Reducing contrast contamination in radial turbo-spin-echo acquisitions by combining a narrow-band KWIC filter with parallel imaging

PURPOSE

Cartesian turbo spin-echo (TSE) and radial TSE images are usually reconstructed by assembling data containing different contrast information into a single k-space. This approach results in mixed contrast contributions in the images, which may reduce their diagnostic value. The goal of this work is to improve the image contrast from radial TSE acquisitions by reducing the contribution of signals with undesired contrast information.

METHODS

Radial TSE acquisitions allow the reconstruction of multiple images with different T2 contrasts using the k-space weighted image contrast (KWIC) filter. In this work, the image contrast is improved by reducing the band-width of the KWIC filter. Data for the reconstruction of a single image are selected from within a small temporal range around the desired echo time. The resulting dataset is undersampled and, therefore, an iterative parallel imaging algorithm is applied to remove aliasing artifacts.

RESULTS

Radial TSE images of the human brain reconstructed with the proposed method show an improved contrast when compared with Cartesian TSE images or radial TSE images with conventional KWIC reconstructions.

CONCLUSION

The proposed method provides multi-contrast images from radial TSE data with contrasts similar to multi spin-echo images. Contaminations from unwanted contrast weightings are strongly reduced.

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26-27 June 2013, Research Triangle Park, NC, USA

A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.

It's Good to Share: Why Environmental Scientists' Ethics Are Out of Date

Although there have been many recent calls for increased data sharing, the majority of environmental scientists do not make their individual data sets publicly available in online repositories. Current data-sharing conversations are focused on overcoming the technological challenges associated with data sharing and the lack of rewards and incentives for individuals to share data. We argue that the most important conversation has yet to take place: There has not been a strong ethical impetus for sharing data within the current culture, behaviors, and practices of environmental scientists. In this article, we describe a critical shift that is happening in both society and the environmental science community that makes data sharing not just good but ethically obligatory. This is a shift toward the ethical value of promoting inclusivity within and beyond science. An essential element of a truly inclusionary and democratic approach to science is to share data through publicly accessible data sets.

Attitudes of research participants and the general public towards genomic data sharing: a systematic literature review

AIM

Introducing data sharing practices into the genomic research arena has challenged the current mechanisms established to protect rights of individuals and triggered policy considerations. To inform such policy deliberations, soliciting public and research participants' attitudes with respect to genomic data sharing is a necessity.

METHOD

The main electronic databases were searched in order to retrieve empirical studies, investigating the attitudes of research participants and the public towards genomic data sharing through public databases.

RESULTS

In the 15 included studies, participants' attitudes towards genomic data sharing revealed the influence of a constellation of interrelated factors, including the personal perceptions of controllability and sensitivity of data, potential risks and benefits of data sharing at individual and social level and also governance level considerations.

CONCLUSION

This analysis indicates that future policy responses and recruitment practices should be attentive to a wide variety of concerns in order to promote both responsible and progressive research.

Building and strengthening infrastructure for data exchange: lessons from the beacon communities

Introduction:

The Beacon Community Cooperative Agreement Program supports interventions, including care-delivery innovations, provider performance measurement and feedback initiatives, and tools for providers and consumers to enhance care. Using a learning health system framework, we examine the Beacon Communities’ processes in building and strengthening health IT (HIT) infrastructures, specifically successes and challenges in sharing patient information to improve clinical care.

Background:

In 2010, the Office of the National Coordinator for Health Information Technology (ONC) launched the three-year program, which provided $250 million to 17 Beacon Communities to invest in HIT and health information exchange (HIE) infrastructure. Beacon Communities used this funding to develop and disseminate HIT-enabled quality improvement practices found effective in particular community and practice environments.

Methods:

NORC conducted 7 site visits, November 2012–March 2013, selecting Communities to represent diverse program features. From August–October 2013, NORC held discussions with the remaining 10 Communities. Following each visit or discussion, NORC summarized the information gathered, including transcripts, team observations, and other documents the Community provided, to facilitate a within-Community analysis of context and stakeholders, intervention strategies, enabling factors, and challenges.

Results:

Although each Community designed and implemented data-sharing strategies in a unique environment, similar challenges and enabling factors emerged across the Beacons. From a learning health system perspective, their strategies to build and strengthen data-sharing infrastructures address the following crosscutting priorities: promoting technical advances and innovations by helping providers adapt EHRs for data exchange and performance measurement with customizable IT and offering technical support to smaller, independent providers; engaging key stakeholders; and fostering transparent governance and stewardship of the infrastructure with neutral conveners.

