Journal articles: 'Indicator data'

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BIERKENS, M. F. P., and P. A. BURROUGH. "The indicator approach to categorical soil data." Journal of Soil Science 44, no. 2 (June 1993): 361–68. http://dx.doi.org/10.1111/j.1365-2389.1993.tb00458.x.

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BIERKENS, M. F. P., and P. A. BURROUGH. "The indicator approach to categorical soil data." Journal of Soil Science 44, no. 2 (June 1993): 369–81. http://dx.doi.org/10.1111/j.1365-2389.1993.tb00459.x.

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Bates-Jensen, Barbara M., Cathy A. Alessi, Mary Cadogan, Len?? Levy-Storms, Jennifer Jorge, June Yoshii, Nahla R. Al-Samarrai, and John F. Schnelle. "The Minimum Data Set Bedfast Quality Indicator." Nursing Research 53, no. 4 (July 2004): 260–72. http://dx.doi.org/10.1097/00006199-200407000-00009.

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Hendry, David F., and Carlos Santos. "Regression Models with Data-based Indicator Variables*." Oxford Bulletin of Economics and Statistics 67, no. 5 (October 2005): 571–95. http://dx.doi.org/10.1111/j.1468-0084.2005.00132.x.

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Jennings, Bonnie Mowinski, Lori A. Loan, Debra DePaul, Laura R. Brosch, and Pamela Hildreth. "Lessons Learned While Collecting ANA Indicator Data." JONA: The Journal of Nursing Administration 31, no. 3 (March 2001): 121–29. http://dx.doi.org/10.1097/00005110-200103000-00007.

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Rowell, Patricia. "Lessons Learned While Collecting ANA Indicator Data." JONA: The Journal of Nursing Administration 31, no. 3 (March 2001): 130–31. http://dx.doi.org/10.1097/00005110-200103000-00008.

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Guyonvarch, Laurette, Thierry Hermitte, Frederic Duvivier, Clement Val, and Anne Guillaume. "Driving style indicator using UDRIVE NDS data." Traffic Injury Prevention 19, sup1 (February 28, 2018): S189—S191. http://dx.doi.org/10.1080/15389588.2018.1426920.

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Berti, Peter R., Milena Nardocci, Minh Hung Tran, Malek Batal, Rebecca Brodmann, Nicolas Greliche, and Naomi M. Saville. "Using DHS and MICS data to complement or replace NGO baseline health data: an exploratory study." F1000Research 10 (February 4, 2021): 69. http://dx.doi.org/10.12688/f1000research.47618.1.

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Background: Non-government organizations (NGOs) spend substantial time and resources collecting baseline data in order to plan and implement health interventions with marginalized populations. Typically interviews with households, often mothers, take over an hour, placing a burden on the respondents. Meanwhile, estimates of numerous health and social indicators in many countries already exist in publicly available datasets, such as the Demographic and Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS), and it is worth considering whether these could serve as estimates of baseline conditions. The objective of this study was to compare indicator estimates from non-governmental organizations (NGO) health projects’ baseline reports with estimates calculated using the Demographic and Health Surveys (DHS) or the Multiple Indicator Cluster Surveys (MICS), matching for location, year, and season of data collection. Methods: We extracted estimates of 129 indicators from 46 NGO baseline reports, 25 DHS datasets and three MICS datasets, generating 1,996 pairs of matched DHS/MICS and NGO indicators. We subtracted NGO from DHS/MICS estimates to yield difference and absolute difference, exploring differences by indicator. We partitioned variance of the differences by geographical level, year, and season using ANOVA. Results: Differences between NGO and DHS/MICS estimates were large for many indicators but 33% fell within 5% of one another. Differences were smaller for indicators with prevalence <15% or >85%. Difference between estimates increased with increasing year and geographical level differences. However, <1% of the variance of the differences was explained by year, geographical level, and season. Conclusions: There are situations where publicly available data could complement NGO baseline survey data, most importantly when the NGO has tolerance for estimates of low or unknown accuracy.

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Vink, Karina, Md Nasif Ahsan, Hisaya Sawano, and Miho Ohara. "Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements." Journal of Disaster Research 12, no. 2 (March 16, 2017): 355–67. http://dx.doi.org/10.20965/jdr.2017.p0355.

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Despite a long developmental history of water-related disaster risk indicators, there is still no consensus or reliable system for selecting objective data, no methodological system for choosing and verifying the relevancy of water-related disaster risk indicators, and no linking results back to root causes or addressing possible impacts on policies or actors to instigate change.Global policy documents such as the Sendai Framework for Disaster Risk Reduction (DRR) 2015–2013 [1] emphasize the urgent need for indicators capable of measuring risk reduction. However, developing and determining risk indicators faces many issues. Most disaster risk indices published do not yet include a basic overview of what data was used and how it was collected, let alone provide a systematic explanation of why each indicator was included, and why others were not. This consequently complicates linking the findings to their potential policy impacts. It also complicates the providing of clear-cut recommendations for improving resilience, which is a common intent of disaster risk indices.This study, which focuses on water-related hazards, aims to provide disaster managers with a set of criteria for evaluating existing datasets used in disaster risk indices, index construction methods, and the links back to policy impacts. So far, there has been no comprehensive overview of indicator requirements or scoring systems. Previous studies concerning indicator evaluating metrics [2] have fewer metrics and have not yet addressed the different tiers of requirements, namely objective indicator data quality, methodological/epistemological aspects of index composition, and, most importantly, policy and actors of change (impact requirements). Further testing of these metrics in local studies can lead to the greatly needed scientific justification for indicator selection and can enhance index robustness.The results aid in developing an evaluation system to address issues of data availability and the comparability of commonly used indicator sources, such as the World Bank. Once indicators can be scientifically linked to impacts through policy devices, national governments or other actors can become more likely to claim ownership of the data management of indicators. Future studies should expand this evaluation system to other natural hazards and focus on investigating the links between indicators and DRR in order to further validate indicator selection robustly.

