Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Science
Title: Situational Knowledge Representation for Traffic Observed by a Pavement Vibration Sensor Network
Abstract: Information systems that build on sensor networks often process data produced by measuring physical properties. These data can serve in the acquisition of knowledge for real-world situations that are of interest to information services and, ultimately, to people. Such systems face a common challenge, namely the considerable gap between the data produced by measurement and the abstract terminology used to describe real-world situations. We present and discuss the architecture of a software system that utilizes sensor data, digital signal processing, machine learning, and knowledge representation and reasoning to acquire, represent, and infer knowledge about real-world situations observable by a sensor network. We demonstrate the application of the system to vehicle detection and classification by measurement of road pavement vibration. Thus, real-world situations involve vehicles and information for their type, speed, and driving direction.
Extracted statement — employs: Machine Learning
Research field: Information Science
Title: Curating Scientific Information in Knowledge Infrastructures
Abstract: Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information – meaningful secondary or derived data – about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation – as machine readable data and their machine-readable meaning – is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Extracted statement — Material: machine readable data
Research field: Information Science
Title: A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers 
Abstract: Abstract Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.
Extracted statement — Process: manuscript peer review
Research field: Information Science
Title: Scalable SPARQL querying of large RDF graphs
Abstract: The generation of RDF data has accelerated to the point where many data sets need to be partitioned across multiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, current solutions that allow the data to be partitioned across multiple machines are highly inefficient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more efficient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query processing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster.
Extracted statement — Material: many data sets
Research field: Information Science
Title: Curating Scientific Information in Knowledge Infrastructures
Abstract: Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information – meaningful secondary or derived data – about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation – as machine readable data and their machine-readable meaning – is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Extracted statement — Data: meaningful secondary or derived data
Research field: Information Science
Title: A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers 
Abstract: Abstract Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.
Extracted statement — Method: metadata import workflow
Research field: Information Science
Title: Multi-Agent Systems: A Survey
Abstract: Multi-agent systems (MASs) have received tremendous attention from scholars in different disciplines, including computer science and civil engineering, as a means to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous entities, known as agents. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with its neighboring agents, and its goal. The MAS has found multiple applications, including modeling complex systems, smart grids, and computer networks. Despite their wide applicability, there are still a number of challenges faced by MAS, including coordination between agents, security, and task allocation. This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation. A classification on MAS applications and challenges is provided along with references for further studies. We expect this paper to serve as an insightful and comprehensive resource on the MAS for researchers and practitioners in the area.
Extracted statement — applicable in: Modeling Complex Systems
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Data: modeling patterns
Research field: Information Science
Title: More Complete Resultset Retrieval from Large Heterogeneous RDF Sources
Abstract: Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Extracted statement — Data: My URI" (WIMU)
Research field: Information Science
Title: A Systematic Review of Information Literacy Programs in Higher Education: Effects of Face-to-Face, Online, and Blended Formats on Student Skills and Views
Abstract: Objective – Evidence from systematic reviews a decade ago suggested that face-to-face and online methods to provide information literacy training in universities were equally effective in terms of skills learnt, but there was a lack of robust comparative research. The objectives of this review were (1) to update these findings with the inclusion of more recent primary research; (2) to further enhance the summary of existing evidence by including studies of blended formats (with components of both online and face-to-face teaching) compared to single format education; and (3) to explore student views on the various formats employed. Methods – Authors searched seven databases along with a range of supplementary search methods to identify comparative research studies, dated January 1995 to October 2016, exploring skill outcomes for students enrolled in higher education programs. There were 33 studies included, of which 19 also contained comparative data on student views. Where feasible, meta-analyses were carried out to provide summary estimates of skills development and a thematic analysis was completed to identify student views across the different formats. Results – A large majority of studies (27 of 33; 82%) found no statistically significant difference between formats in skills outcomes for students. Of 13 studies that could be included in a meta-analysis, the standardized mean difference (SMD) between skill test results for face-to-face versus online formats was -0.01 (95% confidence interval -0.28 to 0.26). Of ten studies comparing blended to single delivery format, seven (70%) found no statistically significant difference between formats, and the remaining studies had mixed outcomes. From the limited evidence available across all studies, there is a potential dichotomy between outcomes measured via skill test and assignment (course work) which is worthy of further investigation. The thematic analysis of student views found no preference in relation to format on a range of measures in 14 of 19 studies (74%). The remainder identified that students perceived advantages and disadvantages for each format but had no overall preference. Conclusions – There is compelling evidence that information literacy training is effective and well received across a range of delivery formats. Further research looking at blended versus single format methods, and the time implications for each, as well as comparing assignment to skill test outcomes would be valuable. Future studies should adopt a methodologically robust design (such as the randomized controlled trial) with a large student population and validated outcome measures.
Extracted statement — Has result: no preference in relation to format
Research field: Information Science
Title: AUPress: A Comparison of an Open Access University Press with Traditional Presses
Abstract: This study is a comparison of AUPress with three other traditional (non-open access) Canadian university presses. The analysis is based on the rankings that are correlated with book sales on Amazon.com and Amazon.ca. Statistical methods include the sampling of the sales ranking of randomly selected books from each press. The results of one-way ANOVA analyses show that there is no significant difference in the ranking of printed books sold by AUPress in comparison with traditional university presses. However, AUPress, can demonstrate a significantly larger readership for its books as evidenced by the number of downloads of the open electronic versions.
Extracted statement — Result: no significant difference
Research field: Information Science
Title: Expanding horizons in historical linguistics with the 400-million word Corpus of Historical American English
Abstract: The Corpus of Historical American English (COHA) contains 400 million words in more than 100,000 texts which date from the 1810s to the 2000s. The corpus contains texts from fiction, popular magazines, newspapers and non-fiction books, and is balanced by genre from decade to decade. It has been carefully lemmatised and tagged for part-of-speech, and uses the same architecture as the Corpus of Contemporary American English (COCA), BYU-BNC, the TIME Corpus and other corpora. COHA allows for a wide range of research on changes in lexis, morphology, syntax, semantics, and American culture and society (as viewed through language change), in ways that are probably not possible with any text archive (e.g., Google Books) or any other corpus of historical American English.
Extracted statement — Corpus genres: non-fiction books
Research field: Information Science
Title: A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers 
Abstract: Abstract Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.
Extracted statement — Method: omics data paper structure
Research field: Information Science
Title: A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers 
Abstract: Abstract Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.
Extracted statement — Material: omics data paper template
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Data: ontology-driven conceptual model patterns