scholarly journals An Australian collector's authority file, 1973–2020

2021 ◽  
Vol 9 ◽  
Author(s):  
Robert Mesibov

Biodiversity databases contain omissions and errors, including those resulting from data entry mistakes and from the use of outdated or incorrect data sources. Some of these omissions and errors can be minimised by the use of authority files, such as expert-compiled taxonomic name databases. However, there are few publicly available authority files for collecting events, and the "where", "when" and "by whom" of specimen data are typically entered into biodiversity databases separately and directly, item by item from specimen labels. Here I describe a publicly available compilation of 3829 of my own collecting events over a 48-year period in Australia. Each record contains a unique combination of date, georeferenced location and location notes.

2020 ◽  
Vol 27 (11) ◽  
pp. 1648-1657
Author(s):  
Tiago K Colicchio ◽  
Pavithra I Dissanayake ◽  
James J Cimino

Abstract Objective To develop a collection of concept-relationship-concept tuples to formally represent patients’ care context data to inform electronic health record (EHR) development. Materials and Methods We reviewed semantic relationships reported in the literature and developed a manual annotation schema. We used the initial schema to annotate sentences extracted from narrative note sections of cardiology, urology, and ear, nose, and throat (ENT) notes. We audio recorded ENT visits and annotated their parsed transcripts. We combined the results of each annotation into a consolidated set of concept-relationship-concept tuples. We then compared the tuples used within and across the multiple data sources. Results We annotated a total of 626 sentences. Starting with 8 relationships from the literature, we annotated 182 sentences from 8 inpatient consult notes (initial set of tuples = 43). Next, we annotated 232 sentences from 10 outpatient visit notes (enhanced set of tuples = 75). Then, we annotated 212 sentences from transcripts of 5 outpatient visits (final set of tuples = 82). The tuples from the visit transcripts covered 103 (74%) concepts documented in the notes of their respective visits. There were 20 (24%) tuples used across all data sources, 10 (12%) used only in inpatient notes, 15 (18%) used only in visit notes, and 7 (9%) used only in the visit transcripts. Conclusions We produced a robust set of 82 tuples useful to represent patients’ care context data. We propose several applications of our tuples to improve EHR navigation, data entry, learning health systems, and decision support.


Author(s):  
Dennis O. Laryea ◽  
Fred K. Awittor

ObjectiveTo discuss the implementation of confidentiality practices at theKumasi Cancer Registry.IntroductionCancer registration involves collecting information on patientswith cancer. Population-based cancer registries in particular areuseful in estimating the disease burden and to inform the institutionof prevention and control measures. Collecting personal informationon patients with cancer requires strict adherence to principles ofconfidentiality to ensure the safety of the collected data. Failure mayhave legal and medical implications. The Kumasi Cancer Registrywas established as a population-based cancer Registry in 2012. Theregistry collects data on cases of cancer occurring among residentsof the Kumasi Metropolitan area of Ghana. Issues bordering onconfidentiality were an integral part of the establishment of theregistry. We discuss the implementation of confidentiality plansduring the four years of existence of the Kumasi Cancer Registry.MethodsThe registry has a designed abstraction form which is used to collectdata. Data sources for the Registry are all major hospitals in Kumasiproviding cancer treatment services. Data sources also include privatepathology laboratories and the Births and Deaths Registry. Trainedresearch assistants collect data from the folders of patients. This isfollowed by coding and then entering into the Canreg 5 software.Coded and entered into the Canreg5 software for management andanalysis. After data entry, the forms are filed in order of registrynumbers as generated by the canreg5 software for easy reference.ResultsConfidentiality of KsCR data is ensured through the followingmeasures. The signing of a confidentiality agreement by all registrystaff. The confidentiality agreement spells out terms for the releaseof data to third parties in particular but even staff of the variousfacilities. The agreement also spells out the consequences of a breachof any of the clauses. No direct contact is made with patients duringthe process of abstraction of data by registrars. The data abstractionforms are kept in a secured safe in the registry office. The computersthat house the registry data are password enabled and are changedon a regular basis to ensure security. The Canreg5 software usedfor electronic data management also has individual profiles withpasswords for all registrars and supervisors. The scope of accessto Canreg data is limited by the profile status of the respectivestaff members. Supervisors have full access to all data includingsummarized reports. Registrars have limited access mostly restrictedto data entry. Access to the registry office is restricted to registry staffand other personnel authorized by the Registry Manager or Director.An established Registry Advisory Board is responsible for assessingrequests and approval of data from the registry. Where files have tobe sent electronically, they are password protected and sent in severalparts in separate emails.ConclusionsDespite the potential challenges to maintaining confidentialityof data in developing outcries, evidence from four years of cancerdata management in Kumasi suggests stringent measure can ensureconfidentiality. The use of multiple measures to ensure confidentialityis essential in surveillance data management


Author(s):  
Nariman Barati ◽  
J Juliët Vrolijk ◽  
Babette E Becherer ◽  
Annelotte C M van Bommel ◽  
Juliëtte E Hommes ◽  
...  

