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Over the last decade, a plethora of training datasets have been compiled for use in language-based machine perception and in human-centered AI, alongside research regarding their compilation methods. From a primarily linguistic perspective, we add to these studies in two ways. First, we provide an overview of sixty-six training datasets used in automatic image, video, and audio captioning, examining their compilation methods with a metadata analysis. Second, we delve into the annotation process of crowdsourced datasets with an interest in understanding the linguistic factors that affect the form and content of the captions, such as contextualization and perspectivation. With a qualitative content analysis, we examine annotator instructions with a selection of eleven datasets. Drawing from various theoretical frameworks that help assess the effectiveness of the instructions, we discuss the visual and textual presentation of the instructions, as well as the perspective-guidance that is an essential part of the language instructions. While our analysis indicates that some standards in the formulation of instructions seem to have formed in the field, we also identified various reoccurring issues potentially hindering readability and comprehensibility of the instructions, and therefore, caption quality. To enhance readability, we emphasize the importance of text structure, organization of the information, consistent use of typographical cues, and clarity of language use. Last, engaging with previous research, we assess the compilation of both web-sourced and crowdsourced captioning datasets from various perspectives, discussing factors affecting the diversity of the datasets. [ABSTRACT FROM AUTHOR]

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Genomic selection (GS) is increasingly used in tree breeding because of the possibility to hasten breeding cycles, increase selection intensity or facilitate multi-trait selection, and to obtain less biased estimates of quantitative genetic parameters such as heritability. However, tree breeders are aiming to obtain accurate estimates of such parameters and breeding values while optimizing sampling and genotyping costs. We conducted a metadata analysis of results from 28 GS studies totalling 115 study-traits. We found that heritability estimates obtained using DNA marker-based information for a variety of traits and species were not significantly related to variation in the total number of markers ranging from about 1500 to 116 000, nor by the marker density, ranging from about 1 to 60 markers/centimorgan, nor by the status number of the breeding populations ranging from about 10 to 620, nor by the size of the training set ranging from 236 to 2458. However, the predictive accuracy of breeding values was generally higher when the status number of the breeding population was smaller, which was expected given the higher level of relatedness in small breeding populations, and the increased ability of a given number of markers to trace the long-range linkage disequilibrium in such conditions. According to expectations, the predictive accuracy also increased with the size of the training set used to build marker-based models. Genotyping arrays with a few to many thousand markers exist for several tree species and with the actual costs, GS could thus be efficiently implemented in many more tree breeding programs, delivering less biased genetic parameters and more accurate estimates of breeding values. [ABSTRACT FROM AUTHOR]

Microplastic pollution in surface waters is a global concern, but the driving factors remain unclear. The characteristics of microplastics were assessed at 2,430 sampling sites in Chinese aquatic environments using published data. Geographical detectors, correlation analysis, analysis of variance, correspondence analysis, and structural equation modeling were used to examine both the effects of human activities and natural processes on microplastic characteristics and the potential influence of experimental methods on these characteristics. The abundance of microplastics was found to be significantly influenced by both human activities and natural processes, including farmland areas, landscape connectivity and annual mean precipitation. Furthermore, significant variations in the characteristics of microplastics were observed between experimental treatments, with the sampling method identified as a key factor influencing their observed abundance. Microplastic abundance showed a significant negative correlation with sample size, but no clear linear relationship with sieve aperture. The influence of the experimental methods reduced the association between the characteristics of microplastics and both human activities and natural processes. In water, the experimental methods reduced the correlation between natural processes and microplastic characteristics by 70 %, and in sediment by 39.6 %. However, the impact of human activities proved to be more complex, with the correlation increasing in water but decreasing by 47.9% in sediment. These findings suggest that variations in experimental methods can distort the accurate depiction of microplastic characteristics, obscuring their true relationship with environmental factors. This experimental inconsistency creates confusion about how microplastics spread and behave in the environment. This highlights the necessity for the development of standardized experimental methods and protocols for microplastics.

