cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/hxdhnmv4mr.2
title: Electronic System for Energy Consumption Profiling and Monitoring of IoT Devices, and Test System

description: This platform is composed of two main systems. The first system corresponds to a measurement and characterization module for analyzing the energy consumption of IoT devices. It features two power measurement inputs: the first supports up to 24 W and includes a voltage stabilization circuit, while the second supports up to 240 W, allowing voltages up to 34 V and currents up to 8 A. Additionally, the system integrates two 10 kΩ NTC temperature sensors for monitoring both the ambient temperature and the internal temperature of the device under test. The system operates automatically, and the acquired data are stored individually on microSD cards to facilitate later analysis.

The second system is an IoT test module designed to validate the performance of the measurement and profiling system. This module communicates and synchronizes with the measurement unit, enabling the profiling of energy consumption according to the computational tasks performed. As an example, a linear regression algorithm was implemented to evaluate energy behavior during the execution of processes with varying computational complexity.

Both systems can be assembled using low-cost components and are suitable for energy consumption characterization in open hardware-based IoT devices. The accompanying project files include the complete list of required components with supplier links, the schematic diagrams and PCB designs developed in Autodesk Fusion 360, the firmware for both systems, and the data files obtained during a test of the IoT system running the linear regression algorithm, used to validate the measurement platform.
DOI: 10.17632/93ndkttytv
title: Improving fecundity estimates to inform transborder legal size limits of soft-shell clams (Mya arenaria L.) in eastern North America

description: Supplementary source data and R Markdown files (RMD, HTML) for manuscript titled "Improving fecundity estimates to inform transborder legal size limits of soft-shell clams (Mya arenaria L.) in eastern North America", submitted to Fisheries Research.
DOI: 10.17632/93ndkttytv.3
title: Improving fecundity estimates to inform transborder legal size limits of soft-shell clams (Mya arenaria L.) in eastern North America

description: Supplementary source data and R Markdown files (RMD, HTML) for manuscript titled "Improving fecundity estimates to inform transborder legal size limits of soft-shell clams (Mya arenaria L.) in eastern North America", submitted to Fisheries Research.
DOI: 10.15468/dl.kda78e
title: Occurrence Download

description: A dataset containing 307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Brasenia schreberi)",
 "Geometry POLYGON((72.2 56, 137.5 56, 137.5 0, 72.2 0, 72.2 56))",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 307 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012382-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/853d6wm7d9
title: Do Geopolitical Risks Raise or Lower Inflation?

description: Replication files for the paper "Do Geopolitical Risks Raise or Lower Inflation?" (Journal of International Economics)
DOI: 10.17632/853d6wm7d9.1
title: Do Geopolitical Risks Raise or Lower Inflation?

description: Replication files for the paper "Do Geopolitical Risks Raise or Lower Inflation?" (Journal of International Economics)
DOI: 10.17632/n4n5y34p3s
title: Bangla and Banglish E-Commerce Reviews Dataset for Aspect-Based Sentiment Analysis

description: This dataset is a manually annotated collection of 3,587 Bangla and Banglish e-commerce product reviews, curated for Aspect-Based Sentiment Analysis (ABSA) research in low-resource languages. Reviews were collected from Daraz, covering products in the electronics domain such as mobile phones, laptops, earphones, headphones, and CCTV cameras.

The raw dataset initially contained 19,638 reviews, which were preprocessed to remove noise, normalize text, and filter irrelevant entries. Preprocessing steps included removing URLs, emails, and mentions; converting emojis to text; normalizing Banglish; reducing character elongation; ensuring meaningful sentence boundaries; and removing all reviews shorter than 5 words to maintain data quality and reliability. After cleaning, 10,657 reviews were retained.

A subset of 3,587 reviews was manually annotated for ABSA. Each review contains one or more aspect-level sentiment labels (positive, negative, or neutral) covering five aspects: product quality, price, delivery, packaging, and seller service.

