Data reconciliation

All process measurements contain some element of error. Typically, a distinction is made between random errors, with zero expected value, and gross errors with non-zero magnitude. Data Reconciliation (DR) and Gross Error Detection (GED) comprise a collection of techniques designed to attenuate measurement errors in process data in order to reduce the effect of the errors on subsequent use of the data. DR proceeds by finding the optimum adjustments so that reconciled measurement data satisfy imposed process constraints, such as material and energy balances. The DR solution is optimal under the assumed statistical random error model, typically Gaussian with zero mean and known covariance. The presence of outliers and gross errors in the measurements or imposed process constraints invalidates the assumptions underlying DR, so that the DR solution may become biased. GED is required to detect, identify and remove or otherwise compensate for the gross errors. Typically GED relies on formal hypothesis testing of constraint residuals or measurement adjustment-based statistics derived from the assumed random error statistical model. Classification methodologies are methods by which observations are classified as belonging to one of several possible groups. For the GED problem, artificial neural networks (ANN's) have been applied historically to resolve the classification of a data set as either containing or not containing a gross error. The hypothesis investigated in this thesis is that classification methodologies, specifically classification trees (CT) and linear or quadratic classification functions (LCF, QCF), may provide an alternative to the classical GED techniques. This hypothesis is tested via the modelling of a simple steady-state process unit with associated simulated process measurements. DR is performed on the simulated process measurements in order to satisfy one linear and two nonlinear material conservation constraints. Selected features from the DR procedure and process constraints are incorporated into two separate input vectors for classifier construction. The performance of the classification methodologies developed on each input vector is compared with the classical measurement test in order to address the posed hypothesis. General trends in the results are as follows: Stellenbosch University 8 VISUALIZING GROSS ERRORS .

All process measurements contain some element of error. Typically, a distinction is made between random errors, with zero expected value, and gross errors with non-zero magnitude. Data Reconciliation (DR) and Gross Error Detection (GED) comprise a collection of techniques designed to attenuate measurement errors in process data in order to reduce the effect of the errors on subsequent use of the data. DR proceeds by finding the optimum adjustments so that reconciled measurement data satisfy imposed process constraints, such as material and energy balances. The DR solution is optimal under the assumed statistical random error model, typically Gaussian with zero mean and known covariance. The presence of outliers and gross errors in the measurements or imposed process constraints invalidates the assumptions underlying DR, so that the DR solution may become biased. GED is required to detect, identify and remove or otherwise compensate for the gross errors. Typically GED relies on formal hypothesis testing of constraint residuals or measurement adjustment-based statistics derived from the assumed random error statistical model. Classification methodologies are methods by which observations are classified as belonging to one of several possible groups. For the GED problem, artificial neural networks (ANN's) have been applied historically to resolve the classification of a data set as either containing or not containing a gross error. The hypothesis investigated in this thesis is that classification methodologies, specifically classification trees (CT) and linear or quadratic classification functions (LCF, QCF), may provide an alternative to the classical GED techniques. This hypothesis is tested via the modelling of a simple steady-state process unit with associated simulated process measurements. DR is performed on the simulated process measurements in order to satisfy one linear and two nonlinear material conservation constraints. Selected features from the DR procedure and process constraints are incorporated into two separate input vectors for classifier construction. The performance of the classification methodologies developed on each input vector is compared with the classical measurement test in order to address the posed hypothesis. General trends in the results are as follows: Stellenbosch University 8 VISUALIZING GROSS ERRORS .

On-line optimisation provides a means for maintaining a process around its optimum operating plant. An important component of optimisation relies in data reconciliation which is used for obtaining consistent data. On a mathematical point of view, the formulation is generally based on the assumption that the measurement errors have normally pdf with zero mean. Unfortunately, in the presence of gross errors, all of the adjustments are greatly affected by such biases and would not be considered as reliable indicators of the state of the process. This paper proposes a data reconciliation strategy that deals with the presence of such gross errors. Application to size flowrates and concentration data in mineral processing is provided.

