Data Analysis Platform Research Articles

Integrating Digital Health Solutions with Immunization Strategies: Improving Immunization Coverage and Monitoring in the Post-COVID-19 Era.

The COVID-19 pandemic underscored the critical importance of vaccination to global health security and highlighted the potential of digital health solutions to improve immunization strategies. This article explores integrating digital health technologies with immunization programs to improve coverage, monitoring, and public health outcomes. It examines the current landscape of digital tools used in immunization initiatives, such as mobile health apps, electronic health records, and data analytics platforms. Case studies from different regions demonstrate the effectiveness of these technologies in addressing challenges such as vaccine hesitancy, logistics, and real-time monitoring of vaccine distribution and adverse events. The paper also examines ethical considerations, data privacy issues, and the need for a robust digital infrastructure to support these innovations. By analyzing the successes and limitations of digital health interventions in immunization campaigns during and after the COVID-19 pandemic, we provide recommendations for future integration strategies to ensure resilient and responsive immunization systems. This research aims to guide policymakers, health professionals, and technologists in leveraging digital health to strengthen immunization efforts and prepare for future public health emergencies.

The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform.

The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research. Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: (1) environmental data integration and harmonization system, (2) clinical and omics data integration, (3) advanced analytical algorithm development, and (4) visualization interface development and targeted population-based cohort recruitment. The CHiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.

Digital Twin System of Substation Equipment

This paper adopts the following technical solutions to achieve: a digital twin system for substation, including: database, database configuration module, data warehousing module, background data processing module and front-end browsing module; The front-end browsing module is used to display the data sent by the background data processing module in the corresponding form according to the user's selection; The form includes building the three-dimensional model of the substation based on BIM technology, and displaying the corresponding real-time data and status in the three-dimensional components of the three-dimensional model. Compared with the existing technology, it organically integrates the three-dimensional model and panoramic monitoring data of the substation, realizes the three-dimensional panoramic display of substation assets and the acquisition of substation monitoring data, improves the spatial experience of daily operation and maintenance comprehensive monitoring, and provides a solid data platform for the subsequent in-depth mining and intelligent analysis of multidimensional data of the substation.

Developing a consensus-based motivational care pathway for individuals with lower limb fractures: a Delphi protocol.

Older adults with lower limb fractures often harbor concerns about losing their mobility, fearing a loss of independence. It is vital to develop strategies that foster their active engagement in the rehabilitation process. The present protocol aims to create a care pathway tailored to motivate individuals with lower limb fractures to adhere to rehabilitation. We will develop an observational, cross-sectional, and descriptive study using the Delphi data-gathering approach. Purposive sampling will recruit a panel of healthcare professionals and experts who care for patients with lower limb fractures. Aligned with the Delphi method, a series of iterative rounds will be developed to gather consensus around the motivational strategies used by health professionals in the rehabilitation of people with lower limb fractures. We will employ the Qualtrics platform for data collection and analysis, and a consensus target of 75% has been predetermined. For quantitative data analysis, we will use descriptive statistics encompassing a range of measures, including count, mean, standard deviation, median, minimum, maximum, and range. An inductive thematic analysis procedure will be employed to extract meaningful themes and patterns from qualitative data. The study results are expected to significantly impact clinical practice by creating a specialized care pathway to motivate individuals with lower limb fractures to adhere to rehabilitation. Adopting these explicit standards by professionals will ensure uniform and high-quality care.

