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United States

  1. Education and academic field
  2. Professional developer
  3. How time is spent
  4. Previous employment
  5. Collaboration and training
  6. Publications and citations
  7. Open source and DOI
  8. Good practices
  9. Tools and programming languages
  10. Job satisfaction
  11. Research software engineer

Education and academic field

This section contains the information about the type of education the participants have, as well as their highest degree obtained.

We asked the participants, in which field they are working. With that question, it is possible to see which current field employed the most of RSE/RSD. The questions was specific to each country and was multiple choice. Each participant could choose several fields. We then calculate the different proportion by dividing each field by the total of participants that have selected at least one option.

Questions in this section

  • What is the highest level of education you have attained? (one choice list)
  • In which discipline is your highest academic qualification? (one choice list)
  • Which professional qualification do you hold? (free text)

Levels of education

Highest level of education for United States Count Percentage Percentage in 2017 Difference with previous year
Doctorate 67 45.5782 60.1227 -14.5445
Master degree 46 31.2925 25.1534 6.13914
Undergraduate degree 33 22.449 11.6564 10.7925
Other 1 0.680272 0.613497 0.0667752

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2021-08-04T21:03:43.442441 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 Other Undergraduate degree Master degree Doctorate 1% 22% 31% 46% Highest level of education, United States −10 0 10 Δ

Academic field for education and professional development

Alongside of question about education level we also asked the participants in which field they finished their highest level of education. Here again the propositions were specific to each countries so the comparison is difficult despite lot of overlapping in the categories.

Field of education for United States Count Percentage Percentage in 2017 Difference with previous year
Physics and Astronomy 44 30.3448 14.1104 16.2344
Computer Science 38 26.2069 25.1534 1.05352
Biological Sciences 14 9.65517 14.7239 -5.06875
Mathematics 13 8.96552 5.52147 3.44404
Electrical & Electronic Engineering 6 4.13793 3.06748 1.07045
Geography & Environmental Sciences 4 2.75862 0.613497 2.14512
Music 2 1.37931 0.613497 0.765813
Chemistry 2 1.37931 8.58896 -7.20965
Mechanical Engineering 2 1.37931 4.29448 -2.91517
Economics 2 1.37931 nan nan
History 2 1.37931 nan nan
Materials Technology 2 1.37931 nan nan
Chemical Engineering 2 1.37931 1.84049 -0.46118
Philosophy 2 1.37931 nan nan
Geology 2 1.37931 2.45399 -1.07468
Psychology 1 0.689655 1.22699 -0.537339
Accounting & Finance 1 0.689655 nan nan
East & South Asian Studies 1 0.689655 nan nan
Politics 1 0.689655 0.613497 0.0761582
Business & Management Studies 1 0.689655 0.613497 0.0761582
Civil Engineering 1 0.689655 1.22699 -0.537339
Linguistics 1 0.689655 0.613497 0.0761582
Education 1 0.689655 nan nan

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2021-08-04T21:03:59.113681 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 5 10 15 20 25 30 Education Linguistics Civil Engineering Business & Management Studies Politics East & South Asian Studies Accounting & Finance Psychology Geology Philosophy Chemical Engineering Materials Technology History Economics Mechanical Engineering Chemistry Music Geography & Environmental Sciences Electrical & Electronic Engineering Mathematics Biological Sciences Computer Science Physics and Astronomy 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 3% 4% 9% 10% 26% 30% Field of education, United States 0 10 Δ 2021-08-04T21:04:00.474410 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Professional qualification, United States

