Minimal Information for Neural Electromagnetic Ontologies (MINEMO): A standards-compliant method for analysis and integration of event-related potentials (ERP) data

We present MINEMO (Minimal Information for Neural ElectroMagnetic Ontologies), a checklist for the description of event-related potentials (ERP) studies. MINEMO extends MINI (Minimal Information for Neuroscience Investigations)to the ERP domain. Checklist terms are explicated in NEMO, a formal ontology that is designed to support ERP data sharing and integration. MINEMO is also linked to an ERP database and web application (the NEMO portal). Users upload their data and enter MINEMO information through the portal. The database then stores these entries in RDF (Resource Description Framework), along with summary metrics, i.e., spatial and temporal metadata. Together these spatial, temporal, and functional metadata provide a complete description of ERP data and the context in which these data were acquired. The RDF files then serve as inputs to ontology-based labeling and meta-analysis. Our ultimate goal is to represent ERPs using a rich semantic structure, so results can be queried at multiple levels, to stimulate novel hypotheses and to promote a high-level, integrative account of ERP results across diverse study methods and paradigms.

The association between health information exchange and measures of patient satisfaction

OBJECTIVE

Health information exchange (HIE) is the interorganizational sharing of patient information and is one of many health information technology initiatives expected to transform the U.S. healthcare system. Two outcomes expected to be improved by HIE are patient-provider communication and patient satisfaction . This analysis examined the relationship between the level of HIE engagement and these two factors in a sample of U.S. hospitals.

METHODS

Independent variables came from existing secondary sources and the dependent measures were from the Hospital Consumer Assessment of Healthcare Providers and Systems. The analysis included 3,278 hospitals. Using ordinary least squares regression, implemented HIE was positively associated with the percentage of patients reporting nurses communicated well and higher satisfaction. Due to the potential for selection bias, results were further explored using a propensity score analysis.

RESULTS

Hospitals that had adopted HIE, but not yet implemented saw no benefits. Hospitals' level of HIE was not associated with the percentage of patients reporting doctors communicated well. According to propensity score corrected estimates, implemented HIE was associated with the percentage of patients who reported nurses always communicated well and who would definitely recommend the hospital.

CONCLUSION

Few studies have examined the impact of HIE at the organizational level. This examination provides some evidence that hospitals engaging in HIE are associated with higher patient satisfaction.

Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): standardised reporting for model reproducibility, interoperability, and data sharing

Cardiac experimental electrophysiology is in need of a well-defined Minimum Information Standard for recording, annotating, and reporting experimental data. As a step towards establishing this, we present a draft standard, called Minimum Information about a Cardiac Electrophysiology Experiment (MICEE). The ultimate goal is to develop a useful tool for cardiac electrophysiologists which facilitates and improves dissemination of the minimum information necessary for reproduction of cardiac electrophysiology research, allowing for easier comparison and utilisation of findings by others. It is hoped that this will enhance the integration of individual results into experimental, computational, and conceptual models. In its present form, this draft is intended for assessment and development by the research community. We invite the reader to join this effort, and, if deemed productive, implement the Minimum Information about a Cardiac Electrophysiology Experiment standard in their own work.

Understanding identifiability in secondary health data

Secondary health data are becoming important sources of information for health research. Unfortunately, there are few national guidelines outlining clear criteria for data custodians when adjudicating requests for access to these data. This presents a particular challenge to researchers and even public health officials interested in multi-jurisdictional analysis of health data. While some of the problem in developing consistent national standards may stem from differences in provincial privacy law, it may also be the result of imprecise terminology, particularly around the concept of identifiability. This commentary discusses the differences between identifiability and self-identification with the hope of contributing to a broader discussion establishing national guidelines for the use of secondary health data in research.

Democracies restricting democratic rights: some classical sources and implications for ethics of biometrics

Ancient Greek and 17th century English philosophy are not usually discussed along with the ethics of biometrics and data sharing. Academic ethics today, however, suffers from a lack of background in classical texts. We may discuss whether biometrics and data sharing are consistent with democracy, but if we do not know what democracy is, then we cannot know what actions are consistent with it. I shall discuss how and why democracies have restricted the rights of their citizens. I will give the most attention to two paradigms that have most influenced modern democratic thinking: 17th century English democracy and ancient Athens. I do not accept the dogma that the Athenians were obviously wrong to try and then to condemn Socrates. His death-loving doctrine could not but have weakened the will of the youth to work and fight for the good of Athens. I will try to understand the Athenians' point of view and their need to defend their security. At the end, I will apply these lessons to biometrics and data sharing for security reasons.

Data standards for Omics data: the basis of data sharing and reuse

To facilitate sharing of Omics data, many groups of scientists have been working to establish the relevant data standards. The main components of data sharing standards are experiment description standards, data exchange standards, terminology standards, and experiment execution standards. Here we provide a survey of existing and emerging standards that are intended to assist the free and open exchange of large-format data.

