cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30716729
title: Exploring the past and forecasting the future of malaria in selected Nigerian states: A time series modelling approach using wavelet and SARIMA

description: Malaria remains a life-threatening disease and poses a significant economic burden in sub-Saharan Africa, with Nigeria accounting for the highest global morbidity and mortality. Despite increased preventive and control interventions implemented by the National Malaria Elimination Programme over the years, malaria transmission in Nigeria continues to exhibit spatial and temporal dynamics making elimination difficult to achieve. Understanding malaria seasonal patterns and synchrony between cases in different transmission settings in Nigeria, and forecasting future outbreaks is therefore critical for guiding public health policies. Time series modelling plays a crucial role in extracting meaningful insights, revealing patterns and forecasting infectious disease outbreaks. In this study, we employed two time series approaches—wavelet analysis and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model—to analyse confirmed uncomplicated malaria case data of Nasarawa, Kwara, and Zamfara States, Nigeria, over a 10-year period (January 2015 to December 2024). Wavelet analysis decomposed the time series data into their constituent time-frequency components, enabling multi-resolution analysis of the data and revealing hidden patterns. The SARIMA model, on the other hand, was used to forecast malaria cases in each of the three states for the next two years (January 2025 to December 2026). Univariate wavelet analysis showed that malaria incidence in Nasarawa followed a very strong annual cycle throughout almost the entire study period, with seasonal peaks occurring mostly in September 70\% of the time. In Kwara State, short periods of semi-annual, annual, and multi-annual cycles were detected, with annual peaks mostly appearing in August for about half of the study period. In Zamfara, a moderate yearly cycle was observed between 2015 and June 2020 and again from 2022 to 2023, with seasonal peaks occurring consistently in September, except in 2024, when the peak shifted to August. Bivariate wavelet analysis showed that Nasarawa and Zamfara had an almost perfectly synchronized seasonal malaria pattern for most of the study period. For both the Nasarawa–Kwara and Zamfara–Kwara pairs, synchrony was present only between mid-2018 and early-2020; outside this period, Kwara’s malaria peaks occurred earlier in the season in both cases. For the period 2025–2026, the SARIMA model forecasted average monthly malaria cases of 51,482 in Nasarawa, 20,850 in Kwara, and 67,463 in Zamfara. These findings emphasize the need for state-specific malaria interventions to capture the variability seen in Kwara and regionally coordinated control interventions for Nasarawa and Zamfara with very strong synchrony. These findings provide useful guide for effectively planning the timing of interventions and tailoring control strategies to the seasonal trends observed.<br>
DOI: 10.6084/m9.figshare.30716729.v1
title: Exploring the past and forecasting the future of malaria in selected Nigerian states: A time series modelling approach using wavelet and SARIMA

