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Results for “Data sharing”

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At least 127 records · Page 7Linked to original sources
PubMed2006

Whose data set is it anyway? Sharing raw data from randomized trials.

BACKGROUND: Sharing of raw research data is common in many areas of medical research, genomics being perhaps the most well-known example. In the clinical trial community investigators routinely refuse to share raw data from a randomized trial without giving a reason. DISCUSSION: Data sharing benefits numerous research-related activities: reproducing analyses; testing secondary hypotheses; developing and evaluating novel statistical methods; teaching; aiding design of future trials; meta-analysis; and, possibly, preventing error, fraud and selective reporting. Clinical trialists, however, sometimes appear overly concerned with being scooped and with misrepresentation of their work. Both possibilities can be avoided with simple measures such as inclusion of the original trialists as co-authors on any publication resulting from data sharing. Moreover, if we treat any data set as belonging to the patients who comprise it, rather than the investigators, such concerns fall away. CONCLUSION: Technological developments, particularly the Internet, have made data sharing generally a trivial logistical problem. Data sharing should come to be seen as an inherent part of conducting a randomized trial, similar to the way in which we consider ethical review and publication of study results. Journals and funding bodies should insist that trialists make raw data available, for example, by publishing data on the Web. If the clinical trial community continues to fail with respect to data sharing, we will only strengthen the public perception that we do clinical trials to benefit ourselves, not our patients.

Editorial↗
PubMed2005

Surrogate data--a secure way to share corporate data.

The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical compounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections of surrogate molecules without the danger of disclosing the underlying molecular structures.

Chemical Engineering↗
PubMed1991

Ethical issues in sharing epidemiologic data.

Concurrent with the explosion in large data files and computers capable of handling both linkage of large data sets and analyzing multiple studies for meta-analysis, this decade has seen a rise in professional concern about the need for researchers to share their data. As scientific groups began to address this question, its importance and complexity became quickly apparent. In this paper recent developments on the ethics of data sharing in statistics, sociology, psychology, and other fields related to epidemiology are summarized, followed by a discussion on why data should be shared, what kinds of data should be shared, who among epidemiologists should be sharing data, when it is appropriate to share data, and how data sharing should be conducted.

Access to Information↗
PubMed2005

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.

Confidentiality↗
PubMed2001

Future of benchmarking: more data, more sharing, and better patient care.

Automated systems that provide whatever regulatory information is needed when it is needed; sharing of data to improve quality; data mined for specific groups of patients: Those are just a few of the trends predicted by health care experts asked to comment on the future of benchmarking and data strategies. Such improvements are needed; many hospitals continually run into problems when it comes to finding the right data sets for targeted patient groups.

Benchmarking↗
PubMed2001

The sharing of cDNA microarray data.

During the initial development of microarrays, much discussion revolved around the technology itself. The discussion has now shifted to data analysis and data sharing. There is great interest in the sharing of cDNA microarray data, but several issues related to format, quality and validation will need to be resolved before microarray data can be meaningfully integrated into other molecular databases.

Animals↗
PubMed2000

Should data and safety monitoring boards share confidential interim data?

Consider the following situation: Two clinical trials are underway, closely related in terms of the interventions being compared and the target populations. In preparing for a planned interim analysis, the statistician for trial 1 finds that the results support a recommendation to stop the trial early. Should the statistician ask the investigators for trial 2 to make interim results of their trial available to the data and safety monitoring board (DSMB) for trial 1? If so, in what form? Would the answers change if the trial 1 results showed a strong but not convincing trend? What is the obligation of the trial 2 investigators to respond to such a request? What role do the two DSMBs have, either in initiating a request or in agreeing to respond to it? In this article, we examine this situation in some detail, having faced it occasionally in our own experience with clinical trials and DSMBs. The chief argument in favor of sharing data is that data from trial 2 are obviously relevant to the question being addressed by trial 1 and therefore ought to be available to those who must interpret the results from that trial. On the other hand, there are several reasons for not sharing interim data. For example, sharing is incompatible with the independence of the trials; the time for synthesizing evidence from both trials is after the two teams of investigators have presented the full analysis and interpretation of their separate trials. For this and other conceptual and practical reasons we conclude that it is better, in most cases, for DSMBs to consider only information that has already been made public in some form.

Clinical Trials as Topic↗
PubMed2002

Neuroinformatics: the integration of shared databases and tools towards integrative neuroscience.

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools. The exchange of neuroscience data and tools between groups affords the opportunity to differently re-analyze previously collected data, encourage new neuroscience interpretations and foster otherwise uninitiated collaborations, and provide a framework for the further development of theoretically based models of brain function. Data sharing will ultimately reduce experimental and analytical error. Many small Internet accessible database initiatives have been developed and specialized analytical software and modeling tools are distributed within different fields of neuroscience. However, in addition large-scale international collaborations are required which involve new mechanisms of coordination and funding. Provided sufficient government support is given to such international initiatives, sharing of neuroscience data and tools can play a pivotal role in human brain research and lead to innovations in neuroscience, informatics and treatment of brain disorders. These innovations will enable application of theoretical modeling techniques to enhance our understanding of the integrative aspects of neuroscience. This article, authored by a multinational working group on neuroinformatics established by the Organization for Economic Co-operation and Development (OECD), articulates some of the challenges and lessons learned to date in efforts to achieve international collaborative neuroscience.

Computational Biology↗