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Build an author trajectory analysis

Source: R/trajectory-build.R, R/trajectory-print.R
sm_author_trajectory.Rd

Analyses the career trajectory of a single author within a corpus, partitioning their publication record into n_periods career stages. For each period, the function computes:

  • Career stages: dominant topics and publication volume.

  • Topic pivots: how much the research focus shifted between periods, measured as the Jaccard distance of concept sets.

  • Collaborator turnover: Jaccard similarity of co-author sets across consecutive periods, plus counts of new, kept, and lost collaborators.

  • Emerging collaborators: co-authors who first appear in the most recent period.

  • H-index curve: cumulative h-index at the end of each period.

  • Novelty curve: average concept novelty per period.

  • Citation acceleration: mean citations per period relative to the corpus-wide mean for the same years.

Usage

sm_author_trajectory(
  corpus,
  orcid = NULL,
  author_id = NULL,
  n_periods = 5L,
  call = rlang::caller_env()
)

# S3 method for class 'sm_trajectory'
print(x, ...)

# S3 method for class 'sm_trajectory'
format(x, ...)

Arguments

corpus

An sm_corpus object.

orcid

Character. ORCID of the author to analyse. Either orcid or author_id must be supplied.

author_id

Character. Internal author ID. Either orcid or author_id must be supplied.

n_periods

Integer. Number of career periods to divide the publication span into. Default 5.

call

Caller environment for error reporting.

x

An sm_trajectory object.

...

Ignored.

Value

An sm_trajectory S3 object.

See also

Other trajectory: is_sm_trajectory(), sm_plot_collab_turnover(), sm_plot_topic_pivots(), sm_plot_trajectory(), sm_researcher_profile(), sm_trajectory

Examples

corpus <- sm_example_corpus()
# Use the first author's ID
traj <- sm_author_trajectory(corpus, author_id = "A000000001")
print(traj)
#> 
#> ── <sm_trajectory> ─────────────────────────────────────────────────────────────
#> Author: Elena Fischer
#> Author ID: A000000001
#> ORCID: 0000-0003-6689-5331
#> Periods: 5
#> 
#> 
#> ── Career stages 
#> Period 1 (2015-2016): 5 works, h=6 | gene expression, biomarker discovery, drug
#> resistance
#> Period 2 (2016-2018): 6 works, h=9 | colorectal cancer, machine learning,
#> clinical outcomes
#> Period 3 (2018-2020): 6 works, h=11 | single-cell RNA-seq, biomarker discovery,
#> clinical outcomes
#> Period 4 (2020-2022): 10 works, h=13 | biomarker discovery, colorectal cancer,
#> single-cell RNA-seq
#> Period 5 (2022-2024): 13 works, h=14 | immune checkpoint, machine learning,
#> clinical outcomes
#> 
#> 
#> ── Topic pivots 
#> Period 2: score=0.3 (stable)
#> Period 3: score=0.3 (stable)
#> Period 4: score=0.3 (stable)
#> Period 5: score=0 (stable)
#> 
#> 
#> ── Collaborator turnover 
#> Period 2: Jaccard=0.194 (new=14, kept=7, lost=15)
#> Period 3: Jaccard=0.081 (new=16, kept=3, lost=18)
#> Period 4: Jaccard=0.085 (new=28, kept=4, lost=15)
#> Period 5: Jaccard=0.315 (new=22, kept=17, lost=15)
#> 
#> 
#> ── Emerging collaborators (9) 
#> James Ibrahim (since 2024)
#> Carlos Smith (since 2022)
#> Wei Johansson (since 2024)
#> Carlos Patel (since 2024)
#> David Andersson (since 2023)
#> ... and 4 more
#> 
#> 
#> ── H-index curve 
#> 2015-2016:6 -> 2016-2018:9 -> 2018-2020:11 -> 2020-2022:13 -> 2022-2024:14
#> 
#> 
#> ── Citation acceleration 
#> 2015-2016: mean=9.4 (-5.3 vs field)
#> 2016-2018: mean=18.3 (+4 vs field)
#> 2018-2020: mean=17 (+2 vs field)
#> 2020-2022: mean=11.2 (-6.1 vs field)
#> 2022-2024: mean=11.8 (-2.5 vs field)