cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/jbbv466n8j.1
title: 20251115-DTEHM_research data

description: Title: How Collective-Intelligence Collaboration Influences Resource Evolution in Open Networked Communities: Mechanisms and Evidence
This study was exploratory and was not pre-registered prior to data collection. This study, through a comprehensive literature review, explores the influence of group collaborative interactions on resource content evolution in the process of community-based learning. It focuses on three major categories of characteristics: contextual characteristics (topic type), group structural characteristics (group size and diversity), and interaction characteristics (interaction intensity and interaction depth). The findings from prior studies indicate that the characteristics influencing the evolution of high-quality resource content are diverse and complex. Given the inconsistencies in existing research conclusions, this study proposes the following preliminary hypotheses:
1)In topic-based community learning processes, certain characteristics may serve as key conditions influencing resource content evolution.
2)The quality of resource evolution in community learning is affected by the synergistic effects of multiple characteristics, and different combinations of these characteristics may lead to equivalent evolutionary pathways.
Based on these hypotheses, this study integrates Group Communication Analysis (GCA) and Discrete-Time Event History Analysis (DTEHA) to investigate the mechanisms through which collective collaboration in resource production promotes resource content evolution. Compared with traditional linear or static analytical approaches, this combined method captures both the dynamic communicative features of collaborative processes and the temporal patterns of resource change, thereby providing a more effective means of uncovering the sustained influence of group collaborative behaviors on resource evolution.
DOI: 10.15468/dl.yzx7yd
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Calacogloea",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039417-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/b74-svm-5c9
title: †Cyclonema J. Hall, 1852

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.17632/wrs5sx4bhb.1
title: Organize raw data according to the images in the article

description: Gallium nitrate, a non-redox analog of iron (III), suppresses bacterial biofilms and virulence within the framework of bacterial regulation. This study investigates the molecular mechanisms and regulatory pathways through which gallium nitrate modulates bacterial activity and function.
DOI: 10.17632/wvxzjs4pr5
title: AI_leadship_ML_PSE-SEM

description: AI_leadship_ML_PSE-SEM
DOI: 10.17632/wvxzjs4pr5.1
title: AI_leadship_ML_PSE-SEM

description: AI_leadship_ML_PSE-SEM
DOI: 10.17632/g9zpc9w39k.2
title: Supplementary material 3 The BIOPIGEE project (‘Biosecurity practices for pig farming across Europe’) online survey questions. Open text answers from online survey.

description: Supplementary material 3

The BIOPIGEE project (‘Biosecurity practices for pig farming across Europe’) online survey questions

Open text answers from survey. All responses translated into English and if needed minimally edited for clarification. Responses were sorted in alphabetical to assure anonymity.

The BIOPIGEE project has received funding from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreement No. 773830: One Health European Joint Programme.
DOI: 10.17632/2wnkk97zdd
title: Legacy effects of biochar application increase fungal necromass carbon but exhibit divergent influence on soil organic carbon

description: The potential of biochar for carbon sequestration is widely recognized, but its long-term residual effects after application cessation remain poorly quantified, particularly regarding microbially mediated pathways for soil organic carbon (SOC) formation and stabilization. This study fills this critical gap through a unique 8-year field experiment, providing novel insights into the regulatory mechanisms governing SOC dynamics in agricultural soils.
DOI: 10.17632/2wnkk97zdd.1
title: Legacy effects of biochar application increase fungal necromass carbon but exhibit divergent influence on soil organic carbon

description: The potential of biochar for carbon sequestration is widely recognized, but its long-term residual effects after application cessation remain poorly quantified, particularly regarding microbially mediated pathways for soil organic carbon (SOC) formation and stabilization. This study fills this critical gap through a unique 8-year field experiment, providing novel insights into the regulatory mechanisms governing SOC dynamics in agricultural soils.
DOI: 10.13140/rg.2.2.18048.08962
title: Certificate of acceptance of a conference paper at the 6th international conference on agriculture food industry and environment
DOI: 10.17632/mzwyhj52tf
title: A Multilingual & Multimodal Text and Image Corpus Dataset for Political Misinformation

