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SOFTWARE USE IN ASTRONOMY: AN INFORMAL SURVEY
Ivelina Momcheva
1
, Erik Tollerud
1, 2
Draft version July 19, 2015
ABSTRACT
We report on an informal survey about the use of software in the worldwide astronomical community.
The survey was carried out between December 2014 and February 2015, collecting responses from 1142
astronomers, spanning all career levels. We find that all participants use software in their research. The
vast majority of participants, 90%, write at least some of their own software. Even though writing
software is so wide-spread among the survey participants, only 8% of them report that they have
received substantial training in software development. Another 49% of the participants have received
“little” training. The remaining 43% have received no training. We also find that astronomers’
software stack is fairly narrow. The 10 most popular tools among astronomers are (from most to least
popular): Python, shell scripting, IDL, C/C++, Fortran, IRAF, spreadsheets, HTML/CSS, SQL and
Supermongo. Across all participants the most common programing language is Python (67 ± 2%),
followed by IDL (44 ± 2%), C/C++ (37 ± 2%) and Fortran (28 ± 2%). IRAF is used frequently by
24 ± 1% of participants. We show that all trends are largely independent of career stage, area of
research and geographic location.
1. INTRODUCTION
Much of modern Astronomy research depends on soft-
ware. Digital images and numerical simulations are cen-
tral to the work of most astronomers today, and any-
one who is actively involved in astronomy research has
a variety of software techniques in their toolbox. Fur-
thermore, the sheer volume of data has increased dra-
matically in recent years. The efficient and effective use
of large data sets increasingly requires more than rudi-
mentary software skills. Finally, as astronomy moves to-
wards the open code model, propelled by pressure from
funding agencies and journals as well as the community
itself, readability and reusability of code will become in-
creasingly important (Figure 1). Yet we know few details
about the software practices of astronomers. In this work
we aim to gain a greater understanding of the prevalence
of software tools, the demographics of their users, and
the level of software training in astronomy.
The astronomical community has, in the past, provided
funding and support for software tools intended for the
wider community. Examples of this include the God-
dard IDL library (funded by the NASA ADP), IRAF
(supported and developed by AURA at NOAO), STS-
DAS (supported and developed by STScI), and the Star-
link suite (funded by PPARC). As the field develops, new
tools are required and we need to focus our efforts on ones
that will have the widest user base and the lowest bar-
rier to utilization. For example, as our work here shows,
the much larger astronomy user base of Python relative
to the language R suggests that tools in the former lan-
guage are likely to get many more users and contributers
than the latter.
More recently, there has been a growing discussion of
the importance of data analysis and software develop-
ment training in astronomy (e.g., the special sessions at
1
Astronomy Department, Yale University, P.O. Box 208101,
2
Hubble Fellow
the 225th AAS “Astroinformatics and Astrostatistics in
Astronomical Research Steps Towards Better Curricula”
and “Licensing Astrophysics Codes”, which were stand-
ing room only). Although astronomy and astrophysics
went digital long ago, the formal training of astronomy
and physics students rarely involves software develop-
ment or data-intensive analysis techniques. Such skills
are increasingly critical in the era of ubiquitous “Big
Data” (e.g., Berriman & Groom (2011), or the 2015
NOAO Big Data conference). Better information on the
needs of researchers as well as the current availability
of training opportunities (or lack thereof) can be used
to inform, motivate and focus future efforts towards im-
proving this aspect of the astronomy curriculum.
In 2014 the Software Sustainability Institute carried
out an inquiry into the software use of researchers in the
UK (Hettrick et al. (2014), see also the associated presen-
tation). This survey provides useful context for software
usage by researchers, as well as a useful definition of “re-
search software”:
Software that is used to generate, process or
analyze results that you intend to appear in
a publication (either in a journal, conference
paper, monograph, book or thesis). Research
software can be anything from a few lines of
code written by yourself, to a professionally
developed software package. Software that
does not generate, process or analyze results
- such as word processing software, or the use
of a web search - does not count as research
software for the purposes of this survey.
However, this survey was limited to researchers at UK
institutions. More importantly, it was not focused on as-
tronomers, who may have quite different software prac-
tices from scientists in other fields.
