Research Publications Affiliated with Luxembourg: A Data-Driven Analysis

1 Introduction

This report presents a comprehensive analysis of Luxembourg’s research publication landscape using data extracted from OpenAlex, an open-access scholarly metadata platform. OpenAlex serves as a freely accessible alternative to proprietary academic databases, providing structured information about publications, authors, institutional affiliations, and research collaborations across all academic disciplines.

The data collection methodology focuses on identifying all scholarly works with Luxembourg institutional affiliations recorded in OpenAlex over the past decade. This approach captures both research led by Luxembourg-based scholars and international collaborative projects where Luxembourg institutions participate as co-authors. The temporal scope provides a current snapshot of the country’s scientific output and evolving research partnerships.

OpenAlex aggregates metadata from multiple sources including institutional repositories, publisher databases, and citation networks. While this multi-source approach enhances coverage comprehensiveness, data quality depends on the accuracy of source reporting and the platform’s ability to correctly identify and link Luxembourg-affiliated works. These methodological considerations should be kept in mind when interpreting the analytical findings presented throughout this report.

2 Data Structure and Document Types

The initial dataset encompasses all document types recorded in OpenAlex for Luxembourg-affiliated scholarly works during the study period. The following table displays the distribution of work types and the presence of Digital Object Identifiers (DOIs), which serve as persistent identifiers linking to original publications:

Table 1: Distribution of types of work and missingness of DOI

Based on the substantial predominance of journal articles in the dataset and their central importance in academic research communication, this analysis restricts its focus to articles exclusively. This selection encompasses both publications with DOI identifiers and those without, ensuring comprehensive coverage of Luxembourg’s peer-reviewed research output.

The analytical framework employs a critical distinction based on first authorship status: whether the primary author maintains affiliation with a Luxembourg institution or represents an international collaboration where Luxembourg institutions participate as secondary contributors. This classification enables differentiation between research leadership and research participation within the national research ecosystem:

Table 2: Number of articles where the first author is LU-affiliated

3 Research Domain Analysis

The following visualization examines the temporal distribution of Luxembourg-affiliated research across major scientific domains. The data utilizes OpenAlex’s domain classification system, which categorizes research fields into broad disciplinary areas. The analysis tracks publication volumes over time while maintaining the distinction between Luxembourg-led research (first author affiliation) and collaborative research (non-first author participation):

import {Plot} from "@observablehq/plot"
import {Inputs} from "@observablehq/inputs"
import {rangeInput} from "@mootari/range-slider@1846"


// Convert R data to JavaScript format
raw_data = transpose(primary_domain_lu_ojs)

// Convert data types to ensure Observable Plot can use them
data = raw_data.map(d => ({
  publication_year: +d.publication_year,  // Convert to number
  primary_domain_name: d.primary_domain_name,
  total: +d.total,  // Convert to number
  is_lu_first_author: d.is_lu_first_author === "TRUE" || d.is_lu_first_author === true  // Convert to boolean
}))

// Create color mapping
domain_colors = new Map([
  ["Health Sciences", "#FF6B35"],
  ["Life Sciences", "#003399"],
  ["MISSING-DOMAIN", "#228B22"],
  ["Physical Sciences", "#FF1493"],
  ["Social Sciences", "#800080"]
])

// Get unique values for controls
unique_domains = [...new Set(data.map(d => d.primary_domain_name))].sort()

html`<div style="margin-top: 20px; padding: 15px;">
  <h4 style="margin-top: 0; margin-bottom: 15px;">Inputs:</h4>
  <label style="font-size: 16px; color: #333;">Select year range:</label>
</div>`

viewof year_range = rangeInput({
  min: 2015,
  max: 2025,
  value: [2015, 2025],
  step: 1
})

viewof selected_domains = Inputs.checkbox(
  unique_domains,
  {
    value: ["Health Sciences"],
    label: "Research Domains"
  }
)

viewof selected_author_type = Inputs.radio(
  ["LU-affiliated first author", "Non LU-affiliated first author", "Both"],
  {
    value: "Both",
    label: "First Author Affiliation"
  }
)