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Jiang, Ruoxi, Shunying Zhu, Hongguang Chang, Jingan Wu, Naikan Ding, Bing Liu, and Ji Qiu. "Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data." Sustainability 13, no. 16 (August 18, 2021): 9278. http://dx.doi.org/10.3390/su13169278.

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Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator Ti was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/Ti indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for Ti, which Ti indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved Ti can overcome these shortcomings; thus, Ti has the highest correlation. More data are needed to verify and support the study.

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Helseth, L. E., E. I. Il’yashenko, M. Baziljevich, and T. H. Johansen. "Detection of magnetic data using a magnetooptic indicator." Journal of Applied Physics 92, no. 1 (July 2002): 543–48. http://dx.doi.org/10.1063/1.1480479.

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Atallah, L. N., J. P. Barbot, and P. Larzabal. "SNR Threshold Indicator in Data-Aided Frequency Synchronization." IEEE Signal Processing Letters 11, no. 8 (August 2004): 652–54. http://dx.doi.org/10.1109/lsp.2004.831683.

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van Deursen, Ton, Paul Koster, and Milan Petković. "Hedaquin: A Reputation-based Health Data Quality Indicator." Electronic Notes in Theoretical Computer Science 197, no. 2 (February 2008): 159–67. http://dx.doi.org/10.1016/j.entcs.2007.12.025.

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Meland, Per, Shukun Tokas, Gencer Erdogan, Karin Bernsmed, and Aida Omerovic. "A Systematic Mapping Study on Cyber Security Indicator Data." Electronics 10, no. 9 (May 5, 2021): 1092. http://dx.doi.org/10.3390/electronics10091092.

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A security indicator is a sign that shows us what something is like or how a situation is changing and can aid us in making informed estimations on cyber risks. There are many different breeds of security indicators, but, unfortunately, they are not always easy to apply due to a lack of available or credible sources of data. This paper undertakes a systematic mapping study on the academic literature related to cyber security indicator data. We identified 117 primary studies from the past five years as relevant to answer our research questions. They were classified according to a set of categories related to research type, domain, data openness, usage, source, type and content. Our results show a linear growth of publications per year, where most indicators are based on free or internal technical data that are domain independent. While these indicators can give valuable information about the contemporary cyber risk, the increasing usage of unconventional data sources and threat intelligence feeds of more strategic and tactical nature represent a more forward-looking trend. In addition, there is a need to take methods and techniques developed by the research community from the conceptual plane and make them practical enough for real-world application.

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Ahmad, Zuriyati, and Abdul Ghafar Ismail. "ALTERNATIVE OF MONETARY POLICY INDICATOR: PANEL DATA ANALYSIS FROM ISLAMIC BANKS IN MALAYSIA." Journal of Islamic Monetary Economics and Finance 4, no. 2 (February 9, 2019): 279–92. http://dx.doi.org/10.21098/jimf.v4i2.1017.

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The monetary policy indicators such as monetary aggregates, interest rates andMonetary Condition Index (MCI) are the traditional monetary policy indicatorsused in order to obtain early indication of the impact of monetary policy. Theseindicators could function as appropriate monetary policy indicators that will provideinformation to the monetary policy makers. The development in Islamic financialsystem however creates a challenge to find a monetary policy indicator, which is inconformity with Islamic teaching. Therefore, this paper is aimed to examine the futuregrowth of nominal GDP as an alternative variable for monetary policy indicator inIslamic monetary system. The investigation will benefit from data of 17 Islamic banksin Malaysia which implement full fledge or Islamic windows scheme spanning from2005 to 2010. GMM system method is used to analyze the data in hope to validatethe appropriateness of the alternative monetary policy indicator. The result suggeststhat the future growth of nominal GDP is significant as monetary policy indicator andcould be applied by the central bank in implementing the monetary policy especiallyin the dual banking system.Keywords: Monetary Policy; Monetary Policy Indicators; Bank Financing.JEL Classification: E52; E58; C33; G21.

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McKool, Marissa, Kimberley Freire, Kathleen C. Basile, Kathryn M. Jones, Joanne Klevens, Sarah DeGue, and Sharon G. Smith. "A Process for Identifying Indicators With Public Data: An Example From Sexual Violence Prevention." American Journal of Evaluation 41, no. 4 (June 12, 2020): 510–28. http://dx.doi.org/10.1177/1098214019891239.

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Despite advances in the sexual violence (SV) prevention field, practitioners still face challenges with identifying indicators to measure the impact of their prevention strategies. Public data, such as existing administrative and surveillance system data, may be a good option for organizations to examine trends in indicators for the purpose of program evaluation. In this article, we describe a framework and a process for identifying indicators with public data. Specifically, we present the SV Indicator Framework and a five-step indicator review process, which we used to identify indicators for a national SV prevention program. We present the findings of the indicator review and explain how the process could be used by evaluators and program planners within other developing topic areas. Tracking indicators with public data, in conjunction with other evaluation methods, may be a viable option for state-level program evaluations. We discuss limitations and implications for practice and research.

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Zhang, Shuai, Yongxiang Zhang, Lin Li, Shengjie Wang, and Yujie Xiao. "An effective health indicator for rolling elements bearing based on data space occupancy." Structural Health Monitoring 17, no. 1 (December 19, 2016): 3–14. http://dx.doi.org/10.1177/1475921716682432.

