cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.j00104.00093
title: AOL4PS
title: AOL4PS

description: This dataset is distributed for NON-COMMERCIAL RESEARCH USE ONLY.Brief description:The dataset is suitable for personalized search tasks and is built based on AOL query logs.The dataset contains 3 files.data: Real query log data that is based on real users for 3 months.AnonID - an anonymous user ID number.QueryIndex - the index of query issued by the userQueryTime - the time at which the query was submitted for search.SessionNo - the number of sessionsDataType - 0,1,2,3 representes historical data,train data, valid data, test data respectivelyDocIndex - the index of document clicked by the userCandiList - the candidate document list for query issued by userClickPos - the position of clicked document in candidate document listquery: The content and index of query.Query - the content of query issued by the userQueryIndex - the index of querydoc: The content and index of document.Url - the url of document clicked by the userDocIndex - the index of documentTitle - the title of Url
description: This dataset is distributed for NON-COMMERCIAL RESEARCH USE ONLY.Brief description:The dataset is suitable for personalized search tasks and is built based on AOL query logs.The dataset contains 3 files.data: Real query log data that is based on real users for 3 months.AnonID - an anonymous user ID number.QueryIndex - the index of query issued by the userQueryTime - the time at which the query was submitted for search.SessionNo - the number of sessionsDataType - 0,1,2,3 representes historical data,train data, valid data, test data respectivelyDocIndex - the index of document clicked by the userCandiList - the candidate document list for query issued by userClickPos - the position of clicked document in candidate document listquery: The content and index of query.Query - the content of query issued by the userQueryIndex - the index of querydoc: The content and index of document.Url - the url of document clicked by the userDocIndex - the index of documentTitle - the title of Url
DOI: 10.57760/sciencedb.j00104.00052
title: Knowledge Graph Construction and Applications for Web Search and Beyond
title: Knowledge Graph Construction and Applications for Web Search and Beyond

description: One table and seven figures of this paper. Table 1 presents Wang Zai’s personal information for more consistent question answering. Figure 1 is an overview of Sogou knowledge graph construction framework. The framework could be divided into three parts: Data Preparation contains operations including collecting data from various sources, extracting data from both structured source and free text and normalizing data; Knowledge graph construction contains all models to build a knowledge graph based on the extracted and normalized data; Application is composed of applications or services of a knowledge graph. A box with solid line represents an operation or model to process data while a box with dashed line represents the intermediate data. Figure 2 shows overview of Sogou knowledge graph storage architecture. Figure 3 shows after query entity linking, entity-related information and pictures are shown in search results. Figure 4 shows that search engine also gives related entity recommendations, due to entity relations in our KG. Figure 5 shows long text entity service used for Anchor Text generation. Figure 6 presents KBQA in Sogou Search Engine, which answers the query directly in the ﬁrst search result. In Figure 7, a user is having a dialogue with our conversational assistant, Wang Zai.
description: One table and seven figures of this paper. Table 1 presents Wang Zai’s personal information for more consistent question answering. Figure 1 is an overview of Sogou knowledge graph construction framework. The framework could be divided into three parts: Data Preparation contains operations including collecting data from various sources, extracting data from both structured source and free text and normalizing data; Knowledge graph construction contains all models to build a knowledge graph based on the extracted and normalized data; Application is composed of applications or services of a knowledge graph. A box with solid line represents an operation or model to process data while a box with dashed line represents the intermediate data. Figure 2 shows overview of Sogou knowledge graph storage architecture. Figure 3 shows after query entity linking, entity-related information and pictures are shown in search results. Figure 4 shows that search engine also gives related entity recommendations, due to entity relations in our KG. Figure 5 shows long text entity service used for Anchor Text generation. Figure 6 presents KBQA in Sogou Search Engine, which answers the query directly in the ﬁrst search result. In Figure 7, a user is having a dialogue with our conversational assistant, Wang Zai.
DOI: 10.57760/sciencedb.j00104.00054
title: Paving the Way to Open Data
title: Paving the Way to Open Data

description: Four tables and one figure of this paper. Table 1 summarizes definitions of open research and open science. Table 2 shows four data-sharing policies adopted at Wiley and their features. Table 3 presents ten example journals that have adopted the Wiley Expects Data policy. Table 4 shows template data availability statements from Ref. [15]. Figure 1 shows selected insights from Wiley Open Science survey, 2016. The whole infographic with many more insights is described in detail by Wiley and is available on Figshare.
description: Four tables and one figure of this paper. Table 1 summarizes definitions of open research and open science. Table 2 shows four data-sharing policies adopted at Wiley and their features. Table 3 presents ten example journals that have adopted the Wiley Expects Data policy. Table 4 shows template data availability statements from Ref. [15]. Figure 1 shows selected insights from Wiley Open Science survey, 2016. The whole infographic with many more insights is described in detail by Wiley and is available on Figshare.
DOI: 10.57760/sciencedb.j00104.00061
title: KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access
title: KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

