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ARTÍCULO DE INVESTIGACIÓN

Researchers and their data: A study based on the
use of the word data in scholarly articles

un acceso abierto

diario

Frédérique Bordignon1,2

and Marion Maisonobe3

1Ecole des Ponts, Marne-la-Vallée, Francia
2LISIS, INRAE, Univ Gustave Eiffel, CNRS, Marne-la-Vallée, Francia
3Laboratoire Géographie-cités, CNRS, Université Paris 1, Université Paris Cité, EHESS, Aubervilliers, Francia

Palabras clave: corpus-based study, datos, research article, science studies, cienciometría, textometrics

ABSTRACTO

Data is one of the most used terms in scientific vocabulary. This article focuses on the
relationship between data and research by analyzing the contexts of occurrence of the word
data in a corpus of 72,471 research articles (1980–2012) from two distinct fields (Social
sciences, Physical sciences). The aim is to shed light on the issues raised by research on data,
namely the difficulty of defining what is considered as data, the transformations that data
undergo during the research process, and how they gain value for researchers who hold them.
Relying on the distribution of occurrences throughout the texts and over time, it demonstrates
that the word data mostly occurs at the beginning and end of research articles. Adjectives and
verbs accompanying the noun data turn out to be even more important than data itself in
specifying data. The increase in the use of possessive pronouns at the end of the articles
reveals that authors tend to claim ownership of their data at the very end of the research
proceso. Our research demonstrates that even if data-handling operations are increasingly
frecuente, they are still described with imprecise verbs that do not reflect the complexity of
these transformations.

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INTRODUCCIÓN

While data are an essential component of the scientific method, it is commonly accepted that
they play a growing role in our contemporary societies. This evolution is related to a change of
scale in our access and means to process data (Chen & zhang, 2014). As this change of scale
also concerns research data, one may wonder if it has an effect on the way researchers use and
refer to data. Given that some consider the changes under way as capable of transforming how
research is done across disciplines, the investigation we provide is timely. The aim of this
paper is indeed to contribute to the science studies that focus on what Borgman (2015) calls
“data scholarship,” that is, the complex arrangement of the relationship between data and
investigación. Para tal fin, we propose to explore the traces of this relationship in scientific articles.

Most scientometrics studies dealing with the advent of the “Big Data era” document the
development of research areas specifically related to data handling (Huang, Schuehle et al.,
2015; Maisonobe, 2022), but no comprehensive, longitudinal and transversal study has been
performed on the content of scholarly articles’ full text to monitor the way academic scholars
refer to data. Sin embargo, Coxhead (2000) has evidenced that the word data is among the most
used terms of academic vocabulary, as did Hyland and Tse (2007) who also insisted on field-
to-field variations.

Citación: Bordignon, F., & Maisonobe,
METRO. (2022). Researchers and their data:
A study based on the use of the word
data in scholarly articles. Quantitative
Science Studies, 3(4), 1156–1178.
https://doi.org/10.1162/qss_a_00220

DOI:
https://doi.org/10.1162/qss_a_00220

Revisión por pares:
https://publons.com/publon/10.1162
/qss_a_00220

Recibió: 10 Puede 2022
Aceptado: 2 Octubre 2022

Autor correspondiente:
Frédérique Bordignon
frederique.bordignon@enpc.fr

Editor de manejo:
Juego Waltman

Derechos de autor: © 2022 Frédérique
Bordignon and Marion Maisonobe.
Publicado bajo Creative Commons
Atribución 4.0 Internacional (CC POR 4.0)
licencia.

La prensa del MIT

Researchers and their data

According to Rosenberg (2013, 2018), the word data progressively gained popularity
among scholars during the 19th century and throughout the 20th century, when it started to
be more often used with the meaning it is attributed today. A quick glance at the Web of Sci-
ence Core Collection suggests the continuation of this positive dynamic. Between 1991 y
2019 the share of article abstracts containing the word data increased from 13.6% a 18.6%. En
2020, this share exceeds 20%, which means that the word data is now present in the abstracts
of one out of five academic papers1.

The widespread use of the word and its frequent appearance in the abstracts of scientific
artículos, sin embargo, give us only limited insight into its use by researchers within their articles
and over time. Además, as many analysts have pointed out in reaction to the sometimes
excessive enthusiasm generated by the Big Data movement (chico & Crawford, 2012; Tenopir,
2016), the term data is so polysemous that it seems difficult to understand it independently of
su contexto. En particular, most specialists share the idea that data does not have a stable mean-
En g, its significance and utility varying throughout the research process (Hilgartner & Brandt-
Rauf, 1994; Latour & Woolgar, 1986; Leonelli, 2020; Longino, 2020).

To account for this diversity of use and meaning, and to better understand how data are mobi-
lized by researchers, we offer to investigate how the rhetorical function of data emerges in aca-
demic writing by analyzing in which contexts data occurs in scientific articles, which are among
the written outputs that researchers use to formulate their argument and reasoning. To carry out this
investigación, we use a computational method of discourse analysis, starting from the pivot term
data and looking for different lexicogrammatical realizations in our corpus of full texts. We under-
take the textometric analysis of a corpus of 72,471 scientific articles with 896,159 occurrences of
the word data retrieved from scholarly articles available in the ISTEX database (Excellence Initiative
for Scientific and Technical Information; see below for details). We consider the evolution of the
use of the term and its context of use (its neighborhood) over the course of the articles and over a
30-year span (1980s to 2010s). To echo the work revealing the differences in practices between
disciplinary fields, we provide a comparative study between physical sciences and social sciences.

Our main research question is therefore the following: What does the linguistic environment of
the word data tell us about the very nature of data in two different disciplinary fields, and over a 30-
year period? This study is indeed a corpus-based lexical study that aims to shed light on the differ-
ent issues raised by research on data, namely the difficulty of defining what is considered as data,
the transformations that data undergo during the research process, and how they gain value for the
researchers who hold them. After the state of research, the article presents the data and methods
used to carry out this study and the results obtained by focusing on the occurrence of data through-
out the article, and in different contexts of use, including the combination with adjectives, verbos,
and possessive pronouns. In addition to a tailored categorization of the types of adjectives and
verbs pertaining to the word data, our research brings out interesting stylized facts about the evo-
lution of the use of the term across articles and interesting distinctions between scientific domains.

2. BACKGROUND

2.1. Drawing on Scientific Articles

Each research field develops its own inscriptions (Latour, 1999) to record, describe, y
represent what it considers to be data. Some of those traces can be found in scientific

1 Analysis performed on January 5, 2022, on the online version of the Web of Science Core Collection, con un
restriction to articles containing an abstract written in English (all the abstracts with at least one occurrence
of the word the are considered in this analysis).