Abstract Background Correct registration of implant characteristics is essential to monitor the safety of implants within implant registries. Currently, in the nationwide Dutch Breast Implant Registry (DBIR) these characteristics are being registered manually by plastic surgeons, resulting in administrative burden and potentially incorrect data entry. Objectives This study evaluated the accuracy of manually registered implant data, possible consequences of incorrect data, and the potential of a Digital Implant Catalog (DIC) on increasing data quality and reducing the administrative burden. Methods Manually entered implant characteristics (fill, shape, coating, texture) of newly inserted breast implants in DBIR, from 2015 to 2019, were compared with the corresponding implant characteristics in the DIC. Reference numbers were used to match characteristics between the two databases. The DIC was based on manufacturers’ product catalogs and was set as the gold standard. Results 57,361 DBIR records could be matched with the DIC. Accuracy of implant characteristics varied from 70.6 to 98.0 percent, depending on the implant characteristic. The largest discrepancy was observed for ‘texture’, the smallest for ‘coating’. All manually registered implant characteristics resulted in different conclusions about implant performance when compared to the DIC (P<0.01). Implementation of the DIC reduced the administrative burden from 14 to 7 variables (50 percent). Conclusions Implementation of a Digital Implant Catalog increases data quality in DBIR and reduces the administrative burden. However, correct registration of reference numbers in the registry by plastic surgeons remains key for adequate matching. Furthermore, all implant manufacturers should be involved and regular updates of the DIC are required.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Samir T. Parmar ◽  
Brittany Kasumi Yarnell

