cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/knt9kc7g6d
title: Data for Impact of the Superblock Model on Key Components of the Urban Water Cycle: Trace Metals and Dissolved Organic Matter Dynamics across the Built Environment [Dataset]

description: Data from sampling campaigns of rainwater, stormwater runoff (first-flush), and the influent and effluent of vegetated Sustainable Urban Drainage Systems (SUDS). The data includes results from bulk water quality parameters, trace metal concentrations, and dissolved organic matter (DOM) analyses.
DOI: 10.17632/knt9kc7g6d.1
title: Data for Impact of the Superblock Model on Key Components of the Urban Water Cycle: Trace Metals and Dissolved Organic Matter Dynamics across the Built Environment [Dataset]

description: Data from sampling campaigns of rainwater, stormwater runoff (first-flush), and the influent and effluent of vegetated Sustainable Urban Drainage Systems (SUDS). The data includes results from bulk water quality parameters, trace metal concentrations, and dissolved organic matter (DOM) analyses.
DOI: 10.17632/x3f8w84s7h
title: A Bilingual Corpus for Twi-English Translation

description: This dataset is a multi-domain Twi-English parallel corpus of 16,085 sentence pairs, covering five thematic domains: casual text, depressed text, medical, toxic language, and agriculture. Unlike most existing Twi corpora that draw primarily from religious or formal texts, our dataset captures everyday and specialized language use. Data was collected through native Twi-speaking household collectors and a custom web-based Streamlit platform, with a three-tier quality control process yielding an acceptance rate of 95.3%. The Twi side contains 111,253 tokens with a vocabulary of 8,393 unique forms, while the English side contains 113,487 tokens and 7,427 unique forms. A sentence-length correlation of r = 0.7738 confirms strong translational fidelity across the corpus. The dataset was develop to support machine translation, sentiment analysis, mental health text classification, and content moderation research.
DOI: 10.17632/x3f8w84s7h.1
title: A Bilingual Corpus for Twi-English Translation

description: This dataset is a multi-domain Twi-English parallel corpus of 16,085 sentence pairs, covering five thematic domains: casual text, depressed text, medical, toxic language, and agriculture. Unlike most existing Twi corpora that draw primarily from religious or formal texts, our dataset captures everyday and specialized language use. Data was collected through native Twi-speaking household collectors and a custom web-based Streamlit platform, with a three-tier quality control process yielding an acceptance rate of 95.3%. The Twi side contains 111,253 tokens with a vocabulary of 8,393 unique forms, while the English side contains 113,487 tokens and 7,427 unique forms. A sentence-length correlation of r = 0.7738 confirms strong translational fidelity across the corpus. The dataset was develop to support machine translation, sentiment analysis, mental health text classification, and content moderation research.
DOI: 10.17632/9xjny9rgry
title: Aurevia Digital CEO Earnings Update Experiment Dataset: Accountability Attribution, Trust, and Investment Allocation (N=266)

description: This dataset contains participant-level survey and behavioral-decision data from a randomized, video-based earnings disclosure experiment examining how accountability attribution cues shape perceived credibility and investment decisions. Participants were randomly assigned to one of two conditions: High Accountability vs Low Accountability attribution, while all other stimulus elements (earnings content, duration, and delivery format) were held constant. The study focuses on whether attributing responsibility to an executive speaker versus an AI-supported communication process changes (i) perceived accountability, (ii) trust/credibility, and (iii) investment allocation.

The Excel file includes four sheets: (1) Data (raw responses plus derived variables; N=266), (2) Codebook (variable definitions and coding), (3) Summary_Tables (key descriptives, balance checks, manipulation checks, and main outcomes), and (4) Figures (figure-ready values/outputs).

