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Research Culture: A survey-based analysis of the academic job market

  1. Jason D Fernandes
  2. Sarvenaz Sarabipour
  3. Christopher T Smith
  4. Natalie M Niemi
  5. Nafisa M Jadavji
  6. Ariangela J Kozik
  7. Alex S Holehouse
  8. Vikas Pejaver
  9. Orsolya Symmons
  10. Alexandre W Bisson Filho
  11. Amanda Haage  Is a corresponding author
  1. Department of Biomolecular Engineering, University of California, Santa Cruz, United States
  2. Institute for Computational Medicine, Johns Hopkins University, United States
  3. Department of Biomedical Engineering, Johns Hopkins University, United States
  4. Office of Postdoctoral Affairs, North Carolina State University Graduate School, United States
  5. Morgridge Institute for Research, United States
  6. Department of Biochemistry, University of Wisconsin-Madison, United States
  7. Department of Biomedical Sciences Midwestern University, United States
  8. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, United States
  9. Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, United States
  10. Department of Biomedical Informatics and Medical Education, University of Washington, United States
  11. The eScience Institute, University of Washington, United States
  12. Department of Bioengineering, University of Pennsylvania, United States
  13. Department of Biology, Brandeis University, United States
  14. Rosenstiel Basic Medical Science Research Center, Brandeis University, United States
  15. Department of Biomedical Sciences, University of North Dakota, United States
Feature Article
Cite this article as: eLife 2020;9:e54097 doi: 10.7554/eLife.54097
11 figures and 43 additional files

Figures

An overview of the academic job search process.

The first column defines common terms in the academic job search; while the second column outlines how the search for an academic job progresses, from a job being posted to an offer being accepted.

Demographics of academic job applicants.

(A) Distribution of survey respondents by self-identified gender and scientific field (Supplementary file 2). Fields highlighted in green were grouped together as life-science related fields for subsequent analyses. (B) Distribution of countries where respondents were researching at the time of the survey (top, see Supplementary file 3) and the countries in which they applied to faculty jobs (green slices of pie charts, bottom; see Supplementary file 4). (C) Self-reported positions of applicants when applying for faculty jobs (Supplementary file 5). (D) The number of years spent as a postdoctoral researcher ranges from 1 year or fewer (4% of applicants) to eight or more years (9% of applicants; maximum of 13 years, top). Life-science related postdoctoral training (n = 268 respondents) takes significantly longer than in other fields (n = 49 respondents; p=6.5×10−6, bottom; for data see Supplementary file 6; for statistical analysis see Supplementary file 7). (E) Number of postdoctoral positions held by survey applicants (Supplementary file 8). (F) Median values for metrics of research productivity in the applicant pool (Supplementary file 9).

Applicant scholarly metrics by gender.

(A) Distribution of gender (male, female, did not disclose) amongst survey respondents (Supplementary file 2, first row). (B) Publication metrics of survey respondents including number of first author papers (top), total publications (middle top), total citations (middle bottom), and h-index (bottom) for male and female respondents. Men in our survey reported more first-authored papers than women (medians of 7 and 5, respectively; p=1.4×10−4), more total publications (medians of 16 and 11; p=3.0×10−3), more overall citations (medians of 343 and 228; p=1.5×10−2), and a statistically significant higher h-index (medians of 9.0 and 7.0; p=5.40×10−3; see Supplementary files 7 and 9). (C) Although most applicants (83.6%) did not have first-author papers in CNS, those in the life sciences had more than applicants in other fields (p=0.012), and men had more than women (p=0.45; see Supplementary files 7 and 11). Note: CNS papers do not include papers in spin-off journals from Cell, Nature or Science. (D) Distribution of funding reported within training period (doctoral fellowship only in blue, postdoctoral fellowship only in red, fellowships during PhD and postdoc in purple, and no fellowship in gray). Females reported significantly more fellowship funding than males (42% of women vs 36% of men for predoctoral fellowships, and 72% of women, 58% of men for postdoctoral fellowships, p=2.40×10−3, χ2 = 12.10, Chi-squared test, df = 2, see Supplementary files 7 and 13). (E) Preprints were posted by 148 of 270 (55%) individual candidates, with an average of 1.57 preprints reported per candidate (top). Number of preprints posted which were not yet accepted for journal publication (bottom) while applying for faculty jobs (see Supplementary file 14).

