cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30341143.v3
title: RoadSens-4M Dataset

description: The <b>RoadSens-4M Dataset</b> provides a multimodal collection of sensor, video, weather, and GIS data designed to support research in intelligent transportation systems, road condition monitoring, and machine-learning-based anomaly detection. This dataset integrates synchronized smartphone sensor data (accelerometer, gyroscope, magnetometer, GPS) with video annotations, weather, and geospatial information to accurately identify and classify road surface anomalies, including bumps, potholes, and normal road segments.<b>The dataset comprises 103 data sessions organized in a hierarchical structure to facilitate flexible access and multi-level analysis.</b> It is divided into four main components: <b>Raw Data</b>, <b>Combined CSV with GIS and Weather Data</b>, <b>Isolated Data</b>, and <b>GIS Data</b>. Each session folder contains all corresponding sensor CSV files, including both calibrated and uncalibrated readings from the accelerometer, gyroscope, magnetometer, barometer, compass, gravity, and GPS sensors, along with annotation and metadata files. Within every session, a dedicated <b>camera subfolder</b> holds annotation data and a text file linking to the corresponding video stored on Google Drive, allowing researchers to access complete recordings without manual segmentation.The <b>merged CSV files</b> combine synchronized sensor, GIS, and weather information (temperature, humidity, wind speed, and atmospheric pressure) with a sampling interval of <b>0.01 seconds</b>, ensuring high temporal resolution. The <b>Isolated Data</b> folder further separates normal and anomaly samples to enable focused comparative analysis, while the <b>GIS Data</b> folder contains QGIS and elevation files for spatial and topographical visualization.This well-structured organization ensures seamless integration of sensor, video, geographic, and environmental data, supporting efficient navigation and in-depth multimodal research. The <b>raw data</b> are hosted separately on Google Drive and can be accessed via the following link:<br>🔗 https://drive.google.com/drive/folders/16tRSgXy6bjgIcJZzdw3U5unw7jpsKAHB?usp=drive_link
DOI: 10.6084/m9.figshare.30341143.v2
title: RoadSens-4M Dataset

description: The <b>RoadSens-4M Dataset</b> provides a multimodal collection of sensor, video, weather, and GIS data designed to support research in intelligent transportation systems, road condition monitoring, and machine-learning-based anomaly detection. This dataset integrates synchronized smartphone sensor data (accelerometer, gyroscope, magnetometer, GPS) with video annotations, weather, and geospatial information to accurately identify and classify road surface anomalies, including bumps, potholes, and normal road segments.<b>The dataset comprises 103 data sessions organized in a hierarchical structure to facilitate flexible access and multi-level analysis.</b> It is divided into four main components: <b>Raw Data</b>, <b>Combined CSV with GIS and Weather Data</b>, <b>Isolated Data</b>, and <b>GIS Data</b>. Each session folder contains all corresponding sensor CSV files, including both calibrated and uncalibrated readings from the accelerometer, gyroscope, magnetometer, barometer, compass, gravity, and GPS sensors, along with annotation and metadata files. Within every session, a dedicated <b>camera subfolder</b> holds annotation data and a text file linking to the corresponding video stored on Google Drive, allowing researchers to access complete recordings without manual segmentation.The <b>merged CSV files</b> combine synchronized sensor, GIS, and weather information (temperature, humidity, wind speed, and atmospheric pressure) with a sampling interval of <b>0.01 seconds</b>, ensuring high temporal resolution. The <b>Isolated Data</b> folder further separates normal and anomaly samples to enable focused comparative analysis, while the <b>GIS Data</b> folder contains QGIS and elevation files for spatial and topographical visualization.This well-structured organization ensures seamless integration of sensor, video, geographic, and environmental data, supporting efficient navigation and in-depth multimodal research. The <b>raw data</b> are hosted separately on Google Drive and can be accessed via the following link:<br>🔗 https://drive.google.com/drive/folders/16tRSgXy6bjgIcJZzdw3U5unw7jpsKAHB?usp=drive_link
DOI: 10.5281/zenodo.17693328
title: Nbiol-GNA-UdeA/E280A-Serum-Lipidomics-Repository-GNA-Cohort: E280A Serum Lipidomics Repository – GNA Cohort

description: Full Changelog: https://github.com/Nbiol-GNA-UdeA/E280A-Serum-Lipidomics-Repository-GNA-Cohort/commits/v1
DOI: 10.6084/m9.figshare.31400436
title: <b>the </b><b>China–Africa trade</b>

