Quality Of Tags Research Articles

What drives social shopping in social commerce platform? The effects of affordance, self-congruity and functional-congruity

PurposeSocial group system and social tagging system, which leverage the psychological mechanisms underlying group influence and social tags to drive consumer behaviors, have been prevalent in the social commerce platform. However, limited studies have examined how the affordances of social group system and social tagging system influence consumers’ social shopping behavior. The purpose of this study is to examine the formation of social shopping behavior in the social commerce platform.Design/methodology/approachCombining affordance theory with dual-congruity theory, we develop a model to examine how the affordances of social group system and social tagging system influence consumers’ social shopping behavior through the underlying self-congruity and functional-congruity processes. We empirically validate the research model using a multimethod approach, including an instrument development study and a field survey study.FindingsOur empirical findings show that social support positively influences relational identity, while it has a nonsignificant effect on social identity. Social interactivity positively influences relational identity and social identity. Furthermore, social tagging quality and social endorser credibility positively affect perceived diagnosticity and perceived serendipity. Finally, relational identity, social identity, perceived diagnosticity and perceived serendipity collectively determine consumers’ social shopping intention.Originality/valueThis study contributes to the theoretical understanding of social shopping in social commerce and offers practical implications for designing an effective social group system and social tagging system to boost product sales.

Comparison of Diatrizoate and Iohexol for Patient Acceptance and Fecal-Tagging Performance in Noncathartic CT Colonography.

The aim of this study was to compare diatrizoate and iohexol regarding patient acceptance and fecal-tagging performance in noncathartic computed tomography colonography. This study enrolled 284 volunteers with fecal tagging by either diatrizoate or iohexol at an iodine concentration of 13.33 mg/mL and an iodine load of 24 g. Patient acceptance was rated on a 4-point scale of gastrointestinal discomfort. Two gastrointestinal radiologists jointly analyzed image quality, fecal-tagging density and homogeneity, and residual contrast agent in the small intestine. The results were compared by the generalized estimating equation method. Patient acceptance was comparable between the 2 groups (3.95 ± 0.22 vs 3.96 ± 0.20, P = 0.777). The diatrizoate group had less residual fluid and stool than the iohexol group ( P = 0.019, P = 0.004, respectively). There was no significant difference in colorectal distention, residual fluid, and stool tagging quality between the 2 groups (all P 's > 0.05). The mean 2-dimensional image quality score was 4.59 ± 0.68 with diatrizoate and 3.60 ± 1.14 with iohexol ( P < 0.001). The attenuation of tagged feces was 581 ± 66 HU with diatrizoate and 1038 ± 117 HU with iohexol ( P < 0.001). Residual contrast agent in the small intestine was assessed at 55.3% and 62.3% for the diatrizoate group and iohexol group, respectively ( P = 0.003). Compared with iohexol, diatrizoate had better image quality, proper fecal-tagging density, and more homogeneous tagging along with comparable excellent patient acceptance, and might be more suitable for fecal tagging in noncathartic computed tomography colonography.

GF-1/6 Satellite Pixel-by-Pixel Quality Tagging Algorithm

The Landsat and Sentinel series satellites contain their own quality tagging data products, marking the source image pixel by pixel with several specific semantic categories. These data products generally contain categories such as cloud, cloud shadow, land, water body, and snow. Due to the lack of mid-wave and thermal infrared bands, the accuracy of traditional cloud detection algorithm is unstable when facing Chinese Gaofen-1/6 (GF-1/6) data. Moreover, it is challenging to distinguish clouds from snow. In order to produce GF-1/6 satellite pixel-by-pixel quality tagging data products, this paper builds a training sample set of more than 100,000 image pairs, primarily using Sentinel-2 satellite data. Then, we adopt the Swin Transformer model with a self-attention mechanism for GF-1/6 satellite image quality tagging. Experiments show that the model’s overall accuracy reaches the level of Fmask v4.6 with more than 10,000 training samples, and the model can distinguish between cloud and snow correctly. Our GF-1/6 quality tagging algorithm can meet the requirements of the “Analysis Ready Data (ARD) Technology Research for Domestic Satellite” project.

