Data Processing Tools Research Articles

Project Planning Analysis with the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT): Study on the Development of Facilities and Infrastructure of SMK Negeri 1 Garut (DOOM)

Success in implementing a project often hinges on thorough planning and effective control. This study examines the infrastructure development project at SMK Negeri 1 Garut (DOOM), applying the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) to estimate project completion time, costs, probability, and potential planning alternatives. The goal is to achieve maximum efficiency and project success. The research began by determining the activities, their sequence, and duration using the PERT and CPM methods, with POM QM as the data processing tool. Based on the PERT method, the estimated project duration is 128 days with a 50% probability. The critical path, identified through the CPM method, includes activities B through P and predicts a project time of 116 days with a 0.13% probability. Additionally, Crashing the project reduces the time to 97 days, with a minuscule probability of 3.095359E-15. The probability of meeting the contract deadline of 126 days is 31%. To achieve a 95% probability, the project time extends to 130 days with an acceleration cost of IDR 2,575,898,910.73. The study suggests that when making addendums, companies should not only focus on cost changes but also consider adjustments to project timelines to meet the expected deadlines. It is recommended that companies incorporate CPM and PERT as alternative planning methods to select the most optimal cost-effective approach for project acceleration, thereby minimizing the risk of financial losses.

ІНФОРМАЦІЙНИЙ ШУМ ТА МАРКЕТИНГОВІ ІНСТРУМЕНТИ ДЛЯ ЙОГО ПОДОЛАННЯ

Introduction. In the modern conditions of the formation of the digital economy and the intensive growth of the volume of information to be processed by analysts, fundamentally new requirements are being formed for consumers and businesses. At the same time, data processing tools do not always provide adequate analytical information regarding changes occurring both in the global and local markets. The reason for this is information noise, which is an excessive flow of data that complicates the perception of important messages and affects the effectiveness of marketing strategies. All this requires the formation of a systematic approach to managing information flows and optimizing communications between the enterprise and the target audience. The purpose of the article. The purpose of the study is to analyze information noise as a barrier to the effective perception of marketing communications, as well as to identify tools and strategies that allow minimizing its impact on consumers and increasing the effectiveness of marketing activities. Methods. The research is based on the use of an interdisciplinary approach, which includes content analysis, abstract modeling methods for studying consumer perception of information, as well as empirical methods for studying the effectiveness of modern marketing technologies, based on the analysis of approaches to managing specialized marketing tools for interacting with consumers. Results. The article considers the conceptual aspects of information noise and its functional impact on consumer behavior. The effectiveness of using various marketing tools based on the automation of business processes, the use of communication platforms and data for analyzing consumer behavior is analyzed, which results in increasing the efficiency of communications and reducing noise effects. Key strategies for combating information noise are identified, which contribute to strengthening the communication interaction of the enterprise in the context of dynamic changes in market conditions. The need for implementing innovative approaches to developing advertising campaigns that take into account the specifics of modern media channels and consumer behavioral models is proven. Conclusions. It has been proven that minimizing information noise is an important factor in ensuring the long-term effectiveness of a company's marketing communications. It has been determined that the use of modern data analysis tools, process automation, and an individual approach to the consumer allows a company to increase the level of trust and loyalty from customers, which results in an increase in sales and profitability of the business entity.

Pengaruh Pelayanan Publik dan Lingkungan Kerja terhadap Kepuasan Karyawan PT. Samudera Agencies Indonesia

This research aims to analyze the influence of Public Services and Work Environment on Employee Satisfaction at PT. Samudera Agencies Indonesia. The population in this study were employees of PT. Samudera Agencies Indonesia. The sampling technique used was a total sampling technique or saturated sample with a total of 30 respondents. Data collection techniques by distributing questionnaires. The analysis used is multiple regression analysis with t test and F test and coefficient of determination. The data processing tool used is SPSS version 24. The results of this research show that the Public Service variable (X1) has a significance value of 0.030 <0.05 so it can be concluded that the Public Service variable (X1) has a positive and significant effect on the Employee Satisfaction variable , as well as the Work Environment variable (X2) with a significance value of 0.032 <0.05 so it can be concluded that the Work Environment variable (X2) has a positive and significant effect on Employee Satisfaction at the PT Office. Samudera Agencies Indonesia. Simultaneously, the F test results with a significance value of 0.006 <0.05, so it can be concluded that the Public Service and Work Environment variables simultaneously have a positive and significant effect on Employee Satisfaction at the PT Office. Samudera Agencies Indonesia. The RSquare value of the independent variable is 31.8% in influencing the dependent variable Employee Satisfaction at the PT Office. Samudera Agencies Indonesia, while the rest is influenced by other factors outside this research.

