Comma-separated Values Research Articles

Post-occupancy evaluation (POE) of high-rise flats built using prefabricated prefinished volumetric construction (PPVC)

PurposeThere has been an increasing use of prefabricated prefinished volumetric construction (PPVC) in the construction of high-rise flats to enhance productivity and sustainability, but there have been little studies to evaluate if the use of PPVC satisfies construction quality and overall satisfaction with the flat. This study aims to evaluate how homeowners perceive living in PPVC flats after they have moved into their homes. This is done with a post-occupancy evaluation (POE) looking at total building performance (TBP) mandates covering spatial performance (SP), acoustics performance (AP), visual performance (VP), indoor air quality performance (IAQP) and thermal performance (TP).Design/methodology/approachData were collected through the use of a survey questionnaire which was developed based on literature review. The data gathered from 145 respondents were then analysed quantitatively using Jeffrey’s Amazing Statistics Program (JASP), Microsoft Excel and Comma-Separated Values (CSV) files. The data analysed were further validated through the use of interviews with industry professionals.FindingsBased on descriptive and inferential statistics, the key findings of this study are that most respondents are satisfied with their PPVC flats in terms of quality of construction, satisfaction, IAQP, TP, AP and VP. However, they are less satisfied with the interior of their PPVC flats.Originality/valueThis is the first ever POE in the world conducted with homeowners living in high-rise flats built using PPVC. The findings from the study highlighted homeowners’ concerns regarding the quality of construction, SP, and AP, particularly in the interior areas of the flats, and underscores the importance of considering homeowners' experiences and perceptions in the design and construction of PPVC housing.

Coral skeletal proxy records database for the Great Barrier Reef, Australia

Abstract. The Great Barrier Reef (GBR), Australia, has a long history of palaeoenvironmental coral research. However, it can be logistically difficult to find the relevant research and records, which are often unpublished or exist as “grey literature”. This hinders researchers' abilities to efficiently assess the current state of coral core studies on the GBR and thus identify any key knowledge gaps. This study presents the Great Barrier Reef Coral Skeletal Records Database (GBRCD), which compiles 208 records from coral skeletal research conducted since the early 1990s. The database includes records from the Holocene, from ∼8000 years ago, to the present day; records are from the northern, central, and southern GBR from inshore and offshore locations. Massive Porites spp. coral records comprise the majority (92.5 %) of the database, and the remaining records are from Acropora, Isopora, or Cyphastrea spp. The database includes 78 variables, with Sr/Ca, U/Ca, and Ba/Ca being the most frequently measured. Most records measure data over 10 or more years and are at monthly or lower resolution. The GBRCD is machine readable and easily searchable so that users can find records relevant to their research, e.g. by filtering for site name, time period, or coral type. It is publicly available as comma-separated values (CSV) data and metadata files with entries linked by the unique record ID and as Linked Paleo Data (LiPD) files. The GBRCD is publicly available from the NOAA National Center for Environmental Information World Data Service for Paleoclimatology at https://doi.org/10.25921/hqxk-8h74 (Arzey et al., 2024). The intention is to update the GBRCD annually, depending on the availability of relevant new GBR records or submission of legacy records to the GBRCD for archiving. The collection and curation of existing GBR coral research data provide researchers with the ability to analyse common proxies such as Sr/Ca across multiple locations and/or examine regional to reef-scale trends. The database is also suitable for multi-proxy comparisons and combination or composite analyses to determine overarching changes recorded by the proxies. This database represents the first comprehensive compilation of coral records from the GBR. It enables the investigation of multiple environmental factors via various proxy systems for the GBR, northeastern Australia, and potentially the broader Indo-Pacific region.

How research contributions of professor Jiju Antony have influenced the field of quality management: an appraisal and critique

