        "channel_name": "Phase",
+        "field_name": "focus_slice",
+        "scope": "fov",
+        "timepoint": None,
+        "z_focus_mean": 5.0,
+    }
+    write_meta_rows_csv(tmp_path, store_path, [norm_row])
+    write_meta_rows_csv(tmp_path, store_path, [focus_row])
+
+    result = read_meta_rows_csv(tmp_path, store_path)
+    assert len(result) == 2
+    assert set(result["field_name"]) == {"normalization", "focus_slice"}
+
+
+def test_write_empty_rows_is_noop(tmp_path):
+    """write_meta_rows_csv with an empty list does not create a file."""
+    write_meta_rows_csv(tmp_path, "/data/exp.zarr", [])
+    assert read_meta_rows_csv(tmp_path, "/data/exp.zarr") is None
+
+
+def test_two_stores_get_separate_files(tmp_path):
+    """Each store gets its own CSV; writing one doesn't affect the other."""
+    row_a = {
+        "store_path": "/data/exp_a.zarr",
+        "position_path": "A/1/0",
+        "channel_name": "Phase",
+        "field_name": "normalization",
+        "scope": "fov",
+        "timepoint": None,
+        "mean": 1.0,
+    }
+    row_b = {
+        "store_path": "/data/exp_b.zarr",
+        "position_path": "A/1/0",
+        "channel_name": "Phase",
+        "field_name": "normalization",
+        "scope": "fov",
+        "timepoint": None,
+        "mean": 2.0,
+    }
+    write_meta_rows_csv(tmp_path, "/data/exp_a.zarr", [row_a])
+    write_meta_rows_csv(tmp_path, "/data/exp_b.zarr", [row_b])
+
+    result_a = read_meta_rows_csv(tmp_path, "/data/exp_a.zarr")
+    result_b = read_meta_rows_csv(tmp_path, "/data/exp_b.zarr")
+    assert result_a.iloc[0]["mean"] == 1.0
+    assert result_b.iloc[0]["mean"] == 2.0
+    assert isinstance(pd.concat([result_a, result_b]), pd.DataFrame)
diff --git a/packages/viscy-data/tests/test_preprocess_cell_index.py b/packages/viscy-data/tests/test_preprocess_cell_index.py
new file mode 100644
index 000000000..ffbc5ecce
--- /dev/null
+++ b/packages/viscy-data/tests/test_preprocess_cell_index.py
@@ -0,0 +1,124 @@
+"""Tests for viscy_data.cell_index.preprocess_cell_index — zattrs vs CSV sidecar paths."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from iohub import open_ome_zarr
+
+from viscy_data.cell_index import preprocess_cell_index, read_cell_index, write_cell_index
+from viscy_data.meta_csv import metadata_to_rows, write_meta_rows_csv
+
+NORM_META = {
+    "fov_statistics": {"mean": 1.0, "std": 0.1, "median": 1.0, "iqr": 0.2, "max": 5.0, "min": 0.0},
+    "timepoint_statistics": {
+        "0": {"mean": 1.0, "std": 0.1, "median": 1.0, "iqr": 0.2, "max": 5.0, "min": 0.0},
+        "1": {"mean": 1.2, "std": 0.15, "median": 1.1, "iqr": 0.25, "max": 5.5, "min": 0.0},
+    },
+}
+FOCUS_META = {
+    "fov_statistics": {"z_focus_mean": 4.0, "z_focus_std": 0.5},
+    "per_timepoint": {"0": 4, "1": 5},
+}
+
+
+@pytest.fixture
+def zarr_store(tmp_path):
+    """Single-FOV, single-channel, 2-timepoint HCS store (no metadata written)."""
+    dataset_path = tmp_path / "store.zarr"
+    with open_ome_zarr(dataset_path, layout="hcs", mode="w", channel_names=["GFP"]) as plate:
+        pos = plate.create_position("A", "1", "0")
+        pos.create_image("0", np.zeros((2, 1, 2, 4, 4), dtype=np.float32))
+    return dataset_path
+
+
+@pytest.fixture
+def cell_index_df(zarr_store):
+    """Minimal 2-row (one per timepoint) cell index for a single FOV/channel."""
+    return pd.DataFrame(
+        {
+            "cell_id": ["c0", "c1"],
+            "experiment": "exp",
+            "store_path": str(zarr_store),
+            "tracks_path": str(zarr_store),
+            "fov": "A/1/0",