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Researchers and their data

publicaciones, and in particular in the articles that we intend to analyze here. While the articles
are of course not a faithful account of what happened in the laboratory, according to Knorr and
Knorr (1978), they provide the selected measurement inscriptions of the laboratory with
“contexture.”

In discourse epistemetrics, a specialty at the interface between quantitative science studies
and applied linguistics (Demarest & Sugimoto, 2015), it is agreed that the style, composition
units, rhetorical moves, and registers found in peer-reviewed research articles demonstrate reg-
ularities and specificities allowing us to distinguish them from other kinds of documents and to
submit them to specific bibliometric and textometric analyses. También, from a diachronic linguis-
tic observation, it is possible to derive a sociohistorical interpretation, in line with those who
consider that the linguistic study of scientific discourses can inform the research process itself
(Bazerman, 1981; Mullins, Snizek, & Oehler, 1988). We follow the footsteps of works that take
the scholarly article as an object and as a reflection of research practices, social context, y
“discourse community” (Swales, 1988), in which researchers are involved.

By studying the use of the word data in research articles, we assume that it can inform us

about the way researchers use and value data.

2.2. The Polysemy of the Term Data

The heterogeneity of the objects falling under the term data has been highlighted by many
studies on the topic, which then hardly succeed in providing a consensual and accurate def-
inition of what this term covers (Borgman, 2015; Zins, 2007). Others have focused on the word
data itself. Rosenberg (2013) relies on datum, the Latin etymology of the word data, cual
refers to something given in an argument, something taken for granted. According to him
the term data is thus used to refer to claims accepted for the sake of argument and has no
intrinsic formal characteristics. Rosenberg concludes that the use of the word data provides
a rhetorical basis.

In sociology of science, the use of the word data has been criticized when it refers to
objects that are rather constructed (Latour, 1999; Terrier, 2011). Latour (1999) suggests stop-
ping speaking of data and using sublata instead, meaning achievements, and illustrating the
cumulative nature of knowledge (Perret & Le Deuff, 2019). In the same line of thought, but in a
reflection on digital humanities, Drucker (2011) also suggests reconsidering data and proposes
to speak of capta: “data are capta, taken not given, constructed as an interpretation of the phe-
nomenal world, not inherent in it.” Even if neither capta nor sublata have passed into common
usage, there is a consensus that data are not indeed already there (Borgman, 2015).

Drawing on the fact that adjectives qualify nouns, by their very nature, we propose to
explore the meaning of the word data by taking advantage of the semantic content conveyed
by the adjectives it combines with.

2.3. Data Transformation, Value, and Ownership

Data are the outcome of a number of operations, manipulations, and translations so that they
can be given meaning and direction for action by those who process them (Hilgartner & Brandt-
Rauf, 1994; Martin-Scholz, 2017). Many studies have explored the complexity of these oper-
ations, such as Walford (2013), drawing on an ethnographic study of scientific research carried
out in the Amazon, Denis and Goëta (2017) and Plantin (2019), who have highlighted several
dimensions of the process of cleaning for opening data (and make them suitable for reuse), o
Ruppert and Scheel (2021) who study the “data practices” within the field of official population

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Researchers and their data

Estadísticas. They argue that these numerous operations are the central features of the transactional
process through which data are both obtained for some and given for others, just as Hilgartner
and Brandt-Rauf (1994) propose to use the term data in a broad manner that encompasses both
inputs and outputs. Respectivamente, data should be conceptualized as part of an evolving “data
stream” (Hilgartner & Brandt-Rauf, 1994; Smolczewska Tona, 2021) or “data journey”
(Leonelli, 2020). This is what leads Gitelman (2013) to consider raw data as an oxymoron, datos
being always “cooked” and never entirely “raw”; there are phenomena of the world filtered by
our means of perception (Longino, 2020). Sin embargo, raw data is a common term for data that
has not yet been cleaned; they are indeed awaiting transformation and do not yet have the
status of certified data (Walford, 2013). We will consider that an article constitutes the authors’
narrative of a part of the “data journey,” the one they wished to share with the reader.

It is in this transformation that data become more valuable and ownership is shaped. El
data have become a symbol of “what the researcher seeks and more importantly, needs”
(Markham, 2013).

Whether observations, traces or other entities become data depends on who collects them
y cómo, with different practices in different disciplines (Borgman, 2015; Leonelli, 2020;
Ruppert & Scheel, 2021; Zins, 2007).

This body of literature prompted us to analyze verbs (and the actions they refer to) y para

investigate the use of the possessive pronouns my and our.

Despite contradictory recommendations and still much controversy about whether to use
the plural or the collective singular to refer to data, usage allows both forms (Rosenberg, 2013)
and we opt for the plural, which seems more natural to us as native speakers of French.

3. DATA AND METHODS

3.1. Datos
This study is a use case of the ISTEX database, an archive of scientific publications2. It offers
French researchers online access to a retrospective collection of more than 23 million scien-
tific publications in all disciplines and from many different publishers including Elsevier,
wiley, Saltador, prensa de la Universidad de Oxford, British Medical Journal, IOP Publishing, Naturaleza,
Royal Society of Chemistry, De Gruyter, Ecco Press, Emerald, and Brill. We therefore take
advantage of this very extensive database, which offers a legal framework for text mining
and saves us the tedious task of (manually) downloading full texts from the various publishers’
websites. Finalmente, ISTEX offers technical services to export plain texts in different formats
expected by text-mining tools, and also enriches the metadata provided by publishers with
several discipline classifications (Dazy, 2014).

It seems essential to us that researchers of quantitative science studies take advantage of this
type of database, partly designed for them3, as they can access and process them to answer
their research questions without the advanced skills of computer scientists.

We used the ISTEX API to build our corpus. We simply searched for the word data every-
where in the documents. After trials and visual checks, we chose to filter out documents whose
quality index was under 5 (out of 10; the score is calculated by ISTEX according to checks on

2 https://www.istex.fr.
3 It should be noted that ISTEX is a public resource supported by the main French research organization: el

CNRS (Centre National de la Recherche Scientifique).

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Researchers and their data

OCR quality and the integrity of texts and metadata) to avoid possible textual residues impos-
sible to interpret.

