Best Practices for Ethical Sharing of Individual-Level Health Research Data From Low- and Middle-Income Settings

Sharing individual-level data from clinical and public health research is increasingly being seen as a core requirement for effective and efficient biomedical research. This article discusses the results of a systematic review and multisite qualitative study of key stakeholders' perspectives on best practices in ethical data sharing in low- and middle-income settings. Our research suggests that for data sharing to be effective and sustainable, multiple social and ethical requirements need to be met. An effective model of data sharing will be one in which considered judgments will need to be made about how best to achieve scientific progress, minimize risks of harm, promote fairness and reciprocity, and build and sustain trust.

Feasibility of individual patient data meta-analyses in orthopaedic surgery

BACKGROUND

The number of individual patient data meta-analyses published is very low especially in surgical domains. Our aim was to assess the feasibility of individual patient data (IPD) meta-analyses in orthopaedic surgery by determining whether trialists agree to send IPD for eligible trials.

METHODS

We performed a literature search to identify relevant research questions in orthopaedic surgery. For each question, we developed a protocol synopsis for an IPD meta-analysis and identified all related randomized controlled trials (RCTs) with results published since 2000. Corresponding authors of these RCTs were sent personalized emails that presented a project for an IPD meta-analysis corresponding to one of the research questions, with a link to the protocol synopsis, and asking for IPD from their RCT. We guaranteed patient confidentiality and secure data storage, and offered co-authorship and coverage of costs related to extraction.

RESULTS

We identified 38 research questions and 273 RCTs related to these questions. We could contact 217 of the 273 corresponding authors (79 %; 56 had unavailable or non-functional email addresses) and received 68/273 responses (25 %): 21 authors refused to share IPD, 10 stated that our request was under consideration and 37 agreed to send IPD. Four corresponding authors required authorship and three others asked for financial support to send the IPD. Overall, we could obtain IPD for 5,110 of 33,602 eligible patients (15 %). Among the 38 research questions, only one IPD meta-analysis could be potentially initiated because we could receive IPD for more than 50 % of participants.

CONCLUSION

The present study illustrates the difficulties in initiating IPD meta-analyses in orthopaedic surgery. Significant efforts must be made to improve data sharing.

IRB practices and policies regarding the secondary research use of biospecimens

BACKGROUND

As sharing and secondary research use of biospecimens increases, IRBs and researchers face the challenge of protecting and respecting donors without comprehensive regulations addressing the human subject protection issues posed by biobanking. Variation in IRB biobanking policies about these issues has not been well documented.

METHODS

This paper reports on data from a survey of IRB Administrative Directors from 60 institutions affiliated with the Clinical and Translation Science Awards (CTSAs) about their policies and practices regarding secondary use and sharing of biospecimens. Specifically, IRB ADs were asked about consent for future use of biospecimens, assignment of risk for studies using biobanked specimens, and sharing of biospecimens/data.

RESULTS

Our data indicate that IRBs take varying approaches to protocol review, risk assessment, and data sharing, especially when specimens are not anonymized.

CONCLUSION

Unclear or divergent policies regarding biospecimen research among IRBs may constitute a barrier to advancing genetic studies and to inter-institutional collaboration, given different institutional requirements for human subjects protections.

Ontology-based approach for in vivo human connectomics: the medial Brodmann area 6 case study

Different non-invasive neuroimaging modalities and multi-level analysis of human connectomics datasets yield a great amount of heterogeneous data which are hard to integrate into an unified representation. Biomedical ontologies can provide a suitable integrative framework for domain knowledge as well as a tool to facilitate information retrieval, data sharing and data comparisons across scales, modalities and species. Especially, it is urgently needed to fill the gap between neurobiology and in vivo human connectomics in order to better take into account the reality highlighted in Magnetic Resonance Imaging (MRI) and relate it to existing brain knowledge. The aim of this study was to create a neuroanatomical ontology, called “Human Connectomics Ontology” (HCO), in order to represent macroscopic gray matter regions connected with fiber bundles assessed by diffusion tractography and to annotate MRI connectomics datasets acquired in the living human brain. First a neuroanatomical “view” called NEURO-DL-FMA was extracted from the reference ontology Foundational Model of Anatomy (FMA) in order to construct a gross anatomy ontology of the brain. HCO extends NEURO-DL-FMA by introducing entities (such as “MR_Node” and “MR_Route”) and object properties (such as “tracto_connects”) pertaining to MR connectivity. The Web Ontology Language Description Logics (OWL DL) formalism was used in order to enable reasoning with common reasoning engines. Moreover, an experimental work was achieved in order to demonstrate how the HCO could be effectively used to address complex queries concerning in vivo MRI connectomics datasets. Indeed, neuroimaging datasets of five healthy subjects were annotated with terms of the HCO and a multi-level analysis of the connectivity patterns assessed by diffusion tractography of the right medial Brodmann Area 6 was achieved using a set of queries. This approach can facilitate comparison of data across scales, modalities and species.

NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

Here we present NeuroVault—a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.