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An effective degradation indicator created from the general features is still a hotspot for the condition monitoring of bearing. Since the single general feature–based indicator cannot fully describe the degradation performance, some new indicators based on the characteristics of multiple general features are proposed using pattern recognition and dimensional reduction algorithms. Although most of these indicators can show the incipient fault and good degradation tendency only across the failure stage, also with oscillation, seldom of them can give the whole health condition of the entire useful life. Hence, further research is still needed to reduce the oscillation impact on the sensitivity of the indicator and make remarkable tendency of the degradation. For that purpose, a new indicator is proposed. For the capability of preserving the full structure of data manifold, Orthogonal Neighbourhood Preserving Projections is used for the data preprocessing to obtain the low-dimensional features with the main information remained. Since the preprocessed data can describe different health conditions, K-means clustering algorithm is used to classify the data into different sets according to different health conditions without human intervention. Then, the summation of mean maximum radius of the data set can be used as the health indicator. In order to further decrease the disturbance of the background noise, local outlier coefficient algorithm is used to eliminate the outliers’ influence on the effectiveness of the health indicator. The experimental results illustrate that the proposed indicator can show distinct tendency of different health conditions with less influence of the randomness and more sensitive to the incipient fault.

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Ozkaya, Gokhan, Mehpare Timor, and Ceren Erdin. "Science, Technology and Innovation Policy Indicators and Comparisons of Countries through a Hybrid Model of Data Mining and MCDM Methods." Sustainability 13, no. 2 (January 12, 2021): 694. http://dx.doi.org/10.3390/su13020694.

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Science, technology and innovation (STI) policies are of great importance for countries to reach their sustainable development goals. Numerous global databases have many indicators that measure and compare the performance of STI policies of countries. However, many problems arise regarding how to identify, classify and systematically analyze these indicators in order to measure, monitor and improve the performance of STI. The study includes a literature review on global problems and new trends in STI policies, while mentioning the necessity of an internationally comparable STI indicator set, current STI indicator studies and efforts, and studies for each continent. In light of these, all the indicators selected are introduced in detail. The strengths and weaknesses of the countries in the study in terms of evaluation indicator values are indicated. After determining the indicator weights objectively with the entropy method, 40 countries are compared with TOPSIS, VIKOR, PROMETHEE I-II, ARAS, COPRAS, MULTIMOORA, ELECTRE, SAW and MAUT methods. In addition, countries that show similarities with each other are evaluated by cluster analysis, which is one of the data mining classification methods. This study offers a new and original approach with MCDM methods on this subject. Considering all the results obtained in the study together, these rankings are compared among themselves and with the rankings specified in the Global Innovation (2019) and Global Competitiveness (2019) indices, and it is seen that the results are consistent. In addition, it is possible to update and publish this study every year with updated data.

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Kido, Hachiro, and Kaoru Yamaguchi. "Combustion Data Analysis by Portable Indicator and Personal Computer." JOURNAL OF THE MARINE ENGINEERING SOCIETY IN JAPAN 27, no. 2 (1992): 201–12. http://dx.doi.org/10.5988/jime1966.27.201.

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Hsu, Shih-Chieh, Yi-Tang Huang, Jr-Chung Huang, Jien-Yi Tu, Guenter Engling, Chuan-Yao Lin, Fei-Jan Lin, and Chao-Hao Huang. "Evaluating real-time air-quality data as earthquake indicator." Science of The Total Environment 408, no. 11 (May 2010): 2299–304. http://dx.doi.org/10.1016/j.scitotenv.2010.02.023.

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Melby, Michael J. "A mid-sized hospital's experience in indicator data collection." American Journal of Health-System Pharmacy 48, no. 9 (September 1, 1991): 1937–40. http://dx.doi.org/10.1093/ajhp/48.9.1937.

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Petitgas, Pierre, Mathieu Woillez, Mathieu Doray, and Jacques Rivoirard. "Indicator-Based Geostatistical Models For Mapping Fish Survey Data." Mathematical Geosciences 50, no. 2 (February 2018): 187–208. http://dx.doi.org/10.1007/s11004-018-9725-2.

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Ferreira, Aracéli Cristina de S., Vinicius Mothe Maia, Dilo S. de Carvalho Vianna, and Juliana Molina Queiroz. "Financial Situation Unique Indicator for Electric Sector Firms." Accounting and Finance Research 10, no. 3 (August 23, 2021): 72. http://dx.doi.org/10.5430/afr.v10n3p72.

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This paper develops a unique indicator to identify the financial situation of firms in the electric sector in Brazil. The National Electric Energy Agency (ANEEL) regulates this sector through five dimensions: indebtedness, efficiency, investment, profitability, and pay-out ratio. Each of these dimensions contains one or two indicators. Based on these indicators, we develop a unique indicator that shows companies' financial situation. To create a unique indicator, we follow the idea of Altman’s solvency indicator. But, we use a logit regression. Our dependent variable is Global Performance of Continuity which indicates the financial situation of the firm. Our independent variables are based on the five dimensions of the ANEEL indicators for financial situation. We collect data from 2011 to 2018. This research follows three main steps: (1) Collection of the data from the ANEEL database; (2) Creation of variables based on ANEEL’s five dimensions of performance; and (3) Econometric proceedings with variables according to ANEEL’s data and indicators of each dimension. First, we estimate one regression with all variables created based on ANEEL’s five dimensions. Then, we make improvements to find a more suitable model with different combinations of variables. We chose the best model by analysing the Akaike information criterion (AIC). Our results show that the unique indicator we create to evaluate firm performance is based on Debt, Efficiency, Investment (CapexA) and the Pay-out Ratio.

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Ahn, Tae In, Jung-Seok Yang, Soo Hyun Park, Heon Woo Moon, and Ju Young Lee. "Translation of Irrigation, Drainage, and Electrical Conductivity Data in a Soilless Culture System into Plant Growth Information for the Development of an Online Indicator Related to Plant Nutritional Aspects." Agronomy 10, no. 9 (September 2, 2020): 1306. http://dx.doi.org/10.3390/agronomy10091306.