Motivated by these issues and related discussions dur-
ing the .Astronomy 6 conference, we created a survey
to explore software use in astronomy. In this paper, we
discuss the methodology of the survey in §2, the results
arXiv:1507.03989v1 [astro-ph.IM] 14 Jul 2015
2
from the multiple-choice sections in §3 and the free-form
comments in §4. In §5 we compare our results to the
aforementioned SSI survey and in §6 we conclude.
We have made the anonymized results of the survey
and the code to generate the summary figures avail-
able at https://github.com/eteq/software_survey_
analysis. This repository may be updated in the future
if a significant number of new respondents fill out the
survey
3
.
2. DATA AND METHODS
The survey was constructed as a Google form question-
naire with seven questions and one comment box. Four
of the questions were about software use and inspired by
the SSI survey:
1. Do you use software in your research?
2. Have you had formal training in software develop-
ment?
3. Which of these is more common in your work? (on
writing one’s own software)
4. Select any of these that you use regularly to write
code for your research. (on most commonly used
software tools)
The remaining three questions requested basic demo-
graphic information:
1. What is your field of research?
2. What is your career stage?
3. What is the location of your institution?
The survey was opened on December 9, 2014. The at-
tendees of the .Astronomy 6 conference were asked to for-
ward a link to the survey to their home departments, in-
cluding a prompt to send it on to any other interested as-
tronomer groups. A link to the survey was also posted on
the Astronomers Facebook group. The survey received
758 responses on the first day and another 210 during
the following day. The data for this work was collected
on February 3, 2015. The number of participants at that
time was 1145. Three responses from participants who
indicated that they work in fields other than astronomy
were removed for a final tally of 1142 participants.
2.1. Survey Demographics
The demographics of the sample at the time of col-
lection were the following. Of the 1142 participants, 380
are graduate students, 340 are postdocs, 385 are research
scientists and faculty (175 and 200, respectively). The re-
maining 37 were undergraduate students (10), emeritus
professors, support scientists at observatories, adjunct
faculty, post-bachelor’s researchers, etc. These 37 “mis-
cellaneous” career levels will be included in the analysis
of the full sample but will not be included in any of the
other career subgroups. For the analysis, we combine the
research scientist and faculty subgroups to create three
groups of similar size, roughly corresponding to “early”,
“intermediate” and “late” career stages.
3
http://tinyurl.com/pvyqw59
In terms of areas of research, 823 participants chose
“Observational Astronomy/Astrophysics”, 353 selected
“Theoretical Astronomy/Astrophysics”, 130 indicated
that they work in astronomical instrumentation and 66
in planetary science. 22 participants did not choose any
of these four main categories. Of these, nine partici-
pants selected “Other”, three did not choose an area of
research, and 10 entered custom values such as physics,
astro-statistics, cosmology, astroparticle physics, space
physics, etc. Participants were allowed to choose more
than one area of research, which is why the numbers for
the individual categories add to more than 1142.
The final piece of demographic data we collected was
the geographic location of the participants’ home insti-
tution. The majority of participants are from the USA
(546), followed by Germany (170), UK (90), Australia
(69) and Chile (35). 80% of the participants come from
these five top countries, with 48% from the USA. The
remaining 232 participants come from 41 different coun-
tries. The break down is the following:
Netherlands (31), Sweden (21), Argentina (19),
Canada (18), Brazil (14), France (13), Spain (12), Italy
(11), Poland (9), Mexico (9), Switzerland (9), Israel (6),
Denmark (5), Finland (3), India (3), Ireland (3), Por-
tugal (3), Japan (3), United Arab Emirates (2), South
Korea (2), South Africa (2), Russia (2), South Korea (2),
Belgium (2), Austria (2), New Zealand (2), Greece (1),
Lithuania (1), Georgia (1), Malaysia (1), Norway (1),
Slovakia (1), Czech Republic (1), China (1), Swaziland
(1), Taiwan (1), Turkey (1), Uzbekistan (1), Hungary
(1), Vatican (1), Ghana (1).
