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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]

Scientific data sharing (SDS) has become essential for scientific progress, technological innovation and socioeconomic development. Identifying the key influencing factors of SDS can effectively promote SDS programmes and give full play to the critical role of scientific data. This study used grounded theory and information ecology theory to construct an SDS influencing factor model that encompassed five dimensions and 28 influencing factors and followed the fuzzy decision-making trial and evaluation laboratory (fuzzy-DEMATEL) approach to measure and analyse the degree of influence of each influencing factor and identify the key factors. The results show that (1) there are interactions and mutual interactions between the various influencing factors of SDS, which can form a complex network system. (2) 16 influencing factors, such as data-sharing policies, data-sharing regulations and data-sharing standards, comprise the key influencing factors in SDS. (3) The optimisation path of SDS is ‘Scientific Researchers’ → ‘Scientific Data’ → ‘Policy Environment’ → ‘Research Organisations → ‘Information Technologies’. In this regard, we proposed the following management suggestions to promote the development of SDS programmes in China: focusing on researchers' subjective willingness to share, enhancing the integrated governance of scientific data, fulfilling the role of policy support and guidance, strengthening the support of research organisations and improving SDS platforms with information technology.

FAIR Principles are a set of high-level guidelines for sharing digital resources. The growing global adoption of the FAIR Principles by policymakers, funders, and organizations compels data professionals, projects, and repositories to demonstrate the level of FAIR-compliance (referred to as FAIRness) of their digital data, metadata, and infrastructures. Because the FAIR Principles offer general objectives rather than specific implementation instructions, discrepancies exist due to different interpretations, domain-specific requirements, and intended applications. These discrepancies hinder direct comparisons and integration of assessment outcomes. To address this issue, we propose a novel framework, including a consolidated FAIR vocabulary. This framework establishes quality measures upfront in FAIRness assessment workflows to surpass the intricacies arising from the aforementioned dependencies. The established quality measures encapsulate the distinctive core concepts inherent in individual FAIR principles and can serve as common, fundamental pillars of holistic FAIRness assessment workflows. Building upon this fundamental set of the quality measures, we introduce a FAIRness quality maturity matrix (FAIR-QMM) as a structured, tiered, and progressive approach for evaluating and reporting the degree of FAIR-compliance. The FAIR-QMM can be used as a FAIRness assessment tool independently and/or as a translator between other FAIRness assessment tools or models.

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.)

The ability to work with data to extract valuable information is a useful skill set, especially in STEM disciplines that produce large amounts of scientific data. This paper reports on the pedagogy and experience of incorporating fundamental database concepts into a STEM Success course, which included a hands-on project. Students worked in groups to design a database for existing tabular data available on the Web and then derived the data to populate the database using SQL queries. This paper reflects on the experience both from a pedagogical perspective as well as a student perspective, including qualitative code analysis of student feedback and survey results regarding attitudes of confidence using, interest in, and usefulness of databases.

[Purpose/Significance] With the advent of the algorithmic era, libraries' information delivery channels have shifted from offline physical entities to online digital platforms. This transformation has brought about significant changes in the way users access and utilize scientific data. The frequency of use of scientific data has increased exponentially, as more and more users rely on data to support their research, education, and innovation activities. The huge demand and application of use of data poses challenges to the development of libraries, among which the service guarantee of intellectual property rights (IPR) for scientific data is becoming a key factor affecting the development of digital libraries. IPR is a legal concept that protects the ownership and control of data creators and providers over their data. It also regulates the rights and obligations of data users and re-users. Therefore, this study aims to explore the influencing factors and optimization strategies of libraries' IPR service quality for scientific data. [Method/Process] To achieve this goal, this study used a questionnaire analysis method to collect data from a sample of 252 individuals belonging to a highly knowledgeable group, such as researchers, academics, and students. These individuals are the main users and producers of scientific data, and their perceptions and expectations of the quality of IPR services by libraries are crucial for improving the service. The questionnaire consists of four parts: demographic information, IPR awareness, IPR satisfaction, and IPR improvement suggestions. The reliability of the questionnaire factors is between 0.724 and 0.913, indicating a high level of internal consistency. The validity of the questionnaire is verified by confirmatory factor analysis, which shows a good fit between the data and the model. Based on the data, this study conducts a path analysis to test the hypotheses and obtain the results. [Results/Conclusions] The results show that the following factors have a significant positive impact on the quality of libraries' IPR services for scientific data: the implementation efficiency of policies and regulations (beta=0.326, p

