cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e242281v1
title: River State University of Science and Technology 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: River State University of Science and Technology 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e242282v1
title: Olabisi Onabanjo University, Ago Iwoye 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Olabisi Onabanjo University, Ago Iwoye 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.5281/zenodo.18131658
title: PC4-Paranoid Filter: Adaptive Physics-Constrained Sensor Fusion Dataset

description: This dataset accompanies the development and stress-testing of the PC4-Paranoid Filter —a physics-constrained, environment-agnostic adaptive sensor fusion algorithm.

The PC4-Paranoid Filter is designed as a universal pre-processor for noisy, adversarial,or partially corrupted sensor arrays, inspired by biological perceptual filteringmechanisms preceding higher-level cognition.

Unlike classical Kalman-based approaches, PC4 does not require environment-specificpre-tuning of noise covariances and remains stable under:• sensor drift• freeze faults• anti-phase corruption• partial sensor failure• snap-back transients

The dataset contains:• reference Python implementations of PC4 filter variants• stress-test scripts reproducing all reported scenarios• comparative benchmarks against Kalman filtering• full experimental figures corresponding to Figures 1–29

All experiments are deterministic and reproducible.The dataset is intended for control systems research, robotics, autonomous vehicles,neuroscience-inspired signal processing, and adaptive AI systemssensor fusionadaptive filteringfault tolerancecontrol systemsbiomorphic filteringkalman comparisonautonomous systemsroboticssignal processingneuroscience-inspired AI
DOI: 10.5281/zenodo.18131659
title: PC4-Paranoid Filter: Adaptive Physics-Constrained Sensor Fusion Dataset

description: This dataset accompanies the development and stress-testing of the PC4-Paranoid Filter —a physics-constrained, environment-agnostic adaptive sensor fusion algorithm.

The PC4-Paranoid Filter is designed as a universal pre-processor for noisy, adversarial,or partially corrupted sensor arrays, inspired by biological perceptual filteringmechanisms preceding higher-level cognition.

Unlike classical Kalman-based approaches, PC4 does not require environment-specificpre-tuning of noise covariances and remains stable under:• sensor drift• freeze faults• anti-phase corruption• partial sensor failure• snap-back transients

The dataset contains:• reference Python implementations of PC4 filter variants• stress-test scripts reproducing all reported scenarios• comparative benchmarks against Kalman filtering• full experimental figures corresponding to Figures 1–29

All experiments are deterministic and reproducible.The dataset is intended for control systems research, robotics, autonomous vehicles,neuroscience-inspired signal processing, and adaptive AI systemssensor fusionadaptive filteringfault tolerancecontrol systemsbiomorphic filteringkalman comparisonautonomous systemsroboticssignal processingneuroscience-inspired AI
DOI: 10.15468/dl.pjersf
title: Occurrence Download

description: A dataset containing 8989 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is GBR.3.2_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Aglais urticae"
 ]
}

 The dataset includes 8989 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071029-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e242314
title: Benue State University of Agriculture Science and Technology 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Benue State University of Agriculture Science and Technology 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.6084/m9.figshare.30987517
title: <b>Prevalence and risk factors of disordered eating risk among Yemeni medical students: a national multicenter cross-sectional study</b>

description: This dataset accompanies the study <b>“Prevalence and risk factors of disordered eating risk among Yemeni medical students: a national multicenter cross-sectional study.”</b> It contains anonymized, self-reported data collected from undergraduate medical students across Yemen between June and July 2025.The primary aim of the dataset is to facilitate transparency, reproducibility, and secondary analyses related to disordered eating risk and its associated sociodemographic, lifestyle, medical, and psychological factors in a conflict-affected, resource-limited setting.
DOI: 10.6084/m9.figshare.30987517.v1
title: <b>Prevalence and risk factors of disordered eating risk among Yemeni medical students: a national multicenter cross-sectional study</b>

description: This dataset accompanies the study <b>“Prevalence and risk factors of disordered eating risk among Yemeni medical students: a national multicenter cross-sectional study.”</b> It contains anonymized, self-reported data collected from undergraduate medical students across Yemen between June and July 2025.The primary aim of the dataset is to facilitate transparency, reproducibility, and secondary analyses related to disordered eating risk and its associated sociodemographic, lifestyle, medical, and psychological factors in a conflict-affected, resource-limited setting.
DOI: 10.15468/dl.egzrfy
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hibiscus peterianus"
 ]
}

 The dataset includes 7 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071031-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34847/nkl.34ae2v34
title: 5 Parisiens ayant une retraite au décès en 1986
title: 5 Parisians receiving a pension at the time of death in 1986

description: Pour estimer à quel rythme les pensions ont continué à se diffuser apres 1977, on a constitué un échantillon des actes de décès pour 1986 (dernière année pour laquelle ces documents sont facilement accessibles).

