Add 2026 poster

.pre-commit-config.yaml

@@ -39,7 +39,7 @@ repos:
     -   id: yamllint
         exclude: pre-commit-config.yaml
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.15.10"
+    rev: "v0.15.11"
     hooks:
     -   id: ruff-format
     -   id: ruff-check

README.md

@@ -1,4 +1,4 @@
-<img width="600" src="https://raw.githubusercontent.com/wayscience/ome-arrow/main/docs/src/_static/logo.png?raw=true">
+<img width="600" src="https://raw.githubusercontent.com/wayscience/ome-arrow/main/docs/src/_static/ome-arrow-with-text.png?raw=true">
 
 ![PyPI - Version](https://img.shields.io/pypi/v/ome-arrow)
 [![Build Status](https://github.com/wayscience/ome-arrow/actions/workflows/run-tests.yml/badge.svg?branch=main)](https://github.com/wayscience/ome-arrow/actions/workflows/run-tests.yml?query=branch%3Amain)
@@ -198,6 +198,8 @@ Please see our [contributing documentation](https://github.com/wayscience/ome-ar
 OME Arrow is used or inspired by the following projects, check them out!
 
 - [`napari-ome-arrow`](https://github.com/WayScience/napari-ome-arrow): enables you to view OME Arrow and related images.
-- [`nViz`](https://github.com/WayScience/nViz): focuses on ingesting and visualizing various 3D image data.
 - [`CytoDataFrame`](https://github.com/cytomining/CytoDataFrame): provides a DataFrame-like experience for viewing feature and microscopy image data within Jupyter notebook interfaces and creating OME Parquet files.
 - [`coSMicQC`](https://github.com/cytomining/coSMicQC): performs quality control on microscopy feature datasets, visualized using CytoDataFrames.
+- [`pycytominer`](https://github.com/cytomining/pycytominer): supports feature profiling, normalization, and downstream analysis workflows for image-based profiling datasets.
+- [`iceberg-bioimage`](https://github.com/WayScience/iceberg-bioimage): defines warehouse-oriented patterns for connecting bioimage formats and analytical tables at scale.
+- [`CytoTable`](https://github.com/cytomining/CytoTable): converts image-based profiling outputs into analysis-ready tabular formats such as Parquet.

docs/presentations/2026-OME-community-meeting/_extensions/quarto-ext/poster/_extension.yml

@@ -0,0 +1,10 @@
+title: Poster (modified)
+author: Carlos Scheidegger, Dave Bunten
+version: 1.0.0
+quarto-required: ">=1.4.415"
+contributes:
+  formats:
+    typst:
+      template-partials:
+        - typst-template.typ
+        - typst-show.typ

docs/presentations/2026-OME-community-meeting/_extensions/quarto-ext/poster/typst-show.typ