// Filter data based on selections
filtered_data = data.filter(d => {
  // Year filter
  const yearOk = d.publication_year >= year_range[0] && d.publication_year <= year_range[1];
  
  // Domain filter
  const domainOk = selected_domains.includes(d.primary_domain_name);
  
  // Author filter
  let authorOk = false;
  if (selected_author_type === "Both") {
    authorOk = true;
  } else if (selected_author_type === "LU-affiliated first author") {
    authorOk = d.is_lu_first_author === true;
  } else if (selected_author_type === "Non LU-affiliated first author") {
    authorOk = d.is_lu_first_author === false;
  }
  
  return yearOk && domainOk && authorOk;
})

// Combined HTML legend with domain colors and author types
html`
<div style="margin-top: 20px; padding: 15px;">
  <h4 style="margin-top: 0; margin-bottom: 15px;">Legend:</h4>
  
  <!-- Domain Colors -->
  <div style="margin-bottom: 15px;">
    <h5 style="margin: 0 0 10px 0;">Research Domains:</h5>
    <div style="display: flex; flex-wrap: wrap; gap: 15px;">
      ${unique_domains.map(domain => `
        <div style="display: flex; align-items: center; gap: 6px;">
          <div style="width: 16px; height: 16px; background-color: ${domain_colors.get(domain)}; border-radius: 2px;"></div>
          <span style="font-size: 14px;">${domain}</span>
        </div>
      `).join('')}
    </div>
  </div>
  
  <!-- Author Types -->
  <div>
    <h5 style="margin: 0 0 10px 0;">Author Types:</h5>
    <div style="display: flex; gap: 30px; align-items: center; flex-wrap: wrap;">
      <div style="display: flex; align-items: center; gap: 8px;">
        <svg width="30" height="20">
          <line x1="0" y1="10" x2="30" y2="10" stroke="#333" stroke-width="2.5" />
          <circle cx="15" cy="10" r="4" fill="#333" stroke="white" stroke-width="1"/>
        </svg>
        <span><strong>LU-affiliated first author</strong> (solid line + circle)</span>
      </div>
      <div style="display: flex; align-items: center; gap: 8px;">
        <svg width="30" height="20">
          <line x1="0" y1="10" x2="30" y2="10" stroke="#333" stroke-width="2.5" stroke-dasharray="8,8"/>
          <polygon points="15,6 19,14 11,14" fill="#333" stroke="white" stroke-width="1"/>
        </svg>
        <span><strong>Non-LU-affiliated first author</strong> (dashed line + triangle)</span>
      </div>
    </div>
  </div>
</div>
`

Plot.plot({
  width: 800,
  height: 500,
  marginLeft: 60,
  marginBottom: 60,
  x: {
    label: "Publication Year",
    domain: [Math.min(...filtered_data.map(d => d.publication_year)), 
             Math.max(...filtered_data.map(d => d.publication_year))],
    tickFormat: "d",
    ticks: d3.range(
      Math.min(...filtered_data.map(d => d.publication_year)),
      Math.max(...filtered_data.map(d => d.publication_year)) + 1
    ) // Force integer ticks only
  },
  y: {
    label: "Number of Publications",
    grid: true
  },
  color: {
    domain: unique_domains,
    range: unique_domains.map(d => domain_colors.get(d)),
    legend: false
  },
  marks: [
    // Your existing marks here...
    // Solid lines for LU-affiliated authors
    Plot.line(filtered_data.filter(d => d.is_lu_first_author === true), {
      x: "publication_year",
      y: "total",
      stroke: "primary_domain_name",
      strokeWidth: 2.5,
      z: d => `${d.primary_domain_name}-LU`
    }),
    // Dashed lines for non-LU-affiliated authors
    Plot.line(filtered_data.filter(d => d.is_lu_first_author === false), {
      x: "publication_year",
      y: "total",
      stroke: "primary_domain_name",
      strokeWidth: 2.5,
      strokeDasharray: "8,8",
      z: d => `${d.primary_domain_name}-NonLU`
    }),
    // Points with different symbols
    Plot.dot(filtered_data, {
      x: "publication_year",
      y: "total",
      fill: "primary_domain_name",
      symbol: d => d.is_lu_first_author ? "circle" : "triangle",
      r: 5,
      stroke: "white",
      strokeWidth: 1
    }),
    // Vertical line rule (only when "Both" is selected)
    ...(selected_author_type === "Both" ? [
      Plot.ruleX(filtered_data, Plot.pointerX({
        x: "publication_year",
        stroke: "gray",
        strokeWidth: 1,
        strokeDasharray: "3,3",
        opacity: 0.7
      }))
    ] : []),
    // Enhanced tooltips
Plot.tip(filtered_data, selected_author_type === "Both" ? 
  Plot.pointerX({
    x: "publication_year",
    title: (d, i, data) => {
      // Group all data points by year for this x-position
      const year = d.publication_year;
      const allPointsAtYear = data.filter(point => point.publication_year === year);
      