The Evolution of Exoplanet Detection Techniques using Artificial Intelligence

The discovery and study of exoplanets have made tremendous strides, particularly with the aid of Artificial Intelligence (AI). The surge in data from space missions like Kepler, TESS, and the upcoming James Webb Space Telescope has necessitated the development of automated tools for efficient data processing. Machine learning (ML) and deep learning (DL) algorithms have significantly improved exoplanet detection, identifying planetary signals and refining the analysis of light curves, radial velocities, and other astronomical data. This review traces the evolution of exoplanet detection techniques, from traditional methods to AI-driven approaches, and explores the future of exoplanet exploration using AI.

Pengaruh LDR Dan NPL Terhadap ROA Pada Bank Umum Swasta Nasional Yang Terdaftar Di BEI Periode 2019-2023

This study aims to determine the effect of Loan to Deposit Ratio (LDR) and Non Performing Loan (NPL) on Return on Assets (ROA) at National Private Commercial Banks Listed on the Indonesia Stock Exchange for the period 2019- 2023. The sampling technique used was purposive sampling method, namely 18 samples of National Private Commercial Banks. The data in this study are secondary data in the form of bank financial reports available on the company's website or the IDX website. The data processing tools used in this research are Microsoft Excel and Eviews 12 software. The data analysis used is descriptive statistical analysis, chow test, hausman test, lagrange multiplier test, panel data regression, t test, f test, and coefficient of determination test. The results showed that partially Loan to Deposit Ratio (LDR) has no significant effect on Return on Assets (ROA) and Non Performing Loan (NPL) has a significant effect on Return on Assets (ROA). Meanwhile, based on the test results simultaneously obtained that the Loan to Deposit Ratio (LDR) and Non Performing Loan (NPL) simultaneously have a significant effect on Return on Assets (ROA) of National Private Commercial Banks listed on the Indonesia Stock Exchange.

Linking the role of supply chain service, collaborative governance and multiple stakeholder participation in the immigration services quality

In this digital era, collaborative management is needed to improve service quality and be supported by multiple stakeholder participation to provide optimal service. This research aims to investigate the role of collaborative governance in the quality of immigration services and the role of numerous stakeholder participation in the quality of immigration services. Investigate the correlation between supply chain service and service quality. This research method uses a quantitative method approach, research data was obtained by distributing online questionnaires via the Google Form platform. The questionnaire is designed to contain statement items on a Likert scale of 1 to 7. A 7-point Likert scale can minimize measurement errors and be more precise. The Likert scale used in this research is (1) strongly disagree, (2) disagree, (3) quite disagree, (4) Neutral, (5) quite agree, (6) agree, (7) Strongly agree. The respondents for this research were 567 senior employees of the immigration department in Indonesia who were determined using a simple random sampling method. Research data analysis uses the partial least squares (PLS) structural equation modelling (SEM) approach with data processing tools using SmartPLS 4.0 software. The variables in this research are the dependent variables, namely collaborative governance, and multiple stakeholder participation and the dependent variable is the quality of immigration services. The stages of data analysis are validity testing, reliability testing and significance testing of hypothesis testing. Based on the results of the analysis and discussion that have been presented in this research, this research uses the Partial Least Square (PLS) method for data analysis, it can be concluded as follows, it is concluded that collaborative governance has a positive and significant relationship to the quality of immigration services, multiple stakeholder participation has a positive and significant relationship to the quality of immigration services. Supply chain service has a positive and significant relationship with service quality. Implementing collaborative governance can encourage improvements in the quality of immigration services. Implementing multiple stakeholder participation can encourage improvements in the quality of immigration services.