[Purpose/Significance] With the advent of the algorithmic era, libraries' information delivery channels have shifted from offline physical entities to online digital platforms. This transformation has brought about significant changes in the way users access and utilize scientific data. The frequency of use of scientific data has increased exponentially, as more and more users rely on data to support their research, education, and innovation activities. The huge demand and application of use of data poses challenges to the development of libraries, among which the service guarantee of intellectual property rights (IPR) for scientific data is becoming a key factor affecting the development of digital libraries. IPR is a legal concept that protects the ownership and control of data creators and providers over their data. It also regulates the rights and obligations of data users and re-users. Therefore, this study aims to explore the influencing factors and optimization strategies of libraries' IPR service quality for scientific data. [Method/Process] To achieve this goal, this study used a questionnaire analysis method to collect data from a sample of 252 individuals belonging to a highly knowledgeable group, such as researchers, academics, and students. These individuals are the main users and producers of scientific data, and their perceptions and expectations of the quality of IPR services by libraries are crucial for improving the service. The questionnaire consists of four parts: demographic information, IPR awareness, IPR satisfaction, and IPR improvement suggestions. The reliability of the questionnaire factors is between 0.724 and 0.913, indicating a high level of internal consistency. The validity of the questionnaire is verified by confirmatory factor analysis, which shows a good fit between the data and the model. Based on the data, this study conducts a path analysis to test the hypotheses and obtain the results. [Results/Conclusions] The results show that the following factors have a significant positive impact on the quality of libraries' IPR services for scientific data: the implementation efficiency of policies and regulations (beta=0.326, p<0.001), talent team building (beta=0.274, p<0.001), the data management technology (beta=0.211, p<0.001), the diversification of service models (beta=0.358, p<0.001), and the number of data IPR sharing agreements (beta=0.329, p<0.001). These factors reflect the importance of improving the legal, human, technical, and organizational aspects of libraries' scientific data IPR services. Based on the findings, this study proposes five optimization strategies for libraries in scientific data IPR service: strengthening the implementation of policies and regulations, improving the training and motivation of talent teams, upgrading the data management technology, innovating the service model, and increasing the number of data IPR sharing agreements. These strategies can help libraries to improve the quality of their scientific data IPR services and meet the needs of the users in the algorithmic era. [ABSTRACT FROM AUTHOR]

The open scientific data platform is the information infrastructure for open data activities. construction of relevant platforms in universities is an important step in strengthening scientific data services. By investigating the current situation both domestically and internationally, the paper proposes issues to be explored in the construction logic, data collaboration functions and data harmonization standards of domestic platforms. Based on the practice of Zhejiang University, it explains the overall framework of independent platform developing path, the major issues of construction about collaborative processes and data harmonization, as well as the effectiveness and reflection of the practice, in order to provide reference for domestic universities to carry out relevant practices and promote the further implementation of domestic open scientific data activities. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] To explore the influencing factors of the added value of scientific data and reveal the inherent development laws of the added value of scientific data. [Method/Process] First, based on the interview data of 18 experts and the research results related to the value appreciation of scientific data in existing literature, the grounded theory method was adopted. Through open coding, main-axis coding, and selective coding, 19 categories, 6 main categories, and 3 core categories were finally obtained. From this, a theoretical model of the factors influencing the value appreciation of scientific data was obtained. On this basis, the Vensim PLE tool was used to establish a dynamic model of the value appreciation system of scientific data, and the process of value appreciation of scientific data was dynamically simulated and analyzed to reveal the relationship between various influencing factors and the value appreciation of scientific data. [Results/Conclusions] In the process of increasing the value of scientific data, the quality factor of raw data is a prerequisite, and high-quality raw scientific data are conducive to the integration and secondary development of subsequent scientific data. The data literacy of data producers represented by researchers has the most significant impact on the quality of scientific data. The level of data storage and payment has a significant impact on the added value of scientific data. When the perceived effort and perceived risk of data storage and payment by researchers decrease, accompanied by the pressure of scientific data sharing policies, researchers become more likely to increase their willingness to save and pay, thereby significantly improving the scale effect of data storage and payment. The organization and integration of scientific data is the key to the formation of value-added scientific data. Overall, the metadata quality has the most significant impact on the level of scientific data organization and integration. The quality of metadata is the foundation of scientific data processing, classification, and integration. The higher the quality of metadata, the more it helps to improve the level of scientific data organization and integration. The sharing and development of scientific data is the key to realizing the value-added of scientific data. It is the final step in realizing the value-added of scientific data. In this process, the scale of scientific data openness, the quality of scientific data sharing platforms, and development capabilities all have a positive promoting effect on the level of scientific data sharing and development.

