ARTICLE - IA de Investigación especializada en el MIT

ARTÍCULO

Dimensions: Bringing down barriers between
scientometricians and data

Christian Herzog

, Daniel Hook

, and Stacy Konkiel

Digital Science

un acceso abierto

diario

Palabras clave: bibliometría, cienciometría, Dimensions, indicator development

Citación: Herzog, C., Hook, D., &
Konkiel, S. (2020). Dimensions:
Bringing down barriers between
scientometricians and data.
Estudios de ciencias cuantitativas, 1(1),
387–395. https://doi.org/10.1162/
qss_a_00020

DOI:
https://doi.org/10.1162/qss_a_00020

Recibió: 21 Junio 2019
Aceptado: 05 December 2019

Autor correspondiente:
Stacy Konkiel
s.konkiel@digital-science.com

Handling Editors:
Ludo Waltman and Vincent Larivière

Derechos de autor: © 2020 Christian Herzog,
Daniel Hook, and Stacy Konkiel.
Publicado bajo Creative Commons
Atribución 4.0 Internacional (CC POR 4.0)
licencia.

La prensa del MIT

ABSTRACTO

Until recently, comprehensive scientometrics data has been made available only in siloed,
subscription-based tools that are inaccessible to researchers who lack institutional support and
resources. As a result of limited data access, research evaluation practices have focused upon
basic indicators that only take publications and their citation rates into account. This has
blocked innovation on many fronts. Dimensions is a database that links and contextualizes
different research information objects. It brings together data describing and linking awarded
subsidios, clinical trials, patents, and policy documents, as well as altmetric information,
alongside traditional publications and citations data. This article describes the approach that
Digital Science is taking to support the scientometric community, together with the various
Dimensions tools available to researchers who wish to use Dimensions data in their research
at no cost.

1. AN INTRODUCTION TO DIMENSIONS DATA

Nosotros (Digital Science) are honored to contribute to this special issue of Quantitative Science
Studies by summarizing what Dimensions can offer to the scientometrics research community.
We would like to state upfront and as clearly as possible:

Dimensions is available at no cost for scientometric research purposes.

To register for no-cost access, simply fill out the form at https://dimensions.ai/data_access.

For those readers with a little more time, we would also like to provide a conceptual and
technical introduction to Dimensions in a bit more detail, answering questions such as the
following: What is Dimensions? Why did we develop Dimensions? What sets Dimensions
apart from other similar databases? How does Digital Science see collaboration with the scien-
tometrics research community evolving today and in the future?

Dimensions is a database of linked information that describes the research life cycle more
completely than any similar system to date. It encompasses awarded grants, publications and
their citations, clinical trials, patents, and policy papers.

Dimensions was intentionally built to help to contextualize the research, discovery, y
evaluation environments (Hook, Portero, & Herzog, 2018). One of several motivations for
Digital Science to build Dimensions was to widen the reach of research evaluators beyond
publication citation analysis (Cifra 1). Al mismo tiempo, we wanted to offer the community
the capacity to perform bibliometric analyses that evolve both academic discussions and

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

Cifra 1. An illustration of the scholarly communication life cycle, from the moment that research is funded to its downstream impacts upon
patents and public policy.

practical impacts beyond the limitations of databases that focused solely on a subset of the
researcher workflow.

En el momento de escribir, the Dimensions database included over 105 million publications and
their citations, along with content types that illustrate the larger information life cycle: de
funding of an idea (via grants data for 5 million funded projects), to the eventual publications
that result from such support, to the impact of the publications (illustrated through the 1.1 billion
citations to 100 million research outputs and altmetrics for 11 million research outputs, respetar-
activamente), to the artifacts of the real-world application of research (más que 497,000 clinical trials,
39 million patents, y 434,000 policy documents). Tomados juntos, these data points can paint
a richer, fuller picture of research than was previously available (Cifra 2).

Dimensions sources data from a number of organizations. Indices such as Crossref and
PubMed Central serve as a “backbone” for publication data. To this backbone is grafted data de-
rived from full-text access to more than 75 million of the 105 million books and articles. Estos
full-text publications are mined to enhance metadata, citas, funding acknowledgements, y

Cifra 2. An illustration of the linkages between altmetric data, citas, publicaciones, subsidios, clinical trials, patents, and public policy
documents in Dimensions.

