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Complex operations carried out in the oil and gas industry such as drilling require constant and accurate real-time monitoring of the process. To this aim, a real-time model of the drilling operation is required. Such a model is used to estimate the state of the well when and where direct and reliable measurements are not available and it helps the driller gain an overview of the drilling process. Given the harsh operating environment, sensor reliability and sensor calibration are known problem areas, and bad data quality is a common problem, affecting the accuracy of the model. As a result, the driller may be misled about the downhole situation or receive conflicting claims about operating conditions. A way to reduce uncertainty and increase confidence is to aggregate the opinion of different experts. When the expert is a computer program, such aggregation is often referred to as an ensemble approach. The principle underlies techniques that have become popular in the oil industry in recent years, such as probabilistic forecasting and ensemble Kalman filters. In this paper, we discuss this trend and develop an ensemble system for predicting the bottom-hole pressure during a managed pressure drilling operation. The improved accuracy and robustness of the ensemble approach in situations with bad data quality is demonstrated.

Mobile vehicles, equipped with low-cost sensors, can provide unprecedented opportunities for urban monitoring with a wide coverage. The quality of collected data is very important for many applications. The low-cost mobile sensors can, however, suffer from limited accuracy, high instability, and sensor drift. Therefore, they need to be frequently calibrated to preserve a good data quality. Frequent sensor calibration can be achieved by deploying static high-precision sensors (called reference sensors) and exploiting meeting points (i.e., rendezvous) between the reference sensors and mobile sensors. A calibrated mobile sensor can also be used to calibrate an uncalibrated mobile sensor when they meet, referred to as multihop calibration. In this paper, we introduce a novel concept, k-hop calibratability, in the context of multihop calibration: a sensor is k-hop calibratable if the calibration path is no larger than k. We consider the problem of how to deploy the reference sensors to ensure that all sensors in the network are k-hop calibratable. To address this problem, we formally define the rendezvous connection graph to precisely describe the relationship between mobile sensors and (virtual) reference sensors. Based on this definition, we formulate the reference sensor deployment problem as a set cover problem. We also extend our problem to consider practical deployment requirements. We propose efficient algorithms and conduct an extensive evaluation in the context of mobile air quality monitoring. We present a detailed prototype implementation of the mobile sensors and reference sensors. Evaluations using real-world datasets show the effectiveness of the algorithms.

The digital economy has been termed a data-driven economy. Many digital business models are based on the collection and the processing of various types of data. Whereas the protection of personal data falls under the EU legal framework, and first and foremost the General Data Protection Regulation, the protection of data that goes beyond personal data may seem to be not protected at all. The latter circumstance is important in light of the EU Commission’s considerations on the introduction of rights related to the protection of non-personal data. Indeed, the question of whether there is a need for the protection of such data – called “industrial data” - and the issue of access to such data have also been at the center of a recent academic debate. Yet, whereas the protection of different types of data is highly relevant when it comes to the transfer and the processing of data, for the question of “access to data” data has to be considered more broadly, i.e. as a digital resource. Since in the digital economy it is no longer individual pieces of data (personal and/or non-personal) that play a role, but rather collections of data, digital data sets (which may consist of both personal and non-personal data) have become a digital asset of companies. The value of such digital data sets derives from the analysis of data – importantly though, the analysis of real-time data. Due to this feature of data, i.e. the fact that data is constantly changing, the question that arises as regards access to data in the digital economy relates not so much to access to data as such, but rather to access to the sources of data. After all, it is only in this case that access can be provided to real-time data. In light of this, the current debate on so-called “industrial data” may appear too static and, besides, it considers only part of the data that is relevant for the companies operating in the digital economy. The question is what the legal framework for an optimal allocation of such a digital resource as data should be and whether the question of access can be solved with the currently available legal tools.

There is great potential for clinical trials designed with adaptive features to result in more efficient decision making within a drug development program. However, clinical trials with adaptive features are more complex to implement than traditional designs such as fixed-sample or group sequential. Workarounds and/or inefficiencies in adaptive design (AD) trial execution may result in human and material wastes. Further, they may result in the introduction of operational biases that may potentially negate any gains in designing an AD trial and may even render trial results not interpretable. In this article, we present and share our experience and best practices in AD trial implementation in the areas of resource planning, randomization considerations including the importance of randomization schemes on clinical supply, Interactive Voice Randomization System vendor capability assessment and quality control, clinical supply strategy considerations, enrollment management and patient enrollment modeling and simulation, data quality and interim analysis planning, managing blinding and unblinding, and the use of a data monitoring committee.