Rather than querying the database as a whole without anticipating possible biases due to
volumetric differences between disciplines, whether as a result of random extraction, irregular
ISTEX updates (es decir., according to new agreements with publishers), or heterogeneity in the
OCR quality across time and editors, we have chosen to create two subcorpora based on
two very broad subject areas which will allow us to make comparisons. We chose Social sci-
ences and Physical sciences because they are unlikely to overlap too much and because they
comprise distinct discourse communities. To distinguish between publications in Social sci-
ences and Physical sciences, we relied on the Scopus classification scheme, which is available
within ISTEX (Elsevier, 2021). We extracted, for each of the two domains under study, a set of
2,500 articles in 2-year slices to ensure that we have a comparable and sufficient number of
articles per period, especially because, to make our results clearer in the charts, we will later
group them by decade. En la práctica, we use the following model query with the random export
option to query the database:

data AND host.genre.raw:(“journal”) AND genre.raw:(“research-article”) AND publication-
Date:[year TO year+1] AND language.raw:(“eng”) AND qualityIndicators.score:[5 TO 10]
AND categories.scopus.raw:“1 – Social sciences”

This query was repeated for each 2-year set since 1980 to extract the publications in Social
sciences and those in Physical sciences (changing: “1 – Social sciences” to “2 – Físico
sciences”). Each time, we exported the corpus files in TXT (for the plain text) and JSON
formats (for the metadata). We then took 2012 as the upper limit, because from 2013 onwards,
the ISTEX database is unbalanced with fewer and fewer articles available in Social sciences.

We therefore have a homogeneous corpus whose text files are of good quality and which
allows us to carry out a comparative analysis over time and within two major scientific fields.

As these files are copyright protected, we cannot make them available, but this methodol-
ogy allows authorized users to access the database and reproduce the operations. In any case,
the data we have extracted from texts are available for download (Bordignon & Maisonobe,
2022).

3.2. Tools

We used the software TXM (Heiden, Magué, & Pincemin, 2010) to index this corpus and, en
particular, to annotate it with TreeTagger. TreeTagger is a tool that processes words from a text
and labels them with a part-of-speech tag. Part-of-speech tagging is commonly used in corpus
linguistics to identify word categories (p.ej., verb, noun, adjective, …) and also grammatical
características (verb tense, plural/singular, …). Por ejemplo, it helps in distinguishing the verb leaves
from the noun leaves thanks to the context of the sequence of words they occur in.

Then TXM is used to query the corpus and retrieve the contexts where data occurs and its
position in the text (es decir., the rank of the sentence in the whole document). The resulting data
can then be exported in CSV and processed in Tableau Software.

3.3. Queries

Queries for TXM must be constructed according to the Corpus Query Language (CQL) based
on the combination of regular expressions and the parts of speech previously tagged. Estos

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Researchers and their data

queries were elaborated through a long iterative process, including quality controls and
numerous tests on our corpus.

For the purposes of this study, we develop queries able to extract adjectives combined with
datos, to extract the verbs and past participles that authors use to express their actions on data to
assess, and the use of the possessive pronouns my and our combined with data.

Given this example:

(…) in Fig. 8, we compare the uncorrected experimental data with the LA 150 datos (…)

and with the following query in TXM

[word=»I|nosotros» & enpos=»PÁGINAS»] []{0,3} [!word=»no|.*n’t|cannot»] [word=»datos»%C] | [word=
«datos»%C][word =»eso»]* [word=»I|nosotros» & enpos=»PÁGINAS»] []{0,3} [!word=»no|.*n’t|cannot»]
[enpos=»V.*»]

we can retrieve the following result

we_PP compare_VVP the_DT uncorrected_JJ experimental_JJ data_NNS

where the verb to compare preceded by the pronoun we can be identified easily thanks to
part-of-speech tagging (where the tag “VVP” stands for “verb, present non-3rd-person” and
thus enables the identification of the verb).

For each occurrence retrieved by these queries, we also recorded the position in the text
with the sentence reference (es decir., rank) provided by TXM; this means that each occurrence
position in the text is between 1 y 100%, el valor 1% representing the beginning of the
text and the value 100% corresponding to the very end of the article. Our aim here was to
resonate with existing studies that consider data as mobile entities and to check whether this is
apparent in the course of the text.

We also built a special query not intended to identify the word data in any context, but to
identify the position of the bibliography section in each document (es decir., the rank of the section
título). This query served to populate a variable that we used to exclude all occurrences of data
that are beyond this limit (es decir., occurring in the titles of publications or sources present in the
articles’ reference section). Our objective here is to prevent the results from being biased by
occurrences of the word data in the full reference of a cited document, con, Por ejemplo, datos
occurring in the title of a cited document.

By removing all publications where the term data appears only in the reference section, nosotros
finally obtained a corpus of 896,159 occurrences of data in the main text, provided by 72,471
research articles (Mesa 1).

Mesa 1.

Corpus overview. Distribution of articles and occurrences of the term data by year and subject area

Subject area
Physical sciences

1980–1990
11,945

1991–2001
12,277

2002–2012
11,889

Todo
36,111

Number of articles

1980–1990
158,678

Number of occurrences of data
2002–2012
137,246

1991–2001
195,177

Todo
491,101

Social sciences

Todo

12,022

23,859

12,700

24,874

12,090

36,812

114,451

153,878

143,995

412 324

23,738

72,471

271,377

346,812

277,970

896,159

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Researchers and their data

4. RESULTADOS

4.1. Data Occurrences Throughout the Article

The following figures present the distribution of occurrences of the word data throughout the
texto, following the examples of Bertin, Atanassova et al. (2016) and Hsiao and Schneider
(2021) while they study the location of in-text citations. De este modo, En figura 1, the x-axis refers
to the progression of the text. The y-axis indicates the share of occurrences related to each
10% interval of the text. The figure shows that it is at the beginning and the end of the article
that the authors use the word data the most, with slightly different curves for Social sciences
and Physical sciences. Sin embargo, we cannot conclude that less data is mobilized in between.
On the contrary, we think that this is indicative of the authors specifying the nature of the data
throughout the text by naming them differently (es decir., without using the word data, but by nam-
ing what can instantiate the word data, such as surveys, measurements, subjects, materiales,
numbers, and photos).

The fact that the word data occurs more often at the beginning and end of articles might
come from the fact that the introduction and conclusion are the sections that contain more

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Cifra 1. Data occurrences throughout the article.

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Cifra 2. Evolution of the distribution of data occurrences throughout the article.

general statements. The end of articles is also often dedicated to discussion. It gives an oppor-
tunity to authors to step back and theorize the results obtained.

Cifra 2 shows that this distribution has not changed much over time, reflecting the regu-

larity of the structure of research articles over the period under study.

4.2. On the Contribution of Adjectives to the Definition of Data

When we see in the literature how difficult it is to define data, and that there is a consensus on
their evolving nature, it becomes clear that it is relevant to study adjectives combined with
data insofar as the role of the adjective is to modify the noun, by bringing further details. Este
is precisely what the authors need to specify the nature of the data they are talking about.