PurposeThe purpose of this study is to apply bibliometric techniques to critically examine the contributions of Professor (Dr) Jiju Antony in the domain of quality management research over a period close to 30 years (i.e. 1995 to 2023).Design/methodology/approachThe study incorporates a bibliometric analysis approach using VoSviewer software package to critically examine the contributions and trends in publications on Scopus indexed publications of Antony, followed by an industry wide evaluation of contributions made. Thematic analysis of the bibliographic data was imported in comma-separated values (CSV) format by selecting Antony (last name) and Jiju (first name) in the author search tab in Scopus. The search was made on September 09, 2023 and bibliographic records of 429 documents were considered for the analysis. The analysis was carried out in terms of most frequent keywords used, sources with most frequent publications, thematic analysis of most cited works and global collaborations.FindingsAntony has focused his research interest largely in the domain of quality management, publishing his research in top rated journals in the domain with a broad network of international collaborators. Antony has received 18,802 and 40,947 citations in Scopus and Google Scholar, respectively. This signifies the impact that Antony has created through his research publications. His major contributions are on the topics of six sigma, lean six sigma, continuous improvement, critical success factors and quality management practice implementations in various organizations. Diverse methodologies both qualitative and quantitative were utilized to conduct his research. However, his body of work is not without criticism. Such criticism includes the limited scope of work, with narrow focus on ISO 9001 and QMS standards, Total Quality Management (TQM). Critique also highlights the necessity for more depth, following insufficient exploration of distinctions between TQM and operational excellence (OPEX) methodologies like lean, six sigma, kaizen and agile. Antony’s work has yet to consider a diverse range of industry sectors, in terms of implementation of quality management principles, geographical location, the impact of national culture on corporate performance and explore data quality influence on decision-making. Notably, there is clear opportunity to consider the service sector in future research.Originality/valueTo best of the authors’ knowledge, there are few previous studies conducted using bibliometric analysis for analyzing the work of an individual. Therefore, the present study aims to set a trend whereby bibliometric analysis can be used to recognize and critically asses the contributions of other researchers in their respective domains.

A systematic analysis of literature on academic writings, attitude, and excellence in the literary world as published in Scopus-indexed journals from 1991 to 2020.

Objective: To assess the role of excellence and competencies in academic writing and how these have evolved, particularly in light of changes in the education system and technological advances.Methodology: A comprehensive search was conducted to gather literature published across ten formats, which was subsequently downloaded from journals indexed in the Scopus database in a comma-separated values (CSV) file for systematic analysis. The search utilized the terms "Academic Writings," "Academic Attitude," and "Academic Excellence," and was restricted to the timeframe from 1991 to 2020.Results: The available data encompasses ten distinct publication formats, which include articles (1820, 76.5%), books (30, 1.2%), book chapters (198, 8.3%), conference papers (119, 5%), editorials (39, 1.6%), errata (6, 0.2%), letters (13, 0.5%), notes (32, 1.3%), reviews (111, 4.6%), and short surveys (10, 0.4%). This information is derived from a total of 2378 documents collected for analysis. In the second decade, there was a 14% increase in the literature ratio compared to the first decade, while the third decade saw a 51.8% rise in the publication ratio relative to the first decade, based on the same total of 2378 publications. Specifically, the first decade accounted for 6.7% of the documents, the second decade for 20.6%, and the third decade for 72.5%.Conclusion: Excellence in academic writing can be achieved by several means. It involves developing a positive attitude towards academic writing, which includes a commitment to producing high-quality work. By adopting these qualities, writers striving for academic excellence can create work that meets the highest academic writing standards and significantly advance knowledge. Keywords: Academic writings, academic attitude, academic excellence, higher education, plagiarism, literary world, andacademic performance.

High-Precision Analysis Using μPMU Data for Smart Substations

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea’s Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships.

Gas Detection and Classification Using Multimodal Data Based on Federated Learning.

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long-short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client's data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches.

CO2 emission heat map of Gulf Cooperation Council region using Python for Geographic Information Systems

Abstract As part of the efforts to ensure a place in the global competitive landscape, Oman is aligning itself with sustainable development through diversified economic measures. To address the United Nations thirteenth Sustainable Development Goal (SDG) which is climate action, the stakeholders of Oman need to understand the carbon dioxide (CO2) emissions and compare the amount of CO2 with the emissions in neighbouring Gulf Cooperation Council (GCC) countries such as United Arab Emirates (UAE), Bahrain, Qatar, Kuwait, and Kingdom of Saudi Arabia (KSA). In this research, an attempt is being made to collect the CO2 emission for all these countries and show them on a web map using python programming language. The data ranging from year 1964 to 2021 is collected from CO2 and Greenhouse Gas Emissions in Comma Separated Values (csv) format. For each country, an approximate location for Capital in terms of Latitude and Longitude is taken from google maps. Using folium library of python, these locations and CO2 emission for a particular year is plotted on google map Application Programming Interface (API). The findings reveal that Oman’s CO2 emissions from fossil fuels and industry in 2022 year is 72 million tonnes which is relatively lower when compared to those of smaller GCC countries such as Qatar (101 million tonnes) and the UAE (244 million tonnes). Nonetheless, a gradual upward trend of 12 million tonnes of CO2 emissions has been observed in Oman between the years 2019 to 2022. The python program provides an easy way to visualise the CO2 emission heatmap without the need of any proprietary software or program.

