Meaningful Data Research Articles

A Narrative Inquiry on Learners of Home-Based Exercise

Exercises performed at home are called Home-based Exercises. The suspension of classes exposed the learners to home-based exercises. The goal of this study is to explore the ideas and insights of learners on Home-based Exercise. The study utilized Basic Qualitative Research, specifically, Narrative Inquiry to gather the stories of learners. In a semi-structured interview, seven conversation partners participated. The seven conversation partners were chosen using inclusion criteria. The rigorous data analysis and data explication using Creswell’s six-step data explication elicited four central themes: (1) The Obstacle: Hindrances in Performing Home-based Exercise, (2) The Push: A way to overcome the Hindrances, (3) The Goal: An Exercise Plan, and (4) The Drive: A motivation to keep going. The findings of this study provided a picture of how learners have coped with the challenges posed by exercising at home. This study carries meaningful data as a reference for future innovation in Home-based Exercise.

Using citizen science image analysis to measure seabird phenology

Developing standardized methodology to allow efficient and cost‐effective ecological data collection, particularly at scale, is of critical importance for understanding species' declines. Remote camera networks can enable monitoring across large spatiotemporal scales and at relatively low researcher cost, but manually analysing images and extracting biologically meaningful data is time‐consuming. Citizen science image analysis could reduce researcher workload and increase output from large datasets, while actively raising awareness of ecological and conservation issues. Nevertheless, testing the validity of citizen science data collection and the retention of volunteers is essential before integrating these approaches into long‐term monitoring programmes. In this study, we used data from a Zooniverse citizen science project, Seabird Watch, to investigate changes in breeding timing of a globally declining seabird species, the Black‐legged Kittiwake Rissa tridactyla. Time‐lapse cameras collected >200 000 images between 2014 and 2023 across 11 locations covering the species' North Atlantic range (51.7°N–78.9°N), with over 35 000 citizen science volunteers ‘tagging’ adult and juvenile Kittiwakes in images. Most volunteers (81%) classified images for only a single day, and each volunteer classified a median of five images, suggesting that high volunteer recruitment rates are important for the project's continued success. We developed a standardized method to extract colony arrival and departure dates from citizen science annotations, which did not significantly differ from manual analysis by a researcher. We found that Kittiwake colony arrival was 2.6 days later and departure was 1.2 days later per 1° increase in latitude, which was consistent with expectations. Year‐round monitoring also showed that Kittiwakes visited one of the lowest latitude colonies, Skellig Michael (51.8°N), during winter, whereas birds from a colony at similar latitude, Skomer Island (51.7°N), did not. Our integrated time‐lapse camera and citizen science system offers a cost‐effective means of measuring changes in colony attendance and subsequent breeding timing in response to environmental change in cliff‐nesting seabirds. This study is of wide relevance to a broad range of species that could be monitored using time‐lapse photography, increasing the geographical reach and international scope of ecological monitoring against a background of rapidly changing ecosystems and challenging funding landscapes.

RNA quality score evaluation: A preliminary study of RNA integrity number (RIN) and RNA integrity and quality number (RNA IQ)

In the past several years, with the in-depth development of RNA-related research, exploring the application of transcriptome and corresponding RNA biomarkers has become one of the research hotspots in the field of forensic science. High-quality RNA is essential for successful downstream workflows, especially in the steps of screening biomarkers by microarray or RNA sequencing (RNA-seq). Thus, accurately evaluating the quality of RNA samples is a critical step in obtaining meaningful expression data. The RNA integrity number (RIN) generated from the Agilent Bioanalyzer system has been widely used for RNA quality control in the past two decades. Recently, Thermo Fisher Scientific launched a ratiometric fluorescence-based method to quickly check whether an RNA sample has degraded, and the results are presented as RNA integrity and quality number (RNA IQ). Both quality score systems determine RNA quality using a numerical system based on a scale of 1–10, with 1 denoting significantly degraded specimens and 10 representing high-quality, intact RNA samples. In this preliminary study, we evaluated the consistency, reproducibility and linearity of two quality scores in RNA quality determination by analyzing heat- and RNase- artificially degraded samples. Meanwhile, the expression levels of three microRNAs (hsa-let-7 g-5p, hsa-miR-93–5p and hsa-miR-191–5p) in intact and severely degraded RNA samples were estimated by TaqMan-qPCR and droplet digital PCR. Overall, both quality scores showed good repeatability and reproducibility in their respective tests. In the samples subjected to thermal degradation, RIN showed a trend corresponding to heating time, while RNA IQ value showed almost no change on the time gradient. However, in RNase A mediated degradation, RNA IQ value observed better linearity. Furthermore, the expression levels of three microRNAs in the severely degraded samples did not show significant changes compared to the intact RNA samples. RNA degradation is a very complex and highly variable process, which is difficult to comprehensively evaluate through any one index and cannot directly compare these two parameters. Nevertheless, combined with previous research results and the expression levels of three microRNAs in this study, analyzing RNA biomarkers with stable regions or small sizes in challenged samples may be a conservative and reliable approach.