Textual tag recommendation with multi-tag topical attention

Tagging can be regarded as the action of connecting relevant user-defined keywords to an item, indirectly improving the quality of the information retrieval services that rely on tags as data sources. Tag recommendation dramatically enhances the quality of tags by assisting users in tagging. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics in real applications, i.e., the long-tail distribution of tags and the topic-tag correlation. In this paper, we propose a Topic-Guided Tag Recommendation (TGTR) model to recommend tags by jointly incorporating dynamic neural topic. Specifically, TGTR first generates dynamic neural topic that would indicate the tags by a neural topic generator. Then, a sequence encoder is used to distill indicative features from the post. To effectively leverage the topic and alleviate the data imbalance, we design a multi-tag topical attention mechanism to get a tag-specific post representation for each tag with the help of dynamic neural topic. These three modules are seamlessly joined together via an end-to-end multi-task learning model, which is helpful for the three parts to enhance each other and balance the effects of topics and tags. Extensive experiments have been conducted on four real-world datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin, especially on tail-tags. The code, data and hyper-parameter settings are publicly released for reproducibility.

A case study of the real-time framework for the implementation of the ITER plasma control system

ITER CODAC is in the process of developing a tailored software solution to carry out necessary control functions for tokamak operation. CODAC has successfully completed the final review of the implementation of the ITER Plasma Control System (PCS) for first plasma (FP), and formally commenced its production in accordance with the conversion mechanism of the Real-Time Framework (RTF) software suite. Rich functionality of RTF built-ins and its flexibility of reconfigurable applications facilitates the development and deployment of complex real-time applications such as commonly found within PCS and the ITER diagnostics.In the practical application of the framework, CODAC has prototyped PCS architecture components to verify how actual operation complies with the intended design. Prototyping initiates CODAC preparation for system validation and verification, and its commissioning. Agile production is based on the model presented in the PCS Simulation Platform (PCSSP). Several fundamental functions such as exception handling, quality tag controlling, and signal watchdog functionality have been implemented and continue to evolve. CODAC demonstrated a mechanism by which individual function models coordinate and cooperate with each other to achieve desired higher-level functions. Operation of prototype and real-life experiments verified the application is compliant with the design input and even adaptable and extendable in various system attributes.This paper will introduce the PCS prototyping and its execution as well as integrated operation function in the ITER environment.

QUALITY EVALUATION OF UHF RFID ANTENNAS AND PASSIVE TAGS ON PAPER SUBSTRATES

The effect of antenna design modification, paper substrates and relative electrical permittivity of background materials on the reflection coefficient of UHF RFID antennas was studied. Simulation software was used to modify the design and calculate the reflection coefficient of the antennas. By modifying the coupling of the dipole with the induction loop of the antennas, areduction of the simulated reflection coefficient was achieved compared to thecommercial antenna. The positive effect of antenna modification was also confirmed by measuring thereflection coefficient of antennas printed on paper by thermal transfer printing, placed on extruded polystyrene and particle board. The reflection coefficient of the modified antennas was lower when placed on extruded polystyrene, whose relative electrical permittivity was lower than particle board. After installing the memory chip to the antennas printed on paper and paperboard, the identification, reading and recording range of passive UHF RFID tags were measured after they were placed on thicker paperboard, extruded polystyrene and particle board. The positive effect of antenna modification on improving the communication quality of passive UHF RFID tags placed on background materials with a relative electrical permittivity of 2.4 to 6.7 wasconfirmed.

Using Metamorphic Testing to Improve the Quality of Tags in OpenStreetMap

We present a metamorphic testing approach to validate the information included in OpenStreetMap, a collaborative effort to produce a free map of the world. We focus on the quality of the tags storing the information about the elements of the map. We identified metamorphic relations with the potential to detect different types of tagging errors. In particular, we carefully designed mechanisms to automatically generate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">follow-up inputs</i> , a fundamental component in the successful application of a metamorphic testing approach. The intrinsic nature of automatically analysing tags implies that we will detect real errors but some false positives as well. In order to obtain a good trade-off between real errors and false positives, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">thresholds</i> . Our MRs will raise an error associated with a certain value if, depending on the nature of the MR, we have a certain number of elements (not) fulfilling a given condition. In order to evaluate the goodness and versatility of our framework, we chose four cities in different continents with the goal of analysing very heterogeneous contributors adding information in different languages. The application of this framework to the analysis of the chosen cities revealed errors in all of them and in all the considered categories. In addition, around 66% of the errors found by our MRs in the analysed areas have not been previously reported by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Osmose</i> , the de facto standard OSM error checker.

