Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Environmental Sciences
Title: Role of Plants and Microbes in Bioremediation of Petroleum Hydrocarbons Contaminated Soils
Abstract: Petroleum hydrocarbons contamination of soil, sediments and marine environment associated with the inadvertent discharges of petroleum–derived chemical wastes and petroleum hydrocarbons associated with spillage and other sources into the environment often pose harmful effects on human health and the natural environment, and have negative socio–economic impacts in the oil–producing host communities. In practice, plants and microbes have played a major role in microbial transformation and growth–linked mineralization of petroleum hydrocarbons in contaminated soils and/or sediments over the past years. Bioremediation strategies has been recognized as an environmental friendly and cost–effective alternative in comparison with the traditional physico-chemical approaches for the restoration and reclamation of contaminated sites. The success of any plant–based remediation strategy depends on the interaction of plants with rhizospheric microbial populations in the surrounding soil medium and the organic contaminant. Effective understanding of the fate and behaviour of organic contaminants in the soil can help determine the persistence of the contaminant in the terrestrial environment, promote the success of any bioremediation approach and help develop a high–level of risks mitigation strategies. In this review paper, we provide a clear insight into the role of plants and microbes in the microbial degradation of petroleum hydrocarbons in contaminated soil that have emerged from the growing body of bioremediation research and its applications in practice. In addition, plant–microbe interactions have been discussed with respect to biodegradation of petroleum hydrocarbons and these could provide a better understanding of some important factors necessary for development of in situ bioremediation strategies for risks mitigation in petroleum hydrocarbon–contaminated soil.
Extracted statement — has research problem: Petroleum hydrocarbons contamination of soil, sediments and marine environment associated with the inadvertent discharges of petroleum–derived chemical wastes and petroleum hydrocarbons associated with spillage and other sources into the environment often pose harmful effects on human health and the natural environment, and have negative socio–economic impacts in the oil–producing host communities.
Research field: Environmental Sciences
Title: Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble: FUTURE CHANGES OF WIND ENERGY OVER EUROPE IN A CMIP5 ENSEMBLE
Abstract: A statistical‐dynamical downscaling method is used to estimate future changes of wind energy output (Eout) of a benchmark wind turbine across Europe at the regional scale. With this aim, 22 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble are considered. The downscaling method uses circulation weather types and regional climate modelling with the COSMO‐CLM model. Future projections are computed for two time periods (2021–2060 and 2061–2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 ensemble mean response reveals a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an ensemble mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra‐annual variability for most of Europe. The latter is, in particular, probable during the second half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061–2100 compared to 2021–2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter‐annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an ensemble of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi‐model ensembles are needed to provide a better quantification and understanding of the future changes.
Extracted statement — has research problem: wind energy potentials
Research field: Ethics and Political Philosophy
Title: Individual homogenization in large-scale systems: on the politics of computer and social architectures
Abstract: Abstract One determining characteristic of contemporary sociopolitical systems is their power over increasingly large and diverse populations. This raises questions about power relations between heterogeneous individuals and increasingly dominant and homogenizing system objectives. This article crosses epistemic boundaries by integrating computer engineering and a historicalphilosophical approach making the general organization of individuals within large-scale systems and corresponding individual homogenization intelligible. From a versatile archeological-genealogical perspective, an analysis of computer and social architectures is conducted that reinterprets Foucault’s disciplines and political anatomy to establish the notion of politics for a purely technical system. This permits an understanding of system organization as modern technology with application to technical and social systems alike. Connecting to Heidegger’s notions of the enframing ( Gestell ) and a more primal truth ( anfänglicheren Wahrheit) , the recognition of politics in differently developing systems then challenges the immutability of contemporary organization. Following this critique of modernity and within the conceptualization of system organization, Derrida’s democracy to come (à venir) is then reformulated more abstractly as organizations to come . Through the integration of the discussed concepts, the framework of Large-Scale Systems Composed of Homogeneous Individuals (LSSCHI) is proposed, problematizing the relationships between individuals, structure, activity, and power within large-scale systems. The LSSCHI framework highlights the conflict of homogenizing system-level objectives and individual heterogeneity, and outlines power relations and mechanisms of control shared across different social and technical systems.