Utopia: Automatic Pivot Table Assistant

Data summarization is required to comprehend large datasets, and aggregations are effective ways to summarize data. A pivot table is a mechanism to aggregate numerical attributes grouped by categorical attributes and spreadsheet pivot tables are particularly suitable for novices, where they can rearrange, group, and aggregate data using intuitive interfaces. However, real-world spreadsheet data is often disorganized, with attributes having multiple values and synonymous variants. For instance, in the IMDb data, multiple genres are stored as a comma-separated value ("Action, Comedy, Drama") and "Science Fiction" can be represented in various ways: "Sci-Fi", "scifi", "Technological Fiction", etc. Such data issues pose barriers for novices while constructing pivot tables, and result in noisy and incomprehensible summarization. Parsing multi-valued attributes forces users to resort to external tools (Power Query) or methods (Python), requiring additional expertise; and consolidating synonymous variants demand manual effort, which is a tedious task. We introduce Utopia, an a UTO matic PI vot table A ssistant that extends the functionality of spreadsheet pivot tables, overcoming data issues such as multi-valued attributes and synonymous variants. Utopia helps construct pivot tables without requiring additional expertise by (1) automatically detecting multi-valued attributes and organizing the values, achieving implicit data normalization , and (2) leveraging SimCSE embeddings and K-Means clustering to consolidate synonymous variants, enabling semantic aggregation. We will demonstrate how Utopia enables effective pivot table construction, relieving technical novices from tedious data preprocessing while allowing them to remain in their familiar spreadsheet environment, without requiring external tools or additional expertise.

Investigating the Acceptability of Students on Generative AI Tools: A Correlation Analysis among Selected Tertiary Schools in Metro Manila, Philippines

Artificial Intelligence has become the emphasis on the needs of future society. The digital era of education demands critical thinking skills, digital literacy and fundamental abilities to navigate and verify information. This development can be supported by the ability of AI chatbots to provide large volume of information in an interactive and efficient way. The goal of this paper is to examine the interest of the chosen BSCS and BSIT students in Metro Manila in utilizing three generative AI tools: Copilot, ChatGPT, and Gemini in the accomplishment of their academic requirements. A one-shot case study was carried out by the authors on six (6) selected universities in Metro Manila. A voluntary response sampling approach was employed to gather the participants in this study. The Slovin's formula was used to calculate statistically enough samples from the population. This study involved 209 CS and IT students, 157 or 75.12% of whom were males and 52 or 24.88% of whom were females, 45 or 21.53% were BSCS students and 164 or 78.47% were BSIT students. The data was initially recorded in a comma-separated value file. The extended TAM instrument was facilitated online using Google Forms by the participants which then imported into SPSS for data understanding and statistical treatment. It was found that out of eighteen (18) observed inter-item relationships, only six (6) or 33.33% were statistically significant and had considerable impacts. Moreover, SN → AT has the highest coefficient value of 0.788 followed by the factor PU → AT of 0.732 indicating their influence on generative AI tools attitude of use (AT). Lastly, PE → AT was shown having the lowest among relationships with 0.533 coefficient value. The study concluded that computer tertiary students in Metro Manila were generally amenable to the idea of introducing generative AI techniques into their academic responsibilities.

FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients

Lower limb amputation is a medical intervention which causes motor disability and may compromise quality of life. Several factors determine patients’ health outcomes, including an appropriate prosthetic provision and an effective rehabilitation program, necessitating a thorough quantitative observation through different data sources. In this context, the role of interoperability becomes essential, facilitating the reuse of real-world data through the provision of structured and easily accessible databases. This study introduces a comprehensive 10-year dataset encompassing clinical features, mobility measurements, and prosthetic knees of 1006 trans-femoral amputees during 1962 hospital stays for rehabilitation. The dataset is made available in both comma-separated values (CSV) format and HL7 Fast Healthcare Interoperability Resources (FHIR)-based representation, ensuring broad utility and compatibility for researchers and healthcare practitioners. This initiative contributes to advancing community understanding of post-amputation rehabilitation and underscores the significance of interoperability in promoting seamless data sharing for meaningful insights into healthcare outcomes.