Academic field of work

field of work for United States Count Percentage Percentage in 2017 Difference with previous year
Physics and Astronomy 63 42.8571 24.5399 18.3173
Computer Science 59 40.1361 56.4417 -16.3057
Biological Sciences 39 26.5306 32.5153 -5.98473
Geography & Environmental Sciences 23 15.6463 12.2699 3.37632
Mathematics 15 10.2041 14.1104 -3.90635
Medicine 9 6.12245 7.97546 -1.85301
Economics 8 5.44218 3.68098 1.7612
Electrical & Electronic Engineering 8 5.44218 7.97546 -2.53328
Geology 6 4.08163 14.1104 -10.0288
Chemical Engineering 6 4.08163 5.52147 -1.43984
Materials Technology 5 3.40136 7.97546 -4.5741
Education 5 3.40136 6.13497 -2.73361
Social Policy 5 3.40136 0.613497 2.78786
Sociology 5 3.40136 3.68098 -0.279621
Chemistry 4 2.72109 14.1104 -11.3893
Pharmacology & Pharmacy 4 2.72109 1.22699 1.49409
Anthropology 4 2.72109 1.84049 0.880598
Mechanical Engineering 4 2.72109 12.2699 -9.54885
Politics 3 2.04082 2.45399 -0.413171
Communication & Media Studies 3 2.04082 0.613497 1.42732
Anatomy & Physiology 3 2.04082 1.22699 0.813822
Psychology 3 2.04082 3.68098 -1.64017
Business & Management Studies 3 2.04082 1.22699 0.813822
History 3 2.04082 0.613497 1.42732
Librarianship & Information Management 3 2.04082 3.68098 -1.64017
Linguistics 3 2.04082 1.22699 0.813822
Robotics 2 1.36054 4.29448 -2.93393
Theology & Religious Studies 2 1.36054 nan nan
Middle Eastern and African Studies 2 1.36054 nan nan
Accounting & Finance 2 1.36054 0.613497 0.747047
General Engineering 2 1.36054 5.52147 -4.16093
Agriculture & Forestry 2 1.36054 3.06748 -1.70694
Civil Engineering 2 1.36054 5.52147 -4.16093
Art & Design 2 1.36054 0.613497 0.747047
Architecture 2 1.36054 0.613497 0.747047
American Studies 2 1.36054 nan nan
Veterinary Medicine 2 1.36054 1.22699 0.13355
Marketing 1 0.680272 1.84049 -1.16022
Law 1 0.680272 nan nan
Land & Property Management 1 0.680272 nan nan
English 1 0.680272 nan nan
Philosophy 1 0.680272 nan nan
Criminology 1 0.680272 nan nan
Complementary Medicine 1 0.680272 nan nan
Classics & Ancient History 1 0.680272 nan nan
Town & Country Planning and Landscape Design 1 0.680272 0.613497 0.0667752

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2021-08-04T21:04:14.491941 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 Town & Country Planning and Landscape Design Classics & Ancient History Complementary Medicine Criminology Philosophy English Land & Property Management Law Marketing Veterinary Medicine American Studies Architecture Art & Design Civil Engineering Agriculture & Forestry General Engineering Accounting & Finance Middle Eastern and African Studies Theology & Religious Studies Robotics Linguistics Librarianship & Information Management History Business & Management Studies Psychology Anatomy & Physiology Communication & Media Studies Politics Mechanical Engineering Anthropology Pharmacology & Pharmacy Chemistry Sociology Social Policy Education Materials Technology Chemical Engineering Geology Electrical & Electronic Engineering Economics Medicine Mathematics Geography & Environmental Sciences Biological Sciences Computer Science Physics and Astronomy 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 2% 2% 3% 3% 3% 3% 3% 3% 3% 3% 4% 4% 5% 5% 6% 10% 16% 27% 40% 43% field of work, United States 0 20 Δ

Professional developer

In this section we investigate the relationship between RSEs/RSDs and their own experience in software development Understandably, we expect them having several years of software development experience. However, as shown in previous years, it is not necessarily reflected upon their own feeling of being considered as professional.

Questions in this section:

  • Do you consider yourself a professional software developer? (Yes/No)
  • How many years of software development experience do you have? (integer)

How many professional developers?

Professional developer for United States Count Percentage Percentage in 2017 Difference with previous year
Yes 90 63.8298 61.25 2.57979
No 51 36.1702 38.75 -2.57979

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2021-08-04T21:05:10.821393 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 No Yes 36% 64% Professional developer, United States −2.5 0.0 2.5 Δ

Years of software development experience

How many years of software development experience for United States (without 95 percentile) Results in 2018 Results in 2017
count 139 149
mean 14.3165 12.8557
std 10.0355 8.84099
min 0 0
25% 5 5
50% 12 10
75% 20 20
max 38 32

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2021-08-04T21:05:11.568263 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 2017 2018 Year 0 5 10 15 20 25 30 35 Value 0 10 20 30 Value 0 5 10 15 20 25 30 Count Year 2017 2018 How many years of software development experience for United States (without 95 percentile)

How time is spent

RSE/RSE are supposed to be an hybrid role, compared to pure software developer. They bring a knowledge from their field but also are developing software. To capture this different tasks they may do during their work, we asked them how they spend their time but also how they wish to spend their time to investigate any difference between what they do and what they want to do.

How to read the plots

Respondents were asked how much time is spent in a particular activity using a Likert scale from from 1 (None at all) to 10 (All my time).

The same questions asked them how much time they wanted to spend on these activities. With that it was possible to see if discrepancies exist between what they actually do and what they want to do.

To read the results, when the bars shift to the right (in blue), it means they reported positive values (from 6 to 10); when the bars are on the left (in red), it means they reported more negative values (relative to the scale). Each bar has a number that represents the percentage of participants that selected that value. The total bar represents 100%.

