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"Database Management Systems - organization "
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Data Sharing in the Post-Genomic World: The Experience of the International Cancer Genome Consortium (ICGC) Data Access Compliance Office (DACO)
ICGC and the Development of Controlled Access Policies Controlled access mechanisms may be viewed as the product of dual imperatives: 1) the legal and ethical requirements of regulators and research ethics committees, as well as research funders and study participants, to protect the confidentiality of data from re-identification and misuse by third parties; and 2) pressure, largely from within the science community, to protect data-producing investigators from acts of free riding by other members of the community (e.g., by ensuring they are properly acknowledged in publications and that no parasitic patents are deposited on the data by subsequent data users). Early models of databases having a two-tiered open/controlled access system included the database of Genotypes and Phenotypes (dbGaP) at the US National Institutes of Health (http://www.ncbi.nlm.nih.gov/gap), the Wellcome Trust Case Control Consortium (WTCCC) (http://www.wtccc.org.uk/), the Malaria Genomic Epidemiology Network (MalariaGEN) (http://www.malariagen.net/), and the European Genome-phenome Archive (EGA) (https://www.ebi.ac.uk/ega/).
Journal Article
Research data management
Librarians have begun to provide a range of services in this area and now teach data management to researchers, work with individual researchers to improve their data management practices, create data management subject guides, and assist in supporting funding agency and publisher data requirements. The data from such an experiment might be composed of: (1) a folder filled with MRI image files or perhaps multiple folders, each filled with MRI images for an individual study participant; (2) data about treatment timing and dosage stored in spreadsheets or possibly in paper forms; (3) spreadsheets of processed data, tumor size for our example; and (4) final analyzed data used to create a figure for publication.
Journal Article
The read-write Linked Data Web
This paper discusses issues that will affect the future development of the Web, either increasing its power and utility, or alternatively suppressing its development. It argues for the importance of the continued development of the Linked Data Web, and describes the use of linked open data as an important component of that. Second, the paper defends the Web as a read-write medium, and goes on to consider how the read-write Linked Data Web could be achieved.
Journal Article
Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints
Cochrane has developed a linked data infrastructure to make the evidence and data from its rich repositories more discoverable to facilitate evidence-based health decision-making. These annotated resources can enhance the study and understanding of biomarkers and surrogate endpoints.
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Multisite collaboration using REDCap to capture library data
In January 2018, library services at Providence St. Joseph Health merged to form a single, unified system, incorporating nine libraries and sixteen full-time staff. As a small, nonclinical team of librarians, we needed to make sure our work and value were visible to clinicians, administrators, and other nonlibrary stakeholders. Using REDCap, we developed a form to seamlessly collect statistics about our services.Virtual Projects are published on an annual basis in the Journal of the Medical Library Association (JMLA) following an annual call for virtual projects in MLAConnect and announcements to encourage submissions from all types of libraries. An advisory committee of recognized technology experts selects project entries based on their currency, innovation, and contribution to health sciences librarianship.
Journal Article
LAS: A Software Platform to Support Oncological Data Management
The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data quality.
Journal Article
Managing the Impact of the ICD-10 Transition on a Data Warehouse
The U.S. Centers for Medicare and Medicaid (CMS) mandated transition from the 9th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-9) to the 10th Edition (ICD-10) on October 1, 2015. ICD-10 differs from ICD-9 in several important ways. First, the total number of codes is larger (approximately 68,000 vs. 13,000). Second, the ICD-9 codes were exclusively numeric, whereas ICD-10 uses alphanumeric codes. Finally, ICD-10 enables the specification of both laterality and the combination of multiple diagnoses in one ICD-10 code (i.e. diabetes mellitus with renal failure, or pressure ulcer right elbow). To accommodate this substantial change in billing code sets, health care organizations have poured extraordinary resources into modifying electronic systems and educating individuals who interact with code sets including providers, coders, and billing clerks. At Vanderbilt University Medical Center (VUMC), our team maintains a large perioperative data warehouse the Vanderbilt Perioperative Data Warehouse (VPDW). In this brief technical communication, we provide an overview of our experience in handing the ICD-9 to ICD-10 migration from a data warehouse perspective.
Journal Article
Federated Web-accessible Clinical Data Management within an Extensible NeuroImaging Database
Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data to be incorporated into existing data management systems. In this paper, an extensible data management system for clinical neuroimaging studies is introduced: The Human Clinical Imaging Database (HID) and Toolkit. The database schema is constructed to support the storage of new data types without changes to the underlying schema. The complex infrastructure allows management of experiment data, such as image protocol and behavioral task parameters, as well as subject-specific data, including demographics, clinical assessments, and behavioral task performance metrics. Of significant interest, embedded clinical data entry and management tools enhance both consistency of data reporting and automatic entry of data into the database. The Clinical Assessment Layout Manager (CALM) allows users to create on-line data entry forms for use within and across sites, through which data is pulled into the underlying database via the generic clinical assessment management engine (GAME). Importantly, the system is designed to operate in a distributed environment, serving both human users and client applications in a service-oriented manner. Querying capabilities use a built-in multi-database parallel query builder/result combiner, allowing web-accessible queries within and across multiple federated databases. The system along with its documentation is open-source and available from the Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) site.
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