Effects of carbodiimide combined with ethanol-wet bonding pretreatment on dentin bonding properties: an in vitro study.

PurposeThis study evaluated the combined effects of Carbodiimide (EDC) and ethanol–wet bonding (EWB) pretreatment on the bond strength and resin–dentin surface.MethodsPhosphoric acid-etched dentin specimens were randomly divided into five groups based on the following pretreatments: deionized water (control), EWB, 0.3M EDC in water (EDCw), EDC water solution combined EWB (EDCw + EWB), and 0.3M EDC in ethanol (EDCe). A scanning electron microscope (SEM) was used to observe the morphology of collagen fibrils on the demineralized dentin matrix in each group after pretreatment. The adhesives Prime & Bond NT (PB) (Dentsply De trey, Konstanz, Germany) or Single bond 2 (SB) (3M ESPE, St. Paul, MN, USA) was applied after pretreatments, and a confocal laser scanning microscope (CLSM) was used to evaluate the quality of resin tags. The degree of conversion (DC) of the adhesive was investigated by Fourier transform infrared spectroscopy (ATR-FTIR). The dentin was first bonded with resin and bathed in water at 37 °C for 24 h. Half of them were subjected to 10, 000 cycles in a thermocycler between 5 °C and 55 °C before a microshear bond strength (µSBS) test. The statistical methods were Analysis of Variance (ANOVA) and a Tukey post hoc test at α = 0.05.ResultsThe µSBS was significantly affected by pretreatments (p < 0.001), adhesives (p < 0.001), and aging conditions (p < 0.001) as revealed by the three-way ANOVA. The EDCw, EDCw + EWB, and EDCe groups significantly increased the µSBS; the EDCw + EWB and EDCe groups produced the highest µSBS. In the EDC-containing groups, the SEM showed at the collagen fibrils in the dentin matrix formed a three-dimensional network structure in the tubules after cross-linking into sheets, and the hybrid layer formed thicker resin tags under a CLSM. In the EDC-containing groups, the CLSM observed an increase in the length of resin tags. PB showed a higher DC and bonding strength than SB, and the five pretreatment groups tested did not affect the DC of the two adhesives.ConclusionsIn etch–and–rinse bonding system, EDC combined with EWB pretreatment can improve the quality of the hybrid layer and enhance the mechanical properties of demineralized dentin matrix. Pretreatment with EDC–ethanol solution may be a new clinically friendly option for enhancing dentin bonding durability.

Reasoning about Confidence in Goal Satisfaction

Goal models are commonly used requirements engineering artefacts that capture stakeholder requirements and their inter-relationships in a way that supports reasoning about their satisfaction, trade-off analysis, and decision making. However, when there is uncertainty in the data used as evidence to evaluate goal models, it is crucial to understand the confidence or trust level in such evaluations, as uncertainty may increase the risk of making premature or incorrect decisions. Different approaches have been proposed to tackle goal model uncertainty issues and risks. However, none of them considers simple quality measures of collected data as a starting point. In this paper, we propose a Data Quality Tagging and Propagation Mechanism to compute the confidence level of a goal’s satisfaction level based on the quality of input data sources. The paper uses the Goal-oriented Requirement Language (GRL), part of the User Requirements Notation (URN) standard, in examples, with an implementation of the proposed mechanism and a case study conducted in order to demonstrate and assess the approach. The availability of computed confidence levels as an additional piece of information enables decision makers to (i) modulate the satisfaction information returned by goal models and (ii) make better-informed decisions, including looking for higher-quality data when needed.