Database management systems are an essential research field in data engineering, data mining, and computer science. The development and research in this field have evolved from manual bookkeeping to automatic data storage, rapidly growing with time. In this bibliometric study, the articles from four premier venues of publication in the field of database systems have been collected for analysis, including ACM Special Interest Group on Management of Data SIGMOD, a well-reputed symposium PODS (Principles of Database Systems) and two journals of the domain namely: Transactions on Database Systems (TODS) by ACM, and Transactions on Knowledge and Data Engineering (TKDE). This study includes the articles published between 2010 and 2019 in the selected venues to perform bibliometric analysis. Both TKDE and TODS are highly reputed journals in the field of database systems but publish quite different research. The SIGMOD conference began in 1975 and specializes in the research area of databases and data management problems, whereas PODS is the research conference that specializes in research related to database theory. This study presents the analysis of the collected articles from three major aspects: 1) metadata analysis, 2) content-based analysis, and 3) citation-based analysis. Furthermore, it also looked at the leading institutes, countries, and authorities based on article citation and publication count. As a whole, this study proposes a methodology for performing the extensive bibliometric analysis for the database management system and conducts a bibliometric analysis for the aforementioned top publication venues in this area of research. [ABSTRACT FROM AUTHOR]

This article traces historical trends in film titling practices in Poland, based on quantitative analysis of film metadata. The authors analyse a corpus of 2,519 films produced in Poland until 2023, looking at regularities in the naming of Polish original films: the average length of titles, title duplication, the most frequent content words typical of successive phases in the history of Polish cinematography, lexical diversity, and the presence of proper names and foreignisms. Then they replicate some of the tests on the corpus of foreign films translated into Polish, which includes 5,585 foreign films distributed in Poland between 1945 and 1989 and 33,794 films released in Poland after 1989. The authors compare the peculiarities of the original titles and those translated into Polish in search of similarities and discrepancies, potentially symptomatic of translationese, or titlese.

Application of organic fertilizers is an important strategy for sustainable agriculture. The biological source of organic fertilizers determines their s pecific functional characteristics, but few studies have systematically examined these functions or assessed their health risk to soil ecology. To fill this gap, we analyzed 16S rRNA gene amplicon sequencing data from 637 soil samples amended with plant- and animal-derived organic fertilizers (hereafter plant fertilizers and animal fertilizers). Results showed that animal fertilizers increased the diversity of soil microbiome, while plant fertilizers maintained the stability of soil microbial community. Microcosm experiments verified that plant fertilizers were beneficial to plant root development and increased carbon cycle pathways, while animal fertilizers enriched nitrogen cycle pathways. Compared with animal fertilizers, plant fertilizers harbored a lower abundance of risk factors such as antibiotic resistance genes and viruses. Consequently, plant fertilizers might be more suitable for long-term application in agriculture. This work provides a guide for organic fertilizer selection from the perspective of soil microecology and promotes sustainable development of organic agriculture. [ABSTRACT FROM AUTHOR]

Large scale enterprise networks often use Enterprise Key-Management (EKM) platforms for unified management of cryptographic keys. Monitoring access and usage patterns of EKM Systems (EKMS) may enable detection of anomalous (possibly malicious) activity in the enterprise network that is not detectable by other means. Analysis of enterprise system logs has been widely studied (for example at the operating system level). However, to the best of our knowledge, EKMS metadata has not been used for anomaly detection. In this article we present a framework for anomaly detection based on EKMS metadata. The framework involves automated outlier rejection, normal heuristics collection, automated anomaly detection, and system notification and integration with other security tools. This is developed through investigation of EKMS metadata, determining characteristics to extract for dataset generation, and looking for patterns from which behaviors can be inferred. For automated labeling and detection, a deep learning-based model is applied to the generated datasets: Long Short-Term Memory (LSTM) auto-encoder neural networks with specific parameters. This generates heuristics based on categories of behavior. As a proof of concept, we simulated an enterprise environment, collected the EKMS metadata, and deployed this framework. Our implementation used QuintessenceLabs EKMS. However, the framework is vendor neutral. The results demonstrate that our framework can accurately detect all anomalous enterprise network activities. This approach could be integrated with other enterprise information to enhance detection capabilities. Further, our proposal can be used as a general-purpose framework for anomaly detection and diagnosis.

