Creation Of Data Research Articles

Molecular quantum chemical data sets and databases for machine learning potentials

Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials.
In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis.
Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

Raudatul Athfal (RA) Job Satisfaction in Colomadu Karanganyar District

This study aims to analyze the influence of Job Characteristics on Job Satisfaction, Burden on Job Satisfaction, Environment on Job Satisfaction, Compensation on Job Satisfaction of Teacher Raudatul Athfal (RA) in Colomadu District, Karanganyar. The research site is in each Raudatul Athfal (RA) location in Colomadu Karanganyar District. The research was conducted for 6 (six) months with a span of time between January to June 2024. Starting from the initial step is observation, sample determination, to the creation of data results from the research conducted, as well as determining the results and concluding the results of the research conducted. This study uses a quantitative method with the distribution of questionnaires using a Likert scale with multiple linear regression analysis with the help of SPSS 24 software. This study was conducted on 66 samples who were RA teachers in Colomadu Karanganyar District. The results showed that the characteristics of work and environment had a positive and significant effect on job satisfaction, while burden and compensation did not have a positive and significant effect on job satisfaction. Keywords: Job satisfaction, Characteristics, Burden, Environment, Compensation

Data Creation Practices of Archaeologists in the Field

Data Creation Practices of Archaeologists in the Field

Multilinguality and LLOD: A survey across linguistic description levels

Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

Exploring the Curation Practices of 3D Data Creators

ABSTRACT3D data creation methods and virtual reality (VR)‐based visualization techniques are increasingly common in interdisciplinary research and pedagogy. 3D data represent a new challenge to existing digital curation frameworks for academic libraries and other support institutions. This poster describes the research methodology, background, and research plan for an in‐progress, 3‐year project funded in part by the Institute of Museum and Library Services (IMLS grant LG‐254830‐OLS‐23). This project is exploring 3D data creation and curation practices of researchers across a range of 3D creation modalities and disciplines.

Performance Evaluation of an Object Detection Model Using Drone Imagery in Urban Areas for Semi-Automatic Artificial Intelligence Dataset Construction.

Modern image processing technologies, such as deep learning techniques, are increasingly used to detect changes in various image media (e.g., CCTV and satellite) and understand their social and scientific significance. Drone-based traffic monitoring involves the detection and classification of moving objects within a city using deep learning-based models, which requires extensive training data. Therefore, the creation of training data consumes a significant portion of the resources required to develop these models, which is a major obstacle in artificial intelligence (AI)-based urban environment management. In this study, a performance evaluation method for semi-moving object detection is proposed using an existing AI-based object detection model, which is used to construct AI training datasets. The tasks to refine the results of AI-model-based object detection are analyzed, and an efficient evaluation method is proposed for the semi-automatic construction of AI training data. Different FBeta scores are tested as metrics for performance evaluation, and it is found that the F2 score could improve the completeness of the dataset with 26.5% less effort compared to the F0.5 score and 7.1% less effort compared to the F1 score. Resource requirements for future AI model development can be reduced, enabling the efficient creation of AI training data.

Kepentingan Bioinformatik Dalam Analisa Genomik Ke Arah Perubatan Jitu Untuk Covid-19

Genomic medicine is the use of an individual's genomic information to benefit the decision making in clinical treatment procedures such as disease diagnosis, prognosis, and therapy. As a result, genomic medicine has implications in cancer, pharmacology, rare and unknown diseases, and infectious diseases. With the advent of high - resolution and rapid genetic testing, thousands of genes have been able to be identified in complex biological samples. Bioinformatics is a crucial step in dealing with big data that may represent a large number of genes or genetic information that are significant in medical and health sciences research. Consequently, bioinformatics has become an essential component of clinical laboratories for the creation, processing, preservation, and interpretation of data from molecular genetic testing. This article provides an overview of the bioinformatics ideas and aspects used in genomic testing. Furthermore, we explore the application of bioinformatics in cancer research, as well as the implications of bioinformatics for the COVID-19 pandemic. Integrating multidisciplinary knowledge is important for overcoming the challenges of acquiring bioinformatics data suited for genomic medicine.

Daily life in the Open Biologist's second job, as a Data Curator.

Data reusability is the driving force of the research data life cycle. However, implementing strategies to generate reusable data from the data creation to the sharing stages is still a significant challenge. Even when datasets supporting a study are publicly shared, the outputs are often incomplete and/or not reusable. The FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to promote data reusability in research, but the practical implementation of FAIR principles in research groups is still falling behind. In biology, the lack of standard practices for a large diversity of data types, data storage and preservation issues, and the lack of familiarity among researchers are some of the main impeding factors to achieve FAIR data. Past literature describes biological curation from the perspective of data resources that aggregate data, often from publications. Our team works alongside data-generating, experimental researchers so our perspective aligns with publication authors rather than aggregators. We detail the processes for organizing datasets for publication, showcasing practical examples from data curation to data sharing. We also recommend strategies, tools and web resources to maximize data reusability, while maintaining research productivity. We propose a simple approach to address research data management challenges for experimentalists, designed to promote FAIR data sharing. This strategy not only simplifies data management, but also enhances data visibility, recognition and impact, ultimately benefiting the entire scientific community.