Impact of Insurance Contract Implementation and Financial Performance on Company Value in the Insurance Sector

Financial performance is a description of the condition of a company that is analyzed to provide information about the work achievements and as a basis for evaluation and assistance in financial decision- making for the company. This research aims to determine the influence of the application of Statement of Financial Accounting Standards (Pernyataan Standar Akuntansi Keuangan/PSAK) 117 with financial performance variables, profitability, and solvency on the value of the company in the insurance sector. This study uses a quantitative descriptive method and the data used are secondary data obtained from reports or documents with the help of Eviews 12 software as a data processing tool. The population in this study is general insurance companies listed by the Financial Services Authority during the period 2018-2023, totaling 76 companies. Through purposive sampling method, 6 companies were selected as samples for a 6-year observation period. The results of the study show that Profitability does not affect the company value, while Solvency significantly influences the company value. Profitability and solvency moderated by PSAK 117 can moderate the company value.

Financial Communication in Crisis Situations

Financial communication plays a key role in shaping stakeholders' decision-making processes, acquiring even greater importance in today's turbulent global landscape marked by various overlapping crises. Thus, the aim of the research revolves around a theoretical exploration, supported by a bibliometric analysis, to emphasize the intrinsic importance of financial communication. The research methodology is based on a bibliometric analysis of 3,000 scientific papers indexed in the Web of Science database, from 1975 to 2023, focusing on financial communication. To facilitate this analysis, specialized software tools for data processing and bibliometric exploration, namely VOSviewer and Bibliometrix, were used. The research findings underline the complexity and interdependence of the financial communication process in organizations, highlighting its evolution from transparency and reduction of information asymmetry in periods of stability, to the need for rapid adoption of digital technologies and integration of ESG dimensions during recent crises, all of which are essential to meet stakeholders' demands and maintain investors' trust.

INFORMATION AND ANALYTICAL PROVISION OF URBAN DEVELOPMENT OF TERRITORIAL COMMUNITIES AND AGGLOMERATIONS

The rapid pace of urbanization, climate change, and economic challenges have heightened the importance of effective urban management, especially in territorial communities and agglomerations. Ensuring sustainable development requires advanced information-analytical tools for decision-making processes. Current systems of urban planning often suffer from inadequate integration of data and limited use of modern analytical technologies, which constrains effective governance and planning. The proposed solutions can enhance the quality of urban management by providing comprehensive, timely, and reliable data. This will facilitate more accurate forecasts, strategic planning, and the sustainable development of territories. The study aims to develop and test a model for an integrated system of information-analytical support to enhance urban development management for territorial communities and agglomerations. The research involved analyzing current methods and tools for data collection, processing, and analysis; developing a model for the integrated system; and assessing its applicability through practical scenarios. The object of the study is the urban development processes of territorial communities and agglomerations, while the subject is the information-analytical support system used in urban management. The study employed a combination of qualitative and quantitative analysis, geospatial modeling, data integration techniques, and predictive modeling using machine learning algorithms. The findings highlight the effectiveness of a comprehensive approach that integrates various data sources and technologies, offering valuable insights for improving decision-making and resource allocation. By leveraging diverse datasets, this approach enables more accurate and timely responses to urban challenges, fostering sustainable development. Future research should focus on further refining the model, enhancing data standardization, and ensuring security and accessibility to promote efficient urban management practices in Ukraine and beyond. Additionally, it will be essential to explore the potential for scalability, adapting the model to different urban contexts while maintaining flexibility and responsiveness to local needs. Collaboration between government, academia, and the private sector will be key to driving this innovation forward

Aerial Images Segmentation with Graph Neural Network

Abstract. The development of remote sensing platforms and sensors, as well as the improvement of remote data processing tools and methods, create new opportunities for automatic updating of maps. Currently, aerial photographs serve as the main source for automatic map updates due to their accessibility and significant informational value. One of the core elements for image to maps transition is accurate image segmentation. Nowadays, machine learning methods demonstrate the best results in task of image segmentation. At its core, maps represent information about a certain area in a vector form, that not only contains visual information about area, but also reflects some relations between objects in the map. This quality makes a map more convenient for human perception than an aerial photograph (raster image). This study addresses the problem of accurate aerial image segmentation with taking the advantages of using graph neural network as the more adequate model of map structure. We use graph neural network for retrieving semantic and vector information about a captured area from its aerial image. The developed framework at first phase utilizes visual transformer for retrieving deep features from the input aerial image. The graph neural network then performs clustering of the extracted deep features to obtain semantic segmentation of the image. To train and evaluate the developed framework, a special dataset is collected and annotated. It contains more than 10k aerial photographs representing various types of objects taken in different years and seasons. The evaluation results on the created dataset proved the state-of-the-art performance of the developed framework.