The word data can also be preceded by a noun (adjunct/attributive noun) which specifies its
meaning in the manner of an adjective. But the numerous tests on our corpus lead us to prefer
the analysis of adjectives and past participles. En efecto, the combination of these attributive
nouns and data generates mainly types of data (p.ej., population data, morbidity data, pollen
datos, citation data), leaving aside some of the nuances that the authors wish to specify con-
cerning their data and that only adjectives can convey.

Without engaging in an in-depth linguistic study of the adjective in the English language,
we propose a simple and intuitive classification, with seven semantic classes for adjectives
combined with data. This proposal is the result of our reflection after a thorough examination
of all our extractions and manual tagging of all lexical combinations. It does not derive from
any other classification and is not intended to be applicable beyond co-occurrences with the
word data.

(cid:129) Kinds of data. We borrow the term kind from Morgan (2020), who uses it to refer to what
is produced by different kinds of instruments, to be taken in a broad sense as it can also
refer to strategies developed in the Social sciences. These kinds of data are revealed by

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the properties conveyed by the adjectives. These properties might be general (simulado,
estadístico, isotopic, global, aleatorio, teórico, exploratory, archival, relational, raw, dig-
ital, analytical, synthetic), or specific to science and technique generally speaking
(experimental, observational, empirical, structural, numerical, electronic), or specific to
a particular field (thermodynamic, kinetic, nuclear, molecular, crystallographic, astrome-
tric, photometric, seismic, atómico, meteorological, optical ).

(cid:129) Dimension. These adjectives can be used to define the extent of the data ( pocos, extensive,
pequeño, full, comprehensive), but in a large majority of cases (big, grande, entire), this dimen-
sion applies to a set designated by the names bank, base, colocar, package, por ejemplo.
(cid:129) Spatiotemporal properties. These adjectives provide a temporal information about the
datos (nuevo, actual, preliminary, initial, recent, current, present, existing, primero, final, pri-
mary, secondary, historical ) or specify their spatial extent (local, national, international,
Brazilian, geocoded, western).

(cid:129) Value. The value of data might be subjective or at least results from the researcher’s
análisis. We only include in this category adjectives that convey a value (or lack of
valor) inherently, independently of any context (accurate, correcto, false, falsified, inac-
curate, confiable, sufficient, limitado, original, basic, relevant, alta calidad, noisy, útil ).
(cid:129) Availability. These adjectives provide information on the availability of data (disponible,
observado, adicional, measured, correspondiente, internal, published ), or on the contrary
on the fact that they are missing for the researcher (lossy, incomplete, inaccessible, insuf-
ficient, complete, desaparecido, unpublished ).

(cid:129) Data to data comparison. These adjectives qualify the data by comparing them to each
otro (alternativa, otro (it might be considered as a determiner as well), mismo, conflicto-
En g, diferente) or by placing them in relation to other data of the same group (articulación, era-
ious, mezclado, homogeneous, combined, balanced ). In both cases, these same adjectives
can be used for a comparison with a completely different data set.

(cid:129) Alpha-numerical adjectives. We also make a special class of adjectives that have an
alpha-numeric form. We are unable to qualify them with certainty because it is neces-
sary to have both context and more detailed knowledge of the scientific field in question
(p.ej.: 1o-2 data, 1s data, 4dvar data, 18-winter data)

We manually tagged into one of these seven classes all the adjectives for which we iden-
tified at least 20 occurrences in the whole corpus (es decir., 3,556 adjectives for 228,988 occur-
rences involving the word data) occurring before the bibliography section. The distribution is
detailed in Table 2 and the most frequent adjectives by class and domain are presented in
Cifra 3.

The identification of adjectives and the fact that 3,556 different adjectives are used more
than 20 times in the corpus show that it is by combining data with an adjective that the word
data takes on its meaning. Adjectives provide the semantic precision that the word data alone
lacks, giving rise to the abundant considerations in the literature in an attempt to provide a
definition. To confirm this, we can see that adjectives expressing a kind of data are the most
frequent with more than half of the total number of uses. This proportion is even more impor-
tant in Physical sciences (59.2%, against 44.5% in Social sciences), but this difference is com-
pensated by a more important proportion of adjectives expressing a spatiotemporal property in
Social sciences (20.9% against 11.3% in Physical sciences). En segundo lugar, adjectives related to the
availability of data are used in more than 11% of the occurrences of data. The access to the
data and their intrinsic characteristics thus seems to be more discussed than their size. Enterrar-
estingly, while the issue of “big data” and its use in science is very important, this aspect does

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Mesa 2. Distribution of data across the classes of adjectives it combines with

# occurrences

% occurrences

# occurrences

% occurrences

Físico
sciences
78,745

Social
sciences
43,570

15,101

20,504

14,493

10,351

7,715

4,334

2,324

12,421

9,944

7,213

3,568

714

Físico
sciences
59.2

Social
sciences
44.5

11.3

10.9

7.8

5.8

3.3

1.7

20.9

12.7

10.2

7.4

3.6

0.7

Todo
121,358

35,292

26,659

20,085

14,745

7,822

3,027

Todo
53.0

15.4

11.6

8.8

6.4

3.4

1.3

Kinds of data

Spatiotemporal
propiedades

Availability

Value

Data to data

Dimension

Alpha-numerical

Todo

133,063

97,934

100.0

228,988

100.0

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not emerge clearly in our corpus. Adjectives expressing the dimension of data sets constitute
barely more than 3% of occurrences. Además, the phrase big data is almost absent from our
cuerpo (alrededor 0.001% of occurrences).

Cifra 3 shows the 20 most frequent adjectives of each class by subject area; the percent-

age is calculated on the whole field.

The most frequent adjective in Physical sciences, es, by far, experimental, con 20.9% de todo
occurrences of adjectives in the domain (vs. 2% in Social sciences). The second most frequent
adjective in Physical sciences, which is also the most frequent in Social sciences, is available,
with around 4% of the occurrences in both disciplines.

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The distribution of adjectives qualifying the word data throughout the text generally follows
that of the word data itself (Cifra 4). Sin embargo, it is interesting to note that the adjectives
expressing relations between data have a flatter curve in Physical sciences, denoting the fact
that the authors continuously assemble their data or balance them with other teams’ data to
progress in their demonstration, up to the results and conclusions. A diferencia de, in Social sci-
ences, it is at the beginning and especially at the very end that these relationships are rather
settled. This can be put in relation to the adjectives of availability, the use of which drops off
rapidly at the beginning of the articles in Physical sciences, whereas it rebounds and lasts for at
least a third of the text in Social sciences. Finalmente, it should be noted that the kinds of data are
also expressed during the first third of the text in Social sciences, whereas these details seem to
be dealt with more quickly in Physical sciences.