Estudios de ciencias cuantitativas

388

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

so on. Además, initiatives and resources, such as OpenCitations1 and I4OC2, clinical trial reg-
istries, funding agencies, and openly available public policy data, supplement the richness of the
Dimensions database. Finalmente, Digital Science companies, such as Altmetric and IFI Claims, contrib-
ute further data to create the Dimensions database. We have linked and “harmonized” millions of
records, using open standards such as GRID3 and ORCID,4 to make the data as interoperable as
posible. A fuller description of the sources and process is given in Hook, Portero, and Herzog (2018).

The Dimensions data can be accessed in a number of ways: from online interfaces that offer
rich, contextual search and data visualization (Dimensions, Dimensions Plus, and Dimensions
Analytics), to powerful APIs that allow users to search and aggregate programmatically across
the entire Dimensions database with precision and retrieve indicators for millions of publica-
ciones, subsidios, etc.. (Dimensions API and Dimensions Metrics API), to bulk data access that al-
lows “power users” and research teams to perform high-powered analyses across the entire
Dimensions database, at scale.

As Dimensions is a relatively young database, there are some known caveats to its data and
what can be done with it. Dimensions does not yet index the entirety of the research eco-
system and may never do so. Sin embargo, our aim is to provide researchers and others in the
ecosystem with a dependable, identifier-driven, verifiable index of objects that are important
to them in support of the widest variety of use cases.

We have recently added preprints as a content type in Dimensions and are still working to
ensure that the methodology that we use to integrate these records into the core data set makes
sense for all the relevant use cases, from personal (p.ej., creating CVs), to institutional (p.ej.,
collaboration benchmarking), to publisher (p.ej., assessing feasibility of new journals), and bib-
liometric or scientometrics (p.ej., calculation of global citation benchmarks).

In the normal course of running, we add new grant and publication data every few months, y
have plans to add data from new patent jurisdictions and policy makers over time. Además, nosotros
continue to work with publishers to improve the coverage and quality of their data in Dimensions.

Over time, we hope that Dimensions will become a data set that truly represents research
across regions and disciplines. Con ese fin, we continue to increase our coverage of non-
English language content, as well as working to include more outputs that represent humanities
and social sciences research. We are also working toward integrating more monograph content,
which we know to be so much more important than journal articles in many disciplines.

Dimensions data can only be as “open” as the data sources that we draw upon allow it to
ser: We work closely with our content providers to allow data to be used in as many contexts
as possible. Sin embargo, much of the data comes with some restrictions, and this can have an
effect upon data sharing. Por ejemplo, applications such as open source analytics dashboards
or archiving of significant segments of Dimensions data in open data repositories for sciento-
metrics studies are use cases that we cannot currently support without discussion. We consider
each request carefully and work with all parties involved (the user, the data provider, and our
product and sales teams) to come to an agreeable solution.

Dimensions data is constantly improving: Since Dimensions launched in 2018, tenemos
received valuable feedback from the scientometrics community on the quality of the
Dimensions data, including our field classification data (Bornmann, 2018; Orduña-Malea &

1 https://opencitations.net/
2 https://i4oc.org/
3 https://www.grid.ac
4 https://orcid.org/

Estudios de ciencias cuantitativas

389

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

Delgado-López-Cózar, 2018). This has helped us to improve Dimensions, and we welcome
further constructive feedback from the community to this effect.

Above all else, we aim for Dimensions to be an innovative and comprehensive data source

and a responsive and thoughtful member of the research community.

2. THE PHILOSOPHY DRIVING DIMENSIONS

The Digital Science team developed Dimensions with three major questions in mind:

1. How can we make publication and citation data more accessible, both broadly for the
benefit of the academic community and specifically for the benefit of researchers who
focus on research on research?

2. How can we move beyond simply lamenting the incorrect use of prevailing research
impact metrics (p.ej., Journal Impact Factor) to instead provide a broader, richer, y
more connected view of the research life cycle and all its impacts?