Human Resource Management in the Transformative Digital Era

Digitalization in human resource management helps companies to modernize HR functions and gives them a competitive edge. At the same time, it requires a change in work style and require changes in demand for HR competencies. Study this aims to introduce the phenomenon of digitalization in literature, explore its current benefits and key risks, and analyze the competencies and role of HR professionals. Innovative strategies that can be applied in human resource management (HRM) to enhance organizational performance in the digital era. With rapid changes in technology and the business environment, organizations need to develop innovative approaches in managing their human resources. This research method is a qualitative research method with a descriptive approach. This research operationally examines various sources of literature relevant to the context of the above research. The data taken, identified in the following order: (1) data collection (2) data sorting (3) data analysis (4) conclusion making. As for data analysis, there is a predetermined sequence in accordance with the empirical steps taken, namely as follows: (1) Examination of data (2) suspected data findings, (3) Data confirmation (4) Diagnosis, (5) Action. The results obtained show that HR professionals tend to be a little reluctant to adopt technology the. The results of this research confirm the importance of digitalization in recent years for human resources and increasing demand for digital skills. Every organization should develop its digital strategy to improve productivity. In the present scenario, DHRM is considered a more significant and growing research topic as well as an important topic for a business organization. With the use of DHR practice and through social media, the internet, AI, and other technology organization can maintain their performance and employees' quality standard for the smooth running of the organization. In exploring "Strategic Human Resource Planning in the Era of Digital Transformation" through the Systematic Literature Review method, this research reveals important findings. Technology integration, especially through artificial intelligence, big data analysis and technology platforms, is proven to be a deep foundation in responsive and adaptive HR planning in the era of digital transformation. The success of this planning also depends heavily on the involvement of key stakeholders, particularly upper management and business units, who contribute to the identification of skills relevant to technological change.

Brain image data processing using collaborative data workflows on Texera.

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called "Texera." The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

Finding the Right Data Analytics Platform for Your Enterprise

Finding the Right Data Analytics Platform for Your Enterprise

Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies

Purpose: The aim of the study was to examine the consumer behavior analysis in the age of big data for effective marketing strategies. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that consumer behavior analysis in the age of big data presents a transformative opportunity for businesses to enhance their marketing strategies and better understand their target audiences. The studies discussed demonstrate the effectiveness of leveraging Big Data analytics in various domains such as social media sentiment analysis, personalized marketing, predictive analytics, customer segmentation, location-based marketing, and real-time personalization. These strategies enable businesses to tailor their marketing efforts more precisely, leading to increased consumer interest, improved engagement, and higher conversion rates. Unique Contribution to Theory, Practice and Policy: Social Identity Theory, Information Processing Theory & Technology Acceptance Model (TAM) may be used to anchor future studies on Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies. Promote the adoption of advanced data analytics tools and techniques by businesses to enhance their marketing strategies. Companies should invest in robust data infrastructure, analytics platforms, and talent to effectively collect, analyze, and interpret consumer data. By leveraging predictive analytics, sentiment analysis, customer segmentation, and real-time personalization, businesses can tailor their marketing efforts to individual consumer preferences and behaviors, leading to improved engagement, customer satisfaction, and ultimately, increased sales and profitability. Advocate for the development of regulatory frameworks that balance consumer privacy rights with the benefits of data-driven marketing. Policymakers should collaborate with industry stakeholders to establish guidelines and standards for ethical data collection, usage, and storage.

Optimizing the Detective's Burden: A Data-Driven Approach to Enhancing Investigative Productivity

