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The article explores the integration of fundamental database concepts into a Science, Technology, Engineering, and Mathematics (STEM) Success course, emphasizing hands-on projects and ethical considerations. Topics discussed include the pedagogical approach of using database design and Structured Query Language (SQL) queries, the importance of data ethics, and the practical application of relational databases in scientific data management.

Pneumonia in Peru is a very serious problem. Its impact in recent years has been aggravated due to the Covid-19 pandemic, generating an increase in infections and deaths without distinguishing the age range, which placed this country on the mortality list due to the pandemic. That is why this research seeks the causes of this problem and evaluates what patterns were detected between the years 2019-2022 in patients with pneumonia in Peru from data set from the Comprehensive Health Insurance (SIS). The data presented values related to age, gender, medication and other significant values to understand the disease. The results of the research were achieved by using the PCA technique where the dimensionality of the data was reduced from 28 to 4 main features (Patient's year of health care, Age, BMI, Department). Finally, with this processed data set, the K-Means algorithm was used, where it was determined that patients in the 60 to 85 years range are the most affected by J189 pneumonia. In addition, an environmental pattern was found in J189 pneumonia. J128, resulting in a focus on patients on the Peruvian coast in places like Lima or La Libertad. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Faced with the growing importance of data security, this research aims to examine the Guinean legal and regulatory framework relating to the protection of scientific data. Through a systematic analysis of legal and regulatory provisions, enriched by interviews with key players in the academic and scientific community, the study evaluates the coherence and efficiency of legislation in Guinea with regard to international standards. Preliminary findings reveal the existence of notable progress but also significant deficits, particularly with regard to alignment with international data protection standards. On this basis, recommendations are put forward with the aim of improving the current legal and regulatory structure for optimal safeguarding of scientific data in the Republic of Guinea. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics. Design/methodology/approach: The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper. Findings: The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality. Originality/value: Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Academic Libraries is the property of Journal of Academic Libraries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although academic journals are often considered to be reliable sources of evidence for informing practice, the extent to which funders turn to them to shape their decisions is unclear. We carried out a survey to examine the types of evidence and knowledge that food systems funders use--and need--to make informed decisions, and to understand how research, particularly from journal publications, is or is not informing food systems investments. The majority of the 19 respondents worked for private foundations or community-based nonprofits that offer grants, loans, or other program-related investments to U.S.-based initiatives focused on local food, sustainable agriculture, and food access. Many respondents indicated that they draw primarily on local stakeholders and grantees to make funding decisions and do not prioritize scientific knowledge or externally derived evidence. For most, peer-reviewed academic journals, including the Journal of Agriculture, Food Systems, and Community Development (JAFSCD), are one of the last places they currently turn to for information that could shape their funding priorities. In other sections of the survey, however, respondents indicated that they value utilizing a blend of scientific research and local knowledge. Only four respondents require grantees or borrowers to carry out formal evaluations, and over a third of respondents are not satisfied with how their organization uses data and evidence to make decisions. They suggested a variety of ways that research could be made more accessible and relevant to them as funders and investors in the food systems arena. We suggest various ways that more comprehensive research could be conducted to study what drives the decision-making processes of the diverse U.S. food systems funding community. [ABSTRACT FROM AUTHOR]

Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely on trial and error to assess lossy compression performance. As a strong data-driven effort toward quantifying lossy compressibility of scientific datasets, we provide a statistical framework to predict compression ratios of lossy compressors. Our method is a two-step framework where (i) compressor-agnostic predictors are computed and (ii) statistical prediction models relying on these predictors are trained on observed compression ratios. Proposed predictors exploit spatial correlations and notions of entropy and lossyness via the quantized entropy. We study 8+ compressors on 6 scientific datasets and achieve a median percentage prediction error less than 12%, which is substantially smaller than that of other methods while achieving at least a 8.8× speedup for searching for a specific compression ratio and 7.8× speedup for determining the best compressor out of a collection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Australia's distinctive colonial administrative history has resulted in the generation and capture of large quantities of personal data about Indigenous Peoples in Australia, which is currently controlled and processed by government agencies and departments without coherent regulation. From an Indigenous standpoint, these data constitute stranded assets. Established legal frameworks for pursuing recovery of other classes of asset alienated by governments from Indigenous Peoples in Australia, including land, natural resources, and unpaid wages, have not yet been extended to the recovery of Indigenous data assets. This legacy scenario has created a disproportionate administrative burden for Indigenous organisations by sustaining their dependency on government for necessary data, while simultaneously suppressing the value of their own contemporary community-owned data assets. In this article, we outline leading international legal, economic, and scientific frameworks by which an equitable arrangement for the governance of Indigenous data might be restored to Indigenous Peoples in Australia. [ABSTRACT FROM AUTHOR]