Dataset Counts:

 Total original reviews: 19,638

 Total preprocessed reviews: 10,657

 Total annotated reviews: 3,587

 Languages (preprocessed):

 Bangla: 6,731

 Banglish: 2,797

 Mixed: 1,129

Folder Structure:

 original/ – raw scraped reviews

 preprocessed/ – cleaned and normalized reviews, filtered for quality

 annotated/ – manually labeled ABSA reviews

 metadata/ – summary of review counts, language distribution

Data quality was ensured through duplicate removal, filtering short reviews, manual validation, and annotation consistency checks. This dataset is ready to train and evaluate multilingual transformer models such as BanglaBERT, XLM-RoBERTa, and mBERT for multi-label ABSA tasks, and serves as a benchmark for Bangla–Banglish e-commerce sentiment research.
DOI: 10.17632/n4n5y34p3s.1
title: Bangla and Banglish E-Commerce Reviews Dataset for Aspect-Based Sentiment Analysis

description: This dataset is a manually annotated collection of 3,587 Bangla and Banglish e-commerce product reviews, curated for Aspect-Based Sentiment Analysis (ABSA) research in low-resource languages. Reviews were collected from Daraz, covering products in the electronics domain such as mobile phones, laptops, earphones, headphones, and CCTV cameras.

The raw dataset initially contained 19,638 reviews, which were preprocessed to remove noise, normalize text, and filter irrelevant entries. Preprocessing steps included removing URLs, emails, and mentions; converting emojis to text; normalizing Banglish; reducing character elongation; ensuring meaningful sentence boundaries; and removing all reviews shorter than 5 words to maintain data quality and reliability. After cleaning, 10,657 reviews were retained.

A subset of 3,587 reviews was manually annotated for ABSA. Each review contains one or more aspect-level sentiment labels (positive, negative, or neutral) covering five aspects: product quality, price, delivery, packaging, and seller service.

Dataset Counts:

 Total original reviews: 19,638

 Total preprocessed reviews: 10,657

 Total annotated reviews: 3,587

 Languages (preprocessed):

 Bangla: 6,731

 Banglish: 2,797

 Mixed: 1,129

Folder Structure:

 original/ – raw scraped reviews

 preprocessed/ – cleaned and normalized reviews, filtered for quality

 annotated/ – manually labeled ABSA reviews

 metadata/ – summary of review counts, language distribution

Data quality was ensured through duplicate removal, filtering short reviews, manual validation, and annotation consistency checks. This dataset is ready to train and evaluate multilingual transformer models such as BanglaBERT, XLM-RoBERTa, and mBERT for multi-label ABSA tasks, and serves as a benchmark for Bangla–Banglish e-commerce sentiment research.
DOI: 10.17632/m8g2wynwyr
title: Growth Stage Flower Image Dataset from Bangladesh

description: This dataset is a collection of 23,334 color images capturing the growth stages of eight flower species common in Bangladesh. Each species is represented across three growth stages—Early, Mid, and Full—forming 24 unique classes.

The dataset includes 3,889 original photographs and 19,445 augmented images, generated using five augmentation techniques (rotation, flipping, hue adjustment, zoom, and shift). All images are stored in JPG format with a uniform resolution of 640 × 640 pixels.
DOI: 10.17632/m8g2wynwyr.2
title: Growth Stage Flower Image Dataset from Bangladesh

description: This dataset is a collection of 23,334 color images capturing the growth stages of eight flower species common in Bangladesh. Each species is represented across three growth stages—Early, Mid, and Full—forming 24 unique classes.