The geographic distribution of the participants indi-
cates that our methods of circulating the survey were un-
able to reach a broad base of researchers in Asia, Africa,
and Eastern Europe. Compared to the IAU membership,
the USA is over-represented by a factor of 2.1, Germany
by a factor of 2.7, and China is under-represented by a
factor of 60. Major contributors to this imbalance are the
language of the survey (English) and the method of dis-
tribution (social media and friends-of-friends networks).
Hence, any conclusions we make will be only applica-
ble to the researchers working in the countries which are
predominantly represented.
2.2. Survey Completeness
This survey should not be viewed as a systematically
representative sample of the astronomy community. The
request to fill out the survey was primarily spread via so-
cial networks (i.e., departmental e-mails, Facebook, twit-
ter, etc.), so it is possible that the sample of astronomers
surveyed is biased in ways that may affect the results
presented below. Given that we are astronomers (not so-
cial scientists), we are not trained in methods to address
these effects, and therefore simply present the raw results
of the survey with no correction for selection bias. That
said, the sheer number of responses implies these results
represent a significant part of the community. Regard-
less, we would certainly be happy if this work inspires
a more rigorous survey by social scientists with domain-
specific expertise.
3. RESULTS
In this section we show and describe the results from
the survey. We first focus on software use and whether
Astro Software Survey 3
Fig. 1.— An xkcd.com comic strip that captures one of the problems with the lack of software training. This strip was widely shared
among astronomers on social networks, showing that the problem is known in at least part of the community.
Fig. 2.— Responses to the question “Do you use software in
your research?”. 100% of survey participants answered in the af-
firmative.
astronomers write their own software. We then examine
the training we receive in software development. Finally,
we discuss the most commonly used software tools. In
all cases we consider how career stage, research area and
geographic location alter these results. Where relevant,
we assume Poisson statistics for error bars and to provide
estimates of significance.
3.1. Software Use
The first question of the survey aims to establish a
baseline of software use. The answers to the question “Do
you use software in your research?” are shown in Figure
2. Unanimously, all participants responded with “Yes”.
This unanimity is not surprising: It would be difficult
to imagine pursuing astronomical research today which
does not rely on software at least to some extent. But
the unanimity does serve to underscore the importance
of software in the field.
3.2. Do Astronomers Use Their Own Software?
We ask the survey participants to best describe the au-
thorship of the software that they use. The goal of this
question is to find what is the predominant practice in
the community: do most of us use “black box” software
packages written by few or do most of us write custom
software? We find that most often we do both (Figure
3, first panel): 57 ± 2% choose this option. One third
of survey participants say they mostly write their own
software (33 ± 2%), while only a small portion of survey
participants predominantly use software written by oth-
ers: 11 ± 1%. Overall, 89% of all participants write some
of their own software.
In Figure 3 we also explore the breakdown of the an-
swers as a function of career stage. The answers vary
slightly between the three groups. One curious result is
that the answers of the “early” and “late” career stages
closely resemble each other. The reason for such a trend
may be that students follow their advisors’ recommen-
dations on software practices. In both of these groups,
∼ 30±3% predominantly write their own software, while
∼ 11 ± 2%) mostly use software written by others. In
contrast, a larger portion of postdocs write their own
software: 39 ± 3%.
We further consider the breakdown of answers as a
function of research area in Figure 4. The groups are
not fully independent because participants were allowed
to choose more than one research area. In all groups the
largest portion of astronomers, ≥ 50%, use both soft-
ware written by others and write their own. Researchers
working in theory and instrumentation are more likely
to primarily depend on their own software (42 ± 4% and
38 ± 5%, respectively) than planetary and observational
astronomers (32 ± 7% and 29 ± 2%, respectively). The
latter two groups are more likely to primarily use soft-
ware written by others, 17±5% and 12±1% for planetary
and observational researchers, respectively, versus 6±1%
and 5 ± 2% for theory and instrumentation.
Finally, we break down the answers by country of the
researchers’ home institution as shown in Figure 5. These
and the following plots by country are more difficult to in-
terpret because they only contain information about the
researchers’ current institutions rather than their insti-
tutional history. All countries show similar trends, with
≥ 50% of astronomers choosing the “Both” option. At
the extremes, the survey respondents from Germany are
most likely to write their own software, 38 ± 5%, and the
respondents from the UK are least likely to use software
written by others, 6 ± 3%.