To calculate the difference between what they want and what they do, we subtract the answers to the the time that they wished to have spent from the the answer to actual time spent. It is therefore possible to understand the results as:

  1. The result is zero: The time spent matches, they do as much as they want.
  2. The result is negative: They wish to spend less time to do that activity
  3. The result is positive: They wish to spend more time to do that activity

Questions in this section

All questions were asked on a 1 to 10 Likert scale.

  • On average, how much of your time is spent developing software?
  • On average, how much of your time is spent on research?
  • On average, how much of your time is spent on management?
  • On average, how much of your time is spent on teaching?
  • On average, how much of your time is spent on other activities?
2021-08-04T21:03:37.881148 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Percentage Developing software Research Management Teaching Other activities 18 33 59 16 22 26 23 33 10 21 20 11 22 10 14 8 14 15 7 6 11 14 14 5 24 7 Time spent 1 2 3 4 5 6 7 8 9 10 Percentage Developing software Research Management Teaching Other activities 10 42 37 26 17 33 21 32 18 12 18 24 10 11 5 14 10 11 13 6 6 16 6 20 13 23 6 8 Time wish to spent 1 2 3 4 5 6 7 8 9 10 100 90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Percentage Developing software Research Management Teaching Other activities 6 13 10 14 20 6 25 35 37 50 48 44 20 29 18 13 8 11 6 14 8 6 6 Difference between time spent and wish -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 United States: Difference between time wish to spent and actually spent for each type of activity

Previous employment

Several questions were about the participants’ previous job. The idea is to collect insights of their career path and understand what their motivations are to be an RSE.

We also asked the participants to rank the reasons why they chose their actual position among 8 different ones:

  • Desire to work in a research environment
  • Freedom to choose own working practices
  • Desire to advance research
  • I want to learn new skills
  • Opportunity to develop software
  • Flexible working hours
  • Ability to work across disciplines
  • Opportunity for career advancement
  • The salary

Questions in this section

  • Where was your previous job based? (single choice)
  • What were the reasons to choose the current job? (ranking)

Where the previous job was based

Where the previous job was based for United States Count Percentage Percentage in 2017 Difference with previous year
University 76 53.9007 48.4076 5.49307
Private company 40 28.3688 22.9299 5.43886
National laboratory 8 5.67376 6.36943 -0.695668
Other 7 4.96454 nan nan
Non-profit organization 6 4.25532 nan nan
Government 4 2.83688 nan nan

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2021-08-04T21:05:24.915659 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 Government Non-profit organization Other National laboratory Private company University 3% 4% 5% 6% 28% 54% Where the previous job was based, United States 0 5 Δ

What were the reasons to choose the current job

Reasons to choose current job Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7 Rank 8
Desire to work in a research environment 27.7778 20.1389 11.8056 11.8881 9.79021 nan nan nan
Desire to advance research 22.9167 13.1944 13.1944 7.69231 6.29371 nan nan nan
Opportunity to develop software 12.5 7.63889 12.5 17.4825 12.5874 nan nan nan
I want to learn new skills 9.02778 8.33333 11.8056 11.8881 15.3846 nan nan nan
The salary 8.33333 9.72222 6.25 6.29371 8.39161 nan nan nan
Freedom to choose own working practices 7.63889 11.8056 15.2778 12.5874 9.79021 nan nan nan
Opportunity for career advancement 7.63889 12.5 9.02778 6.99301 6.29371 nan nan nan
Flexible working hours 2.77778 6.25 10.4167 16.0839 17.4825 nan nan nan
Ability to work across disciplines 1.38889 10.4167 9.72222 9.09091 13.986 nan nan nan

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2021-08-04T21:05:25.696172 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 20 40 60 80 100 Ranking of importance Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Percentage 28 20 12 12 10 23 13 13 8 6 12 8 12 17 13 9 8 12 12 15 8 10 6 6 8 8 12 15 13 10 8 12 9 7 6 6 10 16 17 10 10 9 14 Reasons to choose current job: United States Desire to work in a research environment Desire to advance research Opportunity to develop software I want to learn new skills The salary Freedom to choose own working practices Opportunity for career advancement Flexible working hours Ability to work across disciplines

Collaboration and training

Questions in this section:

  • Who uses the code that you write? (one choice)
  • Do you always work with the same researchers, or do you regularly change the researchers you work with? (one choice)
  • Are you part of a dedicated research software group within your institution? (yes-no)
  • How many software projects are you currently involved in? (numeric)
  • How many software developers typically work on your projects? (numeric)
  • On average, how many times a year do you take part in providing training? (numeric)
  • What training programs are you involved with (comma separated list, e.g., Software Carpentry, local university training, etc.)? (free text)