Key variable groups in the Data sheet include: demographics (age, gender, country), investing background (prior investing, experience 1–5, trading frequency), a financial knowledge screener, watch time (seconds), manipulation checks (Q9 responsibility attribution; Q10 personal accountability; Q11 AI-prepared perception), comprehension checks (Q12–Q14, with item-level correctness and ComprehensionScore_0to3), perceived accountability mediators (Q15–Q16), trust/credibility mediators (Q17–Q22), presence/automation perceptions (Q23–Q24), the primary behavioral outcome (Q25_Alloc_Aurevia_USD, 0–10,000; with the residual risk-free allocation), willingness-to-invest items (Q26–Q28), controls (Q29 risk tolerance; Q30 baseline AI trust), and an attention check (Q31, correct response = 6).

Derived composites are provided for replication and analysis: PerceivedAccountability_Comp (mean of Q15–Q16), TrustCredibility_Comp (mean of Q17–Q22), and Willingness_Comp (mean of Q26–Q28). Exclusion flags are included (Exclude_Attention, Exclude_Watch for watch time &lt; 60s, Exclude_Comprehension for comprehension score &lt; 2) along with Include_Analysis to identify the analysis-ready sample used in the summary tables. Researchers can reproduce the main tests using simple t-tests/OLS or mediation models by filtering on Include_Analysis = 1.
DOI: 10.17632/9xjny9rgry.1
title: Aurevia Digital CEO Earnings Update Experiment Dataset: Accountability Attribution, Trust, and Investment Allocation (N=266)

description: This dataset contains participant-level survey and behavioral-decision data from a randomized, video-based earnings disclosure experiment examining how accountability attribution cues shape perceived credibility and investment decisions. Participants were randomly assigned to one of two conditions: High Accountability vs Low Accountability attribution, while all other stimulus elements (earnings content, duration, and delivery format) were held constant. The study focuses on whether attributing responsibility to an executive speaker versus an AI-supported communication process changes (i) perceived accountability, (ii) trust/credibility, and (iii) investment allocation.

The Excel file includes four sheets: (1) Data (raw responses plus derived variables; N=266), (2) Codebook (variable definitions and coding), (3) Summary_Tables (key descriptives, balance checks, manipulation checks, and main outcomes), and (4) Figures (figure-ready values/outputs).

Key variable groups in the Data sheet include: demographics (age, gender, country), investing background (prior investing, experience 1–5, trading frequency), a financial knowledge screener, watch time (seconds), manipulation checks (Q9 responsibility attribution; Q10 personal accountability; Q11 AI-prepared perception), comprehension checks (Q12–Q14, with item-level correctness and ComprehensionScore_0to3), perceived accountability mediators (Q15–Q16), trust/credibility mediators (Q17–Q22), presence/automation perceptions (Q23–Q24), the primary behavioral outcome (Q25_Alloc_Aurevia_USD, 0–10,000; with the residual risk-free allocation), willingness-to-invest items (Q26–Q28), controls (Q29 risk tolerance; Q30 baseline AI trust), and an attention check (Q31, correct response = 6).

Derived composites are provided for replication and analysis: PerceivedAccountability_Comp (mean of Q15–Q16), TrustCredibility_Comp (mean of Q17–Q22), and Willingness_Comp (mean of Q26–Q28). Exclusion flags are included (Exclude_Attention, Exclude_Watch for watch time &lt; 60s, Exclude_Comprehension for comprehension score &lt; 2) along with Include_Analysis to identify the analysis-ready sample used in the summary tables. Researchers can reproduce the main tests using simple t-tests/OLS or mediation models by filtering on Include_Analysis = 1.
DOI: 10.17632/4s5pjwyhrt
title: Conceptual framework for evaluating cognitive and discursive characteristics of LLMs’ narratives in environmental science