description: One table and five figures of this paper. Table 1 is a summary of the distinguishing features of varying computational cost. Figure 1 is a high-level overview of the four approaches, where Lx means (declarative) representation language and Qx the query language. K is shorthand for the information and knowledge-level content and D is shorthand for instance-level content, irrespective of the formalism and technology used. Figure 2 shows two graphical renderings of a sample ARM schema, resembling a relational model graphical notation (left; source: based on a section of the diagram in ref. [10]) and in ontology-like notation similar to the ARM notation in ref.[25]. Figure 3 shows extending ARM+SQLP toward KnowID, i.e., adding a knowledge layer to the architecture, into the Knowledge-driven Information and Data access, KnowID, architecture. Figure 4 shows possible EER diagram reconstructed from the ARM diagram of Example 2. Figure 5 is an overview of the KnowID transformation tool architecture.
description: One table and five figures of this paper. Table 1 is a summary of the distinguishing features of varying computational cost. Figure 1 is a high-level overview of the four approaches, where Lx means (declarative) representation language and Qx the query language. K is shorthand for the information and knowledge-level content and D is shorthand for instance-level content, irrespective of the formalism and technology used. Figure 2 shows two graphical renderings of a sample ARM schema, resembling a relational model graphical notation (left; source: based on a section of the diagram in ref. [10]) and in ontology-like notation similar to the ARM notation in ref.[25]. Figure 3 shows extending ARM+SQLP toward KnowID, i.e., adding a knowledge layer to the architecture, into the Knowledge-driven Information and Data access, KnowID, architecture. Figure 4 shows possible EER diagram reconstructed from the ARM diagram of Example 2. Figure 5 is an overview of the KnowID transformation tool architecture.
DOI: 10.57760/sciencedb.j00104.00070
title: Distributed Analytics on Sensitive Medical Data: The Personal Health Train
title: Distributed Analytics on Sensitive Medical Data: The Personal Health Train

description: One table and two figures of the paper. Table 1 shows that The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remain in their original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations. FAIR principles supported by the PHT components. Figure 1 shows main components of the PHT architecture. Figure 2 presents applicability of FAIR principles to the components of the PHT.
description: One table and two figures of the paper. Table 1 shows that The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remain in their original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations. FAIR principles supported by the PHT components. Figure 1 shows main components of the PHT architecture. Figure 2 presents applicability of FAIR principles to the components of the PHT.
DOI: 10.57760/sciencedb.j00104.00071
title: FAIR Computational Workflows
title: FAIR Computational Workflows

description: Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. Figure 1 shows a workflow for detecting variants in genome sequences. The workflow is specified in the Common Workflow Language, displayed using the CWL Viewer. Any CWL compliant WfMS execution engine should be able to execute this standardized workflow description and obtain the same results independent of their underlying infrastructure, for instance using Toil executed on SLURM, or REANA on Kubernetes.
description: Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. Figure 1 shows a workflow for detecting variants in genome sequences. The workflow is specified in the Common Workflow Language, displayed using the CWL Viewer. Any CWL compliant WfMS execution engine should be able to execute this standardized workflow description and obtain the same results independent of their underlying infrastructure, for instance using Toil executed on SLURM, or REANA on Kubernetes.
DOI: 10.57760/sciencedb.j00104.00080
title: Social Data: CESSDA Best Practices
title: Social Data: CESSDA Best Practices

description: Three tables and one figure of the paper. Table 1 shows the overview of European research infrastructures. Table 2 presents index to FAIR Action Plan Recommendations. Table 3 shows elements of a Data Catalogue. Figure 1 is a screenshot from CESSDA Data Catalogue.
description: Three tables and one figure of the paper. Table 1 shows the overview of European research infrastructures. Table 2 presents index to FAIR Action Plan Recommendations. Table 3 shows elements of a Data Catalogue. Figure 1 is a screenshot from CESSDA Data Catalogue.
DOI: 10.57760/sciencedb.j00115.00004
title: Sequence information of the lysozyme gene CtG.lys of Chanoria tritonis
title: 法螺G型溶菌酶基因CtG.lys序列信息