Crowdsourced psychological and other biobehavioral research using platforms like Amazon’s Mechanical Turk (MTurk) is increasingly common – but has proliferated more rapidly than studies to establish data quality best practices. Thus, this study investigated whether outcome scores for three common screening tools would be significantly different among MTurk workers who were subject to different sets of quality control checks. We conducted a single-stage, randomized controlled trial with equal allocation to each of four study arms: Arm 1 (Control Arm), Arm 2 (Bot/VPN Check), Arm 3 (Truthfulness/Attention Check), and Arm 4 (Stringent Arm – All Checks). Data collection was completed in Qualtrics, to which participants were referred from MTurk. Subjects (n = 1100) were recruited on November 20–21, 2020. Eligible workers were required to claim U.S. residency, have a successful task completion rate > 95%, have completed a minimum of 100 tasks, and have completed a maximum of 10,000 tasks. Participants completed the US-Alcohol Use Disorders Identification Test (USAUDIT), the Patient Health Questionnaire (PHQ-9), and a screener for Generalized Anxiety Disorder (GAD-7). We found that differing quality control approaches significantly, meaningfully, and directionally affected outcome scores on each of the screening tools. Most notably, workers in Arm 1 (Control) reported higher scores than those in Arms 3 and 4 for all tools, and a higher score than workers in Arm 2 for the PHQ-9. These data suggest that the use, or lack thereof, of quality control questions in crowdsourced research may substantively affect findings, as might the types of quality control items. Supplementary Information The online version contains supplementary material available at 10.3758/s13428-021-01665-8.

Longitudinal studies are important because they can help provide answers to questions about cause and effect, although their complexity leads to a number of challenges for the researcher. By their very nature longitudinal studies may continue over a long period of time and/or have many data points and therefore good documentation of procedures is essential. In addition, it is important to develop dynamic databases that reflect the current status of participants in the project and to develop protocols for dealing with inconsistent or missing responses over time. This paper provides some guidance about these issues as well as information about longitudinal data structure and ways to summarise and display the information obtained from longitudinal studies.

The rigorous economic analysis of peer group formation is a burgeoning subject. Much has been written about how peers influence an individual’s behavior, and these effects are quite prevalent. However, less has been written on how exactly these peer groups begin and the resulting consequences of their formation. A reason for the dearth of knowledge on peer group formation is the lack of quality data sets that clearly define one’s peers. To resolve this issue, this paper explores data which allows a peer group to be defined openly through self nominations. Using these nominations as well as characteristics of the students and their friends, it is possible to see on what dimensions these individuals are sorting into friendships. The data suggests that there is heavy sorting within race and academic ability. Additionally, tests for statistical discrimination on race and academics show that it is exhibited towards blacks and Hispanics. There is also weak evidence of statistical discrimination against whites. Empirical analysis also shows that the degree of statistical discrimination decreases for blacks and Hispanics over a year. however, there is little change for whites over the same period.

ONE of the things which led mo to develop the idea of “fixed period renewal”, was 12 months' experience of Quality Control with a famous aero‐engine firm. During that time I kept life‐charts and quality data in regard to numerous interesting variables relating to the failures and use‐characteristics of components within the power‐plant structure as a whole. Despite failure percentages of rarely more than 5 per cent, as a proportion of total production, I was absolutely amazed at the utter uncertainty as to failure probability, cither in terms of timing or quantity. It was equally appalling to realize what a low life‐expectation there is for all components. During the war the comparative lack of reliability in many minor components, within important assemblies, has led, in my personal and it is true restricted experience, to a terrible wastage of man‐power. Even though material weaknesses and shortages have to some extent excused this (with regard for example to the repair schemes) the result has not infrequently been to endanger or actually to end human life. When a component would be expected, Tor both safety and economic reasons to last χ hours, it has been found to last instead perhaps χ/3 or χ/4 hours.