AbstractAssessing damage to civil infrastructure is a resource-intensive process that is critical during the response to a disaster. Various datasets facilitate this process but are often collected on an individual ad hoc basis by multiple separate entities. Consequently, there is a lack of a coordinated approach when collecting disaster data, which prevents effective data interoperability. Rather than viewing datasets individually, this paper provides a comprehensive analysis of post-disaster damage data to demonstrate the merits of a dynamic data collection process accounting for both spatial and temporal variations. Specifically, datasets from Hurricane Maria and the Indios Earthquake in Puerto Rico are used to illustrate the entities involved, resources used, and resulting datasets for this purpose. The paper analyzes the evolution of key metadata features as a function of time, including data availability, coverage, and resolution. The results show distinct stages of the data collection process and reveal challenges in collaboration between entities and a lack of data integration for disaster response. The findings also lead to recommendations about the essential metadata for increased shareability. With these outcomes, entities in the field can improve the quality of information extracted and facilitate interoperability and information integration across datasets for damage assessment.

Organisations are attempting to make their supply chain eco-friendly due to rising carbon emissions and unsustainable use of natural resources. In this context, this study seeks to give an up-to-date literature review on green supply chain management (GSCM) from 2011 to December 2021. Initially, 375 articles were collected from the Web of Science (WoS) database for metadata analysis. In metadata analysis, the descriptive statistics of research trends of GSCM; most contributing authors, countries/regions and institutions; and most prominent journals, keywords and subject areas are discussed in detail. Later, 50 scholarly publications were selected according to their citations for content analysis. Based on their contents, the papers were classified into four categories: GSCM practices and performances, mathematical techniques, drivers and barriers of GSCM, and general articles related to GSCM. According to the in-depth analysis, most of the publications are theoretical works that contribute to the theory-building of GSCM. Likewise, mathematical techniques are gaining appeal among researchers, whereas research on drivers and barriers is limited. In articles regarding GSCM practices and performance, the structural equation modelling methodology was often employed. The results and future research directions presented may assist beginners in exploring new GSCM research domains. [ABSTRACT FROM AUTHOR]

Abstract A revision of the popular equation of Richardson and Zaki (1954a, Transactions of the Institute of Chemical Engineering, 32, 35–53) for the hindered settling of suspensions of non‐cohesive particles in fluids is proposed, based on 548 data sets from a broad range of scientific disciplines. The new hindered settling equation enables predictions of settling velocity for a wide range of particle sizes and densities, and liquid densities and viscosities, but with a focus on sediment particles in water. The analysis of the relationship between hindered settling velocity and particle size presented here shows that the hindered settling effect increases as the particle size decreases, for example, a 50% reduction in settling velocity is reached for 0.025 mm silt and 4 mm pebbles at particle concentrations of 13% and 25% respectively. Moreover, hindered settling starts to influence the settling behaviour of sediment particles at volumetric concentrations of merely a few per cent. For example, the particle settling velocity in flows that carry 5% silt is reduced by at least 22%. These observations suggest that hindered settling greatly increases the efficiency of natural flows to transport sediment particles, but also particulate carbon and pollutants, such as plastics, over large distances.