      // Create title showing all domains and their values at this year
      const yearTitle = `Year: ${year}\n${'-'.repeat(20)}\n`;
      const entries = allPointsAtYear.map(point => 
        `${point.primary_domain_name}: ${point.total} (${point.is_lu_first_author ? 'LU' : 'Non-LU'})`
      ).join('\n');
      
      return yearTitle + entries;
    },
    fontSize: 12
  }) : 
  Plot.pointer({
    x: "publication_year",
    y: "total",
    fill: "primary_domain_name",
    title: d => `${d.primary_domain_name}\n${d.publication_year}\nPublications: ${d.total}\nLU First Author: ${d.is_lu_first_author ? "Yes" : "No"}`
  })
)
  ],
  title: "Luxembourg Research Publications by Domain Over Time"
})

OpenAlex employs a more granular classification system through subfields, which provides greater specificity than broad domains. Given that this taxonomy encompasses over 200 distinct subfields, this analysis focuses on the ten most frequently represented subfields in Luxembourg’s research output:

raw_data_subfield = transpose(primary_subfield_lu_ojs)

// Convert data types to ensure Observable Plot can use them
data_subfield = raw_data_subfield.map(d => ({
  publication_year: +d.publication_year,  // Convert to number
  primary_subfield_name: d.primary_subfield_name,
  total: +d.total,  // Convert to number
  is_lu_first_author: d.is_lu_first_author === "TRUE" || d.is_lu_first_author === true  // Convert to boolean
}))

// Create color mapping for your actual subfields
subfield_colors = new Map([
  ["Aerospace Engineering", "#FF6B35"],           // Orange-red
  ["Artificial Intelligence", "#003399"],        // Deep blue
  ["Computer Networks and Communications", "#228B22"], // Forest green
  ["Computer Vision and Pattern Recognition", "#FF1493"], // Deep pink
  ["Economics and Econometrics", "#800080"],     // Purple
  ["Electrical and Electronic Engineering", "#FFD700"], // Gold
  ["Information Systems", "#DE2910"],            // Red
  ["Materials Chemistry", "#C8102E"],            // Crimson
  ["Molecular Biology", "#009246"],              // Green
  ["Neurology", "#AA151B"],                      // Dark red
  ["Political Science and International Relations", "#FF7F00"], // Orange
  ["Pulmonary and Respiratory Medicine", "#4B0082"], // Indigo
  ["Sociology and Political Science", "#8B4513"] // Saddle brown
])

// Get unique values for controls
unique_subfields = [...new Set(data_subfield.map(d => d.primary_subfield_name))].sort()

html`<div style="margin-top: 20px; padding: 15px;">
  <h4 style="margin-top: 0; margin-bottom: 15px;">Inputs:</h4>
  <label style="font-size: 16px; color: #333;">Select year range:</label>
</div>`

viewof year_range_subfield = rangeInput({
  min: 2015,
  max: 2025,
  value: [2015, 2025],
  step: 1
})

viewof selected_subfields = Inputs.checkbox(
  unique_subfields,
  {
    value: ["Artificial Intelligence", "Computer Vision and Pattern Recognition", "Neurology"],
    label: "Research Subfields"
  }
)

viewof selected_author_type_subfield = Inputs.radio(
  ["LU-affiliated first author", "Non LU-affiliated first author", "Both"],
  {
    value: "Both",
    label: "First Author Affiliation"
  }
)