PERAN PINJAMAN PERBANKAN TERHADAP PERTUMBUHAN EKONOMI DI PROVINSI BALI

Sustainable economic growth reflects increased economic activity, which ultimately contributes to improving people's welfare. Economic growth in Bali Province has experienced fluctuations over the past five years, one of the causes is the covid-19 pandemic which has shaken the regional economy. One of the instruments needed to support post-pandemic economic recovery is the use of financial instruments, namely bank lending. This research is focused on discussing the description of bank loans associated with economic growth in Bali Province. The purpose of this study is to determine the effect of working capital loans, investment loans, and consumption loans partially and simultaneously on economic growth in Bali Province. The approach used in this research is a quantitative approach. This study uses secondary data obtained from BPS Bali Province. The data used in this study are time series data for each regency / city in Bali Province in 2019-2023. This research uses data processing tools, namely Microsoft Excel and SPSS. The results showed that: (1) working capital loans and consumption loans have a partially positive effect on economic growth, while investment loans have a partially negative effect on economic growth in Bali Province; and (2) capital loans, investment loans, and consumption loans simultaneously affect economic growth in Bali Province.

How to use DIALS to process chemical crystallography 3D ED rotation data from pixel array detectors.

The DIALS package provides a set of tools for crystallographic data processing. The open-source nature of the project, and a flexible interface in which individual command-line programs each have a dedicated job, have enabled the adaptation of DIALS to a wide range of experiment types, including electron diffraction. Here we present detailed instructions for the use of DIALS to process chemical crystallography diffraction data from continuous rotation electron diffraction experiments. We demonstrate processing and structure solution from three different samples from three different instruments, including two commercial instruments dedicated to electron diffraction. Each instrument has a pixel array detector, allowing low-noise data to be obtained, resulting in high quality structures. Various new features were added to DIALS to simplify the workflow for these use cases. These are described in detail, along with useful program options for electron diffraction work.

Illegal ship identification system based on trajectory similarity measurement

The Automatic Identification System (AIS) relies on the identification code (MMSI) of a vessel to identify it and records its trajectory data, which is an important technical means for modern maritime management. However, there are some vessels that violate regulations by opening multiple AIS transponders, resulting in multiple location trajectory records for the same vessel. This behavior is difficult to distinguish through traditional means and has a negative impact on the accuracy and effectiveness of maritime management, thereby threatening the safety of navigation. This system uses big data processing tool Spark to efficiently process and analyze large amounts of AIS data and detects the similarity of vessel trajectories to automatically identify the violating vessels that open multiple AIS transponders. In this way, the system not only improves the detection efficiency of violations, but also provides more reliable evidence for maritime management departments to ensure the safety and order of navigation routes.

Analisis Organizational Culture dan Communication Terhadap Employee Engagement Melalui Job Satisfaction Pada Karyawan Bank Swasta di Jawa Tengah

This study investigates the influence of work satisfaction, organizational culture, and communication on employee engagement within private banks in Central Java. The study's population comprised 150 private bank employees in Central Java, categorized as millennials aged 27 to 41, representing the most significant workforce demographic. Utilizing a non-probability sampling strategy through purposive sampling methodology. Data gathering uses a questionnaire method, with each item evaluated using a Likert scale. The study employed the SEM-PLS technique for data analysis, utilizing the SmartPLS 4.1.0.3 tool for data processing. The research findings indicate that communication positively and significantly influences employee engagement and job satisfaction, while organizational culture also positively and significantly affects both employee engagement and job satisfaction. The mediation test revealed that job satisfaction does not mediate substantially the connection between organizational culture and communication about employee engagement in private banks in Central Java.