Figure 4 with 1 supplement
Job application benchmarks and their impact on success.

(A) Total and median numbers of applications, off-site interviews, on-site interviews and offers recorded in survey responses (Supplementary file 19). (B) Correlations between the total number of applications submitted and off-site interviews (top; R2 = 0.28), onsite interviews (middle) and offers (bottom; R2 = 4.77×10−2). (C) Correlations between the number of interviews completed and offers received (R2 = 0.62). See Figure 4—figure supplement 1 for more details. (D) Total number of off-site interviews (top, p<4.10×10−24, on-site interviews (middle, p=1.20×10−13) and offers (bottom, p=5.0×10−5) for applicants who submitted at least 15 (the median) applications (in red) and less than 15 applications (in blue). (E) Fraction of applications that resulted in offers (offer percentages) for survey respondents who did not apply for jobs outside of faculty positions is significantly higher (p=2.0×10−3, Supplementary file 7) than for those who also applied for both academic and other types of jobs (Supplementary file 14).

Figure 4—figure supplement 1
Correlations between offer percentage and a number of traditional scholarly metrics.

Pearson correlation coefficient (R2) between offer percentage total number of publications (top), number of first author publications (second graph), number of corresponding author publications (third graph), h-index (fourth graph), preprints posted (overall total, fifth graph; as well as those in which the peer-reviewed article was not published at the time of application, sixth graph), and number of patents filed, bottom graph). Yellow dots represent candidates with an offer, blue dots received no offers; black line represents linear best-fit and gray fill represents the 95% confidence interval for that fit. We examined several other publication metrics and found no correlation with the number of offers. Specifically, the total number of publications (R2 = 8×10−2), the number of first author (R2 = 2×10−2), the number of corresponding author publications (R2 = 9×10−4), and h-index (R2 = 4×10−3) did not significantly correlate with offer percentage.

Figure 5 with 2 supplements
Traditional research track record metrics slightly impact job search success.

(A) Pie charts show the fraction of candidates with authorship of any kind on a CNS paper (purple) versus those without (gray), and fraction of candidates who were first author on a CNS paper (purple) versus those who were not (gray). Distributions of off-site interviews (top; p=0.33), onsite interviews (middle; p=2.70×10−4) and offers (bottom; p=1.50×10−4) for applicants without a first-author paper in CNS (gray), and those with one or more first-author papers in CNS (purple; Supplementary files 11, 12, 17). (B) Significant associations were found between offer percentage and the number of first-author papers in CNS (top panel, p=1.70×10−3), career transition awards (second panel, p=2.50×10−2), total citations (third panel, p=2.92×10−2), and years on the job market (fourth panel, p=3.45×10−2). No significant associations were found between offer percentage and having a postdoc fellowship (fifth panel), being above the median in the total number of publications (sixth panel), being an author in any position on a CNS paper (seventh panel), h-index (eighth panel), years as a postdoc (ninth panel), number of first-author papers (tenth panel), number of patents (eleventh panel), or graduate school fellowship status (twelfth panel; Supplementary files 6, 7, 9, 10, 11, 12, 13 and 21). (C) The plots show total citations for those without an offer (blue) and those with one or more offers (gold), for all applicants with one or more first-author papers in CNS (top left); for all applicants without a first-author paper on CNS (bottom left); for all applicants with independent funding (top right); and for all applicants without independent funding (bottom right). In two cases the p value is below 0.05. The bar charts show the offer percentages (gold) for the four possible combinations of career award (yes or no) and first-author paper in CNS (yes or no): for applicants with a first-author paper in CNS, p=0.56, χ2 = 0.34; for applications without, p=0.17, χ2 = 1.92). (D) Summary of significant results testing criteria associated with offer outcomes through Wilcoxon analyses (Supplementary file 7) or logistic regression (Supplementary file 24).