Following the industrial revolution, major economic and populational growth took place, and, therefore, solid waste generation increased exponentially. Nowadays, waste management still generates major impacts because the current wide offer of waste management strategies includes many solutions that produce suboptimal results, such as landfill or waste incineration. From a circular economy perspective, composting is a potentially sustainable option to treat the organic fraction of solid waste and has the advantage of recycling many organic compounds that can be reintroduced into the natural processes. This study aimed to provide a meta-analysis using the Life Cycle Assessment (LCA) method to evaluate the impacts of composting by performing a systematic literature review of the diversity of approaches and assessing environmental impacts. The results of the impact assessment were highly dependent on the choices made over the system boundary and the functional units. The most cited environmental impacts were Global Warming Potential, Acidification Potential, Eutrophication Potential, Photochemical Oxidation Potential, and Ozone Layer Depletion, as gaseous emissions from the transport and decomposition represent the main contributors to these categories. Using a smaller dataset and evaluating the use of the CML method and the most cited impacts categories, it was found that In-vessel Composting and Home Composting were considered the best environmental options among the studied composting methods. Composting environmental impacts were also highly related to the use of non-renewable energy sources, which puts composting at a disadvantage when compared with the use of anaerobic digestion. Such results emphasize the benefits of using these waste management technologies as complementary instead of substitutes. [ABSTRACT FROM AUTHOR]

Despite the importance of the construction sector for economic development, labour productivity in construction is lower than the productivity of other sectors. Construction productivity has been declining continuously for decades, especially in developing countries. The challenges of low productivity in construction are considered chronic problems. This has raised concerns among construction stakeholders to address the myriad of challenges that are undermining labour productivity growth. This study performed a metadata analysis of factors affecting construction labour productivity (CLP) growth in the Middle East. A systematic review of existing studies on labour productivity in construction was presented. Ten studies from the Middle East were selected for metadata analysis. The key factors affecting CLP in the region were identified and quantitative data from the selected studies were synthesised. Effect summaries derived from the analysis revealed that delays in responding to requests for information, inadequate workers' supervision, a shortage of skilled labour, the extent of change orders and clarity of technical specifications are the major factors affecting construction productivity. The study is limited to journal articles published in the Scopus database between 2000 and 2020. Middle East contractors can adopt the study's interventions to develop productivity improvement strategies for their organisations. [ABSTRACT FROM AUTHOR]

American film remakes have enjoyed growing academic attention over the past 50 years. Together with prequels, sequels, reboots and spin-offs, they have been often viewed as exponents of contemporary recycling culture, symptomatic of Hollywood's recent creative exhaustion and commercial risk aversion. In our article, we adopt a diachronic quantitative perspective to analyse and interpret the available metadata on 986 Hollywood remakes produced between 1915 and 2020. Our quantitative research shows the number of American remakes produced every year, their ratio in the total number of feature films produced annually, the percentage of remakes in the top most watched movies among the users of IMDb, as well as remake recency and percentage of remakes with titles recognizably linked to the original over the years. We find that popular convictions concerning the remarkable derivativity of contemporary American cinema stem from availability bias (accessibility of statistics concerning recent productions and relative neglect of historical data) and systemic differences in the function and prestige ascribed to feature film remakes by Old and New Hollywood filmmakers. [ABSTRACT FROM AUTHOR]

Relationships between literary texts, identity constitution, and genre are explored in this digital analysis of 19th century Spanish American novels from Mexico, Argentina, and Cuba, of their subgenres, and their function in the formation of collective identities, starting from references to identity which were found in the subtitles of the novels. In particular, the label “novela original”, as well as identity references that can be subsumed under the terms “novela americana”, “novela mexicana”, “novela argentina”, and “novela cubana” are analyzed. It is found that each type of identity novel, that is, novels with explicit references to a linguistic, cultural, or national context in their subtitle, has its own characteristics. There is not the one genre, theme, or style that serves to represent and constitute identity, but there are forms that are specific for each case, a result which corresponds with findings from memory studies on group-defining stories.