DE acknowledges a NERC studentship whilst at RHUL. LA-ICP-MS work at RHUL over the years was co-funded by SRIF3 (HEFCE) and NERC (NERC CC073) equipment grants, NERC and RHUL PhD studentships, EU-FP7-ITN (‘Throughflow’), Leverhulme Trust grants as well as funding from Resonetics, Laurin Technic and Australian Scientific Instruments (ASI). LA-ICP-MS is increasingly applied to obtain quantitative multi-element data with minimal sample preparation, usually achieved by calibration using reference materials. However, some ubiquitous reference materials, e.g., the NIST SRM 61x series glasses, suffer from reported value uncertainties for certain elements. Moreover, no long-term dataset of analyses conducted over a range of ablation and tuning conditions exists. Thus, there has been little rigorous examination of the extent to which offsets between measured and reported values are the result of error in these values rather than analytically induced fractionation. We present new software (‘LA-MINE’), capable of extracting LA-ICP-MS data with no user-input, and apply this to our system, yielding over five years of data (~ 5700 analyses of ten glass and carbonate reference materials). We examine the relative importance of systematic analytical bias and possible error in reported values, through a mass-specific breakdown of fourteen of the most commonly determined elements. Furthermore, these data, obtained under a wide range of different ablation conditions, enable specific recommendations of how data quality may be improved, e.g., the role of diatomic gas, the effect of differential inter-glass fractionation factors, and choice of transport tubing material. Finally, these data demonstrate that the two-volume Laurin ablation cell is characterised by no discernible spatial heterogeneity in measured trace element ratios. Postprint

There is an ongoing movement in society to provide open data as an enabler for innovation and growth. As more data is passed on beyond organizational borders to trigger third party development of services, the expectations on what could be developed using open data increase. Developers have been observed to express frustration over different challenges in the wake of open data provision, such as localization issues, lack of quality in data etc. Knowledge and tools are missing to assess available open data. This absence risks ensuring continual improvement of open data markets and the management of hampering gaps between developers' requests of data and data providers' provision of data. This paper explores the benefits with open data assessment based on an exploratory single case research design.

The U.S. Geological Survey (USGS) changed the management and delivery of Landsat products to the public in its archive through the implementation of Collections. The Collections process ensures consistent data quality through time and across all the Landsat sensors with a few modifications to the metadata. The consistent data products from Collections are more conducive for applications such as time-series analysis, because the data can be used without a need for sensor-specific geometric or radiometric adjustments. The Collections process also allows for calibration improvements from updated reference sources, model trending, and enhanced algorithms that are grouped together and applied at one time, thus limiting the operational impacts to users. The first collection, Collection-1 was released in 2016, and Collection-2 is expected to be released in 2020. This paper addresses the geometric improvements to the Collection -2 dataset. Geometric improvements in Collection-2 include improvements to the geometric accuracy and interoperability of all Landsat products by implementing the Sentinel 2 Global Reference Image (GRI), improved elevation dataset using NASADEM and other sources of Digital Elevation Model (DEM) for terrain correction, improvements to the precision correction process for Enhanced Thematic Mapper Plus (ETM+) and Thematic Mapper (TM) sensors, changes to the Thermal Infrared Sensor (TIRS) to Operational Land Imager (OLI) alignment estimates, thermal band detector alignments and focal plane adjustments for ETM+, and improvements to the ETM+ sensor to attitude control system (ACS).

One common serious issue of training a prediction model is that the process data significantly outnumber the quality data. Such discrepancy exists because of the time lag for obtaining quality data. This paper proposes semi-supervised variational autoencoder-generative adversarial network (S2-VAE/GAN), that is able to make use of all the data even with some missing quality data. The key idea in S2-VAE/GAN is the capability of enhancing the performance of the decoder/generator in learning the true distribution of both process and quality data in a competition between the decoder/generator and the discriminator in S2-VAE/GAN through Nash Equilibrium, allowing the model to improve the qualities of reconstruction and prediction data. The S2-VAE/GAN model is also flexible enough to automatically adjust itself according to the input data. If the quality data are missing, the model can fill up the data through the trained prediction model and the same network structure defined in the supervised case can still be re-used. With the probabilistic distribution format, the proposed method is also capable of capturing the nonlinear feature of the process and representing the stochastic nature of operating plants. The results of the numerical case and the industrial case in this paper show that S2-VAE/GAN outperforms conventional methods in terms of predictabilities of the missing quality data.

