The Dawn of Open Access to Phylogenetic Data

The scientific enterprise depends critically on the preservation of and open access to published data. This basic tenet applies acutely to phylogenies (estimates of evolutionary relationships among species). Increasingly, phylogenies are estimated from increasingly large, genome-scale datasets using increasingly complex statistical methods that require increasing levels of expertise and computational investment. Moreover, the resulting phylogenetic data provide an explicit historical perspective that critically informs research in a vast and growing number of scientific disciplines. One such use is the study of changes in rates of lineage diversification (speciation - extinction) through time. As part of a meta-analysis in this area, we sought to collect phylogenetic data (comprising nucleotide sequence alignment and tree files) from 217 studies published in 46 journals over a 13-year period. We document our attempts to procure those data (from online archives and by direct request to corresponding authors), and report results of analyses (using Bayesian logistic regression) to assess the impact of various factors on the success of our efforts. Overall, complete phylogenetic data for ~60% of these studies are effectively lost to science. Our study indicates that phylogenetic data are more likely to be deposited in online archives and/or shared upon request when: (1) the publishing journal has a strong data-sharing policy; (2) the publishing journal has a higher impact factor, and; (3) the data are requested from faculty rather than students. Although the situation appears dire, our analyses suggest that it is far from hopeless: recent initiatives by the scientific community -- including policy changes by journals and funding agencies -- are improving the state of affairs

Towards agreement on best practice for publishing raw clinical trial data

Many research-funding agencies now require open access to the results of research they have funded, and some also require that researchers make available the raw data generated from that research. Similarly, the journal Trials aims to address inadequate reporting in randomised controlled trials, and in order to fulfil this objective, the journal is working with the scientific and publishing communities to try to establish best practice for publishing raw data from clinical trials in peer-reviewed biomedical journals. Common issues encountered when considering raw data for publication include patient privacy – unless explicit consent for publication is obtained – and ownership, but agreed-upon policies for tackling these concerns do not appear to be addressed in the guidance or mandates currently established. Potential next steps for journal editors and publishers, ethics committees, research-funding agencies, and researchers are proposed, and alternatives to journal publication, such as restricted access repositories, are outlined

Mine, Yours, Ours? Sharing Data on Human Genetic Variation

The achievement of a robust, effective and responsible form of data sharing is currently regarded as a priority for biological and bio-medical research. Empirical evaluations of data sharing may be regarded as an indispensable first step in the identification of critical aspects and the development of strategies aimed at increasing availability of research data for the scientific community as a whole. Research concerning human genetic variation represents a potential forerunner in the establishment of widespread sharing of primary datasets. However, no specific analysis has been conducted to date in order to ascertain whether the sharing of primary datasets is common-practice in this research field. To this aim, we analyzed a total of 543 mitochondrial and Y chromosomal datasets reported in 508 papers indexed in the Pubmed database from 2008 to 2011. A substantial portion of datasets (21.9%) was found to have been withheld, while neither strong editorial policies nor high impact factor proved to be effective in increasing the sharing rate beyond the current figure of 80.5%. Disaggregating datasets for research fields, we could observe a substantially lower sharing in medical than evolutionary and forensic genetics, more evident for whole mtDNA sequences (15.0% vs 99.6%). The low rate of positive responses to e-mail requests sent to corresponding authors of withheld datasets (28.6%) suggests that sharing should be regarded as a prerequisite for final paper acceptance, while making authors deposit their results in open online databases which provide data quality control seems to provide the best-practice standard. Finally, we estimated that 29.8% to 32.9% of total resources are used to generate withheld datasets, implying that an important portion of research funding does not produce shared knowledge. By making the scientific community and the public aware of this important aspect, we may help popularize a more effective culture of data sharing

Who Shares? Who Doesn't? Factors Associated with Openly Archiving Raw Research Data

Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn't, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication

Sharing clinical research data in the United States under the health insurance portability and accountability act and the privacy rule

Sharing of final research data from clinical research is an essential part of the scientific method. The U.S. National Institutes of Health require some grant applications to include plans for sharing final research data, which it defines as the factual materials necessary to document, support, and validate research findings. In the U.S., however, the Privacy Rule adopted under the Health Insurance Portability and Accountability Act impedes the sharing of final research data. In most situations, final research data may be shared only where all information that could possibly be used to identify the subject has been deleted, or where the subject has given authorization for specific research, or an Institutional Review Board has granted a waiver

Security and privacy requirements for a multi-institutional cancer research data grid: an interview-based study