description: Our database is a richly annotated multimodal database designed to facilitate strong fake-news detection research. It consists of two complementary but separate components: an image directory and a text spreadsheet. The image directory consists of a folder-level organization with a title as a topic; within each topic directory, the images are then placed in real and fake subdirectories based on the expert labeling. Such an organization allows loading and processing images for cross-modal testing or supervised learning. In contrast, text data are kept in a single Excel sheet where a record is one piece of news. Four separate columns keep the title, source, full news report, and real/fake indicator. Together, these modalities cover a broad range of temporal and topical domains not only social-media posts, mainstream-media news reports, and election-related posts but allowing the training of models on both linguistic aspects (sensational or objective tone, grammaticality, metadata quality) and visual aspects (original vs. photo-manipulated images). With a combination of a sparse folder hierarchy for images and a richly annotated spreadsheet for text, the dataset is well-specified, reproducible, and easy to pipe into any subsequent machine-learning pipeline.
DOI: 10.17632/mzwyhj52tf.1
title: A Multilingual & Multimodal Text and Image Corpus Dataset for Political Misinformation

description: Our database is a richly annotated multimodal database designed to facilitate strong fake-news detection research. It consists of two complementary but separate components: an image directory and a text spreadsheet. The image directory consists of a folder-level organization with a title as a topic; within each topic directory, the images are then placed in real and fake subdirectories based on the expert labeling. Such an organization allows loading and processing images for cross-modal testing or supervised learning. In contrast, text data are kept in a single Excel sheet where a record is one piece of news. Four separate columns keep the title, source, full news report, and real/fake indicator. Together, these modalities cover a broad range of temporal and topical domains not only social-media posts, mainstream-media news reports, and election-related posts but allowing the training of models on both linguistic aspects (sensational or objective tone, grammaticality, metadata quality) and visual aspects (original vs. photo-manipulated images). With a combination of a sparse folder hierarchy for images and a richly annotated spreadsheet for text, the dataset is well-specified, reproducible, and easy to pipe into any subsequent machine-learning pipeline.
DOI: 10.25829/7q2e-tf69
title: Cleaned tree inventory data including basal area of focal and neighbouring trees, MyDiv experiment (2016–2021)

description: Climate change is predicted to increase the frequency and duration of drought events, threatening the functioning of Central European forests. To study how individual tree growth during the drought event of 2018-2020 was dependend on the hydro-functional strategy of the tree species and if the tree growth reductions during drought could be buffered by the hydro-functional dissimilarity of the focal tree to its neighbouring trees, we created this dataset. 
Climate change is expected to increase the frequency and duration of drought events, threatening the functioning and stability of Central European forests. To investigate how individual tree growth during the 2018–2020 drought was influenced by species-specific hydro-functional strategies, and whether growth reductions could be mitigated by the hydro-functional dissimilarity of neighboring trees, this dataset was established.

The dataset is derived from tree inventory data collected in the MyDiv Experiment between 2016 and 2021. It includes only trees that remained alive throughout the observation period and focuses on the inner 36 trees per plot to ensure complete information on neighborhood composition. For each tree, basal diameter measurements were cleaned using a standardized procedure, and annual growth was calculated as the increment in basal area based on the corrected diameters (d₀). Neighborhood variables were then computed to describe species identity, size, and relative dominance of surrounding trees.

This dataset represents a curated subset of the full MyDiv inventory, optimized for analyses linking tree growth dynamics to neighborhood composition, competition, and drought response.
DOI: 10.17632/jzgnjsff9f.4
title: EmoTweetID: Indonesian Emotion Tweet Dataset

description: The EmoTweetID dataset is a publicly available resource of Indonesian tweets collected from X (formerly Twitter) using emotion-related keywords. 

