The development and implementation of MIGen standards fills the gap between the initial genotyping experiment information providers and the genotyping data users. While MIGen follows the high-level structure of other well-established minimum information checklists, it also leverages foundational concepts from the OBI ontology. The use of planned processes and a hierarchical structure allows MIGen to accommodate the many varied and unique aspects of different genotyping experiments. A similar but distinct hierarchical architecture of checklists has been proposed by the Geomic Standards Consortium community, where the minimum information about any (x) sequence (MIxS) was created by reverse engineering an ��overarching framework�� [9] to serve as a single entry point for different technology-specific checklists, such as Minimum information about a marker gene sequence (MIMARKS) [9], the minimum information about a genome sequence (MIGS) [10], etc. Independently developed checklists are collected under the MIxS, sharing the same central set of core descriptors but having checklist specific descriptors as well. The MIGen hierarchical architecture not only provides a means for all modules to share common high-level structure, but also the specifications provide the guidelines for development of each module. Further discussion within the research community must take place to reach the final consensus on the proposed standard. We welcome comments on the documentation and additions to the MIGen modules for specific genotyping experiment types. MIGen will facilitate data sharing in the research community, making independent data interpretation, validation and reproduction more efficient and unambiguous. MIGen can also serve as a framework for the development of data models to capture and store genotyping result data and experiment metadata in a structured way, to facilitate data exchange and sharing.
Over the last few decades, neuroscience has witnessed an explosion of methods for the measurement of human brain function, including high-density (multi-sensor) event-related potentials (ERPs). In comparison with other techniques, the ERP method has several advantages: it is completely safe and noninvasive, it is inexpensive and portable, and �� unlike methods such as functional magnetic resonance imaging (fMRI) �� it is a direct measure of neuronal activity. The ERP method also has excellent (millisecond) temporal resolution, which is critical for representation of neural dynamics. Remarkably, despite these many virtues, there are few quantitative comparisons (��meta-analyses��) of ERP results, reflecting the complexity of ERP data and the wide variety of methods that are used to extract and analyze ERP metadata [1-3]. To address this gap, we have gathered an interdisciplinary team of researchers in informatics and human neuroscience to form project NEMO (Neural ElectroMagnetic Ontologies).