Figure 5—figure supplement 1
Life-science specific analysis of applicant survey outcomes.

We performed identical analysis as in Figure 5 but restricted to applicants (n = 269) who described their field as life-science related (as defined in Figure 2).

Figure 5—figure supplement 2
Visualization of possible paths to an offer using the C5.0 decision tree algorithm.

Each rounded node represents an independent variable and each rectangular node represents one of two possible outcomes (offer (gold) or no offer (blue)). Only those variables in Figure 5B were included. In the case of binary variables such as funding and fellowships, ">0" indicates a "yes" and "<=0" indicates a "no". All other variables, except for h-index, were split based on counts. The outcome nodes are labeled with three pieces of information: (Cyranoski et al., 2011) the number of applicants who fell into the given branch (n), (Ghaffarzadegan et al., 2015) the most common outcome in that branch, and (Schillebeeckx et al., 2013) the fraction of individuals with that outcome. For example, the rightmost branch shows applicants who had a career transition award and h-index >4. They constitute the largest group in our dataset (61 individuals). However, only 77% of these applicants received an offer. Similarly, the second and third largest groups included 51 applicants (63% with offer) and 42 applicants (67% with offer) respectively (see eighth outcome box from right and leftmost box). These three groups accounted for 48.6% of our survey respondents. Note that while decision trees have often been used as prediction models, this tree is only reflective of our dataset and choice of algorithm and parameters. We have used this solely for visualization purposes and advise against using this prospectively to evaluate chances of success on the job market as there may be alternative trees that are equally plausible and accurate. In fact, the accuracy of the overall decision tree in distinguishing between candidates with offers and those without was only 58.5%. Furthermore, no group with more than two applicants consisted purely of those with offers and those without. Even in the nine groups where the most common outcome was "no offer", on average, 25% of the applicants did receive offers.

Summary of applicant teaching experience and impact on job search success.

(A) Distribution of institution types targeted by survey applicants for faculty positions (PUI only in blue, R1 institutions only in green, or both in red, Supplementary file 26). (B) Distribution of teaching experience reported by applicants as having TA only experience (in purple), beyond TA experience (e.g. teaching certificate, undergraduate and/or graduate course instructorship, guest lectureship and college adjunct teaching, (in orange), or no teaching credentials (in green; Supplementary files 27 and 28). (C) Distribution of teaching experience (TA experience, right, vs. Beyond TA experience, left) for applicants who applied to R1 institutions only (in green), PU institutions only (blue), or both R1 and PUIs (in red), (Supplementary file 27). The degree of teaching experience did not change based on the target institution of the applicant (p=0.56 (ns), χ2 = 0.41; Chi-squared test). (D) Association between offer percentage and teaching experience is not significant (p=0.16; Supplementary files 7, 27 and 28).

PUI focused applicants differ only in teaching experience from the rest of the application pool.

(A) The gender distribution applicants who focused on applying to PUIs (Supplementary file 26). (B) The gender distribution and number of first-author publications of the applicant who focused on applying to PUIs (p=0.88). (C) Summary of the fellowship history by gender for PUI focused applicants (Supplementary file 13). (D) Distribution of teaching experience of PUI focused applicants (Supplementary file 27). (E) The median number of applications, off-site interviews, on-site interviews and offers for PUI focused applicants. (F) Percentage of survey respondents who identified having "adjunct teaching" experience (Figure 1) based on target institution (p=5.0×10−4; χ2 = 27.5, Chi-squared test). (G) The number of offers received segregated by "adjunct teaching" experience in either PUI focused applicants (p=0.55) or R1/both R1 and PUI focused applicants (p=0.98).

Perceptions of the job application process.