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Electrical conductivity of the growing media or drainage indicates the nutritional conditions in the cultivation system. However, the nutrient uptake phenomenon has not been related well to the soilless culture system. Herein, we report on the design, theoretical analyses, and verification of a method for an online indicator related to plant nutritional aspects. Models for simulating nutrient and water transport in a porous medium were constructed for analyses of the nutrient uptake estimation method. In simulation analyses, we summarized the theoretical relationships between flow rates of total nutrients in a substrate and nutrient uptake. For concept validation, we conducted a greenhouse experiment for correlation analysis with the growth of tomato plants, conventional nutrient, and water management indicators, and developed online indicators related to plant nutritional aspects. Onsite application of the indicator showed a higher correlation with tomato yield than conventional management indicators, such as transpiration, irrigation, drainage ratio, leaching fraction, and electrical conductivity of drainage. In addition, to assess the usability of a nutrient uptake indicator as an onsite decision-making technique, data normalization was conducted. Through this, the time series responsiveness of a nutrient uptake indicator to the yield change was confirmed.

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Santos, Alexandre F., Pedro D. Gaspar, and Heraldo J. L. de Souza. "Ecoenergetic Comparison of HVAC Systems in Data Centers." Climate 9, no. 3 (March 4, 2021): 42. http://dx.doi.org/10.3390/cli9030042.

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The topic of sustainability is of high importance today. Global efforts such as the Montreal Protocol (1987) and the Kigali Amendment (2016) are examples of joint work by countries to reduce environmental impacts and improve the level of the ozone layer, the choice of refrigerants and air conditioning systems, which is essential for this purpose. But what indicators are to be used to measure something so necessary? In this article, the types of air conditioning and GWP (Global Warming Potential) levels of equipment in the project phase were discussed, the issue of TEWI (Total Equivalent Warming Impact) that measures the direct and indirect environmental impacts of refrigeration equipment and air conditioning and a new methodology for the indicator was developed, the TEWI DC (DC is the direct application for Data Center), and using the formulas of this new adapted indicator it was demonstrated that the TEWI DC for Chicago (USA) was 2,784,102,640 kg CO2/10 years and Curitiba (Brazil) is 1,252,409,640 kg CO2/10 years. This difference in value corresponds to 222.30% higher annual emissions in Chicago than in Curitiba, showing that it is much more advantageous to install a Data Center in Curitiba than in Chicago in terms of environmental impact. The TEWI indicator provides a more holistic view, helping to combine energy and emissions into the same indicator.

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Mamdoohi, Amir Reza, Mohsen Fallah Zavareh, Christer Hydén, and Trond Nordfjærn. "Comparative Analysis of Safety Performance Indicators Based on Inductive Loop Detector Data." PROMET - Traffic&Transportation 26, no. 2 (April 26, 2014): 139–49. http://dx.doi.org/10.7307/ptt.v26i2.1273.

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Conflicts in traffic stream have been detected by different safety performance indicators. This study aims to empirically investigate the differences between different indicators in detecting rear-end conflicts and assessing the risk in an uninterrupted flow. Micro-level data of a 24-hr traffic stream (including 6,657 vehicles) were captured using inductive loop detectors installed on a rural freeway section. Different indicators (Time Headway (H), Time to Collision (TTC), Proportion of Stopping Distance (PSD), Deceleration Rate to Avoid Collision (DRAC) and Stopping Distance Index (SDI)) were used to measure each car following event in a bivalent state (safe/unsafe). Unsafe events associated with each indicator were detected and common unsafe events characterized by different indicators were identified. Temporal distributions of rear-end collision risks associated with each indicator at 15-min intervals were also compared. Finally, the 15-min risk values based on different indicators were categorized and compared across three levels (Low, Medium and High). Data mining and statistical techniques showed that while SDI is the single most conservative indicator, DRAC and TTC detect a few risky events but very equal ones. In almost all conflicts associated with TTC, headway is still lower than the critical threshold. However, there exist considerable risky events based on headway which are still safe according to TTC. Comparison of PSD and TTC also declares that almost all conflicts associated with TTC are also risky according to PSD.

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He, Y., D. Chen, B. G. Li, Y. F. Huang, K. L. Hu, Y. Li, and I. R. Willett. "Sequential indicator simulation and indicator kriging estimation of 3-dimensional soil textures." Soil Research 47, no. 6 (2009): 622. http://dx.doi.org/10.1071/sr08218.

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The complex distribution characteristics of soil textures at a large or regional scale are difficult to understand with the current state of knowledge and limited soil profile data. In this study, an indicator variogram was used to describe the spatial structural characteristics of soil textures of 139 soil profiles. The profiles were 2 m deep with sampling intervals of 0.05 m, from an area of 15 km2 in the North China Plain. The ratios of nugget-to-sill values (SH) of experimental variograms of the soil profiles in the vertical direction were equal to 0, showing strong spatial auto-correlation. In contrast, SH ratios of 0.48–0.81 in the horizontal direction, with sampling distances of ~300 m, showed weaker spatial auto-correlation. Sequential indicator simulation (SIS) and indicator kriging (IK) methods were then used to simulate and estimate the 3D spatial distribution of soil textures. The outcomes of the 2 methods were evaluated by the reproduction of the histogram and variogram, and by mean absolute error of predictions. Simulated results conducted on dense and sparse datasets showed that when denser sample data are used, complex patterns of soil textures can be captured and simulated realisations can reproduce variograms with reasonable fluctuations. When data are sparse, a general pattern of major soil textures still can be captured, with minor textures being poorly simulated or estimated. The results also showed that when data are sufficient, the reproduction of the histogram and variogram by SIS was significantly better than by the IK method for the predominant texture (clay). However, when data are sparse, there is little difference between the 2 methods.