In the German Focus on Tropospheric Research (TFS) independent quality assurance procedures were implemented in order to obtain information on data quality and comparability of the different measurements made in the different field campaigns. This paper describes the results for measurements of hydrocarbons using in-situ gas chromatographic techniques and off line analysis of samples collected in canisters (analysed by two central laboratories CL-1 and CL-2) and samples collected on adsorption tubes. The QA-procedures included comparisons with synthetic standards (prepared by EN 45001 certified laboratories), absolute calibration with a diffusion source, and an instrument/methodology comparison in ambient air. Harmonisation of the ambient measurements was achieved with a complex mixture (NCAR/BERLIOZ) containing 70 commonly observed hydrocarbons at mixing ratios of 0.2 to 10 ppb (mole fraction) in nitrogen, which was calibrated by referencing to hydrocarbon standards of the National Institute for Standards and Technology (NIST).

Network-based services have become a ubiquitous part of our lives, to the point where individuals and businesses have often come to critically rely on them. Building and maintaining such reliable, high performance network and service infrastructures requires the ability to rapidly investigate and resolve complex service and performance impacting issues. To achieve this, it is important to collect, correlate and analyze massive amounts of data from a diverse collection of data sources in real time. We have designed and implemented a variety of data systems at AT&T Labs-Research to build highly scalable databases that support real time data collection, correlation and analysis, including (a) the Daytona data management system, (b) the DataDepot data warehousing system, (c) the GS tool data stream management system, and (d) the Bistro data feed manager. Together, these data systems have enabled the creation and maintenance of a data warehouse and data analysis infrastructure for troubleshooting complex issues in the network. We describe these data systems and their key research contributions in this talk.

Read-across and QSAR have different traditions and drawbacks. We address here two main questions: (1) How do we solve the issue of the subjectivity in the evaluation of data and results, which may be particularly critical for read-across, but may have a role also for the QSAR assessment? (2) How do we take advantage of the results of both approaches to support each other? The QSAR model starts from the training set. The presence of similar chemicals with property values close to that predicted can support the result. The approach in read-across is the opposite. The assessment is focused on the few substances similar to the target. The data quality of the similar chemicals is fundamental. A risk is poor standardization in the definition of 'similarity', because different approaches may be applied. Inspired by the principles of high transparency and reproducibility, a new program for read-across, called ToxRead, has been developed and made freely available ( www.toxgate.eu ). The output of ToxRead can be compared and integrated with the output of QSAR, within a weight-of-evidence strategy. We discuss the evaluation and integration of ToxRead and QSAR with examples of the assessment of bioconcentration factors of chemicals.

This is the final report of results of a cooperative study using a kneading compactor and Hveem Stabilometer done between the Indiana State Highway Department and Joint Highway Research Project bituminous laboratories. The final phase of the Purdue work is presented in the first section. The sections following present comparisons of the data obtained in the two laboratories for the final phase of the program in which a type B surface gradation was used. Comparisons are made by presenting average test values in tabular form, by graphical illustrations, and by statistical evaluation of the data. Conclusions and recommendations are presented.

Many providers rely on electronic billing systems to report information to immunization registries. If billing data fail to capture some administered immunizations, the registry will not reflect a child's true immunization status. Our objective was to assess differences between immunizations administered and immunizations reported to a registry from electronic billing systems. Philadelphia's Department of Public Health conducted chart audits in 45 providers serving 50 or more children aged 7-35 months and using electronic billing systems to report data to Philadelphia's immunization registry in 2001-2003. Chart records were compared to registry records to identify immunizations administered in these practices but not reported to the registry. The study practices administered 256,969 immunizations to 20,611 children. Of these 256,969 administered immunizations, 62,213 (24%) were not in the registry. The electronic billing systems submitted data for all administered immunizations for 69% of immunization visits, some but not all for 11% of visits, and none for 20% of visits. Immunizations administered but not billed cost these providers up to $980,477 in lost revenue from administrative fees alone. Improvement of billing data quality would result in more complete registries, higher reported immunization coverage rates, and recovered revenue for immunization providers.

