cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.sezvb4
title: Occurrence Download

description: A dataset containing 121719 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Myioborus miniatus",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2026",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 121719 records from 182 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007347-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p3ec6v
title: Occurrence Download

description: A dataset containing 698 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Elymus smithii",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 698 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007389-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5284/1139402
title: Digital Archive from a Magnetometry Survey at Gryphon Lodge Farm, Leigh, Wiltshire, March 2024

description: This collection consists of a vector graphic, GIS and geophysics data from a geophysical survey, comprising detailed magnetometry, by Archaeological Surveys Ltd. Work occurred ahead of the construction of a new fishing lake near Gryphon Lodge Farm, Leigh, Wiltshire in March 2024. A number of anomalies correspond with a shallow depression, although its origin is uncertain. Other anomalies appear to be associated with a low north to south bank within the field. A small number of discrete, pit-like anomalies have also been located as well as a number of positive linear anomalies; however, they lack clarity and their date and function are uncertain. Evidence of land drainage and agricultural activity have also been located.
DOI: 10.5281/zenodo.18759232
title: Raster data of the Songnen Plain:population density, precipitation, soil moisture, soil type, groundwater depth, and CO₂ emissions.
DOI: 10.5281/zenodo.18759233
title: Raster data of the Songnen Plain:population density, precipitation, soil moisture, soil type, groundwater depth, and CO₂ emissions.
DOI: 10.5281/zenodo.17777755
title: Code and dataset for "F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization"

description:  

project brief

 

This project has implemented the innovative prediction scheme proposed in the paper titled "F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization". For the first time, we have applied wavelet decomposition to the prediction of the F10.7 index. By extracting multi-scale signal features and combining them with the iTransformer model, we have significantly improved the prediction performance.

 

Core innovation points

 



🌊 First application of wavelet decomposition: Utilizing wavelet decomposition to extract the approximate and detail signals of the F10.7 index

🔄 Multi-scale feature fusion: Using both the original signal and decomposed signals as model inputs

☀️ Multi-source data integration: Incorporating the International Sunspot Number (ISN) and its decomposed signals

📊 Comprehensive performance comparison: Systematically comparing with the latest research and prediction models from international institutions

🔬 Strong generalization ability: Validating the effectiveness of the optimal combination model on different datasets and model architectures


 

project structure

 

├── 1_Wavelet-decomposed_data/ # Section 4.1 Wavelet-decomposed data

│ ├── code file # The implementation of wavelet decomposition

│ └── folder # The decomposed data and the visualization results

├── 2_F10.7_wavelet_signals/ # Section 4.2 Performance comparison: integrating wavelet-decomposed F10.7 signals

│ └── code file # model training

│ └── results/ # Trained model and experimental result files

├── 3_ISN_wavelet_signals/ # Section 4.3 Performance comparison: integrating wavelet-decomposed ISN signals

│ ├── code file # model training

│ └── results/ # Trained model and experimental result files

├── 4_Comparison_study/ # Section 4.4 Comparison with the latest research and international institutions

│ ├── 1Compare.py # Comparison between different years

│ ├── 2Compare.py # Comparison between different conditions

│ ├── period_average_metrics.csv # The comparison results between different years

│ └── four_conditions.csv # The comparison results between different conditions

└── 5_Generalization_test/ # Section 4.5 Generalization performance on Dataset B

  ├── code file # model training

  └── results/ # Trained model and experimental result files

 

Data sources

 

• F10.7 index and International Sunspot Number(ISN):DRAO

 

• Langfang data:L&S observations

 

 

environmental requirement

 

python==3.12

torch==2.8.0

numpy==1.26.4

pandas==2.2.2

scipy==1.13.0

scikit-learn==1.7.1

matplotlib==3.8.4

seaborn==0.13.2

PyWavelets==1.9.0

 

Last update by Ma, xuran: January 24th, 2026
DOI: 10.5281/zenodo.17777754
title: Code and dataset for "F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization"

description:  

project brief

 

This project has implemented the innovative prediction scheme proposed in the paper titled "F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization". For the first time, we have applied wavelet decomposition to the prediction of the F10.7 index. By extracting multi-scale signal features and combining them with the iTransformer model, we have significantly improved the prediction performance.