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Saillour-Glénisson, Florence, Karine Nouette-Gaulain, Vianney Jouhet, Louis-Rachid Salmi, and Aurélie Petit-Monéger. "Comparing Graphical Formats for Feedback of Clinical Practice Data." Methods of Information in Medicine 56, no. 01 (2017): 28–36. http://dx.doi.org/10.3414/me15-01-0163.

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Summary Objectives: Although graphical formats used to feedback clinical practice data may have an important impact, the most effective formats remain unknown. Using prevention of postoperative nausea and vomiting by anesthesiologists as an application, the objective of this study was to assess which graphical formats for feedback of clinical practice data are the most incentive to change practice. Methods: We conducted a multicenter cross-sectional study among anesthesiologists randomized in two groups between March andJune 2014. Each anesthesiologist assessed 15 graphical formats displaying an indicator of either prescription conformity or prescription effectiveness. Graphical formats varied by: type of graph (bar charts, linear sliders, or pictographs), presence or not of a target to reach, presence or not of a contrast between a hypothetical physician and his/her team, direction of the difference between the physician and his/her team, and restitution or not of the quality indicator evolution over the previous six months. The primary outcome was a numerical scale score expressing the anesthesiologists’ motivation to change his/her practice (ranging from 1 to 10 points). A linear mixed model was fitted to explain variation in motivation. Results: Sixty-six anesthesiologists assessed the conformity indicator and 67 assessed the effectiveness indicator. Factors associated with an increased motivation to change practice were: (i) presence of a clearly defined target to reach (conformity: β = 0.24 points, p = 0.0046; effectiveness: β = 1.11 points, p < 0.0001); (ii) contrast between the physician and his/her team (conformity: β = 0.38 points, p < 0.0001; effectiveness: β = 0.33 points, p = 0.0021); (iii) better results for the team than for the physician (conformity: β = 0.65 points, p < 0.0001; effectiveness β = 1.16 points, p < 0.0001). For the effectiveness indicator, anesthesiologists were more motivated to change practice with bar charts (β = 0.24 points, p = 0.0447) and pictographs (β = 0.45 points, p = 0.0001) than with linear sliders. Conclusions: Graphs associated with a defined target to reach should be preferred to deliver feedback, especially bar graphs or pictographs for indicators which are more complex to represent such as effectiveness indicators. Anesthesiologists are also more motivated to change practice when graphs report contrasted data between the physician and his/her team and a lower conformity or effectiveness for the physician than for his/her team.

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Aksoy, Hilal, Abdullah Ozturk, Dilek Tarhan, Ibrahim Dolukup, and Duygu Ayhan Baser. "Biochemistry laboratory errors and patient safety: Turkey data." Turkish Journal of Biochemistry 46, no. 4 (August 1, 2021): 377–85. http://dx.doi.org/10.1515/tjb-2020-0193.

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Abstract Objectives Our aim in this study is to provide information about the rate of errors in the process of the biochemistry laboratories in the hospitals in Turkey with the “Indicators”. Methods The hospitals calculate their own data according to the indicator cards defined by the Ministry of Health of Turkey and enter into the system once in a year. In this study we examined the quality indicators related to the disruptions in the biochemistry laboratory of hospitals for the year of 2018. Results All indicators except “Non-timely reported result rate in biochemistry laboratory” are found to be significantly higher in university hospitals. This indicator is found to be significantly higher in private hospitals(p:0.030) “Lost sample rate in biochemistry laboratory” is found to be significantly higher in Eastern Anatolia Region (p:0.000) and “Non-timely reported result rate in biochemistry laboratory” is found to be significantly higher in Aegean Region (p:0.008). Conclusions The ratio of non-timely reported result rate is the most seen disruption in biochemistry laboratories. It may be due to lots of reasons; lack of biochemistry equipment, lack of staff, problems in transportation, etc. The management of hospitals and the staff should take measures and regulations about problems.

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Menzel, Peter. "Constrained indicator data resampling — A parameter constrained irregular resampling method for scattered point data." GEOPHYSICS 81, no. 2 (March 1, 2016): F17—F26. http://dx.doi.org/10.1190/geo2015-0220.1.

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Resampling of high-resolution data sets is often required for real-time applications in geosciences, e.g., interactive modeling and 3D visualization. To support interactivity and real-time computations, it is often necessary to resample the data sets to a resolution adequate to the application. Conventional resampling approaches create uniformly distributed results, which are not always the best possible solution for particular applications. I have developed a new resampling method called constrained indicator data resampling (CIDRe). This method results in irregular point distributions that are adapted to local parameter signal wavelengths of the given data. The algorithm identifies wavelength variations by analyzing gradients in the given parameter distribution. A higher point density is ensured in areas with larger gradients than in areas with smaller gradients, and thus the resulting data set shows an irregular point distribution. A synthetic data test showed that CIDRe is able to represent a data set better than conventional resampling algorithms. In a second application, CIDRe was used to reduce the number of gravity stations for interactive 3D density modeling, in which the resulting point distribution still allows accurate interactive modeling with a minimum number of data points.

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Ilie, C. M., M. A. Brovelli, and S. Coetzee. "MONITORING SDG 9 WITH GLOBAL OPEN DATA AND OPEN SOFTWARE – A CASE STUDY FROM RURAL TANZANIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1551–58. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1551-2019.