@@ -0,0 +1,74 @@
+// Typst custom formats typically consist of a 'typst-template.typ' (which is
+// the source code for a typst template) and a 'typst-show.typ' which calls the
+// template's function (forwarding Pandoc metadata values as required)
+//
+// This is an example 'typst-show.typ' file (based on the default template
+// that ships with Quarto). It calls the typst function named 'article' which
+// is defined in the 'typst-template.typ' file.
+//
+// If you are creating or packaging a custom typst template you will likely
+// want to replace this file and 'typst-template.typ' entirely. You can find
+// documentation on creating typst templates here and some examples here:
+//   - https://typst.app/docs/tutorial/making-a-template/
+//   - https://github.com/typst/templates
+
+#show: doc => poster(
+  $if(title)$ title: [$title$], $endif$
+  // TODO: use Quarto's normalized metadata.
+  $if(poster-authors)$ authors: [$poster-authors$], $endif$
+  $if(departments)$ departments: [$departments$], $endif$
+  $if(size)$ size: "$size$", $endif$
+
+  // Institution logo.
+  $if(institution-logo)$ univ_logo: "$institution-logo$", $endif$
+
+  // Footer text.
+  // For instance, Name of Conference, Date, Location.
+  // or Course Name, Date, Instructor.
+  $if(footer-text)$ footer_text: [$footer-text$], $endif$
+
+  // Any URL, like a link to the conference website.
+  $if(footer-url)$ footer_url: [$footer-url$], $endif$
+
+  // Emails of the authors.
+  $if(footer-emails)$ footer_email_ids: [$footer-emails$], $endif$
+
+  // Color of the footer.
+  $if(footer-color)$ footer_color: "$footer-color$", $endif$
+
+  // DEFAULTS
+  // ========
+  // For 3-column posters, these are generally good defaults.
+  // Tested on 36in x 24in, 48in x 36in, and 36in x 48in posters.
+  // For 2-column posters, you may need to tweak these values.
+  // See ./examples/example_2_column_18_24.typ for an example.
+
+  // Any keywords or index terms that you want to highlight at the beginning.
+  $if(keywords)$ keywords: ($for(keywords)$"$it$"$sep$, $endfor$), $endif$
+
+  // Number of columns in the poster.
+  $if(num-columns)$ num_columns: $num-columns$, $endif$
+
+  // University logo's scale (in %).
+  $if(univ-logo-scale)$ univ_logo_scale: $univ-logo-scale$, $endif$
+
+  // University logo's column size (in in).
+  $if(univ-logo-column-size)$ univ_logo_column_size: $univ-logo-column-size$, $endif$
+
+  // Title and authors' column size (in in).
+  $if(title-column-size)$ title_column_size: $title-column-size$, $endif$
+
+  // Poster title's font size (in pt).
+  $if(title-font-size)$ title_font_size: $title-font-size$, $endif$
+
+  // Authors' font size (in pt).
+  $if(authors-font-size)$ authors_font_size: $authors-font-size$, $endif$
+
+  // Footer's URL and email font size (in pt).
+  $if(footer-url-font-size)$ footer_url_font_size: $footer-url-font-size$, $endif$
+
+  // Footer's text font size (in pt).
+  $if(footer-text-font-size)$ footer_text_font_size: [$footer-text-font-size$], $endif$
+
+  doc,
+)

docs/presentations/2026-OME-community-meeting/_extensions/quarto-ext/poster/typst-template.typ

