EDITORIAL
A Journal for Human and Machine
James Hendler1†, Ying Ding2† & Barend Mons3
1 Rensselaer Institute for Data Exploration and Applications, Rensselaer Polytechnic Institute, Troy, NY12180, EE.UU
2 School of Informatics, Informática, and Engineering, Universidad de Indiana, Bloomington, EN 47408, EE.UU
3 Leiden University Medical Centre, Los países bajos, Poortgebouw N-01, Rijnsburgerweg 10 2333 AA Leiden, Los países bajos
Citación: j. Hendler, Y. Ding, & B. Mons. A journal for human and machine. Data Intelligence 1(2019), 1-5.
doi: 10.1162/dint_e_00001
It is with great pride to bring you this new journal of Data Intelligence. This journal has at least two major
purposes that we hope embrace. Primero, it will embrace the traditional role of a journal in helping to facilitate
the communication of research and best practices in scientific data sharing, especially across disciplines,
an area that is continually growing in importance for the modern practice of science. Segundo, we will be
experimenting with new methods of enhancing the sharing of this communication, and examples of the
campo, by utilizing the increasing power of intelligent computing systems to further facilitate the growth of
the field. The journal’s title, combining “data,” the field we will support, and “intelligence,” a means to that
end, is meant to connote this growing interaction.
Since the establishment of the first academic journals in the mid 1600’s, academic publishing has been
a key part of scientific infrastructure, facilitating knowledge sharing and scholarly communication. Journals,
at their best, publish high-quality scientific articles so that researchers can be aware of recent advancements
in their fields and can have access to archival publications of the “giants” whose shoulders they stand on.
The best papers can also inspire researchers to pursue new scientific adventures.
In the past few decades, journals have taken on another, somewhat unexpected role, being used to rank
scientists and often impacting their future careers (p.ej., hiring, promoción, and future funding). As such,
academic publishing can not only help support the communication of science, but increasingly they are
taking on a role in defining new subfields where researchers can come together and share information
while enhancing their careers. Despite the changing nature of publications, and the search for alt-metrics,
we still today see journals as necessary to enhancing scientific communication among researchers and
practitioners with common interests, enabling them to forge scientific sub-disciplines and/or work across
current disciplines to share their ideas.
Somewhat ironically, as scientists across a number of fields are under pressure to share their scientific
datos, a growing community of researchers has struggled to find a place to share their ideas about how best
Corresponding authors: James Hendler (Correo electrónico: hendler@cs.rpi.edu; ORCID: 0000-0003-3056-1960) and Ying Ding (Correo electrónico:
†
dingying@indiana.edu; ORCID: 0000-0003-2567-2009).
© 2019 Chinese Academy of Sciences Published under a Creative Commons Attribution 4.0 Internacional (CC POR 4.0)
licencia
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A Journal for Human and Machine
to do this. Por ejemplo, the US National Academies of Science, Engineering and Medicine, recently held
a symposium on “International Coordination for Science Data Infrastructure,” and a number of emerging
efforts were discussed among participants who largely were unaware of many of the other ongoing efforts
in the area. In this vein, we can see this journal, Data Intelligence, as a publication aimed at providing a
common communications space for a community of researchers who have not had a place to exchange
their ideas as to how best to share data across a wide range. También, without a reputable journal to publish
en, researchers working in this emerging field have been at a real disadvantage academically as papers have
been spread over a wide range of publications from different disciplines making it hard to find, y por lo tanto
to cite, research that builds on common techniques across these domains.
Sin embargo, as well as this important academic goal, this journal will strive to do more. For these past 350
years of journal publication, the intended readers of the journals have been other scientists. But in the past
two decades, with the advent of the Semantic Web with its increasingly powerful knowledge graphs, mejor
metadata standards, and new linked-data tools, there has been a growing interest in the use of machines
to enhance scientific information sharing and an increasing capability of artificial intelligence systems to
help facilitate the practice of science.
Given the speed with which AI technologies are advancing, and the better processing available when
data are machine readable, it is clear that we need to start to explore how to build a new generation of
journal publication which has the ability to accumulate, disseminate, and create knowledge that is
simultaneously contributed to both humans and machines.
De este modo, as we hope the name of the journal implies, a key goal of our publication is to go beyond the
traditional journal practice and to increasingly help, as it were, to deliver intelligence using data. We admit
that it is not yet crystally clear to us how we should differentiate data intelligence from the more general
field of artificial intelligence and machine learning technologies. Sin embargo, our focus is on the sharing of
data using these technologies.
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Más, we are living in the cusp of an exponentially increasing curve with respect to the data that are
becoming available to scientists and researchers (and many others, por supuesto, but our focus as a scientific
journal is on the use of data in scientific research and engineering). The advent of the “Internet of Things”
will make even more data from sensors and devices available, and scientific instrumentation will produce
ever more machine-readable outputs, which will need to be processed for the human scientist to digest.
Metaphorically speaking, the only way to control this breaking wave of data (or some might say to tame
the data monster) humans need help from machines.
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One of the key methods for providing an interface between the machines that are increasingly producing
data and the humans who need to process data has been the development of better metadata approaches
and the linking of this metadata across applications. The use of metadata is not a new idea, and it has been
used to help humans to represent and categorize data even before computerization, for example in the
century-long practice in libraries for managing the retrieval of books or periodicals. Sin embargo, one of the
goals of this Journal will be to better understand the needs of scientific metadata and to explore how
https://www.nap.edu/catalog/25015/international-coordination-for-science-data-infrastructure-proceedings-of-a-workshop
http://science.sciencemag.org/content/346/6206/171
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Data Intelligence
A Journal for Human and Machine
humans and machines can collaboratively create and reuse metadata to empower knowledge generation
and sharing.