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Abstract. The 17 goals adopted by the United Nations (UN) are aimed at achieving a better and more sustainable future for all. For each goal, a set of indicators has been defined. The indicators measure progress towards achieving the respective SDG. For the majority of these indicators, geospatial information is needed to evaluate the current state of the indicator. While geospatial information is largely available in developed countries, this is not the case in many developing countries of the world. Furthermore, skills and capacity for calculating indicator values are also limited in many developing countries. To address these shortcomings, the third challenge of the 2018 UN OSGeo Committee Educational Challenges called for the development of training material for using open source software together with freely available high resolution global geospatial datasets in support of monitoring SDG progress. The resulting training material provides a step-by-step guide for calculating the state of SDG indicator 9.1.1, Proportion of the rural population who live within 2km of an all-season road, using open software and open data with global coverage. Through the development of this training material, we showed that anyone can monitor progress towards achieving SDG indicator 9.1.1 for their specific part of the world. Because open source software and open data were used, the indicator calculation is cost effective and completely sustainable.

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Ding, Jianfei, Guangya Si, Baoqiang Li, Jingyu Yang, and Yu Zhang. "Construction of composite indicator system based on simulation data mining." Journal of Systems Engineering and Electronics 28, no. 1 (February 20, 2017): 81–87. http://dx.doi.org/10.21629/jsee.2017.01.10.

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Ball, John. "New Firm Formation — is VAT Registration Data an Accurate Indicator?" Local Economy: The Journal of the Local Economy Policy Unit 22, no. 3 (August 2007): 279–92. http://dx.doi.org/10.1080/02690940701584698.

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This paper utilises a novel and original data set collected from a survey of entirely new firms in south Wales. It compares this survey with published VAT data to address the extent to which published VAT registration actually reflects the amount of new firm formation taking place. Using this survey and a chi square test, this article offers evidence of the deficiencies of published VAT data when used as a proxy for new firm formation and suggests caution in the use of VAT data.

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Schnelle, John F., Stacey Wood, Elizabeth R. Schnelle, and Sandra F. Simmons. "Measurement Sensitivity and the Minimum Data Set Depression Quality Indicator." Gerontologist 41, no. 3 (June 1, 2001): 401–5. http://dx.doi.org/10.1093/geront/41.3.401.

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Bradley, Vicky, and Reina Heiser. "Using data to discover new patterns: A triage quality indicator." Journal of Emergency Nursing 22, no. 5 (October 1996): 435–38. http://dx.doi.org/10.1016/s0099-1767(96)80171-1.

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VAN DER MEER, F. "Performance characteristics of the indicator classifier on simulated image data." International Journal of Remote Sensing 17, no. 3 (February 1996): 621–27. http://dx.doi.org/10.1080/01431169608949031.

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Jones, Andrew, John P. Monagle, Susan Peel, Matthew W. Coghlan, Vangy Malkoutzis, and Andrea Groom. "Validity of anaesthetic complication coding data as a clinical indicator." Australian Health Review 36, no. 2 (2012): 229. http://dx.doi.org/10.1071/ah11004.

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Clinical indicators using routinely collected International Statistical Classification of Diseases, Australian Modification (ICD–10–AM) data offer promise as tools for improvement of quality. The ICD–10–AM is the coding system used by Australian administrators to summarise information from the clinical record to describe a patient’s hospital encounter. The use of anaesthesia complications as coded by this system has been proposed by two jurisdictions as a monitor of the quality of anaesthetic services. We undertook a review of cases identified by such indicators in a large tertiary hospital. Our results indicate the anaesthesia indicator dataset proposed by the Victorian and Queensland Health departments appears to have little clinical or quality improvement relevance. What is known about the topic? Quality assurance relies on reviewing performance, highlighting issues and eliminating or minimising the identified risks. Case or risk identification in the medical arena relies heavily on self reporting, which has many flaws. A system not dependent on self reporting that was reliable would be a positive development in the pursuit of quality improvement. What does this paper add? ICD-AM-10 coding was used to identify complications attributable to anaesthesia as defined by the coding system. The cases identified were then reviewed for the clinical accuracy of this information. The clinical coding was accurate, but the clinical case load so identified did not accurately reflect real incidents of anaesthesia-related complications. The ICD AM 10 codes, as they relate to anaesthesia complications, do not provide a reliable method of identifying cases that contribute to anaesthetic quality assurance activities. What are the implications for practitioners? Anaesthesia quality assurance continues to be dependent on self reporting of relevant cases. Coded data do not provide an adequate substitute for the self reporting mechanisms.

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Müller, Hans-Georg, Brad Love, and Robert D. Hoppa. "Semiparametric method for estimating paleodemographic profiles from age indicator data." American Journal of Physical Anthropology 117, no. 1 (December 12, 2001): 1–14. http://dx.doi.org/10.1002/ajpa.10000.

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Gosoniu, Laura, Andre Mia Veta, and Penelope Vounatsou. "Bayesian Geostatistical Modeling of Malaria Indicator Survey Data in Angola." PLoS ONE 5, no. 3 (March 23, 2010): e9322. http://dx.doi.org/10.1371/journal.pone.0009322.

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Almeida, Arlindo, Tomás Figueiredo, and Anabela Fernandes-Silva. "Data Used as an Indicator of Mechanical Olive Harvest Season." Agriculture and Agricultural Science Procedia 7 (2015): 2–5. http://dx.doi.org/10.1016/j.aaspro.2015.12.006.

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Tuholske, Cascade, Andrea E. Gaughan, Alessandro Sorichetta, Alex de Sherbinin, Agathe Bucherie, Carolynne Hultquist, Forrest Stevens, Andrew Kruczkiewicz, Charles Huyck, and Greg Yetman. "Implications for Tracking SDG Indicator Metrics with Gridded Population Data." Sustainability 13, no. 13 (June 30, 2021): 7329. http://dx.doi.org/10.3390/su13137329.

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Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.

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Czvetkó, Tímea, Gergely Honti, and János Abonyi. "Regional development potentials of Industry 4.0: Open data indicators of the Industry 4.0+ model." PLOS ONE 16, no. 4 (April 19, 2021): e0250247. http://dx.doi.org/10.1371/journal.pone.0250247.