Ces actes qui portent sur tous les décès survenus à Paris, indiquent en effet si les défunts sont retraites (ils portent donc aussi sur les décès de mineurs et de personnes dont la résidence légale est à Paris mais sont décédées ailleurs).

Pour estimer la part des retraites parmi les Parisiens adultes, ont été exclus les défunts de moins de 20 ans et ceux qui n'avaient pas leur résidence légale à Paris.
description: To estimate the pace at which pensions continued to spread after 1977, a sample of death certificates for the year 1986 was compiled (the last year for which these documents are easily accessible).

These certificates, which cover all deaths occurring in Paris, indicate whether the deceased were pensioners. They therefore also include the deaths of minors and of individuals whose legal residence was in Paris but who died elsewhere.

To estimate the share of pensioners among adult Parisians, individuals under the age of 20 and those who did not have their legal residence in Paris were excluded.
DOI: 10.5281/zenodo.15548305
title: A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption

description: Zenodo Repository Metadata and Description

Title: Supplemental Material for "A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption"

Description

This repository contains the dataset and methodological artifacts developed for a Systematic Literature Review (SLR) investigating Machine Learning (ML) applications in residential water demand forecasting. The review synthesizes evidence from 80 primary studies published between 2009 and 2024, following the PRISMA-2020 guidelines.

Repository Contents





DataExtraction.csv: The master evidence matrix containing metadata, objectives, and findings for all 80 primary studies.




Taxonomy Maps: Standardized mapping files that document how raw textual terms from the literature were normalized into canonical categories.




Supplementary Material: The file provides detailed methodological documentation and extended analyses that support the results reported in the main manuscript, while preserving conciseness in the primary text.

The supplementary material includes:





A detailed methodological workflow and execution timeline, describing the implementation of the seven-step SLR protocol in accordance with PRISMA guidelines, including database search, screening, quality assessment, data consolidation, and analysis.




A complete list of the 80 primary studies analyzed in the review, including publication type and reference identifiers.




An extended characterization of machine learning model properties (RQ1), documenting the conceptual taxonomy used to classify model capabilities and methodological features across studies.




A qualitative synthesis of reported benefits associated with evaluation metrics (RQ3), complementing the quantitative results presented in the main article.




A citation-based contextual analysis, including total citations and citations per year (CPY), stratified by model families, to identify influential studies and thematic trends within the literature.



All quantitative mappings, cross-references, and classifications described in this document are aligned with the evidence matrix made publicly available alongside this repository. This supplementary file is intended to enhance transparency, reproducibility, and methodological rigor for researchers interested in machine-learning-based residential water demand forecasting.



Key Research Insights





Methodological Evolution: A capability shift around 2020 where Deep Learning models (notably LSTM) displaced traditional methods.




Predictor Patterns: Climatic and historical consumption dominate, with limited use of socio-economic variables due to privacy constraints.




Evaluation Standards: RMSE and MAE are the most widely used evaluation metrics.



Practical Tools for Researchers





Model-Predictor Crosswalk: A co-occurrence matrix of model families and predictor categories.




Prescriptive Decision Tree: A heuristic framework guiding model selection based on data and computational constraints.
DOI: 10.5281/zenodo.18131836
title: A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption

description: Zenodo Repository Metadata and Description

Title: Supplemental Material for "A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption"

Description

This repository contains the dataset and methodological artifacts developed for a Systematic Literature Review (SLR) investigating Machine Learning (ML) applications in residential water demand forecasting. The review synthesizes evidence from 80 primary studies published between 2009 and 2024, following the PRISMA-2020 guidelines.

Repository Contents





DataExtraction.csv: The master evidence matrix containing metadata, objectives, and findings for all 80 primary studies.




Taxonomy Maps: Standardized mapping files that document how raw textual terms from the literature were normalized into canonical categories.