Quantitative Non-Destructive Testing (NDT) of Submerged Civil Concrete Structures Using Remotely Operated Robotic Drones

Periodic inspection is of paramount importance to assess structural integrity and safety. Conventional concrete inspection procedures ranging from rebound hammer, to state-of-the-art ground penetrating radar and ultrasonic tomography focus on superstructures. Often, the part of the structure underwater, is subject to significant levels of corrosion. Today, qualitative visual inspection of the submerged surface can be performed by diving teams, wherein internal defects often go undetected. Furthermore, divers are limited to shallow depths and face several risks, especially when visibility is poor and operating in confined spaces. Presently, there are few commercially available quantitative NDT techniques for submerged structures. This paper presents an alternate solution to inspection of submerged concrete structures — using lightweight remotely operated vehicles (ROV) manufactured by Planys Technologies to perform concrete NDT underwater. Rebound hammer and ultrasonic pulse velocity techniques, although elementary, have been redesigned for use on a submersible drone. Several challenges were addressed including long-range communication and data acquisition, marinizing transducers and pulser-receivers, obtaining measurements in water currents and over deformed surfaces. Planys’ ROVs carry payloads including high-definition cameras, lasers, sonar, and underwater positioning systems to improve data-acquisition quality and location tagging of defects. Key results from experimental studies in a laboratory on canonical specimens are presented. Furthermore, the technology has been successfully tested in a port — details of the structure, operational arrangement and sample results from the field trial are also presented. Following calibration procedures, ROV based underwater concrete NDT was used to acquire data from the structure in grids and subsequently, identify zones of potential non-homogeneity. Numerous challenges, including interpretation of the time traces obtained, and correlation of results to structural integrity and to structures above water, are discussed in the paper. This work will be of interest to asset owners in marine, water management and power generation industries.

Effect of dose splitting of a low-volume bowel preparation macrogol-based solution on CT colonography tagging quality

PurposeTo compare examination quality and acceptability of three different low-volume bowel preparation regimens differing in scheduling of the oral administration of a Macrogol-based solution, in patients undergoing computed tomographic colonography (CTC). The secondary aim was to compare CTC quality according to anatomical and patient variables (dolichocolon, colonic diverticulosis, functional and secondary constipation).MethodsOne-hundred-eighty patients were randomized into one of three regimens where PEG was administered, respectively: in a single dose the day prior to (A), or in a fractionated dose 2 (B) and 3 days (C) before the examination. Two experienced radiologists evaluated fecal tagging (FT) density and homogeneity both qualitatively and quantitatively by assessing mean segment density (MSD) and relative standard deviation (RSD). Tolerance to the regimens and patient variables were also recorded.ResultsCompared to B and C, regimen A showed a lower percentage of segments with inadequate FT and a significantly higher median FT density and/or homogeneity scores as well as significantly higher MSD values in some colonic segments. No statistically significant differences were found in tolerance of the preparations. A higher number of inadequate segments were observed in patients with dolichocolon (p < 0.01) and secondary constipation (p < 0.01). Interobserver agreement was high for the assessment of both FT density (k = 0.887) and homogeneity (k = 0.852).ConclusionThe best examination quality was obtained when PEG was administered the day before CTC in a single session. The presence of dolichocolon and secondary constipation represent a risk factor for the presence of inadequately tagged colonic segments.

Intelligent Video Tag Recommendation System Improving Video Popularity

A successful marketing activity requires attracting high social popularity to their contents, since higher popularity usually indicates stronger influence, more fame and higher revenue. In this, we focus on the question of how to improve popularity of videos sharing on websites like YouTube in mobile computing environment. Obviously, composing high quality titles and tags is beneficial for viewers to discover videos of their interests and increase their tendency to watch more videos. However, it is not an easy task for uploaders,which is especially true since the screen is tight for most mobile devices. To this end, this proposes a novel hybrid method based on multi-modal content analysis that recommends keywords for video uploaders to compose titles and tags of their videos and then to gain higher popularity. The method generates candidate keywords by integrating techniques of textual semantic analysis of original tags and recognition of video content. On one hand, taking the original keywords of a video as input, the method obtains most relevant words from WordNet and related video titles gathered from the three top video sharing sites (YouTube, Yahoo Video, Bing Video). On the other hand, through recognizing video content with deep learning technology,the method extracts the entity name of video content as candidate keywords. Finally, a TFSIM algorithm is proposed to rank the candidate keywords and the most relevant keywords are recommended to uploaders for optimizing the titles and tags of their videos