Linking the mothers and index children in the Avon Longitudinal Study of Parents and Children (ALSPAC) with the Avon and Wiltshire Mental Health Partnership NHS Trust (AWP)

This data note describes electronic mental-health records of a portion of the Avon Longitudinal Study of Parents and Children cohort. The records were obtained by the linking of identifiers of the participants of the Avon Longitudinal Study of Parents and Children (ALSPAC – also known as ‘Children of the 90s’) with those of the clients of the Avon and Wiltshire Mental Health Partnership NHS Trust (AWP). The patient records from AWP includes data about some of the original mothers as well as some of the study’s index children. There was insufficient linkage data to include the original fathers/partners in this exercise. There are a smaller number of families with AWP records on both mother and child. Software syntax was written using STATA (StataCorp LLC, version 17) to convert the original ‘comma separated values’ (.csv) files into a de-identified, but consistent, format for use in research. The cleaned AWP records provide a contemporaneous record of a subset of the ALSPAC cohort over a period of the ALSPAC study in an easily accessible format, in a data base for use in future research.

Bibliometric Analysis of Philosophical Dimensions in Sport Based on Scopus Data

This study aims to explore the literature on the dimensions of philosophy and sports based on scopus indexed articles. The research method used to conduct the analysis is bibliometric. Search and filtering strategy using PRISMA flow chart. The research review uses data in comma-separated values (CSV) format, which is then exported to Microsoft Excel and to visualize the search results of bibliometric analysis the tool used is VOSviewer. The conclusion of the study resulted in the development of publications with the domain of philosophy and sports dimensions experiencing fluctuating conditions, but in the last 10 years the number of publications has tended to be stable.

Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification.

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.

Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

Intelligent preliminary diagnosis system for diseases with similar clinical presentation

Accurately identifying diseases with similar symptoms, especially in resource-limited medical settings, is a key challenge to diagnostic accuracy. This paper presents a preliminary diagnostic system to address the challenge of diagnosing diseases with similar symptoms. The system has been implemented using the PyQt5 library and employs a unique symptom identification algorithm developed in Python. Furthermore, to carry out the diagnosis, it uses comma-separated values (CSV) and excel files as databases where the diseases and their respective symptoms are stored. The results show that the system has a precision of 95%, a sensitivity of 90%, and a specificity of 93% after evaluating 35 clinical cases covering seven diseases with similar initial symptoms, namely: dengue, zika, chikungunya, COVID-19, influenza, monkey-pox, and the common cold. Furthermore, the positive evaluation of the technical performance of the system by experts supports its practical feasibility and its potential as a valuable tool in medical practice. In conclusion, the system diagnoses diseases by analyzing the symptoms of the information file, highlighting its usefulness in improving the diagnostic accuracy of similar cases and optimizing medical care for the benefit of patients.

Hand Motion Analysis Using Accelerometer-Based Sensors and Sheep's Head Model for Basic Training in Functional Endoscopic Sinus Surgery.

Motion analysis, the study of movement patterns to evaluate performance, plays a crucial role in surgical training. It provides objective data that canbe usedto assess and improve trainee's precision, efficiency, and overall surgical technique. The primary aim of this study is to employ accelerometer-based sensors placed on the wrist to analyze hand motions during endoscopic sinus surgery training using the sheep's head. By capturing detailed movement data, the study seeks to quantify the motion characteristics that distinguish different levels of surgical expertise. This approach seeks to quantify motion characteristics indicative of surgical expertise and enhance the objectivity and effectiveness of surgical training feedback mechanisms. Twenty-four participantswere dividedinto three groups based on their experience with endoscopic endonasal surgery. Each participantwas tasked with performingspecified procedures on an individual sheep's head, concentrating on exploring both nasal passages. A single Bluetooth Accelerometer WitMotion sensorwas mountedon the dorsal surface of each hand.Thisfacilitates the evaluation of efficiency parameters such as time, path length, and acceleration during the training procedures. Accelerometer data were collected and imported in CSV format (comma-separated values) for each group of surgeons-senior, specialist, and resident-mean values and standard deviationswere computed. The Shapiro-Wilk Test assessed the normality of the distribution. The Kruskal-Wallis test was employed to compare procedural time, acceleration, and path length differences across the three surgeon experience levels. For the procedural time, statistical significance appears in all surgical steps (p<0.001), with thebiggestdifference in the septoplasty group in favor of the senior group. A clear difference can be observed between the resulting acceleration of the dominant hands (instrument hand) and the non-dominant hand (endoscopic hand) and between the study groups. The difference between groups reaches statistical significance with a p-value <0.001. A statistically significant difference canbe seenbetween the paths covered by each hand of every participant (p<0.001). Also, senior doctors covered significantly less movement with both hands than the specialists and the resident doctors (p<0.001). The data showa clearlearning curve from resident to senior,with residents taking more time and using more hand movementsto complete the same tasks. Specialists are in the intermediate phase, showing signs of honing their technique towards efficiency. This comprehensive data set can help tailor training programs to focus onbothefficiency (quicker procedures) and economy of motion (reduced path length and acceleration), especially in more complex procedures where the difference in performance ismore pronounced.