Developing code for others

developing code for others for United States Count Percentage Percentage in 2017 Difference with previous year
0 - Mostly me 3 2.04082 3.68098 -1.64017
1 16 10.8844 8.58896 2.2954
2 22 14.966 12.2699 2.69605
3 24 16.3265 28.2209 -11.8943
4 31 21.0884 21.4724 -0.383957
5 - Mostly other people 51 34.6939 25.7669 8.92701

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2021-08-04T21:02:54.251846 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 5 10 15 20 25 30 35 5 - Mostly other people 4 3 2 1 0 - Mostly me 35% 21% 16% 15% 11% 2% developing code for others, United States −10 0 Δ

Working with same researchers

Working with same researchers for United States Count Percentage Percentage in 2017 Difference with previous year
Different researchers, same research group 24 17.3913 nan nan
Regularly change researcher(s) 47 34.058 51.6556 -17.5977
Same researcher(s) 67 48.5507 48.3444 0.206354

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2021-08-04T21:02:54.654721 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 Same researcher(s) Regularly change researcher(s) Different researchers, same research group 49% 34% 17% Working with same researchers, United States −10 0 Δ

Part of dedicated group

member of a dedicated group for United States Count Percentage Percentage in 2017 Difference with previous year
No 68 49.2754 56.25 -6.97464
Yes 70 50.7246 43.75 6.97464

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2021-08-04T21:02:54.908574 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 Yes No 51% 49% member of a dedicated group, United States −5 0 5 Δ

Number of projects

Number of software projects for United States Count Percentage Percentage in 2017 Difference with previous year
0 2 1.52672 2.05479 -0.528077
1 17 12.9771 13.0137 -0.0365994
2 37 28.2443 20.5479 7.69633
3 29 22.1374 32.1918 -10.0544
4 14 10.687 8.21918 2.46784
5 17 12.9771 11.6438 1.33326
6 5 3.81679 2.05479 1.762
7 1 0.763359 2.05479 -1.29144
8 2 1.52672 2.05479 -0.528077
10 4 3.05344 2.73973 0.313709
12 1 0.763359 0.684932 0.0784273
14 1 0.763359 0.684932 0.0784273
20 1 0.763359 0.684932 0.0784273

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2021-08-04T21:02:55.381575 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 5 10 15 20 25 20.0 14.0 12.0 10.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1% 1% 1% 3% 2% 1% 4% 13% 11% 22% 28% 13% 2% Number of software projects, United States −10 0 Δ
Number of software developers per projects for United States Count Percentage
0 1 0.757576
1 33 25
2 41 31.0606
3 20 15.1515
4 11 8.33333
5 12 9.09091
6 3 2.27273
7 1 0.757576
8 1 0.757576
10 4 3.0303
12 1 0.757576
15 1 0.757576
20 1 0.757576
30 1 0.757576
100 1 0.757576

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2021-08-04T21:02:55.891054 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 5 10 15 20 25 30 100.0 30.0 20.0 15.0 12.0 10.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Number of software developers per projects, United States

Training

Number of time per year providing training for United States (without 95 percentile) Results in 2018 Results in 2017
count 131 100
mean 1.58779 4.3525
std 1.96848 4.83948
min 0 0
25% 0 2
50% 1 3
75% 2 5
max 8 30

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2021-08-04T21:02:56.695228 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 2017 2018 Year 0 5 10 15 20 25 30 Value 0 10 20 30 Value 0 10 20 30 40 50 60 70 Count Year 2017 2018 Number of time per year providing training for United States (without 95 percentile) 2021-08-04T21:02:58.275964 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Number of time per year providing training, United States

Publications and citations

RSEs is an hybrid role between a researcher and a software developer. We investigated both of these aspects concerning publication and dissemination of their work, one on the traditional aspect of it (publications and conference).

One essential aspect of career in academia is the publications and the conferences to gain recognition. However, the role of RSE being less about writing articles than creating the infrastructure and the software for the article to exist, there is some fear that they will fail to have recognition through the papers and conferences.