For process control improvement, coherency of information supplied by sensors must first be ensured. Because of the presence of random and possibly gross errors, the model equations of the process are not generally satisfied. The problem of data reconciliation in order to satisfy the model constraints is considered in this article. The simultaneous presence of errors in process input and output measurements poses serious problem in the rectification of data. The proposed procedure to solve this problem involves the use of a special filter to estimate both the parameters, the states and the inputs of a process; smoothing of both estimations of the input and the output is increased by adding in the filter a variance term. Application is proposed for rainfall data validation in order to improve urban sewer network control.

After un slight rearangement one eliminates the state vector x in order to generate the redundancy equations between the measured variables. If q is the forward shift operator (q u(t) = u(t+1)), equations (1) are rewriten : After a short view about the fundamental problem of data reconciliation, the paper presents some majors tests (the balance residuals analysis known as method of pseudoequations, the parity space approach, the analysis of residuals known as method of measurement test, the analysis of the sum of squares deviations) and shows their equivalence.

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In the steel industry, the determination of the control system set-points of batch processes is a common problem. It consists in adjusting the set-points in order to reach the given product specifications thanks to a process model. Small changes in operating conditions may impact final product quality. This is particularly true for the Basic Oxygen Furnace (BOF) where the information collected during a specific batch serves to adjust the set-points of the next batch. For being able to control that type of process, measurements must be made coherent and it may be convenient to use data reconciliation procedure. The proposed paper describes a method allowing simultaneous data reconciliation and model parameter estimation. Parameter estimation results can either be used to update the process model or to detect abnormal parameter variations due, e.g. to fouling, corrosion, degradation of parts of the process.

In this survey, the authors try to summarize the various aspects of data reconciliation, to point out the main difficulties and to present the state of the art in this field specially for systems described by generalized bilinear models. In practise, these models are used to describe conservation of material in total flow and partial flows for different chemical or minerallurgical species. The authors present the steps of the data reconciliation problem in the following order : techniques of data reconciliation, classification of the data by the observability concept, gross error detection and localisation, variance of measurement error estimation, sensor positioning.

The quality of the state estimation of a system is strongly conditioned by the number of measurements obtained from this system. Here, this dependence is studied by specifying especially the influence of the number and the position of sensors as well as of their precision. When some variables are necessary for the control, the possibility of failure of some sensors is analysed from the calculation of the whole system reliability. The reciprocal problem is also presented: what is the measurement system allowing to respect some constraints on the observability, the redundancy and the reliability.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

This paper deals with reconciling measurements characterized by an unknown variance from processes described by linear equations. We use the technique of maximum likelihood to give an estimate of the real values and of the measurement errors variance. To make this estimation we assume that the measurement errors follow a normal probability density function with a zero mean and a constant variance. We have developed a relaxation algorithm based on direct iteration to solve the problem of the optimisation of the likelihood function under linear constraints. In the first instance we apply this algorithm to systems operating near a single steady state regime. then we extend the results obtained to the case of several steady state regimes.

This l:,aper presents an analysis of processes with regard to their circulating flows and mainly focuses on the characterization of variables depending on their redundancy level. Previous studies in this field were performed on processes described by linear equations (i.e. when considering a model of total conservation of material or energy). Here, the authors propose a novel methodology, permitting extension of observability analysis to processes involving linear and bilinear equations (i.e. partial and total balance equations). The aim here is the classification of process variables according to their observability level and, on a more practical side, the extraction of the subset of unmeasured variables deducible from those measured and from balance equations. The proposed technique is based on the analysis of the circuit matrix associated to the graph of the process under consideration. This analysis is performed depending on the number and position of the measurement devices.

The data validation consists in obtaining an estimation of the true values of process variables that respect the balance equations. Generally, the procedure needs the knowledge of the variance of the measurement errors; unfortunately, in most situations, we only have a rough estimation of this variance and therefore the data validation procedure gives results depending on this poor estimation. A pioneer work of presents a solution to this problem based on the analysis of the constraint residuals. developed a slighty different approach based on a maximum likelihhod estimator. Here we present a direct method that simultaneously estimates the variances of the measurement errors and reconciles the data with respect to the balance equations. Some numerical results illustrate the efficiency of the proposed method.

This paper deals with the problem of data validation in large scale steady state linear systems which are described by algebraic equations. The conditions for observability and redundancy are defined and a procedure is presented for classifying the system variables into observable, unobservable, redundant and no redundant variables. Reconciliation of the redundant process measurements is developped using a recurrent estimation technique which is well suited in real time data processing. Statistical tests based on the residuals or ...

