Seamount Catalong: Seamount Morphology, Maps, and Data Files

Seamount research, more often than not, is carried out by highly specialized science teams with narrowly focused science objectives. As a result, different seamount science disciplines often do not collaborate or are not even aware of each other. However, it is obvious that interdisciplinary collaboration is the most successful approach to help understand the integrated chemical, physical, and biological systems at seamounts. The Seamount Biogeoscience Network (SBN) was founded to promote the necessary cooperation through workshops, publications, and the development of a database that allows all seamount sciences to share data. Among such data, bathymetric maps are the most fundamental to all disciplines

Towards more biologically-plausible computational models for cognition, texture classification, and network replication

Neuroscience and machine learning often operate at two ends of a spectrum. The former sometimes finds itself entrenched in the details of experimentation, and the latter sometimes finds itself drifting into the expanse of theory. Both fields can mutually coexist, and when they do, have produced invaluable results in computational neuroscience towards more plausible models of biological solutions. This dissertation presents two detailed investigations into the benefits of this interdisciplinary field: a model for cognition and a model for vision. Experiments during these investigations led us to a third result: a new learning approach called neural network tomography. We introduce our universal theory of cognition, Confabulation Theory, and discuss its biological plausibility. Confabulation Theory posits that the cerebral cortex, in conjunction with the thalamus, is implementing a repeated functional architecture of thalamocortical modules, each encoding one attribute which an object in the individual's mental universe may possess. These modules are interconnected with concurrence statistics called knowledge links, are capable of confabulating a state, and are carefully controlled with action commands. We use Confabulation Theory to build a model for natural language processing and present striking results in sentence generation with context. Subsequently, we focus on the task of texture classification, which we argue is a more primitive operation than object recognition, and therefore, appropriate for investigation with the goal of elucidating biology's solution for processing visual stimuli. We develop a hierarchical model for texture classification, carefully informed by neuroscience results, and demonstrate state-of-the-art performance on a challenging texture classification dataset in the context of our human psychophysical experiment. Finally, we survey existing methods in neural network learning and propose a new approach with several valuable theoretical advantages. By rephrasing the task of function approximation as replicating the topology and weights of an existing universal approximator network, we show that several of the drawbacks of classical backpropagation learning can be avoided. We define a new objective function, mean squared curvature (MSC), and demonstrate that minimizing the MSC of the difference between the networks during the replication process produces favorable results and allows networks to be reverse-engineered iterativel

Seamount Catalog: Seamount Morphology, Maps, and Data Files

Seamount research, more often than not, is carried out by highly specialized science teams with narrowly focused science objectives. As a result, different seamount science disciplines often do not collaborate or are not even aware of each other. However, it is obvious that interdisciplinary collaboration is the most successful approach to help understand the integrated chemical, physi-cal, and biological systems at seamounts. The Seamount Biogeoscience Network (SBN) was founded to promote the necessary cooperation through workshops, publications, and the devel-opment of a database that allows all seamount sciences to share data. Amongst such data, bathymetric maps are the most fundamental to all disciplines

The Magnetics Information Consortium (MagIC) Data Repository: Successes and Continuing Challenges

MagIC (earthref.org/MagIC) is an organization dedicated to improving research capacity in the Earth and Ocean sciences by maintaining an open community digital data archive for rock and paleomagnetic data with portals that allow users access to archive, search, visualize, download, and combine these versioned datasets. We are a signatory of the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS)'s Enabling FAIR Data Commitment Statement and an approved repository for the Nature set of journals. We have been in collaboration with EarthCube's GeoCodes data search portal, adding schema.org/JSON-LD headers to our data set landing pages and suggesting extensions to schema.org when needed. Collaboration with the European Plate Observing System (EPOS)'s Thematic Core Service Multi-scale laboratories (TCS MSL) is ongoing with MagIC sending its contributions' metadata to TCS MSL via DataCite records.Improving and updating our data repository to meet the demands of the quickly changing landscape of data archival, retrieval, and interoperability is a challenging proposition. Most journals now require data to be archived in a "FAIR" repository, but the exact specifications of FAIR are still solidifying. Some journals vet and have their own list of accepted repositories while others rely on other organizations to investigate and certify repositories. As part of the COPDESS group at Earth Science Information Partners (ESIP), we have been and will continue to be part of the discussion on the needed and desired features for acceptable data repositories.We are actively developing our software and systems to meet the needs of our scientific community. Some current issues we are confronting are: developing workflows with journals on how to publish the journal article and data in MagIC simultaneously, sustainability of data repository funding especially in light of the greater demands on them due to data policy changes at journals, and how to best share and expose metadata about our data holdings to organizations such as EPOS, EarthCube, and Google

The Magnetics Information Consortium (MagIC) Data Repository: Successes and Continuing Challenges

MagIC (earthref.org/MagIC) is an organization dedicated to improving research capacity in the Earth and Ocean sciences by maintaining an open community digital data archive for rock and paleomagnetic data with portals that allow users access to archive, search, visualize, download, and combine these versioned datasets. We are a signatory of the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS)'s Enabling FAIR Data Commitment Statement and an approved repository for the Nature set of journals. We have been in collaboration with EarthCube's GeoCodes data search portal, adding schema.org/JSON-LD headers to our data set landing pages and suggesting extensions to schema.org when needed. Collaboration with the European Plate Observing System (EPOS)'s Thematic Core Service Multi-scale laboratories (TCS MSL) is ongoing with MagIC sending its contributions' metadata to TCS MSL via DataCite records.Improving and updating our data repository to meet the demands of the quickly changing landscape of data archival, retrieval, and interoperability is a challenging proposition. Most journals now require data to be archived in a "FAIR" repository, but the exact specifications of FAIR are still solidifying. Some journals vet and have their own list of accepted repositories while others rely on other organizations to investigate and certify repositories. As part of the COPDESS group at Earth Science Information Partners (ESIP), we have been and will continue to be part of the discussion on the needed and desired features for acceptable data repositories.We are actively developing our software and systems to meet the needs of our scientific community. Some current issues we are confronting are: developing workflows with journals on how to publish the journal article and data in MagIC simultaneously, sustainability of data repository funding especially in light of the greater demands on them due to data policy changes at journals, and how to best share and expose metadata about our data holdings to organizations such as EPOS, EarthCube, and Google