16128019177056 1..5


EQUITY, DIVERSITY AND INCLUSION

A call to eradicate non-inclusive
terms from the life sciences
Abstract Since the Black Lives Matter movement rose to mainstream prominence, the academic

enterprise has started recognizing the systematic racism present in science. However, there have

been relatively few efforts to make sure that the language used to communicate science is inclusive.

Here, I quantify the number of research articles published between 2000 and 2020 that contained

non-inclusive terms with racial connotations, such as “blacklist” and “whitelist”, or “master” and

“slave”. This reveals that non-inclusive language is being increasingly used in the life sciences

literature, and I urge the global academic community to expunge these archaic terms to make science

inclusive for everyone.

AZIZ KHAN*

Historically, many terms are associated with

racial connotations. In the tech world, the words

“master” and “slave” are often used to refer to

types of storages, circuits, databases or code, in

which the slave type is subservient to the master.

Other commonly used terms are “blacklist” and

“whitelist” — where the blacklists are the prob-

lematic entities and whitelists are the good ones

(Alter et al., 2016).

These, and several other archaic and non-

inclusive terms, are also widely used in scientific

manuscripts (Baeckens et al., 2020; Herb-

ers, 2007; Houghton and Houghton, 2018). In

publishing, the term “blacklist” is used to filter

out predatory journals and publishers from non-

predatory and more trustworthy

journals that are added to the “whitelist”

(Houghton and Houghton, 2018; Silver, 2017).

In the life sciences, the term “blacklist” is com-

monly used to represent problematic genomic

regions, variations, genes, or proteins which

need to be filtered out as an artifact or noise

(Wimberley and Heber, 2019; Maffucci et al.,

2019; Collins et al., 2019; Wilfert et al., 2016).

For example, the ENCODE blacklist regions are

a curated list of non-coding regions in the

genome, which is used by the gene regulation

community – including myself – as an essential

quality filter when analyzing genomic and epige-

nomic data (Amemiya et al., 2019).

The terms “master” and “slave” are also fre-

quently used in molecular biology to group tran-

scription factors (TFs) or genes based on their

function. For example, proteins that are at the

top of the regulatory hierarchy and control key

biological programs, such as determining a cell’s

fate, are commonly named “master regulators”

or “master TFs”. While some may argue that it is

acceptable to use the term “master”, the prob-

lem gets worse when some researchers intro-

duce "slave TFs" (Ocone and Sanguinetti,

2011).

Use of non-inclusive terms in life
sciences literature is growing
To estimate the use of the terms blacklist/white-

list and master/slave, I performed searches on

the open-access repository Europe PMC which

contains millions of biomedical research articles.

A search for articles containing blacklist/whitelist

returned more than 2,000 articles published in

more than 600 journals between 2000 and 2020

(Figure 1), with blacklist appearing more often

(1,994 articles) than whitelist (439 articles).

The first use of the term “blacklist” dates

back to the seventeenth century and has a long

history of being used in the labor market

(Weir, 2013). However, these terms started

appearing in the biomedical literature around

*For correspondence:

azizk@stanford.edu

Competing interests: The author

declares that no competing

interests exist.

Funding: See page 3

Reviewing editor: Julia

Deathridge, eLife, United

Kingdom

Copyright Khan. This article is

distributed under the terms of

the Creative Commons

Attribution License, which

permits unrestricted use and

redistribution provided that the

original author and source are

credited.

Khan. eLife 2021;10:e65604. DOI: https://doi.org/10.7554/eLife.65604 1 of 5

FEATURE ARTICLE

http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/
https://doi.org/10.7554/eLife.65604
https://creativecommons.org/
https://creativecommons.org/
https://elifesciences.org/?utm_source=pdf&utm_medium=article-pdf&utm_campaign=PDF_tracking
https://elifesciences.org/?utm_source=pdf&utm_medium=article-pdf&utm_campaign=PDF_tracking
http://en.wikipedia.org/wiki/Open_access
http://en.wikipedia.org/wiki/Open_access


the mid-nineteenth century. In 1899, an article in

the journal The Hospital suggested maintaining

a “whitelist” of firms that treat their employees

fairly instead of a “blacklist" of firms with a bad

reputation (The Hospital, 1899). Since then, the

use of these non-inclusive terms has continued

to grow (Figure 1).