In the steel industry, the determination of the control system set-points of batch processes is a common problem. It consists in adjusting the set-points in order to reach the given product specifications thanks to a process model. Small changes in operating conditions may impact final product quality. This is particularly true for the Basic Oxygen Furnace (BOF) where the information collected during a specific batch serves to adjust the set-points of the next batch. For being able to control that type of process, measurements must be made coherent and ...

Before improving the control of a plant, we must make sure of information coherence issued from instrument lines or sensors. In fact, these informations can be corrupted by errors and can also deviate from the optimum functioning range. The operator must make precautions not to be outside of this range. The detection of errors is used to point out the deviations. The detection, the location, the different error characterizations and the estimation of true values are steps in the data reconciliation problem. Process measurements are subject to two ...

In this survey, the authors try to summarize the various aspects of data reconciliation, to point out the main difficulties and to present the state of the art in this field specially for systems described by generalized bilinear models. In parctice, these models are used to describe conservation of material in total flow and partial flows for different chemical or minerallurgical species. The authors present the steps of the data reconciliation problem in the following order; techniques of data reconciliation, classification of the data by the observability ...

Quantum key distribution (QKD) is a concept of secret key exchange supported by fundamentals of quantum physics. Its perfect realization offers unconditional key security, however, known practical schemes are potentially vulnerable if the quantum channel loss exceeds a certain realizationspecific bound. This discrepancy is caused by the fact that any practical photon source has a non-zero probability of emitting two or more photons at a time, while theory needs exactly one. We report an essentially different QKD scheme based on both quantum physics and theory of relativity. It works flawlessly with practical photon sources at arbitrary large channel loss. Our scheme is naturally tailored for free-space optical channels, and may be used in ground-to-satellite communications, where losses are prohibitively large and unpredictable for conventional QKD.

RESUMO -As operações de partida de processos químicos e em particular, colunas de destilação, representam interessantes problemas de operabilidade, controle e simulação. Durante a operação de partida de uma coluna de destilação ocorrem mudanças simultâneas em diversas variáveis relevantes do processo o que torna esta operação um dos processos mais complicados observados na prática industrial. Para que se possa propor um procedimento de partida é necessário estudar seu comportamento dinâmico. O comportamento dinâmico de uma coluna de destilação durante a operação de partida é altamente complexo. O trabalho tem por objetivo o estudo do comportamento dinâmico de um sistema de colunas de destilação termicamente acopladas durante a operação de partida e baseado neste estudo propor um procedimento de partida apropriado para o sistema. O sistema estudado consiste em duas colunas termicamente acopladas no qual uma delas é uma coluna extrativa utilizada para separar a mistura azeotrópica etanol-água tendo como solvente o etilenoglicol. O modelo e o procedimento de partida adotado foram construídos no pacote computacional Aspen Tech. A partir dos resultados foi possível observar o comportamento dinâmico do sistema durante a operação de partida e propor um procedimento adequado.

The research field of data integration is an area of growing practical importance, especially considering the increasing availability of huge amounts of data from more and more source systems. According current research includes approaches for solving the problem of conflicts on the data level addressed in this thesis. Dealing with discrepancies in data still is a big challenge, relevant for instance during eliminating duplicates from semantically overlapping sources as well as for combining complementary data from different sources. According operations most often cannot only be based on equality of values, because only in rare cases there are identifiers valid across system boundaries. Using other attribute values is problematic, because erroneous data and varying conventions for information representation are common problems in this field. Therefore, according operations have to be based on the similarity of data objects and values. The concept of similarity itself is problematic regarding its usage and foundations of its semantics. Successful applications often have a very specific view of similarity measures and predicates that represent a narrow focus on the context of similarity for this given scenario. To provide similarity-based operations for data integration purposes requires a broader view on similarity, suitable to include for instance a number of generic and tailor-made similarity measures useful in a given data integration system. These problems are addressed in this thesis by providing similarity-based operations according to a small, generic framework. Similarity-based selection, join, and grouping operations are discussed regarding their general semantics and special aspects of underlying similarity relations. According algorithms suitable for data processing are described for materialised and virtual integration scenarios. Implementations are given and evaluated to prove the applicability and efficiency of the proposed approaches. On the predicate level the thesis is focused on string similarity, namely based on the Levenshtein or edit distance. The efficient processing of similarity-based operations mainly depends on an efficient evaluation of similarity predicates, which is illustrated for string similarity based on index support in materialised and pre-selection in virtual data integration scenarios.