 

Core innovation points

 



🌊 First application of wavelet decomposition: Utilizing wavelet decomposition to extract the approximate and detail signals of the F10.7 index

🔄 Multi-scale feature fusion: Using both the original signal and decomposed signals as model inputs

☀️ Multi-source data integration: Incorporating the International Sunspot Number (ISN) and its decomposed signals

📊 Comprehensive performance comparison: Systematically comparing with the latest research and prediction models from international institutions

🔬 Strong generalization ability: Validating the effectiveness of the optimal combination model on different datasets and model architectures


 

project structure

 

├── 1_Wavelet-decomposed_data/ # Section 4.1 Wavelet-decomposed data

│ ├── code file # The implementation of wavelet decomposition

│ └── folder # The decomposed data and the visualization results

├── 2_F10.7_wavelet_signals/ # Section 4.2 Performance comparison: integrating wavelet-decomposed F10.7 signals

│ └── code file # model training

│ └── results/ # Trained model and experimental result files

├── 3_ISN_wavelet_signals/ # Section 4.3 Performance comparison: integrating wavelet-decomposed ISN signals

│ ├── code file # model training

│ └── results/ # Trained model and experimental result files

├── 4_Comparison_study/ # Section 4.4 Comparison with the latest research and international institutions

│ ├── 1Compare.py # Comparison between different years

│ ├── 2Compare.py # Comparison between different conditions

│ ├── period_average_metrics.csv # The comparison results between different years

│ └── four_conditions.csv # The comparison results between different conditions

└── 5_Generalization_test/ # Section 4.5 Generalization performance on Dataset B

  ├── code file # model training

  └── results/ # Trained model and experimental result files

 

Data sources

 

• F10.7 index and International Sunspot Number(ISN):DRAO

 

• Langfang data:L&S observations

 

 

environmental requirement

 

python==3.12

torch==2.8.0

numpy==1.26.4

pandas==2.2.2

scipy==1.13.0

scikit-learn==1.7.1

matplotlib==3.8.4

seaborn==0.13.2

PyWavelets==1.9.0

 

Last update by Ma, xuran: January 24th, 2026
DOI: 10.5281/zenodo.18759527
title: IMPROVEMENT OF THE TREATMENT AND PREVENTION OF POSTOPERATIVE COMPLICATIONS IN CHILDREN WITH ANORECTAL MALFORMATIONS

description: Anorectal malformations in children are complex congenital defects often associated with postoperative complications. Anorectal sphincter insufficiency remains one of the most severe outcomes and significantly affects bowel function and quality of life. Repeated reconstructive surgeries are frequently required and are associated with increased risks.

Aim: To improve the results of surgical treatment of anorectal malformations in children by reducing postoperative complications.
DOI: 10.5281/zenodo.18759528
title: IMPROVEMENT OF THE TREATMENT AND PREVENTION OF POSTOPERATIVE COMPLICATIONS IN CHILDREN WITH ANORECTAL MALFORMATIONS

description: Anorectal malformations in children are complex congenital defects often associated with postoperative complications. Anorectal sphincter insufficiency remains one of the most severe outcomes and significantly affects bowel function and quality of life. Repeated reconstructive surgeries are frequently required and are associated with increased risks.