description: This study employs multi-regional input-output (MRIO) analysis, integrating Eora and UNCTAD data from 1990 to 2021. It goes beyond aggregate trade indicators by decomposing China-African trade into domestic and foreign value-added components. Our research makes key contributions: First, value capture remains asymmetric: African economies exhibit stronger forward participation in upstream segments, whereas China retains a larger share of downstream, manufacturing-intensive value added. Second, bilateral trade deepening is associated with persistent sectoral concentration in many African economies, in contrast to China’s broader industrial diversification. Third, these structural configurations display substantial path dependence, with limited convergence in relative value capture over time. Fourth, distinct components of value capture exhibit systematic associations with differences in GDP composition across country groups, linking trade structure to macroeconomic patterns without implying causal effects. These findings offer valuable insights into the debates surrounding global value chains and South-South cooperation, while also providing evidence-based policy implications aimed at enhancing value retention and fostering sustainable economic partnerships. These findings are closely aligned with the scope of the journal.
DOI: 10.6084/m9.figshare.31400436.v1
title: <b>the </b><b>China–Africa trade</b>

description: This study employs multi-regional input-output (MRIO) analysis, integrating Eora and UNCTAD data from 1990 to 2021. It goes beyond aggregate trade indicators by decomposing China-African trade into domestic and foreign value-added components. Our research makes key contributions: First, value capture remains asymmetric: African economies exhibit stronger forward participation in upstream segments, whereas China retains a larger share of downstream, manufacturing-intensive value added. Second, bilateral trade deepening is associated with persistent sectoral concentration in many African economies, in contrast to China’s broader industrial diversification. Third, these structural configurations display substantial path dependence, with limited convergence in relative value capture over time. Fourth, distinct components of value capture exhibit systematic associations with differences in GDP composition across country groups, linking trade structure to macroeconomic patterns without implying causal effects. These findings offer valuable insights into the debates surrounding global value chains and South-South cooperation, while also providing evidence-based policy implications aimed at enhancing value retention and fostering sustainable economic partnerships. These findings are closely aligned with the scope of the journal.
DOI: 10.6084/m9.figshare.31400433
title: Smart Furniture Publication Dataset

description: This dataset is a compiles Scopus-indexed scholarly records on the topic of Smart Furniture for the period 1996–2024, covering 356 English-language publications retrieved in November 2025. Records were collected using a transparent Scopus query that targets smart furniture and closely related expressions in titles, abstracts, and keywords: (TITLE-ABS-KEY (smart furniture OR intelligent furniture OR connected furniture OR IoT furniture OR interactive furniture) OR TITLE-ABS-KEY (furniture AND (smart home OR smart object OR smart product))) AND PUBYEAR &gt; 1995 AND PUBYEAR &lt; 2025 AND (LIMIT-TO (LANGUAGE, English)). The repository includes three complementary components: (1) a master smart furniture publication dataset (CSV) containing the retrieved bibliographic metadata, (2) a smart furniture annual publication file (CSV) that aggregates yearly scientific production from 1996 to 2024 for trend analysis and modeling, and (3) a research workflow figure (PNG) that documents the end-to-end pipeline from data collection and screening to bibliometric visualization and modeling forecasting. This dataset supports reproducible studies of publication dynamics and enables downstream analyses in bibliometrics, modeling, and design-driven research management.
DOI: 10.6084/m9.figshare.31400433.v1
title: Smart Furniture Publication Dataset

description: This dataset is a compiles Scopus-indexed scholarly records on the topic of Smart Furniture for the period 1996–2024, covering 356 English-language publications retrieved in November 2025. Records were collected using a transparent Scopus query that targets smart furniture and closely related expressions in titles, abstracts, and keywords: (TITLE-ABS-KEY (smart furniture OR intelligent furniture OR connected furniture OR IoT furniture OR interactive furniture) OR TITLE-ABS-KEY (furniture AND (smart home OR smart object OR smart product))) AND PUBYEAR &gt; 1995 AND PUBYEAR &lt; 2025 AND (LIMIT-TO (LANGUAGE, English)). The repository includes three complementary components: (1) a master smart furniture publication dataset (CSV) containing the retrieved bibliographic metadata, (2) a smart furniture annual publication file (CSV) that aggregates yearly scientific production from 1996 to 2024 for trend analysis and modeling, and (3) a research workflow figure (PNG) that documents the end-to-end pipeline from data collection and screening to bibliometric visualization and modeling forecasting. This dataset supports reproducible studies of publication dynamics and enables downstream analyses in bibliometrics, modeling, and design-driven research management.
DOI: 10.5281/zenodo.18760673
title: vis-spectra
DOI: 10.5281/zenodo.18760674
title: vis-spectra
DOI: 10.5281/zenodo.18675043
title: Post-Ischemia Lipidome Trajectory - Nbiol

description: This dataset provides longitudinal phospholipid-class lipidomics in hippocampus and plasma from a transient middle cerebral artery occlusion (tMCAO) model, paired with time-matched sham controls. Lipid class composition is reported as molar percentage (%mol) and includes PA, PG, PI, PS, PE, PC, ether-linked subclasses (ePS, ePE, ePC), lysophospholipids (LPE, LPC), and sphingomyelin (SM). Samples were collected at 6 h, 24 h, 7 d, 15 d, 1 month, and 4 months post-ischemia to capture acute-to-chronic remodeling of membrane lipid composition across tissues and time.