New applications from the areas of analytical data processing and data integration require powerful features to condense and reconcile available data. Object-relational and other data management systems available today provide only limited concepts to deal with these requirements. The general concept of grouping and aggregation appears to be a fitting paradigm for a number of the mentioned issues, but in its common form of equality based groups and restricted aggregate functions a number of problems remain unsolved. Various extensions to this concept have been introduced over the last years, especially regarding user-defined functions for aggregation and derivation of grouping properties. We propose generic interfaces for user-defined grouping and aggregation as part of a SQL extension, allowing for more complex functions, for instance integration of data mining algorithms. Furthermore, we discuss high-level language primitives for common applications and illustrate the approach by introducing new concepts for similarity-based duplicate detection and elimination.

On-line optimisation provides a means for maintaining a process around its optimum operating range. This optimisation heavily relies on process measurements and accurate process models. However, these measurements often contain random and possibly gross errors as a result of miscalibration or failure of the measuring instruments. This paper proposes a data reconciliation strategy that deals with the presence of such gross errors. Instead of constructing the objective function to be minimized on the basis of random errors only, the proposed method takes into account both contributions from random and gross errors using a so-called contaminated Gaussian distribution. It is shown that this approach introduces less bias in the estimation due to its natural property to reject gross errors.

A new method of data reconciliation for linear static systems which is able to take into account the knowledge about the uncertainties of the used model is proposed. The uncertainties are represented by bounded variables. The elementary operations relating to the intervals make it possible to propose a state estimation of the system taking into account the uncertainties without a priori knowledge on their statistic laws of distribution. The estimation result is also provided in an interval form. Resume: On propose une nouvelle m´thode de r´conciliation de donn´es de syst`mes statiques lin´aires capable de prendre en compte les incertitudes param´triques du mod`le utilis´. Les incertitudes sont repr´sent´es par des variables born´es. Des op´rations ´l´mentaires portant sur les intervalles permettent de proposer une estimation de l'´tat du syst`me prenant en compte ces incertitudes et sans connaissance a priori sur leurs lois de distribution statistique. Le r´sultat de l'estimation est ´galement fourni sous une forme intervalle.

Resumo. Este trabalho apresenta um estudo sobre reconciliação de dados em estado estacionário em beneficiamento de minério, com base em uma unidade industrial publicada por Alhaj-Dibo et al. [1]. É realizada uma breve revisão bibliográfica sobre reconciliação de dados e sua aplicação em beneficiamento de minério, basicamente: cominuição, flotação e hidrometalurgia. O problema estudado é constituído por 48 variáveis sendo 3 não medidas e 7 corrompidas por erros grosseiros, que invalidam a base estatística comumente adotada. O problema é resolvido no pacote computacional EMSO (Ambiente para Modelagem, Simulação e Otimização) que dispõe de rotinas computacionais propriamente desenvolvidas para reconciliação de dados em estado estacionário com restrições lineares ou não lineares. Resultados satisfatórios são obtidos e o desempenho do simulador EMSO pode ser comprovado, utilizando estimadores robustos para detecção dos múltiplos erros grosseiros.

In chemical and hydrocarbon industry operational efficiency is improved by model-based solutions. Historical process data plays an important role in the identification and verification of models utilized by these tools. Since most of the used information are measured values, they are affected by errors influencing the quality of these models. Data reconciliaton aims the reduction of random errors to enhance the quality of data used for model development resulting in more reliable process simulators. This concept is applied to the development and validation of the complex process model and simulator of an industrial hydrogenation system. The results show the applicability of the proposed scheme in industrial environment.