An automatic marine seismic monitoring and recording instrument of recoverable design is discussed. The instrument is capable of measuring three components of earth motion and water pressure waves and is intended for use on the ocean bottom at depths to 10,000 feet. Most previous seismic data has been recorded at land-based stations and the collection of ocean-floor seismic information will materially add to the knowledge of origin and propagation phenomena. Data are recorded on magnetic tape in digital form. Seismic motion is recorded in the 1-10 cps band with a sensitivity below 0.1 millimicron of earth movement. Pressure information is recorded over the same bandwidth with comparable sensitivity. Up to eight hours of data can be recorded on a programmed cycle during each implanting of the instrument. Initial sea tests of the instrument indicate that the unit functioned as desired. Several events recorded during the test period appeared to give valid seismic information. One of these events is described. Simultaneous recording of pressure and velocity is expected to enhance detection capability. It is planned in a future program to collect additional seismic information in much deeper waters and at sites chosen for maximum quality data return. The data will represent the first extensive effort to collect and analyze three-component seismic (plus pressure) information at great ocean depths.

Missile rocket‐engine noise‐ and vibration‐measurement system components are subjected to severe environments which may introduce spurious signals in the data. Thus, all components and systems must be subjected to environmental tests prior to their use to assure good data quality. Such a program is described in this paper. Several types of commercial microphones. accelerometers, preamplifiers, and cables were subjected to environmental tests, and their outputs analyzed to determine effect upon data. In addition, an entire data‐acquisition system (to recorder input), including mounting brackets, was subjected to vibration tests. The results are discussed in the following manner: (a) Test data are graphically presented. (b) Test results are analyzed to predict their effect on the data. (c) The results of the prediction are compared to measured missile data. (d) Individual components and system test data are compared to show differences in results. Both good and poor data taken from missile firings are also presented to show environmental effects.

Satellite tracking systems (notably optical and doppler) have been developed to such a degree that the accuracy of orbit determination is no longer limited by the accuracy of the tracking data itself but is almost wholly limited by other parameters associated with the satellite motion and tracking system. The principal parameters limiting this accuracy are those associated with the earth's gravitation force field and the locations of the satellite tracking stations. Significant improvement in the values of a few of these parameters has already been made via studies of satellite motion but, in most cases, they have been made without the use of statistical inference techniques. The Navy's doppler tracking system provides ample high quality data for statistically inferring very accurate values of these parameters. However, employing inference techniques to tracking data obtained from such a system gives rise to some unusual problems. In particular the validity of inference techniques depends very strongly on correctly estimating the character of and correlations in the experimental tracking data. This paper will discuss briefly some of the more difficult problems associated with specifying the errors in the data, the consequent problems in determining optimum statistical weighting factors, and finally the difficulties in realistically estimating the probable errors in the inferred values for the parameters.

The current-revision of the Process Optimization Program contains two empirical plutonium-exposure expressions. One relates total Pu conversion to exposure and the other, Pu-240 buildup to exposure. The former was incorporated to save computer time and the latter to provide product quality data. Both of these relationships, in addition to a third which relates Pu-241 buildup to exposure, are used in PULP-3, the data generator for LP-90. EMCON-3, a 7090 computer program which determines arbitrary constants for all three empirical expressions, has been devised. It uses, as input, official conversion data and will facilitate rapid evaluation of the effect, of periodic revisions in official conversion data, changes in reactor loadings, and/or changes in product specification. This document contains a discussion of the empirical expressions, the program mechanics, and program input/output. A Fortran listing of the program logic a deck assembly diagram, and sample program output are included in the appendix to aid the reader in using and/or revising the program.

Data for this study came from questionnaires2 on municipal water plant operating data and from raw-water quality data 3 for 1960 (Table 1). The questionnaires yielded data on yearly chemical costs or yearly chemical usage that was converted to costs. The water quality data were averaged so as to determine the mean yearly value of the selected indicators. Despite the large amount of data found in the two sources, only fourteen water plants existed in which both costs and rawwater quality were known. Graphs were plotted of chemical costs per thousand gallons treated against the various indicators. A typical graph, showing cost against color, is shown in Fig. 1. For the graphs whose points approximated a straight line, a simple correlation analysis was performed that involved computing the following three types of measurements 4 : 1. An estimating line, or equation, which describes the functional relation

