AOAC INTERNATIONAL's Technical Division on Reference Materials (TDRM) Reference Materials Database

2016 ◽  
Vol 99 (5) ◽  
pp. 1146-1150 ◽  
Author(s):  
Donna Zink
Keyword(s):  
Reference Materials ◽  
Ease Of Use ◽  
Data Sets ◽  
Online Data ◽  
Essential Information ◽  
Additional Information ◽  
Guiding Principle ◽  
Technical Division ◽  
Voluntary Basis ◽  
Selection Of

Abstract The Technical Division on Reference Materials (TDRM) of AOAC INTERNATIONAL recommends policy and criteria to facilitate the development and use of reference materials (RMs) in the validation, implementation, and routine use of AOAC INTERNATIONAL methods. To aid analysts in these areas, TDRM has developed a searchable online database to identify RMs suitable for use with AOAC Official Methods of AnalysisSM (OMA). RMs can be queried by analyte, by analyte and matrix, or by the selection of an OMA, based on analytes and matrixes described within the scope of the selected method. Only essential information is included in the database, to maximize usefulness and minimize the effort required to keep information current. Additional information, such as measurement uncertainty and purchasing instructions, is available through a link to the producer's Web site, when that information is available online. Data sets are solicited on a voluntary basis from National Metrology Institutes and accredited producers. Consideration of ease-of-use and ease-of-operation is a guiding principle in this database, as is cost management.

scholarly journals Benchmark Bird Surveys Help Quantify Counting Accuracy in a Citizen-Science Database

2021 ◽  
Vol 9 ◽  
Author(s):  
W. Douglas Robinson ◽  
Tyler A. Hallman ◽  
Rebecca A. Hutchinson
Keyword(s):  
Citizen Science ◽  
Count Data ◽  
Time Of Day ◽  
Data Sets ◽  
Online Data ◽  
Additional Information ◽  
Model Sets ◽  
Long Term Study ◽  
Species Abundances ◽  
Error Bias

The growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the world’s largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species’ abundances. We quantified counting accuracy in a sample of 1,406 eBird checklists by comparing numbers contributed by birders (N = 246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily bird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. Median count discrepancies were −29.1% (range: 0 to −42.8%; N = 20 species). Model sets revealed an important influence of each species’ reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, smaller count differences. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. (1) Assess additional options for analytically determining how to select checklists that include less biased count data, as well as exploring options for correcting bias during the analysis stage. (2) Add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. (3) Explore opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data.

scholarly journals Duck, duck, goose: Benchmark bird surveys help quantify counting errors and bias in a citizen-science database

2020 ◽  
Author(s):  
W. Douglas Robinson ◽  
Tyler A. Hallman ◽  
Rebecca A. Hutchinson
Keyword(s):  
Citizen Science ◽  
Count Data ◽  
Time Of Day ◽  
Data Sets ◽  
Online Data ◽  
Additional Information ◽  
Model Sets ◽  
Long Term Study ◽  
Species Abundances ◽  
Error Bias

AbstractThe growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the world’s largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species’ abundances. We quantified counting errors in a sample of 1406 eBird checklists by comparing numbers contributed by birders (N=246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily waterbird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. When only checklists that actually reported a species known to be present were included, median count errors were −29.1% (range: 0 to −42.8 %; N=20 species). Model sets revealed an important influence of each species’ reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, lower counting errors. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. One is to assess additional options for analytically determining how to select checklists that have the highest probability of including less biased count data, as well as exploring options for correcting bias during the analysis stage. Another is to add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. We also recommend exploration of opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data.

Enhancing Flow Field Measurements Through Adaptive Multi-Dimensional Data Sampling

2005 ◽  
Author(s):  
Arnoud R. C. Franken ◽  
Paul C. Ivey
Keyword(s):  
Flow Field ◽  
High Speed ◽  
Sampling Technique ◽  
Field Measurements ◽  
Cost Effective ◽  
Pressure Probe ◽  
Data Sets ◽  
Data Sampling ◽  
Essential Information ◽  
Selection Of

A way to gain insights into the flow field conditions in turbomachinery is by carrying out a series of point measurements in a cross-section of the flow, for example, with a miniature multi-hole pressure probe. A problem commonly encountered in situations like these is the selection of a suitable measurement grid layout and density for obtaining all essential information in a cost-effective and timely manner. In order to achieve the latter, a novel adaptive multi-dimensional data sampling technique has been developed at Cranfield University. This paper describes the underlying principles of this technique, the algorithms utilized, and the results obtained during its successful application to data sets of two different flow fields in a high-speed research compressor.

scholarly journals Construction of economic indicators using internet searches

10.26458/1513 ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 25 ◽  
Author(s):  
Mioara POPESCU
Keyword(s):  
Economic Analysis ◽  
Large Data ◽  
Large Data Sets ◽  
The Internet ◽  
Data Sets ◽  
Online Data ◽  
Additional Information ◽  
Search Data ◽  
Internet Searches ◽  
Further Development

The volume of online data searches can be used as indicators of economic analysis and forecasting. This paper reviews some of the applications that use the large data sets provided by the Internet user searches and presents a very specific case for Romanian economy. These data provide some additional information relative to existing surveys and with further development, internet search data could become an important tool for analysis and prediction. 