Integrating plasmasphere, ionosphere and thermosphere observations and models into a standardised open access research environment: The PITHIA-NRF international project

The PITHIA-NRF project “Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities” aims at building a European distributed network that integrates observations from space and ground, data processing tools and models to support scientific research on the Plasmasphere-Ionosphere-Thermosphere system. PITHIA-NRF is designed to provide formalised open access to experimental facilities, data and models, standardised data products, and training services. Participating organisations that operate these facilities, formed twelve nodes in eleven European countries. These nodes work on optimising their observing facilities and offer trans-national access to scientists and engineers. The PITHIA-NRF e-Science Centre is a core element of the project. Its design and evolution are controlled by a systematic ontology which governs the collection of scientific observations and research models, jointly termed data collections, which are registered with the e-Science Centre. Several tens of data collections are being registered. Data collection registrations adhere to FAIR principles and transparent quality measures to a large extent. The e-Science Centre facilitates the execution of research projects proposed by researchers from inside and outside the PITHIA-NRF consortium which require trans-national access to and understanding of data collections (observations and models) residing at one or several PITHIA-NRF nodes. Upon completion of the project a comprehensive collection of observations and models will have been gathered by the e-Science Centre for the benefit of efficient scientific research which relies on Europe-wide collaboration.

Analysis of vaccination policy in the era of the COVID-19 pandemic in Indonesia

This study aims to reflect on the pattern of vaccination policies implemented by the Indonesian government and to analyze public sentiment (pro/con) towards vaccination policies and the government's strategy in formulating democratic policies, prioritizing the aspirations of those affected by the policies adopted. This paper uses qualitative research methods with NVivo 12 Plus as a data processing tool. This study's results indicate that vaccination policies tend to be mandatory in Indonesia with an indirect compulsory application polarization. Hence, the government still uses a coercive and restrictive approach to vaccination programs. In Indonesia, vaccination policy intersects with ethical aspects, especially religious values, resulting in diverse public sentiments.

Cerebrovascular reactivity as a candidate biomarker in small vessel disease related VCID

AbstractBackgroundThe brain’s ability to perform a cognitive task is a dynamic process and requires small blood vessels to dilate or constrict in real time to adjust blood flow in a region‐specific manner. Cerebrovascular Reactivity (CVR) measures the ability of vessels to react to vasoactive challenges. In this work, we investigated the role of CVR as a possible biomarker in small vessel disease related vascular contributions to cognitive impairment and dementia (VCID), as part of the NINDS‐funded MarkVCID study.MethodsThis work consisted of 4 studies. CVR was measured by MRI signal changes in response to inhalation of 5% carbon dioxide (CO2) (Figure 1). Study 1 was a single‐site study of 72 participants (aged 69±8 years, 28 normal cognition and 44 cognitive impairment), in which we examined the relationship between CVR and cognitive function, CSF‐measured AD pathological markers, and CDR‐sum‐of‐boxes. Study 2 conducted an instrumental validation to benchmark the test‐retest reproducibility of the CVR measure. Study 3 developed an automatic, cloud‐based tool for automatic and standardized processing of CVR MRI data. Study 4 was a multi‐site biological validation study in which CVR was measured across 3 sites and data from each site were separately analyzed to investigate the relationship between CVR and cognitive function.ResultsStudy 1 revealed that whole‐brain CVR was associated with MoCA (β=29.64, 95% CI, 9.94 to 49.34) and global composite scores (β=4.32, 95% CI, 0.05 to 8.58), and these associations were independent of CSF‐measured amyloid and tau markers. CVR was also associated with CDR‐SB score. Study 2 revealed that inter‐scanner CoV of CVR was 6.90 ± 5.08% and had an ICC of 0.8498. Study 3 showed that CVR processing can be standardized and the processing pipeline can be accessed by any researchers around the world through an installation‐free cloud platform referred to as MRICloud (https://braingps.mricloud.org/cvr.v5). Study 4 showed that CVR was significantly associated with MoCA in data analyzed separately in each of the three sites through a pre‐defined analysis (p=0.003, 0.002, 0.046 for the three sites, respectively; when pooling data together p=3.7*10^‐6) (Figure 2).ConclusionCVR measured with MRI is a promising candidate biomarker in VCID.

Digital Mapping of Soil Salinity in the Southern Steppe Zone of Russia Based on Artificial Neural Networks and Linear Regression

