University data governance is an essential requirement for the informatization of universities and holds significant importance in advancing the modernization of university governance systems and governance capabilities. This study focuses on the data governance policies released by “Double First-Class” universities in China since 2015. Based on policy text mining and the PMC index model, the paper developed an evaluation system for university data governance policies consisting of 9 primary indicators and 43 secondary indicators and conducted quantitative assessment. The results indicate that the policies are of good quality overall, with 25% rated as excellent, 66.1% as good, and 8.9% as moderate. Many universities have made significant progress in formulating data governance policies. However, there is still considerable room for improvement. For example, while the policy objectives are clearly defined, certain aspects require further refinement; the stakeholder involvement is relatively narrow, lacking diversity; and the mix of policy instruments is imbalanced. To address these issues, it is recommended that policies be optimized by balancing regulatory priorities, establishing a multi-stakeholder collaborative governance framework, and rationalizing the policy instruments mix.

Agriculture in the Pacific is driven primarily by small-scale private farmers, many of whom do not have access to soil testing services or advice, nor the means to interpret analytical results into soil management and agronomic recommendations. Soil degradation through the process of acidification poses a significant risk to food and income security as it directly threatens crop productivity. The nutritional quality of food crops may also be affected through sub-optimal nutrient uptake by plants and nutrient imbalances. The dataset reported here provides a useful platform for the development of a decision-support tool (DST) that will assist Fiji farmers in understanding and managing soil pH and soil acidity. The DST will enable making informed decisions about liming to help correct soil pH. To support this development, historical soil pH data available from the Pacific Soils Portal were combined with updated analyses of agricultural soils from 17 locations in Viti Levu Island (Fiji) collected during a field campaign undertaken in August 2025. The soils were sampled at two depth intervals (0–15 and 15–30 cm) and analyzed for pH using a variety of methods. These methods included direct field measurements using a portable pH-meter as well as traditional laboratory determinations. Of the soils sampled, it was found that most soils exhibited pH levels below 7, which were observed for both depth intervals. Across all samples taken in 2025, it was found that 54.3% of them had soil pH < 5, 38.6% had soil pH between 5 and 6, and 7.1% had pH > 6 (based on soil pH1:5 soil-to-water method). Depending upon specific land uses, climate and cropping intensity, it was recommended that routine liming be built into soil fertility management programs to help farmers overcome soil acidity-related constraints to production. Liming frequency, timing of application and application rate will need to be determined for specific soil and cropping situations; however, it was suggested that soil pH was not changed by more than 1 unit each time lime was applied. Such an approach should reduce the risk of soil organic matter loss through accelerated mineralization, which would be challenging to restore in that environment if soils remained under continuous cropping. The analytical information contained in this article expanded and updated the datasets available in the Pacific Soils Portal. Furthermore, this work provided an opportunity to build analytical expertise in aspects of soil chemistry at local organizations to support academic and extension activities as well as the ongoing development of the Pacific Soils Portal.

Arthropod pest species represent a serious threat to agriculture and forestry. Canada is no exception, with over 1200 recorded pest species. Although a consolidated dataset would benefit research, management, and policy, information on these species has never been compiled into a unitary database. This publication merges available information to create a dataset for the agroforest pest insects of Canada and updates the taxonomy for these records based on the Catalog of Life and GBIF. Each species record includes (1) their global distribution, (2) feeding guild for adults and larval stages (when available), (3) host plant data (when available) and (4) identifiers to obtain DNA barcodes from the Barcode of Life Data Systems (when available).

As Stack Overflow considers asking for any sort of recommendation to be off-topic, Stack Exchange Network launched Software Recommendations, a particular question-answering (Q&A) community for this purpose. Out of 182 such Q&A sites, Software Recommendations has been the least-answered site for the last 5 years, with only a 57% answered rate. We analyzed the complete data dump of Software Recommendations, containing data from its inception in 2014 to October 2025, to determine what makes it the least-answered site. We observed that incorrect tagging reduces the chances of questions being discovered by experts, and for this reason, certain topics receive better answers than others. Most questions are asked by users with a reputation of less than 300, while most of the answers are provided by those having a higher reputation. In total, 78.86% of users registered on the site act as silent observers and only 21.14% are involved in Q&A activities. Although knowledge-sharing activities on the site have decreased over this period, new users keep joining, which shows the need for software recommendations despite the increasing popularity of AI tools. Although similar questions are often asked and get closed as off-topic on sister sites, a very small proportion of such questions are migrated to Software Recommendations due to the lengthy migration process and reputation reversal on that site, which holds Software Recommendations back from attracting knowledgeable users and gaining popularity. Our findings suggest that the Stack Exchange community needs to revise its incentive mechanisms and devise ways to attract knowledgeable Stack Overflow users to the Software Recommendations site.

This study compiles and harmonizes a dataset on the reference service life (RSL) of non-structural building envelope materials in North America. Data was collected from survey-based published reports and Environmental Product Declarations (EPDs) sourced from either manufacturers’ websites or recognized EPD databases. Relevant North American EPDs were reviewed for RSL or equivalent terms, and the extracted RSL values were recorded and classified by material type. The resulting dataset consolidates survey-based service life values, EPD-derived RSLs, together with their reported frequencies, and the extent of missing data. Analysis of the dataset revealed significant data gaps and strong inconsistencies in currently available RSLs. Many published values are based on client surveys or fixed categorical assumptions, overlooking product-specific durability and often relying on data more than 20 years old. These values do not reflect current manufacturing technologies or construction practices. Furthermore, over half of the reviewed EPDs do not report any RSL, while most of the remaining EPDs simply use default values of 60 or 75 years without clear justification. Although the dataset supports sensitivity and uncertainty analyses in building environmental assessments, the findings underscore the lack of reliability and transparency in existing RSL data and highlight the necessity for a consistent and transparent RSL dataset.