Supplementary Material: The file provides detailed methodological documentation and extended analyses that support the results reported in the main manuscript, while preserving conciseness in the primary text.

The supplementary material includes:





A detailed methodological workflow and execution timeline, describing the implementation of the seven-step SLR protocol in accordance with PRISMA guidelines, including database search, screening, quality assessment, data consolidation, and analysis.




A complete list of the 80 primary studies analyzed in the review, including publication type and reference identifiers.




An extended characterization of machine learning model properties (RQ1), documenting the conceptual taxonomy used to classify model capabilities and methodological features across studies.




A qualitative synthesis of reported benefits associated with evaluation metrics (RQ3), complementing the quantitative results presented in the main article.




A citation-based contextual analysis, including total citations and citations per year (CPY), stratified by model families, to identify influential studies and thematic trends within the literature.



All quantitative mappings, cross-references, and classifications described in this document are aligned with the evidence matrix made publicly available alongside this repository. This supplementary file is intended to enhance transparency, reproducibility, and methodological rigor for researchers interested in machine-learning-based residential water demand forecasting.



Key Research Insights





Methodological Evolution: A capability shift around 2020 where Deep Learning models (notably LSTM) displaced traditional methods.




Predictor Patterns: Climatic and historical consumption dominate, with limited use of socio-economic variables due to privacy constraints.




Evaluation Standards: RMSE and MAE are the most widely used evaluation metrics.



Practical Tools for Researchers





Model-Predictor Crosswalk: A co-occurrence matrix of model families and predictor categories.




Prescriptive Decision Tree: A heuristic framework guiding model selection based on data and computational constraints.
DOI: 10.3886/e242315
title: Abubakar Tafawa Balewa University 2026/2027 Diploma Form, Direct-Entry Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Abubakar Tafawa Balewa University 2026/2027 Diploma Form, Direct-Entry Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.6084/m9.figshare.30919733
title: Real-world cardiac events and outcomes in cBTKi-treated chronic lymphocytic leukemia patients

description: To evaluate the real-world incidence of cardiovascular adverse events (CVAE) and clinical outcomes among patients with chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) treated with covalent Bruton’s Tyrosine Kinase inhibitors (cBTKis). Patients initiating cBTKi treatment from 1 January 2020 to 1 January 2023 were identified using claims data. Demographics, first-line (1L) and second-line or later (2L+) therapy, incident CVAEs, and clinical outcomes were assessed. In total, 2,163 patients (81.4% in 1L and 18.6% in 2L+) were identified with a mean age of 73.8 ± 9.2 years and 40.6% female. The most common incident CVAEs were hypertension (23.4%), atrial fibrillation (10.2%), ventricular arrhythmias (10.1%), heart failure (8.5%), and atrial flutter (4.2%). Those with CVAEs experienced higher switching to next treatment + death and worse overall survival than those without CVAEs. Incidence rates were ~2–3 fold lower with acalabrutinib than ibrutinib for hypertension, atrial fibrillation, and atrial flutter. Kaplan-Meier estimates for the likelihood of not experiencing a CVAE were 83% (acalabrutinib) and 72% (ibrutinib) at 12-months. Despite improvements in 2nd-generation cBTKi cardiotoxicity, patients with CLL/SLL receiving cBTKis have a considerable cardiovascular disease burden. These findings highlight the unmet need for CLL/SLL treatment options with improved efficacy and safety profiles.
DOI: 10.6084/m9.figshare.30919733.v1
title: Real-world cardiac events and outcomes in cBTKi-treated chronic lymphocytic leukemia patients

description: To evaluate the real-world incidence of cardiovascular adverse events (CVAE) and clinical outcomes among patients with chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) treated with covalent Bruton’s Tyrosine Kinase inhibitors (cBTKis). Patients initiating cBTKi treatment from 1 January 2020 to 1 January 2023 were identified using claims data. Demographics, first-line (1L) and second-line or later (2L+) therapy, incident CVAEs, and clinical outcomes were assessed. In total, 2,163 patients (81.4% in 1L and 18.6% in 2L+) were identified with a mean age of 73.8 ± 9.2 years and 40.6% female. The most common incident CVAEs were hypertension (23.4%), atrial fibrillation (10.2%), ventricular arrhythmias (10.1%), heart failure (8.5%), and atrial flutter (4.2%). Those with CVAEs experienced higher switching to next treatment + death and worse overall survival than those without CVAEs. Incidence rates were ~2–3 fold lower with acalabrutinib than ibrutinib for hypertension, atrial fibrillation, and atrial flutter. Kaplan-Meier estimates for the likelihood of not experiencing a CVAE were 83% (acalabrutinib) and 72% (ibrutinib) at 12-months. Despite improvements in 2nd-generation cBTKi cardiotoxicity, patients with CLL/SLL receiving cBTKis have a considerable cardiovascular disease burden. These findings highlight the unmet need for CLL/SLL treatment options with improved efficacy and safety profiles.
DOI: 10.6084/m9.figshare.30747170
title: A deep learning radiomics model for predicting non-sentinel lymph node metastases in early-stage breast cancer patients