Balancing scientific and community interests in community-based participatory research

Community-based participatory research is an approach to studying human populations that emphasizes extensive partnerships between researchers and community members. While there are many advantages of this approach, it also faces a number of conceptual and practical challenges, one of which is managing the conflict that sometimes arises between promoting scientific and community interests. This essay explores the potential conflict between scientific and community interests in several different stages of community-based participatory research, including research design, data interpretation, and publication, and makes some suggestions for practice and policy. To manage potential conflicts between scientific and community interests, investigators and community partners should enter into written agreements at the beginning of the study. In some cases, it may be necessary for a third party, such as a review committee from a supporting institution, the community, or a funding agency, to help investigators and community partners resolve disagreements. It may also be useful, in some situations, to publish a dissenting opinion when investigators and community partners cannot agree on how to interpret findings resulting from a study. These strategies may help address some of the challenges of implementing community-based participatory research.

PhysiomeSpace: digital library service for biomedical data

Every research laboratory has a wealth of biomedical data locked up, which, if shared with other experts, could dramatically improve biomedical and healthcare research. With the PhysiomeSpace service, it is now possible with a few clicks to share with selected users biomedical data in an easy, controlled and safe way. The digital library service is managed using a client-server approach. The client application is used to import, fuse and enrich the data information according to the PhysiomeSpace resource ontology and upload/download the data to the library. The server services are hosted on the Biomed Town community portal, where through a web interface, the user can complete the metadata curation and share and/or publish the data resources. A search service capitalizes on the domain ontology and on the enrichment of metadata for each resource, providing a powerful discovery environment. Once the users have found the data resources they are interested in, they can add them to their basket, following a metaphor popular in e-commerce web sites. When all the necessary resources have been selected, the user can download the basket contents into the client application. The digital library service is now in beta and open to the biomedical research community.

Public sharing of research datasets: a pilot study of associations

The public sharing of primary research datasets potentially benefits the research community but is not yet common practice. In this pilot study, we analyzed whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact. Across 397 recent biomedical microarray studies, we found investigators were more likely to publicly share their raw dataset when their study was published in a high-impact journal and when the first or last authors had high levels of career experience and impact. We estimate the USA's National Institutes of Health (NIH) data sharing policy applied to 19% of the studies in our cohort; being subject to the NIH data sharing plan requirement was not found to correlate with increased data sharing behavior in multivariate logistic regression analysis. Studies published in journals that required a database submission accession number as a condition of publication were more likely to share their data, but this trend was not statistically significant. These early results will inform our ongoing larger analysis, and hopefully contribute to the development of more effective data sharing initiatives.

Recall and bias of retrieving gene expression microarray datasets through PubMed identifiers

BACKGROUND

The ability to locate publicly available gene expression microarray datasets effectively and efficiently facilitates the reuse of these potentially valuable resources. Centralized biomedical databases allow users to query dataset metadata descriptions, but these annotations are often too sparse and diverse to allow complex and accurate queries. In this study we examined the ability of PubMed article identifiers to locate publicly available gene expression microarray datasets, and investigated whether the retrieved datasets were representative of publicly available datasets found through statements of data sharing in the associated research articles.

RESULTS

In a recent article, Ochsner and colleagues identified 397 studies that had generated gene expression microarray data. Their search of the full text of each publication for statements of data sharing revealed 203 publicly available datasets, including 179 in the Gene Expression Omnibus (GEO) or ArrayExpress databases. Our scripted search of GEO and ArrayExpress for PubMed identifiers of the same 397 studies returned 160 datasets, including six not found by the original search for data sharing statements. As a proportion of datasets found by either method, the search for data sharing statements identified 91.4% of the 209 publicly available datasets, compared to only 76.6% found by our search carried out using PubMed identifiers. Searching GEO or ArrayExpress alone retrieved 63.2% and 46.9% of all available datasets, respectively. There was no difference in the type of datasets found by PubMed identifier searches in terms of research theme or the technology used. However, the studies identified were more likely to have larger sample sizes, were more frequently cited, and published in higher impact journals.

CONCLUSIONS

Searching database entries using PubMed identifiers can identify the majority of publicly available datasets, but caution is required when this method is used to collect data for policy evaluation since studies in low impact journals are disproportionately excluded. We urge authors of all datasets to complete the citation fields for their dataset submissions once publication details are known, thereby ensuring their work has maximum visibility and can contribute to subsequent studies.

