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At least 127 records · Page 7Linked to original sources
PubMed1995

Practical application of sharing surveillance data.

This article provides several practical and effective mechanisms for reporting meaningful information on nosocomial infections to critical care and other specific units. Roadblocks and a small sample of hospital practices for reporting unit-specific infections are described. Graphic presentations, especially line-stay histograms, are recommended.

Data Interpretation, Statistical↗
PubMed1997

Guaranteeing anonymity when sharing medical data, the Datafly System.

We present a computer program named Datafly that maintains anonymity in medical data by automatically generalizing, substituting, and removing information as appropriate without losing many of the details found within the data. Decisions are made at the field and record level at the time of database access, so the approach can be used on the fly in role-based security within an institution, and in batch mode for exporting data from an institution. Often organizations release and receive medical data with all explicit identifiers, such as name, address and phone number, removed in the incorrect belief that patient confidentiality is maintained because the resulting data look anonymous; however, we show the remaining data can often be used to re-identify individuals by linking or matching the data to other databases or by looking at unique characteristics found in the fields and records of the database itself. When these less apparent aspects are taken into account, each released record can be made to ambiguously map to many possible people, providing a level of anonymity determined by the user.

Confidentiality↗
PubMed1999

DNA fingerprinting data and the analysis of population genetic structure by comparing band-sharing patterns.

Genetic isolation among populations can be effectively investigated by multilocus DNA fingerprinting. If populations have diverged, it is expected that the mean proportion of bands shared by individuals from the same population, Bw, exceeds the corresponding mean, Bb, calculated from pairs of individuals from distinct populations. A problem arises in deciding whether any difference between Bw and Bb is statistically significant. In fact, any two band-sharing data (bij), contributing to Bw or Bb, are not independent if they share a common individual (like bij and bjl). This prevents a correct application of parametric tests, such as the Student's t-test. Recently, a modification of this test has been proposed that should avoid the independence problem. Using a large number of samples of fingerprints, simulated from an appropriate 'genetic' model, under a wide range of conditions, we compared the performances of the Student's t-test, the modified t-test and five new permutation tests, where individuals, rather than bij values, are permuted. We found that: (i) the Student's t-test can be very permissive, rejecting too often the null hypothesis when true, but is correct or conservative in certain cases; (ii) the modified t-test is extremely conservative when the null hypothesis is true and very inefficient otherwise; (iii) all five permutation tests are strictly correct, provided that individuals are ordered randomly on gels; and (iv) in this case, the permutation tests are equally efficient, and are not inferior to the Student's t-test when the latter is approximately correct and provides a fair benchmark.

DNA Fingerprinting↗
PubMed1998

Sharing and archiving data is fundamental to scientific progress.

The persuasive argument for sharing and archiving data is that scientists must build on the shoulders of other scientists, that science is cumulative and replicative, and that science must be open. Sharing and archiving data are just a small part of all that is implied by that principle, but it is inextricably part of our obligation as social and behavioral scientists to conduct our work in the open. Only then can others see and understand what we did, and only then will someone have a chance to confirm that we were right, or to prove that we were wrong. Moreover, data archiving and sharing create opportunities for addressing questions not envisioned by the initial investigators. Indeed, by supplementing or pooling archived data, new and original data sets can be created that permit analyses well beyond the purpose or scope of the initial data collection. Of course, the creativity and labor of initial investigators should be protected, and the privacy of research participants must be safeguarded. These protections and safeguards, however, are not antithetical to data archiving and sharing. They simply raise questions about when and how data archiving and sharing should take place. In our view, the benefits of properly archived and shared data for outweigh the potential for harm. As indicated above, this is a perspective shared by several funding agencies of behavioral and social research, including the NIA.

Data Collection↗
PubMed2003

The tissue microarray data exchange specification: a community-based, open source tool for sharing tissue microarray data.

BACKGROUND: Tissue Microarrays (TMAs) allow researchers to examine hundreds of small tissue samples on a single glass slide. The information held in a single TMA slide may easily involve Gigabytes of data. To benefit from TMA technology, the scientific community needs an open source TMA data exchange specification that will convey all of the data in a TMA experiment in a format that is understandable to both humans and computers. A data exchange specification for TMAs allows researchers to submit their data to journals and to public data repositories and to share or merge data from different laboratories. In May 2001, the Association of Pathology Informatics (API) hosted the first in a series of four workshops, co-sponsored by the National Cancer Institute, to develop an open, community-supported TMA data exchange specification. METHODS: A draft tissue microarray data exchange specification was developed through workshop meetings. The first workshop confirmed community support for the effort and urged the creation of an open XML-based specification. This was to evolve in steps with approval for each step coming from the stakeholders in the user community during open workshops. By the fourth workshop, held October, 2002, a set of Common Data Elements (CDEs) was established as well as a basic strategy for organizing TMA data in self-describing XML documents. RESULTS: The TMA data exchange specification is a well-formed XML document with four required sections: 1) Header, containing the specification Dublin Core identifiers, 2) Block, describing the paraffin-embedded array of tissues, 3)Slide, describing the glass slides produced from the Block, and 4) Core, containing all data related to the individual tissue samples contained in the array. Eighty CDEs, conforming to the ISO-11179 specification for data elements constitute XML tags used in the TMA data exchange specification. A set of six simple semantic rules describe the complete data exchange specification. Anyone using the data exchange specification can validate their TMA files using a software implementation written in Perl and distributed as a supplemental file with this publication. CONCLUSION: The TMA data exchange specification is now available in a draft form with community-approved Common Data Elements and a community-approved general file format and data structure. The specification can be freely used by the scientific community. Efforts sponsored by the Association for Pathology Informatics to refine the draft TMA data exchange specification are expected to continue for at least two more years. The interested public is invited to participate in these open efforts. Information on future workshops will be posted at http://www.pathologyinformatics.org (API we site).

Community Health Services↗