Conclusion:

While all the Communities developed or strengthened data-exchange infrastructure, each did this in a unique environment of existing health care market and legal factors. The Communities, however, encountered similar challenges and enabling factors. Organizations undertaking collaborative data sharing, performance measurement and clinical transformation can learn from the Beacon Communities’ experience.

Benefits and Risks in Secondary Use of Digitized Clinical Data: Views of Community Members Living in a Predominantly Ethnic Minority Urban Neighborhood

BACKGROUND

There is potential to increase the speed of scientific discovery and implement personalized health care by using digitized clinical data collected on the patient care experience. The use of these data in research raises concerns about the privacy and confidentiality of personal health information. This study explored community members' views on the secondary use of digitized clinical data to (1) recruit participants for clinical studies; (2) recruit family members of persons with an index condition for primary studies; and (3) conduct studies of information related to stored biospecimens.

METHODS

A qualitative descriptive design was used to examine the bioethical issues outlined from the perspective of urban-dwelling community members. Focus groups were used for data collection, and emergent content analysis was employed to organize and interpret the data.

RESULTS

Thirty community members attended one of four focus groups ranging in size from 4 to 11 participants. Five critical themes emerged from the focus-group material: (1) perceived motivators for research participation; (2) objective or "real-life" barriers to research participation; (3) a psychological component of uncertainty and mistrust; (4) preferred mechanisms for recruitment and participation; and (5) cultural characteristics that can impact understanding and willingness to engage in research.

CONCLUSIONS

The overriding concern of community members regarding research participation and/or secondary clinical and nonclinical use of digitized information was that their involvement would be safe and the outcome would be meaningful to them and to others. According to participants, biospecimens acquired during routine clinical visits or for research are no longer possessions of the participant. Although the loss of privacy was a concern for participants, they preferred that researchers access their personal health information using a digitized clinical file rather than through a paper-based medical record.

Harnessing modern web application technology to create intuitive and efficient data visualization and sharing tools

Neuroscientists increasingly need to work with big data in order to derive meaningful results in their field. Collecting, organizing and analyzing this data can be a major hurdle on the road to scientific discovery. This hurdle can be lowered using the same technologies that are currently revolutionizing the way that cultural and social media sites represent and share information with their users. Web application technologies and standards such as RESTful webservices, HTML5 and high-performance in-browser JavaScript engines are being utilized to vastly improve the way that the world accesses and shares information. The neuroscience community can also benefit tremendously from these technologies. We present here a web application that allows users to explore and request the complex datasets that need to be shared among the neuroimaging community. The COINS (Collaborative Informatics and Neuroimaging Suite) Data Exchange uses web application technologies to facilitate data sharing in three phases: Exploration, Request/Communication, and Download. This paper will focus on the first phase, and how intuitive exploration of large and complex datasets is achieved using a framework that centers around asynchronous client-server communication (AJAX) and also exposes a powerful API that can be utilized by other applications to explore available data. First opened to the neuroscience community in August 2012, the Data Exchange has already provided researchers with over 2500 GB of data.

KiMoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems

BACKGROUND

The kinetic modeling of biological systems is mainly composed of three steps that proceed iteratively: model building, simulation and analysis. In the first step, it is usually required to set initial metabolite concentrations, and to assign kinetic rate laws, along with estimating parameter values using kinetic data through optimization when these are not known. Although the rapid development of high-throughput methods has generated much omics data, experimentalists present only a summary of obtained results for publication, the experimental data files are not usually submitted to any public repository, or simply not available at all. In order to automatize as much as possible the steps of building kinetic models, there is a growing requirement in the systems biology community for easily exchanging data in combination with models, which represents the main motivation of KiMoSys development.

DESCRIPTION

KiMoSys is a user-friendly platform that includes a public data repository of published experimental data, containing concentration data of metabolites and enzymes and flux data. It was designed to ensure data management, storage and sharing for a wider systems biology community. This community repository offers a web-based interface and upload facility to turn available data into publicly accessible, centralized and structured-format data files. Moreover, it compiles and integrates available kinetic models associated with the data.KiMoSys also integrates some tools to facilitate the kinetic model construction process of large-scale metabolic networks, especially when the systems biologists perform computational research.

CONCLUSIONS

KiMoSys is a web-based system that integrates a public data and associated model(s) repository with computational tools, providing the systems biology community with a novel application facilitating data storage and sharing, thus supporting construction of ODE-based kinetic models and collaborative research projects.The web application implemented using Ruby on Rails framework is freely available for web access at http://kimosys.org, along with its full documentation.