A novel probabilistic load shifting approach for demand side management of residential users

Demand side management is a beneficial set of techniques which leads improving consumption profile of residential users. Practice of dynamic pricing and appropriate shifting of electrical appliances at home are important tasks at that point. In this study, a novel probabilistic load shifting strategy is proposed and its beneficial effects on economic and technical parameters are demonstrated. For that purpose, a survey on energy consumption is applied for residential users and its results are used for creating a probabilistic model. 300 virtual residents are created in this way and a probabilistic simulation technique is developed. Next, the developed simulation technique is applied for creation of the monthly consumption data. Finally, it is demonstrated that electricity bills are decreased significantly when the proposed approach is applied. Besides that, peak-to-average ratio which is calculated by considering all residential users is also decreased as a result. In conclusion, when the results are compared with the ones of conventional constant tariff, time-of-use tariff with three time zones and a deterministic load shifting strategy, it is observed that the best results of economic and technical parameters in question are achieved by using the proposed probabilistic load shifting strategy together with dynamic pricing mechanism involved.

A quantitative study of vocabulary categorization and reuse in the LIS field linked data

PurposeExploring diverse knowledge organization systems and metadata schemes in linked data, aiming to promote vocabulary usability and high-quality linked data creation within the LIS field.Design/methodology/approachWe used content analysis to select 77 articles from 13 library and information science journals around our research theme. We identified four dimensions: vocabularies participation, reuse, functions, and naming variations in linked data.FindingsThe vocabulary comprises seven main categories and their corresponding 126 vocabularies, which participate in linked data in single, two, and multiple dimensions. These vocabularies are used in the eight LIS subfields. Reusing vocabularies has become integral to linked data publishing, with six categories and their corresponding 66 vocabularies being reused. Ontologies are the most engaged and widely reused category of vocabulary in linked data practice. The mutual support among the three major categories and seven subfunctions of vocabulary promotes the sustainable development of linked data. Under a combination of factors, the phenomenon of terminology name changes and cross-usage between “vocabulary” and “ontology.”Research limitations/implicationsThis study has limitations. Although 77 articles on the topic of vocabularies applied in linked data were analyzed and presented with quantitative statistics and visualizations, the exploration of the topic tends to be a practical activity, with limited presence in scholarly articles. Moreover, this study’s analysis of the practical applications of linked data is relatively limited, and the sample literature focused on articles published in English, which may have affected the diversity and inclusiveness of the research sample.Practical implicationsPractically, this study does not confine the application of content analysis solely to the traditional exploration of knowledge organization topics, development trends, or course content. Instead, it integrates the dual perspectives of linked data and vocabularies, employing content analysis to analyze and objectively reveal the application issues of vocabularies in linked data. The conclusions can provide specific guidelines for future applications of vocabularies in the LIS subfields and contribute to promoting interoperability of vocabularies.Social implicationsThis research explores the relationship between linked data and vocabularies, highlighting the diverse manifestations and challenges of vocabularies in linked data. It provides theoretical references for the construction and further development of vocabularies considering technologies such as linked data, drawing attention to the potential and existing issues associated with linked open data vocabularies.Originality/valueThis study extends the application of content analysis to exploring vocabularies, especially Knowledge Organization Systems and metadata schemes in the LIS field linked data, highlighting the mutually beneficial interactions between linked data and vocabularies. It provides guidance for future vocabularies applications in the LIS field and offers insights into vocabularies construction and the healthy development of linked data ecosystems in the era of information technology.

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method's detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

‘I like to dance with the flowers!’: Exploring the possibilities for biodiverse futures in an urban forest school

AbstractThis article explores the ways in which ‘forest school’, an educational approach where children engage in creative and play based activities in a ‘natural’ environment, can contribute towards Sustainable Development Goal 15 (SDG 15) by promoting sustainable use of terrestrial ecosystems and by helping address biodiversity loss. Drawing on data from an inquiry that explored childhoodnature play in an inner‐city forest school in London, England, this article discusses the ways in which nature relations emerged over weekly visits to an urban park over the course of one school year. We, the adult and young child researchers and more‐than‐human nature, enacted ‘shared play’ as part of a post‐qualitative approach that involved playing together as a form of data creation. Co‐created ‘play tales’ from the inquiry foreground encounters between children, grass, flowers, soil, trees, fruits and insects and illuminate the ways in which nature relations ignited small, yet powerful, acts to protect and restore the flora and fauna in the urban park. This article asserts that post‐anthropocentric, postdevelopmental educational approaches create opportunities for multi‐species relations to slowly emerge and flourish. These stories illuminate the ways these ethico‐political relations and onto‐epistemological transformations can prevent the continuation of terrestrial ecosystem degradation and contribute towards biodiverse futures in urban areas.