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Computer vision has become an important technology for obtaining meaningful data from visual content and providing valuable information for enhancing security controls, marketing, and logistic strategies in diverse industrial and business sectors. The retail sector constitutes an important part of the worldwide economy. Analyzing customer data and shopping behaviors has become essential to deliver the right products to customers, maximize profits, and increase competitiveness. In-person shopping is still a predominant form of retail despite the appearance of online retail outlets. As such, in-person retail is adopting computer vision models to monitor store products and customers. This research paper presents the development of a computer vision solution by Lytica Company to detect customers in Steren’s physical retail stores in Mexico. Current computer vision models such as SSD Mobilenet V2, YOLO-FastestV2, YOLOv5, and YOLOXn were analyzed to find the most accurate system according to the conditions and characteristics of the available devices. Some of the challenges addressed during the analysis of videos were obstruction and proximity of the customers, lighting conditions, position and distance of the camera concerning the customer when entering the store, image quality, and scalability of the process. Models were evaluated with the F1-score metric: 0.64 with YOLO FastestV2, 0.74 with SSD Mobilenetv2, 0.86 with YOLOv5n, 0.86 with YOLOv5xs, and 0.74 with YOLOXn. Although YOLOv5 achieved the best performance, YOLOXn presented the best balance between performance and FPS (frames per second) rate, considering the limited hardware and computing power conditions.

Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.

Automated weather forecasting and field monitoring using GRU-CNN model along with IoT to support precision agriculture

India's agricultural sector is dwindling, which has an impact on ecosystem output. Smart farming increases crop productivity by reducing waste and making effective use of fertiliser. Various Machine Learning techniques together with IoT enabled are developed for precision agriculture. However, the existing techniques face difficulties in forecasting weather and predicting disease accurately. In this work, a system is developed to automate weather forecasting and field monitoring using sensors. Wireless transmission is used to transfer sensor data to a web server database. In the first step, the collected data is pre-processed using normalization and mean based missing value imputation to convert the raw data into meaningful data. Pre-processed data is then fed into Gated Recurrent Unit (GRU) for forecasting the weather condition. In the second step, crop images acquired from sensors for disease prediction are pre-processed using Adaptive Gaussian Filter for noise removal and Dynamic Histogram Equalization for contrast enhancement of image. Pre-processed images are then fed into ResNet50 for feature extraction and classification. Using these predicted data, in case of any horrible weather conditions and soil conditions, remedial action will be automatically taken by the systems. On the other hand, regarding the information about horrible conditions and in presence of pests and diseases, an alert message will be sent to the farmers. The proposed method is tested with several metrics which attain better performance like 94% accuracy for weather forecasting and 98% accuracy for field monitoring. Thus the proposed IoT and deep learning based model can support farmers to achieve a high quantity of crop production in lesser time.

Predicting materials properties with generative models: applying generative adversarial networks for heat flux generation

In the realm of materials science, the integration of machine learning techniques has ushered in a transformative era. This study delves into the innovative application of generative adversarial networks (GANs) for generating heat flux data, a pivotal step in predicting lattice thermal conductivity within metallic materials. Leveraging GANs, this research explores the generation of meaningful heat flux data, which has a high degree of similarity with that calculated by molecular dynamics simulations. This study demonstrates the potential of artificial intelligence (AI) in understanding the complex physical meaning of data in materials science. By harnessing the power of such AI to generate data that is previously attainable only through experiments or simulations, new opportunities arise for exploring and predicting properties of materials.

