The ease of data retrieval, analysis, and distribution can accelerate the pace of scientific research. A difference in philosophies and methodologies in the manner of data-collection and storage leads to a lack of semantic-consistency in naming-conventions across disciplines. Previous studies have opened this discussion within various research disciplines, however there has yet to be a study accomplished within a Convergence Science Lab such ours, Genetically Engineered Materials Science and Engineering Center (GEMSEC). Well-defined naming-conventions, clear-cut data standards, and set programmatic interfaces are required to provide improved data access to researchers and the public. This consistency is necessary to create a robust database with the ability to query large amounts of related information. The key point is that integrating design (of variables, research questions, etc.) with traditional, empirical research approaches in the natural sciences will result in more robust data and clearer analysis. Here we discuss the need for standardized protocol and metadata standards for data-collection and storage. We began by creating a data analysis and storage pipeline for two computationally involved experiments, Molecular Dynamics (MD) and Next Generation Sequencing (NGS), within GEMSEC. A database schema storing metadata associated with raw, cleaned, and analyzed files was created. We found homogenous collections of metadata with inconsistencies, especially in peptide naming conventions, between experiments. The target-database includes those from other labs some of which may store unconventional file types, use other naming schemes for key variables such as sequences, and have different standards for what metadata is stored and what is computationally retrieved at a later time. This schema is formed via interviewing various other researchers, determining similarities and differences within metadata, and creating a standard for all to use based on this collected information.

The long term goal of this project is to study and mathematically characterize how the mammalian (mainly Homo sapiens) brain creates and maintains meaningful neuronal connections and organizes into connectomes and cortexes, and to compare what we learn to an existing (patented and under development at GEMSEC) brain-like computational neural network. A key point in understanding the formation, organization and long-term existence of new brain neural networks is the fundamental relationship between the geometric entropy of the physical network embedding, and information entropy of the network adjacency, connectivity. To our knowledge, there is currently no study in the literature that focuses on understanding biological neural networks through the entropy of their connections. Here we pose the question whether entropy related learning rules emerge from biological network connections or are the driving force for these connections. We also ask whether such learning rules can be imposed on artificial neural networks for enhanced functionality. To find the information entropy analogue of the geometric entropy term from a network point of view, we need to define the information entropy of the neuronal adjacency matrix. We define the information entropy of the neuronal adjacency, with random numbers between 0 and 1 symbolizing strength of the connections, i.e., synaptic plasticity, as the Shannon’s entropy, i.e., information entropy, of the spectral distribution of the neuronal adjacency matrix. To aid us in this study, two public neuronal datasets have been found: the Janelia FlyEM research group’s Hemibrain, and the NeuralEnsemble simulated spike train data. We test the entropy of the inferred neuronal connections from these datasets toward achieving our goal of the mechanism of formation of neural connections and connectomes. This project is supported by the UW Computational Neuroscience Center, and the DMREF Program of NSF through the MGI platform under DMR# 1629071, 1848911, and 1922020.