The dataset consists of three main components:
1. EmoTweetID-Corpus.csv: 3,126,987 unlabeled tweets for unsupervised tasks such as word embedding construction.
2. EmoTweetID-Lexicon.csv: 2,243 tweets automatically annotated using the Indonesian NRC EmoLex.
3. EmoTweetID-Human.csv: 2,243 tweets manually annotated by three psychology students, with inter-annotator agreement measured using Cohen’s and Fleiss’ Kappa.

Both annotated files (EmoTweetID-Lexicon.csv and EmoTweetID-Human.csv) provide labels following Ekman’s six basic emotions: anger, disgust, fear, joy, sadness, and surprise.

Additionally, two pre-trained word embedding models (Wors2Vec and FastText) trained on the corpus, TweetID-Word2Vec.zip and TweetID-FastText.zip, are provided for various downstream NLP tasks.

All code used to construct the dataset is available in the GitHub repository: https://github.com/ksnugroho/EmoTweetID

This dataset offers a valuable benchmark for affective computing and natural language processing in Indonesian, supporting research in emotion recognition, social media analysis, and the development of empathetic AI systems.
DOI: 10.5281/zenodo.17632843
title: Material Suplementar - O papel da Inteligência Artificial no apoio ao Desenvolvimento Ágil de Software: uma revisão da literatura
DOI: 10.5281/zenodo.17632844
title: Material Suplementar - O papel da Inteligência Artificial no apoio ao Desenvolvimento Ágil de Software: uma revisão da literatura
DOI: 10.17632/c9nj9mjdcw
title: Snow depth

description: A long-term daily snow depth dataset for the Northern Hemisphere was prepared by combining multi-source snow depth product data, environmental factor variables, and ground-observed snow depth data using machine learning methods. The spatiotemporal characteristics of average snow depth and maximum snow depth from 1980 to 2019 were then analyzed on the Qinghai-Tibet Plateau using this data.
DOI: 10.3535/kaw-q6j-3fh
title: †Cyclonema J. Hall, 1852

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.17632/c9nj9mjdcw.1
title: Snow depth

description: A long-term daily snow depth dataset for the Northern Hemisphere was prepared by combining multi-source snow depth product data, environmental factor variables, and ground-observed snow depth data using machine learning methods. The spatiotemporal characteristics of average snow depth and maximum snow depth from 1980 to 2019 were then analyzed on the Qinghai-Tibet Plateau using this data.
DOI: 10.17632/ttvg2w68rj
title: Synchronous sequential thrusting and extension-controlled exhumation of the Higher Himalayan Crystalline in the Bhagirathi Valley, NW Himalaya

description: This thermochronological fission-track dataset was collected along the Bhagirathi Valley in the NW Himalaya, India. The data belongs to the part of the PhD work of Sanjeeb Behera and is an outcome of a Ministry of Earth Sciences (MoES) funded project. All analytical work was conducted at the National Facility for Low-Temperature Thermochronology, Kurukshetra University, Kurukshetra, Haryana, India. The dataset corresponds to the research article titled “Synchronous Sequential Thrusting and Extension-Controlled Exhumation of the Higher Himalayan Crystalline in the Bhagirathi Valley, NW Himalaya.” We employed 3D thermokinematic modeling to constrain exhumation patterns, controls, and fault parameters. This repository includes the forward and inverse modeling tables, relevant figures, and a compilation table of cooling ages.
DOI: 10.17632/ttvg2w68rj.1
title: Synchronous sequential thrusting and extension-controlled exhumation of the Higher Himalayan Crystalline in the Bhagirathi Valley, NW Himalaya

