FDI-Lab-Website/about.html at main · SciCrunch/FDI-Lab-Website · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>About — FDI Lab</title>
  <meta name="description" content="Learn about the FAIR Data Informatics Lab at UC San Diego, our mission, history, and approach to biomedical data infrastructure.">
  <link rel="stylesheet" href="css/style.css">
</head>
<body>

  <header>
    <div class="header-inner">
      <a href="index.html" class="logo">
        <img src="images/site/fdi-logo-white.png" alt="FDI Lab" style="height:36px; object-fit:contain;">
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        <a href="about.html" class="active">About</a>
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  </header>

  <div class="page-hero">
    <div class="container">
      <h1>About FDI Lab</h1>
      <p>Building the open data infrastructure that biomedical science needs.</p>
    </div>
  </div>

  <!-- Mission -->
  <section>
    <div class="container">
      <h2>Our Mission</h2>
      <div class="divider"></div>
      <p>
        The FAIR Data Informatics (FDI) Lab at the University of California, San Diego is
        a leader in developing and providing novel informatics infrastructure and tools for
        making data <strong>Findable, Accessible, Interoperable, and Reusable (FAIR)</strong>.
      </p>
      <p>
        Our work spans the development of technology, services, and policy — thereby
        improving accuracy, reproducibility, and analysis in biomedical research. We bring
        together expertise in biology, computer science, library science, and ontology
        engineering to build infrastructure that works for scientists across disciplines.
      </p>
    </div>
  </section>

  <!-- History -->
  <section>
    <div class="container">
      <h2>Our History</h2>
      <div class="divider"></div>
      <p>
        FDI Lab originated in <strong>2008</strong> as the
        <strong>Neuroscience Information Framework (NIF)</strong>, an NIH Blueprint Consortium
        initiative. NIF began cataloging neuroscience resources in 2006 and created one of the
        first cross-database search engines for biomedical research, indexing hundreds of
        databases, tools, and resources.
      </p>
      <p>
        Building on this foundation, the lab developed <strong>SciCrunch</strong> — a
        customizable platform for creating domain-specific data portals. SciCrunch now powers
        infrastructure for multiple biomedical communities, including the
        <strong>dkNET</strong> portal for digestive, diabetes, and kidney disease research.
      </p>
      <p>
        A particularly impactful initiative has been the
        <strong>Resource Identification Initiative (RRID)</strong>, which provides unique,
        persistent identifiers for research resources including antibodies, cell lines,
        organisms, and biosamples cited in scientific literature — dramatically improving
        reproducibility across the published record.
      </p>
      <p>
        FDI Lab actively participates in open-science organizations including
        <strong>FORCE11</strong> and the
        <strong>International Neuroinformatics Coordinating Facility (INCF)</strong>.
      </p>
    </div>
  </section>

  <!-- FAIR explained -->
  <section>
    <div class="container">
      <h2>About FAIR</h2>
      <div class="divider"></div>
      <p>
        The FAIR Guiding Principles, published in <em>Scientific Data</em> in 2016, provide
        a framework for making data and digital assets maximally reusable. FDI Lab was an
        early adopter and active contributor to FAIR practice across the biomedical community.
      </p>
      <div class="cards">
        <div class="card">
          <h3>Findable</h3>
          <p>Data and metadata are assigned globally unique, persistent identifiers and are deposited in searchable resources. Rich metadata helps both humans and machines discover datasets efficiently.</p>
        </div>
        <div class="card">
          <h3>Accessible</h3>
          <p>Once found, data and metadata can be retrieved using open, standardized protocols. Authentication and authorization rules are transparent, and metadata remain accessible even when data are not.</p>
        </div>
        <div class="card">
          <h3>Interoperable</h3>
          <p>Data use a formal, accessible, shared, and broadly applicable language for knowledge representation. Vocabularies follow FAIR principles, and qualified references to other data are included.</p>
        </div>
        <div class="card" style="grid-column: span 1;">
          <h3>Reusable</h3>
          <p>Data have rich, accurate metadata; a clear, accessible data usage license; detailed provenance; and meet domain-relevant community standards to support replication and reuse.</p>
        </div>
      </div>
    </div>
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