+            "well": "A/1",
+            "y": np.array([1.0, 2.0], dtype=np.float32),
+            "x": np.array([1.0, 2.0], dtype=np.float32),
+            "z": np.array([0, 0], dtype=np.int16),
+            "perturbation": "none",
+            "channel_name": "GFP",
+            "microscope": "",
+            "t": pd.array([0, 1], dtype="Int32"),
+        }
+    )
+
+
+def _write_zattrs(zarr_store):
+    with open_ome_zarr(zarr_store, mode="r+") as plate:
+        for _, pos in plate.positions():
+            pos.zattrs["normalization"] = {"GFP": NORM_META}
+            pos.zattrs["focus_slice"] = {"GFP": FOCUS_META}
+
+
+def _write_csv_sidecar(csv_dir, zarr_store):
+    rows = [
+        *metadata_to_rows(
+            NORM_META, store_path=zarr_store, position_path="A/1/0", channel_name="GFP", field_name="normalization"
+        ),
+        *metadata_to_rows(
+            FOCUS_META, store_path=zarr_store, position_path="A/1/0", channel_name="GFP", field_name="focus_slice"
+        ),
+    ]
+    write_meta_rows_csv(csv_dir, zarr_store, rows)
+
+
+def test_zattrs_path_adds_norm_and_focus_columns(cell_index_df, zarr_store, tmp_path):
+    """Default (zattrs) path populates norm_* and z_focus_mean, and rewrites z from per_timepoint."""
+    _write_zattrs(zarr_store)
+    input_path = tmp_path / "cell_index.parquet"
+    write_cell_index(cell_index_df, input_path)
+
+    output_path = tmp_path / "out.parquet"
+    preprocess_cell_index(input_path, output_path=output_path)
+    result = read_cell_index(output_path)
+
+    assert len(result) == 2
+    row_t0 = result[result["t"] == 0].iloc[0]
+    assert row_t0["norm_mean"] == pytest.approx(1.0)
+    assert row_t0["z_focus_mean"] == pytest.approx(4.0)
+    assert row_t0["z"] == 4
+
+
+def test_csv_dir_path_matches_zattrs_path(cell_index_df, zarr_store, tmp_path):
+    """csv_dir path produces the same norm_*/z_focus_mean/z columns as the zattrs path."""
+    csv_dir = tmp_path / "csvs"
+    _write_csv_sidecar(csv_dir, zarr_store)
+
+    input_path = tmp_path / "cell_index.parquet"
+    write_cell_index(cell_index_df, input_path)
+
+    csv_output = tmp_path / "csv_out.parquet"
+    preprocess_cell_index(input_path, output_path=csv_output, csv_dir=csv_dir)
+    csv_result = read_cell_index(csv_output).sort_values("t").reset_index(drop=True)
+
+    _write_zattrs(zarr_store)
+    zattrs_output = tmp_path / "zattrs_out.parquet"
+    preprocess_cell_index(input_path, output_path=zattrs_output)
+    zattrs_result = read_cell_index(zattrs_output).sort_values("t").reset_index(drop=True)
+
+    for col in ("norm_mean", "norm_std", "norm_median", "norm_iqr", "norm_max", "norm_min", "z_focus_mean", "z"):
+        np.testing.assert_allclose(
+            csv_result[col].to_numpy(dtype=float),
+            zattrs_result[col].to_numpy(dtype=float),
+        )
+
+
+def test_csv_dir_missing_sidecar_raises(cell_index_df, zarr_store, tmp_path):
+    """csv_dir set but no sidecar exists for the store -> ValueError, not a silent skip."""
+    input_path = tmp_path / "cell_index.parquet"
+    write_cell_index(cell_index_df, input_path)
+
+    with pytest.raises(ValueError, match="no CSV sidecar"):
+        preprocess_cell_index(input_path, csv_dir=tmp_path / "empty")
diff --git a/packages/viscy-utils/src/viscy_utils/meta_utils.py b/packages/viscy-utils/src/viscy_utils/meta_utils.py
index 2dbe70e92..4128fe457 100644
--- a/packages/viscy-utils/src/viscy_utils/meta_utils.py
+++ b/packages/viscy-utils/src/viscy_utils/meta_utils.py
@@ -7,6 +7,7 @@
 from skimage.filters import threshold_otsu
 from tqdm import tqdm
 