description: This thermochronological fission-track dataset was collected along the Bhagirathi Valley in the NW Himalaya, India. The data belongs to the part of the PhD work of Sanjeeb Behera and is an outcome of a Ministry of Earth Sciences (MoES) funded project. All analytical work was conducted at the National Facility for Low-Temperature Thermochronology, Kurukshetra University, Kurukshetra, Haryana, India. The dataset corresponds to the research article titled “Synchronous Sequential Thrusting and Extension-Controlled Exhumation of the Higher Himalayan Crystalline in the Bhagirathi Valley, NW Himalaya.” We employed 3D thermokinematic modeling to constrain exhumation patterns, controls, and fault parameters. This repository includes the forward and inverse modeling tables, relevant figures, and a compilation table of cooling ages.
DOI: 10.17632/jmnk6vkdy2
title: IPO Automation

description: The dataset comprises detailed financial and market data related to companies planning or having conducted Initial Public Offerings (IPOs). It includes identifiers such as the serial number and company name for reference purposes. Key financial metrics like Current Market Price, Price-to-Earnings (P/E) ratio, Market Capitalization, Dividend Yield, and Return on Capital Employed (ROCE) provide insight into the valuation, profitability, and operational efficiency of these companies. Performance indicators such as Net Profit of the last quarter, Quarterly Profit Variation, Quarterly Sales, and Sales Variation track recent growth trends, helping to assess momentum and financial health. IPO-specific features like Issue Price, Listing Price, Listing Premium or Discount, and Current Returns offer information regarding the pricing strategy and the market's reception of the IPO. The dataset also includes a Classification feature that categorizes companies by sector or business type, aiding comparative analysis. Together, these features form a comprehensive profile that allows in-depth evaluation of IPO candidates. This dataset is invaluable for building predictive models to assist investors and analysts in deciding whether to apply for or invest in an IPO, by leveraging company fundamentals, historical financial performance, and market data. Its richness and diversity in feature types enable robust machine learning applications aimed at forecasting IPO success or suitability for investment.
DOI: 10.17632/jmnk6vkdy2.2
title: IPO Automation

description: The dataset comprises detailed financial and market data related to companies planning or having conducted Initial Public Offerings (IPOs). It includes identifiers such as the serial number and company name for reference purposes. Key financial metrics like Current Market Price, Price-to-Earnings (P/E) ratio, Market Capitalization, Dividend Yield, and Return on Capital Employed (ROCE) provide insight into the valuation, profitability, and operational efficiency of these companies. Performance indicators such as Net Profit of the last quarter, Quarterly Profit Variation, Quarterly Sales, and Sales Variation track recent growth trends, helping to assess momentum and financial health. IPO-specific features like Issue Price, Listing Price, Listing Premium or Discount, and Current Returns offer information regarding the pricing strategy and the market's reception of the IPO. The dataset also includes a Classification feature that categorizes companies by sector or business type, aiding comparative analysis. Together, these features form a comprehensive profile that allows in-depth evaluation of IPO candidates. This dataset is invaluable for building predictive models to assist investors and analysts in deciding whether to apply for or invest in an IPO, by leveraging company fundamentals, historical financial performance, and market data. Its richness and diversity in feature types enable robust machine learning applications aimed at forecasting IPO success or suitability for investment.
DOI: 10.17632/n7262bffjn
title: Multi-Source Seismological Data Compilation for the 2017 Hojedk Events

description: This dataset brings together strong-motion parameters, focal-mechanism information, mainshock location data, relocated depths, and a multi-year regional earthquake catalogue for the 2017 Hojedk events in southeastern Iran. All entries were compiled from national seismic networks and are organized into five structured sheets within a single Excel file, allowing straightforward reuse in seismological and hazard-related applications.
DOI: 10.17632/n7262bffjn.1
title: Multi-Source Seismological Data Compilation for the 2017 Hojedk Events