Remote sensing data are an important source of information for monitoring and mapping vegetation cover. Machine-learning methods are a modern and powerful tool for data processing. However, machine-learning methods combined with remote sensing data have hardly been used for soil salinity assessment and mapping in the southern steppe zone of Russia. This paper examines the possibility of applying different spectral characteristics to map soil salinization in solonetzic complexes in the southern steppe zone of Russia (Republic of Kalmykia) using machine-learning methods. A number of predictors were considered, including reflectance coefficients in blue, green, red, and infrared spectral zones; vegetation indices (NDVI, NDVIt, TVI, SAVI, MSAVI, EVI1–EVI4); salinity indices (SI1–SI6); intensity indices (Int1, Int2); brightness index (BI); and an index proposed by the authors. High-resolution images from the QuickBird (2.4 m) and SuperView-1 (2 m) satellites were used. Soil salinity was assessed using two indicators: specific electrical conductivity in water suspension (EC1:5) and sodium activity (aNa1 : 5). Two different machine-learning models were applied in the study: linear regression and neural networks. According to the results obtained, the linear regression model for EC1 : 5 in 0- to 30-, 0- to 50-, and 0- to 100-cm layers has coefficients of determination (R2) of 0.53, 0.59, and 0.79 on the training sample; the test sample managed to obtain coefficients of determination of 0.49, 0.58, and 0.70, respectively. The neural-network model has significantly higher coefficients of determination: R2 for EC1 : 5 in 0- to 30-, 0- to 50-, and 0- to 100-cm layers on the training sample is equal to 0.68, 0.91, and 0.97, and on the test sample, 0.87, 0.86, and 0.88, respectively. This fact indicates a greater potential of this model for cartographic modeling of soil salinity. The best predictors were the following indices: NDVIt, TVI, EVI1, and Int1. The study has shown the potential of using the neural-network model and spectral indices obtained with SuperView-1 images for soil salinity mapping of solonetzic complexes in the south of the steppe zone of Russia.

Multi-omics approach in gut and environmental microbiota research under the One Health concept.

The One Health concept, although formulated two decades ago, remains challenging to implement. It necessitates the integration of numerous scientific disciplines, diverse techniques and various professional expertise. Furthermore, it often requires the collaboration of different institutions, encompassing both scientific and administrative entities. This concept posits that human health is intrinsically linked to and dependent on the well-being of animals, plants and the broader environment, while the environment not only sustains life but can also serve as a source of xenobiotics that affect the health-disease balance. In this context, all components of the potential exposome, encompassing the entirety of factors of various natures that influence health throughout life, must be considered comprehensively. Achieving this holistic understanding typically demands the application of multiple research techniques, known as the multi-omics approach and the adoption of an integrated method for data analysis. This project endeavoured to utilise such an integrated approach, examining data from diverse origins: human (children stool for gut microbiota analysis) and environmental (groundwater for hyporheic zone microbial analysis), as well as implementing comprehensive informatic tools for data processing. Analysis of stool samples revealed significant differences in gut microbiota composition across various taxonomic levels between normal weight, overweight and obese children. Additionally, a potential link between certain xenobiotics and gut microbiota composition, body weight and overall health status was identified. Analysis of groundwater samples revealed significant differences in hyporheic zone microbial composition at various taxonomic levels based on the sampling location and depth. Key geochemical factors influencing sample diversity were also identified. The promising results obtained not only demonstrate the viability of this methodology but also pave the way for future research initiatives.

Automated high-throughput RP-HPLC-MS and SFC-MS analytical and purification platforms to support drug discovery.

In recent years, the need to accelerate drug discovery processes in the pharmaceutical industry has revived the interest of implementing automated workflows, allowing the simultaneous processing of multiple samples on global processes that are referred as High-Throughput Purification (HTP). In this work, SAPIO Laboratory Information Management System (SAPIO LIMSSM) has been customized at the HTP laboratories of Janssen R&D to accommodate the needs of global purification groups on several automated HTP workflows, integrating Analytical Studio™ data processing tool on multiple steps. Herein we describe the workflow details from crude analysis via RP-LC-MS or SFC-MS systems to sample redissolution and delivery to Compound Logistics (CL) in tubes ready for assay plate preparation. This includes robotic platforms to streamline sample handling and automation tools to facilitate chromatographic and Nuclear Magnetic Resonance (NMR) analyses. The combination of Analytical StudioTM and the SAPIO LIMSSM has increased the productivity of Janssen HTP teams since 2020, having a big impact on reducing the Design-Make-Test-Analyze (DMTA) cycles. To the best of our knowledge, this is the first time that a complete HTP workflow with a LIMS is being reported, including RP-HPLC and/or SFC, High-Throughput Nuclear Magnetic Resonance (HT-NMR), redissolution and submission as DMSO solutions of registered compounds to CL, ready for biological assay distribution.