Meta-Research: The changing career paths of PhDs and postdocs trained at EMBL
- Genome Biology Unit, European Molecular Biology Laboratory, Germany
- EMBL International Centre for Advanced Training, European Molecular Biology Laboratory, Germany
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Meta-Research: The changing career paths of PhDs and postdocs trained at EMBLeLife 12:e78706.https://doi.org/10.7554/eLife.78706 - Cite
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Figures
Figure 1 with 3 supplements
Career outcomes for EMBL alumni.
(A) Charts showing the percentage of PhD alumni (n=969) and postdoc alumni (n=1315) from EMBL in different careers in 2021 (see Table 1). (B) Charts showing percentage of PhD (left, n=800) and …
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Figure 1—source data 1
Summary data plotted in Figure 1A, B.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig1-data1-v1.xlsx
Figure 1—figure supplement 1
Sankey diagrams showing movement between different careers.
(A) Sankey diagram showing that of the 539 alumni who have held an Academia: PI (AcPI) position at some time, 75.3% moved into their first AcPI position from an Academia: Postdoc (AcPD) position, …
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Figure 1—figure supplement 1—source data 1
Summary data plotted in the simplified Sankey plots in Figure 1—figure supplement 1.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig1-figsupp1-data1-v1.xlsx
Figure 1—figure supplement 2
Career outcomes for EMBL alumni at five different time points for three different cohorts.
Charts showing the percentage of PhD and postdoc alumni in different roles at five different time points for three different cohorts. See Table 3 for cohort sizes; alumni who have not yet reached a …
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Figure 1—figure supplement 2—source data 1
Summary data plotted in Figure 1—figure supplement 2.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig1-figsupp2-data1-v1.xlsx
Figure 1—figure supplement 3
Comparing EMBL alumni with alumni from institutions in Canada and the United States.
Charts comparing percentage of PhD alumni in PI or PI-like positions for EMBL and Stanford University, the University of California San Francisco (UCSF), the University of Chicago (Bioscience …
Figure 2 with 1 supplement
Changes in career outcomes for more recent cohorts.
(A) Kaplan–Meier plots showing the estimated probability of an individual being in a PI position (y-axis) as a function of time after EMBL (x-axis) for three cohorts of PhD alumni (left) and three …
Figure 2—figure supplement 1
Length of time taken to become a PI.
(A) Box plot with overlaid dot plot showing the distribution of the length of time between PhD and first PI role for two cohorts of alumni who defended their PhD between 1997 and 2012 and became a …
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Figure 2—figure supplement 1—source data 1
Summary data plotted in Figure 2—figure supplement 1.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig2-figsupp1-data1-v1.xlsx
Figure 3 with 2 supplements
Gender differences in career outcomes.
(A) Charts showing the percentage of female (n=415) and male (n=554) PhD alumni, and female (n=492) and male (n=823) postdoc alumni, in different careers in 2021. (B) Kaplan–Meier plots showing the …
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Figure 3—source data 1
Summary data plotted in Figure 3A.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig3-data1-v1.xlsx
Figure 3—figure supplement 1
Length of time taken to become a PI for female and male alumni.
(A) Box plot with overlaid dot plot showing the distribution of the length of time between PhD and first PI role for female alumni (left) and male alumni (right) who defended their PhD between 1997 …
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Figure 3—figure supplement 1—source data 1
Summary data plotted in Figure 3—figure supplement 1.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig3-figsupp1-data1-v1.xlsx
Figure 3—figure supplement 2
Rate of entry into different types of role by gender.
(A) Kaplan–Meier plots showing the estimated probability of an individual working in Academia: Other (y-axis) as a function of time after EMBL, stratified by gender for PhD alumni (left) and postdoc …
Figure 4 with 2 supplements
Publication factors are highly correlated with becoming a PI.