Traditional problems in the preparation of climatic maps of insolation involve the quality and scarcity of data. There is little the climatologist can do to improve data quality, but, in some cases, he can take steps to increase the quantity of usable data. In some parts of the world insolation is measured regularly at a few locations as part of a widespread and systematic climatic data-collection program. Several investigators have shown that analysis of data from the few available insolation stations in such areas will reveal empirical relationships between insolation and climatic elements measured at many other locations, and that the resulting relationships can be used successfully to estimate insolation for places lacking data and to fill gaps in the records of insolation stations. The application of this technique to the United States in the subject of this paper. Between 1950 and 1962, 59 stations in the contiguous United States maintained records of insolation suitable for climatic analysis. The relationships between insolation and more widely available variables—percent of possible sunshine, cloud cover, presence or absence of snow on the ground, and station elevation—are established through simple and multiple regression analysis, and tested for use in estimating insolation for 113 stations where insolation is not measured. The regression relationships are also tested and applied to filling in missing data in the records of the insolation stations. The final product consists of 12 monthly maps of the United States showing mean daily insolation.

An essential feature of the oral history project is the comparability of material once it has been collected. It should be made clear that for comparative analysis it is not essential (although it makes the work easier, if this has already been standardized) for all categories of data collection to be standardized in all countries studied; it is essential only that a full picture of all significant aspects of each movement will be covered--a task, as Professor Blanksten suggests, made easier by some forethought as to which categories should be covered in the interview. We assume that the categories not covered but very important in one or two countries will be recorded there if the interviewers perform their roles adequately. Certainly there are a number of extremely interesting problems that comparison would solve. What are the different speeds with which success or failure of these movements occurs? Why did some simply die out, others become dominant and successful, while still others become separatist tribal groups? If we use Professor Zolberg's categories plus others that turn up in the pilot study, and take account of Professor Cohen's strictures concerning quantification, then some answers may be forthcoming.

The basic objective of Data Manager-1 (DM-1) is to provide business organizations with the comprehensive data handling capability outlined in this section.

A crustal refraction experiment was shot in August 1965 in Hudson Bay as part of a comprehensive survey encompassing the fields of geophysics, geology, and oceanography. Nine university and government crews participated. Eight stations were located on the periphery of the bay, while telemetering sonobuoys were monitored at a central location in the bay. Good quality data were obtained from 41 shots. All preliminary data pertaining to participants, instrumentation, shot times and distances, travel times, etc. are tabulated in this report.

This paper describes the research, development and evaluation process of a solution based on computer vision for the detection and prevention of Computer Vision Syndrome, a type of eye fatigue characterized by the appearance of ocular symptoms during or after prolonged periods watching digital screens. The system developed targets users of computers and mobile devices, detecting and warning users to the occurrence of eye fatigue situations and suggesting corrective behaviours in order to prevent more complicated health consequences. The implementation resorts to machine learning techniques, using eye images datasets for training the eye state detection algorithm. OpenCV Lib was used for eye’s segmentation and subsequent fatigue analysis. The final goal of the system is to provide users and health professionals with quality data analysis of eye fatigue levels, in order to raise awareness over accumulated stress and promote behaviour change.

Automatic vehicle location (AVL) and automatic passenger counting (APC) systems are powerful tools for transit agencies to archive large, detailed data sets for transit operations. Ensuring data quality is an important first step to exploiting these data sets. An automated quality assurance methodology is presented to identify unreliable archived AVL-APC data for exclusion from further operational analyses. The approach is based on observed or expected pattern limitations of travel and passenger activity, which are derived from archived data. Stop-level tests identify suspect data, which are then flagged at the trip level. A methodology case study is presented for AVL-APC data from Grand River Transit in the region of Waterloo, Ontario, Canada.

In a spring wheat breeding nursery at the University of Manitoba in 1967, the wheat protein content of systematic control plots of Triticum aestivum cv. Manitou varied from 10.3% to 16.5% (at 13.5% moisture basis). The correlation between grain yield and protein content of these plots was 0.88 and significant at the 99% confidence level. Correlations calculated for control plots at specified distances apart indicate that for all breadmaking quality test characteristics except bushel weight and flour yield, contiguous plots are significantly more similar in quality than those further apart. The correlation between control plots 2.7 m (9 ft) apart is 0.84 (P = 0.05) for wheat protein percent and of similar order for those quality characteristics which are dependent on total protein. Areas of high and low quality "potential" can be identified in a wheat nursery by using quality data from controls at frequent intervals, and this information should be used by the breeder when assessing the single quality test of a breeding line from a given area of the nursery.