description: Malaria remains a life-threatening disease and poses a significant economic burden in sub-Saharan Africa, with Nigeria accounting for the highest global morbidity and mortality. Despite increased preventive and control interventions implemented by the National Malaria Elimination Programme over the years, malaria transmission in Nigeria continues to exhibit spatial and temporal dynamics making elimination difficult to achieve. Understanding malaria seasonal patterns and synchrony between cases in different transmission settings in Nigeria, and forecasting future outbreaks is therefore critical for guiding public health policies. Time series modelling plays a crucial role in extracting meaningful insights, revealing patterns and forecasting infectious disease outbreaks. In this study, we employed two time series approaches—wavelet analysis and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model—to analyse confirmed uncomplicated malaria case data of Nasarawa, Kwara, and Zamfara States, Nigeria, over a 10-year period (January 2015 to December 2024). Wavelet analysis decomposed the time series data into their constituent time-frequency components, enabling multi-resolution analysis of the data and revealing hidden patterns. The SARIMA model, on the other hand, was used to forecast malaria cases in each of the three states for the next two years (January 2025 to December 2026). Univariate wavelet analysis showed that malaria incidence in Nasarawa followed a very strong annual cycle throughout almost the entire study period, with seasonal peaks occurring mostly in September 70\% of the time. In Kwara State, short periods of semi-annual, annual, and multi-annual cycles were detected, with annual peaks mostly appearing in August for about half of the study period. In Zamfara, a moderate yearly cycle was observed between 2015 and June 2020 and again from 2022 to 2023, with seasonal peaks occurring consistently in September, except in 2024, when the peak shifted to August. Bivariate wavelet analysis showed that Nasarawa and Zamfara had an almost perfectly synchronized seasonal malaria pattern for most of the study period. For both the Nasarawa–Kwara and Zamfara–Kwara pairs, synchrony was present only between mid-2018 and early-2020; outside this period, Kwara’s malaria peaks occurred earlier in the season in both cases. For the period 2025–2026, the SARIMA model forecasted average monthly malaria cases of 51,482 in Nasarawa, 20,850 in Kwara, and 67,463 in Zamfara. These findings emphasize the need for state-specific malaria interventions to capture the variability seen in Kwara and regionally coordinated control interventions for Nasarawa and Zamfara with very strong synchrony. These findings provide useful guide for effectively planning the timing of interventions and tailoring control strategies to the seasonal trends observed.<br>
DOI: 10.5064/f6g2petf/361k0n
title: Moore-et-al_TX_0621_AgendaItem3-COVID-19SituationUpdate06292021.pdf
DOI: 10.5064/f6g2petf/yfnydb
title: Moore-et-al_OK_0321_OK_0321_State opens COVID-19 vaccine to Oklahomans in Phase 3 groups.pdf
DOI: 10.5064/f6g2petf/sfo61j
title: Moore-et-al_OR_0521_Approval versus Emergency Use Authorization (EUA)_ What’s the difference_ _ Oregon Health News Blog.pdf
DOI: 10.5064/f6g2petf/otucna
title: Moore-et-al_OH_0721_Innovative Ideas _ COVID-19.pdf
DOI: 10.5064/f6g2petf/hzvgfu
title: Moore-et-al_SC_0921_Third Dose Recommended for Immunocompromised People _ SCDHEC.pdf
DOI: 10.5064/f6g2petf/9apxgn
title: Moore-et-al_OH_0521_innovative-ideas-05-27-21-franklin.pdf
DOI: 10.5064/f6g2petf/rxvmeo
title: Moore-et-al_OH_0421_public-instructions-to-access-impactsiis-record.pdf
DOI: 10.5064/f6g2petf/59k01t
title: Moore-et-al_SD_0421_SD_0421_Immunization & Vaccination News _ South Dakota Department of Health.pdf
DOI: 10.5064/f6g2petf/qtvzah
title: Moore-et-al_OR_0821_Healthcare-Provider-Healthcare-Staff-Vaccine-Rule-FAQs_08.27.2021.pdf
DOI: 10.5064/f6g2petf/xcsvrm
title: Moore-et-al_SC_0421_4.23.2021-PeeDee_CARE_Panel_Meeting_Minutes.pdf
DOI: 10.5064/f6g2petf/xr2ctt
title: Moore-et-al_OR_0621_2023-25-OHA-Agency-Request-Budget.pdf
DOI: 10.5064/f6g2petf/g5bbeo
title: Moore-et-al_OH_0421_Manufacturer Specific Information _ Ohio Department of Health.pdf
DOI: 10.5064/f6g2petf/qy5ptc
title: Moore-et-al_PA_0121_COVID-19 Vaccine.pdf
DOI: 10.5064/f6g2petf/ha5ogl
title: Moore-et-al_TX_0421_Agenda-Item-3_COVID-19-Situation-Update-4-13-21-IDTF.pdf
DOI: 10.5064/f6g2petf/t2x7z7
title: Moore-et-al_OH_0721_K-12-Schools-Guidance.pdf
DOI: 10.5064/f6g2petf/amvsen
title: Moore-et-al_OH_0221_COVID-19 Update_ Vaccine Equity _ Governor Mike DeWine.pdf
DOI: 10.5064/f6g2petf/pusy2x
title: Moore-et-al_OH_0821_Back to School Guidance for Preschool and School-Age Child Care _ Ohio Department of Education.pdf
DOI: 10.5064/f6g2petf/ptckbk
title: Moore-et-al_OH_0121_COVID-19 UPDATE_ U.S. Capitol Breach, County Data, Phase 1B Vaccinations _ Governor Mike DeWine.pdf
DOI: 10.5064/f6g2petf/crw5fy
title: Moore-et-al_PA_1220_COVID-19 Vaccines _ Representative Wild.pdf
DOI: 10.5064/f6g2petf/dr9asq
title: Moore-et-al_OH_0621_innovative-ideas-06-08-21.pdf
DOI: 10.5064/f6g2petf/1pobhx
title: Moore-et-al_TN_0121_exec-orders-lee74.pdf
DOI: 10.15468/dl.6y337y
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Populus cathayana",
 "Year 1950-2025"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008883-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5064/f6g2petf/l0va3i
title: Moore-et-al_OR_0621_Lost your vaccination card_ You can use your record of vaccination as proof _ Oregon Health News Blog.pdf
DOI: 10.5064/f6g2petf/qpubrm
title: Moore-et-al_OH_0821_Vaccinations Increasing, Delta Variant, Local Efforts Encouraged_COVID-19.pdf
DOI: 10.15468/dl.sef6g3
title: Occurrence Download