Proposal of a Knowledge Management Model for Complex Systems: Case of the Supervision and Control Subsystem of the Colombian Health System.

Considering regulatory, supervision, and control health policy, an innovative knowledge management model is proposed for the Colombian health system, which is recognized as a complex system. A model is constructed through a comparative analysis of various theoretical and conceptual frameworks, and an original methodology is proposed based on an analysis of the macroprocesses of the Supervision and Control System (SSC) of the Colombian General Social Security System in Health (SGSSS). After formulating hypotheses and conceptual references, information errors are determined within the different macroprocesses of the SGSSS, including those of governance and the SSC. The risks of generating duplicate, wrong, hidden, or non-existent information arise when the associated regulations need more specificity to be applied in all cases, thus leading to the risk of different interpretations by some actors. In this way, it is possible to hinder the generation of unified information, as there is no clarity as to who is responsible for the generation or creation of certain data. The proposed model is characterized by its flexibility and adaptability, integrating several processes that can be executed simultaneously or cyclically (depending on the system's needs) and allowing for the generation and feedback of knowledge at different stages, with some processes simultaneously executed to complement each other.

Integration of Mapbox and Google Maps to Support Additional Features in Geographic Information Systems

An integrated geographic information system is a geographic information system that aims to facilitate the creation and integration of mapping data so that researchers from various backgrounds can present their research results in the form of a map and can be combined on a larger scale. In making an integrated geographic information system, the use of the maps box was chosen because the maps box is a tool for making mapping that is free of charge both in making mappings and displaying mapping results so; it can save costs because the number of accessors and mappings made will incur substantial costs if using a paid alternative. However, the Maps box has many shortcomings in terms of features, so we researched to integrate third-party applications to fulfill the features that need to be added in the maps box. To overcome the shortcomings of the map box, the researcher integrates the route feature of Google Maps, integrates the location details feature on Google Maps, and integrates Google Maps coordinate data via the Google Maps link, which can be stored in the system so that these features can be used in an integrated geographic information system. Integrating third-party applications can create an integrated geographic information system using a map box but can also have more features.

Mechanochemical Synthesis of Sodium-Ion Conducting Chloride NaTaCl6

All-solid-state sodium batteries have been attracting attentions owing to their high energy density and safety. Solid electrolytes play a significant role in the electrochemical performances of all-solid-state batteries. Chloride-based solid electrolytes are particularly important for achieving high energy densities because of their high oxidation stability, ionic conductivity, and formability. Fewer sodium-ion conducting chlorides have been developed compared to lithium-ion conducting materials. It is necessary to discover the chlorides as the end-member of sodium-ion conductors. In this study, we report the development of a new NaTaCl6 chloride as an end-member sodium-ion conductor with high ionic conductivity. NaTaCl6 sample was mechanically prepared using a planetary ball mill apparatus. The X-ray diffraction patterns of the obtained sample displayed a broad pattern similar to the reported NaTaCl6 (ICSD#36519), suggesting that the sample contains both amorphous and crystalline phases. The reference intensity ratio method was used to investigate the weight ratios of the amorphous phases in the sample. The ratio was approximately 45 %. Transmission electron microscopy observations suggested that the sample consisted of the amorphous matrix and crystalline with the crystallite size of approximately 20 nm. The electrical conductivities of the pelletized samples were measured using two-terminal AC electrochemical impedance spectroscopy. The sample pellets were prepared by uniaxial pressing at 360 MPa without heat treatment. The pellet sample had the relative density of 85 % (powder density: 3.32 g cm–3, pellet density: 2.83 g cm–3). The sample shows σ 25ºC of 6.2×10–5 S cm–1, and the activation energy of 0.37 eV. To determine the NaTaCl6 onset oxidation voltage (OOV), linear sweep voltammetry measurement was carried out. The OOV of the sample at 60 ºC was 3.8 V vs. Na15Sn4. To demonstrate the high oxidation stability of NaTaCl6 in all-solid-state sodium batteries, it was used in a positive electrode by mixing Na3V2(PO4)3 as the active material and the buffer layer. The cell exhibited an initial reversible capacity of 96 mAh g–1 and stable cycling after 40 cycles with the capacity retention of 99.5 %. A 3.3 V all-solid-state sodium battery without oxide solid electrolytes was successfully operated. In this study, the NaTaCl6 electrolyte was developed with a high ionic conductivity and oxidation stability.Acknowledgement: This work was partly supported by JSPS KAKENHI (Grant Numbers: JP21H04701 and JP19H05816) and MEXT Program: Data Creation and Utilization-Type Material Research and Development Project Grant Number JPMXP1122712807.

