Data Science 1 (2017) 1–5 1
Data Science – Methods, infrastructure, and
Michel Dumontier aand Tobias Kuhn b
aMaastricht University, Maastricht, The Netherlands
E-mail: firstname.lastname@example.org; ORCID: https://orcid.org/0000-0003-4727-9435
bDepartment of Computer Science, Vrije Universiteit Amsterdam, The Netherlands
E-mail: email@example.com; ORCID: https://orcid.org/0000-0002-1267-0234
1. About Data Science
Science has always been about data. Observational data points have served as the evidence that has al-
lowed us assess, accept, and discard scientiﬁc theories. But in the last decades, scientiﬁc data has grown
dramatically in both size and importance. Data Science is therefore not a new science discipline, but
rather a new pair of glasses – a new paradigm – to look at problems and questions in the existing disci-
plines with the new possibilities of data analytics in mind. It also stands for the development that data,
when properly linked, transcend disciplines and can enable new sorts of interdisciplinary research ﬁelds
and even breed entirely new areas. The focus on data also immediately highlights other important and
urgent issues in contemporary science, namely the reproducibility of results, the responsible treatment of
potentially sensitive data, the transparency and openness of scientiﬁc data and processes, the attribution
and recognition of data gathering and curation efforts, and the now widely accepted requirement that
scientiﬁc data should be Findable, Accessible, Interoperable, and Reusable (FAIR). With this journal,
called Data Science, we intend to give this type of research the focus and attention we think it deserves.
2. The journal
Data Science is an interdisciplinary journal that covers aspects around scientiﬁc data over the whole
range from data creation, mining, discovery, curation, modeling, processing, and management to analy-
sis, prediction, visualization, user interaction, communication, sharing, and re-use. The ultimate goal is
to unleash the power of scientiﬁc data to deepen our understanding of physical, biological, and digital
systems, gain insight into human social and economic behaviour, and design new solutions for the future.
We are interested in general methods and concepts, as well as speciﬁc tools, infrastructures, and applica-
tions. The rising importance of scientiﬁc data, both big and small, brings with it a wealth of challenges
to combine structured, but often siloed data with messy, incomplete, and unstructured data from text,
audio, visual content such as sensor and weblog data. New methods to extract, transport, pool, reﬁne,
store, analyze, and visualize data are needed to unleash their power while simultaneously making tools
This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
2451-8484 © 2017 – IOS Press and the authors.
2M. Dumontier and T. Kuhn / Data Science – Methods, infrastructure, and applications
and workﬂows easier to use by the public at large. The journal invites contributions ranging from theo-
retical and foundational research to platforms, methods, applications, and tools in all areas. We welcome
papers which add a social, geographical, and temporal dimension to Data Science research, as well as
application-oriented papers that prepare and use data in discovery research.
Our journal has a number of features to maximize the transparency, speed, and quality with which
results are published and made available for current and future reuse and interpretation. First of all, Data
Science is an open access journal, which increases the visibility and enables simple access and use of
the reported results. Article processing charges will be waived for the ﬁrst year and charges thereafter
will be reasonable and competitive. We are furthermore committed to minimizing the time it takes to
obtain a decision on a submitted manuscript. For that reason, Data Science gives reviewers only 10 days
to respond and aims for sending out ﬁrst decisions on submissions within weeks rather than months.
In order to increase the visibility and recognition of reviewers and to promote accountability in the
reviewing process, Data Science has opted for reviews to be open and attributable. All reviews are made
freely available under CC-BY licenses after a decision has been made for the submission (independent of
whether the decision was to accept or reject). In addition to solicited reviews, everybody is welcome to
submit additional reviews and comments for papers that are under review. Reviews are non-anonymous
by default, although reviewers can request to stay anonymous. The journal attributes the work of editors
and non-anonymous reviewers in all published articles.
All Data Science submissions will moreover be publicly available as preprints right away. Publishing
preprints has the advantage of establishing a precedent for the work while it undergoes peer review.