+from viscy_data.meta_csv import build_provenance_fields, metadata_to_rows, write_meta_rows_csv
 from viscy_utils.mp_utils import get_val_stats
 
 
@@ -59,7 +60,13 @@ def _grid_sample(position, grid_spacing, channel_index):
 
 
 def generate_normalization_metadata(
-    zarr_dir, num_workers=4, channel_ids=-1, grid_spacing=32, compute_otsu=False, otsu_grid_spacing=8
+    zarr_dir,
+    num_workers=4,
+    channel_ids=-1,
+    grid_spacing=32,
+    compute_otsu=False,
+    otsu_grid_spacing=8,
+    csv_dir=None,
 ):
     """Generate pixel intensity metadata for normalization.
 
@@ -83,10 +90,15 @@ def generate_normalization_metadata(
         Grid spacing for Otsu sampling, by default 8. Denser than the
         default ``grid_spacing=32`` to capture inter-cell gaps. A median
         filter is applied before thresholding to smooth noise.
+    csv_dir : str or Path or None, optional
+        If given, the store is opened read-only and statistics are written
+        to a per-store CSV sidecar under this directory instead of into
+        the store's ``.zattrs`` — for datasets mounted without write
+        access. By default None (write to ``.zattrs`` as usual).
     """
     with ngff.open_ome_zarr(
         zarr_dir,
-        mode="r+",
+        mode="r" if csv_dir else "r+",
         implementation="tensorstore",
         implementation_config=TensorStoreConfig(data_copy_concurrency=num_workers),
     ) as plate:
@@ -108,7 +120,7 @@ def generate_normalization_metadata(
             dataset_sample_values = []
             position_and_statistics = []
 
-            for _, pos in tqdm(position_map, desc="Positions"):
+            for pos_name, pos in tqdm(position_map, desc="Positions"):
                 samples = _grid_sample(pos, grid_spacing, channel_index)
                 dataset_sample_values.append(samples)
                 fov_stats = get_val_stats(samples)
@@ -128,7 +140,7 @@ def generate_normalization_metadata(
                 for t in range(num_timepoints):
                     fov_timepoint_statistics[str(t)] = get_val_stats(samples[t])
                 fov_statistics["timepoint_statistics"] = fov_timepoint_statistics
-                position_and_statistics.append((pos, fov_statistics))
+                position_and_statistics.append((pos_name, pos, fov_statistics))
 
             dataset_statistics = {
                 "dataset_statistics": get_val_stats(np.stack(dataset_sample_values)),
@@ -140,21 +152,44 @@ def generate_normalization_metadata(
                 all_fov_samples_at_t = np.stack([samples[t] for samples in dataset_sample_values])
                 dataset_timepoint_statistics[str(t)] = get_val_stats(all_fov_samples_at_t)
 
-            write_meta_field(
-                position=plate,
-                metadata=dataset_statistics | {"timepoint_statistics": dataset_timepoint_statistics},
-                field_name="normalization",
-                subfield_name=channel_name,
-            )
-
-            for pos, position_statistics in position_and_statistics:
+            if csv_dir is not None:
+                provenance = build_provenance_fields()
+                rows = metadata_to_rows(
+                    dataset_statistics | {"timepoint_statistics": dataset_timepoint_statistics},
+                    store_path=zarr_dir,
+                    position_path=None,
+                    channel_name=channel_name,
+                    field_name="normalization",
+                )
+                for pos_name, _, position_statistics in position_and_statistics:
+                    rows.extend(
+                        metadata_to_rows(
+                            dataset_statistics | position_statistics,
+                            store_path=zarr_dir,
+                            position_path=pos_name,
+                            channel_name=channel_name,
+                            field_name="normalization",
+                        )
+                    )
+                for row in rows:
+                    row.update(provenance)
+                write_meta_rows_csv(csv_dir, zarr_dir, rows)
+            else:
                 write_meta_field(
-                    position=pos,
-                    metadata=dataset_statistics | position_statistics,
+                    position=plate,
+                    metadata=dataset_statistics | {"timepoint_statistics": dataset_timepoint_statistics},
                     field_name="normalization",
                     subfield_name=channel_name,
                 )
 
+                for _, pos, position_statistics in position_and_statistics:
+                    write_meta_field(
+                        position=pos,
+                        metadata=dataset_statistics | position_statistics,
+                        field_name="normalization",
+                        subfield_name=channel_name,
+                    )
+
 
 def generate_fg_masks(
     zarr_dir,
diff --git a/packages/viscy-utils/src/viscy_utils/trainer.py b/packages/viscy-utils/src/viscy_utils/trainer.py
index be5bcb653..a52042015 100644
--- a/packages/viscy-utils/src/viscy_utils/trainer.py
+++ b/packages/viscy-utils/src/viscy_utils/trainer.py
@@ -30,6 +30,7 @@ def preprocess(
         compute_fg_masks: bool = False,
         fg_mask_channels: list[str] | None = None,
         fg_mask_key: str = "fg_mask",
+        csv_dir: Path | None = None,
         model: LightningModule | None = None,
     ):
         """Compute dataset statistics for normalization.
@@ -57,11 +58,19 @@ def preprocess(
             that had Otsu thresholds computed (``channel_names``).
         fg_mask_key : str, optional
             Zarr array key for the mask, by default ``"fg_mask"``.
+        csv_dir : Path or None, optional
+            If given, statistics are written to a per-store CSV sidecar
+            under this directory instead of into the store's ``.zattrs``,
+            and the store is opened read-only. Use for datasets mounted
+            without write access. Incompatible with ``compute_fg_masks``,
+            which writes a mask array into the store. By default None.
         model : LightningModule, optional
             Ignored placeholder, by default None.
         """
         if model is not None:
             _logger.warning("Ignoring model configuration during preprocessing.")
+        if csv_dir is not None and compute_fg_masks:
+            raise ValueError("compute_fg_masks writes into the store and is incompatible with csv_dir.")
         with open_ome_zarr(data_path, layout="hcs", mode="r") as dataset:
             channel_indices = (
                 [dataset.channel_names.index(c) for c in channel_names] if channel_names != -1 else channel_names
@@ -76,6 +85,7 @@ def preprocess(
             grid_spacing=block_size,
             compute_otsu=compute_otsu,
             otsu_grid_spacing=otsu_grid_spacing,
+            csv_dir=csv_dir,
         )
         if compute_fg_masks:
             if not compute_otsu:
diff --git a/packages/viscy-utils/tests/test_meta_utils.py b/packages/viscy-utils/tests/test_meta_utils.py
index 08ed7e5b2..6b4809ca5 100644
--- a/packages/viscy-utils/tests/test_meta_utils.py
+++ b/packages/viscy-utils/tests/test_meta_utils.py
@@ -2,6 +2,7 @@
 import pytest
 from iohub import open_ome_zarr
 
+from viscy_data.meta_csv import read_meta_rows_csv
 from viscy_utils.meta_utils import generate_fg_masks, generate_normalization_metadata
 
 GRID_SPACING = 8
@@ -188,3 +189,62 @@ def test_generate_fg_masks_no_overwrite(bimodal_hcs_dataset):
 
     with pytest.raises(FileExistsError):
         generate_fg_masks(bimodal_hcs_dataset, channel_names=["Fluorescence"])
+
+
+def test_csv_dir_does_not_write_zattrs(tmp_path, small_hcs_dataset):
+    """csv_dir opens the store read-only and never touches .zattrs."""
+    generate_normalization_metadata(small_hcs_dataset, num_workers=1, grid_spacing=GRID_SPACING, csv_dir=tmp_path)
+
+    with open_ome_zarr(small_hcs_dataset, mode="r") as plate:
+        assert "normalization" not in plate.zattrs
+        for _, fov in plate.positions():
+            assert "normalization" not in fov.zattrs
+
+
+def test_csv_dir_matches_manual_computation(tmp_path, small_hcs_dataset):
+    """CSV sidecar timepoint rows match manually computed per-FOV statistics."""
+    generate_normalization_metadata(small_hcs_dataset, num_workers=1, grid_spacing=GRID_SPACING, csv_dir=tmp_path)
+    sidecar = read_meta_rows_csv(tmp_path, small_hcs_dataset)
+    assert sidecar is not None
+
+    with open_ome_zarr(small_hcs_dataset, mode="r") as plate:
+        num_timepoints = next(plate.positions())[1]["0"].shape[0]
+        for fov_name, fov in plate.positions():
+            raw = fov["0"][:]
+            for ch_idx, channel in enumerate(plate.channel_names):
+                for t in range(num_timepoints):
+                    sampled = raw[t, ch_idx, :, ::GRID_SPACING, ::GRID_SPACING]
+                    expected_mean = float(np.nanmean(sampled))
+                    row = sidecar[
+                        (sidecar["position_path"] == fov_name)
+                        & (sidecar["channel_name"] == channel)
+                        & (sidecar["field_name"] == "normalization")
+                        & (sidecar["scope"] == "timepoint")
+                        & (sidecar["timepoint"] == t)
+                    ]
+                    assert len(row) == 1, f"missing row for {fov_name}/{channel}/t={t}"
+                    np.testing.assert_allclose(row.iloc[0]["mean"], expected_mean, rtol=1e-5)
+
+
+def test_csv_dir_stores_otsu_threshold(tmp_path, bimodal_hcs_dataset):
+    """csv_dir + compute_otsu writes otsu_threshold on the fov-scope row."""
+    generate_normalization_metadata(
+        bimodal_hcs_dataset,
+        num_workers=1,
+        grid_spacing=GRID_SPACING,
+        compute_otsu=True,
+        otsu_grid_spacing=4,
+        csv_dir=tmp_path,
+    )
+    sidecar = read_meta_rows_csv(tmp_path, bimodal_hcs_dataset)
+    fov_rows = sidecar[(sidecar["field_name"] == "normalization") & (sidecar["scope"] == "fov")]
+    assert not fov_rows["otsu_threshold"].isna().any()
+
+
+def test_csv_dir_includes_provenance_columns(tmp_path, small_hcs_dataset):
+    """Every sidecar row carries written_at, git_commit, cli_invocation."""
+    generate_normalization_metadata(small_hcs_dataset, num_workers=1, grid_spacing=GRID_SPACING, csv_dir=tmp_path)
+    sidecar = read_meta_rows_csv(tmp_path, small_hcs_dataset)
+    for col in ("written_at", "git_commit", "cli_invocation"):
+        assert col in sidecar.columns
+        assert not sidecar[col].isna().any()
diff --git a/uv.lock b/uv.lock
index 9b52e7d2d..c9ff2c8b2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,18 +1,24 @@
 version = 1
 revision = 3
-requires-python = ">=3.12, <3.14"
+requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'",
     "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
@@ -22,7 +28,6 @@ members = [
     "cytoland",
     "dynacell",
     "dynaclr",
-    "eet-tracking",
     "viscy",
     "viscy-data",
     "viscy-models",
@@ -85,24 +90,6 @@ dependencies = [
     { name = "tifffile" },
 ]
 
-[[package]]
-name = "aiobotocore"
-version = "3.7.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "aiohttp" },
-    { name = "aioitertools" },
-    { name = "botocore" },
-    { name = "jmespath" },
-    { name = "multidict" },
-    { name = "python-dateutil" },
-    { name = "wrapt" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/75/42cce839c2ec263ff74b10b650fe36b066fbb124cbee6f247eac0983e1ab/aiobotocore-3.7.0.tar.gz", hash = "sha256:c64d871ed5491a6571948dd48eabd185b46c6c23b64e3afd0c059fc7593ada30", size = 127054, upload-time = "2026-05-09T10:02:52.332Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/90/5f/85535dfb3cfd6442d66d1df1694062c5d6df02f895329e7e120b2a3d2b8b/aiobotocore-3.7.0-py3-none-any.whl", hash = "sha256:680bde7c64679a821a9312641b759d9497f790ba8b2e88c6959e6273ee765b8e", size = 89539, upload-time = "2026-05-09T10:02:50.389Z" },
-]
-
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.2"
@@ -169,15 +156,47 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/af/cc/c122eabd7a1b7e0c9bbdd6be60e4715905b858399145d9df872bb94f1427/aiohttp-3.14.0-cp313-cp313-win32.whl", hash = "sha256:23f094a1ef64823fd35854ddf5c7a80a078162f37f9d2f7c6142b51a6affa456", size = 448656, upload-time = "2026-06-01T19:39:11.171Z" },
     { url = "https://files.pythonhosted.org/packages/41/a5/bab07d79848a00eedd8ed979ccb302aaea3ac6eb9fa16bd0ed87135869b4/aiohttp-3.14.0-cp313-cp313-win_amd64.whl", hash = "sha256:e03abdaa17d553f17e1d1d06bb266b3970106c78051d06795723e748d8e49d11", size = 475803, upload-time = "2026-06-01T19:39:13.439Z" },
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-]
-
-[[package]]
-name = "aioitertools"
-version = "0.13.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/fd/3c/53c4a17a05fb9ea2313ee1777ff53f5e001aefd5cc85aa2f4c2d982e1e38/aioitertools-0.13.0.tar.gz", hash = "sha256:620bd241acc0bbb9ec819f1ab215866871b4bbd1f73836a55f799200ee86950c", size = 19322, upload-time = "2025-11-06T22:17:07.609Z" }
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