// Filter data based on selections
filtered_data_subfield = data_subfield.filter(d => {
  // Year filter
  const yearOk = d.publication_year >= year_range_subfield[0] && d.publication_year <= year_range_subfield[1];
  
  // Subfield filter
  const subfieldOk = selected_subfields.includes(d.primary_subfield_name);
  
  // Author filter
  let authorOk = false;
  if (selected_author_type_subfield === "Both") {
    authorOk = true;
  } else if (selected_author_type_subfield === "LU-affiliated first author") {
    authorOk = d.is_lu_first_author === true;
  } else if (selected_author_type_subfield === "Non LU-affiliated first author") {
    authorOk = d.is_lu_first_author === false;
  }
  
  return yearOk && subfieldOk && authorOk;
})

// Combined HTML legend with subfield colors and author types
html`
<div style="margin-top: 20px; padding: 15px;">
  <h4 style="margin-top: 0; margin-bottom: 15px;">Legend:</h4>
  
  <!-- Subfield Colors -->
  <div style="margin-bottom: 15px;">
    <h5 style="margin: 0 0 10px 0;">Research Subfields:</h5>
    <div style="display: flex; flex-wrap: wrap; gap: 15px;">
      ${unique_subfields.map(subfield => `
        <div style="display: flex; align-items: center; gap: 6px;">
          <div style="width: 16px; height: 16px; background-color: ${subfield_colors.get(subfield) || '#888'}; border-radius: 2px;"></div>
          <span style="font-size: 14px;">${subfield}</span>
        </div>
      `).join('')}
    </div>
  </div>
  
  <!-- Author Types -->
  <div>
    <h5 style="margin: 0 0 10px 0;">Author Types:</h5>
    <div style="display: flex; gap: 30px; align-items: center; flex-wrap: wrap;">
      <div style="display: flex; align-items: center; gap: 8px;">
        <svg width="30" height="20">
          <line x1="0" y1="10" x2="30" y2="10" stroke="#333" stroke-width="2.5" />
          <circle cx="15" cy="10" r="4" fill="#333" stroke="white" stroke-width="1"/>
        </svg>
        <span><strong>LU-affiliated first author</strong> (solid line + circle)</span>
      </div>
      <div style="display: flex; align-items: center; gap: 8px;">
        <svg width="30" height="20">
          <line x1="0" y1="10" x2="30" y2="10" stroke="#333" stroke-width="2.5" stroke-dasharray="8,8"/>
          <polygon points="15,6 19,14 11,14" fill="#333" stroke="white" stroke-width="1"/>
        </svg>
        <span><strong>Non-LU-affiliated first author</strong> (dashed line + triangle)</span>
      </div>
    </div>
  </div>
</div>
`

