Dataset Information

SpotCard: an optical mark recognition tool to improve field data collection speed and accuracy.

ABSTRACT: Background:When taking photographs of plants in the field, it is often necessary to record additional information such as sample number, biological replicate number and subspecies. Manual methods of recording such information are slow, often involve laborious transcription from hand-written notes or the need to have a laptop or tablet on site, and present a risk by separating written data capture from image capture. Existing tools for field data capture focus on recording information rather than capturing pictures of plants. Results:We present SpotCard, a tool comprising two macros. The first can be used to create a template for small, reusable cards for use when photographing plants. Information can be encoded on these cards in a human- and machine-readable form, allowing the user to swiftly make annotations before taking the photograph. The second part of the tool automatically reads the annotations from the image and tabulates them in a CSV file, along with picture date, time and GPS coordinates. The SpotCard also provides a convenient scale bar and coordinate location within the image for the flower itself, enabling automated measurement of floral traits such as area and perimeter. Conclusions:This tool is shown to read annotations with a high degree of accuracy and at a speed greatly faster than manual transcription. It includes the ability to read the date and time of the photograph, as well as GPS location. It is an open-source ImageJ/Fiji macro and is available online. Its use requires no knowledge of the ImageJ macro coding language, and it is therefore well suited to all researchers taking pictures in the field.

SUBMITTER: Symington HA

PROVIDER: S-EPMC6385457 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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SpotCard: an optical mark recognition tool to improve field data collection speed and accuracy.

Symington Hamish A HA Glover Beverley J BJ

Plant methods 20190222

<h4>Background</h4>When taking photographs of plants in the field, it is often necessary to record additional information such as sample number, biological replicate number and subspecies. Manual methods of recording such information are slow, often involve laborious transcription from hand-written notes or the need to have a laptop or tablet on site, and present a risk by separating written data capture from image capture. Existing tools for field data capture focus on recording information rat ...[more]

PMID: 30833981

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Project description:Obtaining robust survival estimates is critical, but sample size limitations often result in imprecise estimates or the failure to obtain estimates for population subgroups. Concurrently, data are often recorded on incidental reencounters of marked individuals, but these incidental data are often unused in survival analyses.We evaluated the utility of supplementing a traditional survival dataset with incidental data on marked individuals that were collected ad hoc. We used a continuous time-to-event exponential survival model to leverage the matching information contained in both datasets and assessed differences in survival among adult and juvenile and resident and translocated Mojave desert tortoises (Gopherus agassizii).Incorporation of the incidental mark-encounter data improved precision of all annual survival point estimates, with a 3.4%-37.5% reduction in the spread of the 95% Bayesian credible intervals. We were able to estimate annual survival for three subgroup combinations that were previously inestimable. Point estimates between the radiotelemetry and combined datasets were within |0.029| percentage points of each other, suggesting minimal to no bias induced by the incidental data.Annual survival rates were high (>0.89) for resident adult and juvenile tortoises in both study sites and for translocated adults in the southern site. Annual survival rates for translocated juveniles at both sites and translocated adults in the northern site were between 0.73 and 0.76. At both sites, translocated adults and juveniles had significantly lower survival than resident adults. High mortality in the northern site was driven primarily by a single pulse in mortalities.Using exponential survival models to leverage matching information across traditional survival studies and incidental data on marked individuals may serve as a useful tool to improve the precision and estimability of survival rates. This can improve the efficacy of understanding basic population ecology and population monitoring for imperiled species.

Project description:Background and aimsPatient-reported outcome measures (PROMs) are increasingly used in health services. Paper forms are still often used to register such data. Manual double data entry (DDE) has been defined as the gold standard for transferring data to an electronic format but is laborious and costly. Automated form processing (AFP) is an alternative, but validation in a clinical context is warranted. The study objective was to examine and validate a local hospital AFP setup.MethodsPatients over 18 years of age who were scheduled for knee or hip replacement at Stavanger University Hospital from 2014 to 2017 who answered PROMs were included in the study and contributed PROM data. All paper PROMs were scanned using the AFP techniques of optical mark recognition (OMR) and intelligent character recognition (ICR) and were processed by DDE by health secretaries using a data entry program. OMR and ICR were used to capture different types of data. The main outcome was the proportion of correctly entered numbers, defined as the same response recorded in AFP and DDE or by consulting the original paper questionnaire at the data field, item, and PROM level.ResultsA total of 448 questionnaires from 255 patients were analyzed. There was no statistically significant difference in error proportions per 10 000 data fields between OMR and DDE for data from check boxes (3.52 95% confidence interval (CI) 2.17 to 5.72 and 4.18 (95% CI 2.68-6.53), respectively P = .61). The error proportion for ICR (nine errors) was statistically significantly higher than that for DDE (two errors), that is, 3.53 (95% CI 1.87-6.57) vs 0.78 (95% CI 0.22-2.81) per 100 data fields/items/questionnaires; P = .033. OMR (0.04% errors) outperformed ICR (3.51% errors; P < .001), Fisher's exact test.ConclusionsOMR can produce an error rate that is comparable to that of DDE. In our setup, ICR is still problematic and is highly dependent on manual validation. When AFP is used, data quality should be tested and documented.

Dataset Information

SpotCard: an optical mark recognition tool to improve field data collection speed and accuracy.

Publications

SpotCard: an optical mark recognition tool to improve field data collection speed and accuracy.

Similar Datasets

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