A revision of the popular equation of Richardson and Zaki (1954a, Transactions of the Institute of Chemical Engineering, 32, 35–53) for the hindered settling of suspensions of non‐cohesive particles in fluids is proposed, based on 548 data sets from a broad range of scientific disciplines. The new hindered settling equation enables predictions of settling velocity for a wide range of particle sizes and densities, and liquid densities and viscosities, but with a focus on sediment particles in water. The analysis of the relationship between hindered settling velocity and particle size presented here shows that the hindered settling effect increases as the particle size decreases, for example, a 50% reduction in settling velocity is reached for 0.025 mm silt and 4 mm pebbles at particle concentrations of 13% and 25% respectively. Moreover, hindered settling starts to influence the settling behaviour of sediment particles at volumetric concentrations of merely a few per cent. For example, the particle settling velocity in flows that carry 5% silt is reduced by at least 22%. These observations suggest that hindered settling greatly increases the efficiency of natural flows to transport sediment particles, but also particulate carbon and pollutants, such as plastics, over large distances. [ABSTRACT FROM AUTHOR]

Interdisciplinarity involves the interaction, combination, and integration of theories, concepts, and methods across different disciplines—and fan studies is commonly seen as an interdisciplinary field of research. This contribution sheds light on the question of interdisciplinarity by investigating contemporary notions of theory and methods used in two discipline-related scholarly journals through a metadata analysis of the keywords as well as a content analysis of fifty randomly selected abstracts in order to investigate the dominant theoretical approaches and methods used in the field of fan studies.

As libraries commit to equity, diversity, and inclusion (EDI) initiatives, it is critical to measure the strengths and weaknesses of collections in response. Building off already established approaches of collection diversity audits, the author argues that it is possible to use MARC metadata to perform exploratory analyses at a large scale. This paper discusses the process of planning, preparing the data, and performing an analysis, following the trajectory of an analysis of the 2.8 million geographic subject headings present in the University of Toronto Libraries' MARC records. The tools developed during this project are cited and available for reuse. [ABSTRACT FROM AUTHOR]

The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics. [ABSTRACT FROM AUTHOR]

On most collegiate sports teams’ injuries are plaguing rosters. The issue with the injuries on a football team is the amount of them that come from training overloads and lack of recovery from their training sessions. Coaches are not being able to take their team to their full potential and athletes are not being able to perform at their full potential as well. We are getting direct real-time data from wearable technology called Whoop. It collects the strain that the athlete goes through, their Heart Rate Variability (HRV), Respiration efficiency, and so many more things that could help improve the health of the athletes. Therefore, this paper will present the research proposal that the use of Big Data Analysis will be able to incorporate predictive analysis methods. With this proposal system in place coaches, athletes, and athletic trainers will be able to help prevent injuries and improve the overall performance of the team. By retrieving the real-time data from the Whoop band, we will be able to make real-time decisions and create correlations between the collected data and the performance of the athletes. The purpose of this proposed research is to create a system that will be beneficial to all types of sports teams, athletes, and university athletics budgets with big data analysis. [ABSTRACT FROM AUTHOR]

Introduction. Duplication in inventions produced by research institutions in Indonesia becomes an issue. It is important to map the specialization of the invention in research institutions. This study examines the mapping of the innovation in research institutions in Indonesia. Data Collection Method. This study uses a patent-based technology document analysis method to map the potential of technology. The data used is patent data registered in the Direktorat Jenderal Kekayaan Intelektual (DJKI) database. Data Analysis. Metadata analysis was conducted by using the K-Means Klastering method with R software. Results and Discussions. The findings in the pre-analysis show that when the independent variable involved in the model are very large, the Localized feature selection method can effectively select variables without losing much information. There are 5 dominant technology groups that can be produced by research institutions in Indonesia, namely 1) Technology related to the development of measurement and testing instrument technology; 2) Technologies related to food and food ingredients; and 3) microstructural test equipment / detectors; 4) radar technology; 5) Technology in agriculture. Conclusion. The findings show that there are still overlapping inventions by several research institutions in the same technology cluster. K-means clustering with LFSBSS pre analysis has a clear performance in the technology cluster space.