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Our corpus allows us to generate Figure 5, which shows the evolution of the distribution of

each type of adjective over time in each domain.

Even if we can see some oscillations (which may be due to the very nature of the ISTEX
cuerpo), we cannot say that there is a clear shift over this 30-year period. Sin embargo, if we look
very closely, we can see that the curve for data to data adjectives in Social sciences also tends
to flatten out, with a less sharp decrease in data to data adjectives over the first two-thirds of
the articles. This trend is very subtle indeed and requires further investigation; it may be indic-
ative of a more intensive use of quantitative approaches in Social sciences.

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Cifra 3. Percentage of occurrences of the 20 most frequent adjectives combined with data, by adjective class and subject area (see full list
and reusable version in the available data set (Bordignon & Maisonobe, 2022)).

4.3. Data Transformation Through Verb Usage

As many studies have shown that data undergo transformations, we assume that it is by iden-
tifying verbs whose subjects are the authors of the articles (using we or I ) that we will be able to
have a better picture of how data are used and how transformations are performed. We build a
query for this purpose, taking care to avoid retrieving negative forms that reflect the opposite of

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Cifra 4. Distribution of data throughout the text, represented by the adjective classes it combines with and the subject areas.

what we are looking for. Our query also allows us to identify more complex inverted forms
such as “the data we analyzed.” We also search for past participles, which are somehow half-
way between the adjectives from which they take on the function of qualification (notably after
the verb to be), and the verbs from which they are lexically derived. To process the results
accurately, we homogenize some equivalent forms but whose spellings may differ (p.ej., ana-
lyse and analyze).

As for adjectives qualifying data, we propose a classification of the verbs and past partici-
ples combined with data. We propose three main classes that mirror three phases of the data
scholarship process: assemblage, análisis, and inscription.

(cid:129) Assemblage. Data assemblage verbs and participles are those allowing authors to
explain how they carried out the delineation and curation of their data. We borrow
the term from Law (2004), who presents the concept of method assemblage as “the pro-
cess of enacting or crafting bundles of ramifying relations that condense presence and
(therefore also) generate absence by shaping, mediating and separating these.” Data
assemblage is displayed through verbs related to data collection (collect, gather, acumular-
mulate), to their selection (filter, remove, truncate), and to their arrangement (merge,
aggregate, associate). Some rare verbs within this class express the fact of possessing
these collected data (own, tener, hold ). At this stage, data are inputs and are interrelated,
echoing the data to data adjectives.

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Cifra 5. Evolution of the distribution of data throughout the text, according to the adjective classes it combines with and the subject areas.

(cid:129) Análisis. With data analysis verbs and the use of the passive voice, authors explain the
analysis carried out with the data and the associated methodology. The data are seen
from afar: They are studied (or examined, comparado, interpreted ) or evaluated (o
assessed, checked, verified ) thus reminding us of value adjectives.

(cid:129) Inscription. In this third class, verbs and participles denote inscriptions, in Latourian
terms (Latour, 1999): They refer to “the transformations through which data become
materialized into a sign” (lista, normalize, plot, código, compute). They can also refer to
the presentation of these inscriptions ( present, espectáculo, informe, publish). The data are then
outputs.

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Mesa 3. Distribution of data according to verbs and past participles it combines with in the two subject areas

Assemblage

Number of occurrences

Physical sciences

Social sciences

Past
participles
29,881

Verbos
1,274

Subtotal
31,155

Past
participles
24,553

Verbos
1,998

Subtotal
26,551

Percentage by subject area

95.9

4.1

100

92.5

7.5

100

Todo
57,263

–

Inscription

Number of occurrences

32,665

1,422

34,087

18,842

1,399

20,241

54,004

Percentage by subject area

95.8

4.2

100

93.1

6.9

100

–

Análisis

Number of occurrences

14,886

1,953

16,839

14,915

3,073

17,988

34,561

Percentage by subject area

Others

Number of occurrences

Percentage by subject area

88.4

433

92.3

11.6

7.7

100

469

100

82.9

268

79.5

17.1

20.5

100

337

100

–

799

–

Todo

Number of occurrences

77,865

4,685

82,550

58,578

6,539

65,117

146,627

Percentage by subject area

94.3

5.7

100

90.0

10.0

100

–

We have unified verbs and past participles under their corresponding lemma (p.ej., obtener
and obtained have obtain as lemma). We finally left out the phrases with the term set, porque
the sequence data set (supposed to be equivalent to dataset in one word) was problematic
when tagged in parts-of-speech (set being then often wrongly considered as a participle;
p.ej., “the results were used as a learning data set for a predictor of peptide detectability”).
And eventually, we excluded the different variants of do and make, which do not bring any
information on the action performed on the data itself.

We classified all verbs and participles with more than 20 occurrences (in the whole corpus)
into one of the three categories (es decir., 457 terms for 146,627 occurrences involving the word
datos, occurring before the bibliography section). Past participles account for more than 90% de
the occurrences (Mesa 3), consistent with the fact that the passive voice suggests the
researcher’s actions that the data undergo. The distribution by subject area across classes is
detailed in Table 4 and the most frequent forms associated are presented in Figure 6 by class
and domain.

Mesa 4. Distribution by percentage of occurrences of data across the classes of verbs/past

participles in the two subject areas

Assemblage

Análisis

Inscription

Others

Todo

Physical sciences
37.7

Social sciences
40.8

20.4

41.3

0.6

100.0

27.6

31.1

0.5

100.0

Todo
39.1

23.6

36.8

0.5

100.0

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Cifra 6. Percentage of occurrences of the 20 most frequent verbs/past participles combined with data, by class and subject area (see full list
and reusable version in the available data set (Bordignon & Maisonobe, 2022)).

Cifra 6 displays the 20 most frequent forms of each class by field; the percentage is cal-

culated on the whole field.

The verbs obtain and collect, expressing the task of data collection, are by far the most used
in both subject areas. Next are the verbs use and analyze, as well as present and show (especialmente-
cially in Physical sciences). But these frequently used verbs are lacking specificity. To specify
their actions, the authors then resort to other verbs that are more precise. But still, the class of
assemblage verbs is the smallest, with hardly more than 100 variants. This means that the data
assemblage phase is underdescribed. It is in the class of inscriptions that we find the most
variants, más que 200, which describe the inscriptions generated.