Extracted statement — has research problem: Heterogeneity
Research field: Ethics and Political Philosophy
Title: Individual homogenization in large-scale systems: on the politics of computer and social architectures
Abstract: Abstract One determining characteristic of contemporary sociopolitical systems is their power over increasingly large and diverse populations. This raises questions about power relations between heterogeneous individuals and increasingly dominant and homogenizing system objectives. This article crosses epistemic boundaries by integrating computer engineering and a historicalphilosophical approach making the general organization of individuals within large-scale systems and corresponding individual homogenization intelligible. From a versatile archeological-genealogical perspective, an analysis of computer and social architectures is conducted that reinterprets Foucault’s disciplines and political anatomy to establish the notion of politics for a purely technical system. This permits an understanding of system organization as modern technology with application to technical and social systems alike. Connecting to Heidegger’s notions of the enframing ( Gestell ) and a more primal truth ( anfänglicheren Wahrheit) , the recognition of politics in differently developing systems then challenges the immutability of contemporary organization. Following this critique of modernity and within the conceptualization of system organization, Derrida’s democracy to come (à venir) is then reformulated more abstractly as organizations to come . Through the integration of the discussed concepts, the framework of Large-Scale Systems Composed of Homogeneous Individuals (LSSCHI) is proposed, problematizing the relationships between individuals, structure, activity, and power within large-scale systems. The LSSCHI framework highlights the conflict of homogenizing system-level objectives and individual heterogeneity, and outlines power relations and mechanisms of control shared across different social and technical systems.
Extracted statement — has research problem: System organization
Research field: Genomics
Title: The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia
Abstract: Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1, and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard-of-care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard-of-care diagnostics. KMT2A (MLL) rearrangements, although usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard-of-care testing in 9 patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the most common IKZF1 deletion from RNA-seq data and validated this using an RQ-PCR assay. We applied an expression classifier to identify Philadelphia chromosome-like B-ALL patients. T-ALL proved a rich source of novel gene fusions, which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.
Extracted statement — has research problem: acute lymphoblastic leukemia (ALL)
Research field: Graphics
Title: Formal Linked Data Visualization Model
Abstract: Recently, the amount of semantic data available in the Web has increased dramatically. The potential of this vast amount of data is enormous but in most cases it is difficult for users to explore and use this data, especially for those without experience with Semantic Web technologies. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. In this article we devise a formal Linked Data Visualization Model (LDVM), which allows to dynamically connect data with visualizations. We report about our implementation of the LDVM comprising a library of generic visualizations that enable both users and data analysts to get an overview on, visualize and explore the Data Web and perform detailed analyzes on Linked Data.
Extracted statement — has research problem: Data Visualization
Research field: Human and Clinical Nutrition
Title: Comparative Assessment of Iodine Content of Commercial Table Salt Brands Available in Nigerian Market
Abstract: Iodine deficiency disorders (IDD) has been a major global public health problem threatening more than 2 billion people worldwide. Considering various human health implications associated with iodine deficiency, universal salt iodization programme has been recognized as one of the best methods of preventing iodine deficiency disorder and iodizing table salt is currently done in many countries. In this study, comparative assessment of iodine content of commercially available table salt brands in Nigerian market were investigated and iodine content were measured in ten table salt brands samples using iodometric titration. The iodine content ranged from 14.80 mg/kg – 16.90 mg/kg with mean value of 15.90 mg/kg for Sea salt; 24.30 mg/kg – 25.40 mg/kg with mean value of 24.60 mg/kg for Dangote salt (blue sachet); 22.10 mg/kg – 23.10 mg/kg with mean value of 22.40 mg/kg for Dangote salt (red sachet); 23.30 mg/kg – 24.30 mg/kg with mean value of 23.60 mg/kg for Mr Chef salt; 23.30 mg/kg – 24.30 mg/kg with mean value of 23.60 mg/kg for Annapurna; 26.80 mg/kg – 27.50 mg/kg with mean value of 27.20mg/kg for Uncle Palm salt; 23.30 mg/kg – 29.60 mg/kg with mean content of 26.40 mg/kg for Dangote (bag); 25.40 mg/kg – 26.50 mg/kg with mean value of 26.50 mg/kg for Royal salt; 36.80 mg/kg – 37.20 mg/kg with mean iodine content of 37.0 mg/kg for Abakaliki refined salt, and 30.07 mg/kg – 31.20 mg/kg with mean value of 31.00 mg/kg for Ikom refined salt. The mean iodine content measured in the Sea salt brand (15.70 mg/kg) was significantly P < 0.01 lower compared to those measured in other table salt brands. Although the iodine content of Abakaliki and Ikom refined salt exceed the recommended value, it is clear that only Sea salt brand falls below the World Health Organization (WHO) recommended value (20 – 30 mg/kg), while the remaining table salt samples are just within the range. The results obtained have revealed that 70 % of the table salt brands were adequately iodized while 30 % of the table salt brands were not adequately iodized and provided baseline data that can be used for potential identification of human health risks associated with inadequate and/or excess iodine content in table salt brands consumed in households in Nigeria.
Extracted statement — has research problem: Iodine deficiency disorders (IDD) has been a major global public health problem threatening more than 2 billion people worldwide.