Steps to reproduce





Download the dataset and open Post-Ischemia Lipidome Trajectory - Nbiol.csv.




Subset the data by tissue (hippocampus or plasma) and timepoint (6h, 24h, 7d, 15d, 1m, 4m), including both groups (tMCAO and time-matched sham).




Import the %mol lipid species matrix into LipidSig (or an equivalent lipidomics analysis workflow).




Zero-filtering: within each tissue × timepoint subset, remove lipid classes/species with >80% zero values across samples.




Imputation (timepoint-specific): for each remaining lipid species, replace zero values using the minimum non-zero %mol detected for that species at the same timepoint, computed across sham + tMCAO samples (timepoint-by-timepoint).




Perform downstream analyses (e.g., PCA) using the filtered and imputed matrix, as described in the manuscript.
DOI: 10.5281/zenodo.18675042
title: Post-Ischemia Lipidome Trajectory - Nbiol

description: This dataset provides longitudinal phospholipid-class lipidomics in hippocampus and plasma from a transient middle cerebral artery occlusion (tMCAO) model, paired with time-matched sham controls. Lipid class composition is reported as molar percentage (%mol) and includes PA, PG, PI, PS, PE, PC, ether-linked subclasses (ePS, ePE, ePC), lysophospholipids (LPE, LPC), and sphingomyelin (SM). Samples were collected at 6 h, 24 h, 7 d, 15 d, 1 month, and 4 months post-ischemia to capture acute-to-chronic remodeling of membrane lipid composition across tissues and time.

Steps to reproduce





Download the dataset and open Post-Ischemia Lipidome Trajectory - Nbiol.csv.




Subset the data by tissue (hippocampus or plasma) and timepoint (6h, 24h, 7d, 15d, 1m, 4m), including both groups (tMCAO and time-matched sham).




Import the %mol lipid species matrix into LipidSig (or an equivalent lipidomics analysis workflow).




Zero-filtering: within each tissue × timepoint subset, remove lipid classes/species with >80% zero values across samples.




Imputation (timepoint-specific): for each remaining lipid species, replace zero values using the minimum non-zero %mol detected for that species at the same timepoint, computed across sham + tMCAO samples (timepoint-by-timepoint).




Perform downstream analyses (e.g., PCA) using the filtered and imputed matrix, as described in the manuscript.
DOI: 10.15468/dl.pv4vr2
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pentanisia arenaria"
 ]
}

 The dataset includes 55 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007567-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tq7ps8
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Armenia",
 "TaxonKey is Clitopilus prunulus"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007569-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18760740
title: WORKING WITH FUNCTIONS IN JAVA: A COMPREHENSIVE STUDY OF METHOD OVERLOADING

description: Method overloading is one of the fundamental features of object-oriented programming in Java that enables developers to define multiple methods with the same name but different parameter lists within the same class. This mechanism enhances code readability, reusability, and flexibility by allowing methods to perform similar operations on different types or numbers of inputs. As a compile-time polymorphism technique, method overloading plays a critical role in software design and development, especially in large-scale enterprise systems where modularity and maintainability are essential. This paper provides a comprehensive examination of method overloading in Java, including its theoretical foundations, syntax rules, implementation mechanisms, advantages, limitations, and practical applications. The study also explores the relationship between method overloading and other object-oriented concepts such as polymorphism, inheritance, encapsulation, and abstraction. Furthermore, performance considerations, compiler behavior, ambiguity resolution, and best practices are discussed in detail. Through theoretical explanations and illustrative examples, this research demonstrates how method overloading contributes to efficient software engineering practices and robust program architecture.
DOI: 10.5281/zenodo.18760741
title: WORKING WITH FUNCTIONS IN JAVA: A COMPREHENSIVE STUDY OF METHOD OVERLOADING

description: Method overloading is one of the fundamental features of object-oriented programming in Java that enables developers to define multiple methods with the same name but different parameter lists within the same class. This mechanism enhances code readability, reusability, and flexibility by allowing methods to perform similar operations on different types or numbers of inputs. As a compile-time polymorphism technique, method overloading plays a critical role in software design and development, especially in large-scale enterprise systems where modularity and maintainability are essential. This paper provides a comprehensive examination of method overloading in Java, including its theoretical foundations, syntax rules, implementation mechanisms, advantages, limitations, and practical applications. The study also explores the relationship between method overloading and other object-oriented concepts such as polymorphism, inheritance, encapsulation, and abstraction. Furthermore, performance considerations, compiler behavior, ambiguity resolution, and best practices are discussed in detail. Through theoretical explanations and illustrative examples, this research demonstrates how method overloading contributes to efficient software engineering practices and robust program architecture.
DOI: 10.15468/dl.d6n3az
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pentanisia renifolia"
 ]
}