Three word clouds summarizing qualitative responses from the job applicant survey respondents to the following questions: A) "What was helpful for your application? " (top; Supplementary file 17), (B) "What was an obstacle for your application? " (middle; Supplementary file 18), and C) "What is your general perception of the entire application process?" (bottom; Supplementary file 31). The size of the word (or short phrase) reflects its frequency in responses (bigger word corresponds to more frequency). Survey respondents were able to provide longer answers to these questions, as shown in Supplementary files 17, 18 and 31. 'CNS-papers' refers to papers in Cell, Nature or Science; 'Pedigree' refers to the applicant’s postdoc lab pedigree or postdoc university pedigree; 'Grant-Writing' refers to the applicant’s grant writing experience with their PhD or postdoctoral mentor; 'Peer-reviewing' refers to the experience of performing peer-reviewing for journals; 'Interdisciplinary-research' refers to comments stating that Interdisciplinary research was underappreciated; 'two-body problem' refers to the challenges that life-partners face when seeking employment in the same vicinity; 'No-Feedback' refers to lack of any feedback from the search committees on the status, quality or outcome of applications.

Summary of metrics valued by search committees.

Search committee members were asked on how specific factors were weighted in the decision on which applicant to extend an offer to (Supplementary files 3338). All search committee members surveyed were based at R1 universities (Box 1). (A) Distribution of the fields of study and years of experience for the search committee survey respondents. (B) The median number of faculty job openings, number of applicants per opening, applicants that make the first cut, applicants who are invited for phone/Skype interviews, and offers made. (C) The quantitative rating of search committee faculty on metrics: candidate/applicant research proposal, career transition awards, postdoctoral fellowships, graduate fellowships, PI/mentor reputation (lab pedigree), Cell/Nature/Science journal publications, Impact factor of other journal publications, Teaching experience and value of preprints based on a 5-level Likert scale where 1 = not at all and 5 = heavily. (D) Visual summary of the job applicant perception (from word cloud data) and the results of both surveys (statistical analyses of the applicant survey and criteria weighting from the search committee survey). A number of metrics mentioned in short answer responses were not measured/surveyed across all categories. These missing values are shown in gray.

Figure 10 with 1 supplement
Search committee perception of the faculty job application process.

Two word clouds representing responses from members of search committees in response to the following questions: A) "What information do you wish more candidates knew when they submit their application?", and B) "Have you noticed any changes in the search process since the first search you were involved in?" The size of the word/phrase reflects its frequency in responses, with larger phrases corresponding to more frequent responses. Search committee faculty members were able to provide long answers to both questions (Supplementary files 38 and 39).

Figure 10—figure supplement 1
Overview of search committee impressions of the candidates.

Bar chart showing the number of search committee respondents who held each of the opinions shown for candidates applying to academic jobs that they had been in the search committees for (Supplementary file 36). Additional files.

Author response image 1

Data availability

The authors confirm that, for approved reasons, access restrictions apply to the data underlying the findings. Raw data underlying this study cannot be made publicly available in order to safeguard participant anonymity and that of their organizations. Ethical approval for the project was granted on the basis that only aggregated data is provided (as has been provided in the supplementary tables) (with appropriate anonymization) as part of this publication.

Additional files

Supplementary file 1

Common online resources for finding academic jobs.

Resources for finding academic jobs, often mentioned by our applicant survey respondents and cited by others as helpful for locating academic job announcements across different fields.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp1-v2.xlsx
Supplementary file 2

Applicants by field of research and gender.

Overview of job application survey respondents’ (total and by gender) field of study. Fields which had fewer than three respondents in our job applicant survey were aggregated as “Other Fields” in the table. All percentages are calculated out of the total number of respondents.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp2-v2.xlsx
Supplementary file 3

Applicant demographics: country of research origin (applicant location).

Overview of candidates’ country of research origin. Regions which had fewer than five respondents in our job applicant survey were aggregated as “Other countries” in the table. All percentages are calculated out of the total number of respondents to this particular survey question (297) not the total number of overall survey respondents (n = 317).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp3-v2.xlsx
Supplementary file 4

Country to which faculty application was made (job location).

Overview of the countries to which the faculty candidates applied to, for faculty positions. Note: most candidates applied to more than one country. Regions which had fewer than five respondents in our job applicant survey were aggregated as “Other countries and regions” in the table. All percentages are calculated out of the total number of respondents to this particular survey question (n = 317).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp4-v2.xlsx
Supplementary file 5

Current research/academic position for all applicants.