3. How can we ensure a “division of powers” in indicator and metric development, por
offering, for free, aggregated and curated data to the research community to develop
new indicators, rather than developing proprietary metrics?

To achieve our first goal, we decided to do two things: primero, to make a version of Dimensions5
that focuses on publication and citation data freely available for personal use, specifically with-
out the need to register; y segundo, to make access to the entirety of Dimensions data available
at no cost for scientometric research purposes, in a wide variety of formats, to enable large-scale
analysis on scholarly communication trends.

To support our goal of providing a broader, richer view of the research life cycle and all its
impacts, we have invested (and are investing still) in a diverse set of efforts to broaden the
scope of Dimensions beyond existing bibliometric data. Por ejemplo, we continue to work
to integrate more grants, patents, clinical trials, and policy documents, not only aggregating
millions of previously siloed records but also creating links between these records based on
increasing occurrence of persistent identifiers, as well as AI-based techniques, and by mining
relationships referred to in full text. This makes Dimensions an increasingly interesting data set
that is ripe for use in scientometric analysis and gaining evaluative insight. Exploration of the
deep and multifaceted linkages already provides a colorful view of the research process and
the results that it produces (Bode et al., 2018). Además, we have decided to take an “inclusive”
approach to the publications we index in Dimensions. We believe that Dimensions should be a
comprehensive data source, not a judgment call, and so we index as broad a swath of content as
possible and have developed a number of features (p.ej., the Dimensions API, journal list filters
that limit search results to journals that appear in sources such as Pubmed or the 2015 Australian
ERA6 journal list) that allow users to filter and select the data that is most relevant to their specific
necesidades.

To support the third goal that we have highlighted here, we have involved scientometri-
cians in the development of Dimensions since the very beginning, as part of a larger group
de 100+ development partners that also include research organizations and funders.

When we saw the first winds of change in the community regarding the perception of re-
search evaluation, and the rethinking of incentives brought about by DORA and the Open

5 https://app.dimensions.ai
6 https://www.arc.gov.au/excellence-research-australia

Estudios de ciencias cuantitativas

390

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

Data movement, we knew that Dimensions should not be about rankings or the creation of
new metrics. We believe that it is the role of Digital Science as a data provider to continually
add relevant data sets to Dimensions and to enhance those data by establishing more connec-
tions between the different types of research objects in Dimensions. We felt strongly that these
data should be provided at no cost to the scientometrics research community. We also believe
that we should not develop indicators, because to do so would stop us from being a neutral
party when it comes to supporting the indicators of others. Indicators, in our opinion, debería
be developed and owned by the research community to ensure a multifaceted, rich, abierto, y
neutral perspective on the research process and its results.

In June 2018, we launched a year-long pilot phase to provide no-cost Dimensions access
for scientometrics research. This pilot allowed us to test our approach, streamline the applica-
tion and contract process, and optimize our internal review and approval workflows to pro-
vide quick, fácil, no-cost access to Dimensions data. It also allowed us to launch a
Scientometrics User Group for researchers, to provide resources and support to those using
los datos. As of September 2019, we have granted no-cost access to more than 100 investigadores
and research teams and have a growing community of more than 200 User Group members.
There are still many more steps to be taken in support of our goal, one being this communi-
cation in Quantitative Science Studies to reach a broader audience with our offer of no-cost
data access for scientometricians.

We are now entering the next chapter of the Dimensions Scientometrics User Group by
announcing a partnership with the International Society for Scientometrics and Informetrics
(ISSI). ISSI members pursuing personal, noncommercial scientometrics research projects will
have the ability to receive no-cost Dimensions access as a benefit of their ISSI membership. En
doblar, Dimensions will work with ISSI to scale and broaden access to the data to a much greater
number of scientometricians worldwide.

3. USING DIMENSIONS DATA FOR LARGE-SCALE, REPRODUCIBLE ANALYSES

We created Dimensions to allow for the kinds of large-scale analyses that scientometrics re-
searchers and research analysts typically pursue. De hecho, we developed the Dimensions API
specifically to meet this use case—the API not only allows data retrieval but also provides a
business logic layer7 that allows researchers to truly “work” with the data at scale, such as by
developing composite indicators.