Manuscripts submitted to Data Science are made available as preprints prior to reviewing so that review-
ers and others are free to not only read, but also share submitted papers. Preprints will remain available
after reviewing, independent of whether the paper was accepted or rejected for publication.
Enabling access to content in a manner that conforms to community standards is a key part of the
FAIR principles, ensuring that these results can be easily reused in other contexts. Data Science requires
authors to represent and provide any data used or produced in their studies with community-based data
formats and metadata standards. These data should furthermore be made openly available free of charge,
unless privacy or other well founded concerns apply.
Finally, in order to experiment with better communication methods for the future of scientiﬁc com-
munication, we encourage authors to write their papers in HTML and to provide (meta)data with formal
semantics, as a step towards the vision of semantic publishing, which will allow us – to a certain extent –
to automatically integrate, combine, organize, and reuse scientiﬁc knowledge.
3. This issue
This inaugural issue features position papers on various aspects around Data Science. Speciﬁcally,
these aspects cover new types of insights we can gain from data, new types of data that have become
available and require new methods and tools, urgent social issues that stem from these new types of
data-driven research, and new approaches on the role of data in the scientiﬁc publishing process.
3.1. New kinds of insights from data
New data with increased coverage and size might allow us to arrive at new types of insights. For exam-
ple, could such data let us predict the outbreak of conﬂicts and wars? In his paper “conﬂict forecasting
and its limits”, Thomas Chadefaux explores this question, which is both very important for humanity and
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very complicated to answer. He argues that the degree to which we can ﬁnd answers depends on whether
conﬂicts behave like clocks (predictable), clouds (difﬁcult to predict), or black swans (unpredictable).
Before we can make real progress on prediction of conﬂicts, we need to understand the fundamental
nature of such events.
3.2. New kinds of data
In the last decades, we have also witnessed the emergence of entirely new types of data. Speciﬁcally,
three papers of the inaugural issue look into the new kinds of data that are rooted in symbolic logic with
languages of precisely deﬁned syntax and semantics, and how such kind of data can be combined with
the prevalent statistical and machine learning approaches for data analytics. A fourth paper focuses on
the fact that such semantic data increasingly come in the form of continuous and dynamic data streams,
rather than discrete releases of static datasets.
Lawrence E. Hunter argues in his paper entitled “Knowledge-based biomedical Data Science” that
Data Science in general, and in the domain of biomedicine in particular, can beneﬁt a great deal from
the existing body of research on computational knowledge representation and reasoning. Applications
can include logical inference on ontology annotations of domain entities, such as genes and biological
processes, and on the biomedical literature where these entities are further described, as well as the
automated generation and evaluation of hypotheses.
Robert Hoehndorf and Núria Queralt-Rosinach make a similar argument in their paper “Data sci-
ence and symbolic AI: Synergies, challenges and opportunities” proposing a symbiotic combination of
statistical and symbolic Data Science, thereby combining symbolic AI approaches like ontologies and
reasoning with statistical approaches like machine learning and probabilistic models. Such a symbiotic
system can consume data and – importantly – existing knowledge to reliably produce new knowledge.
Xander Wilcke, Peter Bloem, and Victor de Boer explain in their paper “The knowledge graph as the
default data model for learning on heterogeneous knowledge” how we can use techniques and models
that originate from symbolic AI research, speciﬁcally knowledge graphs and Linked Data, as underpin-
ning unifying model for all kinds of machine learning approaches. With the rise of deep learning, raw
data has become the preferred input instead of manually constructed features, but this doesn’t work well
for heterogeneous data, for example data that includes images, sound, and text. A formal knowledge
graph is – maybe surprisingly – in a sense “rawer” than heterogeneous raw data, because all information
is preserved in a general and uniform manner and can potentially be capitalized on by a machine learning
Daniele by Dell’Aglio, Emanuele Della Valle, Frank van Harmelen, and Abraham Bernstein then
focus in their paper “Stream reasoning: A survey and outlook” on dynamic streams of such formally
represented data. They explain how the ﬁeld has evolved in its ten years of existence and sketch the open
challenges they see on the road ahead.