Research field: Information Science
Title: Matching the Blanks: Distributional Similarity for Relation Learning
Abstract: General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. However, both of these approaches are limited in their ability to generalize. In this paper, we build on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task’s training data. We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED
Extracted statement — has research problem: build task agnostic relation representations solely from entity-linked text
Research field: Information Science
Title: Semantic relation classification via bidirectional LSTM networks with entity-aware attention using latent entity typing
Abstract: Classifying semantic relations between entity pairs in sentences is an important task in natural language processing (NLP). Most previous models applied to relation classification rely on high-level lexical and syntactic features obtained by NLP tools such as WordNet, the dependency parser, part-of-speech (POS) tagger, and named entity recognizers (NER). In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information related to the entity, which may be the most crucial feature for relation classification. To address these issues, we propose a novel end-to-end recurrent neural model that incorporates an entity-aware attention mechanism with a latent entity typing (LET) method. Our model not only effectively utilizes entities and their latent types as features, but also builds word representations by applying self-attention based on symmetrical similarity of a sentence itself. Moreover, the model is interpretable by visualizing applied attention mechanisms. Experimental results obtained with the SemEval-2010 Task 8 dataset, which is one of the most popular relation classification tasks, demonstrate that our model outperforms existing state-of-the-art models without any high-level features.
Extracted statement — has research problem: Classifying semantic relations between entity pairs in sentences
Research field: Information Science
Title: Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start Problem
Abstract: E-learning recommender systems are gaining significance nowadays due to its ability to enhance the learning experience by providing tailor-made services based on learner preferences. A Personalized Learning Environment (PLE) that automatically adapts to learner characteristics such as learning styles and knowledge level can recommend appropriate learning resources that would favor the learning process and improve learning outcomes. The pure cold-start problem is a relevant issue in PLEs, which arises due to the lack of prior information about the new learner in the PLE to create appropriate recommendations. This article introduces a semantic framework based on ontology to address the pure cold-start problem in content recommenders. The ontology encapsulates the domain knowledge about the learners as well as Learning Objects (LOs). The semantic model that we built has been experimented with different combinations of the key learner parameters such as learning style, knowledge level, and background knowledge. The proposed framework utilizes these parameters to build natural learner groups from the learner ontology using SPARQL queries. The ontology holds 480 learners’ data, 468 annotated learning objects with 5,600 learner ratings. A multivariate k-means clustering algorithm, an unsupervised machine learning technique for grouping similar data, is used to evaluate the learner similarity computation accuracy. The learner satisfaction achieved with the proposed model is measured based on the ratings given by the 40 participants of the experiments. From the evaluation perspective, it is evident that 79% of the learners are satisfied with the recommendations generated by the proposed model in pure cold-start condition.
Extracted statement — has research problem: Cold-start Problem
Research field: Information Science
Title: A model for contextual data sharing in smartphone applications
Abstract: Purpose The purpose of this paper is to introduce a model for identifying, storing and sharing contextual information across smartphone apps that uses the native device services. The authors present the idea of using user input and interaction within an app as contextual information, and how each app can identify and store contextual information. Design/methodology/approach Contexts are modeled as hierarchical objects that can be stored and shared by applications using native mechanisms. A proof-of-concept implementation of the model for the Android platform demonstrates contexts modelled as hierarchical objects stored and shared by applications using native mechanisms. Findings The model was found to be practically viable by implemented sample apps that share context and through a performance analysis of the system. Practical implications The contextual data-sharing model enables the creation of smart apps and services without being tied to any vendor’s cloud services. Originality/value This paper introduces a new approach for sharing context in smartphone applications that does not require cloud services.
Extracted statement — has research problem: contextual 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 — has research problem: Data Publishing
Research field: Information Science
Title: Directing the development of constraint languages by checking constraints on rdf data
Abstract: For research institutes, data libraries, and data archives, validating RDF data according to predefined constraints is a much sought-after feature, particularly as this is taken for granted in the XML world. Based on our work in two international working groups on RDF validation and jointly identified requirements to formulate constraints and validate RDF data, we have published 81 types of constraints that are required by various stakeholders for data applications. In this paper, we evaluate the usability of identified constraint types for assessing RDF data quality by (1) collecting and classifying 115 constraints on vocabularies commonly used in the social, behavioral, and economic sciences, either from the vocabularies themselves or from domain experts, and (2) validating 15,694 data sets (4.26 billion triples) of research data against these constraints. We classify each constraint according to (1) the severity of occurring violations and (2) based on which types of constraint languages are able to express its constraint type. Based on the large-scale evaluation, we formulate several findings to direct the further development of constraint languages.
Extracted statement — has research problem: Data Quality