In conclusion, the majority of astronomers, ∼ 90%
write at least some of their own software, across all de-
mographics explored in our survey and a third of survey
participants predominantly rely on their own software.
4
Fig. 3.— Answers to “Which of these is more common in your work: I write mostly my own software, I mostly use software written by
others, or somewhere in between”, sub-divided by career stage.
Fig. 4.— Same question as Figure 3, but divided by sub-field.
3.3. Are We Trained?
Considering that, across all demographics, ∼ 90% of
astronomers are involved in writing software (§3.2), it is
important to asses the level of training we receive. We
allowed participants to choose from one of three levels
of training in software development: “A little”, “A lot”,
or “None”. The survey questions did not give guidelines
on how to interpret the first two categories. Rather, we
left it to the survey participants to decide whether they
thought their training was substantial or not. This sec-
tion breaks down the answers into different demograph-
ics.
The first panel in Figure 6 shows the answers from
all participants. Overall, 8 ± 1% of survey participants
have received substantial training, 49 ± 2% have received
a little training and 43 ± 2% have received no training.
Altogether, 57 ± 2% of survey participants have received
some training in software development. Across all career
levels, only ∼ 8% of astronomers have received significant
training. Facutly and scientists are slightly more likely
to have received no training at 50±4% versus 40±3% for
the more junior groups. Postdocs and graduate students
are slightly more likely to have received some training at
53 ± 4%, relative to faculty and scientists (42 ± 3%).
In Figure 7 we specifically focus on the training of sur-
vey participants who, in the previous questions, said that
they primarily write their own software (“My own” op-
tion, ∼ 33% of the sample). Overall, 40 ± 3% of those
participants have received no training and 89 ± 5% have
received at best a little bit of training. The results for
this subset are consistent with the answers from the full
sample within the error bars, i.e. astronomers who pri-
marily write their own software do not have more train-
ing in software development than everyone else. The re-
sults are similar if we also considered the participants
who write some of their software (“Both” option). This
finding is key because it shows that the lack of training
is not because there is no need for such skills. Rather
training simply does not occur. This is of particular im-
portance because it implies that many astronomers have
little to no training in an activity that is a major part
of their research work, despite the fact that they nearly
always have many years of post-secondary education dur-
ing which they could have received such training.
Astro Software Survey 5
Fig. 5.— Same question as Figure 3, but sub-divided by country (for the countries with the highest number of respondents).
Fig. 6.— Answers to the question “Have you had formal training in software development: Yes, a lot; Yes, a little; No”, sub-divided by
career stage.
Fig. 7.— Answers to the question from Figure 6, but only for those who primarily write their own software.
Back to the full sample, in Figure 8 we show the break-
down of answers as a function of research area. The
trends remain the same across all fields. The breakdown
by country (Figure 9) shows that the results are similar
internationally. The fraction of astronomers with signif-
icant training is largely independent of geography. Some
geographical variations exist in the fraction of partici-
pants who have at least a little training: the USA has the
largest fraction with training: 55 ± 3%, while Australia
has the smallest with 35 ± 7%. Based on these results,
we speculate that opportunities to receive at least a little
bit of training are more available at US institutions or
that more US researches seek out such opportunities.
In conclusion, across all career levels, research areas
and countries, only a small fraction of astronomy re-
searchers receive significant training in software develop-
ment. The lack of a strong trend with career level may
indicate that significant training only occurs at the un-
Astro Software Survey 7
dergraduate level (and some participants left comments
to that effect). While graduate students are more likely
to have had a little training, it seems that few gradu-
ate programs offer and/or require CS courses (otherwise
junior astronomers will have a higher level of significant
training). Overall, ∼ 90% of the survey participants have
received only a little bit of training at best, despite all
being software users, and most being writers of their own
software.
3.4. What is in the Astronomer Software Tool Stack?
In this section we consider the most common software
tools for professional astronomers. We refer to the full
set of software tools an astronomer uses as their “stack”.