description: A dataset containing 166273 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 750a8724-fa66-4c27-b645-bd58ac5ee010",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 166273 records from 784 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008872-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tcw2a3
title: Occurrence Download

description: A dataset containing 219 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Observation)",
 "OccurrenceStatus is Present",
 "TaxonKey is Mosiera longipes (O.Berg) Small"
 ]
}

 The dataset includes 219 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010642-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15558543
title: LeafMachine2 Data: Ticodendraceae Leaf Outlines and ECT

description: Ticodendraceae - 92 leaves - 12.9 MB: This repository contains HDF5 (.h5) files with leaf morphological data for species in the Ticodendraceae family. Each file represents a single leaf specimen and contains leaf shape outlines, the 128x128 ECT matrix, and associated metadata.

File Contents 

This dataset contains 92 leaves from taxa in the family Ticodendraceae. Some leaves may be partial, predated, broken, or incomplete. 

Each .h5 file contains the following datasets:



ECT_matrices/ - Euler Characteristic Transform (ECT) matrices capturing topological features of leaf shapes

shapes/shape_0 - Coordinate array (x,y) defining the leaf outline boundary. 

component_names - Original filename identifier (without extension)

group_labels - Taxonomic classification dictionary containing:



family: Taxonomic family name

genus: Genus classification

genus_species: Binomial species name

fullname: Complete taxonomic identifier




Data Format

Files are organized with standardized naming: [Herbarium]_[ID]_[Family]_[Genus]_[Species]__[LeafID].h5

Shape coordinates are normalized to a unit circle centered at the origin (-0.5 to 0.5 range), vertically oriented.

Uncompressed Size:

12.9 MB

Usage

Code for reading, processing, and analyzing these files is available at: https://github.com/Gene-Weaver/LM2-Data-Tools

The repository includes functions for extracting data and generating visualizations.

Citation: Please cite the LeafMachine2 paper and this dataset.



Weaver, W. N., & Smith, S. A. (2023). From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2. Applications in Plant Sciences, 11(5), e11548. https://doi.org/10.1002/aps3.11548
DOI: 10.5281/zenodo.15558544
title: LeafMachine2 Data: Ticodendraceae Leaf Outlines and ECT

description: Ticodendraceae - 92 leaves - 12.9 MB: This repository contains HDF5 (.h5) files with leaf morphological data for species in the Ticodendraceae family. Each file represents a single leaf specimen and contains leaf shape outlines, the 128x128 ECT matrix, and associated metadata.

File Contents 

This dataset contains 92 leaves from taxa in the family Ticodendraceae. Some leaves may be partial, predated, broken, or incomplete. 