Overview of current academic position of our job applicant survey respondents. All percentages are calculated out of the total number of respondents to this particular survey question (n = 317).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp5-v2.xlsx
Supplementary file 6

Postdoctoral training times for all applicants.

Overview of time spent in postdoctoral training by our job applicant survey respondents.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp6-v2.xlsx
Supplementary file 7

Summary of the statistical analysis in this paper.

Summary of statistical analysis. In this table and relevant figures, “ns” stands for not significant.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp7-v2.xlsx
Supplementary file 8

Applicant demographics: applicants with first or multiple postdoctoral position.

Overview of number of postdoctoral positions that the candidates held at the time of their faculty job application. All percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp8-v2.xlsx
Supplementary file 9

Scholarly metrics for all applicants.

Overview of the job applicant publication metrics (average citation number, average h-index, average number of peer-reviewed papers, average number of preprints, average number of peer-reviewed first-author papers, number of Cell/Nature/Science journal publications or “CNS” papers of any type meaning first author, co-author or corresponding author) of our survey respondents by gender breakdown.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp9-v2.xlsx
Supplementary file 10

Scholarly metrics for applicants in the life/biomedical sciences.

Overview of the job applicant publication metrics (average citation number, average h-index, average number of peer-reviewed papers, average number of preprints, average number of peer-reviewed first-author papers, number of Cell/Nature/Science journal publications or “CNS” papers of any type meaning first author, co-author or corresponding author) of our survey respondents in life/biomedical sciences (respondents who indicated their field of research as Chemistry, Biology, Bioengineering or Biomedical or Life Sciences) by gender breakdown.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp10-v2.xlsx
Supplementary file 11

Responses on Cell/Nature/Science or “CNS” journal publications for all applicants.

Overview of the number of Cell/Nature/Science (“CNS”) journal publications of our job applicant survey respondents by gender breakdown. Percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp11-v2.xlsx
Supplementary file 12

Responses on Cell/Nature/Science or “CNS” journal publications from applicants in the life/biomedical sciences.

Overview of the number of Cell/Nature/Science (“CNS”) journal publications of our job applicant survey respondents in life/biomedical sciences (respondents who indicated their field of research as Chemistry, Biology, Bioengineering or Biomedical or Life Sciences) by gender breakdown. Percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp12-v2.xlsx
Supplementary file 13

Fellowships and funding.

Overview of the types of funding held by our job applicant survey respondents. Percentages are calculated out of the total number of respondents to this particular survey question. All percentages are calculated out of the total number of respondents to this particular survey question. Our survey questions did not distinguish between the types (e.g. government funded vs privately funded, full vs partial salary support) or number of fellowships applied to; many of these factors are likely critical in better understanding gender differences in fellowship support.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp13-v2.xlsx
Supplementary file 14

Responses about preprints.

Overview of candidates who had unpublished preprints at the time of their job application. Percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp14-v2.xlsx
Supplementary file 15

Twitter poll: number of offers current faculty received.

Overview of the responses to a twitter poll with the question: “Faculty, when you accepted your first position, how many offers did you have to choose from?”

https://cdn.elifesciences.org/articles/54097/elife-54097-supp15-v2.xlsx
Supplementary file 16

Applicants who also applied to non-faculty jobs.

Overview of candidates who also applied for non-faculty jobs (e.g. Industry positions, government jobs, etc.). Percentages are calculated out of the total number of respondents to this particular survey question (n = 315 applicants).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp16-v2.xlsx
Supplementary file 17

Themes from job applicant survey written responses to helped your application.

Candidate responses to “Was any aspect of your career particularly helpful when applying (preprints, grants etc.)?” Survey participants were able to provide long answers to this comment question. A word cloud referring to this table of comments is provided in Figure 8A.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp17-v2.xlsx
Supplementary file 18

Themes from written responses to question about obstacles.