Pengaruh Persepsi Kegunaan, Persepsi Kemudahan, Kecepatan, Keamanan 75 dan Kerahasiaan Serta Kesiapan Teknologi Informasi Wajib Pajak terhadap Penggunaan e-Filing Bagi Wajib Pajak Orang Pribadi Di Kota Tangerang, Kecamatan Karawaci

2016 ◽  
Vol 8 (1) ◽  
pp. 75-91 ◽  
Author(s):  
Shelby Devina ◽  
Waluyo Waluyo
Keyword(s):  
Ease Of Use ◽  
Perceived Usefulness ◽  
Security And Privacy ◽  
Primary Data ◽  
Perceived Ease Of Use ◽  
Analysis Technique ◽  
Convenience Sampling ◽  
The Individual ◽  
Tax Payers ◽  
Selection Of

The objective of this research was to examine the effect of perceived usefulness, perceived ease of use, speed, security and privacy and readiness technology tax payers information to e-Filing usage. The object of this study is the individual tax payers in Tangerang City, Karawaci District. The selection of the sample is determined based on convenience sampling method. Data used in this study was primary data, id est: questionnaires. The respondent in this study were 110. Data analysis technique in this study using multiple linear regression. The result of this study were (1) perceived usefulness have a significant impact towards e-Filing usage; (2) perceived ease of use have a significant impact towards e-Filing usage; (3) speed does not have a significant impact towards e-Filing usage; (4) security and privacy does not have a significant impact towards e-Filing usage; (5) readiness technology tax payers information does not have a significant impact towards e-Filing usage; (6) perceived usefulness, perceived ease of use, speed, security and privacy and readiness technology tax payers information all simultaneously, have a significant impact towards e-Filing usage. Keywords: e-Filing usage, perceived usefulness, perceived ease of use, readiness technology tax payers information, security and privacy.

Selection of one-dimensional sedimentation: models for on-line use

1995 ◽  
Vol 31 (2) ◽  
pp. 193-204 ◽  
Author(s):  
Koen Grijspeerdt ◽  
Peter Vanrolleghem ◽  
Willy Verstraete
Keyword(s):  
Steady State ◽  
Selection Criteria ◽  
Data Sets ◽  
Concentration Profiles ◽  
A Posteriori ◽  
One Dimensional ◽  
On Line ◽  
Dynamic Concentration ◽  
Selection Of ◽  
Modelling Task

A comparative study of several recently proposed one-dimensional sedimentation models has been made. This has been achieved by fitting these models to steady-state and dynamic concentration profiles obtained in a down-scaled secondary decanter. The models were evaluated with several a posteriori model selection criteria. Since the purpose of the modelling task is to do on-line simulations, the calculation time was used as one of the selection criteria. Finally, the practical identifiability of the models for the available data sets was also investigated. It could be concluded that the model of Takács et al. (1991) gave the most reliable results.

Real-time Approximation of Photometric Polygonal Lights

2020 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Christian Luksch ◽  
Lukas Prost ◽  
Michael Wimmer
Keyword(s):  
Real Time ◽  
Specular Reflection ◽  
Near Field ◽  
Measurement Data ◽  
Data Sets ◽  
Photometric Measurement ◽  
Integration Technique ◽  
Time Approximation ◽  
Light Emitter ◽  
Selection Of

We present a real-time rendering technique for photometric polygonal lights. Our method uses a numerical integration technique based on a triangulation to calculate noise-free diffuse shading. We include a dynamic point in the triangulation that provides a continuous near-field illumination resembling the shape of the light emitter and its characteristics. We evaluate the accuracy of our approach with a diverse selection of photometric measurement data sets in a comprehensive benchmark framework. Furthermore, we provide an extension for specular reflection on surfaces with arbitrary roughness that facilitates the use of existing real-time shading techniques. Our technique is easy to integrate into real-time rendering systems and extends the range of possible applications with photometric area lights.

scholarly journals The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

2017 ◽  
Vol 21 (9) ◽  
pp. 4747-4765 ◽  
Author(s):  
Clara Linés ◽  
Micha Werner ◽  
Wim Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Drought Management ◽  
Ebro Basin ◽  
Sensing Data ◽  
Drought Impacts ◽  
Management Plans ◽  
Drought Indicators ◽  
Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

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