The Share Project: Building Capacity of Justice-Involved Individuals, Policymakers, and Researchers to Collectively Transform Health Care Delivery.

The Share Project (TSP), a US health justice initiative, convened key stakeholders to advance the use of inclusive research methods and data sharing to engage groups that are typically marginalized from research. TSP trained justice-involved patients, community health workers, policymakers, and researchers in participatory research and the use of a data-sharing platform developed with justice-involved patients. The platform allowed users to analyze health and criminal justice data to develop new research that is patient driven and responsive to the needs of providers.

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

Towards Reducing Health Information Inequities in the Caribbean: The Eastern Caribbean Health Outcomes Research Network Data Sharing Platform Usability Study.

There is a dearth of health research among Caribbean populations. Underrepresented individuals are affected by structural and data inequities that limit the usefulness, availability, and accessibility to health information systems and research-generated data. To overcome this limitation, a data sharing platform was created for the Eastern Caribbean Health Outcomes Research Network Cohort Study. This study aimed to evaluate the usability of the platform. Usability testing was conducted remotely, via video conferencing, using a cognitive walkthrough and think-aloud protocol. Participants completed a self-administered web-based survey which included an adapted version of the System Usability Scale (SUS). The results showed (N=16) overall average SUS score was 73.1 (SD±21.0), translating to a 'good' usability rating. Most recommendations for improvement focused on navigation and error prevention. Participatory data sharing platforms have the potential to reduce health information inequities in the Caribbean, however, usability testing should be conducted to improve user satisfaction and increase engagement.

Approaches to neuroscience data integration.

As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.

Toward Reducing Health Information Inequities in the Caribbean: Our Experience Building a Participatory Health Informatics Project.

Precision medicine seeks to leverage technology to improve the health for all individuals. Successful health information systems rely fundamentally on the integration and sharing of data from a range of disparate sources. In many settings, basic infrastructure inequities exist that limit the usefulness of health information systems. We discuss the work of the Yale Transdisciplinary Collaborative Center for Health Disparities focused on Precision Medicine, which aims to improve the health of people in the Caribbean and Caribbean diaspora by leveraging precision medicine approaches. We describe a participatory informatics approach to sharing data as a potential mechanism to reducing inequities in the existing data infrastructure.

Entrez Neuron RDFa: a pragmatic semantic web application for data integration in neuroscience research.

The amount of biomedical data available in Semantic Web formats has been rapidly growing in recent years. While these formats are machine-friendly, user-friendly web interfaces allowing easy querying of these data are typically lacking. We present "Entrez Neuron", a pilot neuron-centric interface that allows for keyword-based queries against a coherent repository of OWL ontologies. These ontologies describe neuronal structures, physiology, mathematical models and microscopy images. The returned query results are organized hierarchically according to brain architecture. Where possible, the application makes use of entities from the Open Biomedical Ontologies (OBO) and the 'HCLS knowledgebase' developed by the W3C Interest Group for Health Care and Life Science. It makes use of the emerging RDFa standard to embed ontology fragments and semantic annotations within its HTML-based user interface. The application and underlying ontologies demonstrate how Semantic Web technologies can be used for information integration within a curated information repository and between curated information repositories. It also demonstrates how information integration can be accomplished on the client side, through simple copying and pasting of portions of documents that contain RDFa markup.

Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons.

Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.

AlzPharm: integration of neurodegeneration data using RDF.

BACKGROUND: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. RESULTS: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. CONCLUSION: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.

Dynamic tables: an architecture for managing evolving, heterogeneous biomedical data in relational database management systems.

Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute-value-based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques. The authors propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. The authors provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design; (2) past approaches used by biomedical informatics researchers; and (3) their sparse, column-store architecture. The performance results show that their architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.

Database architectures for neuroscience applications.

To determine effective database architecture for a specific neuroscience application, one must consider the distinguishing features of research databases and the requirements that the particular application must meet. Research databases manage diverse types of data, and their schemas evolve fairly steadily as domain knowledge advances. Database search and controlled-vocabulary access across the breadth of the data must be supported. We provide examples of design principles employed by our group as well as others that have proven successful and also introduce the appropriate use of entity-attribute-value (EAV) modeling. Most important, a robust architecture requires a significant metadata component, which serves to describe the individual types of data in terms of function and purpose. Recording validation constraints on individual items, as well as information on how they are to be presented, facilitates automatic or semi-automatic generation of robust user interfaces.

Interoperability across neuroscience databases.

