=Paper=
{{Paper
|id=None
|storemode=property
|title=Using Nanopublications to Incentivize the Semantic Exposure of Life Science Information
|pdfUrl=https://ceur-ws.org/Vol-952/paper_20.pdf
|volume=Vol-952
|dblpUrl=https://dblp.org/rec/conf/swat4ls/ThompsonS12
}}
==Using Nanopublications to Incentivize the Semantic Exposure of Life Science Information==
https://ceur-ws.org/Vol-952/paper_20.pdf
Using Nanopublications to Incentivize the
Semantic Exposure of Life Science Information
Mark Thompson and Erik A. Schultes
Leiden University Medical Center
Abstract. The growing rate of data production in the life sciences cre-
ates an urgent need for semantic integration of information. Although
the development of tools and infrastructure will make semantic data
exposure easier with time, presently the effort associated with creating
linked data remains largely unrecognized by peer-review processes, pub-
lishers, and promotion committees. Here, we describe a novel data pub-
lishing framework called nanopublications that provides incentives for re-
searchers to expose their data in semantic form. A nanopublication is the
smallest unit of publishable information and is composed of an assertion
(a semantic triple subject-predicate-object combination) and provenance
metadata such as personal and institutional attribution (which also uses
triples). As RDF named graphs, nanopublications are fully interoperable
and machine readable, and need not be tethered to centralized databases,
research articles or other schema for their retrieval and use. Hence, indi-
vidual nanopublications can be cited and their impact tracked, creating
powerful incentives for compliance with open standards and driving data
interoperability.
1 Background
Large, harmonized datasets, especially from heterogeneous sources, promise to
accelerate discovery in the life sciences, and offer new approaches to managing
intrinsically complex biomedical systems [1]. However, the channels for data con-
sumption have not scaled with data production leading to the loss of valuable
data from scientific discourse [1, 2]. Given the magnitude and diversity of data
production in the life sciences, the identification of trends and the inference of
novel and relevant associations demands automated approaches to analysis and
reasoning. In turn, this requires the automatic and universal interoperability of
data [3]. Semantic technologies have emerged that effectively address these is-
sues, but the legacies of scholarly communication continue to preempt efforts of
data integration [4]. The narrative research article has been, for over a century,
the accepted unit of attribution and scientific productivity. This made sense
when typical datasets were small enough to be included in the research article
itself (as tables or figures). However, as data production becomes increasingly
automated, large-scale datasets must necessarily be hosted independently of the
research article [5, 6]. In response, a dynamic ecosystem of technological solu-
tions to large-scale data deposition, archival, persistence, licensing, access and
attribution has emerged1 . Yet, no consensus around data representation, proto-
cols for data linking or citation has crystallized among the research community
or publishers. Thus, the lack of data interoperability continues to persist as a
sociological, rather than as a technological problem.
2 What is a nanopublication?
Since 2009, the Dutch BioSemantics Group [7] has been developing a data format
standard that scales with the demands of Big Data. This standard, called nanop-
ublications [5, 8, 9], attaches to individual datum provenance metadata such that
data no longer need to be tethered to centralized databases, research articles
or other schema for their retrieval and use. Furthermore, exploiting off-the-shelf
semantic technology, nanopublications are fully interoperable and machine read-
able. Hence, individual nanopublications can be cited and their impact tracked,
creating incentives for individuals and institutions to exchange appropriate data.
Nanopublication packages individual datum as citable, stand-alone publica-
tions using semantic representations. Nanopublication is a schema on top of
existing semantic technology using controlled vocabularies and ontologies. A
nanopublication has two parts: the assertion (datum) and provenance (meta-
data). The assertion and provenance are RDF named graphs composed of seman-
tic triples (subject-predicate-object combinations) [10]. The assertion describes
a minimal unit of actionable scientific information such as a controlled observa-
tion (from the field or the laboratory) or a simple hypothesis (that can later be
tested). The provenance describes how the assertion came to be, and includes
both supporting information (e.g., context, parameter settings, a description of
methods) and attribution information including fine-grained acknowledgment
of institutions supporting the work, funding sources and other information like
date and time stamps and certification [11–13]. A nanopublication represents
the smallest unit of actionable information and combines both the technical so-
lution for interoperability (semantic web representations) with the incentives
(attribution) as a single publishable unit.
3 How to use nanopublications?
Creating a nanopublication requires a one-time effort to model the scientific as-
sertion and provenance as RDF named graphs. After submission to an open,
decentralized nanopublication store (essentially a triple store), nanopublications
will be available as both human-readable and machine-readable information and
will be fully interoperable under semantic queries and to automated inferencing
engines. Nanopublications can be used to expose any data type whatsoever, in-
cluding quantitative and qualitative data, experimental data as well as hypothe-
ses, novel or legacy data and even negative results that usually go unpublished.
1
Some examples are available at www.datadryad.org, www.foaf-project.org,
www.thedatahub.org, www.datamarket.com, www.thedata.org and
www.gigasciencejournal.com
The nanopublication framework can be used to expose data streams from cu-
rated databases as well as from instrumentation (sensors) and communication
sources (internet transactions, email, video, click streams, or other digital sources
available today and in the future). As a data publishing framework, nanopublica-
tions are meant to augment (not replace) traditional narrative research articles,
although nanopublications can be used to expose individual assertions from nar-
rative text.
By linking assertions and provenance using semantic representations, not only
do data become interoperable, but their value can be independently estimated.
Nanopublications provide a common currency for the exchange of data and thus
allow crowd sourced or market-driven assignment of value to individual datum
[4, 5, 14–17]. This is in contrast to traditional peer-review which has not scaled
with the demands of data production and increasingly shows signs of bias and
failure [18–22]. Based on this estimated value, nanopublications can be filtered
and prioritized for the purposes of search and inclusion in automated inferencing
algorithms. Large networks of custom nanopublication mash-ups from diverse
sources can be constructed and searched for novel (implied) associations that
would otherwise escape the human reasoning. Indeed, newly discovered associ-
ations can themselves be represented and shared as nanopublications. In turn,
the value of individual datum can be translated into citation metrics, measures
of scientific impact and other professional and economic indicators incentivizing
interoperability and sharing [14].
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