The terms “master” and “slave” are also

widely used in the scientific literature. A search

for articles with both these terms found over

3,500 research articles published in more than

900 journals between 2000 and 2020 (Figure 1).

Similar to blacklist and whitelist, the use of mas-

ter and slave is growing with time. Furthermore,

a search for “master TFs” or “master regulators”

found more than 50,000 articles from 2000 to

2020, with their use increasing each year (Fig-

ure 1—figure supplement 1). This suggests that

non-inclusive terms are becoming increasingly

pervasive, and possibly the norm in the life sci-

ences literature.

Most of the papers with non-inclusive terms

were published in well-known journals, including

multidisciplinary journals (such as Nature, Nature

Communications, PLOS One, PNAS and Scien-

tific Reports) and journals with broad scopes

within the life sciences and medicine (such as

BMJ, Cell, Cell Reports and eLife). In addition to

these multidisciplinary and broad-scope journals,

the journals that used the terms "blacklist" or

"whitelist" most often were BMC Bioinformatics,

Nature Genetics and Genome Research, and the

journals that used the terms "master" and

"slave" most often were Sensors, Optics

Express, Scientific World Journal and BMC Bioin-

formatics. Inevitably, larger journals (such as

Nature Communications, PLOS One, PNAS, Sci-

entific Reports and Sensors) tended to use these

terms more often than small journals with fewer

publications.

Let’s expunge non-inclusive terms
to make science inclusive for all
Following the Black Lives Matter protests the sci-

entific community has spoken against the sys-

tematic racism in science and called for action to

make science more diverse and inclusive

(Barber et al., 2020; Cell Editorial Team, 2020;

Eisen, 2020; Nature, 2020;

Sanford, 2020; Stevens et al., 2021; Taffe and

Gilpin, 2021). Yet, the growing use of such non-

inclusive terms in scientific literature potentially

reflects a racist research space that endorses

and sustains the use of these terms. The more

we use this language, the more it becomes a

habit, and we need to act now to avoid passing

this behavior on to future generations of

scientists.

Some tech and governmental organizations,

such as Google, GitHub, the UK National Cyber

Security Center, among others Seele, 2020, are

already replacing such terms that reflect a racist

culture (Google, 2020; GitHub, 2020; Emm-

a, 2020; Seele, 2020; Im, 2020). I urge the sci-

entific community (including institutions,

researchers, funders, learned societies, journals

and others) to follow suit, and replace the terms

blacklist/whitelist with excluded/included or

deny/allow lists, and to use the terms primary

and secondary instead of master and slave.

There are several other examples of non-

inclusive terminologies that are used in the life

sciences and beyond. For example, there are

growing concerns over terms with racial etymol-

ogy, such as “slave-making ants” — a slavery

metaphor to describe ant behavior (Herb-

ers, 2020; Herbers, 2007), or the word “noos-

ing” to describe catching lizards, which reminds

people of the racial lynchings of Black people in

the United States (Cahan, 2020). A number of

0

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1.0 M

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2000 2005 2010 2015 2020

Year published

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 P

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Total number of articles in Europe PMC (right axis)

Articles with terms master and slave (left axis)

Articles with terms blacklist or whitelist (left axis)

Figure 1. The growth of non-inclusive terms in the life sciences literature. The number of

articles on Europe PMC containing the terms blacklist or whitelist (blue; left axis), containing

the terms master and slave (orange; left axis), and the total number of articles on Europe

PMC (green; right axis) between 2020 and 2000.

The online version of this article includes the following figure supplement(s) for figure 1:

Figure supplement 1. The number articles in the life sciences literature that just contain the

term "master" or "slave".