Questions in the section:

  • In general, when your software contributes to a paper, are you acknowledged in that paper? (one choice)
  • Have you presented your software work at a conference or workshop? (yes-no)
  • At which conference(s)/workshop(s) have you presented your software work? (free text)

Acknowledgment in paper

Acknowledgment in paper for United States Count Percentage
Not mentioned at all 30 21.7391
Acknowledged in the main text 17 12.3188
Acknowledged in acknowledgements section 23 16.6667
Named as co-author 65 47.1014
Named as main author 3 2.17391

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2021-08-04T21:03:18.278182 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 Named as main author Named as co-author Acknowledged in acknowledgements section Acknowledged in the main text Not mentioned at all Acknowledgment in paper, United States

Participation in conferences

Did you participate in conference for United States Count Percentage Percentage in 2017 Difference with previous year
Yes 79 59.8485 65.9574 -6.10896
No 53 40.1515 34.0426 6.10896

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2021-08-04T21:03:18.539247 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 No Yes 40% 60% Did you participate in conference, United States −5 0 5 Δ

Conference name

2021-08-04T21:03:20.534330 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Did you participate in conference, United States

Open source and DOI

RSEs is an hybrid role between a researcher and a software developer. We investigated both of these aspects concerning publication and dissemination of their work, one on the traditional aspect of it (publications and conference) and, as developed here, on the more software aspect (open source and DOI).

We asked the participants if they have ever released their work under open source licence but also questions about the referencing system. We asked them how often they reference software, and if they use DOI for it, and which tools for it.

We also asked them if they have an ORCID ID, a system that gives a unique reference ID for the researcher.

Questions in this section:

  • How often do you use an open-source licence for your software? (likert scale)
  • How often do you reference software directly or the papers describing the software? (likert scale)
  • How often do you associate your software with a Digital Object Identifier (DOI)? (likert scale)
  • Which tools do you use to mint a DOI (e.g. local library, Zenodo)? (free text)
  • Do you have an ORCID ID? (yes-no)

Open source use

Open source use for United States Count Percentage Percentage in 2017 Difference with previous year
1 (None at all) nan nan nan nan
2 2 1.44928 5.04202 -3.59274
3 2 1.44928 3.36134 -1.91207
4 2 1.44928 2.52101 -1.07173
5 4 2.89855 9.2437 -6.34515
6 4 2.89855 4.20168 -1.30313
7 5 3.62319 7.56303 -3.93984
8 17 12.3188 14.2857 -1.96687
9 25 18.1159 13.4454 4.67056
10 (All the time) 74 53.6232 nan nan

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2021-08-04T21:02:15.180525 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 10 (All the time) 9 8 7 6 5 4 3 2 1 (None at all) 54% 18% 12% 4% 3% 3% 1% 1% 1% Open source use, United States −5 0 5 Δ

Referencing software

Citation of software for United States Count Percentage
1 (None at all) nan nan
2 8 5.7971
3 8 5.7971
4 8 5.7971
5 16 11.5942
6 4 2.89855
7 10 7.24638
8 13 9.42029
9 9 6.52174
10 (All the time) 31 22.4638

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2021-08-04T21:02:17.281791 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 5 10 15 20 10 (All the time) 9 8 7 6 5 4 3 2 1 (None at all) Citation of software, United States

Use of Digital Object Identifier (DOI)

Use of Digital Object Identifier for United States Count Percentage Percentage in 2017 Difference with previous year
1 (None at all) nan nan nan nan
2 9 6.52174 6.38298 0.13876
3 8 5.7971 10.6383 -4.8412
4 1 0.724638 4.25532 -3.53068
5 11 7.97101 25.5319 -17.5609
6 5 3.62319 6.38298 -2.75979
7 6 4.34783 8.51064 -4.16281
8 7 5.07246 21.2766 -16.2041
9 5 3.62319 6.38298 -2.75979
10 (All the time) 7 5.07246 nan nan

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2021-08-04T21:02:19.292923 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 1 2 3 4 5 6 7 8 10 (All the time) 9 8 7 6 5 4 3 2 1 (None at all) 5% 4% 5% 4% 4% 8% 1% 6% 7% Use of Digital Object Identifier, United States −10 0 Δ

Tools used for DOI

2021-08-04T21:02:20.382655 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Which tool is used for Digital Object Identifier, United States

ORCID

Using ORCID for United States Count Percentage
Yes 81 60.9023
No 19 14.2857

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2021-08-04T21:02:20.891591 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 Yes No Using ORCID, United States

Good practices

This section comprises sections that focus on the technical and development aspects of the RSEs’ work. They aim to understand good practices in developing software.

We chose two broad measures to provide an insight into sustainability: the bus factor and technical hand over planning.

  • The bus factor is a measure of the number of developers who understand a specific software project and could, with only a cursory review of the project, maintain or extend the code. A project with a bus factor of 1 is completely reliant on only one developer. If this developer finds new employment, becomes ill or is hit by the titular bus, then the project will fail. A high bus factor provides some confidence that the project can be sustained even if a developer leaves.

  • A technical hand over plan is used to introduce a new developer to a software project. These plans cover basic information, such as the license and location of the software, a repository, a description of the software architecture, a summary of development plans and any other information that a new developer would need to understand the software. A project that has written (and maintained) a technical hand over plan can withstand the departure of a developer, even a key developer, significantly better than one without such a plan.