description: To develop and validate a deep learning radiomics model to predict non-sentinel lymph node (NSLN) metastases in early-stage breast cancer patients with 1–2 positive sentinel lymph node (SLN) metastases. This retrospective and prospective study encompassed 1,647 patients. Clinical, pathological information, and axillary ultrasound (AUS) findings, collected. Radiomic features of breast cancer lesions were extracted from the ultrasound images. We developed predictive models based on clinical factors alone (C model), clinical factors coupled with AUS (CA model), and clinical factors integrated with both AUS and radiomic features (CAR model). The predictive performance of each model was evaluated via the area under the curve (AUC), decision curve analysis (DCA), and calibration curve analysis. The AUC values for the C model, CA model and CAR model in the test cohort were 0.812, 0.850, and 0.994, respectively. Notably, the CAR model exhibited significantly superior predictive capability compared to both the C model and CA model. In subgroups analyses, the CAR model also achieved the optimal predictive performance. The DCA curve confirmed that the CAR model possessed significant clinical implications. The CAR model had the capability to predict NSLN metastases in early-stage breast cancer with 1–2 positive SLN metastases. Axillary lymph node dissection is controversial in patients with 1–2 positive sentinel lymph node metastases because non-sentinel lymph node is negative in some of these patients. In order to identify this group patients, this study built an algorithm with a quite large group of patients. This algorithm built with all valuable informantion of patients including clinical and pathological information, axillary ultrasound findings, as well as ultrasound radiomic features of breast cancer lesions. This algorithm is able to identify above-mentioned patients who would not benefit from axillary lymph node dissection with area under the curve as high as 0.994. The performance of our algorithm is superior to existing models such as MSKCC, Tenon and is robust across different subgroups.
DOI: 10.6084/m9.figshare.30747170.v1
title: A deep learning radiomics model for predicting non-sentinel lymph node metastases in early-stage breast cancer patients

description: To develop and validate a deep learning radiomics model to predict non-sentinel lymph node (NSLN) metastases in early-stage breast cancer patients with 1–2 positive sentinel lymph node (SLN) metastases. This retrospective and prospective study encompassed 1,647 patients. Clinical, pathological information, and axillary ultrasound (AUS) findings, collected. Radiomic features of breast cancer lesions were extracted from the ultrasound images. We developed predictive models based on clinical factors alone (C model), clinical factors coupled with AUS (CA model), and clinical factors integrated with both AUS and radiomic features (CAR model). The predictive performance of each model was evaluated via the area under the curve (AUC), decision curve analysis (DCA), and calibration curve analysis. The AUC values for the C model, CA model and CAR model in the test cohort were 0.812, 0.850, and 0.994, respectively. Notably, the CAR model exhibited significantly superior predictive capability compared to both the C model and CA model. In subgroups analyses, the CAR model also achieved the optimal predictive performance. The DCA curve confirmed that the CAR model possessed significant clinical implications. The CAR model had the capability to predict NSLN metastases in early-stage breast cancer with 1–2 positive SLN metastases. Axillary lymph node dissection is controversial in patients with 1–2 positive sentinel lymph node metastases because non-sentinel lymph node is negative in some of these patients. In order to identify this group patients, this study built an algorithm with a quite large group of patients. This algorithm built with all valuable informantion of patients including clinical and pathological information, axillary ultrasound findings, as well as ultrasound radiomic features of breast cancer lesions. This algorithm is able to identify above-mentioned patients who would not benefit from axillary lymph node dissection with area under the curve as high as 0.994. The performance of our algorithm is superior to existing models such as MSKCC, Tenon and is robust across different subgroups.