description: The data uploaded this time is the cDNA sequence information of the ctG.lys gene of the Chanoria tritonis G-type lysozyme in the prokaryotic expression and antibacterial activity of the prokaryotic expression of the Chanoria tritonis G-type lysozyme recombinant protein.
description: The data uploaded this time is the cDNA sequence information of the ctG.lys gene of the Chanoria tritonis G-type lysozyme in the prokaryotic expression and antibacterial activity of the prokaryotic expression of the Chanoria tritonis G-type lysozyme recombinant protein.
DOI: 10.57760/sciencedb.j00116.00006
title: Raw Data for the article "Development of a rabbit model of Wiskott-Aldrich syndrome"
title: Raw Data for the article "Development of a rabbit model of Wiskott-Aldrich syndrome"

description: This zip package includes all the raw data involving in the article "Development of a rabbit model of Wiskott-Aldrich syndrome" submitted to the FASEB journal.
description: This zip package includes all the raw data involving in the article "Development of a rabbit model of Wiskott-Aldrich syndrome" submitted to the FASEB journal.
DOI: 10.57760/sciencedb.j00104.00074
title: Growing the FAIR Community at the Intersection of the Geosciences and Pure and Applied Chemistry
title: Growing the FAIR Community at the Intersection of the Geosciences and Pure and Applied Chemistry

description: Two figures of the paper. Figure 1 shows research processes that need to be intertwined with good data management practices. Figure 2 shows the Data Management Training Clearinghouse website demonstrating the filter alignment with the FAIR Guiding Principles.
description: Two figures of the paper. Figure 1 shows research processes that need to be intertwined with good data management practices. Figure 2 shows the Data Management Training Clearinghouse website demonstrating the filter alignment with the FAIR Guiding Principles.
DOI: 10.57760/sciencedb.j00104.00059
title: Refining Linked Data with Games with a Purpose
title: Refining Linked Data with Games with a Purpose

description: Two tables, 1 algorithm and six figures. Table 1 shows the number of required contributions for truth inference over N tasks. Table 2 shows the truth inference results comparison between our incremental approach and state-of-the-art techniques. Algorithm 1 shows incremental truth inference algorithm. Figure 1 shows incremental Truth Inference approach. Figure 2 presents architecture of the GWAP enabler. Figure 3 shows Indomilando: gameplay (left) and asset visualisation on a map (right). Figure 4 shows land cover validation game: pixel classification (left) and badges (right). Figure 5 shows Night Knights: the game play. Figure 6 presents sample screenshot from ARGO game to collect analogies.
description: Two tables, 1 algorithm and six figures. Table 1 shows the number of required contributions for truth inference over N tasks. Table 2 shows the truth inference results comparison between our incremental approach and state-of-the-art techniques. Algorithm 1 shows incremental truth inference algorithm. Figure 1 shows incremental Truth Inference approach. Figure 2 presents architecture of the GWAP enabler. Figure 3 shows Indomilando: gameplay (left) and asset visualisation on a map (right). Figure 4 shows land cover validation game: pixel classification (left) and badges (right). Figure 5 shows Night Knights: the game play. Figure 6 presents sample screenshot from ARGO game to collect analogies.
DOI: 10.57760/sciencedb.j00104.00073
title: Taking FAIR on the ChIN: The Chemistry Implementation Network
title: Taking FAIR on the ChIN: The Chemistry Implementation Network

description: The Chemistry Implementation Network (ChIN) is focused on supporting the FAIR Data needs of the research community regarding chemical related data. Figure 1 shows the interoperation between the ChIN and key community-leading organizations.
description: The Chemistry Implementation Network (ChIN) is focused on supporting the FAIR Data needs of the research community regarding chemical related data. Figure 1 shows the interoperation between the ChIN and key community-leading organizations.
DOI: 10.57760/sciencedb.j00104.00094
title: EHR data from MIMIC-III
title: EHR data from MIMIC-III