Developing software requires a set of good practices to ensure the quality of the subsequent analysis as well as the robustness of the developed software, to name a few of important aspects. We wanted to see if the implementation of some simple but essential good practices were a reality beside the bus factor and technical hand over planning.

When developing software, version control and testing can be seen as tool to enhance the quality of the developed software, especially considering the importance of code review and sharing in public funded places such as academia.

For testing, we asked the participants to choose any of the following testing methods:

  • Test engineers conduct testing
  • Developers conduct testing
  • Users conduct testing
  • No formal testing

Obviously, the test engineers conduct testing is the most robust testing method but may not be possible in smaller projects while no formal testing should not occur in any ideal scenario, regardless of the size of the project.

We also asked the participants if they use any version control tools through a list of choice. And finally we asked them which repository they are currently using for their most important project.

Bus factor

Bus factor for United States Count Percentage
0 2 1.44928
1 73 52.8986
2 39 28.2609
3 13 9.42029
4 8 5.7971
5 1 0.724638
7 1 0.724638
20 1 0.724638

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2021-08-04T21:01:37.459847 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 20.0 7.0 5.0 4.0 3.0 2.0 1.0 0.0 Bus factor, United States

Presence of transition plan

Presence of transition plan for United States Count Percentage Percentage in 2017 Difference with previous year
Yes 28 20.2899 18.2482 2.04168
No 110 79.7101 81.7518 -2.04168

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2021-08-04T21:01:37.733010 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 70 80 No Yes 80% 20% Presence of transition plan, United States −2 0 2 Δ

Use of version control

Use of version control for United States Count Percentage
Git 134 91.1565
SVN 22 14.966
CVS 9 6.12245
Mercurial 6 4.08163
None 5 3.40136

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2021-08-04T21:01:38.089889 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 20 40 60 80 Git SVN CVS Mercurial None Use of version control, United States

Testing strategies

Testing strategies for United States Count Percentage Percentage in 2017 Difference with previous year
No formal testing 10 6.80272 11.0429 -4.24022
No formal testing but users provide feedback 43 29.2517 55.8282 -26.5765
The developers do their own testing 125 85.034 83.4356 1.59843
Test engineers conduct testing 16 10.8844 9.20245 1.6819

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2021-08-04T21:01:38.435847 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 20 40 60 80 Test engineers conduct testing The developers do their own testing No formal testing but users provide feedback No formal testing 11% 85% 29% 7% Testing strategies, United States −25 0 Δ

Repository

2021-08-04T21:01:39.928485 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Repository, United States

Tools and programming languages

On technical details we wanted to know which of the programming languages are mostly used by the RSEs. We give them a multi-choice list inspired by the results published by Stackoverflow.

We also wanted to know which operating system they use for work.

Questions in this section:

  • Which operating system do you primarily use for development? (one choice)
  • What programming languages do you use at work? Please select all that apply. (multiple choice)

Programming languages

Programming languages for United States Count Percentage Percentage in 2017 Difference with previous year
Python 115 78.2313 73.6196 4.61166
C++ 51 34.6939 42.9448 -8.25091
C 50 34.0136 44.7853 -10.7717
SQL 47 31.9728 30.0613 1.91144
JavaScript 42 28.5714 27.6074 0.964067
R 34 23.1293 39.8773 -16.748
Java 31 21.0884 18.4049 2.68353
Fortran 30 20.4082 30.6748 -10.2667
Perl 20 13.6054 15.9509 -2.34548
Matlab 20 13.6054 28.2209 -14.6154
PHP 10 6.80272 13.4969 -6.69421
C# 9 6.12245 4.29448 1.82797
Ruby 6 4.08163 8.58896 -4.50732
Assembly 4 2.72109 0.613497 2.10759
VBA 4 2.72109 1.84049 0.880598
Julia 4 2.72109 7.36196 -4.64087
Groovy 4 2.72109 0 2.72109
Scala 3 2.04082 3.68098 -1.64017
Rust 2 1.36054 1.84049 -0.479947
Lua 2 1.36054 6.13497 -4.77443
Go 2 1.36054 4.29448 -2.93393
Clojure 2 1.36054 0.613497 0.747047
CoffeeScript 2 1.36054 0.613497 0.747047
Common Lisp 1 0.680272 0.613497 0.0667752
TypeScript 1 0.680272 1.84049 -1.16022
Swift 1 0.680272 2.45399 -1.77372
Visual Basic 1 0.680272 1.22699 -0.546722
Elixir 1 0.680272 0 0.680272
Objective-C 1 0.680272 1.84049 -1.16022
Hack 0 0 0 0
Dart 0 0 0 0
Smalltalk 0 0 0 0
Erlang 0 0 0 0
F# 0 0 1.22699 -1.22699
VB.NET 0 0 0 0
Haskell 0 0 0.613497 -0.613497