description: The application of large language models (LLMs) for research and scientific discourse is expanding because of their use in generating narratives, explaining complex concepts, and influencing human perception of various issues, including climate change and sustainability. However, a systematic approach for evaluating the quality and cognitive effects of LLMs’ output remains underdeveloped. This study proposes a conceptual framework for the evaluation of narratives generated by LLMs for the cognitive and discursive characterization of texts in a structured, comparable manner. The study integrates the six dimensions: cognitive framing, information selection/exclusion, emotional modulation, cognitive load and accessibility, trustworthiness/epistemic positioning, and persuasive potential. Each dimension reflects how LLMs construct the meaning of narratives and shape users’ perception. Each dimension can be assessed using diverse analytical strategies and openly available tools, such as lexical analysis platforms (e.g., Voyant), readability indices, sentiment analysis, stance detection, and rhetorical analysis. This study may serve as an example of establishing a precise, repeatable framework for comparing the LLMs-generated narratives in English and to enhance the responsible use of generative AI in environmental discussions by standardizing evaluation methods and linking cognitive-discursive analysis with sustainability communication.
DOI: 10.17632/4s5pjwyhrt.1
title: Conceptual framework for evaluating cognitive and discursive characteristics of LLMs’ narratives in environmental science

description: The application of large language models (LLMs) for research and scientific discourse is expanding because of their use in generating narratives, explaining complex concepts, and influencing human perception of various issues, including climate change and sustainability. However, a systematic approach for evaluating the quality and cognitive effects of LLMs’ output remains underdeveloped. This study proposes a conceptual framework for the evaluation of narratives generated by LLMs for the cognitive and discursive characterization of texts in a structured, comparable manner. The study integrates the six dimensions: cognitive framing, information selection/exclusion, emotional modulation, cognitive load and accessibility, trustworthiness/epistemic positioning, and persuasive potential. Each dimension reflects how LLMs construct the meaning of narratives and shape users’ perception. Each dimension can be assessed using diverse analytical strategies and openly available tools, such as lexical analysis platforms (e.g., Voyant), readability indices, sentiment analysis, stance detection, and rhetorical analysis. This study may serve as an example of establishing a precise, repeatable framework for comparing the LLMs-generated narratives in English and to enhance the responsible use of generative AI in environmental discussions by standardizing evaluation methods and linking cognitive-discursive analysis with sustainability communication.
DOI: 10.17632/hjvhb4pj4c
title: smart box machine sensor dataset

description: Funding:
AI REDGIO 5.0 - Regions and (E)DIHs alliance for AI-at-the-Edge adoption by European Industry 5.0 Manufacturing SMEs 101092069
European Commission, Horizon Europe

Data format:
Description Unit Data type
entity_id The enity ID (device ID) string
entity_type The name of Entity string
time_index The creation time of this data Date
fiware_servicepath service path for FIWARE string
__original_ngsi_entity__ not use (null) string
instanceid Instance ID string
powercurrentnode The value of electric current mA/factor integer
temperaturenode The value of temperature Celsius/1000 integer
freqnode Vaule of vibration frequency Hz integer
amplitudenode vibration amplitude integer
fampnode vibration amplitude in spectrum integer
DOI: 10.17632/hjvhb4pj4c.2
title: smart box machine sensor dataset

description: Funding:
AI REDGIO 5.0 - Regions and (E)DIHs alliance for AI-at-the-Edge adoption by European Industry 5.0 Manufacturing SMEs 101092069
European Commission, Horizon Europe

Data format:
Description Unit Data type
entity_id The enity ID (device ID) string
entity_type The name of Entity string
time_index The creation time of this data Date
fiware_servicepath service path for FIWARE string
__original_ngsi_entity__ not use (null) string
instanceid Instance ID string
powercurrentnode The value of electric current mA/factor integer
temperaturenode The value of temperature Celsius/1000 integer
freqnode Vaule of vibration frequency Hz integer
amplitudenode vibration amplitude integer
fampnode vibration amplitude in spectrum integer
DOI: 10.17632/jrk32gn9z6
title: Dataset_Meneses_etal_2026_Transforming Accounting Processes

description: This dataset accompanies the systematic literature review titled “Transforming Accounting Processes Using Artificial Intelligence and the Internet of Things: A Systematic Review of the Literature.” It contains the complete set of academic documents analyzed during the review process, along with their corresponding metadata and classification variables.