description: We conducted our experiments on de-identified EHR data from MIMIC-III. This data set contains various clinical data relating to patient admission to ICU, such as disease diagnoses in the form of International Classification of Diseases (ICD)-9 codes, and lab test results as detailed in Supplementary Materials. We collected data for 5,956 patients, extracting lab tests every hour from admission. There are a total of 409 unique lab tests and 3,387 unique disease diagnoses observed. The diagnoses were obtained as ICD-9 codes and they were represented using one-hot encoding where one represents patients with disease and zero indicates those without. We binned the lab test events into 6, 12, 24, and 48 hours prior to patient death or discharge from ICU. From these data, we performed mortality predictions that are 10-fold, cross validated.
description: We conducted our experiments on de-identified EHR data from MIMIC-III. This data set contains various clinical data relating to patient admission to ICU, such as disease diagnoses in the form of International Classification of Diseases (ICD)-9 codes, and lab test results as detailed in Supplementary Materials. We collected data for 5,956 patients, extracting lab tests every hour from admission. There are a total of 409 unique lab tests and 3,387 unique disease diagnoses observed. The diagnoses were obtained as ICD-9 codes and they were represented using one-hot encoding where one represents patients with disease and zero indicates those without. We binned the lab test events into 6, 12, 24, and 48 hours prior to patient death or discharge from ICU. From these data, we performed mortality predictions that are 10-fold, cross validated.
DOI: 10.57760/sciencedb.j00104.00098
title: A multinational dataset of game players’ behaviors in a virtual world and environmental perceptions
title: A multinational dataset of game players’ behaviors in a virtual world and environmental perceptions

description: The current dataset offers valuable resources regarding environmental worldviews and behaviors in the virtual world of 640 Animal Crossing: New Horizons (ACNH) gameplayers from 29 countries around the globe. The dataset consists of six major categories: 1) socio-demographic profile, 2) COVID-19 concern, 3) environmental perception, 4) game-playing habit, 5) in-game behavior, and 6) game-playing feeling. By making this dataset open, we aim to provide policymakers, game producers, and researchers with valuable resources for understanding the interactions between behaviors in the virtual world and environmental perceptions, which could help produce video games in compliance with the United Nations (UN) Sustainable Development Goals.
description: The current dataset offers valuable resources regarding environmental worldviews and behaviors in the virtual world of 640 Animal Crossing: New Horizons (ACNH) gameplayers from 29 countries around the globe. The dataset consists of six major categories: 1) socio-demographic profile, 2) COVID-19 concern, 3) environmental perception, 4) game-playing habit, 5) in-game behavior, and 6) game-playing feeling. By making this dataset open, we aim to provide policymakers, game producers, and researchers with valuable resources for understanding the interactions between behaviors in the virtual world and environmental perceptions, which could help produce video games in compliance with the United Nations (UN) Sustainable Development Goals.
DOI: 10.57760/sciencedb.j00104.00100
title: Datasets for NLPCC2022.SharedTask5.Track1
title: Datasets for NLPCC2022.SharedTask5.Track1

description: In recent years, the indexing for scientific research literature is becoming more and more important with the increasing number of such literature in various fields. According to incomplete statistics, about 2.5 million research papers published from about 28100 journals every year. Search engines, digital libraries and citation indexes are widely used to search these documents and publications. The intelligent classification of scientific research literature is helpful to improve the retrieval efficiency and quality of users. Hierarchical multi-label classification is a common method for indexing scientific research literature. For example, both arXiv and Microsoft Academic adopt this method. Moreover, hierarchical multi-label classification is also a challenging task in text classification.
description: In recent years, the indexing for scientific research literature is becoming more and more important with the increasing number of such literature in various fields. According to incomplete statistics, about 2.5 million research papers published from about 28100 journals every year. Search engines, digital libraries and citation indexes are widely used to search these documents and publications. The intelligent classification of scientific research literature is helpful to improve the retrieval efficiency and quality of users. Hierarchical multi-label classification is a common method for indexing scientific research literature. For example, both arXiv and Microsoft Academic adopt this method. Moreover, hierarchical multi-label classification is also a challenging task in text classification.
DOI: 10.57760/sciencedb.j00113.00004
title: Structures and electronic states of one-dimensional atomic wires based on first principles calculations
title: Structures and electronic states of one-dimensional atomic wires based on first principles calculations

description: In this database, 367 kinds of one-dimensional (1D) atomic wires (AWs) are included with JSON format. The corresponding structures, electronic structures, magnetic states, phonon spectra and exfoliation energies of these 1D AWs are provided and organized. Besides, a python code is also attached to extract the data from the JSON file based on the keys in each layer. This database will serve as a valuable resource for the scientific community to further explore 1D exotic quantum states and design practical devices using 1D AWs.
description: In this database, 367 kinds of one-dimensional (1D) atomic wires (AWs) are included with JSON format. The corresponding structures, electronic structures, magnetic states, phonon spectra and exfoliation energies of these 1D AWs are provided and organized. Besides, a python code is also attached to extract the data from the JSON file based on the keys in each layer. This database will serve as a valuable resource for the scientific community to further explore 1D exotic quantum states and design practical devices using 1D AWs.