ObjectiveTo assess the data sources used to monitor overdose events in Marion County and improve community overdose surveillance.IntroductionMirroring public health response to infectious disease outbreaks, many public health departments are taking an outbreak management approach to respond to drug overdose surges 1-3. The Marion County Public Health Department (MCPHD) has developed an overdose response plan (ORP) integrating drug overdose surveillance and community stakeholder response strategies. Effective drug overdose surveillance requires accurate and reliable data streams. This work assessed data sources utilized for county overdose surveillance and provided recommendations to improve overdose surveillance.MethodsData sources utilized as of September 2018 for opioid overdose surveillance in Marion County were assessed on utilization history by epidemiologists. General recommendations to improve overdose surveillance were created based on the findings. The three primary sources were emergency department data, ambulance run data, and death certificate data. Secondary sources included Indiana Poison Center (IPC) and toxicology data. General recommendations were generated based on challenges/solutions encountered and good practices observed from other health departments 4,5.ResultsThe assessment of data sources and utilization showed variation of data entry at the hospital level, limited identifiers in some cases, and varying timeliness ranges which may limit combined use of many data sources. The emergency department data source showed particular variation in data entry, limited unique identifier information), and no incident location information which impedes geographical surveillance. Periodic data checks by the ambulance service data holder appears to drastically increase data quality. Intermittent data feed drops from specific emergency departments also interfered with effective surveillance. Recommendations were generated based on lessons learned during successful partnerships with Indianapolis Emergency Medical Services, IPC, and emergency departments and challenges encountered during overdose surveillance work (Figure 1).In application of the strategy, the MCPHD is interested in linking data and looking for other ways to improve our overdose response to get a fuller picture of what is happening with overdoses, so we applied the steps in figure one to find areas of improvement. We found that limited identifiers and incomplete fields are our biggest challenge to linking datasets, so to combat these gaps we identified sources that have the necessary fields of interest and have been working with others to improve the data quality. Additionally, data sources will be evaluated on experiences with three categories: completeness in data fields, timeliness of data delivery, and consistency of data feed. Data quality measures were developed for completeness by fields present per record, timeliness by lag time from time added to time of event, and consistency by record counts per facility over time. We also recognized that meeting with partners is necessary to share how we are using the data and additional datasets that we might use in the future. Additionally we have been meeting with academic researchers so that we can expand our analyses to identify other issues related to overdoses. Finally, in order to make a difference in Marion County we are applying our findings to our outreach and interventions to hopefully prevent more overdoses and deaths.Future plans include data partnerships include police drug arrest data, fire department naloxone administration data, prescription drug monitoring data, Medicaid claims data, and health information exchange overdose data. Future research partnerships will consider a solutions based framework 6.ConclusionsThe results of our work demonstrate the value in surveillance assessment to summarize limitations of the many data sources utilized at a local level to conduct overdose surveillance. Our evaluation approach provides a path to improve and fill in surveillance gaps with new processes. Other health departments interested in optimizing overdose surveillance may seek a similar evaluation approach. Periodic data linkages have not been implemented which presents an opportunity to glean valuable insights on longitudinal patterns of drug use in the population. Future collaboration with researchers presents an opportunity to improve MCPHD ORP, Safe Syringe Access and Support Program, and Substance Use Outreach Services interventions.References[1] Moore K, Boulet M, Lew J, Papadomanolakis-Pakis N. A public health outbreak management framework applied to surges in opioid overdoses. Journal of opioid management. 2017;13(5):273-81.[2] Rudd RA. Increases in drug and opioid-involved overdose deaths—United States, 2010–2015. MMWR. Morbidity and mortality weekly report. 2016;65.[3] Rowe C, Wheeler E, Jones TS, Yeh C, Coffin PO. Community-Based Response to Fentanyl Overdose Outbreak, San Francisco, 2015. Journal of Urban Health. 2018 May 3:1-6.[4] Chen H, Hailey D, Wang N, Yu P. A review of data quality assessment methods for public health information systems. International journal of environmental research and public health. 2014 May 14;11(5):5170-207.[5] Massachusetts. Department of Public Health. An Assessment of Opioid-Related Deaths in Massachusetts (2013-2014). Massachusetts Department of Public Health; 2016.[6] Wiehe SE, Rosenman MB, Chartash D, Lipscomb ER, Nelson TL, Magee LA, Fortenberry JD, Aalsma MC. A Solutions-Based Approach to Building Data-Sharing Partnerships. eGEMs. 2018;6(1).


Author(s):  
M.F. Schmid ◽  
R. Dargahi ◽  
M. W. Tam

Electron crystallography is an emerging field for structure determination as evidenced by a number of membrane proteins that have been solved to near-atomic resolution. Advances in specimen preparation and in data acquisition with a 400kV microscope by computer controlled spot scanning mean that our ability to record electron image data will outstrip our capacity to analyze it. The computed fourier transform of these images must be processed in order to provide a direct measurement of amplitudes and phases needed for 3-D reconstruction.In anticipation of this processing bottleneck, we have written a program that incorporates a menu-and mouse-driven procedure for auto-indexing and refining the reciprocal lattice parameters in the computed transform from an image of a crystal. It is linked to subsequent steps of image processing by a system of data bases and spawned child processes; data transfer between different program modules no longer requires manual data entry. The progress of the reciprocal lattice refinement is monitored visually and quantitatively. If desired, the processing is carried through the lattice distortion correction (unbending) steps automatically.


Author(s):  
Gyeung Ho Kim ◽  
Mehmet Sarikaya ◽  
D. L. Milius ◽  
I. A. Aksay

Cermets are designed to optimize the mechanical properties of ceramics (hard and strong component) and metals (ductile and tough component) into one system. However, the processing of such systems is a problem in obtaining fully dense composite without deleterious reaction products. In the lightweight (2.65 g/cc) B4C-Al cermet, many of the processing problems have been circumvented. It is now possible to process fully dense B4C-Al cermet with tailored microstructures and achieve unique combination of mechanical properties (fracture strength of over 600 MPa and fracture toughness of 12 MPa-m1/2). In this paper, microstructure and fractography of B4C-Al cermets, tested under dynamic and static loading conditions, are described.The cermet is prepared by infiltration of Al at 1150°C into partially sintered B4C compact under vacuum to full density. Fracture surface replicas were prepared by using cellulose acetate and thin-film carbon deposition. Samples were observed with a Philips 3000 at 100 kV.


2008 ◽  
Author(s):  
Kimberly A. Barchard ◽  
Jenna Scott ◽  
David Weintraub ◽  
Larry A. Pace
Keyword(s):  

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