The dataset includes:
 • Bibliographic information (title, authors, year, journal, DOI, source database)
 • Document type (article, conference paper, review, book chapter, etc.)
 • Research focus categories related to the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in accounting processes
 • Methodological characteristics (research design, analysis techniques, sample characteristics)
 • Key themes and contributions identified through qualitative coding
 • Relevance indicators (citations, journal ranking, keywords)

The data were extracted from leading academic databases and filtered using eligibility criteria aligned with PRISMA guidelines. Each record reflects the standardized screening and coding procedures conducted by the authors to ensure consistency, replicability, and transparency.

This dataset enables researchers to explore current trends, methodological approaches, and emerging themes at the intersection of AI, IoT, and accounting, facilitating further empirical and conceptual research in this evolving field.
DOI: 10.17632/jrk32gn9z6.1
title: Dataset_Meneses_etal_2026_Transforming Accounting Processes

description: This dataset accompanies the systematic literature review titled “Transforming Accounting Processes Using Artificial Intelligence and the Internet of Things: A Systematic Review of the Literature.” It contains the complete set of academic documents analyzed during the review process, along with their corresponding metadata and classification variables.

The dataset includes:
 • Bibliographic information (title, authors, year, journal, DOI, source database)
 • Document type (article, conference paper, review, book chapter, etc.)
 • Research focus categories related to the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in accounting processes
 • Methodological characteristics (research design, analysis techniques, sample characteristics)
 • Key themes and contributions identified through qualitative coding
 • Relevance indicators (citations, journal ranking, keywords)

The data were extracted from leading academic databases and filtered using eligibility criteria aligned with PRISMA guidelines. Each record reflects the standardized screening and coding procedures conducted by the authors to ensure consistency, replicability, and transparency.

This dataset enables researchers to explore current trends, methodological approaches, and emerging themes at the intersection of AI, IoT, and accounting, facilitating further empirical and conceptual research in this evolving field.
DOI: 10.17632/z6633hv9j7
title: Local climate action plans for Norwegian municipalities (2024)

description: All the local climate action plans for Norwegian municipalities (retrieved December 2024). Tokenized at the sentence level.
DOI: 10.15468/dl.ymtrua
title: Occurrence Download

description: A dataset containing 1132790 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Denmark",
 "DatasetKey is Danish Mycological Society, fungal records database",
 "TaxonKey is Fungi"
 ]
}

 The dataset includes 1132790 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004451-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/z6633hv9j7.1
title: Local climate action plans for Norwegian municipalities (2024)

description: All the local climate action plans for Norwegian municipalities (retrieved December 2024). Tokenized at the sentence level.
DOI: 10.6084/m9.figshare.29202794
title: Chorus Frog (Pseudacris maculata) survey data Cameron Pass Colorado 1986-2025

description: These data are capture survey data for chorus frogs (Pseudacris maculata) at two locations (Matthews Pond and Lily Pond) on Cameron Pass in Colorado, USA. This dataset includes capture dates and environmental covariates (air and water temps and wind/cloud cover). There are also limited data on frog color, sex, and measurements (snout-vent length and mass). Data were collected as part of the U.S. Geological Survey (USGS) Amphibian Research and Monitoring Initiative, to contribute to an understanding of demography and population dynamics in small hylid frogs.
DOI: 10.6084/m9.figshare.29202794.v2
title: Chorus Frog (Pseudacris maculata) survey data Cameron Pass Colorado 1986-2025

description: These data are capture survey data for chorus frogs (Pseudacris maculata) at two locations (Matthews Pond and Lily Pond) on Cameron Pass in Colorado, USA. This dataset includes capture dates and environmental covariates (air and water temps and wind/cloud cover). There are also limited data on frog color, sex, and measurements (snout-vent length and mass). Data were collected as part of the U.S. Geological Survey (USGS) Amphibian Research and Monitoring Initiative, to contribute to an understanding of demography and population dynamics in small hylid frogs.
DOI: 10.6084/m9.figshare.29202794.v1
title: Chorus Frog (Pseudacris maculata) survey data Cameron Pass Colorado 1986-2025