Plot.plot({
  width: 800,
  height: 500,
  marginLeft: 60,
  marginBottom: 60,
  x: {
    label: "Publication Year",
    domain: [Math.min(...filtered_data_subfield.map(d => d.publication_year)), 
               Math.max(...filtered_data_subfield.map(d => d.publication_year))],
    tickFormat: "d",
    ticks: d3.range(
      Math.min(...filtered_data_subfield.map(d => d.publication_year)),
      Math.max(...filtered_data_subfield.map(d => d.publication_year)) + 1
    )
  },
  y: {
    label: "Number of Publications",
    grid: true
  },
  color: {
    domain: unique_subfields,
    range: unique_subfields.map(d => subfield_colors.get(d) || '#888'),
    legend: false
  },
  marks: [
    // Solid lines for LU-affiliated authors
    Plot.line(filtered_data_subfield.filter(d => d.is_lu_first_author === true), {
      x: "publication_year",
      y: "total",
      stroke: "primary_subfield_name",
      strokeWidth: 2.5,
      z: d => `${d.primary_subfield_name}-LU`
    }),
    // Dashed lines for non-LU-affiliated authors
    Plot.line(filtered_data_subfield.filter(d => d.is_lu_first_author === false), {
      x: "publication_year",
      y: "total",
      stroke: "primary_subfield_name",
      strokeWidth: 2.5,
      strokeDasharray: "8,8",
      z: d => `${d.primary_subfield_name}-NonLU`
    }),
    // Points with different symbols
    Plot.dot(filtered_data_subfield, {
      x: "publication_year",
      y: "total",
      fill: "primary_subfield_name",
      symbol: d => d.is_lu_first_author ? "circle" : "triangle",
      r: 5,
      stroke: "white",
      strokeWidth: 1
    }),
    // Vertical line rule (only when "Both" is selected)
    ...(selected_author_type_subfield === "Both" ? [
      Plot.ruleX(filtered_data_subfield, Plot.pointerX({
        x: "publication_year",
        stroke: "gray",
        strokeWidth: 1,
        strokeDasharray: "3,3",
        opacity: 0.7
      }))
    ] : []),
    // Enhanced tooltips
        Plot.tip(filtered_data_subfield, selected_author_type_subfield === "Both" ? 
      Plot.pointerX({
        x: "publication_year",
        title: (d, i, data) => {
          // Group all data points by year for this x-position
          const year = d.publication_year;
          const allPointsAtYear = data.filter(point => point.publication_year === year);
          // Create title showing all subfields and their values at this year
          const yearTitle = `Year: ${year}\n${'-'.repeat(20)}\n`;
          const entries = allPointsAtYear.map(point => 
            `${point.primary_subfield_name}: ${point.total} (${point.is_lu_first_author ? 'LU' : 'Non-LU'})`
          ).join('\n');
          return yearTitle + entries;
        },
        fontSize: 12
      }) : 
      Plot.pointer({
        x: "publication_year",
        y: "total",
        fill: "primary_subfield_name",
        title: d => `${d.primary_subfield_name}\n${d.publication_year}\nPublications: ${d.total}\nLU First Author: ${d.is_lu_first_author ? "Yes" : "No"}`
      })
    )
  ],
  title: "Luxembourg Research Publications by Subfield Over Time"
})

4 International Collaboration Patterns

The analysis extends to examining Luxembourg’s research collaboration patterns with international partners. The dataset contains information about co-author affiliations, enabling identification of the most frequent collaborating countries and regions. This section presents the geographical distribution of research partnerships, organized by publication year and distinguished by Luxembourg’s role as lead author versus collaborative partner.

The data processing groups countries into meaningful categories, including major individual nations, regional blocs, and an aggregated “Others” category for countries with lower collaboration frequencies. This approach provides clarity while maintaining analytical depth regarding Luxembourg’s primary research partnerships:

raw_country_data = transpose(country_authors_unique_ojs)

// Convert data types to ensure Observable Plot can use them
country_data = raw_country_data.map(d => ({
  publication_year: +d.publication_year,
  is_lu_first_author: d.is_lu_first_author === "TRUE" || d.is_lu_first_author === true,
  country_groups: d.country_groups,
  n: +d.n
}))

// Create color mapping for countries
country_colors = new Map([
  ["European Union", "#003399"],
  ["Others", "#FF1493"],
  ["Luxembourg", "#FF6B35"],
  ["France", "#800080"],
  ["USA", "#228B22"],
  ["Belgium", "#FFD700"],
  ["China", "#DE2910"],
  ["Great Britain", "#C8102E"],
  ["Italy", "#009246"],
  ["Spain", "#AA151B"],
  ["Switzerland", "#FF0000"],
  ["Netherlands", "#FF7F00"]
])

// Get unique values for controls
unique_years = [...new Set(country_data.map(d => d.publication_year))].sort()
unique_countries = [...new Set(country_data.map(d => d.country_groups))].sort()

html`<div style="margin-top: 20px; padding: 15px;">
  <h4 style="margin-top: 0; margin-bottom: 15px;">Inputs:</h4>
  <label style="font-size: 16px; color: #333;">Select year range:</label>
</div>`

viewof country_year_range = rangeInput({
  min: 2015,
  max: 2025,
  value: [2022, 2025],
  step: 1
})

viewof selected_countries = Inputs.checkbox(
  unique_countries,
  {
    value: unique_countries,
    label: "Countries/Regions"
  }
)

viewof country_author_type = Inputs.radio(
  ["LU-affiliated first author", "Non LU-affiliated first author", "Both"],
  {
    value: "Both",
    label: "First Author Affiliation"
  }
)