Aim: To improve the results of surgical treatment of anorectal malformations in children by reducing postoperative complications.
DOI: 10.15468/dl.8w26fg
title: Occurrence Download

description: A dataset containing 796 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mechanitis polymnia",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 796 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007390-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48366/r1586215
title: Nutritional description of a Cameoonian food called "Macabo Ndolè"

description: This table aims to describe the Cameroonian food called "Macabo Ndolè". For a portion of 100g of fresh weight, it describes the composition in terms of Manganese, Iron, Copper,  Zinc, Potassium, Phosporus, Magnesium and Calcium.
DOI: 10.15468/dl.z24zfz
title: Occurrence Download

description: A dataset containing 2618 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cryptoblepharus buchananii"
 ]
}

 The dataset includes 2618 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007391-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18759537
title: EVACUATION RULES, TYPES, AND METHODS

description: This thesis is dedicated to highlighting the organizational and practical aspects of the evacuation process, which is an essential component of the civil protection system during emergency situations. It analyzes, within a scientific and theoretical framework, the rules, types, and methods of conducting evacuations.
DOI: 10.13011/m3-gx3x-ph54
title: Understanding the kinetics of static recrystallization in Mg-Zn-Ca alloys using an integrated PRISMS simulation framework
DOI: 10.5281/zenodo.18759536
title: EVACUATION RULES, TYPES, AND METHODS

description: This thesis is dedicated to highlighting the organizational and practical aspects of the evacuation process, which is an essential component of the civil protection system during emergency situations. It analyzes, within a scientific and theoretical framework, the rules, types, and methods of conducting evacuations.
DOI: 10.5281/zenodo.18757918
title: Modeling Candida albicans complex-mediated evasion and in silico treatment strategies: a systems biology approach

description: General information

Welcome to the Zenodo repository of the publication:

"Modeling Candida albicans complex-mediated evasion and in silico treatment strategies: a systems biology approach"

Yann Bachelot, Carl-Magnus Svensson, Anastasia Solomatina, Joshepine Schötz, Kerstin Hünniger-Ast, Oliver Kurzai, Marc Thilo Figge.

You will find here the data related to the aforementioned study:



Live-cell_imaging_data.zip: live-cell imaging data in ome.tif format.

Supplementary_Videos_1-4.zip: example videos of the live-cell imaging data corresponding to the 4 different conditions.

Survival_assays_CFU.zip: data from the CFU-based survival assays, in .csv format.


 

Code

The code related to this study can be accessed on GitHub:https://github.com/applied-systems-biology/Complex-mediated_evasion_systems_biology_approach.git 

 

License

Copyright (c) 2026, ann Bachelot, Carl-Magnus Svensson, Anastasia Solomatina, Joshepine Schötz, Kerstin Hünniger-Ast, Oliver Kurzai, Marc Thilo Figge.All rights reserved.

Redistribution and use in source and binary forms, with or withoutmodification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,   this list of conditions and the following disclaimer in the documentation   and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THEIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AREDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLEFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIALDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS ORSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVERCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USEOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
DOI: 10.5281/zenodo.18757919
title: Modeling Candida albicans complex-mediated evasion and in silico treatment strategies: a systems biology approach

description: General information

Welcome to the Zenodo repository of the publication:

"Modeling Candida albicans complex-mediated evasion and in silico treatment strategies: a systems biology approach"

Yann Bachelot, Carl-Magnus Svensson, Anastasia Solomatina, Joshepine Schötz, Kerstin Hünniger-Ast, Oliver Kurzai, Marc Thilo Figge.

You will find here the data related to the aforementioned study:



Live-cell_imaging_data.zip: live-cell imaging data in ome.tif format.

Supplementary_Videos_1-4.zip: example videos of the live-cell imaging data corresponding to the 4 different conditions.

Survival_assays_CFU.zip: data from the CFU-based survival assays, in .csv format.


 

Code

The code related to this study can be accessed on GitHub:https://github.com/applied-systems-biology/Complex-mediated_evasion_systems_biology_approach.git 

 

License

Copyright (c) 2026, ann Bachelot, Carl-Magnus Svensson, Anastasia Solomatina, Joshepine Schötz, Kerstin Hünniger-Ast, Oliver Kurzai, Marc Thilo Figge.All rights reserved.