Candidate responses to “Was any aspect of your career particularly an obstacle when applying?” Survey participants were able to provide long answers to this comment question. A word cloud referring to this table of comments is provided in Figure 8B.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp18-v2.xlsx
Supplementary file 19

Application statistics.

Overview of application statistics: total number of applications made, offsite (remote via phone or online via Skype) interviews, onsite interviews, offers made, approximate number of rejections and total number of no feedbacks received from faculty job committees to our survey respondents.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp19-v2.xlsx
Supplementary file 20

Career transition awards.

Overview of the types of transition/independent type funding held by our faculty candidate (applicant survey) respondents. Percentages are calculated out of the total number of respondents to this particular survey question. Being a ‘Co-PI’ of a grant as a postdoctoral researcher or research scientist means co-writing a grant with a PI (an independent investigator). The co-writer may or may not be explicitly mentioned on the grant as a Co-PI.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp20-v2.xlsx
Supplementary file 21

Responses on patenting.

Overview of Candidates who had approved or pending patents from their research at the time of their job application. Percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp21-v2.xlsx
Supplementary file 22

Use of resources that offered information about the application process.

Overview of candidates who were familiar with the Future PI Slack resource and other resources during their application process. Responses to “Did you find the Future PI google sheet/Slack helpful? Yes/No” Survey participants were able to provide a long answer to this comment question (Future PI Slack or FPI Slack is a Slack group comprised of postdoctoral researchers aspiring to apply for faculty/Principal Investigator positions).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp22-v2.xlsx
Supplementary file 23

Responses to “Why did you find the Future PI Google Sheet helpful?”.

Overview of candidates who were familiar with the Future PI Slack resource and other resources during their application process. Responses to “Why did you find the Future PI google sheet/Slack helpful?” Survey participants were able to provide a long answer to this comment question. Note: Future PI Slack is a Slack group of postdoctoral researchers who aspire to apply for faculty positions.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp23-v2.xlsx
Supplementary file 24

Logistic regression with stepwise variable selection analysis on the survey data.

Regression analysis with stepwise variable selection was performed on the job applicant survey data. All variables collected except for the number of remote and on-site interviews were included as potential predictors of receiving (Cyranoski et al., 2011) or not receiving (0) a job offer. Positive coefficients indicate positive associations and negative coefficients indicate negative associations with receiving an offer. Coefficients that are zero indicate no association. Bold values indicate that the associations were found to be significant at a threshold of 0.05. Summary of results testing criteria with offer outcomes either through Wilcoxon analyses or logistic regression. When applicants with missing values were excluded, application number (β=0.5345, p=1.53×10−3), having a postdoctoral fellowship (β=0.4013, p=6.23×10−3), and number of citations (β=0.4178, p=2.01×10−2) positively associated with offer status in a significant manner, while searching for other jobs (β=−0.3902, p=1.04×10−2) negatively associated with offer status in a significant manner. When missing values were imputed, significant positive coefficients were observed for application number (β=0.5171, p=8.55×10−4), funding (β=0.3156, p=1.72×10−2), having a postdoctoral fellowship (β=0.2583, p=3.75×10−2) and citations (β=0.4363, p=1.34×10−2). Moreover, the search for non-academic jobs (β=−0.2944, p=1.98×10−2) and the number of years on the job market (β=−0.2286, p=7.74×10−2) were significantly negatively associated with offer status.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp24-v2.xlsx
Supplementary file 25

Logistic regression with stepwise variable selection analysis on survey data of applicants from only the life sciences applicants.