description: This dataset brings together strong-motion parameters, focal-mechanism information, mainshock location data, relocated depths, and a multi-year regional earthquake catalogue for the 2017 Hojedk events in southeastern Iran. All entries were compiled from national seismic networks and are organized into five structured sheets within a single Excel file, allowing straightforward reuse in seismological and hazard-related applications.
DOI: 10.17632/xkc88dc44d
title: Features of visit time series

description: This dataset contains a five-year daily time series of multiple traffic metrics collected from an educational blog on statistical forecasting, hosted under the alias statforecasting.com. The variables exhibit complex seasonal patterns driven by both weekly cycles and the academic calendar. Specifically, the dataset records daily visits over the period 2014–2020, with pronounced seasonality aligned with academic schedules in the Gregorian calendar as well as weekday effects. These characteristics make the dataset a suitable testbed for evaluating the proposed approach. The data were partitioned into 85% for training and 15% for testing.
DOI: 10.17632/xkc88dc44d.2
title: Features of visit time series

description: This dataset contains a five-year daily time series of multiple traffic metrics collected from an educational blog on statistical forecasting, hosted under the alias statforecasting.com. The variables exhibit complex seasonal patterns driven by both weekly cycles and the academic calendar. Specifically, the dataset records daily visits over the period 2014–2020, with pronounced seasonality aligned with academic schedules in the Gregorian calendar as well as weekday effects. These characteristics make the dataset a suitable testbed for evaluating the proposed approach. The data were partitioned into 85% for training and 15% for testing.
DOI: 10.17632/7xnjxydf8h
title: Numerical computations of the strain energy release rate for a comparative analysis of the virtual crack closure technique and a cohesive zone model and relative meshes

description: The present data includes the numerical results analysed and discussed in the article “A local comparison of the virtual crack closure technique and a cohesive zone model in calculating the strain energy release rate for large and curved delaminations” be the same authors. In this work, three non-standard specimens for composite delaminations are simulated numerically. These are a reinforced Double Cantilever Beam (R-DCB), a reinforced End-Load Split (R-ELS) with standard cohesive length and a reinforced End-Load Split with doubled cohesive length (R-ELS2). The presence of reinforcing plates produces large and curved delamination fronts. A specific comparative strategy is adopted and described in the article.

The simulations are used to compare locally the computations of the strain energy release rate of the virtual crack closure techniques and a cohesive zone model implementing a J-integral formulation. Five load-displacement combinations per specimen are analysed; for each of these combinations, five delamination fronts are considered. This results in 75 possible comparisons of the computed strain energy release rate decomposed in its modes. Given the mesh dependency of the VCCT, the mesh of each VCCT simulation is also included. The presend dataset and its associated article thus provide a first local comparison between the two most used techniques for delamination analysis for large and curved delaminations.
DOI: 10.17632/7xnjxydf8h.1
title: Numerical computations of the strain energy release rate for a comparative analysis of the virtual crack closure technique and a cohesive zone model and relative meshes

description: The present data includes the numerical results analysed and discussed in the article “A local comparison of the virtual crack closure technique and a cohesive zone model in calculating the strain energy release rate for large and curved delaminations” be the same authors. In this work, three non-standard specimens for composite delaminations are simulated numerically. These are a reinforced Double Cantilever Beam (R-DCB), a reinforced End-Load Split (R-ELS) with standard cohesive length and a reinforced End-Load Split with doubled cohesive length (R-ELS2). The presence of reinforcing plates produces large and curved delamination fronts. A specific comparative strategy is adopted and described in the article.

The simulations are used to compare locally the computations of the strain energy release rate of the virtual crack closure techniques and a cohesive zone model implementing a J-integral formulation. Five load-displacement combinations per specimen are analysed; for each of these combinations, five delamination fronts are considered. This results in 75 possible comparisons of the computed strain energy release rate decomposed in its modes. Given the mesh dependency of the VCCT, the mesh of each VCCT simulation is also included. The presend dataset and its associated article thus provide a first local comparison between the two most used techniques for delamination analysis for large and curved delaminations.