Upgrading the Performance Features of the CBSS Indigenous Weather Station

This work presents an upgrade on the legacy version of the Centre for Basic Space Science (CBSS) indigenous Weather Station. The station is a microcontroller-based system that employed the use of sensor modules to acquire atmospheric, environmental and soil parameters values for scientific purpose. These data variables, station’s coordinate and owner’s informations are logged into an on-board Random Access Memory (RAM) in a Comma-Separated Value (CSV) format. The information is transmittable via radio telemetry setup to a handheld user-interactive communication console sub-system. In the event of components’ failure, the system offers precise identification of these failed components for ease of troubleshooting and maintenance.

Automated detection and classification of polioviruses from nanopore sequencing reads using piranha.

Widespread surveillance, rapid detection, and appropriate intervention will be critical for successful eradication of poliovirus. Using deployable next-generation sequencing (NGS) approaches, such as Oxford Nanopore Technologies' MinION, the time from sample to result can be significantly reduced compared to cell culture and Sanger sequencing. We developed piranha (poliovirus investigation resource automating nanopore haplotype analysis), a 'sequencing reads-to-report' solution to aid routine poliovirus testing of both stool and environmental samples and alleviate the bioinformatic bottleneck that often exists for laboratories adopting novel NGS approaches. Piranha can be used for efficient intratypic differentiation of poliovirus serotypes, for classification of Sabin-like polioviruses, and for detection of wild-type and vaccine-derived polioviruses. It produces interactive, distributable reports, as well as summary comma-separated values files and consensus poliovirus FASTA sequences. Piranha optionally provides phylogenetic analysis, with the ability to incorporate a local database, processing from raw sequencing reads to an interactive, annotated phylogeny in a single step. The reports describe each nanopore sequencing run with interpretable plots, enabling researchers to easily detect the presence of poliovirus in samples and quickly disseminate their results. Poliovirus eradication efforts are hindered by the lack of real-time detection and reporting, and piranha can be used to complement direct detection sequencing approaches.

AgTC and AgETL: open-source tools to enhance data collection and management for plant science research.

Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through web-based tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences.

RADCURE: An open-source head and neck cancer CT dataset for clinical radiation therapy insights.

This manuscript presents RADCURE, one of the most extensive head and neck cancer (HNC) imaging datasets accessible to the public. Initially collected for clinical radiation therapy (RT) treatment planning, this dataset has been retrospectively reconstructed for use in imaging research. RADCURE encompasses data from 3346 patients, featuring computed tomography (CT) RT simulation images with corresponding target and organ-at-risk contours. These CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were manually generated and reviewed at weekly RT quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution's radiation treatment planning and oncology information systems using a custom-built data mining and processing system. Furthermore, images were linked to our clinical anthology of outcomes data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system (Tumor-Node-Metastasis Classification System of Malignant Tumors). The median patient age is 63, with the final dataset including 80% males. Half of the cohort is diagnosed with oropharyngeal cancer, while laryngeal, nasopharyngeal, and hypopharyngeal cancers account for 25%, 12%, and 5% of cases, respectively. The median duration of follow-up is five years, with 60% of the cohort surviving until the last follow-up point. The dataset provides images and contours in DICOM CT and RT-STRUCT formats, respectively. We have standardized the nomenclature for individual contours-such as the gross primary tumor, gross nodal volumes, and 19 organs-at-risk-to enhance the RT-STRUCT files' utility. Accompanying demographic, clinical, and treatment data are supplied in a comma-separated values (CSV) file format. This comprehensive dataset is publicly accessible via The Cancer Imaging Archive. RADCURE's amalgamation of imaging, clinical, demographic, and treatment data renders it an invaluable resource for a broad spectrum of radiomics image analysis research endeavors. Researchers can utilize this dataset to advance routine clinical procedures using machine learning or artificial intelligence, to identify new non-invasive biomarkers, or to forge prognostic models.