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

With the development of information technology, various cloud music services are gradually emerging, which has fully changed and enriched people’s music life. How to propose the songs that consumers anticipate from the enormous song data is one of the key goals of the music recommendation system. This research aims to create a better music algorithm that incorporates user data for deep learning, a candidate matrix compression technique for suggestion improvement, accuracy, recall rate, and other metrics as evaluation criteria. In terms of recommendation methods, the music-music recommendation method based on predicting user behavior data and the recommendation method based on automatic tag generation are proposed. The music features obtained by audio processing are fully utilized, and the depth content information in music audio data is combined with other data for recommendation, which improves the tag quality and avoids the problem of low coverage. The results show that this model can extract the effective feature representation of songs in different classification criteria and achieve a good classification effect simultaneously.

A Pulse Distortion Approach for Decoding Frequency-Coded Tags in Multicarrier Chipless RFID Systems

We propose a new approach based on the multicarrier system, in which multiple narrowband pulses emit toward a chipless RFID tag in the UWB, to decode the tag in the time domain. The multicarrier system is known to be able to reduce complexity and interference, and also increase spectrum efficiency and flexibility. In this study, we not only experimentally investigate the backscattered signal of a frequency-coded chipless RFID tag interrogated by narrowband pulses, but we have also derived mathematical equations to describe the tag responses. We have found that the shape of the reflected response from the tag is determined by the emitted signal multiplied by a distortion factor. The distortion amount depends on transmitted signal bandwidth, tag quality factor, and resonance frequency. We further use the distorted tag response as a tag signature in the time domain for decoding purposes, eliminating the signal transformation to the frequency spectrum, which is one of the most time-consuming post-processing algorithms. To further demonstrate the performance of the proposed approach in a more realistic scenario, we have developed a customized multicarrier transceiver to decode a four-bit cross-polarized tag in the time domain. We have demonstrated that the proposed analytical approach can also enhance tag detection in a realistic environment with multiple parasitic reflections.

Prediction of Movie Quality via Adaptive Voting Classifier

Information retrieval from huge social web data is a challenging task for conventional search engines. Recently, information filtering recommender systems may help to find movies, however, their services are limited because of not considering movie quality aspects in detail. Prediction of movies can be improved by using the characteristics of social web content about a movie such as social-quality, tag quality, and a temporal aspect. In this paper, we have proposed to utilize several features of social quality, user reputation and temporal features to predict popular or highly rated movies. Moreover, enhanced optimization-based voting classifier is proposed to improve the performance on proposed features. Voting classifier uses the knowledge of all the candidate classifiers but ignores the performance of the model on different classes. In the proposed model, weight is assigned to each model based on its performance for each class. For the optimal selection of weights for the candidate classifiers, Genetic Algorithm is used and the proposed model is called Genetic Algorithm Voting (GA-V) classifier. After labeling the suggested features by using a fixed threshold, several classifiers like Bayesian logistic regression, Na&#x00EF;ve Bayes, BayesNet, Random Forest, SVM, Decision Tree, LSTM and AdaboostM1 are trained on MovieLens dataset to find high-quality/popular movies in different categories. All the traditional ML models are compared with GA-V in terms of precision, recall and F1 score. The results show the significance of the proposed features and proposed GA-V classifier.

PASSIVE UHF RFID TAGS WITH THERMAL-TRANSFER-PRINTED ANTENNAS

Papers for the thermal transfer printing of UHF RFID antennas were prepared by coating and calendering. Real and imaginary components of the impedance of the UHF RFID antennas depended on their design, coating composition and conditions of paper calendering. Passive UHF RFID tags were constructed from antennas and chips whose real and imaginary components of impedance in the 860–960 MHz frequency band were at approximately the same level. The communication quality of passive UHF RFID tags was evaluated by measuring the reading range using the designed UHF RFID reading unit. The reading range of experimental UHF RFID tags with printed antennas on paper and commercial UHF RFID tags with chemically etched antennas on a PET film were identical in the 860 MHz frequency.

