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        Unified human gene representations
      </p>

      <h1>Gene Embedding Hub</h1>

      <p class="lead">
          Browse standardized gene-level representations spanning
          protein sequence, DNA sequence, RNA sequence, expression,
          biomedical text, functional data, networks, ontologies,
          regulatory genomics, knowledge graphs, single-cell, and
          multimodal representations.
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          href="#contribute"
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          Contribute an embedding
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          href="https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset"
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          Open Core Dataset
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        Community contributions are automatically checked for
        compliance with the technical policy. Inclusion is not
        independent scientific or legal endorsement.
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    <section
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      aria-label="Catalog summary"
    >
      <article>
        <strong id="stat-count">—</strong>
        <span>embedding sets</span>
      </article>

      <article>
        <strong id="stat-genes">—</strong>
        <span>maximum genes</span>
      </article>

      <article>
        <strong id="stat-dims">—</strong>
        <span>dimension range</span>
      </article>
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      <p class="section-label">
        Community contribution
      </p>

      <h2>Submit a gene embedding</h2>

      <p class="contribute-intro">
        Prepare one standardized package and upload it directly to
        the library-owned Hugging Face Dataset as a Pull Request.
      </p>

      <div class="instruction-grid">
        <article class="instruction-card">
          <span class="step-number">1</span>

          <h3>Prepare one folder</h3>

          <pre><code>&lt;embedding_id&gt;/
├── embeddings.npz
├── genes.tsv
├── README.md
└── submission.yaml</code></pre>

          <p>
            The embedding ID must contain 3–80 lowercase letters,
            numbers, dots, underscores, or hyphens and must begin
            with a letter or number.
          </p>
            <p>
              A <code>metadata.json</code> file is automatically
              generated and saved in the embedding's folder after
              acceptance.
            </p>

        </article>

        <article class="instruction-card">
          <span class="step-number">2</span>

          <h3>Check the package contract</h3>

          <ul>
            <li>
              <code>embeddings.npz</code> contains exactly one
              two-dimensional <code>float32</code> array named
              <code>embeddings</code>.
            </li>

            <li>
              <code>genes.tsv</code> contains one unique
              <code>ensembl_gene_id</code> column whose rows match
              the matrix rows.
            </li>
            <li>
              <code>submission.yaml</code> provides the scientific
              context and provenance needed to understand the
              embedding and how it was produced.
            </li>


            <li>
              <code>README.md</code> documents how the representation
              was produced.
            </li>
          </ul>


            <p>
              <a
                class="button"
                href="gene-mapping-tutorial.html"
              >
                Open gene mapping tutorial
              </a>
            </p>
        </article>

                <article class="instruction-card">
          <span class="step-number">3</span>

          <h3>Complete the submission template</h3>

          
          <p>
            Download <code>submission.yaml</code>, replace every
            placeholder and keep the embedding ID identical to the
            folder name. Submitted values are checked automatically
            against the package.
          </p>
</article>

                <article class="instruction-card">
          <span class="step-number">4</span>

          <h3>Upload as a Pull Request</h3>
          <ol>
            <li>
              Open the Hugging Face uploader using the button below.
            </li>

            <li>
              Upload the folder
              <code>&lt;embedding_id&gt;</code>.
            </li>

            <li>
              Select <strong>Open as a pull request</strong>.
            </li>
          </ol>
        </article>

        <article class="instruction-card full-width">
          <span class="step-number">5</span>

          <h3>Automatic validation and publication</h3>

          <p>
            The package structure, metadata consistency, Ensembl
            identifiers, matrix shape, numeric validity, licensing,
            provenance, and attestations are checked automatically.
          </p>

          <p>
            A strict PASS is stored in the central Dataset and added
            to this catalog.
          </p>
        </article>
      </div>

      <div class="button-row">
        <a
          class="button primary"
          href="https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-contributions-staging/upload/main"
          target="_blank"
          rel="noopener noreferrer"
        >
          Open Hugging Face uploader
        </a>

        <a
          class="button"
          href="submission.yaml"
          download
        >
          Download submission template
        </a>
        <a
          class="button"
          href="submission.example.yaml"
          download
        >Download completed example</a>
      </div>

      <details class="cli-alternative">
        <summary>Command-line alternative example</summary>

        <p>
          Before running this example, replace
          <code>my_embedding_v1</code> with the name of your existing
          local embedding folder in both path arguments.
        </p>

        <pre><code>hf upload \
  BoevaLab/Gene-Embedding-Hub-contributions-staging \
  ./my_embedding_v1 \
  my_embedding_v1 \
  --repo-type dataset \
  --create-pr \
  --commit-message "Submit gene embedding"</code></pre>
      </details>
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