Data interoperability between well-defined domains is currently performed by leveraging Web services. In the biosciences, more specifically in neuroscience, robust data interoperability is more difficult to achieve due to data heterogeneity, continuous domain changes, and the constant creation of new semantic data models (Nadkarni et al., J Am Med Inform Assoc 6, 478-93, 1999; Miller et al., J Am Med Inform Assoc 8, 34-48, 2001; Gardner et al., J Am Med Inform Assoc 8, 17-33, 2001). Data heterogeneity in neurosciences is primarily due to its multidisciplinary nature. This results in a compelling need to integrate all available neuroscience information to improve our understanding of the brain. Researchers associated with neuroscience initiatives such as the human brain project (HBP) (Koslow and Huerta, Neuroinformatics: An Overview of the Human Brain Project, 1997), the Bioinformatics Research Network (BIRN), and the Neuroinformatics Information Framework (NIF) are exploring mechanisms to allow robust interoperability between these continuously evolving neuroscience databases. To accomplish this goal, it is crucial to orchestrate technologies such as database mediators, metadata repositories, semantic metadata annotations, and ontological services. This chapter introduces the importance of database interoperability in neurosciences. We also describe current data sharing and integration mechanisms in genera. We conclude with data integration in bioscience and present approaches on neuroscience data sharing.

Using a community-engaged health informatics approach to develop a web analytics research platform for sharing data with community stakeholders.

Marginalized communities are rarely included in the planning of research relevant to their own health or have access to research data. We implemented a community-based participatory research approach in developing a new health informatics system, WARP (Web Analytics Research Platform), to enable stakeholders to access and analyze research data. We leveraged data from a cohort study of 751 patients in the Transitions Clinic Network (TCN), a network of clinical programs serving patients with a history of incarceration. WARP holds de-identified, patient data, streamlines data processing (i.e. transformation, archival, and partitioning), and has a web analytic tool for users to perform statistical analyses. We used feedback from focus groups of patients with a history of incarceration and workshops with TCN research teams, including patients, community health workers and policymakers, to develop WARP. Our approach advances mechanisms to engage stakeholders in research. Future work will evaluate its effect on community-engagement in research.

A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.

This paper describes a natural language processing (NLP)-based clinical decision support (CDS) system that is geared towards colon cancer care coordinators as the end users. The system is implemented using a metadata- driven Structured Query Language (SQL) function (discriminant function). For our pilot study, we have developed a training corpus consisting of 2,085 pathology reports from the VA Connecticut Health Care System (VACHS). We categorized reports as "actionable"- requiring close follow up, or "non-actionable"- requiring standard or no follow up. We then used 600 distinct pathology reports from 6 different VA sites as our test corpus. Analysis of our test corpus shows that our NLP approach yields 98.5% accuracy in identifying cases that required close clinical follow up. By integrating this into our cancer care tracking system, our goal is to ensure that patients with worrisome pathology receive appropriate and timely follow-up and care.

ResourceLog: an embeddable tool for dynamically monitoring the usage of web-based bioscience resources.

The present study described an open source application, ResourceLog, that allows website administrators to record and analyze the usage of online resources. The application includes four components: logging, data mining, administrative interface, and back-end database. The logging component is embedded in the host website. It extracts and streamlines information about the Web visitors, the scripts, and dynamic parameters from each page request. The data mining component runs as a set of scheduled tasks that identify visitors of interest, such as those who have heavily used the resources. The identified visitors will be automatically subjected to a voluntary user survey. The usage of the website content can be monitored through the administrative interface and subjected to statistical analyses. As a pilot project, ResourceLog has been implemented in SenseLab, a Web-based neuroscience database system. ResourceLog provides a robust and useful tool to aid system evaluation of a resource-driven Web application, with a focus on determining the effectiveness of data sharing in the field and with the general public.

Olfactory Receptor Database: a metadata-driven automated population from sources of gene and protein sequences.

The Olfactory Receptor Database (ORDB; http://senselab.med.yale.edu/senselab/ordb) is a central repository of olfactory receptor (OR) and olfactory receptor-like gene and protein sequences. To deal with the very large OR gene family, we have constructed an algorithm that automatically downloads sequences from web sources such as GenBank and SWISS-PROT into the database. The algorithm uses hypertext markup language (HTML) parsing techniques that extract information relevant to ORDB. The information is then correlated with the metadata in the ORDB knowledge base to encode the unstructured text extracted into the structured format compliant with the database architecture, entity attribute value with classes and relationship (EAV/CR), which supports the SenseLab project as a whole. Three population methods: batch, automatic and semi-automatic population are discussed. The data is imported into the database using extensible markup language (XML).

ORDB, HORDE, ODORactor and other on-line knowledge resources of olfactory receptor-odorant interactions.