(A) Histograms showing the number of alumni who have 0, 1, 2, 3,... first-author articles from their time at EMBL and became PIs (bottom; n=662, excluding 23 outliers), and did not become PIs (top; …
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Figure 4—source data 1
Summary data plotted in Figure 4A, B and D.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig4-data1-v1.xlsx
Figure 4—figure supplement 1
Cox models for predicting entry into various careers.
Harrell’s C-Index for various Cox models for predicting entry into Academia: Other (A), Industry Research (B), Science-related Non-research (C), and Non-science-related careers (D). As in Figure 4D, …
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Figure 4—figure supplement 1—source data 1
Summary data plotted in Figure 4—figure supplement 1.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig4-figsupp1-data1-v1.xlsx
Figure 4—figure supplement 2
Entry into various careers and number of first-author publications.
Kaplan–Meier plots showing the estimated probability of an individual being in various careers (y-axis) as a function of time after EMBL (x-axis), stratified by number of first-author publications …
Figure 5
Publications are increasingly collaborative.
(A) Mean number of authors (y-axis) as a function of year (x-axis) for research articles that were published between 1995 and 2020, and have at least one of the alumni included in this study as an …
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Figure 5—source data 1
Summary data plotted in Figure 5.
- https://cdn.elifesciences.org/articles/78706/elife-78706-fig5-data1-v1.xlsx
Tables
Table 1
Career outcomes for 2284 EMBL alumni.
| Career | PhD alumni | Postdoc | Total |
|---|---|---|---|
| Academia: PI (AcPI) | 215 (22.2%) | 421 (32%) | 636 (27.8%) |
| Academia: Other (AcOt) | 102 (10.5%) | 281 (21.4%) | 383 (16.8%) |
| Academia: Postdoc (AcPD) | 168 (17.3%) | 76 (5.8%) | 244 (10.7%) |
| Industry research (IndR) | 153 (15.8%) | 179 (13.6%) | 332 (14.5%) |
| Science-related Non-research (SciR) | 178 (18.4%) | 171 (13%) | 349 (15.3%) |
| Non-science-related (NonSci) | 47 (4.9%) | 44 (3.3%) | 91 (4%) |
| Unknown | 106 (10.9%) | 143 (10.9%) | 249 (10.9%) |
| TOTAL | 969 | 1315 | 2284 |
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See Table 2 for more information on the different jobs covered by Industry Research, Science-related Non-research, and Non-science-related. This classification is based on Stayart et al., 2020.
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AcPI: includes those leading an academic research team with financial and scientific independence – evidenced by a job title such as group leader, professor, associate professor or tenure-track assistant professor. Where the status was unclear from the job title, we classified an alumnus as a Principal Investigator (PI) if one of the following criteria was fulfilled: (a) they appear to directly supervise students/postdocs (based on hierarchy shown on website); (b) they have published a last author publication from their current position; (c) their group website or CV indicates that they have a grant (not just a personal merit fellowship) as a principal investigator. AcOt: differs from Stayart et al., 2020 in that it includes academic research, scientific services or teaching staff (e.g., research staff, teaching faculty and staff, technical directors, research infrastructure engineers).
Table 2
Classification of Industry Research, Science-related Non-research and Non-science-related positions.