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Cifra 7. Distribution of the occurrences of data throughout the text, represented by the verbs/past participles classes it combines with and
the subject areas.

Cifra 7 reveals an important difference between the two domains: The curves in Social
sciences follow those of the distribution of data in the text, whereas they are different in
Physical sciences. En efecto, we can see that even if the actions of assemblage are more
frequent at the beginning and the end of the text, the curve tends to flatten with occur-
rences that are distributed on the whole article. The homogenization of the distribution is
even more striking for the inscriptions, with a nearly flat curve. This reveals that physical
scientists have a permanent and continuous use of data, generate new inscriptions, y
need to rearrange their data to carry out their research and thus their demonstration in
the text.

This result is consistent with the findings in Figure 4 with a similar distribution for the
data to data adjectives; this means that assemblage operations and inscriptions are recur-
rent throughout the text. Cifra 8 shows that these practices have not changed much over
tiempo.

4.4. Data Ownership and Possessive Pronouns

The literature review revealed that the work around data, from collection to interpretation, es
important and value-creating. Bearing in mind that researchers gain recognition with their
datos, we looked for expressions of ownership of data in texts through possessive pronouns
by isolating the use of our/my data.

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Cifra 8. Evolution of the distribution of data throughout the text, according to the verb/past participles classes it combines with and the
subject areas.

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Encontramos 23,102 occurrences of possessive pronouns used before the word data and
recorded before the bibliography. De estos, 15,481 occurrences are related to the pronouns
my or our, o 67%.

Cifra 9 shows very clearly that the authors appropriate the data throughout the article, como si
after having used them, they finally allow themselves to assert that they are the owners and
that it is with this status that they mention them in the conclusion of their article. This is prob-
ably also a way of “sharing” them (at least the associated results) within the community by
insisting on who owns them, and the value added by the analysis.

Authors anticipate the future credit that readers would have to give them by citing the con-
clusions of their research, and sometimes the data themselves. But at the same time, if my/our
data appears as the subject (typically in “our data suggest …” or “our data show …”), it helps

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Cifra 9. Distribution of my/our data throughout the text in the two subject areas.

authors distance themselves, shift responsibility to the data (as Salager-Meyer (1999) states it)
with a typical turn of phrase where some nonhuman entity such as data actually speaks for
sí mismo. Por lo tanto, they anticipate criticism as they progress in presenting their results. As the
article reaches its conclusion, authors have an interest in gaining confidence and may seek
to emphasize the personal and specific contribution to the literature that the article and its
results represent.

Cifra 10 shows the evolution over the three decades we are studying. Without showing
major changes, it nevertheless reveals that in the most recent period, the occurrences distri-
bution curve in Social sciences increasingly looks like that of Physical sciences with a less
marked growth at the end of the text.

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Cifra 10. Evolution of the distribution of my/our data throughout the text in the two subject areas.

5. DISCUSIÓN

As our literature review at the beginning of this article demonstrates, we set our study in a
theoretical framework that takes scientific writings as research objects and considers that their
linguistic analysis can inform the research process itself, even if only partially. Our approach is
intended to complement sociological or ethnographic studies that point out the difficulty of
defining what data are and which place them in a transformation process. Por lo tanto, by choos-
ing to work on adjectives and verbs, we are able to provide results that confirm both the poly-
semous nature of the word data and the difficulty of defining it. But above all, our study draws
on a large corpus of scientific articles overlapping two disciplinary fields and thus sheds new
light on this polysemy by providing a large number of examples of lexical combinations,
which we present with typologies of adjectives and verbs/past participles to help us under-
stand the nature of data.

Our findings confirm the transformation process but also that the task of assemblage is
poorly rewarded, as shown by previous studies whose scope is wider because they deal with

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data curation, mainly in archives, whereas our focus here is rather on what researchers
decide to mention in the articles they publish. Creemos, sin embargo, that the large number
of words we have extracted and which highlight these transformation processes can contrib-
ute to the works related to the description of data curation and data reuse. En efecto, mientras
many instances of data use that we have identified in the articles may be instances of primary
data use (es decir., researchers describe collecting, manipulating, and analyzing their own data),
researchers may also reuse data created by others that have been made available through
data repositories. The lists of terms we provide help to fuel studies on these topics and to
enrich, with a greater level of detail, the Data Practices and Curation Vocabulary (a shared
vocabulary (chao, Cragin, & Palmer, 2015), limited to 187 terms, and intended for use by
data curators and other persons involved in the curation process (including preservation,
sharing or management)).

In line with the principles of epistemetrics, we have built up a corpus that allows for a com-
parative analysis between fields, a diachronic analysis and an analysis along the text, cual
we suggest reflects the «data journey» agreed upon by previous studies. Despite the increasing
use of the word data in titles and abstracts, our research shows a significant regularity over the
period studied in the way researchers refer to data within scholarly articles. En particular,
authors prefer to use this word at the beginning and the end of articles. This predominance
is consistent with other findings suggesting (Master, 1991) that it is in the opening and con-
cluding sections of research articles that we find phrases involving more abstract and general
conceptos, as data can be.

We have identified a few differences between the two subject areas, and those differences

continue over time:

(cid:129) data and their characteristics are discussed longer at the beginning of Social sciences

artículos;

(cid:129) in Physical sciences only, assemblage operations and inscriptions are recurrent through-

out the text; y

(cid:129) there is more description of the spatiotemporal properties of data and more discussion of

their availability in Social sciences and these features continue over time.

In both subject areas, we observe the same tendency to take more ownership of the data
throughout the narrative, which is illustrated by the more frequent use of the possessives my or
our at the end of the articles, actually reaching the end of the “data journey.” We have seen
that the trend in Social sciences to appropriate data at the very end of the text has changed in
the most recent period, with a redistribution of some of the uses of my/our earlier in the text.
This is a discourse pattern similar to the one we have in Physical sciences. It is difficult to draw
conclusions about a possible change in the behavior of social scientists towards their data, y
it would be worth organizing interviews to clarify this. Our work opens up here an opportunity
for further research.

The verb analysis confirms the seminal research of Knorr and Knorr (1978) on the topic:
The complex and painful task of data acquisition is somewhat absent from articles, even if
assemblage verbs are the most frequent. They refer to assemblage operations but with rather
imprecise verbs, such as obtain and collect. It is in the class of inscriptions that we find the
most variants, meaning authors prefer to focus on their results, to produce their own new
inscriptions and thus new data, and to claim ownership of them at the end of the process.
This is what leads us to think that it would be interesting to do the same analysis on another
kind of scientific output, the data paper, to see if other trends emerge.