This study examines the critical issue of detective workload and its impact on investigative effectiveness. While detectives play a vital role in solving crimes and upholding justice, they often face overwhelming workloads that can hinder their ability to focus on core investigative activities. The overall aim of this study is to utilize workload analysis to gain a comprehensive understanding of detective work and identify evidence-based strategies to increase their productivity in criminal investigations. This research analyzes detective workload through a data-driven lens, exploring the challenges and opportunities for improvement. Main topics the impact of heavy workloads on detective productivity and well-being. Current workload distribution and its limitations Innovative strategies for workload management (e.g., data-driven case prioritization, workload sharing). The role of technology adoption (e.g., digital forensics tools, data analysis platforms) in streamlining investigations. The importance of work-life balance for detective well- being and performance. Key Arguments traditional investigative practices and an imbalance between administrative and investigative tasks significantly impede detective productivity. Implementing data- driven workload management strategies can optimize detective time allocation and improve case resolution rates. Utilizing advanced investigative technologies equips detectives with efficient tools to analyze evidence and identify leads. Promoting a healthy work-life balance for detectives reduces stress and burnout, ultimately contributing to investigative effectiveness. Methodology this research employs a comprehensive approach, drawing on. Literature Review analyzing existing research on detective workload, productivity, and innovative investigative techniques. Policy Review: Examining existing policies and practices related to detective workload management and technology adoption within police departments. Significance this research has significant implications for policymakers, police leadership, and detectives themselves. Policymakers can leverage the findings to develop evidence-based strategies for optimizing detective workload and improving investigative efficiency. Police leadership can utilize the recommendations to implement data-driven workload management practices and equip detectives with the necessary technology and training. Detectives can gain valuable insights into workload management strategies and advocate for initiatives that promote well-being and enhance their investigative capabilities. Audience this research is targeted towards a broad audience, including: Policymakers involved in criminal justice reform and police department funding. Police leadership seeking to enhance detective productivity and improve investigative outcomes. Detectives and law enforcement professionals seeking to optimize their workload and improve investigative efficiency. Academic researchers interested in the field of criminal justice and police work.

HumanIslets: An integrated platform for human islet data access and analysis.

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

BioLadder: A bioinformatic platform primarily focused on proteomic data analysis.

BioLadder (https://www.bioladder.cn/) is an online data analysis platform designed for proteomics research, which includes threeclasses of experimental data analysis modules and fourclasses of common data analysis modules. It allows for a variety of proteomics analyses to be conducted easily and efficiently. Additionally, most modules can also be utilized for the analysis of other omics data. To facilitate user experience, we have carefully designed fourdifferent kinds of functions for customers to quickly and accurately utilize the relevant analysis modules.

Computing platform for fast analysis of experimental big data

A multi-purpose computing platform for processing (big) data collected from experiments and large research infrastructures is developed. The platform is validated in a case study on monitoring data aquired by the SCADA system of the High Power Laser System at ELI-NP.

The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

The Palantir Files: public interest archives for platform accountability

ABSTRACT Platforms designed for data integration and analysis introduce unique challenges for understanding the social implications of data management in public services. Researchers who study such platforms in the public interest must negotiate limited access to proprietary systems and the firms that make them but can create accountability through openly available data. This paper introduces an approach to researching a data integration and analysis firm, Palantir Technologies, through what we call ‘public interest archives for platform accountability.’ The paper begins by introducing data integration and analysis platforms as an emerging sector of the platform economy, followed by a summary of academic researchers’ challenges. We then provide a literature review of Palantir research, public interest archives, and contemporary archival projects for platform accountability. Then, we present our Palantir case study and describe our methods for document tracking to build a corpus (n = 2807), including court documents, privacy impact assessments, press releases, contracts, Freedom of Information Act requests, non-governmental organization reports, Securities and Exchange Commission filings, and patents. We end with several vignettes that help researchers understand this material’s significance to platform accountability.

Cross study analyses of SEND data: toxicity profile classification.

A SEND toxicology data transformation, harmonization, and analysis platform were created to improve the identification of unique findings related to the intended target, species, and duration of dosing using data from multiple studies. The lack of a standardized digital format for data analysis had impeded large-scale analysis of in vivo toxicology studies. The CDISC SEND standard enables the analysis of data from multiple studies performed by different laboratories. This work describes methods to analyze data and automate cross-study analysis of toxicology studies. Cross-study analysis can be used to understand a single compound's toxicity profile across all studies performed and/or to evaluate on-target versus off-target toxicity for multiple compounds intended for the same pharmacological target. This work involved development of data harmonization/transformation strategies to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND datasets from the BioCelerate database were used for the analyses. Toxicity profiles for key organ systems were developed for liver, kidney, male reproductive tract, endocrine system, and hematopoietic system using SEND domains. A cross-study analysis dashboard with a built-in user-defined scoring system was created for custom analyses, including visualizations to evaluate data at the organ system level and drill down into individual animal data. This data analysis provides the tools for scientists to compare toxicity profiles across multiple studies using SEND. A cross-study analysis of 2 different compounds intended for the same pharmacological target is described and the analyses indicate potential on-target effects to liver, kidney, and hematopoietic systems.