<p>Abstract</p> <p>Background</p> <p>Data protection is important for all information systems that deal with human-subjects data. Grid-based systems – such as the cancer Biomedical Informatics Grid (caBIG) – seek to develop new mechanisms to facilitate real-time federation of cancer-relevant data sources, including sources protected under a variety of regulatory laws, such as HIPAA and 21CFR11. These systems embody new models for data sharing, and hence pose new challenges to the regulatory community, and to those who would develop or adopt them. These challenges must be understood by both systems developers and system adopters. In this paper, we describe our work collecting policy statements, expectations, and requirements from regulatory decision makers at academic cancer centers in the United States. We use these statements to examine fundamental assumptions regarding data sharing using data federations and grid computing.</p> <p>Methods</p> <p>An interview-based study of key stakeholders from a sample of US cancer centers. Interviews were structured, and used an instrument that was developed for the purpose of this study. The instrument included a set of problem scenarios – difficult policy situations that were derived during a full-day discussion of potentially problematic issues by a set of project participants with diverse expertise. Each problem scenario included a set of open-ended questions that were designed to elucidate stakeholder opinions and concerns. Interviews were transcribed verbatim and used for both qualitative and quantitative analysis. For quantitative analysis, data was aggregated at the individual or institutional unit of analysis, depending on the specific interview question.</p> <p>Results</p> <p>Thirty-one (31) individuals at six cancer centers were contacted to participate. Twenty-four out of thirty-one (24/31) individuals responded to our request- yielding a total response rate of 77%. Respondents included IRB directors and policy-makers, privacy and security officers, directors of offices of research, information security officers and university legal counsel. Nineteen total interviews were conducted over a period of 16 weeks. Respondents provided answers for all four scenarios (a total of 87 questions). Results were grouped by broad themes, including among others: governance, legal and financial issues, partnership agreements, de-identification, institutional technical infrastructure for security and privacy protection, training, risk management, auditing, IRB issues, and patient/subject consent.</p> <p>Conclusion</p> <p>The findings suggest that with additional work, large scale federated sharing of data within a regulated environment is possible. A key challenge is developing suitable models for authentication and authorization practices within a federated environment. Authentication – the recognition and validation of a person's identity – is in fact a global property of such systems, while authorization – the permission to access data or resources – mimics data sharing agreements in being best served at a local level. Nine specific recommendations result from the work and are discussed in detail. These include: (1) the necessity to construct separate legal or corporate entities for governance of federated sharing initiatives on this scale; (2) consensus on the treatment of foreign and commercial partnerships; (3) the development of risk models and risk management processes; (4) development of technical infrastructure to support the credentialing process associated with research including human subjects; (5) exploring the feasibility of developing large-scale, federated honest broker approaches; (6) the development of suitable, federated identity provisioning processes to support federated authentication and authorization; (7) community development of requisite HIPAA and research ethics training modules by federation members; (8) the recognition of the need for central auditing requirements and authority, and; (9) use of two-protocol data exchange models where possible in the federation.</p

Indicators for the Data Usage Index (DUI): an incentive for publishing primary biodiversity data through global information infrastructure

<p>Abstract</p> <p>Background</p> <p>A professional recognition mechanism is required to encourage expedited publishing of an adequate volume of 'fit-for-use' biodiversity data. As a component of such a recognition mechanism, we propose the development of the Data Usage Index (DUI) to demonstrate to data publishers that their efforts of creating biodiversity datasets have impact by being accessed and used by a wide spectrum of user communities.</p> <p>Discussion</p> <p>We propose and give examples of a range of 14 absolute and normalized biodiversity dataset usage indicators for the development of a DUI based on search events and dataset download instances. The DUI is proposed to include relative as well as species profile weighted comparative indicators.</p> <p>Conclusions</p> <p>We believe that in addition to the recognition to the data publisher and all players involved in the data life cycle, a DUI will also provide much needed yet novel insight into how users use primary biodiversity data. A DUI consisting of a range of usage indicators obtained from the GBIF network and other relevant access points is within reach. The usage of biodiversity datasets leads to the development of a family of indicators in line with well known citation-based measurements of recognition.</p

An ecosystem for linked humanities data

The main promise of the digital humanities is the ability to perform scholar studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of suciently well described data. For the eld of socio-economic history, data usually comes in a tabular form. Existing eorts to curate and publish datasets take a top-down approach and are focused on large collections. This paper presents QBer and the underlying structured data hub, which address the long tail of research data by catering for the needs of individual scholars. QBer allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets.We present QBer, and evaluate our rst results by showing how our system facilitates two use cases in socio-economic history