Digital Science also works toward supporting reproducible scientometrics research
(Herzog, Hook, & Adie, 2018), which is why all Dimensions no-cost agreements allow for
scientometrics researchers to retain a copy of their data to reproduce their analyses.
Sin embargo, although we would also like to fully support open data by allowing researchers
to archive their scientometric data in open repositories such as Figshare, we are ourselves re-
stricted by the same legal constraints that make bulk data access relatively rare.

4. REQUIREMENTS FOR FREE DIMENSIONS DATA ACCESS

As part of our commitment to openness and accessibility, we made the Dimensions publica-
tions core search freely available to everyone8. The functionality in the free Dimensions prod-
uct has been described as at least comparable with subscription offerings, in terms of the scope

7 We developed a simple, custom language for working with the Dimensions API, called Dimensions Search
Idioma, which has a number of business logic functions embedded. To learn more, visit our API docu-
mentation https://docs.dimensions.ai/dsl/index.html.

8 Ver https://app.dimensions.ai to use the free version of Dimensions

Estudios de ciencias cuantitativas

391

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

of publications indexed, keyword search functionalities, and contextual information provided
(Harzing, 2019; Thelwall, 2018; Visser, van Eck, & waltman, 2019). Además, Dimensions
includes

(cid:129) Full-text search capabilities for more than 72 million of the 105 million publications

records in the system (and title and abstract search for all articles);

(cid:129) Search results that include detailed author affiliation data and lists of citing articles; y
(cid:129) Supporting grant information, altmetric data, citing patents, clinical trials, and policy

documents as well as links to ancillary data.

En general, the free version of Dimensions offers the full context of all indexed publications in
one place. The full version of Dimensions differs from the free version in the number of anal-
yses that a user can perform, access to the API, and ability to perform searches across our
clinical trial, subsidios, patents, and public policy indices. Users who need access to large-
scale publications data or patent, subsidios, and other data for noncommercial analysis can
apply for no-cost access to the full version of Dimensions.

Given that no-cost Dimensions data access for scientometric research purposes is part of
our overall philosophy, we have developed a quick and lean process that allows us to quickly
provide access to Dimensions data. The only condition for approval is that the data and tools
are only to be used for the agreed upon purpose.

Any researcher can apply for no-cost Dimensions access for use of Dimensions data in their
personal, noncommercial scientometrics research projects. Depending on researchers’ needs,
they can receive access to the Dimensions Analytics database, the Dimensions Analytics API,
the Dimensions Metrics API, or a combination of the three tools.

Applied scientometric use cases (p.ej., consulting, institutional analysis, and reporting) are not
eligible for no-cost bulk data access, because to support a fair and sustainable Dimensions busi-
ness model requires that organizations support our efforts to maintain and operate the platform
with their license contribution for applied or commercial use.

Research groups conducting high-volume analyses may request access to Dimensions data

in bulk; these requests are considered on a case-by-case basis.

Making Dimensions data available for noncommercial scientometrics research is aligned
and engrained in the values that drive Digital Science overall, chief of which are community-
mindedness and supporting open research. We intend to offer no-cost data access in perpetuity,
barring restrictions from copyright holders that might impede our ability to allow no-cost
access in the future. We want to make very clear we see making the data available in such
a way as one of the core missions of Digital Science, even though we are organized as a
commercial group, because Digital Science was founded upon and is driven by open re-
search values.

5. WHO IS CURRENTLY USING DIMENSIONS DATA IN SCIENTOMETRICS RESEARCH?

Hasta la fecha, Dimensions has been contacted by scientometricians from diverse backgrounds who
are studying a variety of research topics. These topics run the gamut: from how research funding
affects publication rates in the developing world, to how diversity of researcher background can
influence the quality of published research, to how national coauthorship trends can benefit
scholarly impact and online attention for research. Researchers and analysts using Dimensions
data include

Estudios de ciencias cuantitativas

392

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

(cid:129) Michael Head, University of Southampton (Reino Unido), who has used Dimensions data as part
of the Research Investments in Global Health study (Marrón & Head, 2018), an ongoing
academic analysis assessing levels of health-related research funding compared to fac-
tors such as the global and national burdens of disease;