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Regarding the limits of this study, we can say that it would have been interesting to investigate
on words belonging to the data family, such as database and dataset. This is something we are
considering for future work, but it made sense to start by investigating the word data to grasp its
polysemy, before studying derived words such as database or dataset. Además, the work of
indexing and tagging terms in such a large corpus is very time-consuming and the many neces-
sary controls are tedious. As for the ISTEX database, it should be noted that it does not cover
recent publications and that its coverage depends on agreements with publishers so that the types
of analyses that can be derived from it are limited. Given the evolution of the coverage, nosotros también
need to be cautious with the interpretations of the longitudinal observations. Sin embargo, por
using a random sampling method, we believe that we have somehow managed to address this
coverage pitfall. Como consecuencia, we found that ISTEX is a resource that can be used if care is taken
to extract a fairly large corpus and to ensure its homogeneity, as we have done. En el otro
mano, getting down to a finer level of detail, such as the subfield, is probably riskier because
the inclusion of a single journal, Por ejemplo, can unbalance the content’s homogeneity.

Because we worked on equal-sized sets of publications for each subperiod studied, nuestro
study makes it difficult to perceive the changes and the enthusiasm of the 2000s for data
and more precisely for big data. As this phrase hardly emerges from our corpus, our analysis
has the advantage of showing that, when it comes to looking at research as a whole, the use of
the word and its context of occurrence have not radically changed over the period under
estudiar. Data scholarship is an intrinsic part of research activity, both in Social sciences and
Physical sciences, and this regularity as well as the frequency of occurrences of the word in
the two subject areas reflect this clearly.

6. CONCLUSIÓN

Para concluir, the results of our corpus-based study demonstrate that there is no point in trying
to define the word data, as researchers do it themselves in the course of their writing, tomando
Firth’s idea to its fullest extent: “You shall know a word by the company it keeps” (Firth, 1957).
The word data serves as a rhetorical base and draws on the context for its meaning, relying on
the properties conveyed by adjectives and verbs associated to it. Adjectives and verbs accom-
panying the noun data turn out to be even more important than data itself in specifying what
data are at stake. And to echo Gitelman (2013), we can say that while data can never be raw,
the word data is, and it only serves as a rhetorical basis, as long as the context and mainly
adjectives have not contributed to achieve its potential with the properties they convey.

CONTRIBUCIONES DE AUTOR

Frédérique Bordignon: Curación de datos, Investigación, Metodología, Validación, Visualización,
Writing—Original draft, Writing—Review & edición. Marion Maisonobe: Conceptualización,
Investigación, Metodología, Validación, Writing—Original draft, Writing—Review & edición.

CONFLICTO DE INTERESES

Los autores no tienen intereses en competencia.

INFORMACIÓN DE FINANCIACIÓN

The research project received no funding by third parties.

DISPONIBILIDAD DE DATOS

The data used in this study are available in a repository (Bordignon & Maisonobe, 2022).

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REFERENCIAS

Bazerman, C. (1981). What written knowledge does: Three
examples of academic discourse. Philosophy of the Social Sciences,
11(3), 361–387. https://doi.org/10.1177/004839318101100305
Bertin, METRO., Atanassova, I., Gingras, y., & Larivière, V. (2016). El
invariant distribution of references in scientific articles. Diario
of the Association for Information Science and Technology,
67(1), 164–177. https://doi.org/10.1002/asi.23367

Bordignon, F., & Maisonobe, METRO. (2022). Data for “Researchers and
datos. A study through the use of the word data in scholarly arti-
cles.” Zenodo. https://doi.org/10.5281/zenodo.5873829

Borgman, C. l. (2015). Big data, little data, no data: Scholarship in
the networked world. Cambridge, MAMÁ: CON prensa. https://doi.org
/10.7551/mitpress/9963.001.0001

chico, D., & Crawford, k. (2012). Critical questions for big data.
Información, Comunicación & Sociedad, 15(5), 662–679. https://
doi.org/10.1080/1369118X.2012.678878

chao, t. C., Cragin, METRO. h., & Palmer, C. l. (2015). Data Practices
and Curation Vocabulary (DPCVocab): An empirically derived
framework of scientific data practices and curatorial processes.
Journal of the Association for Information Science and Technol-
ogia, 66(3), 616–633. https://doi.org/10.1002/asi.23184

Chen, PAG. C. l., & zhang, C.-Y. (2014). Data-intensive applications,
challenges, techniques and technologies: A survey on Big Data.
Information Sciences, 275, 314–347. https://doi.org/10.1016/j.ins
.2014.01.015

Coxhead, A. (2000). A new academic word list. TESOL Quarterly,

34(2), 213–238. https://doi.org/10.2307/3587951

Dazy, A. (2014). ISTEX: A powerful project for scientific and tech-
nical electronic resources archives. Insights, 27(3), 269–273.
https://doi.org/10.1629/2048-7754.157

Demarest, B., & Sugimoto, C. R. (2015). Argue, observe, assess:
Measuring disciplinary identities and differences through
socio-epistemic discourse. Revista de la Asociación de Informa-
ción Ciencia y Tecnología, 66(7), 1374–1387. https://doi.org
/10.1002/asi.23271

Denis, J., & Goëta, S. (2017). Rawification and the careful genera-
tion of open government data. Social Studies of Science, 47(5),
604–629. https://doi.org/10.1177/0306312717712473,
PubMed: 28633611

Drucker, j. (2011). Humanities approaches to graphical display.

Digital Humanities Quarterly, 5(1).

Elsevier. (2021). What are the most used Subject Area categories
and classifications in Scopus?—Scopus: Access and use Support
Center. https://service.elsevier.com/app/answers/detail/a_id
/14882/supporthub/scopus/~/what-are-the-most-frequent-subject
-area-categories-and-classifications-used-in/

Firth, j. R. (1957). A synopsis of linguistic theory, 1930–1955.

Oxford: Basil Blackwell.

Gitelman, l. (Ed.). (2013). ‘Raw data’ is an oxymoron. Cambridge,
MAMÁ: CON prensa. https://doi.org/10.7551/mitpress/9302.001.0001
Heiden, S., Magué, J.-P., & Pincemin, B. (2010). TXM: Une plate-
forme logicielle open-source pour la textométrie—conception et
développement. 10th International Conference on the Statistical
Analysis of Textual Data—JADT 2010, 2(3), 1021–1032.

Hilgartner, S., & Brandt-Rauf, S. I. (1994). Data access, ownership,
and control: Toward empirical studies of access practices.
K n o w l e d g e , 1 5 ( 4 ) , 3 5 5 – 3 7 2 . h t t p s : / / d o i . o rg / 1 0 . 11 7 7
/107554709401500401

Hsiao, T.-K., & Schneider, j. (2021). Continued use of retracted
documentos: Temporal trends in citations and (lack of ) awareness of
retractions shown in citation contexts in biomedicine.