Since past several years, finding relevant documents from plethora of web repositories has become prime attention of the scientific community. To find out relevant research articles, state-of-the-art techniques employ content, metadata, citations, and collaborative filtering based approaches. Among all of them, citation based approaches hold strong potential because most of the time, authors cite relevant papers. Bibliographic coupling is one of the well-known citation based approaches for recommending relevant papers. In this paper, we present an approach SwICS that harnesses number of common references between pair of documents as similarity measure whereas the distribution of in-text citations within the text are not analyzed. The proposed approach explores the in-text citation frequencies within contents of the paper and in-text citation patterns between different logical sections for bibliographically coupled papers. For evaluation, the employed data set contains 1150 research documents are obtained from a well-known autonomous citation index known as: CiteSeer. A comprehensive user study is conducted to build a gold standard for comparing the proposed approach. The approach is compared with the state-of-the-art bibliographic coupling and content similarity based techniques. The comparison results revealed that proposed approach significantly performs better than the contemporary approaches. The comparison result with gold standard yielded an average of 0.73. The average gain achieved by the proposed approach is 60% from state-of-the-art: bibliographic coupling. Whereas, the correlation between gold standard and content based approach remains 20%. The proposed approach can play a significant role for search engines and citation indexers in terms of improving the quality of their results.

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories—events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics' values coincide with the intuitive agreement of the community on its "top conferences". Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors. [ABSTRACT FROM AUTHOR]

Digital humanities are accused of contributing to the decay of Academia in general and of betraying the humanities principles. Through looking at the developments of the field, as well as at its research principles and practices, this article seeks to refute such an allegation, and to show that the passionate debates the digital humanities still raise are related to their critical stance towards ‘traditional’ SSH research. In the first part, the collaborative and FAIR principles (Findable, Accessible, Interoperable and Reusable) that characterise DH approach are examined, in connection with the dissatisfaction they express towards the established research practices and organisation. Based on an example of the exploration of the archives of the Hispanic 20th century vanguard, the second part focuses more specifically on the challenges of working with data and of haptic thinking in the literary and cultural fields. [ABSTRACT FROM AUTHOR]

The purpose of this paper is a discussion of some major concepts and operational definitions involved in tourism statistics within Regulation 692/2011, with a cross-country comparison and an in-depth look at the Italian case. Data and information are derived from main documents of major international and national institutions and Eurostat metadata. Both demand-side and supply-side data sources on tourism are discussed. The work highlights the main differences in the data production processes of different EU countries, although they follow the same regulation. The main critical points are: the definition of usual environment and survey design for the demand-side data, as well as the statistical unit for the supply-side data, which does not coincide with that used in structural business statistics. The analysis is limited to those EU countries with complete available Eurostat metadata. Moreover, only household surveys relating to trips and vacations are considered for demand-side data (border surveys are not treated). The main contribution is concerned with the description of the current situation in the production of tourism statistics, by looking and their comparability for cross-country analyses and at the possibility of their integration and reconciliation in the perspective of building a system of tourism statistics. [ABSTRACT FROM AUTHOR]

One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences. However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution. In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics. In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy. This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work. Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org. [ABSTRACT FROM AUTHOR]

Although the preponderance of collective memory research focuses on particular cultural repository sites, memorials, traumatic events, media channels, texts, or commemorative rituals as objects of study, this article fills a gap in literature by arguing that it is time to refresh established media-memory studies to now also consider how multimodal practices promise insight into the process of shared remembering in the new media ecology. The specific focus here is to propose a conceptual approach for how collective remembering can be observed, experienced, and researched in the digital ecosystem. In addition to a survey of collective memory and media memory studies, this article identifies specific ways to examine this issue by introducing the concepts of multimodal memory practices and platformed communities of memory, and by arguing that metadata analysis of digital practices should be considered a contemporary form of studying collective memory. [ABSTRACT FROM AUTHOR]