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This paper aims to identify the regional potential of Industry 4.0 (I4.0). Although the regional background of a company significantly determines how the concept of I4.0 can be introduced, the regional aspects of digital transformation are often neglected with regard to the analysis of I4.0 readiness. Based on the analysis of the I4.0 readiness models, the external regional success factors of the implementation of I4.0 solutions are determined. An I4.0+ (regional Industry 4.0) readiness model, a specific indicator system is developed to foster medium-term regional I4.0 readiness analysis and foresight planning. The indicator system is based on three types of data sources: (1) open governmental data; (2) alternative metrics like the number of I4.0-related publications and patent applications; and (3) the number of news stories related to economic and industrial development. The indicators are aggregated to the statistical regions (NUTS 2), and their relationships analyzed using the Sum of Ranking Differences (SRD) and Promethee II methods. The developed I4.0+ readiness index correlates with regional economic, innovation and competitiveness indexes, which indicates the importance of boosting regional I4.0 readiness.

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Lucas, Alexandre, Giuseppe Prettico, Marco Flammini, Evangelos Kotsakis, Gianluca Fulli, and Marcelo Masera. "Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis." Energies 11, no. 7 (July 18, 2018): 1869. http://dx.doi.org/10.3390/en11071869.

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Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO2eq/MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.

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Rutala, William A., Suzanne M. Jones, and David J. Weber. "Comparison of a Rapid Readout Biological Indicator for Steam Sterilization With Four Conventional Biological Indicators and Five Chemical Indicators." Infection Control & Hospital Epidemiology 17, no. 7 (July 1996): 423–28. http://dx.doi.org/10.1017/s0195941700006810.

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AbstractObjective:In this study, we compare a new biological indicator that provides results within 3 hours with four conventional, 48-hour biological indicators and five chemical indicators.Design:Biological indicators tested included the conventional Attest 1262, Proof Plus, Assert, and Biosign, and the new Attest 1292 Rapid Readout biological indicator. Chemical indicators tested included Comply, Propper, Chemdi, Sterigage, and Thermalog S. Spore survival following 121°C in a gravity displacement sterilizer was measured by media color change after incubation for 24 and 48 hours at 56°C for the conventional biological indicators, fluorescence at 3 hours for the Attest 1292 Rapid Readout biological indicator, and color change for the chemical indicators. Each exposure time was replicated 12 times with five samples of each indicator per run (ie, 60 replicates per indicator).Results:At 48 hours, the conventional biological indicators Attest 1262, Proof Plus, Assert, and Biosign showed 100%, 95%, 88%, and 93% spore survival at 5 minutes' exposure; 0%, 0%, 0%, and 8% at 10 minutes; and all showed 0% survival at 15 minutes' exposure. Following a 3hour incubation, the Attest 1292 Rapid Readout biological indicator showed fluorescence at 100%, 72%, and 0% at 5, 10, and 15 minutes, respectively. The chemical indicators Comply, Propper, Chemdi, Sterigage, and Thermalog S revealed sterilization failure rates of 100%, 100%, 100%, 100%, and 100% at 5 minutes' exposure; 0%, 0%, 0%, 92%, and 100% at 10 minutes; and, 0%, 0%, 0%, 3%, and 27% at 15 minutes' exposure, respectively.Conclusions:The sensitivity of the Attest 1292 Rapid Readout biological indicator parallels that of conventional biological indicators. These data suggest that a 3hour rapid readout biological indicator is equivalent to a standard 48-hour biological indicator. Some chemical indicators (eg, Thermalog S) failed to indicate adequate sterilization at 15 minutes' exposure. These chemical indicators have the potential of causing unnecessary recall of adequately sterilized items.

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Gouda, K., P. Rycerz, A. Kadiric, and GE Morales-Espejel. "Assessing the effectiveness of data-driven time-domain condition indicators in predicting the progression of surface distress under rolling contact." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 233, no. 10 (March 27, 2019): 1523–40. http://dx.doi.org/10.1177/1350650119838896.

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Condition monitoring of machine health via analysis of vibration, acoustic and other signals offers an important tool for reducing the machine downtime and maintenance costs. The key aspect in this process is the ability to relate features derived from the recorded sensor signals to the physical condition of the monitored asset in real time. This paper uses simple machine learning techniques to examine the ability of specific time-domain features obtained from vibration signals to predict the progression of surface distress in lubricated, rolling-sliding contacts, such as those found in rolling bearings and gears. Controlled experiments were performed on a triple-disc rolling contact fatigue rig using seeded-fault roller specimens where micropitting damage was generated and its progression directly observed over millions of contact cycles. Vibration signals were recorded throughout the experiments. Features known as condition indicators were then extracted from the recorded time-domain signals and their evolution related to the observed physical state of the associated specimens using simple machine learning techniques. Five time-domain condition indicators were examined, peak-to-peak, root-mean-square, kurtosis, crest factor and skewness, three of which were found not to be redundant. First, a classification model using KNN nearest neighbor was built with the three informative condition indicators as training data. The cross-validation results indicated that this classifier was able to predict the presence of micropitting damage with a relatively high precision and a low rate of false positives. Secondly, a k-means clustering analysis was performed to measure the significance of each condition indicator by leveraging patterns. The peak-to-peak condition indicator was found to be a good predictor for progression of micropitting damage. In addition, this indicator was able to distinguish between micropitting and pitting failure modes with a high success rate. Finally, the condition indicator response was correlated with the predicted damage state of the test specimen obtained through an existing physics-based surface distress model in order to illustrate the potential of hybrid models for improved prognostics of damage progression in rolling-sliding tribological contacts.