In the survey form we suggested 19 software tools and
allowed participants to add any options we missed. The
input was edited to standardize spelling and capitaliza-
tion of tools. In total, participants added 64 custom
options. 10 respondents did not provide an answer to
this question. While “C” was an option, “C++” was not
part of our suggestions. Some participants noted in the
comments what they chose “C” even though they actu-
ally use “C++”. For this reason we consider C and C++
together in our analysis. Within the top-20 most used
software tools there are four items that were not on our
original list: C++, Mathematica, gnuplot and awk.
The overall astronomer stack is rather narrow (Figure
10, first panel). Only ten of the software tools are used
by more than 10% of the survey participants. These
are (from most popular to least popular): Python, shell
scripting, IDL, C/C++, Fortran, IRAF, spreadsheets,
HTML/CSS, SQL and Supermongo. Across all partici-
pants the most common programing language is Python
(67± 2%), followed by IDL (44 ± 2%), C/C++ (37 ± 2%)
and Fortran (28±2%). Shell scripting is the second most
popular tool for astronomers (47 ± 2%). The IRAF (Im-
age Reduction and Analysis Facility) environment is used
by 24 ± 1% of the survey participants.
Across the different career stages, we notice that se-
nior astronomers have a broader tool stack, i.e. they
utilize a wider variety of tools in their research. Only
eight tools are used by more than 10% of graduate stu-
dents, nine tools are used by more than 10% of post-
docs and 11 tools are used by more than 10% of faculty
and scientists. Python is the most popular tool at all
career levels, and it is most popular among junior re-
searchers. Four out of five graduate students use Python
(80 ± 5%), as do 70 ± 5% of postdocs and half of faculty
and scientists (53 ± 4%). IDL, IRAF and compiled lan-
guages have a more uniform user base across all career
levels. Some tools are unique to certain demographics.
Graduate students have the highest fraction of Matlab
users (11%), while faculty and research scientists dom-
inate HTML/CSS (21%), Supermongo (16%) and Perl
(16%).
Unsurprisingly, software tools depend strongly on the
research area (Figure 11). Without attempting to be ex-
haustive, we note some interesting differences between
fields. Observational astronomers have the highest frac-
tions of IDL (48 ± 2%) and IRAF (31 ± 2%) users. Theo-
retical researchers have the highest fractions of compiled
language users: C/C++ with 56 ± 4% and Fortran with
50 ± 4%. Researchers in instrumentation have a high
fraction of C/C++ (52 ± 6%) and spreadsheet (28 ± 5%)
users. Other tools, however show little field-to-field vari-
ation. Python use is consistently high across all fields at
60 - 70%, as is shell scripting at ∼ 50%.
Finally, in Figure 12 we consider the software stack
for researchers in different countries. Researches in the
USA have the highest fractions of IDL (49 ± 3%) and
IRAF (25 ± 2%) users, while Australia has the lowest
fraction of users of these tools, 32 ± 7% and 12 ± 4%,
for IDL and IRAF respectively. The UK has the highest
fraction of SQL users (21±5%); Germany has the highest
fraction of C/C++ users (48 ± 5%); and Australia has
the highest fraction of Matlab users (13 ± 4%). However,
these results can be strongly influenced by the research
areas represented for each country within our sample so
we caution against drawing far-reaching conclusions.
We can also compare the USA and non-USA survey re-
spondents, since those two samples are comparable in size
(Figure 12, second and sixth panels). Overall the rank-
ings and fractions of users of different tools are very simi-
lar as can be expected by the global mobility of many as-
tronomers. The only notable exceptions are IDL and R.
The fraction of IDL users in the USA is 10% larger than
of non-USA participants. The user base of the statistical
package R is reversed: 8 ± 1% of non-USA researchers
choose this option vs. only 3 ± 1 of USA researchers.
Considering the wide-spread use of R in other scientific
fields, its popularity among astronomers is strikingly low.
