Research landscape

viewof visible_topics = Inputs.checkbox(
  topic_names,
  { value: topic_names,
    label: "Topics:"
  }
)

Plot

Plot.plot({
  grid: true,
  color: {
    scheme: "spectral",
    domain: topic_names,
    legend: true
  },
  x: {
    label: ""
  },
  y: {
    label: ""
  },
  marks: [
    Plot.dot(
        filtered,
        {
            x: "embedding.x",
            y: "embedding.y",
            title: (d) =>
        `${d.doi}\n ${d.title}\n ${d['journal.abbreviation']} ${d['journal.volume']} (${d['year']})`,
            fill: "embedding.topic_name",
            r: 6,
        }
    )
  ],
  tooltip: {
    stroke: "blue",
  }
})

Data

Inputs.table(filtered, {
  columns: [
    "doi",
    "title",
    "journal.name",
    "journal.volume",
    "year",
  ],
  header: {
    "journal.name": "journal",
    "journal.volume": "volume",
  },
  format: {
    doi: doi => htl.html`<a href=https://doi.org/${doi} target=_blank>${doi}</a>`,
    title: title => htl.html`${title}`,
    "journal.volume": volume => htl.html`<b>${volume}</b>`,
    year: year => htl.html`${year}`,
  },
  width: {
    "title": 10
  },
  layout: "fixed",
})

import {Plot} from "@mkfreeman/plot-tooltip"
papers = FileAttachment("data/all_papers.csv").csv({ typed: true })

topic_names = Array.from(new Set(papers.map(d => d["embedding.topic_name"])))

filtered = papers.filter(function(p) {
  return visible_topics.includes(p["embedding.topic_name"]);
})

Note

This represents a two-dimensional embedding of all the group’s publications, clustered in topics. Publication abstracts are lemmatized and clustered using BERTopic with a UMAP dimensionality reduction.

For a more detailed description of our research, see Topics.