More than 50 million journal papers will have been published by the end of 2019 with 2 million more journal papers published every year. The number of conference papers is even higher, and millions of other types of scientific research are added to the knowledge base every year. Scientific databases such as Web of Science, Scopus, and PubMed index millions of scientific papers and Google Scholar indexes a huge amount of scientific knowledge across diverse domains. However, current systems provide long lists of results when users attempt to find relevant papers, leaving them with little choice other than manually skimming through the lists. This article surveys different techniques used to identify relevant research papers by knowledge-based organizations. We categorized current literature content as content, metadata, collaborative filtering, and citation based techniques and identified the strengths and limitation for each approach. Further, we evaluated the published techniques and research-based products used to identify relevant documents and identified the strengths and limitations of each approach. This research will greatly help to understand current state-of-the-art techniques internal workings for finding relevant papers, understand the relevant strengths and limitations, and explore previously proposed techniques targeting this area. [ABSTRACT FROM AUTHOR]

Nitrogen is the most limiting factor in crop production. Legumes establish a symbiotic relationship with rhizobia and enhance nitrogen fixation. We analyzed 1,624 rhizosphere 16S rRNA gene samples and 113 rhizosphere metagenomic samples from three typical legumes and three non-legumes. The rhizosphere microbial community of the legumes had low diversity and was enriched with nitrogen-cycling bacteria ( Sphingomonadaceae , Xanthobacteraceae , Rhizobiaceae , and Bacillaceae ). Furthermore, the rhizosphere microbiota of legumes exhibited a high abundance of nitrogen-fixing genes, reflecting a stronger nitrogen-fixing potential, and Streptomycetaceae and Nocardioidaceae were the predominant nitrogen-fixing bacteria. We also identified helper bacteria and confirmed through metadata analysis and a pot experiment that the synthesis of riboflavin by helper bacteria is the key factor in promoting nitrogen fixation. Our study emphasizes that the construction of synthetic communities of nitrogen-fixing bacteria and helper bacteria is crucial for the development of efficient nitrogen-fixing microbial fertilizers.

Application of organic fertilizers is an important strategy for sustainable agriculture. The biological source of organic fertilizers determines their specific functional characteristics, but few studies have systematically examined these functions or assessed their health risk to soil ecology. To fill this gap, we analyzed 16S rRNA gene amplicon sequencing data from 637 soil samples amended with plant- and animal-derived organic fertilizers (hereafter plant fertilizers and animal fertilizers). Results showed that animal fertilizers increased the diversity of soil microbiome, while plant fertilizers maintained the stability of soil microbial community. Microcosm experiments verified that plant fertilizers were beneficial to plant root development and increased carbon cycle pathways, while animal fertilizers enriched nitrogen cycle pathways. Compared with animal fertilizers, plant fertilizers harbored a lower abundance of risk factors such as antibiotic resistance genes and viruses. Consequently, plant fertilizers might be more suitable for long-term application in agriculture. This work provides a guide for organic fertilizer selection from the perspective of soil microecology and promotes sustainable development of organic agriculture.IMPORTANCEThis study provides valuable guidance for use of organic fertilizers in agricultural production from the perspective of the microbiome and ecological risk., Competing Interests: The authors declare no conflict of interest.

The conceptual framework of Essential Biodiversity Variables (EBVs) aims to capture the major dimensions of biodiversity change by structuring biodiversity monitoring and by ruling data collection amongst different providers. Amongst the research infrastructures adopting and implementing the EBV framework, LTER-Europe - the European node of ILTER (International Long-Term Ecological Research) - follows the approach to compare site-based biodiversity observations within and across its networks. However, a synoptic overview of their contributions with EBVs-relevant data is still missing, since data are not made available for several reasons. In this paper, we assess the capacity of LTER-Italy, one of the richest and heterogeneous networks of LTER sites in Europe, to provide data to "Species Distribution" and "Species Abundance" EBVs without inspecting and downloading their contents. To this aim, we mine the EBVs information which is publicly structured and shared by LTER site managers through DEIMS-SDR, the LTER-Europe online metadata repository. We classify the sites according to two types of contributions: (i) the actual contribution, based on metadata of datasets and (ii) the potential contribution, based on metadata of sites. Through these assessments, we investigate if LTER-Italy monitoring activities can provide EBVs measures and which sites currently provide datasets. By comparing the two contributions, we pinpoint the factors hampering the accessibility of LTER-Italy data and suggest solutions to increase the discoverability and reusability of LTER-Italy EBVs measurements. The research provides the first overview of EBVs monitored in LTER-Italy and the corresponding data management practices, as well as an evaluation of the interoperability of this network with respect to other research organisations for legal and technical aspects. [ABSTRACT FROM AUTHOR]