3.5. Python vs. IDL?
A recent shift in astronomy has been the favored
choice of interpreted programming language for day-to-
day analysis work. In the previous section we showed
that Python has overtaken IDL in popularity. This may
not have been true three to five years ago, but today
Python is, by a wide margin, the most popular inter-
preted language in astronomy (at least insofar as this
survey is representative). Still, there is a significant over-
lap between the users of both languages as many people
are either transitioning from one to the other or using
both in their research. In Figure 13 we show a Venn di-
agram of the Python and IDL users. In total 984 (86%)
of the survey participants use either Python or IDL. Of
those, 764 use Python and 497 use IDL. Both are chosen
by 277 or 25% of all survey participants. This indicates
substantial overlap: 36% of Python users also use IDL
and 55% of IDL users also use Python. Finally, 158 sur-
vey participants (14% of the full sample) chose neither
option.
3.6. Interactive Visualization Of Software Tools
To facilitate understanding of this multi-dimensional
dataset of how use of the various software tools overlap
with each other, we provide an interactive visualization,
available within the Authorea version of the paper, by
downloading the software repository described in Sec-
tion 1, or at this link. In this visualization, the tools
respondents use are shown as sectors in a radial layout.
Users of multiple tools are represented as stacked sectors:
for example, the fraction of users who use only Python
and IDL are represented as the fraction of the third ring
labeled “idl” with “python” and “idl” as the lower two
layers. Hovering over that sector shows the number of
respondents to the left of the page (for Python and IDL
8
Fig. 10.— Responses to the prompt “Select any of these that you regularly use in your research”, sub-divided by career stage. The
options listed included: IDL, IRAF, Python, C, Fortran, Perl, Javascript, Julia, Matlab, Java, R, SQL, Shell Scripting, STAN, Figaro,
Ruby, HML/CSS, Supermongo (labeled “sm”), and Excel or other spreadsheets (labeled “excel”). Respondents could add additional tools
not listed using an “Other” box. Among the tools in this plot, four items were added by respondents: C++, Mathematica, gnuplot and
awk. Note that the x axis varies between panels.
Fig. 11.— Same question as Figure 10, but sub-divided by sub-field.
only it is 36.) Clicking on a particular sector expands the
visualization to show only those who use the correspond-
ing stack of tools, while clicking on the central circle goes
back to larger segments of the survey.
4. COMMENTS
We allowed participants to leave comments at the end
of the survey. In order to increase the anonymity of com-
ments we detached them from the answers to the other
questions (aside from career stage). We further removed
e-mails, names, or other identifying information from the
content of the comments. If anyone who took the sur-
vey would prefer that their comments not be included,
we ask them to contact us and we would be happy to
remove the information from the public dataset.
We see three recurring topics in the comments. The
first common comment topic is the switch from IDL to
Python. Many users comment that they would like to
or are planning to make the switch from IDL to Python,
frequently because of licensing issues and costs. We find
it particularly striking that several senior astronomers
commented that they are learning Python to be more
helpful to their students.
The second common comment topic is the desire for
more opportunities to improve software development
skills. Many participants voiced interest in attending
Astro Software Survey 9
Fig. 12.— Same question as Figure 10, but sub-divided by country (for the countries with the highest number of respondents).
Fig. 13.— Venn Diagram of the overlap between respondents
who reported using Python, IDL, or neither.
software development classes for astronomers. Several
suggested that classes in programming and statistics
should be an integral part of the undergraduate and/or
graduate curriculum in astronomy. Participants sug-
gested that such training not only aids in better research
efficiency, but also can make code more readable and
reusable.
The third recurring topic in the comments is the lack
of career opportunities for astronomers who write soft-
ware. Comments suggest that more professional recogni-
tion should be given to those who spend most of their
time developing important tools for the astronomical
community and that such efforts should be recognized
in hiring and explicitly funded.
We cite some of the representative comments on each
topic in the Appendix.
5. COMPARISON TO SSI SURVEY
This work was inspired by a UK survey led by the
Software Sustainability Institute. While their findings
are for the wider scientific community in the UK and
ours are for the worldwide astronomical community, a
comparison is still interesting.