// Filter data based on selections
filtered_country_data = country_data.filter(d => {
  // Year filter
  const yearOk = d.publication_year >= country_year_range[0] && d.publication_year <= country_year_range[1];
  
  // Country filter
  const countryOk = selected_countries.includes(d.country_groups);
  
  // Author filter
  let authorOk = false;
  if (country_author_type === "Both") {
    authorOk = true;
  } else if (country_author_type === "LU-affiliated first author") {
    authorOk = d.is_lu_first_author === true;
  } else if (country_author_type === "Non LU-affiliated first author") {
    authorOk = d.is_lu_first_author === false;
  }
  
  return yearOk && countryOk && authorOk;
})

// Get years present in filtered data for x-axis
years_present = [...new Set(filtered_country_data.map(d => d.publication_year))].sort()

// Legend for countries
html`
<div style="margin-top: 20px; padding: 15px;">
  <h4 style="margin-top: 0; margin-bottom: 15px;">Legend:</h4>
  
  <div style="margin-bottom: 15px;">
    <h5 style="margin: 0 0 10px 0;">Countries/Regions:</h5>
    <div style="display: flex; flex-wrap: wrap; gap: 15px;">
      ${unique_countries.map(country => `
        <div style="display: flex; align-items: center; gap: 6px;">
          <div style="width: 16px; height: 16px; background-color: ${country_colors.get(country)}; border: 1px solid black; border-radius: 2px;"></div>
          <span style="font-size: 14px;">${country}</span>
        </div>
      `).join('')}
    </div>
  </div>
</div>
`

Plot.plot({
  width: 900,
  height: 600,
  marginLeft: 80,
  marginBottom: 80,
  marginRight: 40,
  x: {
    label: null,
    axis: null,
    paddingOuter: 0.2
  },
  y: {
    label: "Total Publications",
    grid: true
  },
  fx: {
    label: "Publication Year",
    tickFormat: "d"
  },
  fy: {
    label: null,
    tickFormat: d => d ? "LU First Author" : "LU Not First Author"
  },
  color: {
    domain: unique_countries,
    range: unique_countries.map(d => country_colors.get(d)),
    legend: false
  },
  facet: {
    data: filtered_country_data,
    x: "publication_year",
    y: "is_lu_first_author",
    marginTop: 40
  },
  marks: [
    Plot.barY(filtered_country_data, Plot.groupX(
      {y: "sum"}, 
      {
        x: "country_groups",
        y: "n",
        fill: "country_groups",
        stroke: "black",
        strokeWidth: 0.5,
        tip: true,
        title: d => `${d.country_groups}\n${d.publication_year}\nPublications: ${d.n}\nLU First Author: ${d.is_lu_first_author ? "Yes" : "No"}`
      }
    )),
    Plot.frame({stroke: "black", strokeWidth: 1})
  ],
  style: {
    fontSize: "12px"
  },
  title: "Publications by Country/Region Over Time (Faceted by Luxembourg First Author Status)"
})

The table belows shows some citation statistics by year and domain:

Table 3: Citation statistics

As expected, citation patterns, regardless of discipline, publication year, or author affiliation, tend to follow a power-law distribution. Most research articles receive relatively few citations, as shown by the low median. In the health sciences, for LU-affiliated lead authors, only 25% of articles published in 2024 received at least 2 citations (as shown in the 75th percentile column in the table above), and just 1% reached 16 citations. However, citation counts accumulate over time, so it is more informative to consider older publications. For the same group, among articles published in 2019, 25% received at least 18 citations, and 1% reached 147 or more. The highest citation count observed for a single article in this group was 482.

Luxembourg has three official languages but English is the language of science, regardless of domain: