Add outline for bioimage format skew project

README.md

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 # tesserift
-Measure how bioimaging storage layouts shape performance, analysis, and biological discovery.
+
+Bioimaging data can follow a format specification and still perform poorly.
+
+We will study around **six public datasets from three sources**, with two datasets from each source:
+
+* **Image Data Resource (IDR):** established OME bioimaging datasets with different image shapes and workloads.
+* **SSBD:** multidimensional OME-Zarr data, including 3D and time-series images.
+* **Cell Painting Gallery / JUMP:** large-scale imaging data with associated metadata and feature tables.
+
+Two datasets from each source will help us measure variation both **within** and **between** public data services.
+
+Unlike general-purpose Zarr/Parquet benchmarks, we tie storage properties to bioimaging-specific workloads and test causality directly, not just correlation.
+
+The work will focus on four goals:
+
+* **Measure format skew.** Compare chunking, compression, file counts, metadata, schemas, row groups, and related storage properties.
+* **Find costly patterns.** Identify storage properties that predict slow access or analysis.
+* **Measure real workloads.** Test image reads, table scans, filtering, joins, and related bioimaging tasks.
+* **Turn evidence into rules.** Add tested performance rules to `zarr-lint` and `arrow-lint`.
+
+Cost means wall-clock latency and throughput, measured on a fixed compute and network setup.
+
+We will not search for many examples of the same problem.
+When we find an important pattern, we will create controlled versions of a dataset that change that property.
+
+This lets us test whether the storage difference actually causes the observed performance change.
+
+The main question is:
+
+**How much do storage layouts vary across public bioimaging resources, and can that variation predict the cost of accessing and analyzing the data?**
+
+The manuscript will also examine how these costs affect interactive exploration and large-scale biological analysis.
+
+## Operational outline
+
+Keep the complete study in one public GitHub repository.
+
+* Write the manuscript with **Quarto Manuscripts**.
+* Keep analysis and benchmark code beside the manuscript.
+* Store generated figures and tables in the repository.
+* Make benchmarks reproducible from scripts or workflows.
+* Record dataset versions, inputs, and benchmark settings.
+* Develop the manuscript and analysis together.
+* Publish the completed manuscript as a **bioRxiv preprint**.
+* Select a **right-size peer-reviewed journal** after the results and scope are clear.
+
+The repository becomes the manuscript, analysis, figures, and reproducibility record.
+
+## Timeline
+
+Rough estimate, ~9 months total:
+
+* Months 1-2: select datasets, set up benchmarking infrastructure.
+* Months 3-4: measure format skew and workload cost.
+* Months 5-6: build controlled dataset variants, test causality.
+* Months 7-8: draft the manuscript, publish the bioRxiv preprint.
+* Month 9: submit to a right-size journal.