Estudios de ciencias cuantitativas, 2(4), 1144–1169. https://doi.org
/10.1162/qss_a_00155, PubMed: 36186715

Huang, y., Schuehle, J., Portero, A. l., & Youtie, j. (2015). A system-
atic method to create search strategies for emerging technologies
based on the Web of Science: Illustrated for ‘Big Data’. Sciento-
métrica, 105(3), 2005–2022. https://doi.org/10.1007/s11192-015
-1638-y

Hyland, K., & Tse, PAG. (2007). Is there an “academic vocabulary”?
TESOL Quarterly, 41(2), 235–253. https://doi.org/10.1002/j
.1545-7249.2007.tb00058.x

Knorr, k. D., & Knorr, D. W.. (1978). From scenes to scripts: Sobre el
relationships between laboratory research and published paper in
ciencia. Wien: IHS.

Latour, B. (1999). Pandora’s hope: Essays on the reality of science

estudios. Cambridge, MAMÁ: Prensa de la Universidad de Harvard.

Latour, B., & Woolgar, S. (1986). Laboratory life: The construction
of scientific facts. Princeton, Nueva Jersey: Prensa de la Universidad de Princeton.
https://doi.org/10.1515/9781400820412

Law, j. (2004). After method: Mess in social science research.
Londres: Routledge. https://doi.org/10.4324/9780203481141
Leonelli, S. (2020). Learning from data journeys. In S. Leonelli & norte.
Tempini (Editores.), Data journeys in the sciences (páginas. 1–24). cham:
Springer International Publishing. https://doi.org/10.1007/978-3
-030-37177-7_1

Longino, h. mi. (2020). Afterword: Data in transit. In S. Leonelli & norte.
Tempini (Editores.), Data journeys in the sciences (páginas. 391–399).
cham: Springer International Publishing. https://doi.org/10.1007
/978-3-030-37177-7_20

Maisonobe, METRO. (2022). The future of urban models in the Big Data
and AI era: A bibliometric analysis (2000–2019). AI & SOCIETY,
37(1), 177–194. https://doi.org/10.1007/s00146-021-01166-4
Markham, A. norte. (2013). Undermining ‘data’: A critical examination
of a core term in scientific inquiry. First Monday. https://journals
.uic.edu/ojs/index.php/fm/article/view/4868

Martin-Scholz, A. (2017). Communiquer et organiser en échafau-
dant un ‘big data’ manuel. Le cas d’un projet de formation en
aménagement du territoire [Phd, Université de Toulouse, Uni-
versité Toulouse III – Paul Sabatier]. https://thesesups.ups-tlse.fr
/3803/

Master, PAG. (1991). Active verbs with inanimate subjects in scientific
prosa. English for Specific Purposes, 10(1), 15–33. https://doi.org
/10.1016/0889-4906(91)90013-METRO

morgan, METRO. S. (2020). The datum in context: measuring frame-
obras, data series and the journeys of individual datums. En
S. Leonelli & norte. Tempini (Editores.), Data journeys in the sciences
(páginas. 103–120). cham: Springer International Publishing. https://
doi.org/10.1007/978-3-030-37177-7_6

Mullins, NORTE., Snizek, w., & Oehler, k. (1988). The structural analysis
of a scientific paper. In A. F. j. Van Raan (Ed.), Handbook of quan-
titative studies of science and technology (páginas. 81–105). Elsevier.
https://doi.org/10.1016/B978-0-444-70537-2.50008-8

Perret, A., & Le Deuff, oh. (2019). Documentarité et données, instru-
mentation d’un concept. 12ème Colloque International d’ISKO-
Francia: Données et Mégadonnées Ouvertes En SHS: De Nou-
veaux Enjeux Pour l’état et l’organisation Des Connaissances?
Plantin, J.-C. (2019). Data cleaners for pristine datasets: Visibility
and invisibility of data processors in social science. Ciencia,
Tecnología, & Human Values, 44(1), 52–73. https://doi.org/10
.1177/0162243918781268

Rosenberg, D. (2013). Data before the fact. en l. Gitelman (Ed.), ‘Raw
data’ is an oxymoron (páginas. 15–40). Cambridge, MAMÁ: CON prensa.

Estudios de ciencias cuantitativas

1177

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

3
4
1
1
5
6
2
0
7
0
8
1
0
q
s
s
_
a
_
0
0
2
2
0
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Researchers and their data

Rosenberg, D. (2018). Data as word. Historical Studies in the
Natural Sciences, 48(5), 557–567. https://doi.org/10.1525/hsns
.2018.48.5.557

Ruppert, mi. S., & Scheel, S. (2021). Data practices: Making up a

European people. Goldsmiths Press.

Salager-Meyer, F. (1999). From “Mr. Guthrie is profoundly mis-
tomado…” to “Our data do not seem to confirm the results of a pre-
vious study on…": A diachronic study of polemicity in academic
writing (1810–1995). Ibérica, Revista de La Asociación Europea
de Lenguas Para Fines Especóficos, 1, 5–28.

Smolczewska Tona, A. (2021). Enquête sur les représentations
discursives des temporalités de la donnée à l’œuvre dans des
articles scientifiques. In B. Simonnot, mi. Broudoux, & GRAMO. Chartron
(Editores.), Humains et données: Création, médiation, décision,
narration—Actes du colloque ‘Document numérique et société’,
Nancy, 2020.

Swales, j. (1988). Discourse communities, genres and English as an
international language. World Englishes, 7(2), 211–220. https://
doi.org/10.1111/j.1467-971X.1988.tb00232.x

Tenopir, C. (2016). Big data, little data, no data: Scholarship in the
networked world by Christine L. Borgman. Cambridge, MAMÁ: CON
Prensa, 2015. 400 páginas. (ISBN 9780262028561). Journal of the
Association for Information Science and Technology, 67(3),
751–753. https://doi.org/10.1002/asi.23626

Terrier, C. (2011). The value of the geographical data. L’espace géo-
graphique, 40(2), 103–108. https://doi.org/10.3917/eg.402.0103
Walford, A. (2013). Transforming data: An ethnography of scientific data
from the Brazilian Amazon [PhD thesis]. Universidad de Copenhague.
Zins, C. (2007). Conceptual approaches for defining data, informa-
ción, and knowledge. Journal of the American Society for Infor-
mation Science and Technology, 58(4), 479–493. https://doi.org
/10.1002/asi.20508