Tomato, one of the vegetables popular among Chinese residents, is widely grown throughout the country. In recent years, the price of tomato has shown the characteristics of large fluctuation range and instability. Hebei is the main production area of tomato, while Beijing and Tianjin are important sales areas of tomato. Taking the Beijing-Tianjin-Hebei region as a whole, the historical price data of tomato within the region are analyzed to explore the fluctuations of tomato market price. It is of great significance to perfect circulation pattern of regional agricultural products, balance tomato production structure and stabilize tomato market price. This data set fully combines the vegetable price data provided by the National Agricultural Product Business Information Public Service Platform, the National Wholesale Market Price Information System and other platforms, and the field survey data of the fund project team. Through comparison and integration with the field survey data, the authenticity and consistency of the basic data set are ensured. Through processing the daily average price information of tomato in 29 wholesale markets of agricultural products in the Beijing-Tianjin-Hebei region from 2012 to 2019, the text data and image data reflecting the price information of tomato wholesale markets in the Beijing-Tianjin-Hebei region from 2012 to 2019 were obtained, including weekly and monthly average price of tomato market, a total of 1536 articles; There are 78 historical fluctuation charts of weekly average price and monthly average price, and line charts of month-on-month and year-on-year price change rates. The obtained results have been tested by manual quality, and the data processing has been completed in accordance with standardized steps. The obtained data sets can provide basic data support for the research and monitoring and early warning of tomato market price fluctuations in the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