(cid:129) Janne Seppänen, University of Jyväskylä (FI), who is using Dimensions data to calculate
an open, cocitation network citation rate percentile rank similar to the Relative Citation
Ratio (an indicator developed by the U.S. Institutos Nacionales de Salud [NIH] that can be
used to understand the citation performance of an NIH-funded paper compared to its
cocitation network) (Hutchins, Yuan, anderson, & Santangelo, 2016; Seppänen, 2018);
(cid:129) Pablo García-Sánchez and Manuel Jesús Cobo, University of Cadíz (ES), who are using
interlinked publication, author, and citation data to map the impact of Andalusian uni-
versidades (García-Sánchez & Cobo, 2018); y

(cid:129) The teams behind VOSviewer9 and CiteSpace10, two noncommercial scientometric data
visualization software packages that can create informative network graphs and other
visualizations based on Dimensions publication data.

Beyond these examples, there are many more researchers interrogating our rich, linked data
in innovative and creative ways. Although we are happy to support the use of Dimensions data
in any kind of scientometric study, we are especially keen to offer Dimensions data and sup-
port to researchers who are developing and testing research impact indicators.

6. WHAT WE HOPE TO LEARN IN RETURN

Though we do expect to learn a great deal through partnering with the scientometrics com-
munity, it is not only what we want to learn that matters. Our hope is to establish a long-term
open cooperation where we can play our part in supporting a vibrant research community that
is developing increasingly powerful methods to understand and support researchers and re-
buscar. We believe that we can provide access to interesting data that we aggregate and cu-
tasa, and where scientometricians can seize the opportunity to work with these data to provide
robusto, community-owned indicators and tools to better map new frontiers of research.

Hasta la fecha, this cooperation has allowed the mutual sharing of our experiences and advice,
which has already resulted in a number of new scientometrics publications and presentations,
as well as improvements to Dimensions’ data quality and coverage, improvements to our in-
house machine learning techniques, and the release of several community-created, abierto
source libraries for working with the Dimensions API. We are thankful for the feedback and
hints we received so far, working together to improve Dimensions’ data scope and quality. Nosotros
look forward to continuing that journey!

At the end of the day—as Digital Science is made up of a diverse team of former scientists,
librarians, publishers, hackers, and tinkerers—we are mostly just excited to see how the com-
munity uses Dimensions data to drive new discoveries and insights around scholarly commu-
nication that we could not have imagined ourselves.

7. FREE DATA ACCESS AND THE DIMENSIONS SCIENTOMETRICS USER GROUP

If you are interested in free Dimensions data access for your scientometric research projects,
you can simply fill out the form at https://dimensions.ai/data_access.

9 https://www.vosviewer.com/
10 http://cluster.cis.drexel.edu/~cchen/citespace/

Estudios de ciencias cuantitativas

393

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

In return, we ask that researchers commit to

(cid:129) Acknowledging their use of Dimensions data in all related publications, presentations,

posters, and software;

(cid:129) Sharing their Dimensions-related research with us, so we can share it with others in the

comunidad;

(cid:129) Reaching out to collaborate, allowing us to add context to Dimensions-related research

findings; y

(cid:129) Helping us improve Dimensions by suggesting enhancements and pointing out data is-
sues and inconsistencies—this feedback improves Dimensions data for all who use it.

When you fill out the no-cost data access application form, we will then follow up with
more information about data access terms and conditions (basically, how you can and cannot
use and share Dimensions data; we need to explain this to keep our legal department happy),
which you will need to accept to receive a Dimensions account. Once we have your agree-
ment on file, our team will review your application and, if you qualify for access, get you set
up with the Dimensions credentials you need to begin your research.

We invite members of the community to join the Dimensions Scientometrics User Group11.
Members can receive support for their Dimensions-related research projects provided via
email, office hours with Dimensions data scientists, and in-person meetings at conferences.
We host regular community calls and events that offer networking and collaboration oppor-
tunities with Digital Science’s experts and the discipline’s top researchers.