description: These data are capture survey data for chorus frogs (Pseudacris maculata) at two locations (Matthews Pond and Lily Pond) on Cameron Pass in Colorado, USA. This dataset includes capture dates and environmental covariates (air and water temps and wind/cloud cover). There are also limited data on frog color, sex, and measurements (snout-vent length and mass). Data were collected as part of the U.S. Geological Survey (USGS) Amphibian Research and Monitoring Initiative, to contribute to an understanding of demography and population dynamics in small hylid frogs.
DOI: 10.17632/k45nnfjydt
title: Apple Fruitlet Sizing and Growth Rate Tracking Dataset

description: Supporting data for our manuscript entitled: Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision. The dataset contains rectified stereo images of apple fruitlet clusters, ground-truth caliper measurements, Mask R-CNN training annotations, and stereo camera calibration information. Please see README.txt for full description.
DOI: 10.17632/k45nnfjydt.2
title: Apple Fruitlet Sizing and Growth Rate Tracking Dataset

description: Supporting data for our manuscript entitled: Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision. The dataset contains rectified stereo images of apple fruitlet clusters, ground-truth caliper measurements, Mask R-CNN training annotations, and stereo camera calibration information. Please see README.txt for full description.
DOI: 10.17632/8x4v7yy3ds
title: EcoIrrigate: Multi-Sensor Soil Moisture Monitoring Dataset from Kolkata, India (Jan–Apr 2025)

description: Multi-sensor soil moisture monitoring dataset collected from two experimental farms in Kolkata, India (22.57°N, 88.36°E) over 110 days (Jan–Apr 2025) at 15-minute intervals. Contains 21,312 records across 22 features including calibrated volumetric water content (VWC), raw capacitive ADC readings, atmospheric pressure and temperature from METAR aviation weather reports, soil temperature, and cyclical time encodings. Collected using HW-390 capacitive sensors, Arduino Mega 2560 with ESP8266 WiFi, and solar-powered nodes transmitting via ThingSpeak IoT platform. Supports reproducibility of the associated manuscript on multi-task deep learning for sensor calibration and soil moisture forecasting.
DOI: 10.17632/8x4v7yy3ds.1
title: EcoIrrigate: Multi-Sensor Soil Moisture Monitoring Dataset from Kolkata, India (Jan–Apr 2025)

description: Multi-sensor soil moisture monitoring dataset collected from two experimental farms in Kolkata, India (22.57°N, 88.36°E) over 110 days (Jan–Apr 2025) at 15-minute intervals. Contains 21,312 records across 22 features including calibrated volumetric water content (VWC), raw capacitive ADC readings, atmospheric pressure and temperature from METAR aviation weather reports, soil temperature, and cyclical time encodings. Collected using HW-390 capacitive sensors, Arduino Mega 2560 with ESP8266 WiFi, and solar-powered nodes transmitting via ThingSpeak IoT platform. Supports reproducibility of the associated manuscript on multi-task deep learning for sensor calibration and soil moisture forecasting.
DOI: 10.17632/khv2bdsj7j
title: Integrated Multi-omics Elucidates the Protective Mechanism of Polygonatum odoratum Extract against Cyclophosphamide-Induced Hepatointestinal Injury via the Liver-Gut Axis and JAK/STAT3 Pathway

description: Integrated Multi-omics Elucidates the Protective Mechanism of Polygonatum odoratum Extract against Cyclophosphamide-Induced Hepatointestinal Injury via the Liver-Gut Axis and JAK/STAT3 Pathway
DOI: 10.17632/khv2bdsj7j.1
title: Integrated Multi-omics Elucidates the Protective Mechanism of Polygonatum odoratum Extract against Cyclophosphamide-Induced Hepatointestinal Injury via the Liver-Gut Axis and JAK/STAT3 Pathway

description: Integrated Multi-omics Elucidates the Protective Mechanism of Polygonatum odoratum Extract against Cyclophosphamide-Induced Hepatointestinal Injury via the Liver-Gut Axis and JAK/STAT3 Pathway