The dataset includes 3,889 original photographs and 19,445 augmented images, generated using five augmentation techniques (rotation, flipping, hue adjustment, zoom, and shift). All images are stored in JPG format with a uniform resolution of 640 × 640 pixels.
DOI: 10.18150/yh6w8w
title: Biostimulant effect of Cladophora glomerata extract on garden cress plant growth

description: All raw absorbance measurements for spectrophotometric determinations necessary for semi-quantitative assays (blank signal already removed) used in the preparation of the title manuscript. Assays include: DPPH, ABTS (antioxidant power), TPC, TFC (total phenolics and flavonoids determination), carotenoids and chlorophylls. Results pertain to content of active compounds in garden cress plants under the biostimulant effect of Cladophora glomerata extract of different concentration. Further details are available in the Description file.
DOI: 10.15468/dl.tpnwmr
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "OccurrenceStatus is Present",
 "TaxonKey is Carya",
 "Year 1000-2025"
 ]
}

 The dataset includes 44 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012383-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3xtt7x9g32
title: Data for "From policy to practice: deployment and impact assessment of smart grid photovoltaic energy systems in Nigerian commercial hubs"

description: The data contains the study locations and PVSyst Simulations files for the studies. The study location file contains the latitude and longitude of 37 locations in Nigeria.
Also, the PVSyst files can be used to run the energy generation simulations in all the 37 locations provided to give exact results as the study.
The validation study simulation file is in PVSyst format, it contains the location, weather data and system losses.
DOI: 10.17632/3xtt7x9g32.1
title: Data for "From policy to practice: deployment and impact assessment of smart grid photovoltaic energy systems in Nigerian commercial hubs"

description: The data contains the study locations and PVSyst Simulations files for the studies. The study location file contains the latitude and longitude of 37 locations in Nigeria.
Also, the PVSyst files can be used to run the energy generation simulations in all the 37 locations provided to give exact results as the study.
The validation study simulation file is in PVSyst format, it contains the location, weather data and system losses.
DOI: 10.17632/zkmnz4x2sc.1
title: Mathematical Belief: A Symbolic Dynamical System Exhibiting Emergent Cross-Symmetry

description: Mathematical Belief is a symbolic dynamical system derived from poetic logic. It functions as a 4-dimensional stochastic cellular automaton in which each lattice point represents a semantic state—idea, concept, system, flaw, or dead. Simple recursive update rules governing propagation and decay generate emergent cross-symmetry, modeling the transformation of belief through failure and renewal. The construct merges symbolic computation, complex-systems theory, and synthetic phenomenology, treating language itself as a rule-set that evolves toward self-organizing order. In essence, it is a computational micro-universe where meaning behaves as matter and recursion becomes the physics of faith.

The model incorporates stochastic decay; a random seed may be fixed (e.g., np.random.seed(42)) for deterministic replication or left unset to allow spontaneous emergent variation between runs.

The poem and conceptual framework were written and developed by Dustin Sprenger.
The Python implementation was generated by Grok AI and released to the public domain under CC0 1.0 Universal.

The accompanying poem Mathematical Belief is Copyright Pending © Dustin Sprenger to be licensed under Creative Commons Attribution – NonCommercial – NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
DOI: 10.17632/zjmfphx39x
title: Base datos

description: Base de datos recolectada de las encuestas aplicadas en el estudio
DOI: 10.17632/zjmfphx39x.1
title: Base datos

description: Base de datos recolectada de las encuestas aplicadas en el estudio
DOI: 10.17632/wnvd69jf5j
title: Dataset on collaborative creativity in digital higher education

description: This dataset contains quantitative and qualitative information derived from a multi-cohort study conducted between 2020 and 2025 in a Psychology of Education course at a Mexican public university. The study examined the microdevelopment of collaborative creativity among university students working in digital environments.
Data were collected from 1,038 individual contributions written by students in shared Google Docs while developing creative intervention proposals in teams. Each contribution was coded along two dimensions — Novelty (0–3 scale) and Appropriateness-Feasibility Index (0–5 scale) — following the framework of Kupers et al. (2018).
DOI: 10.17632/wnvd69jf5j.1
title: Dataset on collaborative creativity in digital higher education