Stem cell research funding policies and dynamic innovation: a survey of open access and commercialization requirements

This article compares and contrasts the pressures of both open access data sharing and commercialization policies in the context of publicly funded embryonic stem cell research (SCR). First, normative guidelines of international SCR organizations were examined. We then examined SCR funding guidelines and the project evaluation criteria of major funding organizations in the EU, the United Kingdom (UK), Spain, Canada and the United States. Our survey of policies revealed subtle pressures to commercialize research that include: increased funding availability for commercialization opportunities, assistance for obtaining intellectual property rights (IPRs) and legislation mandating commercialization. In lieu of open access models, funders are increasingly opting for limited sharing models or "protected commons" models that make the research available to researchers within the same region or those receiving the same funding. Meanwhile, there still is need for funding agencies to clarify and standardize terms such as "non-profit organizations" and "for-profit research," as more universities are pursuing for-profit or commercial opportunities.

OWLing Clinical Data Repositories With the Ontology Web Language

Background

The health sciences are based upon information. Clinical information is usually stored and managed by physicians with precarious tools, such as spreadsheets. The biomedical domain is more complex than other domains that have adopted information and communication technologies as pervasive business tools. Moreover, medicine continuously changes its corpus of knowledge because of new discoveries and the rearrangements in the relationships among concepts. This scenario makes it especially difficult to offer good tools to answer the professional needs of researchers and constitutes a barrier that needs innovation to discover useful solutions.

Objective

The objective was to design and implement a framework for the development of clinical data repositories, capable of facing the continuous change in the biomedicine domain and minimizing the technical knowledge required from final users.

Methods

We combined knowledge management tools and methodologies with relational technology. We present an ontology-based approach that is flexible and efficient for dealing with complexity and change, integrated with a solid relational storage and a Web graphical user interface.

Results

Onto Clinical Research Forms (OntoCRF) is a framework for the definition, modeling, and instantiation of data repositories. It does not need any database design or programming. All required information to define a new project is explicitly stated in ontologies. Moreover, the user interface is built automatically on the fly as Web pages, whereas data are stored in a generic repository. This allows for immediate deployment and population of the database as well as instant online availability of any modification.

Conclusions

OntoCRF is a complete framework to build data repositories with a solid relational storage. Driven by ontologies, OntoCRF is more flexible and efficient to deal with complexity and change than traditional systems and does not require very skilled technical people facilitating the engineering of clinical software systems.

Developing a data infrastructure for a learning health system: the PORTAL network

The Kaiser Permanente & Strategic Partners Patient Outcomes Research To Advance Learning (PORTAL) network engages four healthcare delivery systems (Kaiser Permanente, Group Health Cooperative, HealthPartners, and Denver Health) and their affiliated research centers to create a new national network infrastructure that builds on existing relationships among these institutions. PORTAL is enhancing its current capabilities by expanding the scope of the common data model, paying particular attention to incorporating patient-reported data more systematically, implementing new multi-site data governance procedures, and integrating the PCORnet PopMedNet platform across our research centers. PORTAL is partnering with clinical research and patient experts to create cohorts of patients with a common diagnosis (colorectal cancer), a rare diagnosis (adolescents and adults with severe congenital heart disease), and adults who are overweight or obese, including those with pre-diabetes or diabetes, to conduct large-scale observational comparative effectiveness research and pragmatic clinical trials across diverse clinical care settings.

PEDSnet: a National Pediatric Learning Health System

A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.

Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology

The branching structures of neurons are a long-standing focus of neuroscience. Axonal and dendritic morphology affect synaptic signaling, integration, and connectivity, and their diversity reflects the computational specialization of neural circuits. Altered neuronal morphology accompanies functional changes during development, experience, aging, and disease. Technological improvements continuously accelerate high-throughput tissue processing, image acquisition, and morphological reconstruction. Digital reconstructions of neuronal morphologies allow for complex quantitative analyses that are unattainable from raw images or two-dimensional tracings. Furthermore, digitized morphologies enable computational modeling of biophysically realistic neuronal dynamics. Additionally, reconstructions generated to address specific scientific questions have the potential for continued investigations beyond the original reason for their acquisition. Facilitating multiple reuse are repositories like NeuroMorpho.Org, which ease the sharing of reconstructions. Here, we review selected scientific literature reporting the reconstruction of axonal or dendritic morphology with diverse goals including establishment of neuronal identity, examination of physiological properties, and quantification of developmental or pathological changes. These reconstructions, deposited in NeuroMorpho.Org, have since been used by other investigators in additional research, of which we highlight representative examples. This cycle of data generation, analysis, sharing, and reuse reveals the vast potential of digital reconstructions in quantitative investigations of neuronal morphology.