Agricultural pests and diseases pose a serious threat to crop yield and quality, making accurate and efficient detection and identification of pests and diseases crucial in agricultural production. In this paper, we propose a comprehensive agricultural pests and diseases dataset, which includes agricultural pest detection dataset, agricultural disease detection dataset, agricultural disease classification dataset, and rice phenotype segmentation dataset. By collecting and curating data from public sources and academic papers, we ensured the diversity and representativeness of the dataset. Rigorous quality control and validation measures were implemented during the data filtering, cleaning, and annotation processes to ensure the accuracy and reliability of the dataset. This dataset can be used for agricultural pest and disease recognition, as well as rice phenotype identification and other agricultural visual tasks. It provides valuable resources for agricultural pest and disease research and contributes to the sustainable development of agricultural production. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ibero-Americana de Ciência da Informação is the property of Revista Ibero-Americana de Ciencia da Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Brazilian Journal of Information Science is the property of Brazilian Journal of Information Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação is the property of Universidade Estadual de Campinas - Portal de Periodicos Eletronicos Cientificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: The ability to search over large scientific datasets has become crucial to next‐generation scientific discoveries as data generated from scientific facilities grow dramatically. In previous work, we developed and deployed ScienceSearch, a search infrastructure for scientific data which uses machine learning to automate metadata creation. Our current deployment is deployed atop a container based platform at a HPC center. In this article, we present an evaluation and discuss our experiences with the ScienceSearch infrastructure. Specifically, we present a performance evaluation of ScienceSearch's infrastructure focusing on scalability trends. The obtained results show that ScienceSearch is able to serve up to 130 queries/min with latency under 3 s. We discuss our infrastructure setup and evaluation results to provide our experiences and a perspective on opportunities and challenges of our search infrastructure. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] This paper aims to study the structure-utility relationship of descriptive information of scientific data to provide a new perspective for the theoretical study of scientific data description and a reference for the best description of agricultural scientific data in the digital environment. [Method/Process] Based on information processing theory, the lens model, the probabilistic mental model theory and the adaptive decision-making behavior framework, the relationship model between descriptive information structure and informing utility was constructed. A situational experiment was designed according to the model. In this study, 47 postgraduates from 14 institutes were invited for quasi-experimental observation by using qualitative and quantitative methods such as eye-tracking, semi-structured interview and questionnaire. First, this study used a semi-structured interview to obtain a user's cognitive interpretation of fixation points and collected the descriptive items of agricultural scientific data and their use frequency by encoding the interview text. Second, this study combined descriptive item usage path coding and user judgment confidence to obtain the combination of descriptive items with high utility. Finally, the study used multiple regression analysis to identify the descriptive items with high utility and their predictive ability, and analyzed the impact of data literacy and data utilization type on the utility of descriptive items. [Results/Conclusions] The study identified 42 descriptive items of 11 categories of agricultural scientific data and their usage characteristics. Among them, the top 5 frequently used descriptive items were subject, data, overall description, source and data production information, which played an important role in user relevance judgment. Then this study identified the combination of descriptive items with high utility and found that users' use patterns of descriptive items were diverse. Compared with making a judgment with "relevant" result, users often needed less information to achieve a high level of confidence when making an "irrelevant" judgment. This study also found that the descriptive items with high utility include source, data, use and evaluation, and data production information. It is determined that user data literacy and data utilization purpose were the influencing factors of descriptive information utility, and the effects of the two factors were preliminarily analyzed. Based on this research, the paper put forward some suggestions for improving agricultural scientific data metadata and scientific data sharing. In the future, this study will be repeated in groups with different academic backgrounds and data literacy levels, so as to enhance the generalization ability of research conclusions and construct a more effective structure of scientific data descriptive information. [ABSTRACT FROM AUTHOR]

A lot of scientific work is dedicated to the analysis of data. Most of the analyzed data, like data from space missions, are structured. The choice of data format can affect various characteristics - read/write speed of standard files, read/write speed of small files and read/write speed of compressed data formats. In this paper, we analyze binary data formats, proposed types of the tests and testing methods, and compare their performance with human-readable text format. We also discuss compressed and uncompressed modes available for data formats like HDF5 and netCDF. When disregarding precision, the best data format from the size perspective is lossy HDF5 without compression. Losless HDF5 without compression show the best speed performance. Lossy HDF5 without compression is the best balance between size reduction and speed. However, for specific criteria and types of files, there might be better candidates as detailed in the conclusion. [ABSTRACT FROM AUTHOR]

This paper presents the results of an analysis of trends in the development of the information ecosystem of open science based on the study of the practices of the main actors (research and educational organizations, publishers, sponsors of scientific research, and libraries), the dynamics of the number and prevalence of open access publications by fields of knowledge, scientific institutions and countries. Areas of development of the open information space of Russian science are identified. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale, but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which treats the multilevel decomposition as a preconditioner and terminates the decomposition process at an appropriate level. 3) We leverage a set of algorithmic optimization strategies to significantly improve the performance of multilevel decomposition/recomposition. 4) We evaluate our proposed method using four real-world scientific datasets and compare with several state-of-the-art lossy compressors. Experiments demonstrate that our optimizations improve the decomposition/recomposition performance of the existing multilevel method by up to $70 \times$ 70 × , and the proposed compression method can improve compression ratio by up to $2 \times$ 2 × compared with other state-of-the-art error-bounded lossy compressors under the same level of data distortion. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data. Design/methodology/approach: First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers' experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward. Findings: The scientific data manufacturing process and data stakeholders' responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data. Research limitations/implications: As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders. Practical implications: The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues. Originality/value: This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields. [ABSTRACT FROM AUTHOR]