We present here an exploration of the evolution of three well-established, web-based resources dedicated to the dissemination of information related to olfactory receptors (ORs) and their functional ligands, odorants. These resources are: the Olfactory Receptor Database (ORDB), the Human Olfactory Data Explorer (HORDE) and ODORactor. ORDB is a repository of genomic and proteomic information related to ORs and other chemosensory receptors, such as taste and pheromone receptors. Three companion databases closely integrated with ORDB are OdorDB, ORModelDB and OdorMapDB; these resources are part of the SenseLab suite of databases (http://senselab.med.yale.edu). HORDE (http://genome.weizmann.ac.il/horde/) is a semi-automatically populated database of the OR repertoires of human and several mammals. ODORactor (http://mdl.shsmu.edu.cn/ODORactor/) provides information related to OR-odorant interactions from the perspective of the odorant. All three resources are connected to each other via web-links.Database URL: http://senselab.med.yale.edu; http://genome.weizmann.ac.il/horde/; http://mdl.shsmu.edu.cn/ODORactor/.

Informatics approaches to functional MRI odor mapping of the rodent olfactory bulb: OdorMapBuilder and OdorMapDB.

The present study applies informatics tools to aid and extend fMRI analysis of the coding mechanism of neural signals in the rodent olfactory system. Odor stimulation evokes unique spatial patterns of activity in the glomerular layer of the mammalian olfactory bulb (OB). An open-source software program, OdorMap-Builder, has been developed to process the high resolution anatomical and functional MRI images of the OB and to generate single two-dimensional flat maps, called odor maps, that describe the spatial activity patterns in the entire glomerular layer. Odor maps help identify the spatial activity patterns from the tremendous amount of fMRI data and they serve as ideal representation of space coding for the olfactory signals in the OB in response to a given odor stimulation. Based on the fMRI technology, OdorMapBuilder provides comparable odor maps on the intra-subject basis, a significant step towards the detailed analyses of the effects of odor types and/or concentrations. In addition, a new database, OdorMapDB, is developed to provide a repository for the generated odor maps. Web interfaces to the database are provided for the data entry, modification and retrieval. OdorMapDB is based on the EAV/CR (entity-attribute-value with classes and relationships) architecture and it is integrated with two other SenseLab olfactory databases: the olfactory receptor and odor databases. Both OdorMapBuilder and OdorMapDB should serve as useful tools and resources for the field and help facilitate experimental research in understanding the olfactory system and the mechanism for smell perception.

Text mining neuroscience journal articles to populate neuroscience databases.

We have developed a program NeuroText to populate the neuroscience databases in SenseLab (http://senselab.med.yale.edu/senselab) by mining the natural language text of neuroscience articles. NeuroText uses a two-step approach to identify relevant articles. The first step (pre-processing), aimed at 100% sensitivity, identifies abstracts containing database keywords. In the second step, potentially relevant abstracts identified in the first step are processed for specificity dictated by database architecture, and neuroscience, lexical and semantic contexts. NeuroText results were presented to the experts for validation using a dynamically generated interface that also allows expert-validated articles to be automatically deposited into the databases. Of the test set of 912 articles, 735 were rejected at the pre-processing step. For the remaining articles, the accuracy of predicting database-relevant articles was 85%. Twenty-two articles were erroneously identified. NeuroText deferred decisions on 29 articles to the expert. A comparison of NeuroText results versus the experts' analyses revealed that the program failed to correctly identify articles' relevance due to concepts that did not yet exist in the knowledgebase or due to vaguely presented information in the abstracts. NeuroText uses two "evolution" techniques (supervised and unsupervised) that play an important role in the continual improvement of the retrieval results. Software that uses the NeuroText approach can facilitate the creation of curated, special-interest, bibliography databases.

QIS: A framework for biomedical database federation.

Query Integrator System (QIS) is a database mediator framework intended to address robust data integration from continuously changing heterogeneous data sources in the biosciences. Currently in the advanced prototype stage, it is being used on a production basis to integrate data from neuroscience databases developed for the SenseLab project at Yale University with external neuroscience and genomics databases. The QIS framework uses standard technologies and is intended to be deployable by administrators with a moderate level of technological expertise: It comes with various tools, such as interfaces for the design of distributed queries. The QIS architecture is based on a set of distributed network-based servers, data source servers, integration servers, and ontology servers, that exchange metadata as well as mappings of both metadata and data elements to elements in an ontology. Metadata version difference determination coupled with decomposition of stored queries is used as the basis for partial query recovery when the schema of data sources alters.

Achieving evolvable Web-database bioscience applications using the EAV/CR framework: recent advances.

The EAV/CR framework, designed for database support of rapidly evolving scientific domains, utilizes metadata to facilitate schema maintenance and automatic generation of Web-enabled browsing interfaces to the data. EAV/CR is used in SenseLab, a neuroscience database that is part of the national Human Brain Project. This report describes various enhancements to the framework. These include (1) the ability to create "portals" that present different subsets of the schema to users with a particular research focus, (2) a generic XML-based protocol to assist data extraction and population of the database by external agents, (3) a limited form of ad hoc data query, and (4) semantic descriptors for interclass relationships and links to controlled vocabularies such as the UMLS.