Review Paper on a Comprehensive Approach to Detecting Tuberculosis, Asthma, and COVID-19

This study delves deeper into the realm of electronic devices and technologies for the detection of COVID-19, tuberculosis (TB), and asthma, examining recent advancements and future prospects. Electronics, with their versatility and precision, have emerged as a critical tool in combating infectious diseases and chronic conditions. Through a comprehensive review, this paper explores the diverse range of electronic devices used in detection methods for these diseases, including sensors, imaging systems, wearable devices, and data analytics platforms. Moreover, it discusses the integration of emerging technologies, such as artificial intelligence, machine learning, and the Internet of Things (IoT) to enhance the capabilities of electronic devices for disease detection and monitoring.

Optimizing gula apong production with an IoT-based temperature monitoring system

<p>Determining the quality of <em>gula apong</em> is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The <em>gula apong</em> industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of <em>gula apong</em>. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality <em>gula apong</em> is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of <em>gula apong</em> to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of <em>gula apong</em> but also reduce the cooking time and cost.</p>

Advancing team science and health equity in clinical oncology using the All of Us Research Program dataset.

e13741 Background: The All of Us Research Program ( All of Us) is one of the largest, most diverse health databases developed to expedite medical breakthroughs and is a resource for cancer researchers. The size and diversity of participants along with data types within the program, coupled with the program’s cloud based data analytics platform, encourage both team science and equitable health research outcomes. All of Us is dedicated to advancing the science of health equity with a focus on populations underrepresented in biomedical research and workforce, and embedding health equity across all aspects of the program. Methods: All of Us uses a centralized protocol where participants may elect to 1) enroll, consent, and authorize EHR, 2) answer surveys, 3) provide physical measurements, 4) provide biosamples, and 5) share data from wearables. High levels of participant diversity was achieved through an Engagement Ecosystem of outreach leaders, participants, and engagement awardees. Outreach, engagement, enrollment, and retention efforts are strategically directed by a Community and Participant Engagement Framework and a Health Equity Strategic Plan. Results: As of January 2024, health data from 413,450+ participants is available, including eight survey types from 337,500+ participants, electronic health records (EHR) from 287,000+ participants, wearable data from 15,600+ participants and whole genome sequencing from 245,350+ participants. Over 75% of participants self-identify as underrepresented in biomedical research, and about 45% self-identify as racial and ethnic minority groups. Cancer data includes history at enrollment as well as incident ascertainment via EHR (N = 26,323 total cancers and 13,067 incident cancers, from 7,544 participants assigned female at birth and 5,264 participants assigned male at birth). As of February 2024, All of Us data has been used in more than 221 publications. Of these, 35% (78) discuss cancer or oncology. More than 10% of active workspaces (921 out of 8000+ as of January 2024) include “cancer” in workspace descriptions. Commonly studied cancer types include breast, prostate, lung, ovarian, and colorectal. Conclusions: All of Us has established itself as a resource for cancer researchers. This database has the potential to provide insights into the development of treatment guidelines and emerging indicators for screening and early detection guided by health equity built on participant engagement, diversity, and broad data availability. Finally, the breadth of All of Us data also offers potential insight into cancer etiology and opportunities for prevention.

Using Dedoose to “Quantitize” Qualitative Data in Mixed Method Analysis: A Case Analysis

This practice note provides a review of Dedoose®, a software analysis platform capable of mixed methods analysis, based on a case application. The software has been used infrequently by the evaluation community but has significant potential, particularly for those interested in “quantizing” qualitative data. The evaluation of the World Federation of Societies of Anaesthesiologists’ Fellowship Program is briefly described, followed by a description and critique of our application of Dedoose. The authors conclude by encouraging evaluation practitioners to seriously consider adding this mixed-methods data analysis platform to their chest of tools.

Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, and imaging modalities. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.