This paper uses a family of bivariate extreme-value distributions to estimate the probability of a large exceedance of a random variable given that a certain other random variable not independent of the first has exceeded a certain value. A simple method of reasonably good efficiency is given for estimating a bivariate extreme-value distribution from independent bivariate samples. The method is used to analyze the performance of a spacecraft command receiver which has an indication of data quality so that commands likely to be in error can be rejected.

Although business process visibility is considered to be increasingly important for international trade, the visibility of existing supply chains is currently still ambiguous. Information deficiencies such as incorrectness or inconsistency of data are major determinants that decrease clarity. The Data Pipeline principle has been proposed to overcome the data quality shortcomings, enhance the visibility, and improve performance of the supply chain. In a first elaboration, the Data Pipeline model named the Distributed Trust Backbone (DTB) was designed and implemented in three different countries. In order to discover the effectiveness and feasibility of the model, the visualization of the Data Pipeline’s process flow is urgently required in terms of both real-time detection of system failures and process flow representations. However, there is no process visualization feature available for the Data Pipeline. This challenge is taken up in this paper. We propose a Data Pipeline monitoring system and describe the results of performed simulation tests based on a case study of the international trade lane between Southeast Asia and Europe.

Every day there are more techniques that can work with low quality data. As a result, issues related to data quality have become more crucial and have consumed a majority of the time and budget of data mining projects. One problem for researchers is the lack of low quality data in order to test their techniques with this data type. Also, as far as we know, there is no software tool focused on the create/manage low quality datasets which treats, in the widest possible way, the low quality data and helps us to create repositories with low quality datasets for testing and comparison of data mining techniques and algorithms. For this reason, we present in this paper a software tool which can create/manage low quality datasets. Among other things, the tool can transform a dataset by adding low quality data, removing and replacing any data, constructing a fuzzy partition of the attributes, etc. It also allows different input/output formats of the dataset.

Online citizen science projects involve recruitment of volunteers to assist researchers with the creation, curation, and analysis of large datasets. Enhancing the quality of these data products is a fundamental concern for teams running citizen science projects. Decisions about a project's design and operations have a critical effect both on whether the project recruits and retains enough volunteers, and on the quality of volunteers' work. The processes by which the team running a project learn about their volunteers play a critical role in these decisions. Improving these processes will enhance decision-making, resulting in better quality datasets, and more successful outcomes for citizen science projects. This paper presents a qualitative case study, involving interviews and long-term observation, of how the team running Galaxy Zoo, a major citizen science project in astronomy, came to know their volunteers and how this knowledge shaped their decision-making processes. This paper presents three instances that played significant roles in shaping Galaxy Zoo team members' understandings of volunteers. Team members integrated heterogeneous sources of information to derive new insights into the volunteers. Project metrics and formal studies of volunteers combined with tacit understandings gained through on- and offline interactions with volunteers. This paper presents a number of recommendations for practice. These recommendations include strategies for improving how citizen science project team members learn about volunteers, and how teams can more effectively circulate among themselves what they learn., 15 pages

Part 2: Artificial Intelligence for Knowledge Management; International audience; Companies today have several major issues while managing information. Many subsidiaries and departments have developed their own Data Management which has led to a multitude of Operational Databases and sometimes a multitude of Data Marts, policies and processes. Thus, these systems lack sustainability because they are not dynamic and not self-organizing, and so they do not adapt to the continuous needs arising from evolution that the companies experience. The Dynamic Data Mart architecture is built around 6 main functions, namely the 3Ms (Data Mining, Data Marshalling and Data Meshing) and the 3Rs (Recommendation, Reconciliation and Representation), which will address the aforementioned problems. Once the totality of the data have been loaded into a single Data Warehouse, the Dynamics Data Marts address these problems by mining the user’s behavior and the user’s decision making processes and continuously and automatically adapting the Data Mart to the needs of the users. Dynamic Data Marts create adapted dimensions, facts, data associations and views and then automatically find the ones that are not used anymore. These latter are then automatically dropped by the system, or can be presented to the IT manager if needed for validation of their removal.