Tiny Object Tracking: A Large-Scale Dataset and a Baseline.

Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in many vision tasks, such as object detection and segmentation. To promote the research and development of tiny object tracking, we create a large-scale video dataset, which contains 434 sequences with a total of more than 217K frames. Each frame is carefully annotated with a high-quality bounding box. In data creation, we take 12 challenge attributes into account to cover a broad range of viewpoints and scene complexities, and annotate these attributes for facilitating the attribute-based performance analysis. To provide a strong baseline in tiny object tracking, we propose a novel multilevel knowledge distillation network (MKDNet), which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination, and localization abilities in tracking tiny objects. Extensive experiments are performed on the proposed dataset, and the results prove the superiority and effectiveness of MKDNet compared with state-of-the-art methods. The dataset, the algorithm code, and the evaluation code are available at https://github.com/mmic-lcl/Datasets-and-benchmark-code.

Webbing 3D map from UAV images in Da Lat City, Lam Dong Province

Abstract Worldwide, big cities are accelerating to fulfill the establishment of Spatial Data Infrastructure (SDI), which is a key task towards smart city. The mainstay of SDI is to create an important foundation of data capacity to provide necessary and essential information related to the requirements of urban and smart city management. With the advantages of high accuracy, low cost, and flexibility in 3D data creation, UAV (Unmanned Aerial Vehicle) images have been widely applied for the purpose of digital transformation purposes. This article aims to firstly utilize the new opportunities of using UAVs in 3D mapping and sharing 3D map data through the WebGIS platform. Secondly, processing techniques will be described after collecting the UAV images to create orthomosaics, point clouds, and Digital Surface Model (DSM). A classification of ground points was conducted to create a Digital Terrain Model (DTM) in combining with DSM to form a calibrated surface numerical model (Canopy Height Model, CHM). The images map was then digitalized coupled with the object height extracted from the CHM model and assigning attributable data to each object. The results suggested that ArcGIS Online allows users to share information widely, targeting multiple users, and to reduce the cost of GIS software investment in building thematic classes for urban infrastructure management.

Beyond Data Collection: Examining Artificial Intelligence Data Creation in Organizations

Beyond Data Collection: Examining Artificial Intelligence Data Creation in Organizations

Enhancing equity of the post-disaster recovery governance through community data generation

Climate adaptation and disaster risk reduction policy frameworks emphasize the importance of engaging vulnerable communities in data creation and utilization to enhance resilience. Yet, empirical evidence on how capacity strengthening and community data generation can improve post-disaster recovery processes remains limited. To address this gap, this article investigates the post-disaster recovery process in the El Pacífico self-built neighborhood, located at the hillslopes of the rural-urban border of Medellín, Colombia. Utilizing participatory action research and documentation analysis, we examined documents related to the response and recovery committee (called “Mesa de Atención y Recuperación”) established following a severe flooding event. Our analysis reveals a lack of clearly defined procedures for engaging affected communities in the post-disaster efforts. However, we provide evidence that initiatives promoting equitable governance through capacity strengthening and community data generation, supported by our project, have effectively empowered community members, amplifying their voices in the negotiation with municipal authorities. These findings offer valuable insights that can inform more equitable future policies and practices in disaster risk reduction and climate adaptation with increased recognition and representation of marginalized urban residents.

ANALISIS POLA PERUBAHAN LAHAN SAWAH MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS (STUDI KASUS: KELURAHAN MAKROMAN, SAMARINDA)

Changes in rice fields are an important phenomenon that affects the sustainability of agriculture and the environment. This study aims to analyze the pattern of changes in rice fields in Makroman Village, using a Geographic Information System (GIS) approach. The method used is spatial analysis which involves such as spatial data creation, spatial data conversion, spatial data clustering, and mathematical calculation of spatial data. The spatial data used in this study include Ikonos Satellite Images of Makroman Village in 2011 and UAV Aerial Photos of Makroman Village in 2019. The results showed that the area of rice fields in Makroman Village increased from 96.196 ha to 129.343 ha in 2011-2019. Spatially, the increase occurred due to the presence of new rice fields and existing rice fields that did not decrease in number. Shrubs become land uses that have the potential to be converted in rice field extensification activities. GIS application is a method that can be used to answer the problems in this study to the level of detail, but some things that need to be considered in the application of this method are the quality of spatial data, especially remote sensing data used.