The SSI survey finds that 90% of UK scientists use
software in their research. Our survey shows that as-
tronomy is in line with other sciences in the use of soft-
ware – 100% of astronomers use it. 90% of astronomers
write some software (93% of UK astronomers). This is
much larger than the SSI survey, which finds that only
56% of researchers, across all disciplines, write some of
their software. This implies astronomers are much more
dependent on their own software than other sciences.
In the SSI survey, 55% of respondents say they have re-
ceived some training in software development, with 40%
indicating that the training was a formal course and
15% indicating self-directed study. Our categories are
not identical, but we find that a similar fraction of as-
tronomers – 57% (53% of UK astronomers) – say that
they have received some form of training. However, only
8% say that the training was substantial, while 49% say
they received a little training. The decision what con-
stitutes a lot and a little was left to the participants.
Those who chose to expand on their decision indicated
that a lot corresponded to a formal class while a little
corresponded to using on-line materials such as Software
Carpentry and Code Academy.
Finally, 40% of our survey respondents who predomi-
nantly write their own software have received no train-
ing (42% of those who write some of their own software).
This fraction is twice as large as the one reported in
the SSI survey (21%) and may indicate that in astron-
omy there are fewer efforts and opportunities to train re-
searchers in software development. This is all the more
surprising given that many more astronomers write their
own code, according to these surveys.
6. CONCLUSIONS
Based on the responses summarized in Section 3, we
come to the following conclusions:
10
• Unsurprisingly, all 1142 survey participants say
that they do use software in their research (Fig-
ure 2). The unanimous answer to this question
underscores the importance of understanding how
astronomers use software (i.e., the purpose of this
survey).
• 89% of astronomers across all demographics write
their own software (Figure 3). However, only 58%
of those who write software are trained in software.
Moreover, only 8% self-report as having better than
“a little” training. From this we conclude that 42%
of astronomers have no training for a key element
of their work, and 92 % have at most “a little”
training.
• Python is the dominant language among our re-
spondents. Surprisingly, this is true across all
career stages (Figure 10). While a commonly-
expressed mindset, reflected in some of the com-
ments, is that graduate students are more likely to
know the newer languages, it appears that this is
only mildly true, at least in our survey.
• Astronomers have a fairly narrow software “stack”,
with only 10 tools used by more than 10% or re-
spondents. Theorists tend to have a more narrow
stack relative to other fields (Figure 11) as do grad-
uate students relative to more senior researchers.
Independent of career level and field, Python and
shell scripting are are most popular tools for as-
tronomers. These results show that training efforts
can have a significant impact even if they only focus
on a limited number of software tools. We suggest
that the rankings we produce can help in choosing
training topics that would be most useful for the
broadest group of participants.
We caution that these results are tentative because our
sampling methodology was not robust. If nothing else,
we hope this survey will prompt a more formal study
of software use in astronomy to better understand how
we should use the limited resources of our community to
improve software training and software use.
7. ACKNOWLEDGEMENTS
We thank the organizers and participants of the .As-
tronomy 6 conference, the Adler Planetarium, and all
participants in the survey. Without them, this work
would not have been possible. We also thank Demitri
Muna and Camille Avestruz for helpful comments on the
manuscript.
APPENDIX
PARTICIPANT COMMENTS
The section below shows a representative sampling of free-form comments left by the survey participants.
On switching from IDL to Python:
• I recently switched from using IDL as my primary programming language to using Python. (Graduate student)
• Mainly IDL user who wants to switch to Python as it is more open source. (Graduate student)
• I learned IDL as an undergrad and continue to mostly code in it as a grad student. However I’ve been learning
Python lately and plan to mostly switch over within the next year or so. (Graduate student)
• I plan to learn Python but haven’t yet worked with it. (Graduate student)
• At this stage I see Python as the future and am rapidly moving away from IDL. (Graduate student)
• I learned IDL as an undergrad (class of 2004) and used it nearly exclusively [...] until about two years ago. Over
the last two years I’ve been slowly switching to Python [...] (Postdoc)
• I’ve only recently started working in IDL and Python. I expect to do quite a bit of development in Python from
now on. (Postdoc)
• I want to learn Python and R as soon as possible (Postdoc)
• While I haven’t learned it yet many of my colleagues use Python and I make all of my students learn that (instead
of e.g. Matlab). (Faculty)
• I am telling all of my students to learn Python and through that I am also gaining proficiency in Python. This
is different from what my advisor did. He told me to use the language that he used so that he could help me
debug it. (Faculty)
• Moving to Python as IDL needs a license... And I just like the language. (Faculty)
• Currently I code in IDL but I am trying to switch to Python. (Faculty)
• I plan to switch from IDL to Python over the next 2-3 years. (Faculty)
• I’m intending to try out Python soon. (Faculty)
Astro Software Survey 11
• Taking a workshop on Python soon I can encourage and help my students learn a language that can be used
outside of academia as well. (Faculty)
On the desire for more opportunities to improve software development skills:
• If there is any software development training program for astronomers I would love to attend. (Graduate student)
• I think it should be strongly recommended that people going into astronomy should take programming classes.