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Nikitin, V. V., and D. V. Bobin. "Principal Component Analysis for Weighted Data in the Procedure of Multidimensional Statistical Forecasting." Statistics and Economics 18, no. 2 (May 12, 2021): 4–11. http://dx.doi.org/10.21686/2500-3925-2021-2-4-11.

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Purpose of the research. Let’s assume that the dynamics of the state of some object is being investigated. Its state is described by a system of specified indicators. Among them, some may be a linear combination of other indicators. The aim of any forecasting procedure is to solve two problems: first, to estimate the expected forecast value, and second, to estimate the confidence interval for possible other forecast values. The prediction procedure is multidimensional. Since the indicators describe the same object, in addition to explicit dependencies, there may be hidden dependencies among them. The principal component analysis effectively takes into account the variation of data in the system of the studied indicators. Therefore, it is desirable to use this method in the forecasting procedure. The results of forecasting would be more adequate if it were possible to implement different forecasting strategies. But this will require a modification of the traditional principal component analysis. Therefore, this is the main aim of this study. A related aim is to investigate the possibility of solving the second forecasting problem, which is more complex than the first one. Materials and research methods. When estimating the confidence interval, it is necessary to specify the procedure for estimating the expected forecast value. At the same time, it would be useful to use the methods of multidimensional time series. Usually, different time series models use the concept of time lag. Their number and weight significance in the model may be different. In this study, we propose a time series model based on the exponential smoothing method. The prediction procedure is multidimensional. It will rely on the rule of agreed upon data change. Therefore, the algorithm for predictive evaluation of a particular indicator is presented in a form that will be convenient for building and practical use of this rule in the future. The principal component analysis should take into account the weights of the indicator values. This is necessary for the implementation of various strategies for estimating the boundaries of the forecast values interval. The proposed standardization of weighted data promotes to the implementation of the main theorem of factor analysis. This ensures the construction of an orthonormal basis in the factor area. At the same time, it was not necessary to build an iterative algorithm, which is typical for such studies. Results. For the test data set, comparative calculations were performed using the traditional and weighted principal component analysis. It shows that the main characteristics of the component analysis are preserved. One of the indicators under consideration clearly depends on the others. Therefore, both methods show that the number of factors is less than the number of indicators. All indicators have a good relationship with the factors. In the traditional method, the dependent indicator is included in the first main component. In the modified method, this indicator is better related to the second component. Conclusion. It was shown that the elements of the factor matrix corresponding to the forecast time can be expressed as weighted averages of the previous factor values. This will allow us to estimate the limits of the confidence interval for each individual indicator, as well as for the complex indicator of the entire system. This takes into account both the consistency of data changes and the forecasting strategy.

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Kim, Yonsoo, Changhyun Choi, Younghye Bae, Donghyun Kim, Deokhwan Kim, and Hung Soo Kim. "Indicator Development and Evaluation of Storm and Flood Resilience Using Big Data Analysis: (1) Development of Resilience Indicators." Journal of the Korean Society of Hazard Mitigation 18, no. 4 (June 30, 2018): 97–107. http://dx.doi.org/10.9798/kosham.2018.18.4.97.

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Vann, Katie. "Surplus and Indicator." Engaging Science, Technology, and Society 3 (February 17, 2017): 92. http://dx.doi.org/10.17351/ests2017.113.

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This essay offers a perspective on journal impact factor (JIF) centrism in academic evaluation from the vantage point of academic publishing in an increasingly data-driven scholarly environment. The political implications and orientations to the JIF are thought through with respect both to commercial publishing industry consolidation and to the reliance of public-sector scholarly communities on (oligopolistic) commercial academic publishing houses. The author proposes that centrism to the JIF as a legitimizing indicator and incentivizing norm leads to two diametrically opposed forms of “surplus”: for academic communities, surplus emerges in the form of layers of scholarly knowledge effects/impact and labor, which, because they remain foreclosed to formal professional recognition, are inadvertently reconstructed as dispensable (waste); for private sector publishing companies––whose contribution to the publishing process consists foremost in providing scalable content management/distribution platforms and in transforming unique manuscript content into standardized digital objects that are amenable to indexing, aggregation, and comparative calculation––surplus emerges in the form of monetary surplus (profit). The essay describes the inner workings of these phenomena.

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Salazar, Eduardo, Richard Smith, Martin Weale, and Stephen Wright. "A Monthly Indicator of GDP." National Institute Economic Review 161 (July 1997): 84–89. http://dx.doi.org/10.1177/002795019716100106.

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Cyclical indicators have hitherto been used as a means of describing the state of the economy on a month by month basis. However, they have proved unsatisfactory for a number of reasons and are now no longer published. This paper summarises a means of producing monthly estimates of GDP from existing monthly data. The technique can be used to extrapolate as well as to interpolate monthly data and therefore provides an indicator of the current state of the economy.

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Opala, Michał. "Analysis of Experimental Data in the Context of Safety against Derailment of a Railway Vehicle, Using the Energy Method." Key Engineering Materials 518 (July 2012): 16–23. http://dx.doi.org/10.4028/www.scientific.net/kem.518.16.

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This article describes an example analysis of safety against derailment of a railway vehicle. The analysis is based on the experimental data recorded during measurement of the wheel-rail interaction forces and lateral accelerations. The data is used for the calculation of two safety against derailment indicators and then the indicators are compared to each other. The first indicator is the ratio of the lateral to vertical wheel-rail forces Y/Q, based on the Nadal criteria. The second indicator is given in the energy description. In this description, the derailment of a railway vehicle depends on the amount of the work that has been done by the total lateral force acting on the single wheelset. The second indicator can be particularly convenient for a railway vehicle condition monitoring system, because it does not require the measurement of the contact forces.

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Journal articles on the topic 'Indicator data'

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