Globally, one in eight people experience a mental disorder, which constitutes a leading cause of years lived with disability and disproportionately affects young people. Gaps in scientific knowledge have been identified, with limited studies in university students. This article presents an open-access database on mental health and family functioning, collected through a survey of undergraduate students in health sciences programs at a private university in Cali (Colombia). The purpose was to explore suicide risk, substance abuse and family functioning using three structured questionnaires (Family APGAR, Dast-10, and PANSI), together with sociodemographic variables, organized in four sections (family and peer support, substance use, suicidal ideation, and background). The results of the article correspond to the database description, which includes finally 574 records obtained from students of health sciences programs (medicine, dentistry, psychology, prehospital care, nursing, dental mechanics). The data are provided as raw, analyzable files (spreadsheet formats) free of charge from Mendeley Data. In conclusion, the scientific impact of these data lies in their potential to be reused by researchers and higher-education decision-makers for secondary analyses that guide the development of mental and family health interventions for groups linked to undergraduate programs in the health sector.

This article presents a database focused on measuring the experimental performance of a pilot parabolic trough collector (PTC) combined with the meteorological conditions corresponding to the installation site. Water was chosen as the fluid to recirculate through the PTC circuit. The data were recorded between August and September, assuming that global radiation was adequate for use in the concentration process. The database comprises seven experimental tests, which contain variables such as time, inlet temperature, outlet temperature, ambient temperature, global radiation, diffuse radiation, wind direction, wind speed, and volumetric flow rate. Based on the data obtained from this pilot PTC system, it is possible to provide relevant information for the installation and construction of large-scale solar collectors. Furthermore, the climatic conditions considered allow key factors in the design of multiple collectors to be determined, such as the type of arrangement (series or parallel) and manufacturing materials. In addition, the data collected in this study are key to validating future theoretical models of the PTC. Finally, considering the real operating conditions of a PTC in conjunction with meteorological variables could also be useful for predicting the system’s thermal performance using artificial intelligence-based models.

As a member of the North American Free Trade Agreement, the Mexican Ministry of Environment and Natural Resources publishes the Pollutant Releases and Transfers Registry of Substances annually, in accordance with the Official Mexican Norm Standard NOM-165-Semarnat-2013. This registry comprises 19 datasets (one per year, from 2004 to 2022). These have not preserved the same structure and categorical values, making it difficult to avoid their fusion with other datasets and conduct exploratory studies. These datasets contain (a)data on substances released and transferred to the environment and (b) data on producer facilities. They do not include additional data to make any other kind of query. Users must create adapted versions of these datasets while achieving isolated analyses. This paper describes a method for integrating the Pollutant Release and Transfer Registry dataset, enhanced with facilities data and cancer classification from the International Agency for Research on Cancer, to produce an improved and augmented public data source for academic or research purposes. The obtained database contains geospatial information, which enabled us to analyze the dataset at the state or municipal level to create digital products that can be used to inform decisions about environmental pollution.

The article presents a method for detecting low-intensity DDoS attacks, focused on identifying difficult-to-detect “low-and-slow” scenarios that remain undetectable by traditional defence systems. The key feature of the developed method is the statistical criteria’s (χ2 and T statistics, energy ratio, reconstruction errors) integration with a combined neural network architecture, including convolutional and transformer blocks coupled with an autoencoder and a calibrated regressor. The developed neural network architecture combines mathematical validity and high sensitivity to weak anomalies with the ability to generate interpretable artefacts that are suitable for subsequent forensic analysis. The developed method implements a multi-layered process, according to which the first level statistically evaluates the flow intensity and interpacket intervals, and the second level processes features using a neural network module, generating an integral blend-score S metric. ROC-AUC and PR-AUC metrics, learning curve analysis, and the estimate of the calibration error (ECE) were used for validation. Experimental results demonstrated the superiority of the proposed method over existing approaches, as the achieved values of ROC-AUC and PR-AUC were 0.80 and 0.866, respectively, with an ECE level of 0.04, indicating a high accuracy of attack detection. The study’s contribution lies in a method combining statistical and neural network analysis development, as well as in ensuring the evidentiary value of the results through the generation of structured incident reports (PCAP slices, time windows, cryptographic hashes). The obtained results expand the toolkit for cyber-attack analysis and open up prospects for the methods’ practical application in monitoring systems and law enforcement agencies.

Despite the critical importance of biomedical databases like MEDLINE, users are often hampered by search tools with stagnant designs that fail to support complex exploratory tasks. To address this limitation, we synthesized research from visual analytics and related fields to propose a new design framework for non-traditional search interfaces. This framework was built upon seven core principle: visualization, interaction, machine learning, ontology, triaging, progressive disclosure, and evolutionary design. For each principle, we detail its rationale and demonstrate how its integration can transcend the limitations of conventional search tools. We contend that by leveraging this framework, designers can create more powerful and effective search tools that empower users to navigate complex information landscapes.