Parent perspectives on privacy and governance for a pediatric repository of non-biological, research data

Research data repositories (RDRs) are data storage entities where data can be submitted, stored, and subsequently accessed for purposes beyond the original intent. There is little information relating to non-biological RDRs, nor considerations regarding pediatric data storage and re-use. We examined parent perspectives on pediatric, non-biological RDRs. Qualitative, descriptive methods including both interviews and focus groups were used. Purposive sampling of adult participants in two provincial birth cohorts yielded 19 interviewees and 18 focus group participants (4 groups). Transcripts were analyzed by thematic content analysis. Parent research participants strongly supported the sharing of their own, and their child's, non-biological research data. Four themes emerged: that altruism has limits, that participants have ongoing privacy concerns, that some participants need the assurance of congruent values between themselves and researchers/research questions, and that opinions diverge for some governance issues. The establishment of RDRs is important and maximizes participants', researchers', and funders' investments. Participants as data donors have concerns relating to privacy, relationships, and governance that must be considered in RDR development.

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.

Developing a guideline to standardize the citation of bioresources in journal articles (CoBRA)

BACKGROUND

Many biomedical publications refer to data obtained from collections of biosamples. Sharing such bioresources (biological samples, data, and databases) is paramount for the present governance of research. Recognition of the effort involved in generating, maintaining, and sharing high quality bioresources is poorly organized, which does not encourage sharing. At publication level, the recognition of such resources is often neglected and/or highly heterogeneous. This is a true handicap for the traceability of bioresource use. The aim of this article is to propose, for the first time, a guideline for reporting bioresource use in research articles, named CoBRA: Citation of BioResources in journal Articles.

METHODS

As standards for citing bioresources are still lacking, the members of the journal editors subgroup of the Bioresource Research Impact Factor (BRIF) initiative developed a standardized and appropriate citation scheme for such resources by informing stakeholders about the subject and raising awareness among scientists and in science editors' networks, mapping this topic among other relevant initiatives, promoting actions addressed to stakeholders, launching surveys, and organizing focused workshops.

RESULTS

The European Association of Science Editors has adopted BRIF's suggestion to incorporate statements on biobanks in the Methods section of their guidelines. The BRIF subgroup agreed upon a proposed citation system: each individual bioresource that is used to perform a study and that is mentioned in the Methods section should be cited as an individual "reference [BIORESOURCE]" according to a delineated format. The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network mentioned the proposed reporting guideline in their "guidelines under development" section.

CONCLUSIONS

Evaluating bioresources' use and impact requires that publications accurately cite such resources. Adopting the standard citation scheme described here will improve the quality of bioresource reporting and will allow their traceability in scientific publications, thus increasing the recognition of bioresources' value and relevance to research. Please see related article: http://dx.doi.org/10.1186/s12916-015-0284-9.

The growth and gaps of genetic data sharing policies in the United States

The 1996 Bermuda Principles launched a new era in data sharing, reflecting a growing belief that the rapid public dissemination of research data was crucial to scientific progress in genetics. A historical review of data sharing policies in the field of genetics and genomics reflects changing scientific norms and evolving views of genomic data, particularly related to human subjects' protections and privacy concerns. The 2013 NIH Draft Genomic Data Sharing (GDS) Policy incorporates the most significant protections and guidelines to date. The GDS Policy, however, will face difficult challenges ahead as geneticists seek to balance the very real concerns of research participants and the scientific norms that propel research forward. This article provides a novel evaluation of genetic and GDS policies' treatment of human subjects' protections. The article examines not only the policies, but also some of the most pertinent scientific, legal, and regulatory developments that occurred alongside data sharing policies. This historical perspective highlights the challenges that future data sharing policies, including the recently disseminated NIH GDS Draft Policy, will encounter.

Virtual patients on the semantic Web: a proof-of-application study

Background

Virtual patients are interactive computer simulations that are increasingly used as learning activities in modern health care education, especially in teaching clinical decision making. A key challenge is how to retrieve and repurpose virtual patients as unique types of educational resources between different platforms because of the lack of standardized content-retrieving and repurposing mechanisms. Semantic Web technologies provide the capability, through structured information, for easy retrieval, reuse, repurposing, and exchange of virtual patients between different systems.

Objective

An attempt to address this challenge has been made through the mEducator Best Practice Network, which provisioned frameworks for the discovery, retrieval, sharing, and reuse of medical educational resources. We have extended the OpenLabyrinth virtual patient authoring and deployment platform to facilitate the repurposing and retrieval of existing virtual patient material.

Methods

A standalone Web distribution and Web interface, which contains an extension for the OpenLabyrinth virtual patient authoring system, was implemented. This extension was designed to semantically annotate virtual patients to facilitate intelligent searches, complex queries, and easy exchange between institutions. The OpenLabyrinth extension enables OpenLabyrinth authors to integrate and share virtual patient case metadata within the mEducator3.0 network. Evaluation included 3 successive steps: (1) expert reviews; (2) evaluation of the ability of health care professionals and medical students to create, share, and exchange virtual patients through specific scenarios in extended OpenLabyrinth (OLabX); and (3) evaluation of the repurposed learning objects that emerged from the procedure.