3.3. Social aspects around Data Science
Data Science is by nature cross-disciplinary, with the potential to bridge virtually any scientiﬁc en-
deavors within or between sectors. A series of ﬁve articles in this journal address the social aspects and
connecting abilities of Data Science, when optimally engaged in research, education, and business. In
“Maintaining intellectual diversity in Data Science”, Richard P. Mann and Olivia Woolley-Meza argue
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that intellectual diversity leads to the combined approaches that are needed to address complex phenom-
ena. In order to enrich the research community, appropriate incentives should be implemented to support
diverse thought and approaches, which may be initially perceived as unfashionable.
Furthermore, multi-disciplinary teams must include a data scientist to rapidly advance biomedical
research, as Manisha Desai posits in her paper, “The integration of the data scientist into the team: Im-
plications and challenges”. Team science, interdisciplinary collaboration across disciplines to address
important scientiﬁc questions, is key to successful biomedical research. Yet, academic medical centers
do not yet have the professional advancement structures in place to incentivize the integration and pro-
motion of data scientists into the clinical and translational research team.
To promote cross-disciplinary Data Science, Evangelos Pournaras describes in his paper,“Cross-
disciplinary higher education of Data Science – beyond the computer science student”, the experience
of designing and implementing a postgraduate Data Science course for students of all disciplines. While
the experience of developing the course was challenging, the course was deemed to be highly rewarding
for the variety of students engaged. Innovative curricular designs like this demonstrate the possibilities
for disciplines to learn from one another based on connecting nature of Data Science.
In her paper, “Thoughtful artiﬁcial intelligence: Forging a new partnership for Data Science and sci-
entiﬁc discovery”, Yolanda Gil describes a vision for thoughtful artiﬁcial intelligence systems, which
would partner with the scientist in driving research and discovery beyond what individual researchers
can accomplish individually, and perhaps with less error and bias than can be manually achieved. Hu-
man creativity and insight could overcome the shortcomings of AI, just as AI can overcome human
shortcomings in performing science.
Christine Chichester, ﬁnally, argues in her paper “Valorizing omics visualization for discovery” that
we should start appreciate that visualizations are more than just eye candy for presentation purposes,
but rather that they form a crucial step in data-driven knowledge discovery. Visualization efforts need to
be given proper credit and need to be encouraged and supported to advance our knowledge discovery
3.4. Scientiﬁc publishing
Finally, the inaugural issue contains two papers on semantic publishing, which is the ﬁeld of research
on how we can use semantic technologies to improve the communication and dissemination of scientiﬁc
ﬁndings and metadata. This includes our own paper entitled “Genuine semantic publishing”, in which
we argue that we should return to the original and literal meaning of semantic publishing by letting
the authors themselves represent their results in a semantic notation in the ﬁrst place, and release these
representations as prime publication elements.
While we argue for requiring authors to do some extra work, Silvio Peroni takes the opposite stance
in his paper “Automating semantic publishing”, declaring that with appropriate methods a large amount
of formal and semantic structure can automatically extracted from already written papers, thereby mini-
mizing the additional effort needed on the side of the authors. He then presents a speciﬁc approach and
framework to gradually building semantic information starting from low-level syntactic elements in a
markup language like HTML.
M. Dumontier and T. Kuhn / Data Science – Methods, infrastructure, and applications 5
4. Invitation for contributions
For the upcoming issues we are looking for different kinds of papers on the topics outlined above.
Most importantly, we are inviting the submission of research papers that report on original research. But
we are also interested in position papers that present relevant and novel discussion points around the
journal topics in a thorough manner, like the papers of this inaugural issue. We moreover also accept
survey papers on the state of the art of relevant topics to serve as introductory and overview texts for
We furthermore welcome suggestions for special issues. The ﬁrst special issue call on “Special Issue.
Distributed Ledgers: Making Data Science More Open, Transparent, and Accountable” is already open
and accepting submissions. We aim for a continuous stream of such special issues on a variety of Data
Science topics. More information about the journal and its open calls can be found on our website https://