Enabling personalized medicine through an interoperable IT infrastructure: an overview of the cancer Biomedical Informatics Grid®

To implement personalized medicine successfully, multidisciplinary teams of collaborating scientists must manage and analyze vast quantities of genomic and clinical outcomes data in a cohesive and integrated way. This process is complex and not supported by the existing IT infrastructure and tools available to most researchers. To address these needs, the National Cancer Institute initiated the cancer Biomedical Informatics Grid initiative in 2004, to develop and deploy the interoperable IT infrastructure and tools needed to help basic and clinical researchers manage and share these data. Now, the cancer Biomedical Informatics Grid is being deployed to cancer centers and other biomedical research organizations across the USA and around the world, facilitating collaborative research that will ultimately lead to improved patient outcomes.

Slim-prim: a biomedical informatics database to promote translational research

With the current national emphasis on translational research, data-exchange systems that can bridge the basic and clinical sciences are vital. To meet this challenge, we have developed Slim-Prim, an integrated data system (IDS) for collecting, processing, archiving, and distributing basic and clinical research data. Slim-Prim is accessed via user-friendly Web-based applications, thus increasing data accessibility and eliminating the security risks inherent with office or laboratory servers. Slim-Prim serves as a laboratory management interface and archival data repository for institutional projects. Importantly, multiple levels of controlled access allow HIPAA-compliant sharing of de-identified information to facilitate data sharing and analysis across research domains; thus Slim-Prim encourages collaboration between researchers and clinicians, an essential factor in the development of translational research. Slim-Prim is an example of utilizing an IDS to improve organizational efficiency and to bridge the gap between laboratory discovery and practice.

NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF

Neuronal morphology affects network connectivity, plasticity, and information processing. Uncovering the design principles and functional consequences of dendritic and axonal shape necessitates quantitative analysis and computational modeling of detailed experimental data. Digital reconstructions provide the required neuromorphological descriptions in a parsimonious, comprehensive, and reliable numerical format. NeuroMorpho.Org is the largest web-accessible repository service for digitally reconstructed neurons and one of the integrated resources in the Neuroscience Information Framework (NIF). Here we describe the NeuroMorpho.Org approach as an exemplary experience in designing, creating, populating, and curating a neuroscience digital resource. The simple three-tier architecture of NeuroMorpho.Org (web client, web server, and relational database) encompasses all necessary elements to support a large-scale, integrate-able repository. The data content, while heterogeneous in scientific scope and experimental origin, is unified in format and presentation by an in house standardization protocol. The server application (MRALD) is secure, customizable, and developer-friendly. Centralized processing and expert annotation yields a comprehensive set of metadata that enriches and complements the raw data. The thoroughly tested interface design allows for optimal and effective data search and retrieval. Availability of data in both original and standardized formats ensures compatibility with existing resources and fosters further tool development. Other key functions enable extensive exploration and discovery, including 3D and interactive visualization of branching, frequently measured morphometrics, and reciprocal links to the original PubMed publications. The integration of NeuroMorpho.Org with version-1 of the NIF (NIFv1) provides the opportunity to access morphological data in the context of other relevant resources and diverse subdomains of neuroscience, opening exciting new possibilities in data mining and knowledge discovery. The outcome of such coordination is the rapid and powerful advancement of neuroscience research at both the conceptual and technological level.

Confidentiality and spatially explicit data: concerns and challenges

Recent theoretical, methodological, and technological advances in the spatial sciences create an opportunity for social scientists to address questions about the reciprocal relationship between context (spatial organization, environment, etc.) and individual behavior. This emerging research community has yet to adequately address the new threats to the confidentiality of respondent data in spatially explicit social survey or census data files, however. This paper presents four sometimes conflicting principles for the conduct of ethical and high-quality science using such data: protection of confidentiality, the social-spatial linkage, data sharing, and data preservation. The conflict among these four principles is particularly evident in the display of spatially explicit data through maps combined with the sharing of tabular data files. This paper reviews these two research activities and shows how current practices favor one of the principles over the others and do not satisfactorily resolve the conflict among them. Maps are indispensable for the display of results but also reveal information on the location of respondents and sampling clusters that can then be used in combination with shared data files to identify respondents. The current practice of sharing modified or incomplete data sets or using data enclaves is not ideal for either the advancement of science or the protection of confidentiality. Further basic research and open debate are needed to advance both understanding of and solutions to this dilemma.

Exchanging genetic data for public health practice and human subjects research: implications for health practitioners

Advances in genomic science and medicine require collaboration between healthcare providers, researchers and public health practitioners to improve individual and communal health. However, broader collaboration raises significant privacy and other concerns about acquisitions, uses and disclosures of identifiable genetic data. Corresponding legal requirements vary extensively if data are to be used in clinical practice, human subjects research, or public health surveillance. Distinguishing between practice and research activities is not easy. We introduce an enhanced methodology to distinguish public health practice from human subjects research to: help providers, researchers, and public health practitioners navigate the legal requirements underlying the exchange of genetic information; determine when public health data use crosses over to research, thereby triggering differing legal and ethical regulations; and to protect individual and group privacy from the unintended misuse of genetic information.