@@ -0,0 +1,211 @@
+#let poster(
+  // set variables for use throughout
+  // note: some are referenced from `.qmd` file
+  size: "'36x24' or '48x36''",
+  title: "Paper Title",
+  authors: "Author Names (separated by commas)",
+  departments: "Department Name",
+  univ_logo: "Logo Path",
+  footer_text: " ",
+  footer_url: "Footer URL",
+  footer_email_ids: "Email IDs (separated by commas)",
+  footer_color: "Hex Color Code",
+  keywords: (),
+  num_columns: "4",
+  univ_logo_scale: "140",
+  univ_logo_column_size: "10",
+  title_column_size: "33",
+  title_font_size: "42",
+  authors_font_size: "32",
+  footer_url_font_size: "40",
+  footer_text_font_size: "40",
+  body
+) = {
+  // initialize template display formatting
+  set text(font: "Lato", size: 26pt)
+  let sizes = size.split("x")
+  let width = int(sizes.at(0)) * 1in
+  let height = int(sizes.at(1)) * 1in
+  univ_logo_scale = int(univ_logo_scale) * 1%
+  title_font_size = int(title_font_size) * 1pt
+  authors_font_size = int(authors_font_size) * 1pt
+  num_columns = int(num_columns)
+  univ_logo_column_size = int(univ_logo_column_size) * 1in
+  title_column_size = int(title_column_size) * 1in
+  footer_url_font_size = int(footer_url_font_size) * 1pt
+  footer_text_font_size = int(footer_text_font_size) * 1pt
+
+  // create overall page output
+  set page(
+    // total dimensions
+    width: width,
+    height: height,
+    // margin on all sides
+    margin:
+      (top: .8in, left: .8in, right: .8in, bottom: 1.8in),
+    // footer section
+    footer: [
+      #set align(center)
+      #set text(42pt)
+      #block(
+        fill: rgb(footer_color),
+        width: 100%,
+        inset: 20pt,
+        radius: 10pt,
+        // adds text to footer
+        [
+          #text(font: "Lato", size: footer_url_font_size, footer_url)
+          #h(1fr)
+          #text(size: footer_text_font_size, smallcaps(footer_text))
+          #h(1fr)
+          #text(font: "Lato", size: footer_url_font_size, footer_email_ids)
+        ]
+      )
+    ]
+  )
+
+  // set math display properties
+  set math.equation(numbering: "(1)")
+  show math.equation: set block(spacing: 0.65em)
+
+  set enum(indent: 10pt, body-indent: 9pt)
+  set list(indent: 10pt, body-indent: 9pt)
+
+  // set the heading numbering system
+  set heading(numbering: "I.A.1.")
+  show heading: it => context {
+    // Get numbering tuple for *this* heading at the current location.
+    let levels = counter(heading).at(here())
+    let deepest = if levels != () { levels.last() } else { 1 }
+
+    // defines how sub-headers display
+    set text(25pt, weight: 400)
+
+    // sub-header level 0
+    if it.level == 0 [
+      #set text(style: "italic")
+      #v(32pt, weak: true)
+      #if it.numbering != none {
+        numbering("i.", deepest)
+        h(7pt, weak: true)
+      }
+      #it.body
+
+    // sub-header level 1
+    ] else if it.level == 1 [
+      #v(10pt, weak: true)
+      #set align(left)
+      #set text({ 44pt }, weight: 600, font: "Vollkorn", fill: rgb("#2A6F63"))
+      #v(45pt, weak: true)
+      #if it.numbering != none {
+        numbering("I.", deepest)
+        h(7pt, weak: true)
+      }
+      #it.body
+      #v(25pt, weak: true)
+      #line(length: 100%, stroke: rgb(200, 200, 200))
+      #v(25pt, weak: true)
+
+    // all other headers
+    ] else [
+      #set text({ 36pt }, weight: 600, font: "Vollkorn", fill: rgb("#3E877A"), style: "italic")
+      #if it.level == 2 {
+        numbering("⧈  a)", deepest)
+        [ ]
+      }
+      #it.body
+      #v(40pt, weak: true)
+    ]
+}
+
+  // header grid
+align(left,
+  grid(
+    // add one more column at the start for the left-side image
+    rows: (auto, auto),
+    columns: (210pt, title_column_size, univ_logo_column_size),
+    column-gutter: 25pt,
+    row-gutter: 30pt,
+
+    // left-side image cell
+    grid.cell(
+      pad(top: -18pt,
+        image("images/ome-arrow-logo.png", width: 210pt),
+      ),
+      rowspan: 3,
+      align: left,
+    ),
+
+    // main title
+    grid.cell(
+      [
+        #show "➶": it => text(weight: 800, fill: rgb("#000000"))[#it]
+        #set par(linebreaks: "simple")
+        #text(
+          font: ("Vollkorn", "Apple Symbols", "Zapf Dingbats", "Arial Unicode MS"),
+          weight: 500,
+          size: title_font_size,
+          fill: rgb("#2A6F63"),
+        )[#title]
+      ],
+      align: left,
+    ),
+
+    // university logo on the far right
+    grid.cell(
+      pad(top: 2pt, pad( left: 20pt,
+        image(univ_logo, width: 130%),
+      )),
+      rowspan: 3,
+      align: right,
+    ),
+
+    // author display
+    pad(top: 5pt, text(size: 28pt, authors)),
+
+    // department and notes display
+    pad(top: 5pt, text(size: 26pt, emph(departments)))
+    )
+  )
+
+  // spacing between the header and body
+  v(40pt)
+
+  // set main body display
+  show: columns.with(num_columns, gutter: 60pt)
+  // paragraph display properties
+  set par(leading: 10pt,
+    justify: false,
+    first-line-indent: 0em,
+    linebreaks: "optimized"
+  )
+
+  // Style inline code spans from markdown backticks.
+  show raw.where(block: false): set text(weight: 700, fill: rgb("#1E4F8C"))
+
+  // Style hyperlinks with a yellow/orange tone by default.
+  show link: set text(fill: rgb("#A85A00"))
+
+  // Configure figures.
+  show figure: it => block({
+    // Display a backdrop rectangle.
+    it.body
+
+    // Display caption.
+    if it.has("caption") {
+      set align(left)
+      v(if it.has("gap") { it.gap } else { 24pt }, weak: true)
+      let fig_levels = counter(figure).at(here())
+      let fig_num = if fig_levels != () { fig_levels.last() } else { 1 }
+      [
+        #text(weight: "bold")[Figure #fig_num:]
+        #h(6pt)
+        #text(weight: "regular")[#it.caption.body]
+      ]
+    }
+
+  })
+
+  // adds body content to page
+  body
+}