Data-centric artificial olfactory system based on the eigengraph

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

The contextual interview: a cross-cutting patient-interviewing approach for social context

ABSTRACT Patient interviewing pedagogy in medical education has not evolved to comprehensively capture the biopsychosocial model of healthcare delivery. While gathering a patient’s social history targets important aspects of social context it does not adequately capture and account for the real-time reassessment required to understand evolving factors that influence exposure to drivers of health inequities, social determinants of health, and access to supports that promote health. The authors offer a patient interviewing approach called the Contextual Interview (CI) that specifically targets dynamic and ever-changing social context information. To substantiate the use of the CI in medical education, the authors conducted a qualitative review of the Accreditation Council for Graduate Medical Education Milestones for primary care specialties (Family Medicine, Internal Medicine, and Pediatrics). Milestones were coded to the extent to which they reflected the learner’s need to acknowledge, assess, synthesize and/or apply patient contextual data in real-time patient encounters. Approximately 1 in 5 milestones met the context-related and patient-facing criteria. This milestone review further highlights the need for more intentional training in eliciting meaningful social context data during patient interviewing. The CI as a cross-cutting, practical, time-conscious, and semi-structured patient interviewing approach that deliberately elicits information to improve the clinician’s sense and understanding of a patient’s social context. The authors reviewed future directions in researching adapted versions of the CI for undergraduate and graduate medical education.

Towards a smart PCR process

ABSTRACT PCR has transformed DNA profiling, providing the opportunity to analyse trace DNA. Trace DNA often contains degraded DNA, such that many samples generate no meaningful STR data. The PCR process has changed little since first use despite the drive for greater sensitivity: the same steps of denaturation, annealing and extension used in the first STR amplifications are recognizable in current processes. Our aim is to move towards a smart PCR that can monitor amplification and amend cycling conditions. To achieve this, DNA amplification needs to be performed on a real-time PCR machine. Initially, this was the adjustment of the time at the denaturation and annealing steps. Standard STR profiling was performed using the GlobalFilerTM kit on an open-source qPCR machine and reduced in step changes the denaturation time to 2 s from 10 and time of annealing from 90 s to 30. The comparison of the STR profiles resulting from the modified amplification parameter to those of the control set indicated a loss of allelic amplification although the resulting likelihood ratios were extremely high. We identify the groundwork required to attain the ambitious goal of creating a smart PCR system that can respond in real time.

Knee replacement patients and wearable knee pads

This paper proposes a novel solution to the common problem of knee stiffness experienced by patients following knee replacement surgery. The paper suggests designing a wearable knee pad that is fitted with three 6-axis IMU sensors to monitor, evaluate, and process the movement data of the patient’s knee in real-time. The data collected would then be used to provide appropriate recovery methods and encouragement to the patients. This paper highlights the advancements in wearable technology and remote patient monitoring, which allow for the improvement of postoperative care and behavioural change in knee replacement surgery patients. The literature review section examines the role of mHealth technologies and wearable sensors in remote patient monitoring and behaviour change for total knee arthroplasty patients. The research recommends integrating mobile health and wearable sensor technologies for remote patient monitoring and behaviour change interventions in these patients for enhanced postoperative care and improved patient outcomes. The proposed methodology includes user-friendly interfaces that provide continuous monitoring, personalized rehabilitation programs, and enhanced patient engagement using machine learning algorithms to recognize patterns and anomalies in knee motion data. The data analysis section employs various filtering, segmentation, normalization, and statistical methods to ensure accurate and meaningful data analysis. The document concludes by highlighting the need for further research to optimize and validate these technologies and interventions.

The utility of cone-beam computed tomography and multislice computed tomography scan for the evaluation of invasion versus erosion by mandibular squamous cell carcinoma as viewed on medical PACS

Introduction The degree of bone involvement in mandibular squamous cell carcinoma has a significant impact on surgical planning. The purpose of this study was to determine the utility of cone-beam computed tomography (CBCT) for the evaluation of invasion by mandibular squamous cell carcinoma. Methods A retrospective review was conducted to identify subjects treated for oral squamous cell carcinoma (OSCC). All subjects had OSCC adjacent or fixed to the mandible, received preoperative CBCT and multislice computed tomography scan (MSCT), and had resection specimens that included bone. Results Twenty-one subjects met the inclusion criteria. The sensitivity of CBCT was 100% (95% CI 0.75–1), the specificity was 75% (95% CI 0.35–0.97), the positive predictive value was 87% (95% CI 0.66–0.98), and the calculated test accuracy was 91%. The sensitivity of MSCT was 92% (95% CI 0.64–1), the specificity was 100% (95% CI 0.63–1), and the calculated test accuracy was 95%. Discussion CBCT provides meaningful data that may be useful in identifying bony involvement in patients with mandibular squamous cell carcinoma. The utility in delineating erosion versus invasion is limited.