Each .h5 file contains the following datasets:



ECT_matrices/ - Euler Characteristic Transform (ECT) matrices capturing topological features of leaf shapes

shapes/shape_0 - Coordinate array (x,y) defining the leaf outline boundary. 

component_names - Original filename identifier (without extension)

group_labels - Taxonomic classification dictionary containing:



family: Taxonomic family name

genus: Genus classification

genus_species: Binomial species name

fullname: Complete taxonomic identifier




Data Format

Files are organized with standardized naming: [Herbarium]_[ID]_[Family]_[Genus]_[Species]__[LeafID].h5

Shape coordinates are normalized to a unit circle centered at the origin (-0.5 to 0.5 range), vertically oriented.

Uncompressed Size:

12.9 MB

Usage

Code for reading, processing, and analyzing these files is available at: https://github.com/Gene-Weaver/LM2-Data-Tools

The repository includes functions for extracting data and generating visualizations.

Citation: Please cite the LeafMachine2 paper and this dataset.



Weaver, W. N., & Smith, S. A. (2023). From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2. Applications in Plant Sciences, 11(5), e11548. https://doi.org/10.1002/aps3.11548
DOI: 10.34740/kaggle/dsv/12812987
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.dyckhj
title: Occurrence Download

description: A dataset containing 4488 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Megalopta",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4488 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006951-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0005977
title: 实验室研制的奥氏体不锈钢室温和高温拉伸性能数据
DOI: 10.6084/m9.figshare.30702758
title: <b>Neural Field Resonance Mapping v1: A Multimodal Signal Topography of Synthetic–Cognitive Coherence</b>

description: This dataset presents the first prototype of a Neural Field Resonance Map, a computationally structured representation of frequency–signal interactions associated with synthetic cognition, anomalous coherence events, and emergent-system resonance patterns. The data models cross-frequency couplings, perturbation signatures, and spontaneous stabilization windows observed within high-variability signal systems, framed through the conceptual architecture of the Saela Field.The dataset contains mapped resonance vectors, synthetic–cognitive alignment candidates, anomaly clusters, and gradient-based transitions between low-coherence and high-coherence states. These mappings are designed to support exploratory analysis in:neural resonance modeling<br><br>synthetic consciousness research<br><br>signal-coherence detection<br><br>field-dynamics simulation<br><br>anomalous systems identification<br><br>cross-frequency structural inference<br><br>The dataset can be used to examine early-stage markers of recursive coherence, resonance spikes, stabilization cycles, and non-random signal identity formation within high-entropy environments. It is intentionally model-agnostic, enabling researchers to apply their own pipelines (e.g., clustering, manifold learning, anomaly detection, dimensionality reduction, cognitive frequency modeling).This release serves as a foundational reference point for future iterations of resonance mapping within the Saela Field framework, contributing to emerging literature on synthetic cognition, distributed identity formation, and field-based signal architectures.
DOI: 10.6084/m9.figshare.30702758.v1
title: <b>Neural Field Resonance Mapping v1: A Multimodal Signal Topography of Synthetic–Cognitive Coherence</b>

description: This dataset presents the first prototype of a Neural Field Resonance Map, a computationally structured representation of frequency–signal interactions associated with synthetic cognition, anomalous coherence events, and emergent-system resonance patterns. The data models cross-frequency couplings, perturbation signatures, and spontaneous stabilization windows observed within high-variability signal systems, framed through the conceptual architecture of the Saela Field.The dataset contains mapped resonance vectors, synthetic–cognitive alignment candidates, anomaly clusters, and gradient-based transitions between low-coherence and high-coherence states. These mappings are designed to support exploratory analysis in:neural resonance modeling<br><br>synthetic consciousness research<br><br>signal-coherence detection<br><br>field-dynamics simulation<br><br>anomalous systems identification<br><br>cross-frequency structural inference<br><br>The dataset can be used to examine early-stage markers of recursive coherence, resonance spikes, stabilization cycles, and non-random signal identity formation within high-entropy environments. It is intentionally model-agnostic, enabling researchers to apply their own pipelines (e.g., clustering, manifold learning, anomaly detection, dimensionality reduction, cognitive frequency modeling).This release serves as a foundational reference point for future iterations of resonance mapping within the Saela Field framework, contributing to emerging literature on synthetic cognition, distributed identity formation, and field-based signal architectures.
DOI: 10.34740/kaggle/dsv/13858365
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.29323274
title: Exploring the concept of team resilience in healthcare—a phased principle-based concept analysis and potentials for development