description: This dataset contains quantitative and qualitative information derived from a multi-cohort study conducted between 2020 and 2025 in a Psychology of Education course at a Mexican public university. The study examined the microdevelopment of collaborative creativity among university students working in digital environments.
Data were collected from 1,038 individual contributions written by students in shared Google Docs while developing creative intervention proposals in teams. Each contribution was coded along two dimensions — Novelty (0–3 scale) and Appropriateness-Feasibility Index (0–5 scale) — following the framework of Kupers et al. (2018).
DOI: 10.17632/96by2x2gh5
title: SupplementaryData_Immunometabolism

description: Supplementary data about primers used, gating strategy and purity of CD4 enriched population.
DOI: 10.17632/96by2x2gh5.1
title: SupplementaryData_Immunometabolism

description: Supplementary data about primers used, gating strategy and purity of CD4 enriched population.
DOI: 10.17632/skps9txdkm
title: Daily Weather Data from 14 Stations across the Southern Tamaulipas Urban Zone: Supplementary to MoCaALTAMA Air Quality Monitoring

description: These datasets contain monthly average meteorological data collected from 14 Automatic Weather Stations (EMAs) located in the Southern Tamaulipas, Mexico (Tampico, Ciudad Madero, and Altamira). The recorded variables include temperature (°C), dew point (°C), relative humidity (%), wind speed (km/h), atmospheric pressure (hPa), and accumulated precipitation (mm).

The data were originally sourced from the Weather Underground platform, where measurements are recorded at 5-minute intervals and can be retrieved as daily, weekly, or monthly averages. However, data availability may vary due to station outages or device removal from the platform. To ensure continuous and reliable access, this repository compiles and preserves the datasets covering the period from October 2023 to June 2025.

The wind direction dataset from the EMAs was calculated as monthly averages based on the available daily measurements. Some data points are missing due to various factors, such as electrical interruptions or natural wear and tear, common in this coastal region due to high salinity and humidity. The wind vane, the most commonly used device for wind direction measurement, is particularly prone to malfunctioning under these conditions. Therefore, monthly average wind directions were computed from the available data only.
To indicate data coverage and reliability, the following labels are used:

(a) nearly complete monthly data (24 to 30 data points),
(b) most of the month, with approximately one week of scattered missing data (16 to 23 data points),
(c) approximately half of the monthly data (11 to 15 data points),
(d) very limited data, less than 10 data points, roughly one week or less.

For additional information, you may contact Dr. Felipe Caballero (fcaballero@ipn.mx) or M.I.A. Mireya del Socorro Ovando Rocha (movando@utaltamira.edu.mx).
DOI: 10.17632/skps9txdkm.2
title: Daily Weather Data from 14 Stations across the Southern Tamaulipas Urban Zone: Supplementary to MoCaALTAMA Air Quality Monitoring

description: These datasets contain monthly average meteorological data collected from 14 Automatic Weather Stations (EMAs) located in the Southern Tamaulipas, Mexico (Tampico, Ciudad Madero, and Altamira). The recorded variables include temperature (°C), dew point (°C), relative humidity (%), wind speed (km/h), atmospheric pressure (hPa), and accumulated precipitation (mm).

The data were originally sourced from the Weather Underground platform, where measurements are recorded at 5-minute intervals and can be retrieved as daily, weekly, or monthly averages. However, data availability may vary due to station outages or device removal from the platform. To ensure continuous and reliable access, this repository compiles and preserves the datasets covering the period from October 2023 to June 2025.

The wind direction dataset from the EMAs was calculated as monthly averages based on the available daily measurements. Some data points are missing due to various factors, such as electrical interruptions or natural wear and tear, common in this coastal region due to high salinity and humidity. The wind vane, the most commonly used device for wind direction measurement, is particularly prone to malfunctioning under these conditions. Therefore, monthly average wind directions were computed from the available data only.
To indicate data coverage and reliability, the following labels are used:

(a) nearly complete monthly data (24 to 30 data points),
(b) most of the month, with approximately one week of scattered missing data (16 to 23 data points),
(c) approximately half of the monthly data (11 to 15 data points),
(d) very limited data, less than 10 data points, roughly one week or less.