We present a model based algorithm for simultaneous validation of online analysers and process simulators. Reconciled on-line and historical process data satisfying balance and model equations provides the opportunity to validate and improve process models and soft sensors. Accurate simulation results and laboratory measurements can be used for the validation of online analysers. Validated and reconciled data can be used to the iterative and interactive identification of the unknown parameters of the simulator, e.g. for the determination of kinetic parameters. This method can also be used for monitoring and diagnostics of complex processes because situations when the operating conditions have been significantly changed can be discovered. The approach is illustrated by the analysis of an industrial hydrogenation system. We present the proposed iterative and interactive procedure of model development and analyser validation, the applied data reconciliation method, and the details of the case study. The results show the applicability of the proposed scheme in industrial environment and the benefits of the extracted information in the maintenance and monitoring of advanced model based process engineering tools. The developed tool can increase operating efficiency that is the key of reducing energy consumption and environmental impact. This is especially true in hydrocarbon industry where the operation of the technology is supported by process simulator and on-line analyser based advanced process control systems.

Data reconciliation and principal component analysis are two recognised statistical methods used for plant monitoring and fault detection. We propose to combine them for increased efficiency. Data reconciliation is used in the first step of the determination of the projection matrix for principal component analysis (eigenvectors). Principal component analysis can then be applied to raw process data for monitoring purpose. The combined use of these techniques aims at a better efficiency in fault detection. It relies mainly in a lower number of components to monitor. The method is applied to a modelled ammonia synthesis loop.

Analysis of the results of a data reconciliation program is made easier by extracting more information from the Jacobian matrix of the constraint equations. Standard deviation for all state variables (measured or not measured) is related to the standard deviation of measurements. Distinction between variables that are actually corrected by the validation process, and those that are merely derived from a single measurement is straightforward. Based on this information, decisions can be taken : deletion of unnecessary measurements, addition of new measurement points and their optimal selection, or identification of key measurements for which any enhancement of accuracy would result in significant improvement in the quality of the process validation.

The oxygen mass transfer coefficient (K L a) is of paramount importance in conducting aerobic fermentation. K L a also serves to compare the efficiency of bioreactors and their mixing devices as well as being an important scale-up factor. In submerged fermentations, four methods are available to estimate the overall oxygen mass transfer coefficient (K L a): the dynamic method, the stationary method based on a previous determination of the oxygen uptake rate (Q O2 X), the gaseous oxygen balance and the carbon dioxide balance. Each method provides a distinct estimation of the value of K L a. Data reconciliation can be used to obtain the most probable value of K L a by minimizing an objective function that includes measurement terms and oxygen conservation models, each being weighted according to their level of confidence. Another alternative, for a more rapid determination of K L a, is using a neural network which has been previously trained to predict K L a from the series of oxygen conservation models. Results obtained with this new approach show that K L a can be predicted rapidly and gives values that are equivalent to those obtained with the conventional data reconciliation algorithm.

Fermentation experiments using Aspergillus niger result in a very viscous broth due to the growth of filamentous microorganism. For viscous fermentation processes, it is difficult to estimate with confidence the volumetric oxygen mass transfer coefficient (K L a), which can be used for scale-up or design of bioreactors. In the present study, four methods based on dynamic and stationary approaches were used to measure K L a throughout the fermentation. Data reconciliation was used to obtain a more reliable and consistent K L a. The K L a value obtained by a data reconciliation technique was found to be more reliable since it takes into consideration both the reliability of all measured variables and the accuracy of all mass balance equations.

A new technique of data reconciliation, applied to validation of measures collected on a system, is suggested in this paper. This approach is based on the inequality constraints describing the hyper space in which the state of the considered system is probably located. We also use the model of the system in which the residuals must, in the same way, be constrained in an hyper Ž space. Finally, the complete problem of reconciliation appears in the form of LMI linear matrix. inequality and thus can be solved with the software tools that are appropriate to this technique. Some examples show that classical problems of balance equilibration can be solved: total flow balances, partial flow balances, balances with partial measures, consideration of biased measures.

A stepwise experimental design procedure to obtain reliable data from wastewater treatment plants (WWTPs) was developed. The proposed procedure aims at determining sets of additional measurements (besides available ones) that guarantee the identifiability of key process variables, which means that their value can be calculated from other, measured variables, based on available constraints in the form of linear mass balances. Among all solutions, i.e. all possible sets of additional measurements allowing the identifiability of all key process variables, the optimal solutions were found taking into account two objectives, namely the accuracy of the identified key variables and the cost of additional measurements. The results of this multi-objective optimization problem were represented in a Pareto-optimal front. The presented procedure was applied to a full-scale WWTP. Detailed analysis of the relation between measurements allowed the determination of groups of overlapping mass balanc...