The development of an open science information infrastructure requires the presence of highly qualified library staff. He should be capable of responding to and interested in changes in science and technology, able to create innovative information products and make them available to the public, organize new forms of interaction with scientists, implement educational programs, and advise on associated with the concept of open science. The study is conducted in order to determine the areas of training and practice-oriented training for additional professional programs and retraining of specialized specialists. These programs are the specialists to acquire practical skills in the creation and use of open information resources of various types, the formation of information culture of users, the development of scientific communication and new library and information services. The results of the analysis of the required competencies of library specialists are presented. They take into account the trends in the development of the information ecosystem of open science and building relationships with users through partnerships; the system of different training in Russia is studied and the gap between the education of librarians and the practice of their work on information support of research activities is determined.

Standard reference data is a type of high-quality scientific data and has both metrological and technical standard attributes; therefore, it is an important national science foundation and strategic resources. Research on the standard reference data in China lags significantly behind the advanced world level, causing potential risks to scientific research and production. This study explores the scientific connotation, extended characteristics, and strategic values of standard reference data, and analyzes the progress, management system, international influence, and practical application of standard reference data in the United States. Moreover, the current status of standard reference data development in China is viewed, key problems such as unclear concept, insufficient top-level design, lack of data resource accumulation, and missing management system are highlighted, and a construction path for standard reference data in China is proposed. Furthermore, the study recommends establishing a sound policy system for standard reference data, strengthening the financial guarantee for relevant research, strengthening publicity and education to accelerate the cultivation of talents, and encouraging international cooperation.

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.

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.)

[Purpose/Significance] To explore the influencing factors of the added value of scientific data and reveal the inherent development laws of the added value of scientific data. [Method/Process] First, based on the interview data of 18 experts and the research results related to the value appreciation of scientific data in existing literature, the grounded theory method was adopted. Through open coding, main-axis coding, and selective coding, 19 categories, 6 main categories, and 3 core categories were finally obtained. From this, a theoretical model of the factors influencing the value appreciation of scientific data was obtained. On this basis, the VensimPLEtool was used to establish a dynamic model of the value appreciationsystemofscientific data, and the process of value appreciation of scientific data was dynamically simulated and analyzed to reveal the relationship between various influencing factors and the value appreciationofscientific data.[Results/Conclusions] Inthe process of increasing the value of scientific data, the quality factor of raw data is a prerequisite, and high-quality raw scientific data are conducive to the integration and secondary development of subsequent scientific data. The data literacy of data producers represented by researchers has the most significant impact on the quality of scientific data. The level of data storage and payment has a significant impact on the added value of scientific data. When the perceived effort and perceived risk of data storage and payment by researchers decrease, accompanied by the pressure of scientific data sharing policies, researchers become more likely to increase their willingness to save and pay, thereby significantly improving the scale effect of data storage and payment. The organization and integration of scientific data is the key to the formation of value-added scientific data. Overall, the metadata quality has the most significant impact on the level of scientific data organization and integration. The quality of metadata is the foundation of scientific data processing, classification, and integration. The higher the quality of metadata, the more it helps to improve the level of scientific data organization and integration. The sharing and development of scientific data is the key to realizing the value-added of scientific data. It is the final step in realizing the value-added of scientific data. In this process, the scale of scientific data openness, the quality of scientific data sharing platforms, and development capabilities all have a positive promoting effect on the level of scientific data sharing and development. [ABSTRACT FROM AUTHOR]

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.)

Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines. We aim to automatically construct research highlights given certain segments of a research paper. We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings. We test our model on a benchmark dataset, CSPubSum, and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation. For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature. On the CSPubSum dataset, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78, 9.76 and 29.3, respectively, METEOR score of 24.00, and BERTScore F1 of 85.25.

[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.

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.