En esencia, the ultimate goal of this Journal is to help us to explore an emerging ecosystem of scientific
data in which human researchers and increasingly capable machines work together to enhance research
across diverse fields ranging from the traditional sciences and engineering disciplines, the social sciences
and humanities, and to emerging fields that cross the artificial boundaries between these areas. We want
to understand how scientific and research data can be timely captured and represented using metadata to
add to the “giant global graph” of knowledge proposed by Tim Berners-Lee. This vision is that the linked
data of many different kinds form a fusion of linked information where new knowledge can be inferred,
and feedback loops can be created to renew and update older data.
In the natural world, an ecological balance is defined as “a state of dynamic equilibrium within a
community of organisms in which genetic, species, and ecosystem diversity remain relatively stable, sujeto
to gradual changes through natural succession.” With this journal, we want to explore creating exactly
that kind of ecological system within the world of knowledge—where information can be continually
changing, but rarely destabilizing.
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The tools for the creation and curation of such knowledge are still in their infancy, and we hope as the
journal grows over time we will be able to both report on the experiments in data sharing that are being
pursued by our contributors, as well as to see how best to create new models that can enhance the sharing
of information. We will start as an open access purveyor of papers, but at the same time we will be exploring
the development and publication of online information to accompany the articles and/or the publication
of human-readable descriptions of metadata, ontologies, and other sources being shared online.
As an example of the kind of work we hope to enable, consider the introductory paper of this issue in
which Barend Mons describes how FAIR (Findable, Accessible, Interoperable, and Reusable) data principles
can be realized in practice. In particular, he explores an envisioned Internet of FAIR Data and Services
(IFDS) that could play a critical role in helping scientists, especially in the findability aspect of FAIR.
Data Intelligence in its role as “the first journal that is also for machines” hopes to explore how we can
be an exemplar of creating potential solutions in which all journals, data repositories, and software
repositorios, in addition to what they already do and publish, also produce a FAIR data point (FDP) con
rich metadata to be indexed by multiple search and matching engines, so as to participate in this envisioned
IFDS.
En breve, even though we are starting out using a traditional publishing model, enhanced by relatively
simple article-related metadata, as time goes on we hope to be helping to forge a community of data sharers
who can increasingly take advantage of the emerging machine intelligence models that can enhance the
practice of research across our many disciplines. We hope you will join us on this journey of exploration
by reporting on your experiments, your data sharing technologies, and your shared data resources. We look
forward to seeing where we can go together as we experiment with these exciting new data models that
machine intelligence is helping to enable.
see https://en.wikipedia.org/wiki/Giant_Global_Graph
WWF, http://wwf.panda.org
Data Intelligence
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A Journal for Human and Machine
AUTHOR BIOGRAPHY
James Hendler is a Professor and Director of the Rensselaer Institute for Data
Exploration and Applications at Rensselaer Polytechnic Institute. He is one of
the early Semantic Web developers. Hendler held a longstanding position as
professor at the University of Maryland where he was the Director of the Joint
Institute for Knowledge Discovery and held joint appointments in the
Departamento de Ciencias de la Computación, the Institute for Advanced Computer Studies
and the Institute for Systems Research. He was the Director for Semantic Web
and Agent Technology at the Maryland Information and Network Dynamics
Laboratory. Hendler served as an “Internet Web Expert” for the U.S. gobierno,
providing guidance to the Data.gov project and in 2016 he was appointed to
the National Academies Board on Research Data and Information. He is a
Fellow of the American Association for Artificial Intelligence, the British Computer Society, the Institute of
Electrical and Electronics Engineers, the American Association for the Advancement of Science and the
Association for Computing Machinery.
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Ying Ding is a Professor at School of Informatics, Informática, and Engineering,
Indiana University and currently the associate director for data science online
programa. She is the Changjiang Guest Professor at Wuhan University and
Elsevier Guest Professor at Tongji University, Porcelana. She has been involved in
various NIH, NSF and European-Union funded projects. She has published
more than 200 papers in journals, conferences and workshops, and served
as the program committee member for more than 180 international
conferences. She is the co-editor of book series called Semantic Web Synthesis
by Morgan & Claypool publisher. She is co-author of the book Intelligent
Information Integration in B2B Electronic Commerce published by Kluwer
Academic Publishers, and co-author of the book chapter in Spinning the
Semantic Web published by Massachusetts Institute of Technology (CON) Prensa. She is the co-founder of
Data2Discovery Company advancing cutting edge technologies in data science. Her current research
interests include data-driven science of science, data-driven discovery, Web semántica, scientific collaboration
and the application of Web Technology.
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Data Intelligence
A Journal for Human and Machine
Barend Mons is Professor of BioSemantics at the Human Genetics Department
of Leiden University Medical Center and founder of the BioSemantics group.
He was elected CODATA President in 2018. Next to his leading role in the
research of the group, Barend plays a leading role in the international
development of ‘data stewardship’ for biomedical data. Por ejemplo, he was
head-of-node of ELIXIR-NL at the Dutch Techcentre for Life Sciences (until
2015), is Integrator Life Sciences at the Netherlands eScience Center, y
board member of the Leiden Centre of Data Science. En 2014, Barend initiated
the FAIR data initiative and in 2015, he was appointed Chair of the European
Commission’s High Level Expert Group for the “European Open Science
Cloud”, from which he retired by the end of 2016. Presently, Barend is co-leading the GO FAIR initiative,
an initiative to kick start developments towards the Internet of FAIR data and services, which will also
contribute to the implementation of components of the European Open Science Cloud. The focus of the
contribution of the BioSemantics group is on developing an interoperability backbone for biomedical
applications in general and rare disease in particular.
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