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2021-08-04T21:04:26.730206 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 70 80 Haskell VB.NET F# Erlang Smalltalk Dart Hack Objective-C Elixir Visual Basic Swift TypeScript Common Lisp CoffeeScript Clojure Go Lua Rust Scala Groovy Julia VBA Assembly Ruby C# PHP Matlab Perl Fortran Java R JavaScript SQL C C++ Python 0% 0% 0% 0% 0% 0% 0% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 2% 3% 3% 3% 3% 4% 6% 7% 14% 14% 20% 21% 23% 29% 32% 34% 35% 78% Programming languages, United States −10 0 Δ

Operating systems

Operating systems for United States Count Percentage Percentage in 2017 Difference with previous year
GNU/Linux 84 63.1579 54.5455 8.61244
OS X 36 27.0677 34.8485 -7.78082
Windows 13 9.77444 9.84848 -0.0740488

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2021-08-04T21:04:27.374641 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 Windows OS X GNU/Linux 10% 27% 63% Operating systems, United States −5 0 5 Δ

Job satisfaction

Job satisfaction is an essential pulse to take about a community’s health. It helps to track the evolution and the current state of the RSEs within their role and to catch any sign of structural or organisational dysfunction that are translated into well-being. There are a lot of different metrics to measure the quality of a job on a personal and psychological level [1]. Several models exist to understand the link between different factors of job satisfaction and turnover intention [2]–[6]. Turnover intention is an important measure that is highly associated with the risk of employees leaving the organisation [3]. Job satisfaction is important in retaining RSEs. Perceived employability provides information on how workers values their own skills in regard of the market. To measure the different attitudes toward the RSE role, we used scales that have been created in [5], [6], [7], [8]. These are Likert scale [7], which are 5 point ordinal scales graduated from Strongly disagree to Strongly agree. Each scale is composed of several so called items (i.e. questions) that each measure one attitude.

Beside these specific concepts we asked more general question about their satisfaction in their current position and their satisfaction with their career in general with a range of answers from 0 (not at all satisfied) to 10 (completely satisfied).

The specific questions about their job satisfaction reflect, in general, the same opinion as the two more generic questions. However, the granularity helps to identify a couple of issues that would not appears with generic questions:

  • Recognition: These questions ask if the RSEs feel that they receive enough information about their work and their performance.
  • The turnover intention: These questions aim to measure the desire to quit their current position.
  • The perceived employability: This concept is linked to the previous one. People may not have the intention to leave their jobs, not because they like it, but because they fear they are not employable.
  • The possibility of progression: This question aims to study the possibility of evolution for the RSEs, if information is available and if they see a possibility of evolution within their current career. This is the only questions that clearly received negative answers.

Questions in this section:

All questions were asked in a Likert scale.

  • In general, how satisfied are you with your current position?
  • In general, how satisfied are you with your career?
  • Do you feel that your contribution to research is recognised by your supervisor/line manager?
  • Do you feel that your contribution to research is recognised by the researchers you work with?
  • Do you feel that your contribution to research is recognised by your institution?
  • How often do you consider leaving your job?
  • I would accept another job at the same compensation level if I was offered it
  • It would not be very difficult for me to get an equivalent job in a different institution
  • My experience is in demand on the labour market
  • It is likely that I will gain a promotion within my current group
  • The process I have to complete to gain a promotion is clear and understandable
  • There are many opportunities within my chosen career plan
  • It is likely that my next position will be an Research Software Engineer / Research

/References/

  1. B. Aziri, “Job satisfaction: A literature review,” vol. 3, no. 4, pp. 77–86.
  2. N. De Cuyper, S. Mauno, U. Kinnunen, and A. Mkikangas, “The role of job resources in the relation between perceived employability and turnover intention: A prospective two-sample study,” vol. 78, no. 2, pp. 253–263.
  3. A. B. Bakker and E. Demerouti, “The job demands-resources model: State of the art,” vol. 22, no. 3, pp. 309–328.
  4. G. H. L. Cheng and D. K. S. Chan, “Who Suffers More from Job Insecurity? A Meta-Analytic Review.” vol. 57, no. 2, p. 272.
  5. E. R. Thompson and F. T. Phua, “A brief index of affective job satisfaction,” vol. 37, no. 3, pp. 275–307.
  6. L. Greenhalgh and Z. Rosenblatt, “Job insecurity: Toward conceptual clarity,” pp. 438–448.
  7. R. Likert, “A technique for the measurement of attitudes.” vol. 22, no. 140, p. 55.