The promotion of data sharing in pharmacoepidemiology

This article addresses the role of pharmacoepidemiology in patient safety and the crucial role of data sharing in ensuring that such activities occur. Against the backdrop of proposed reforms of European data protection legislation, it considers whether the current legislative landscape adequately facilitates this essential data sharing. It is argued that rather than maximising and promoting the benefits of such activities by facilitating data sharing, current and proposed legislative landscapes hamper these vital activities. The article posits that current and proposed data protection approaches to pharmacoepidemiology--and more broadly, re-uses of data--should be reoriented towards enabling these important safety enhancing activities. Two potential solutions are offered: 1) a dedicated working party on data reuse for health research and 2) the introduction of new, dedicated legislation.

Refining gold from existing data

PURPOSE OF REVIEW

The purpose is to review the arguments for increasing use of existing data in health research.

RECENT FINDINGS

The reuse of data in observational, exploratory and outcome studies, as well as in confirming other findings, is being justified on epistemological grounds as the major path to new knowledge and to the generalizing of findings to 'real world' populations. It is also justified on the grounds of cost, power, and efficiency, even though data reuse comes with real informatics, scientific culture, societal, and regulatory issues.

SUMMARY

Data reuse is becoming more compelling. When contemplating new research for any purpose other than RCTs for efficacy, it is prudent to consider whether partnership with existing data holders should be part of the optimal research plan.

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Big data, open science and the brain: lessons learned from genomics

The BRAIN Initiative aims to break new ground in the scale and speed of data collection in neuroscience, requiring tools to handle data in the magnitude of yottabytes (10²⁴). The scale, investment and organization of it are being compared to the Human Genome Project (HGP), which has exemplified “big science” for biology. In line with the trend towards Big Data in genomic research, the promise of the BRAIN Initiative, as well as the European Human Brain Project, rests on the possibility to amass vast quantities of data to model the complex interactions between the brain and behavior and inform the diagnosis and prevention of neurological disorders and psychiatric disease. Advocates of this “data driven” paradigm in neuroscience argue that harnessing the large quantities of data generated across laboratories worldwide has numerous methodological, ethical and economic advantages, but it requires the neuroscience community to adopt a culture of data sharing and open access to benefit from them. In this article, we examine the rationale for data sharing among advocates and briefly exemplify these in terms of new “open neuroscience” projects. Then, drawing on the frequently invoked model of data sharing in genomics, we go on to demonstrate the complexities of data sharing, shedding light on the sociological and ethical challenges within the realms of institutions, researchers and participants, namely dilemmas around public/private interests in data, (lack of) motivation to share in the academic community, and potential loss of participant anonymity. Our paper serves to highlight some foreseeable tensions around data sharing relevant to the emergent “open neuroscience” movement.

Satellite remote sensing, biodiversity research and conservation of the future

Assessing and predicting ecosystem responses to global environmental change and its impacts on human well-being are high priority targets for the scientific community. The potential for synergies between remote sensing science and ecology, especially satellite remote sensing and conservation biology, has been highlighted by many in the past. Yet, the two research communities have only recently begun to coordinate their agendas. Such synchronization is the key to improving the potential for satellite data effectively to support future environmental management decision-making processes. With this themed issue, we aim to illustrate how integrating remote sensing into ecological research promotes a better understanding of the mechanisms shaping current changes in biodiversity patterns and improves conservation efforts. Added benefits include fostering innovation, generating new research directions in both disciplines and the development of new satellite remote sensing products.

Zoonotic disease surveillance--inventory of systems integrating human and animal disease information

Although 65% of recent major disease outbreaks throughout the world have a zoonotic origin, there is still a sharp division among the disciplines into the human and animal health sectors. In the last few decades, a global integrative concept, often referred to as 'One Health', has been strongly endorsed. Surveillance and monitoring efforts are major components for effective disease prevention and control. As human health and animal health are inextricably linked, it is assumed that a cross-sectoral data interpretation of zoonotic disease information will improve their prevention, prediction and control. To provide an overview of existing systems throughout the world which integrate information from humans and animals on zoonotic diseases, a literature review was conducted. Twenty projects were identified and described regarding their concepts and realization. They all vary widely depending on their surveillance purpose, their structure and the source of information they use. What they have in common is that they quite often use data which have already been collected for another purpose. Therefore, the challenges of how to make use of such secondary data are of great interest.

Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation

The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the “small N” problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries—the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.

Pediatric data sharing in genomic research: attitudes and preferences of parents

OBJECTIVE

In the United States, data from federally funded genomics studies are stored in national databases, which may be accessible to anyone online (public release) or only to qualified researchers (restricted release). The availability of such data exposes participants to privacy risk and limits the ability to withdraw from research. This exposure is especially challenging for pediatric participants, who are enrolled in studies with parental permission. The current study examines genomic research participants' attitudes to explore differences in data sharing (DS) preferences between parents of pediatric patients and adult patients.

METHODS

A total of 113 parents of pediatric patients and 196 adult participants from 6 genomics studies were randomly assigned to 3 experimental consent forms. Participants were invited to a follow-up structured interview exploring DS preferences, study understanding, and attitudes. Descriptive analyses and regression models were built on responses.

RESULTS

Most parents (73.5%) and adult participants (90.3%) ultimately consented to broad public release. However, parents were significantly more restrictive in their data release decisions, not because of understanding or perceived benefits of participation but rather autonomy and control. Parents want to be more involved in the decision about DS and are significantly more concerned than adult participants about unknown future risks.

CONCLUSIONS

Parents have the same altruistic motivations and grasp of genomics studies as adult participants. However, they are more concerned about future risks to their child, which probably motivates them to choose more restrictive DS options, but only when such options are made available.

BIOFRAG - a new database for analyzing BIOdiversity responses to forest FRAGmentation

Habitat fragmentation studies have produced complex results that are challenging to synthesize. Inconsistencies among studies may result from variation in the choice of landscape metrics and response variables, which is often compounded by a lack of key statistical or methodological information. Collating primary datasets on biodiversity responses to fragmentation in a consistent and flexible database permits simple data retrieval for subsequent analyses. We present a relational database that links such field data to taxonomic nomenclature, spatial and temporal plot attributes, and environmental characteristics. Field assessments include measurements of the response(s) (e.g., presence, abundance, ground cover) of one or more species linked to plots in fragments within a partially forested landscape. The database currently holds 9830 unique species recorded in plots of 58 unique landscapes in six of eight realms: mammals 315, birds 1286, herptiles 460, insects 4521, spiders 204, other arthropods 85, gastropods 70, annelids 8, platyhelminthes 4, Onychophora 2, vascular plants 2112, nonvascular plants and lichens 320, and fungi 449. Three landscapes were sampled as long-term time series (>10 years). Seven hundred and eleven species are found in two or more landscapes. Consolidating the substantial amount of primary data available on biodiversity responses to fragmentation in the context of land-use change and natural disturbances is an essential part of understanding the effects of increasing anthropogenic pressures on land. The consistent format of this database facilitates testing of generalizations concerning biologic responses to fragmentation across diverse systems and taxa. It also allows the re-examination of existing datasets with alternative landscape metrics and robust statistical methods, for example, helping to address pseudo-replication problems. The database can thus help researchers in producing broad syntheses of the effects of land use. The database is dynamic and inclusive, and contributions from individual and large-scale data-collection efforts are welcome.

A web-portal for interactive data exploration, visualization, and hypothesis testing

Clinical research studies generate data that need to be shared and statistically analyzed by their participating institutions. The distributed nature of research and the different domains involved present major challenges to data sharing, exploration, and visualization. The Data Portal infrastructure was developed to support ongoing research in the areas of neurocognition, imaging, and genetics. Researchers benefit from the integration of data sources across domains, the explicit representation of knowledge from domain experts, and user interfaces providing convenient access to project specific data resources and algorithms. The system provides an interactive approach to statistical analysis, data mining, and hypothesis testing over the lifetime of a study and fulfills a mandate of public sharing by integrating data sharing into a system built for active data exploration. The web-based platform removes barriers for research and supports the ongoing exploration of data.

Analyzing large-scale spiking neural data with HRLAnalysis(™)

The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in data can make analyzing the results of these simulations difficult. This is further compounded by the fact that many existing analysis packages were not developed with large spiking data sets in mind. Presented here is a software suite developed to not only process the increased amount of spike-train data in a reasonable amount of time, but also provide a user friendly Python interface. We describe the design considerations, implementation and features of the HRLAnalysis(™) suite. In addition, performance benchmarks demonstrating the speedup of this design compared to a published Python implementation are also presented. The result is a high-performance analysis toolkit that is not only usable and readily extensible, but also straightforward to interface with existing Python modules.