The growing rate of data production in the life sciences creates 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 e ort associated with creating linked data remains largely unrecognized by peer-review processes, publishers, and promotion committees. Here, we describe a novel data publishing framework called nanopublications that provides incentives for researchers 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, individual nanopublications can be cited and their impact tracked, creating powerful incentives for compliance with open standards and driving data interoperability.
Large, harmonized datasets, especially from heterogeneous sources, promise to
accelerate discovery in the life sciences, and o er new approaches to managing
intrinsically complex biomedical systems [
Since 2009, the Dutch BioSemantics Group [
Nanopublication packages individual datum as citable, stand-alone publications 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 (metadata). The assertion and provenance are RDF named graphs composed of semantic triples (subject-predicate-object combinations) [10]. The assertion describes a minimal unit of actionable scienti c information such as a controlled observation (from the eld 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 ne-grained acknowledgment of institutions supporting the work, funding sources and other information like date and time stamps and certi cation [11{13]. A nanopublication represents the smallest unit of actionable information and combines both the technical solution for interoperability (semantic web representations) with the incentives (attribution) as a single publishable unit. 3
Creating a nanopublication requires a one-time e ort to model the scienti c assertion 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, including quantitative and qualitative data, experimental data as well as hypotheses, 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 curated 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, nanopublications are meant to augment (not replace) traditional narrative research articles, although nanopublications can be used to expose individual assertions from narrative 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 ltered 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 associations can themselves be represented and shared as nanopublications. In turn, the value of individual datum can be translated into citation metrics, measures of scienti c impact and other professional and economic indicators incentivizing interoperability and sharing [14]. 9. http://www.nanopub.org 10. Klyne, G., Carroll, J.J.: Resource Description Framework (RDF): Concepts and
Abstract Syntax. Technical report 11. Taylor, C.F., Field, D., Sansone, S.A., Aerts, J., Apweiler, R., Ashburner, M., Ball, C.A., Binz, P.A., Bogue, M., Booth, T., Brazma, A., Brinkman, R.R., Michael Clark, A., Deutsch, E.W., Fiehn, O., Fostel, J., Ghazal, P., Gibson, F., Gray, T., Grimes, G., Hancock, J.M., Hardy, N.W., Hermjakob, H., Julian, R.K., Kane, M., Kettner, C., Kinsinger, C., Kolker, E., Kuiper, M., Novere, N.L., Leebens-Mack, J., Lewis, S.E., Lord, P., Mallon, A.M., Marthandan, N., Masuya, H., McNally, R., Mehrle, A., Morrison, N., Orchard, S., Quackenbush, J., Reecy, J.M., Robertson, D.G., Rocca-Serra, P., Rodriguez, H., Rosenfelder, H., Santoyo-Lopez, J., Scheuermann, R.H., Schober, D., Smith, B., Snape, J., Stoeckert, C.J., Tipton, K., Sterk, P., Untergasser, A., Vandesompele, J., Wiemann, S.: Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotech 26(8) (August 2008) 889{896 12. http://isa-tools.org 13. http://www.w3.org/2004/02/skos 14. Bartolini, C., Vukovic, M.: Crowdsourcing human mutations. (2011) 15. Mons, B., Ashburner, M., Chichester, C., van Mulligen, E., Weeber, M., den Dunnen, J., van Ommen, G.J., Musen, M., Cockerill, M., Hermjakob, H., Mons, A., Packer, A., Pacheco, R., Lewis, S., Berkeley, A., Melton, W., Barris, N., Wales, J., Meijssen, G., Moeller, E., Roes, P.J., Borner, K., Bairoch, A.: Calling on a million minds for community annotation in WikiProteins. Genome biology 9(5) (January 2008) R89 16. Ho mann, R.: A wiki for the life sciences where authorship matters. Nat Genet 40(9) (September 2008) 1047{1051 17. Oprea, T.I., Bologa, C.G., Boyer, S., Curpan, R.F., Glen, R.C., Hopkins, A.L., Lipinski, C.A., Marshall, G.R., Martin, Y.C., Ostopovici-Halip, L., Rishton, G., Ursu, O., Vaz, R.J., Waller, C., Waldmann, H., Sklar, L.A.: A crowdsourcing evaluation of the NIH chemical probes. Nature Chemical Biology 5(7) (2009) 441{447 18. Miller, A., Barwell, G.: Science and Technology Peer review in scienti c publications Eighth Report of Session 201012. (July) (2011) 19. Mullard, A.: Reliability of 'new drug target' claims called into question. Nat Rev
Drug Discov 10(9) (September 2011) 643{644 20. Prinz, F., Schlange, T., Asadullah, K.: Believe it or not: how much can we rely on published data on potential drug targets? Nature reviews. Drug discovery 10(9) (September 2011) 712 21. Booth, B.: Academic Bias & Biotech Failures. http://lifescivc.com/2011/03/ academic-bias-biotech-failures/ (2011) 22. Fanelli, D.: How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data. PLoS ONE 4(5) (2009) e5738