 The dataset includes 100 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007570-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1610002201-eng
title: Production of metallic, non-metallic, aggregates, clay and refractory minerals in quantities, annual

description: Metallic, non-metallic, aggregates, clay and refractory minerals extracted in Canada. The variables available in this table are the quantity produced, the quantity shipped and the closing inventories. The data are published at the national, provincial and territorial levels.
DOI: 10.15468/dl.dweacj
title: Occurrence Download

description: A dataset containing 1269 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "NetworkKey is 99d66b6c-9087-452f-a9d4-f15f2c2d0e7e",
 "TaxonKey is Chordata"
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Passer domesticus [GBIF Backbone Taxonomy])",
 "InstitutionCode is one of (FMNH)",
 "StateProvince is one of (Illinois)",
 "Country is one of (United States of America)",
 "EventDate 2000-01-01-2015-01-01"
 ]
 }
 ]
}

 The dataset includes 1269 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007571-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13144/lil-1720
title: Privilèges d’entreprise, France, Royaume Uni, Italie, Allemagne, Autriche - 1443-1831

description: La base Privilèges contribue à l'interrogation des dynamiques institutionnelles des économies européennes au cours de l'époque moderne (XVIe-XVIIIe siècle). Elle recense plusieurs milliers de privilèges d'entreprise dans les entités souveraines suivantes : royaumes d'Angleterre et de France, république de Venise, duchés de Mantoue et de Piémont (puis royaume de Sardaigne), grand-duché de Toscane, Saxe et ville libre de Nuremberg dans le Saint Empire.Les privilèges d'entreprise peuvent être définis comme des droits particuliers concédés par le souverain, droits qui ouvrent des espaces ou des capacités d'action à des individus dans le domaine économique. Ces grâces prennent des formes diplomatiques différentes selon les aires de souveraineté concernées. La particularité de la base Privilèges est donc d'être à la fois plurilingue et constituée de données hétérogènes. C'est à la fois sa richesse et sa difficulté d'utilisation.La base de données indexe les actes constitutifs de ces privilèges. Elle en détaille les différentes clauses affirmatives ou restrictives. Le cas échéant, elle permet de suivre l'évolution des droits octroyés et de repérer les bénéficiaires et les autorités concédantes. Lorsque cela a été possible, elle donne accès à une version numérisée de ces actes.Elle est constituée des six tables versées dans la base de données relationnelles PRIVILEGES consultables en ligne (&lt;https://privileges.huma-num.fr&gt;). Une première table recense les fonds archivistiques et ressources bibliographiques traitées (« Conteneurs », décrits par neuf variables).Trois tables recensent les privilèges et les hiérarchisent sur trois niveau d'abstraction : « Mentions » (45 variables) décrit chaque citation d'un privilège au plus proche de la source, « Graines » (45 variables) permet de regrouper plusieurs mentions liées à la même étape de vie d'un privilège, et « Privileges » (sept variables) permet de suivre les renouvellements de privilèges dans la durée. Enfin la table « Acteurs » (six variables) liste la citation d'un personnage historique dans chaque mention, et la table « Personnes » (29 variables) permet de regrouper les différentes citations et informations d'un même personnage sous le même identifiant.Cette base constitue le socle documentaire de l'ouvrage : Dominique Margairaz, Anne Conchon, Vincent Demont, Guillaume Garner, Pauline Lemaigre-Gaffier, Corine Maitte, L'économie des grâces. Les privilèges d'entreprise dans l'Europe moderne, XVe-XVIIIe siècles (Angleterre, France, Mantoue, Piémont, Toscane, Saxe, Venise), Paris, Comité pour l'histoire économique et financière de la France, 2025.
DOI: 10.5281/zenodo.18760753
title: Enhanced 2D structured illumination microscopy datasets

description: Structured illumination microscopy datasets of LSECs in the visual (Biotracker 555) and near-infrared (Biotracker 790) range.
DOI: 10.5281/zenodo.18760752
title: Enhanced 2D structured illumination microscopy datasets

description: Structured illumination microscopy datasets of LSECs in the visual (Biotracker 555) and near-infrared (Biotracker 790) range.
DOI: 10.25318/1610002201-fra
title: Production des minéraux métalliques, non-métalliques, agrégats, argile et minéraux réfractaires en quantité, annuel

description: Minéraux métalliques, non métalliques, les agrégats, l'argile et les minéraux réfractaires extraits au Canada. Les variables disponibles dans ce tableau sont la quantité produite, la quantité expédiée et les stocks de clôture. Les données sont publiées aux niveaux national, provincial et territorial.