Khan. eLife 2021;10:e65604. DOI: https://doi.org/10.7554/eLife.65604 2 of 5

Feature Article Equity, Diversity and Inclusion A call to eradicate non-inclusive terms from the life sciences

https://doi.org/10.7554/eLife.65604


plant and animal species also have non-inclusive

names or are named after people who were

known for their racist rhetoric (Shiffman, 2019).

Recently, the racially loaded term “quantum

supremacy” was introduced to represent the

power of quantum computers, which is now get-

ting replaced by “quantum advantage” (Pala-

cios-Berraquero et al., 2019; Wiesner, 2017).

Additionally, in response to recent social unrest,

the academic enterprise has started renaming

academic buildings, programs and prizes, and

removing monuments named after people who

were known for their racist comments and ideol-

ogy (Cahan, 2020). Now, it is time for us to also

rethink the language we use to communicate

science.

Language matters — it shapes the way we

think, see and behave. The list of non-inclusive

terms in science is long and widespread across

multiple disciplines. As scientists, we have a

responsibility to fix the problem and to use lan-

guage that is inclusive to everyone.

Methods
The research articles with specific terms were

queried through Europe PMC using the

europepmc R package v0.4 (Ferguson et al.,

2021). The search query was restricted to publi-

cation year between January 01, 2000, to

December 31, 2020. Preprints were excluded

from the search.

The query used to search articles with terms

blacklist and whitelist is as follows: ((blacklist OR

blacklisted OR “black-listed” OR “black-list” OR

blacklisting) OR (whitelist OR whitelisted OR

“white-listed” OR “white-list” OR whitelisting))

AND (FIRST_PDATE:[2000-01-01 TO 2020-12-

31]) NOT (SRC:PPR).

The query used to search articles with terms

master and slave is as follows: (“master” AND

“slave”) AND (FIRST_PDATE:[2000-01-01 TO

2020-12-31]) NOT (SRC:PPR).

The query used to search articles with master

TF(s) or master regulator(s) is as follows: ("mas-

ter TFs" OR "Master transcription factor" OR

"master regulator" OR "master TF") AND

(FIRST_PDATE:[2000-01-01 TO 2020-12-31])

NOT (SRC:PPR).

All the figures were created using ggplot2

v3.3.2 Wickham, 2016 with R v3.6.1. The figures

can be reproduced using the available code in

the code and data availability

section (Wickham, 2016).

Code and data availability

The source code and data used to generate fig-

ures are available on GitHub (https://github.

com/asntech/inclusive-science) and also on Zen-

odo (Khan, 2021).

Acknowledgements

The author thanks Drs. Roza Berhanu Lemma,

Sarvenaz Sarabipour, Anthony Mathelier and

Jaime Abraham Castro-Mondragon for their use-

ful comments and suggestions.

Aziz Khan is a computational biologist in the Stanford

Cancer Institute, School of Medicine, Stanford

University, Stanford, California, United States. He is an

ambassador for ASAPbio and eLife ambassador for

2018-2020. He often tweets about research practices,

preprints, reproducibility in research and EDI in

science from @khanaziz84

azizk@stanford.edu

https://orcid.org/0000-0002-6459-6224

Author contributions: Aziz Khan, Conceptualization,

Data curation, Formal analysis, Visualization, Writing -

original draft, Writing - review and editing

Competing interests: The author declares that no

competing interests exist.

Received 10 December 2020

Accepted 28 January 2021

Published 08 February 2021

Funding

No external funding was received for this work.

Additional files
Supplementary files
. Transparent reporting form

Data availability
The source code and data used to generate figures are
available on GitHub (https://github.com/asntech/inclu-
sive-science; copy archived at https://github.com/asn-
tech/inclusive-science/releases/tag/v1.1) and also on
Zenodo (https://doi.org/10.5281/zenodo.4458453).

The following dataset was generated:

Author(s) Year Dataset
URL

Database and
Identifier

Aziz K 2021 http://doi.org/
10.5281/zeno-
do.4458453

Zenodo, 10.
5281/zenodo.
4458453

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