[Purpose/Significance] With the development of data-intensive scientific research paradigm, the effective discovery and reuse of scientific data is of great significance to the sharing of research results. In order to promote scientific data utilization and improve the scientific value of data resources, the international academic community proposed the FAIR principles in 2016, aiming to promote data openness, which has so far attracted widespread attention from scholars at home and abroad. Many exploratory studies have been carried out around the FAIR principles, such as GO FAIR, and RDA, CODATA and other international organizations have been established to be devoted to open science and data sharing practices; the European Union, as the representative advocate of open science, has strongly promoted the implementation and promotion of the FAIR principles by establishing the European Open Science Cloud (EOSC) and introducing data policies, etc. The purpose of this paper is to provide a systematic and comprehensive overview of the academic achievements of FAIR principles, with the aim of providing references for the promotion and implementation of the principles and scientific data management practice in China. [Method/Process] This paper combines literature research and online research, and through content analysis, provides an in-depth analysis of literature on FAIR principles in terms of content interpretation, assessment methods, domain application, discipline application and regional application. It includes five perspectives of organization guarantee on FAIR principles, four basic principles of FAIR, practical exploration, disciplinary implementation and regional practice of FAIR principles to review the research progress and implementation of FAIR principles at home and abroad, focusing on exploring the practical value of FAIR principles, and systematically summarizing the research status and existing achievements. [Results/Conclusions] International research on FAIR principles focuses on theory, implementation strategies, and evaluation methods, For example, at the level of FAIR content, the four basic principles have been explored and discussed in practice; at the level of implementation strategies, many stakeholders have taken different measures to ensure the implementation of FAIR principles, and different organizations have developed evaluation methods. In addition, FAIR principles have been explored in the fields of medicine and other disciplines and are widely used in Europe and other regions. In contrast, domestic research is still in its infancy and national policy support and follow-up by relevant organizations is needed for further study and implementation of FAIR principles in China.

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]

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]

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]

Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating-point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word-Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.

Introduction: CRIS systems constitute comprehensive information systems over the entire ecosystem of the scientific process. BrCris aims to integrate and organize information regarding research activities, projects, publications, researchers, institutions, financing and other relevant data in the Brazilian scientific context. Objective: This study aims to discuss data processing on the BrCris Platform and analyze the computational tools used for this purpose, exploring three main approaches: data integration and consistency, visualization and validation, in addition to data certification. Methodology: The study is descriptive, presenting in detail the BrCris data processing steps, discussing the challenges encountered in handling large volumes of information. Furthermore, it describes the computational tools used to process scientific and technological information. Results: The study reveals the procedures for data processing and the computational tools developed for information systems, as well as the integration and analysis of the data obtained. The results of the processing and modeling of information based on VIVO, with data and graphic panels, and the opportunities for reusing the generated data are presented. The integration of data into a self-declared repository (Lattes) and the theses and dissertations aggregator repository (Oasisbr) is also detailed, culminating in the issuance of a certification seal. Conclusion: The results show that the adoption of these computational tools provides easy and agile access to an extensive set of consolidated information, previously dispersed across various sources, especially due to the diversity of repositories and individual access limitations. Thus, this study presents a set of computational tools whose functionalities are aligned with the guidelines of Open Science in Brazil. [ABSTRACT FROM AUTHOR]

Digital data have become essential to leverage scientific research. In part, they serve a functional and ephemeral role, but their importance becomes permanent and their value as a substrate for scientific communication can increase or decrease depending on the demands and uses to which they are put. Data preservation has become a demand for researchers as well as for universities and research centers. In response, different repositories have emerged in Brazil, which allow storing data in various areas of knowledge. This research aims to locate repositories that gather primary scientific research data in Brazil. Searches were undertaken in the directories Re3Data, OpenDoar, ROAR and review of the repositories of the Federal Universities registered with the Ministry of Education. This made it possible to locate 20 repositories covering data and to analyze their main characteristics in relation to subject coverage areas and preservation standards. The data obtained show that, although most repositories provide alignment with international standards, some do not have adequate infrastructures for the datasets provided. It is considered that there may be a failure in the management of certain repositories, but most offer adequacy to the needs for availability and search of scientific data in all indicators. [ABSTRACT FROM AUTHOR]

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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] In order to solve the problems of incomplete data chain and insufficient data support decision-making ability in agricultural science and technology management, this paper put forward a set of industrial knowledge service model based on the needs of scientific management in agriculture, and completed the key part of the model, the research and practice on association of scientific data and scientific literature. This study will provide theoretical and practical cases for library and information science (LIS) research institutions to serve agricultural science and technology management and industrial development. [Method/Process] Firstly, this paper reviews the current situation of research into knowledge service and data association. Secondly, based on the analysis of characteristics of industrial requirements and data management life cycle in agricultural science and technology management, the paper constructed a knowledge service model with industrial characteristics. Then, based on the agricultural science data and scientific literature of Sichuan Province, the study designed and constructed an ontology model and system based on industrial elements. [Results/Conclusions] 20 data types, 24 284 scientific data entries and related scientific literature were integrated. And we established multi-source heterogeneous data association in 1 system platform and 1 sub-platform with Chinese and English data. The research results will provide reference for cross-field cooperation between agricultural science and technology management and LIS research, and cross-departmental sharing of scientific data and scientific literature.