A patient-centred and multi-stakeholder co-designed observational prospective study protocol: Example of the adolescent experience of treatment for X-linked hypophosphataemia (XLH).

The importance of patient centricity and keeping the patient at the heart of research design is now well recognised within the healthcare community. The involvement of patient, caregiver and clinician representatives in the study design process may help researchers to achieve this goal and to ensure robust and meaningful data generation. Real-world data collection allows for a more flexible and patient-centred research approach for gaining important insights into the experience of disease and treatments, which is acutely relevant for rare diseases where knowledge about the disease is more likely to be limited. Here, we describe a practical example of a patient-centric, multi-stakeholder approach that led to the co-design of a prospective observational study investigating the lived experience of adolescents with the rare disease, X-linked hypophosphataemia. Specifically, we describe how the knowledge and expertise of a diverse research team, which included expert physicians, research and technology specialists, patients and caregivers, were applied in order to identify the relevant research questions and to ensure the robustness of the study design and its appropriateness to the population of interest within the context of the current clinical landscape. We also demonstrate how a structured patient engagement exercise was key to informing the selection of appropriate outcome measures, data sources, timing of data collection, and to assessing the feasibility and acceptability of the proposed data collection approach.

Diagnosis and Pharmacological Management of Microscopic Colitis in Geriatric Care.

Microscopic colitis, a diagnosis under the umbrella term of inflammatory bowel disease, is a prevalent cause of watery diarrhea, often with symptoms of urgency and bloating, typically observed in older adults aged ≥60years. Its incidence has been reported to exceed those of ulcerative colitis and Crohn's disease in some geographical areas. Although nonpathognomonic endoscopic abnormalities, including changes of the vascular mucosal pattern; mucosal erythema; edema; nodularity; or mucosal defects, e.g., "cat scratches" have been reported, a colonoscopy is typically macroscopically normal. As reliable biomarkers are unavailable, colonoscopy using random biopsies from various parts of the colon is compulsory. Based on the histological examination under a microscope, the disease is divided into collagenous (with a thickened subepithelial collagenous band) and lymphocytic (with intraepithelial lymphocytosis) colitis, although incomplete forms exist. In routine clinical settings, the disease has a high risk of being misdiagnosed as irritable bowel syndrome or even overlooked. Therefore, healthcare providers should be familiar with clinical features and rational management strategies. A 6-8-week oral budesonide treatment course (9 mg/day) is considered the first-line therapy, but patients often experience relapse when discontinued, or might become intolerant, dependent, or even fail to respond. Consequently, other therapeutic options (e.g., bismuth subsalicylate, biologics, loperamide, bile acid sequestrants, and thiopurines) recommended by available guidelines may be prescribed. Herein, clinically meaningful data is provided based on the latest evidence that may aid in reaching a diagnosis and establishing rational therapy in geriatric care to control symptoms and enhance the quality of life for those affected.

Enabling Personalization for Digital Cognitive Stimulation to Support Communication With People With Dementia: Pilot Intervention Study as a Prelude to AI Development.

Maintaining good communication and engagement between people with dementia and their caregivers is a major challenge in dementia care. Cognitive stimulation is a psychosocial intervention that supports communication and engagement, and several digital applications for cognitive stimulation have been developed. Personalization is an important factor for obtaining sustainable benefits, but the time and effort required to personalize and optimize applications often makes them difficult for routine use by nonspecialist caregivers and families. Although artificial intelligence (AI) has great potential to support automation of the personalization process, its use is largely unexplored because of the lack of suitable data from which to develop and train machine learning models. This pilot study aims to evaluate a digital application called Aikomi in Japanese care homes for its potential to (1) create and deliver personalized cognitive stimulation programs to promote communication and engagement between people with dementia and usual care staff and (2) capture meaningful personalized data suitable for the development of AI systems. A modular technology platform was developed and used to create personalized programs for 15 people with dementia living in 4 residential care facilities in Japan with the cooperation of a family member or care staff. A single intervention with the program was conducted with the person with dementia together with a care staff member, and for some participants, smell stimulation was provided using selected smell sticks in conjunction with the digital program. All sessions were recorded using a video camera, and the combined personalized data obtained by the platform were analyzed. Most people with dementia (10/15, 67%) showed high levels of engagement (>40 on Engagement of a Person with Dementia Scale), and there were no incidences of negative reactions toward the programs. Care staff reported that some participants showed extended concentration and spontaneous communication while using Aikomi, which was not their usual behavior. Smell stimulation promoted engagement for some participants even when they were unable to identify the smell. No changes in well-being were observed following the intervention according to the Mental Function Impairment Scale. The level of response to each type of content in the stimulation program varied greatly according to the person with dementia, and personalized data captured by the Aikomi platform enabled understanding of correlations between stimulation content and responses for each participant. This study suggests that the Aikomi digital application is acceptable for use by persons with dementia and care staff and may have the potential to promote communication and engagement. The platform captures personalized data, which can provide suitable input for machine learning. Further investigation of Aikomi will be conducted to develop AI systems and create personalized digital cognitive stimulation applications that can be easily used by nonspecialist caregivers.