docs/presentations/2026-OME-community-meeting/abstract.md

@@ -0,0 +1,19 @@
+# 2026 OME Community Meeting - OME-Arrow
+
+## Authors
+
+Dave Bunten, Jenna Tomkinson, Michael Lippincott, Cameron Mattson, Julia B. Curd, and Gregory P. Way
+
+## Title
+
+OME-Arrow: Unifying Images, Metadata, and Morphology in an Interoperable Data Model for High-Content Imaging
+
+## Abstract
+
+Modern bioimaging workflows combine images, metadata, and derived measurements across many tools, but these components are often stored in incompatible formats and disconnected systems. This fragmentation makes it difficult to join data, reproduce analyses, and scale from small experiments to large, multi-sample studies.
+
+OME-Arrow is a data model and toolkit for working with bioimaging data in modern analytical environments, where data are processed in code, queried with SQL, and analyzed across tools such as Python and R. It brings images, metadata, and derived measurements into a single structure organized as linked tables, rather than leaving them split across separate files and systems. This allows imaging data to be directly joined, filtered, and analyzed using familiar operations, enabling image-derived measurements, metadata, and experimental context to be queried together in a single system. In contrast to existing workflows, where these relationships must be manually reconstructed across files and tools, OME-Arrow makes them explicit and queryable.
+
+OME-Arrow builds on Open Microscopy Environment (OME) conventions and represents data using Apache Arrow, a columnar in-memory data format designed for fast analytics and efficient data sharing across programming languages. It supports ingestion from formats such as TIFF, OME-Zarr, and NumPy, and export to Arrow-native formats (e.g., Parquet, Lance, Vortex) as well as OME-TIFF and OME-Zarr. Data can be processed directly in standalone workflows using these formats, enabling local analysis, scripting, and integration with tools such as SQL engines and DuckDB. For larger-scale use cases, the same data can be organized into an Apache Iceberg-style table structure, which supports dataset versioning, schema evolution, and concurrent access across systems. These two modes use the same underlying data model, allowing workflows to scale from local analysis to warehouse environments without restructuring data. The library also provides lazy scan-style access for large datasets, supports tensor-based pathways for machine learning, and integrates with napari-ome-arrow for advanced visualization and CytoDataFrame for feature-centric analysis within Jupyter notebook environments.
+
+These capabilities enable end-to-end image-based profiling workflows in which raw images, single-cell features, and experimental metadata are analyzed together without intermediate data reshaping. In pediatric cancer research settings, this supports direct querying across imaging data and derived measurements, enabling researchers to relate cellular morphology to perturbations such as compounds, genetic modifications, or treatment conditions. By making these relationships explicit and queryable, OME-Arrow reduces the need for custom data integration steps and improves the consistency of downstream analyses. This approach is being applied to pediatric cancer datasets in collaboration with Alex’s Lemonade Stand Foundation, where integrated access to imaging and profiling data supports systematic exploration of phenotype–treatment relationships and more reproducible analytical workflows.