Results

We evaluated 30 repurposed virtual patient cases. The evaluation, with a total of 98 participants, demonstrated the system’s main strength: the core repurposing capacity. The extensive metadata schema presentation facilitated user exploration and filtering of resources. Usability weaknesses were primarily related to standard computer applications’ ease of use provisions. Most evaluators provided positive feedback regarding educational experiences on both content and system usability. Evaluation results replicated across several independent evaluation events.

Conclusions

The OpenLabyrinth extension, as part of the semantic mEducator3.0 approach, is a virtual patient sharing approach that builds on a collection of Semantic Web services and federates existing sources of clinical and educational data. It is an effective sharing tool for virtual patients and has been merged into the next version of the app (OpenLabyrinth 3.3). Such tool extensions may enhance the medical education arsenal with capacities of creating simulation/game-based learning episodes, massive open online courses, curricular transformations, and a future robust infrastructure for enabling mobile learning.

The DISTANCE model for collaborative research: distributing analytic effort using scrambled data sets

BACKGROUND

Data-sharing is encouraged to fulfill the ethical responsibility to transform research data into public health knowledge, but data sharing carries risks of improper disclosure and potential harm from release of individually identifiable data.

METHODS

The study objective was to develop and implement a novel method for scientific collaboration and data sharing which distributes the analytic burden while protecting patient privacy. A procedure was developed where in an investigator who is external to an analytic coordinating center (ACC) can conduct original research following a protocol governed by a Publications and Presentations (P&P) Committee. The collaborating investigator submits a study proposal and, if approved, develops the analytic specifications using existing data dictionaries and templates. An original data set is prepared according to the specifications and the external investigator is provided with a complete but de-identified and shuffled data set which retains all key data fields but which obfuscates individually identifiable data and patterns; this" scrambled data set" provides a "sandbox" for the external investigator to develop and test analytic code for analyses. The analytic code is then run against the original data at the ACC to generate output which is used by the external investigator in preparing a manuscript for journal submission.

RESULTS

The method has been successfully used with collaborators to produce many published papers and conference reports.

CONCLUSION

By distributing the analytic burden, this method can facilitate collaboration and expand analytic capacity, resulting in more science for less money.

Using PhonBank and Phon in studies of phonological development and disorders

The goal of this paper is to present an overview of new tools that can be used to further our understanding of phonological development and disorders. We begin with a summary of the field of child phonology with a focus on databases and methods of analysis and then move to a description of PhonBank, a shared database for the study of phonology, and Phon, a specialised software system capable of performing various types of phonological analyses based on both phonetic transcriptions and acoustic analyses of speech productions. We provide a detailed example of using PhonBank and Phon to examine the use of velar fronting using longitudinal data from one child with typical development and three children with phonological disorder. We conclude with an emphasis on data sharing and its central relevance to further advances in our field.

Ethical and regulatory considerations in the design of traumatic brain injury clinical studies

Research is essential for improving outcomes after traumatic brain injury (TBI). However, the ubiquity, variability, and nature of TBI create many ethical issues and accompanying regulations for research. To capture the complexity and importance of designing and conducting TBI research within the framework of key ethical principles, a few highly relevant topics are highlighted. The selected topics are: (1) research conducted in emergency settings; (2) maintaining equipoise in TBI clinical trials; (3) TBI research on vulnerable populations; and (4) ethical considerations for sharing data. The topics aim to demonstrate the dynamic and multifaceted challenges of TBI research, and also to stress the value of addressing these challenges with the key ethical principles of respect, beneficence, and justice. Much has been accomplished to ensure that TBI research meets the highest ethical standards and has fair and enforceable regulations, but important challenges remain and continued efforts are needed by all members of the TBI research community.

The semantic web in translational medicine: current applications and future directions

Semantic web technologies offer an approach to data integration and sharing, even for resources developed independently or broadly distributed across the web. This approach is particularly suitable for scientific domains that profit from large amounts of data that reside in the public domain and that have to be exploited in combination. Translational medicine is such a domain, which in addition has to integrate private data from the clinical domain with proprietary data from the pharmaceutical domain. In this survey, we present the results of our analysis of translational medicine solutions that follow a semantic web approach. We assessed these solutions in terms of their target medical use case; the resources covered to achieve their objectives; and their use of existing semantic web resources for the purposes of data sharing, data interoperability and knowledge discovery. The semantic web technologies seem to fulfill their role in facilitating the integration and exploration of data from disparate sources, but it is also clear that simply using them is not enough. It is fundamental to reuse resources, to define mappings between resources, to share data and knowledge. All these aspects allow the instantiation of translational medicine at the semantic web-scale, thus resulting in a network of solutions that can share resources for a faster transfer of new scientific results into the clinical practice. The envisioned network of translational medicine solutions is on its way, but it still requires resolving the challenges of sharing protected data and of integrating semantic-driven technologies into the clinical practice.

Does policy grow on trees?

Policymaking is both an art and a science. It is a long process of research, debate and consensus (where possible). The elaboration of the Framework for Responsible Sharing of Genomic and Health-Related Data serves as an illustration of this process.

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.