8. SUMMARY

If research evaluation is to move forward, then the scientometric research that underpins re-
search evaluation needs also to move forward. We believe that the task of data providers such
as Dimensions should be to lower the barriers that make it difficult for scientometricians to find
and work with data at scale. Since our launch, the Dimensions team has focused on making
such data easier to access and analyze, through providing integrated, easy-to-use tools; offer-
ing no-cost access for researchers who need it; and making billions of linked data points avail-
able for analysis in a single database. Going forward, we look forward to working with the
community to find ways to lower barriers to scientometric reproducibility, también.

CONFLICTO DE INTERESES

The authors of this paper are Digital Science employees. Digital Science runs Dimensions, el
database discussed in this article.

REFERENCIAS

Bode, C., Herzog, C., Hook, D., & McGrath, R. (2018). A guide to
the Dimensions data approach. https://doi.org/10.6084/m9.
figshare.5783094.v7

Bornmann, l. (2018). Field classification of publications in Dimen-
siones: A first case study testing its reliability and validity. Sciento-
métrica, 117(637). https://doi.org/10.1007/s11192-018-2855-y
Marrón, R. J., & Head, METRO. GRAMO. (2018). Sizing up pneumonia research.

https://doi.org/10.6084/m9.figshare.6143060.v1

García-Sánchez P., & Cobo, METRO. j. (2018). Measuring the impact of
the international relationships of the Andalusian universities
using Dimensions database. En H. Yin, D. Camacho, PAG. Novais,
& A. Tallón-Ballesteros (Editores.), Intelligent Data Engineering and
Automated Learning—IDEAL 2018 (páginas. 138–144). Suiza:
Springer Nature. https://doi.org/10.1007/978-3-030-03496-2_16
Harzing, A. (2019). Two new kids on the block: How do Crossref
and Dimensions compare with Google Scholar, Microsoft

11 To join the Dimensions Scientometrics User Group, fill out the form at https://ds.digital-science.com/

DimensionsSUG.

Estudios de ciencias cuantitativas

394

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
/

1
1
3
8
7
1
7
6
0
8
7
6
q
s
s
_
a
_
0
0
0
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

Dimensions

Académico, Scopus and the Web of Science? cienciometria,
120(1). https://doi.org/10.1007/s11192-019-03114-y

Herzog, C., Hook, D. w., & Adie, mi. (2018). Reproducibility or pro-
ducibility? Metrics and their masters. Paper presented at STI 2018,
Leiden, Los países bajos. Retrieved from https://openaccess.
leidenuniv.nl/handle/1887/65257

Hook, D. w., Portero, S. J., & Herzog, C. (2018). Dimensions: Edificio
context for search and evaluation. Frontiers in Research Metrics
and Analytics, 3(23). https://doi.org/10.3389/frma.2018.00023
Hutchins, B. I., Yuan, X., anderson, j. METRO., Santangelo, GRAMO. METRO. (2016).
Relative citation ratio (RCR): A new metric that uses citation rates
to measure influence at the article level. PLOS Biology, 14(9),
e1002541. https://doi.org/10.1371/journal.pbio.1002541

Orduña-Malea, MI., & Delgado-López-Cózar, mi. (2018). Dimen-
siones: Re-discovering the ecosystem of scientific information.

El Profesional de la Información (The Information Professional).
27(2), 420–431. https://doi.org/10.3145/epi.2018.mar.21

Seppänen, j. t. (2018). Conservation Biology is not a single field
of science: How to judge citation impact properly. Paper
presented at 5th European Congress of Conservation Biology,
Jyväskylä, Finland. https://doi.org/10.17011/conference/
eccb2018/108222

Thelwall, METRO. (2018). Dimensions: A competitor to Scopus and the
Web of Science? Journal of Informetrics. 12(2), 430–435. https://
doi.org/10.1016/j.joi.2018.03.006

Visser, METRO., van Eck, norte. J., & waltman, l. (2019). Large-Scale com-
parison of bibliographic data sources: Web of Science, Scopus,
Dimensions, and Crossref. Paper presented at 17th International
Conference on Scientometrics and Informetrics (ISSI 2019),
Roma, Italia.