For additional information, you may contact Dr. Felipe Caballero (fcaballero@ipn.mx) or M.I.A. Mireya del Socorro Ovando Rocha (movando@utaltamira.edu.mx).
DOI: 10.15468/dl.pmj6c6
title: Occurrence Download

description: A dataset containing 2842 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Physodermatales",
 "Year 1000-2026"
 ]
}

 The dataset includes 2842 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003039-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31044832
title: Association between sweet food consumption and preeclampsia: Insights from an observational study and Mendelian randomization analysis

description: Previous studies suggest links between sweet food consumption and metabolic conditions, but its role in preeclampsia (PE) remains unclear. This study combines a case‒control design with Mendelian randomization (MR) to explore the potential causal relationship between sweet food intake and PE. This retrospective cohort study included 1146 pregnant women from Fujian Provincial Maternity and Children's Hospital (60 with PE, 1086 controls). Dietary habits, specifically sweet food consumption (≥3 times/week), were recorded pre-delivery. Logistic regression assessed the association between sweet food intake and PE, adjusting for confounders like age, prenatal weight, blood glucose, and anemia. MR analysis used genetic variants from UK Biobank and FinnGen databases, analyzed via the inverse variance weighted (IVW) method, with sensitivity tests (MR-Egger, weighted median). Frequent sweet food consumption was associated with increased PE risk in the cohort study (OR = 2.51, 95% CI: 1.46–4.44, <i>P</i> = 0.001) after adjustments. However, MR analysis found no causal link between sweet food intake and PE. Instead, a causal association between higher BMI and PE was identified (OR = 1.56, 95% CI: 1.40–1.74, <i>P</i> = 6.51 × 10<sup>−16</sup>). While the case–control study linked frequent sweet food consumption to higher PE risk, MR analysis did not confirm causality. Elevated BMI, driven by excessive energy intake, emerged as a significant PE risk factor. Further research is needed to clarify dietary influences on PE and the interplay of diet, weight, and metabolic health in pregnancy.
DOI: 10.6084/m9.figshare.31044832.v1
title: Association between sweet food consumption and preeclampsia: Insights from an observational study and Mendelian randomization analysis

description: Previous studies suggest links between sweet food consumption and metabolic conditions, but its role in preeclampsia (PE) remains unclear. This study combines a case‒control design with Mendelian randomization (MR) to explore the potential causal relationship between sweet food intake and PE. This retrospective cohort study included 1146 pregnant women from Fujian Provincial Maternity and Children's Hospital (60 with PE, 1086 controls). Dietary habits, specifically sweet food consumption (≥3 times/week), were recorded pre-delivery. Logistic regression assessed the association between sweet food intake and PE, adjusting for confounders like age, prenatal weight, blood glucose, and anemia. MR analysis used genetic variants from UK Biobank and FinnGen databases, analyzed via the inverse variance weighted (IVW) method, with sensitivity tests (MR-Egger, weighted median). Frequent sweet food consumption was associated with increased PE risk in the cohort study (OR = 2.51, 95% CI: 1.46–4.44, <i>P</i> = 0.001) after adjustments. However, MR analysis found no causal link between sweet food intake and PE. Instead, a causal association between higher BMI and PE was identified (OR = 1.56, 95% CI: 1.40–1.74, <i>P</i> = 6.51 × 10<sup>−16</sup>). While the case–control study linked frequent sweet food consumption to higher PE risk, MR analysis did not confirm causality. Elevated BMI, driven by excessive energy intake, emerged as a significant PE risk factor. Further research is needed to clarify dietary influences on PE and the interplay of diet, weight, and metabolic health in pregnancy.
DOI: 10.15468/dl.9y5spv
title: Occurrence Download

description: A dataset containing 252874 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Psophodes olivaceus (Latham, 1802)"
 ]
}

 The dataset includes 252874 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0228381-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.