This chapter proposes an approach to generate predictions for decision support issues. It relies on case-based and reconciliation methods, using an ontology. The objective of the chapter is to provide an overview of the state of the art, but also to describe the proposed method and to illustrate it on a concrete application. In this approach, a reconciliation stage identifies groups of rules expressing a common experimental tendency. A prediction stage generates new rules, using both experimental tendencies obtained in the previous stage and new experimental descriptions. The method has been tested within a case study concerning food quality management. It has been compared to a classic predictive approach, leading to promising results in terms of accuracy, completeness and error rate.

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Process data reconciliation and rectification and their relationship to process performance monitoring functions have been the subject of many recent publications. [See, for instance, Mah (1981) for a review of these publications.] In this note we shall confine our

In this article, we propose an explicit integer optimization formulation for the design of reliable and robust (to uncertainty in reliability data) sensor networks. The robustness is achieved by incorporating simultaneous occurrence of different kinds of uncertainty in the failure rate data in the optimization formulation. We show the use of constraint programming to solve these combinatorial problems to global optimality and also evaluate the globally optimal pareto front between robustness and cost of these sensor networks. Such tradeoffs help the designer in making informed choices for the selection of sensor networks. The applicability of the proposed work has been demonstrated on a case study taken from literature.

The quality of on-line measurement data usually cannot meet the demands of practical process monitoring and control due to the influence of measurement noise. Although extended Kalman filter (EKF) is able to improve the quality by reconciling the measurement data to fit the dynamic process model describing nonlinear and Gaussian processes, it rarely considers the presence of different types of gross errors, such as outliers, bias and drift. However, the three types of gross errors often simultaneously appear in dynamic process systems. We propose a robust EKF combined with measurement compensation (MC-REKF) approach, in which a statistical test method is used to detect the abnormal measurement data, where gross error identification is accomplished within a moving window. Subsequently, the magnitudes of gross errors are estimated and used for measurement compensation. Finally, the compensated measurements are updated to re-estimate the accurate states via EKF. The effectiveness of the proposed MC-REKF is demonstrated through a complex nonlinear dynamic chemical process system, namely the free radical polymerization of styrene. With three different types of gross errors, the mean squared error (MSE) of reconciled measurements based on the MC-REKF decreases 37 fold compared to EKF. The magnitude of the residuals between the estimated states and the true states falls below 1.0E −6 when using the MC-REKF. The implementation results imply that the proposed MC-REKF can identify and estimate different types of gross errors and finally decreases their influence on state estimation and measurement reconciliation.

The availability of small diameter (10-50 mm) hydrocyclones allows classification and clarification of aqueous slurries containing fine particles (down to-2 lam in size). However, in some applications it is necessary to treat ultra-fine sub-micron particles that have been purposely aggregated to increase their apparent size in order to make centrifugal separation feasible. For some time the possibility of processing of slurries containing aggregates (eg ceramics, minerals, pharmaceuticals, fine chemicals, biomaterials, etc) has been considered doubO~ul, since it was presumed that shear forces within the hydrocyclone would tend to render such operations ineffective by breaking up the aggregates. The complex mechanisms involved in the separation of aggregates have not allowed a clear understanding of the breakage-separation process, and no model has been available that considers the breakage and separation of particles simultaneously. This paper demonstrates the feasibility of processing dilute suspensions of aggregates using small diameter hydrocyclones by presenting a critical assessment of previous work, and new experimental measurements employing in-situ particle size, density and mass flow measurements for the process streams coupled with robust data reconciliation procedures. A model to describe the breakage-separation phenomenon is proposed and tested.

Parameters for data collection Kinetic data were recorded at 30 °C. Continuous cell cultures were studied at 37 °C. Further details are explained in the section Experimental Design, Materials and Methods. Description of data collection Kinetic data were obtained by spectrophotometry in a plate reader equipped with monochromator, temperature control and a built-in software controlled by the user through the software Gen5. Data from continuous cultures were obtained by the integration of data obtained by different means. More details in the section Experimental Design, Materials and Methods. Data source location