A scalable parallel Chinese online encyclopedia knowledge denoising method based on entry tags and Spark cluster

Because of the open-collaborative of online encyclopedias, a large number of knowledge triples are improperly classified in online encyclopedia systems, and it is necessary to denoise and refine the open-domain encyclopedia Knowledge Bases (KBs) to improve the quality and precision. However, the lack and inaccuracy of triple semantic features lead to a poor refinement effect. In addition, considering large-scale encyclopedia KBs, the processing of massive knowledge will lead to too much computing time and poor scalability of the algorithm. To solve the problems of knowledge denoising in the Chinese encyclopedia system, first, based on data field theory, this paper proposes a new Cartesian product mapping-based method (TripleES) to calculate the semantic similarity of entity triples, based on which a method for quantifying the quality of entry tags is proposed. Second, to further improve the denoising effect on KBs, this paper proposes a new method (TriplePV) to compute the potential value of triple based on multi-feature fusion strategy to calculate the semantic distance between the “out-of-vocabulary” entry tags and embeds it into the potential function. Third, to ensure our algorithms have good scalability, the proposed denoising algorithms are implemented and optimized in parallel based on the Spark cluster-computing framework. Specifically, Spark-based TripleES (ES_Spark) and Spark-based TriplePV (PV_Spark) algorithms are proposed to calculate the semantic similarity and potential value of triples respectively. Finally, a comprehensive comparative analysis is performed on the denoising effect and time efficiency with the state-of-the-art distributed Chinese encyclopedia knowledge denoising algorithm. The experimental results on real-world datasets show that the parallel denoising algorithm proposed in this paper can improve the efficiency of knowledge denoising and the accuracy of KBs, which outperforms the state-of-the-art methods.

Participatory Description and Metadata Quality in Rapid Response Archives

This article describes the establishment of a participatory, rapid response archive, and an assessment of metadata quality applied by student curators between March and September 2020. A Journal of the Plague Year: An Archive of COVID-19 (JOTPY) was founded to document the COVID-19 pandemic, and is titled after Daniel Defoe’s 1722 book of the same name. As a participatory collection, A Journal of the Plague Year: An Archive of COVID-19 relies on a team of student curators to tag materials. This article summarizes an analysis of metadata tags, and describes strategies that improved the quantity and quality of metadata tags in the collection.

Digital analysis of polydiacetylene quality tags for contactless monitoring of milk

The incorporation of colorimetric sensors as quality indicators in food packaging is an exciting new area of research that could improve food management. The standard approach, however, demands a reliable interface between the sensor and the food and risks food contamination which is a significant consumer concern. To overcome this challenge, herein, we develop a polydiacetylene/phospholipid agarose-based sensor that encapsulates milk in the hydrogel matrix during synthesis. The chemical recognition of free fatty acids, a product of microbial spoilage of the encapsulated milk, induces a gradual blue to red color change in the sensor. We demonstrate that the new composite material exhibits the same spoilage kinetics as regular liquid milk (digital colorimetric response 28 ± 1% and 27 ± 3% respectively), indicating the agarose does not preserve the milk ingredients nor inhibit the detection mechanism of the polydiacetylene sensors. As a result, this sensor can be attached to the external surface of food packaging to provide an indirect indication of food quality without the need for contact with the milk product. The quality tags we present can be “switched” on and off using dehydration and rehydration, removing the need for in situ manufacturing and allowing storage before use. We show that the quality tags produce a similar digital colorimetric response of 21 ± 2% to indicate milk spoilage after rehydration. The color change of the quality tags could not be analyzed using absorption spectroscopy, the standard technique for polydiacetylenes, due to the opacity that milk imparts on the sample. To solve this problem, we develop digital colorimetric analysis software using the Python programming language to describe the extent of color change in polydiacetylene materials and develop a new metric termed the Digital Colorimetric Response that describes polydiacetylene response with excellent linearity (R2 = 0.96). The software is programmed to employ statistical cleaning techniques that automatically remove image noise and outliers based on a pixel’s grayscale Z-score. This new approach to sensor design increases practicality and could be extended to the contactless quality monitoring of other foods, medicines and other products whose safety or quality is jeopardized with direct sensor contact.

CMIID: A comprehensive medical information identifier for clinical search harmonization in Data Safe Havens

Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.