Hydrochars are valuable resources obtained from hydrothermal carbonization (HTC) of biomass. To optimize the reaction conditions of HTC, extensive experimentation is required, which is both costly and time consuming. In order to reduce the time and cost, this study develops new predictive models for higher heating values (HHV) of hydrochar based on Gaussian Process Regression (GPR), Ensemble, and Decision Tree (DT) algorithms using Bayesian Optimization (BO). This approach reduces prediction errors by combining GPR, Ensemble, and DT with BO. This is the first study on the application of BO for the hyperparameter selection as the basic learner. BO-GPR converged during training with the lowest Mean Absolute Error (0.1783) compared to BO-Ensemble (0.5128) and BO-DT (0.7430). The efficiencies of the algorithms were measured by the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R2). During the testing, BO-GPR generated MAE, RMSE and R2 of 0.4435, 0.5961 and 0.9705, respectively, and performed better than BO-Ensemble and BO-DT. The Nemenyi test showed that BO-GPR, BO-Ensemble, and BO-DT were statistically different in terms of their prediction ability. BO-GPR outperformed the other two methods. [Display omitted] • Higher heating value of hydrochars is predicted using machine learning algorithms. • Proximate analysis results for various biomass and process conditions were inputs. • Feature importance analysis showed that temperature is the main influential parameter. • Bayesian optimization (BO) algorithm is used to adjust hyperparameters. • BO- Gaussian Process Regression showed best prediction ability (R2: 0.9705). [ABSTRACT FROM AUTHOR]

This paper develops an aspect of my doctoral thesis which focuses on the German reception of Giuseppe Verdi’s Messa da Requiem. It analyzes the critique and commentary in German newspapers and music journals from the period of the first performances in Austria, Germany and Switzerland (1874–78) and outlines two ways of working with a corpus of heterogeneous texts by mostly unknown authors: Metadata analysis helps to distinguish the different scopes of music journals and newspapers, and a »horizontal reading« approach based on viewing the context of individual semantic entities in parallel gives insight into the spectrum of the reception in all sources together.

Plastic has become a “hot topic” for aquatic ecosystems’ conservation together with other issues such as climate change and biodiversity loss. Indeed, plastics may detrimentally affect habitats and biota. Small plastics, called microplastics, are more easily taken up by freshwater organisms, causing negative effects on growth, reproduction, predatory performance, etc. Since available information on microplastics in freshwater are fragmentary, the aim of this review is twofold: (i) to show, analyse, and discuss data on the microplastics concentration in freshwater and (ii) to provide the main polymers contaminating freshwater for management planning. A bibliographic search collected 158 studies since 2012, providing the scientific community with one of the largest data sets on microplastics in freshwater. Contamination is reported in all continents except Antarctica, but a lack of information is still present. Lentic waters are generally more contaminated than lotic waters, and waters are less contaminated than sediments, suggested to be sinks. The main contaminating polymers are polypropylene and polyethylene for sediment and water, while polyethylene and polyethylene terephthalate are mainly found in biota. Future research is encouraged (1) to achieve a standardised protocol for monitoring, (2) to identify sources and transport routes (including primary or secondary origin), and (3) to investigate trophic transfer, especially from benthic invertebrates.