General satisfaction

2021-08-04T21:04:56.540976 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 100 90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Percentage satisGen1. In general, how satisfied are you with your current position satisGen2. In general, how satisfied are you with your career 8 6 6 10 11 25 29 26 23 19 18 8 6 6 10 11 25 29 26 23 19 18 General satisfaction 1 2 3 4 5 6 7 8 9 10 General satisfaction: United States

Recognition

2021-08-04T21:04:57.265630 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 100 90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Percentage satisGen1. In general, how satisfied are you with your current position satisGen2. In general, how satisfied are you with your career 8 6 6 10 11 25 29 26 23 19 18 8 6 6 10 11 25 29 26 23 19 18 Recognition 1 2 3 4 5 6 7 8 9 10 Recognition: United States

Turn-over intention

2021-08-04T21:04:57.921102 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 100 90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Percentage satisGen1. In general, how satisfied are you with your current position satisGen2. In general, how satisfied are you with your career 8 6 6 10 11 25 29 26 23 19 18 8 6 6 10 11 25 29 26 23 19 18 Consider leaving job 1 2 3 4 5 6 7 8 9 10 Consider leaving job: United States 2021-08-04T21:04:58.586874 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 100 90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Percentage satisGen1. In general, how satisfied are you with your current position satisGen2. In general, how satisfied are you with your career 8 6 6 10 11 25 29 26 23 19 18 8 6 6 10 11 25 29 26 23 19 18 Would accept another job at same compensation 1 2 3 4 5 6 7 8 9 10 Would accept another job at same compensation: United States

Perceived employability

2021-08-04T21:04:59.251686 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 100 90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Percentage satisGen1. In general, how satisfied are you with your current position satisGen2. In general, how satisfied are you with your career 8 6 6 10 11 25 29 26 23 19 18 8 6 6 10 11 25 29 26 23 19 18 Perceived employability 1 2 3 4 5 6 7 8 9 10 Perceived employability: United States

Progression in the current role

2021-08-04T21:04:59.913991 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 100 90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Percentage satisGen1. In general, how satisfied are you with your current position satisGen2. In general, how satisfied are you with your career 8 6 6 10 11 25 29 26 23 19 18 8 6 6 10 11 25 29 26 23 19 18 Progression in the current role 1 2 3 4 5 6 7 8 9 10 Progression in the current role: United States

Research software engineer

In this section we wanted to know if the participants are member or not of local organisations and if they are interested to participate to conference specific for RSE.

We also asked them to tell them which skills is important as RSE and which they and to acquire for their current role.

Questions in this section

  • Are you a member of an association of Research Software Developers (e.g. AUS-RSE, CANARIE, DE-RSE, NZ_RSE, UK RSE, …)? (yes-no)
  • Would you be interested in joining such an organisation? (yes-no)
  • What is important for such an organisation? (multiple choice)
  • Would you like to attend a conference about software development in academia? (yes-no)
  • How did you learn the skills you need to become an Research Software Engineer / Research Software Developer? (free text)
  • What three skills would you like to acquire or improve to help your work as a Research Software Engineer/ Research Software Developer? The skills can be technical and non-technical (free text)

RSE member

RSE Member for United States Count Percentage Percentage in 2017 Difference with previous year
Yes 6 5.08475 5.7971 -0.712356
No 112 94.9153 94.2029 0.712356

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2021-08-04T21:06:35.866748 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 20 40 60 80 No Yes 95% 5% RSE Member, United States −0.5 0.0 0.5 Δ

Joining a RSE/RSD association

Joining a RSE/RSD association for United States Count Percentage
Yes 66 68.75
No 30 31.25

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2021-08-04T21:06:36.119716 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 70 Yes No Joining a RSE/RSD association, United States

What is important for such an organisation

What is important for such an organisation for United States Count Percentage
Networking 55 37.415
Research software standards and interoperability definition 53 36.0544
Training 48 32.6531
Job opportunities 43 29.2517
Research collaborations 39 26.5306

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2021-08-04T21:06:36.452412 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 5 10 15 20 25 30 35 Networking Research software standards and interoperability definition Training Job opportunities Research collaborations What is important for such an organisation, United States

Attending a national conference of RSE/RSD

Attending a national conference of RSE/RSD for United States Count Percentage
Yes 92 76.6667
No 28 23.3333

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2021-08-04T21:06:36.702965 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ 0 10 20 30 40 50 60 70 80 Yes No Attending a national conference of RSE/RSD, United States

Learning skills for RSE/RSD

2021-08-04T21:06:39.018410 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Learning skill to become a RSE/RSD, United States

Which skills to improve

2021-08-04T21:06:42.013471 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Which skill to improve as RSE/RSD, United States