Regression analysis with stepwise variable selection was performed on the subset of the job applicant survey data corresponding to applicants from the life sciences. All variables collected except for the number of remote and on-site interviews were included as potential predictors of receiving (Cyranoski et al., 2011) or not receiving (0) a job offer. Positive coefficients indicate positive associations and negative coefficients indicate negative associations with receiving an offer. Coefficients that are zero indicate no association. Bold values indicate that the associations were found to be significant at a threshold of 0.05. Summary of results testing criteria with offer outcomes either through Wilcoxon analyses or logistic regression. When applicants with missing values were excluded, application number (β=0.5827, p=1.07×10−3) and having a postdoctoral fellowship (β=0.5738, p=1.74×10−3) positively associated with offer status in a significant manner, while searching for other jobs (β=−0.3975, p=3.16×10−2) negatively associated with offer status in a significant manner. When missing values were imputed, significant positive coefficients were observed for application number (β=0.5445, p=4.54×10−4), funding (β=0.3687, p=1.27×10−2), having a postdoctoral fellowship (β=0.3385, p=1.72×10−2) and citations (β=0.5117, p=1.51×10−2). Moreover, the search for non-academic jobs (β=−0.3022, p=3.21×10−2) and the number of years on the job market (β=−0.3226, p=3.32×10−2) were significantly negatively associated with offer status.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp25-v2.xlsx
Supplementary file 26

Applicants by their application type (R1 Universities, PUIs or both) and gender.

Overview of job application survey respondents’ (total and by gender) applications to R1 Universities (high-activity Research Universities), PUIs (Primarily Undergraduate Institutions; see 1 for definitions) or applied to both types of institutions. Percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp26-v2.xlsx
Supplementary file 27

Teaching experience.

Overview of the teaching experience (Teaching Assistantship for a course (lecture-based and/or laboratory-based) for the course instructor only versus beyond teaching assistantship which is independently designing and instructing undergraduate and/or graduate courses) of our applicant survey respondents. Percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp27-v2.xlsx
Supplementary file 28

Themes from responses to question about teaching experiences beyond being a teaching assistant.

Overview of specific types of teaching experience of our job applicant survey respondents detailed in a comment question. The “Adjunct Teaching Instructor for Undergraduate Courses at a Community College or PUI” and “Adjunct Teaching Instructor for Undergraduate Courses at an R1 or PU Institution” were explicitly mentioned in comments by our applicant survey respondents. The “Total Adjunct teaching positions” were the total head-count of “adjunct type” college teaching performed by our job applicant survey respondents. A total of n = 162 applicants responded to this comment type long answer question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp28-v2.xlsx
Supplementary file 29

Frequency of job applicant comments who received an offer.

Overview of candidates who commented on their view in general of the application process. Responses to “Do you have any comments that you would like to share? For example, how did you experience the application process?” Survey participants were able to provide a long answer to this comment question. A word cloud referring to this table of comments is provided in Figure 8C. Percentages are calculated out of the total number of respondents to this particular survey question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp29-v2.xlsx
Supplementary file 30

Applicant demographics: number of times (cycles/years) that the candidates had applied for faculty positions.

Overview of number of times job candidate survey respondents applied for a faculty (PI) position (Box 1). This is in response to the survey question:”How many times have you applied for PI positions? i.e. if the 2018–2019 cycle was the first time, please enter "1", if you also applied last cycle, enter "2", etc. Percentages are calculated out of the total number of respondents to this particular survey question (n = 314).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp30-v2.xlsx
Supplementary file 31

General perceptions of the application process.

Overview of candidates who commented on their view in general of the application process. Responses to “Do you have any comments that you would like to share? For example, how did you experience the application process?” Survey participants were able to provide long answers to this comment question. A word cloud referring to this table of comments is provided in Figure 8C.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp31-v2.xlsx
Supplementary file 32

Search committee survey: other comments.

Overview of search committee members who commented on “Do you have any other comments or thoughts about the state of hiring for tenure track positions?” Survey participants were able to provide a long answer to this comment question.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp32-v2.xlsx
Supplementary file 33

Search committee survey: statistics.

Overview of the search committee survey responses to “Approximately how many applicants for a posted position do you get?”, “Approximately how many applicants make it through the first round of cuts?”, “Approximately how many applicants are invited for off-site interview (Skype/phone)?”, “Approximately how many offers does your committee make per job posting?”, “Approximately how many openings has your department had in the last five years?”, “Approximately how many applicants are invited for on-site interview?”, “How long have you been involved in academic search committees?”.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp33-v2.xlsx
Supplementary file 34

Search committee survey: demographics.