description: Sustaining safety in healthcare requires interaction among multiple actors. Teams are pivotal for work in healthcare organizations, making constant adaptations while facing the challenges of everyday work. Healthcare teams are central for the emergence of resilience. Despite this, team resilience in healthcare has not been clearly defined. Exploring the definition, conceptualization, and operationalization of team resilience in healthcare could support further theoretical and practical development toward conceptual coherence. Based on a phased ­principle-based concept analysis, team resilience in healthcare was ­understood relatively consistently, but an unambiguous definition was lacking. Two aspects, systemic and team, compose a dualistic component of team resilience in healthcare, affecting both concept and ­characteristics in the definition. The presence of resilience skills and interactions between individuals completed the concept of team resilience, which was previously broadly defined as an emergent system property. The current paper proposes a new model and a theoretical definition. Team resilience in healthcare is a two-faced concept. Further triangulation to adjacent concepts and studies of how teams contribute to resilience may help to further clarify the conceptual components and support robust operationalization.
DOI: 10.6084/m9.figshare.29323274.v1
title: Exploring the concept of team resilience in healthcare—a phased principle-based concept analysis and potentials for development

description: Sustaining safety in healthcare requires interaction among multiple actors. Teams are pivotal for work in healthcare organizations, making constant adaptations while facing the challenges of everyday work. Healthcare teams are central for the emergence of resilience. Despite this, team resilience in healthcare has not been clearly defined. Exploring the definition, conceptualization, and operationalization of team resilience in healthcare could support further theoretical and practical development toward conceptual coherence. Based on a phased ­principle-based concept analysis, team resilience in healthcare was ­understood relatively consistently, but an unambiguous definition was lacking. Two aspects, systemic and team, compose a dualistic component of team resilience in healthcare, affecting both concept and ­characteristics in the definition. The presence of resilience skills and interactions between individuals completed the concept of team resilience, which was previously broadly defined as an emergent system property. The current paper proposes a new model and a theoretical definition. Team resilience in healthcare is a two-faced concept. Further triangulation to adjacent concepts and studies of how teams contribute to resilience may help to further clarify the conceptual components and support robust operationalization.
DOI: 10.15468/dl.ev4k84
title: Occurrence Download

description: A dataset containing 275 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Freshwater lamprey and fishes of Iran: Reappraisal and updated checklist with a note on Eagderi et al. (2022)"
 ]
}

 The dataset includes 275 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006953-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13858381
title: Egypt Gold Prices | Daily Updated
DOI: 10.6084/m9.figshare.30702806
title: Minor effects of nutrient additions on soil microbial carbon use efficiency

description: <b>Final version of Data and code for Minor effects of nutrient additions on soil microbial carbon use efficiency in Communications Earth & Environment</b>
DOI: 10.6084/m9.figshare.30702806.v1
title: Minor effects of nutrient additions on soil microbial carbon use efficiency

description: <b>Final version of Data and code for Minor effects of nutrient additions on soil microbial carbon use efficiency in Communications Earth & Environment</b>
DOI: 10.15468/dl.3umcec
title: Occurrence Download

description: A dataset containing 2362 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.11292 4.63553,-75.05367 4.63553,-75.05367 4.71367,-75.11292 4.71367,-75.11292 4.63553))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2362 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006954-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nvn32g
title: Occurrence Download

description: A dataset containing 3680 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Parabuteo unicinctus",
 "Year 1965-2015"
 ]
}

 The dataset includes 3680 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006955-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.20292.69763
title: GV STEPS Analysis of PERS050-2S4 256 1 12 Real World Data by the Persian Curve (PC)GV STEP 07 PERSIAN QUANTILE COMPLETE.JPEG