Abstract
Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters. The context of the annotated content is made explicit in a standard way by associating it with ontological terms (e.g., species, cell types, brain regions). The exact position of the annotated content within a document is specified by the starting character of the annotated text, or the number of the figure, the equation, or the table, depending on the context. Alternatively, the provenance of parameters can also be specified by bounding boxes. Parameter types are linked to curated experimental values so that they can be systematically integrated into models. We demonstrate the use of this approach by releasing a corpus describing different modeling parameters associated with thalamo-cortical circuitry. The proposed framework supports a rigorous management of large sets of parameters, solving common difficulties in their traceability. Further, it allows easier classification of literature information and more efficient and systematic integration of such information into models and analyses.
Highlights
In the context of large-scale, highly detailed, and data-driven realistic modeling of the brain, developers are faced with the daunting task of reviewing voluminous, and ever growing, body of scientific papers to extract all information useful in constraining the large number of parameters involved in the modeling process
More subtle alterations are often applied such as changing the nature of the variable or combining different measures
We report on the development of an annotation format for scientific literature curation and on the public release of open-access tools to assist in the creation and management of annotation corpora
Summary
In the context of large-scale, highly detailed, and data-driven realistic modeling of the brain, developers are faced with the daunting task of reviewing voluminous, and ever growing, body of scientific papers to extract all information useful in constraining the large number of parameters involved in the modeling process. Curated information from the literature that has been embedded into models are often vulnerable to issues regarding the traceability of its origin. This happens for example when the embedding does not provide a means to trace back (1) the publication from which a numerical value has been extracted, (2) the exact place in the paper from where the information has been taken, or (3) the precise method used to transform published numbers into the values inserted into models. More subtle alterations are often applied such as changing the nature of the variable (e.g., passing from area to volume by considering hypotheses or supplementary factors, Collaborative Curation of Neuroscientific Literature as it is the case when using cell counts per area from stereology studies to model neuronal volumetric densities) or combining different measures (e.g., taking the median of values reported by different sources)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