Copyright of Em Questão is the property of Em Questao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scientific exploration and discovery heavily rely on increasing datasets and strong supercomputing power. Surging data pose massive data management challenges in existing data query frameworks. Although many data management techniques have been developed to quickly locate the selected data records, the time and space required to build and store these indexes are often too expensive. To deal with the problem of data location in a parallel file system managing large-scale scientific data, we propose an improved high-performance query data framework called "Bi-cluster+." In the aspect of index generation, a hierarchical index data structure is designed, which effectively balances index granularity and index construction overhead. According to the characteristics of the index offset, the write load balancing strategy is designed. The hierarchical index is written independently and in parallel. The in situ index generation is optimized by resource constraint analysis. In the aspect of data retrieval, optimization techniques are proposed to improve the query performance. Such as the strategy of the logical data block merging and reading. With the experiments by using multiple scientific datasets on a supercomputer, our optimizations improve data query performance by up to a factor of 1.9 compared with the original Bi-cluster implementation. The scalability of Bi-cluster+ can keep a good performance by evaluating on 17496 cores. [ABSTRACT FROM AUTHOR]

Copyright of Em Questão is the property of Em Questao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data infrastructure systems such as the National Aeronautics and Space Administration (NASA) Planetary Data System (PDS), European Space Agency (ESA) Planetary Data Archive (PSA)and Japan Aerospace Exploration Agency (JAXA) Data Archive and Transmission System (DARTS) archive large amounts of scientific data obtained through dozens of planetary exploration missions and have made great contributions to studies of lunar and planetary science. Since China started lunar exploration activities in 2007, the Ground Research and Application System (GRAS), one of the five systems developed as part of China's Lunar Exploration Program (CLEP) and the Planetary Exploration of China (PEC), has gradually established China's Lunar and Planetary Data System (CLPDS), which involves the archiving, management and long-term preservation of scientific data from China's lunar and planetary missions; additionally, data are released according to the policies established by the China National Space Administration (CNSA). The scientific data archived by the CLPDS are among the most important achievements of the CLEP and PEC and provide a resource for the international planetary science community. The system plays a key and important role in helping scientists obtain fundamental and original research results, advancing studies of lunar and planetary science in China, and improving China's international influence in the field of lunar and planetary exploration. This paper, starting from CLEP and PEC mission planning, explains the sources, classification, format and content of the lunar and Mars exploration data archived in the CLPDS. Additionally, the system framework and core functions of the system, such as data archiving, management and release, are described. The system can be used by the international planetary science community to comprehensively understand the data obtained in the CLEP and PEC, help scientists easily access and better use the available data resources, and contribute to fundamental studies of international lunar and planetary science. Moreover, since China has not yet systematically introduced the CLPDS, through this article, international data organizations could learn about this advanced system. Therefore, opportunities for international data cooperation can be created, and the data service capability of the CLPDS can be improved, thus promoting global data sharing and application for all humankind. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bulletin of the Chinese Academy of Sciences / Chung-kuo ko Hsueh Yuan Yuan Kan is the property of Institutes of Science & Development, Chinese Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scientific literature data is now more available than ever before. Specifically, this paper focuses on measures of centrality and dispersion of keywords and author network analysis of research papers published in the 2012–2016 period, in five top journals (in the first and second quartiles of SCImago Journal Rank, in 2016, in the field of Tourism, Leisure and Hospitality Management) within the scope of sport. Based on Social Network Theory and using content analysis for data mining purposes, this paper aims to identify trends in sports literature and authors behaviors during the analyzed period. Also, using ANOVA links between author network characteristics and ranking information are analyzed, such as: self-citations, total citations, and percentage of international collaboration. In the period studied, some topics evolution and social activities patterns are discovered. The most predominant topics in sport literature for analyzed years were sport policy, social capital, and sport participation while predominant topics in other disciplines were not found. Furthermore, according to social patterns, results suggest the predominance of two-author teams and that group of authors tend to vary from one research to another. Furthermore, there are links between author network characteristics and citations received, particularly, between Average Degree, Weighted Average Degree, and Graph Density and total citations and, also, between Graph Density and the percentage of international collaboration. [ABSTRACT FROM AUTHOR]

