e., a research map; Figure 1C), and suggest causal hypotheses (Figure 1D). Just as a GPS map affords different levels of zoom, someone reading a research map would be able to survey a specific
research area at different levels of resolution, from coarse summaries of findings (Figure 1C) to fine-grained accounts of experimental results. The primary function of a research map is to display no more and no less information to a user than is necessary for the researcher’s purposes. Primary research articles often contain summaries of prior research this website and statements concerning the significance of findings presented. Additionally, review articles can help to place specific collections of findings in a broader and more integrated perspective. However valuable they may be, the individual perspectives in research papers and review articles are not always objective and balanced. Frequently, they do not reflect all of the relevant information available for the topic being reviewed. Thus, in addition to these personal perspectives, it would be useful to consult exhaustive, inclusive, and integrated databases (i.e.,
research maps) concerning the results and experimental strategies of an area or topic of interest. To enhance the accessibility of research maps, each assertion would be stated in an unambiguous vocabulary. There are now numerous such vocabularies for automated reasoning, called ontologies (e.g., available through the National Center for Biomedical learn more Ontologies, or NCBO). Unlike natural languages (e.g., English), biomedical ontologies map one entity into one term. For instance, the word “nucleus” is ambiguous and could mean a cluster of cells, the nucleus of a single cell,
and an atomic nucleus. The different senses of “nucleus” receive different terms in biomedical ontologies, so that when data are annotated with one of these terms, there is no ambiguity to confound a search over that data and no ambiguity to confound automated reasoning. To date, the most extensive effort toward developing an ontology for neuroscience has been undertaken by the Neuroscience Information Framework (NIF). The NIF has collected a dynamic lexicon of over 19,000 neuroscience terms to describe neural structures and functions. The lexicon is built from the NIF standard ontologies (NIFSTD) not (Larson and Martone, 2009). To make these vocabularies available to nonspecialists, the NIF group has built a web app, NeuroLex, from which a user can easily find the right terms to describe a phenomenon or protocol. Ontologies like the NIFSTD provide materials for composing unambiguous representations of neuroscience research in a format sometimes called “nanopublication” (Groth et al., 2010). A nanopublication is the smallest unit of publishable information that can be uniquely identified and attributed to its author(s). Each of the eight experiments in Figure 1A could be reported in a single conventional research paper, or in eight nanopublications.