Overview of the search committee faculty demographics of our faculty survey respondents. Percentages are calculated out of the total number of respondents to this particular survey question (n = 15).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp34-v2.xlsx
Supplementary file 35

Search committee survey: preprints.

Overview of the search committee survey responses to “Does your committee look favorably upon preprints?”.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp35-v2.xlsx
Supplementary file 36

Search committee survey: perceptions of the job market.

Overview of the search committee survey responses to “What is your perception of the job market for tenure track faculty as someone involved in the search process (please tick all that are true)”. Percentages are calculated out of the total number of respondents to this particular survey questions (Rockey, 2012).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp36-v2.xlsx
Supplementary file 37

Search committee survey: weighting given to various aspects of an application.

Overview of the search committee survey responses to evaluation of a number of the tenure-track application materials: 1) “To what extent does the research proposal weigh on the selection process (e.g. "This candidate's research statement is incredibly compelling!", 2) “To what extent does good mentorship in the candidate's postdoctoral/graduate student lab explicitly weigh on selection process (e.g. "This candidate's mentor is known to produce good trainees", 3) “How heavily does the committee weigh graduate student fellowships or awards (e.g. The National Science Foundation (NSF) Graduate Research Fellowship (GRF), The National Institutes of Health (NIH) predoctoral fellowship/The Ruth L. Kirschstein National Research Service Awards for Individual Predoctoral Fellowships (F30 or F31), etc.)”, 4) “How heavily does the committee weigh non-transitional postdoctoral fellowships or awards (e.g. NIH F32, AHA etc.)”, 5) “Does your committee weigh Cell, Science, or Nature papers above papers in other journals?”, 6) “To what extent does journal impact factor explicitly weigh in to the selection process (e.g. does the word ‘impact factor’ come up in discussions around applicants)?”, 7)”How heavily does the committee weigh transition awards as a positive factor (i.e. The NIH Pathway to Independence (K99/R00) award, Burroughs Wellcome Career Award, or another award that provides the applicant with money as a new faculty member)?”, 8)”How heavily does the committee weigh prior teaching experience?”. In the survey, a 5-level Likert scale was used to record faculty impressions where a response of 1 = not at all and 5 = heavily. Percentages are calculated out of the total number of respondents to this particular survey question (n = 15).

https://cdn.elifesciences.org/articles/54097/elife-54097-supp37-v2.xlsx
Supplementary file 38

Search committee survey: responses to the question “What information do you wish more candidates knew when they submitted their application?”.

Overview of the search committee who responded to “What information do you wish more candidates knew when they submitted their application?” Survey participants were able to provide a long answer to this comment question. A word cloud referring to this table of comments is provided in Figure 10A.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp38-v2.xlsx
Supplementary file 39

Search committee survey: changes in the search process.

Overview of search committee faculty members who commented on “Have you noticed any changes in the search process since the first search you were involved in?” Survey participants were able to provide a long answer to this question. A word cloud referring to this table of comments is provided in Figure 10B.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp39-v2.xlsx
Supplementary file 40

Applicant survey: scholarly metrics by gender with breakdown by offer status.

Mean and median values for publication-related metrics plotted in Figure 2B broken down by gender and offer status. Additionally, p-values from Wilcoxon rank-sum tests that compare metric values from the female and male groups. “All” shows these values when the full dataset is considered, “With offers” shows values for only those applicants with at least one offer, and “Without offers” shows values for only those without any offers. “F” stands for female and “M” stands for male. Trends in gender differences remain the same even for the applicants with offers, serving as a possible explanation for the similar search outcomes for females and males and the importance of gender in the logistic regression.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp40-v2.xlsx
Supplementary file 41

The job applicant survey.

Survey of the applicants to the tenure-track jobs.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp41-v2.docx
Supplementary file 42

The search committee survey.

Survey of faculty members involved in tenure-track searches.

https://cdn.elifesciences.org/articles/54097/elife-54097-supp42-v2.docx
Transparent reporting form
https://cdn.elifesciences.org/articles/54097/elife-54097-transrepform-v2.docx

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