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive—due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps. [ABSTRACT FROM AUTHOR]

The results of an analysis of trends in the development of the information ecosystem of open science based on the study of the global document flow, open access resources, and scientific data repositories, as well as initiatives in the field of open science, including during the COVID-19 pandemic, are presented. The directions of activity of libraries in these conditions are determined. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Libraries is the property of Journal of Academic Libraries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Anomaly detection tries to find out the data that disobeys the rule of majority data or expected patterns. The traditional hierarchical clustering algorithms have been adopted to detect anomaly, but have the disadvantages of low effectiveness and unstability. So we propose an improved agglomerative hierarchical clustering method for anomaly detection. It dynamically adjusts the optimum clustering number according to the self‐defined criterion to save the trouble of manually picking clustering number, and determines the optimum clustering distance mode according to cophenetic correlation coefficient to reduce the procedure of manually testing the suitable distance mode in each iteration. The performances of proposed method are verified on tensile test, HTRU2 and credit card dataset. Compared with the traditional methods, our method possesses the most comprehensive performance (the highest F‐measure with less iterations), which shows effectiveness of anomaly detection. And compared with the traditional methods (single, complete, average, and centroid mode), our method achieves the best performance on tensile test and HTRU2 dataset, showing stronger generalization. Compared with other methods (Decision + Gradient Boosted Tree, Decision Trees + Decision Stump, etc) on credit card dataset, our method obtains similar accuracy, and ranks in the top level in the aspect of sensitivity. [ABSTRACT FROM AUTHOR]

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In cloud computing, there is a trade-off between SLAV (Service Level Agreement Violation) and system operating cost. Violation rates can be decreased when using more hosts, which increases system operating costs. Therefore, to manage the resources of those hosts efficiently, finding an optimal balancing point between SLAV and system operating cost is critical. In addition, a cost-effective load balancing approach based on the proper migration of VMs (Virtual Machines) in the hosts is needed. For this purpose, some indicators are also necessary to identify the abnormal hosts that violate SLA. One of the primary indicators, CPU usage, is closely related to energy consumption and can be used to reduce SLAV and energy consumption effectively. Our approach focuses on the special environment such as the cloud environment for the scientific data. Here, most of the jobs are data-intensive and a large amount of disk operations is required. Owing to disk operations are likely to affect the performance degradation of the host, disk bandwidth usage as well as CPU usage should be also considered. In this study, we propose the Min-Max Exclusive VM Placement (MMEVMP) strategy to minimize both SLAV and energy consumption. The current working system called KIAF (KISTI Analysis Facility), the CERN ALICE experimental cloud environment for scientific data analysis, is used to analyze the characteristics of data-intensive jobs within it. In this experiment, a lightweight version of the CloudSim simulator was developed and the results were compared to the other methods of different policies. Our evaluation showed that our proposed strategy can reduce SLA violation reasonably as well as the system operating cost-effectively. [ABSTRACT FROM AUTHOR]

Scientific literature data is now more available than ever before. Specifically, this paper focuses on measures of centrality and dispersion of keywords and author network analysis of research papers published in the 2012–2016 period, in five top journals (in the first and second quartiles of SCImago Journal Rank, in 2016, in the field of Tourism, Leisure and Hospitality Management) within the scope of sport. Based on Social Network Theory and using content analysis for data mining purposes, this paper aims to identify trends in sports literature and authors behaviors during the analyzed period. Also, using ANOVA links between author network characteristics and ranking information are analyzed, such as: self-citations, total citations, and percentage of international collaboration. In the period studied, some topics evolution and social activities patterns are discovered. The most predominant topics in sport literature for analyzed years were sport policy, social capital, and sport participation while predominant topics in other disciplines were not found. Furthermore, according to social patterns, results suggest the predominance of two-author teams and that group of authors tend to vary from one research to another. Furthermore, there are links between author network characteristics and citations received, particularly, between Average Degree, Weighted Average Degree, and Graph Density and total citations and, also, between Graph Density and the percentage of international collaboration. [ABSTRACT FROM AUTHOR]