In fact I would make it part of the required course work to get a B.S. in Physics or Astronomy. Most astronomers
I know did not take any formal programming course we learned as we went along. Most of us write our own
code and many do not use good coding practices making reading or adapting code from other astronomers a lot
more painful than it should be. (Graduate student)
• Helps to include courses in computation and statistics in grad curriculum. (Graduate student)
• Astro students should get more formal training in programming and software design (and it should happen at
the undergraduate level whenever possible). (Graduate student)
• I wish I could get more formal training on programming. I have the feeling that Astronomy and Physics depart-
ment usually don’t emphasize the importance of programming until people start doing researches. (Graduate
student)
• The coding skills incoming graduate students possess seem to vary wildly but they are often dismal with respect
to the level required to begin doing serious research right away. In my department there seems to be little
motivation to rectify this with either: (1) requiring undergraduate CS preparation as a condition for admission;
or (2) organizing a programming course for beginning graduate students. This situation seems if not unsustainable
very much non-optimal for the cultivation of strong substantive independent research skills. I imagine many other
departments are currently facing the same dilemma. (Graduate student)
• More formal training in software use/development would be wonderful. (Graduate student)
• Formal training on astronomical software packages as part of my astronomy undergraduate degree would have
been very helpful for me. (Graduate student)
• We need to be teaching undergraduates and graduates good object-oriented design skills from day one. Software
is more important now than it ever was and the “learn as you go” mentality causes a tremendous amount of
wasted efforts as bad code has to be rewritten all too often with the side effect of having astronomers with career
skills that aren’t as well developed as they could have been should they choose to leave astronomy. Also we
should make a concerted effort to rewrite some of our legacy tools (IDL libraries, IRAF, etc.) in a language and
style that is more easily and cleanly extended and maintained. Incentives to put in the time and money for these
[initially] low impact projects are hard to come by though. (Graduate student)
On the lack of career opportunities for people who write software:
• People who develop software that the community use should be recognized more for their efforts! (Research
Scientist)
• It would be great to have more career options for researchers who focus on software development in the astro-
physical community. Too many good researchers who contribute lots of great software to the community have
been forced out of the field because of lack of recognition for their work and lack of funding for people other than
those who publish several science papers a year. (Postdoc)
• Software development is evidently as important a tool in modern science as mathematics and just as it has
historically not been deemed wise to outsource all mathematics to professional mathematicians I believe a large
fraction of scientific software development will have to be accomplished by scientists who are intimately familiar
with the problem at hand. Perhaps more than is the case for mathematics though the paper metric used for
hiring scientists often pushes excellent software developers out of science and into industry who are then lost to
us. (Postdoc)
• I find that my observational colleagues are often unaware that we as computational scientists need to write
proposals for supercomputers like they write for telescopes. Also when we write science proposals (e.g., NASA,
NSF) we have to lie about how much time we will spend developing code say only a few months when in reality it
occupies most of the grant period since code development is frowned upon (except within the new NASA ROSES
program PDART started in 2014). (Research Scientist)
REFERENCES
Berriman, G. B., & Groom, S. L. 2011, Commun. ACM, 54, 52 Hettrick, S., Antonioletti, M., Carr, L., et al. 2014, UK Research
Software Survey 2014, doi:10.5281/zenodo.14809