Assessment of the present capacity for Management of the Loco Concholepas concholepas (BRUGUIERE, 1789) (GASTROPODA, MURICIDAE) in Chile.

In recent years the fishery for loco (Concholepas concholepas) has undergone a number of changes in management strategy. The first was a change in the mínimum size limit from 95 mm in the north of Chile and 105 min in the south to a uniform 100 mm in 1981. An annual 3 month closed season was initiated in 1982, and. starting in September of 1985. There was a closure of nearly two years in all regions except the southernmost three. This two-year closure has been followed by two short open seasons, one of 45 days in 1987. and one with a national 5,000 ton quota in 1988. The response by fishermen to increasingly restrictive regtdation has been some clandestine fishing and the stockpiling of locos prior to the open season. These actions have resulted in i he partial loss of landings data and a nearly total loss of meaningful effort data. This loss of data will have serious impact on the ability of fishery managers to effectively evaluate the State of the fishery and determine future regulatory actions. There is considerable published background information on the basic biology of the species. and some Information on aspects of the population dynamics such as growth and mortality. There are. however, large discrepancies between authors which mav be due lo sub-populalion or regional differences.

Challenges and best practices in omics benchmarking.

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological datasets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers

Classification is one of the main applications of supervised learning. Recent advancements in developing quantum computers have opened a new possibility for machine learning on such machines. Due to the noisy performance of near-term quantum computers, error mitigation techniques are essential for extracting meaningful data from noisy raw experimental measurements. Here, we propose two ensemble-learning error mitigation methods, namely, bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform error-mitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian, in which we observe a significant improvement in accuracy. Our ensemble-learning methods provide a systematic way of utilizing shallow circuits to solve complex classification problems. Published by the American Physical Society 2024

Autonomous Data Association and Intelligent Information Discovery Based on Multimodal Fusion Technology

The effective association of multimodal data is the basis of massive multi-source heterogeneous data sharing in the era of big data. How to realize data autonomous association between massive multimodal databases and the automatic intelligent screening of valuable information from associated data, so as to provide a reliable data source for artificial intelligence (AI), is an urgent problem to be solved. In this paper, a data autonomous association method based on the organizational structure of data cells is proposed, including transaction abstraction based on information nucleuses, symmetric and asymmetric data association based on strategies and data pipes, and information generation based on big data. To screen meaningful data associations, an information-driven intelligent information discovery method and a task-driven intelligent information discovery method are proposed. The former screens meaningful data associations by training the reward and punishment model to simulate the manual scoring of data associations. The latter is task-oriented and screens meaningful data associations by training the reward and punishment model to simulate the manual ranking of data associations related to the task requests. Through the above work, autonomous data association and intelligent information discovery are effectively realized based on multimodal fusion technology, which provides a novel data source mining approach using multimodal data sharing and intelligent information discovery.

Using a 3D heat map to explore the diverse correlations among elements and mineral species

This paper presents an enhanced 3D heat map for exploratory data analysis (EDA) of open mineral data, addressing the challenges caused by rapidly evolving datasets and ensuring scientifically meaningful data exploration. The Mindat website, a crowd-sourced database of mineral species, provides a constantly updated open data source via its newly established application programming interface (API). To illustrate the potential usage of the API, we constructed an automatic workflow to retrieve and cleanse mineral data from it, thus feeding the 3D heat map with up-to-date records of mineral species. In the 3D heat map, we developed scientifically sound operations for data selection and visualization by incorporating knowledge from existing mineral classification systems and recent studies in mineralogy. The resulting 3D heat map has been shared as an online demo system, with the source code made open on GitHub. We hope this updated 3D heat map system will serve as a valuable resource for researchers, educators, and students in geosciences, demonstrating the potential for data-intensive research in mineralogy and broader geoscience disciplines.