| Job function | PhD alumni | Postdoc | Total |
|---|---|---|---|
| Industry research | |||
| R & D scientist* | 138 (14.2%) | 167 (12.7%) | 305 (13.4%) |
| Entrepreneurship† | 6 (0.6%) | 8 (0.6%) | 14 (0.6%) |
| Postdoctoral | 7 (0.7%) | 1 (0.1%) | 8 (0.4%) |
| Business development, consulting & strategic alliances ‡ | 2 (0.2%) | 3 (0.2%) | 5 (0.2%) |
| Total | 153 (15.8%) | 179 (13.6%) | 332 (14.5%) |
| Science-related Non-research | |||
| Administration and training | 35 (3.6%) | 35 (2.7%) | 70 (3.1%) |
| Business development, consulting & strategic alliances | 38 (3.9%) | 20 (1.5%) | 58 (2.5%) |
| Tech support and product development | 20 (2.1%) | 24 (1.8%) | 44 (1.9%) |
| Science writing and communication | 16 (1.7%0) | 21 (1.6%) | 37 (1.6%) |
| Data science, analytics, software engineering § | 15 (1.5%) | 13 (1%) | 28 (1.2%) |
| Intellectual property and law | 16 (1.7%) | 10 (0.8%) | 26 (1.1%) |
| Science education and outreach | 10 (1%) | 11 (0.8%) | 21 (0.9%) |
| Clinical research management | 8 (0.8%) | 4 (0.3%) | 12 (0.5%) |
| Regulatory affairs | 5 (0.5%) | 7 (0.5%) | 12 (0.5%) |
| Clinical services/public health | 4 (0.4%) | 6 (0.5%) | 10 (0.4%0) |
| Sales and Marketing | 4 (0.4%) | 6 (0.5%) | 10 (0.4%) |
| Healthcare provider | 1 (0.1%) | 8 (0.6%) | 9 (0.4%) |
| Other | 4 (0.4%) | 2 (0.2%) | 6 (0.3%) |
| Entrepreneurship | 2 (0.2%) | 2 (0.2%) | 4 (0.2%) |
| Science policy and government affairs | 0 (0%) | 2 (0.2%0) | 2 (0.1%) |
| Total | 178 (18.4%) | 171 (13%) | 349 (15.3%) |
| Non-science-related | |||
| Data science, analytics, software engineering § | 18 (1.9%) | 25 (1.9%) | 43 (1.9%) |
| Business development, consulting & strategic alliances | 16 (1.7%) | 4 (0.3%) | 20 (0.9%) |
| Other (inc retired) | 7 (0.7%) | 12 (0.9%) | 19 (0.8%) |
| Entrepreneurship | 5 (0.5%) | 2 (0.2%) | 7 (0.3%0) |
| Administration and training | 1 (0.1%) | 1 (0.1%) | 2 (0.1%) |
| Total | 47 (4.9%) | 44 (3.3%) | 91 (4%) |
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*
This function differs from the schema in Stayart et al., 2020; it includes alumni carrying out or overseeing scientific research in industry as group leaders, research staff, technical directors and non-directorship research leadership roles, including alumni who appear to be working in computational biology roles of a pharma, biotech, contract research or similar company regardless of job title (i.e. including data science roles that appear to be related to analysis of research-related data.)
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†
Founders of companies whose primary focus is R&D (including contract research organizations).
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‡
Includes director-level senior management roles overseeing the scientific direction & research of a company with R&D focus, e.g. CSOs in biotech start-ups.
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§
Not including computational biology roles linked to R&D functions.
Table 3
Overview of PhD and postdoc cohorts.
| Completion years | PhD cohorts | Postdoc cohorts | All | ||||
|---|---|---|---|---|---|---|---|
| 1997–2004 | 2005–2012 | 2013–2020 | 1997–2004 | 2005–2012 | 2013–2020 | All | |
| n = | 256 | 341 | 372 | 369 | 422 | 524 | 2284 |
| n (%) known current role | 225 (88%) | 306 (90%) | 332 (89%) | 336 (88%) | 364 (86%) | 472 (90%) | 2035 (89%) |
| n (%) detailed career path | 220 (70%) | 258 (79%) | 413 (77%) | 179 (60%) | 271 (61%) | 285 (79%) | 1626 (71%) |
| n (%) female | 85 (33%) | 157 (46%) | 173 (47%) | 136 (37%) | 149 (35%) | 207 (40%) | 907 (40%) |
Additional files
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MDAR checklist
- https://cdn.elifesciences.org/articles/78706/elife-78706-mdarchecklist1-v1.docx
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Supplementary file 1
Supplementary tables (Table S1–11).
- https://cdn.elifesciences.org/articles/78706/elife-78706-supp1-v1.docx
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Supplementary file 2
Supplementary methods.
- https://cdn.elifesciences.org/articles/78706/elife-78706-supp2-v1.docx