The COVID-19 pandemic has resulted in many changes, including restrictions on indoor gatherings and visitation to residential aged care facilities, hospitals and certain communities. Coupled with potential restrictions imposed by health services and academic institutions, these changes may significantly impact the conduct of population health research. However, the continuance of population health research is beneficial for the provision of health services and sometimes imperative. This paper discusses the impact of COVID-19 restrictions on the conduct of population health research. This discussion unveils important ethical considerations, as well as potential impacts on recruitment methods, face-to-face data collection, data quality and validity. In addition, this paper explores potential recruitment and data collection methods that could replace face-to-face methods. The discussion is accompanied by reflections on the challenges experienced by the authors in their own research at an oral health service during the COVID-19 pandemic and alternative methods that were utilised in place of face-to-face methods. This paper concludes that, although COVID-19 presents challenges to the conduct of population health research, there is a range of alternative methods to face-to-face recruitment and data collection. These alternative methods should be considered in light of project aims to ensure data quality is not compromised.

A method is proposed for evaluating the reliability of the details of such recording instructions as may be given to coders in content analysis or to observers of social interaction. The method is applicable to situations in which (1) the data to be generated are nominal, (2) the number of coders or observers are variable during the process, and (3) the instructions specify a multilevel decision process. The chief aim of the analysis is to detect and evaluate sources of unreliability in complex recording instructions and to predict by computational means the reliability of data when parts of a recording instruction are deleted. In contrast to other aims of data quality control, the term instruction reliability is used to designate the results. The method provides several coefficients and an accounting scheme for amount of disagreement. The power of the method is demonstrated by a computational example, and the relation of the proposed measures to other known reliability coefficients is discussed.

Big data changed the way in which we collect and analyze data. In particular, the amount of available information is constantly growing and organizations rely more and more on data analysis in order to achieve their competitive advantage. However, such amount of data can create a real value only if combined with quality: good decisions and actions are the results of correct, reliable and complete data. In such a scenario, methods and techniques for the Data Quality assessment can support the identification of suitable data to process. If for traditional database numerous assessment methods are proposed, in the Big Data scenario new algorithms have to be designed in order to deal with novel requirements related to variety, volume and velocity issues. In particular, in this paper we highlight that dealing with heterogeneous sources requires an adaptive approach able to trigger the suitable quality assessment methods on the basis of the data type and context in which data have to be used. Furthermore, we show that in some situations it is not possible to evaluate the quality of the entire dataset due to performance and time constraints. For this reason, we suggest to focus the Data Quality assessment only on a portion of the dataset and to take into account the consequent loss of accuracy by introducing a confidence factor as a measure of the reliability of the quality assessment procedure. We propose a methodology to build a Data Quality adapter module, which selects the best configuration for the Data Quality assessment based on the user main requirements: time minimization, confidence maximization, and budget minimization. Experiments are performed by considering real data gathered from a smart city case study.

Quality data from Western Canadian Cooperative Wheat Tests were analyzed. Standard deviations of 25 different quality measurements were determined and will allow wheat breeders to assess the significance of differences in their material. Estimates of heritability indicated that protein content and remix loaf volumes were highly heritable while gassing power, diastatic activity and other measures of amylase activity appeared to be influenced mainly by environmental factors. It was recognized that estimates of heritability were probably biased upward due to the nature of the data, and a need for studies designed for the purpose of estimating heritability of quality traits was expressed. Correlations among quality measurements were estimated. Prediction of loaf volumes on the basis of measurements on flour were quite successful, and indicated the importance of measurements such as protein content, farinograph development time and gassing power in early generation selection for quality.

The purpose of this report is to clarify certain issues of data-base support. The main issues are data independence, security, integrity, search, and the integrated data base. The first section of the report is an introduction, which includes data-management history, trends, and terminology. The second section presents a theory of operational data based on the notions of entity sets and data maps. The third section is an exposition of data-bank design, emphasizing structure, search, and maintenance. The fourth section shows why data independence is a necessary feature of a viable data base system. The report should not be construed as representing a commitment or intention of IBM. The opinions expressed are personal and do not represent a corporate position. The intent of the report is tutorial, and the viewpoint is that of a systems programmer. Data independence Integrated data base Data integrity Search Data management Systems programming Data security 21 Programming