Redistribution and use in source and binary forms, with or withoutmodification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,   this list of conditions and the following disclaimer in the documentation   and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THEIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AREDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLEFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIALDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS ORSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVERCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USEOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
DOI: 10.15468/dl.77cy4x
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Nycticebus bengalensis",
 "Year 2001-2026"
 ]
}

 The dataset includes 37 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007393-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.egj429
title: Occurrence Download

description: A dataset containing 6468 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ctenotus inornatus"
 ]
}

 The dataset includes 6468 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007396-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r4hcyg
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "OccurrenceStatus is Present",
 "TaxonKey is Pentalonia nigronervosa"
 ]
}

 The dataset includes 15 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007398-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18759562
title: IMPROVEMENT OF THE TREATMENT AND PREVENTION OF COMPLICATIONS IN CHILDREN WITH ANORECTAL MALFORMATIONS

description: Background: Anorectal malformations in children are complex congenital anomalies. Postoperative anorectal insufficiency remains a frequent and severe complication.

 

Aim: To improve surgical outcomes and prevent postoperative anorectal insufficiency in children with anorectal malformations.

 

Methods: A total of 337 children were analyzed. Postoperative anorectal insufficiency developed in 102 cases (30.3%). Ultrasonography, MRI, and electromyography were used.
DOI: 10.5281/zenodo.18759561
title: IMPROVEMENT OF THE TREATMENT AND PREVENTION OF COMPLICATIONS IN CHILDREN WITH ANORECTAL MALFORMATIONS

description: Background: Anorectal malformations in children are complex congenital anomalies. Postoperative anorectal insufficiency remains a frequent and severe complication.

 

Aim: To improve surgical outcomes and prevent postoperative anorectal insufficiency in children with anorectal malformations.

 

Methods: A total of 337 children were analyzed. Postoperative anorectal insufficiency developed in 102 cases (30.3%). Ultrasonography, MRI, and electromyography were used.
DOI: 10.5281/zenodo.18738248
title: Feldman et al. rainfall frequency and intensity trends dataset

description: These data and scripts are those that were used in the Feldman et al. manuscript accepted in Geophysical Research Letters titled "Widespread co-location of less frequent and more intense daily precipitation over land."

Please consult the README.txt file, which details the workflow and input and output file naming conventions.

The "Scripts_FigureGenerationData.zip" contains the python (.py) scripts used in all steps of the workflow. It also contains the necessary files to only plot the main and supplemental figures in python (only Step 3 in the README.txt file). 

The "StudyDatasets.zip" contains all input and output files used in the full workflow. These files are in compressed .npz files, which are typically agile if mainly using python and numpy (as these scripts here primarily use). However, "StudyDatasets_netcdf_PartX.zip" provide these same datasets as .nc files, which is accessible for a wider range of programming applications.

Please consult the README.txt file for more information about python and names of files and scripts being used. 

For any use of the datasets and scripts, please cite (1) the Feldman et al "Widespread co-location of less frequent and more intense daily precipitation over land" publication in Geophysical Research Letters. Additionally, (2) please acknowledge the use of these scripts and/or datasets in this Zenodo repository in the acknowledgements section of your publication. The use of any scripts or data are not allowed unless this paper is officially in print. For questions, please contact Andrew Feldman at andrew.feldman@nasa.gov or afeld24@umd.edu.

The authors thank the following funding sources: the NASA Postdoctoral Program (NPP) as well as a NASA ROSES A.63 Ecohydrology Grant (80NSSC25K0156). Both grants were acquired and led by PI A. Feldman.
DOI: 10.5281/zenodo.18738247
title: Feldman et al. rainfall frequency and intensity trends dataset

description: These data and scripts are those that were used in the Feldman et al. manuscript accepted in Geophysical Research Letters titled "Widespread co-location of less frequent and more intense daily precipitation over land."

Please consult the README.txt file, which details the workflow and input and output file naming conventions.

The "Scripts_FigureGenerationData.zip" contains the python (.py) scripts used in all steps of the workflow. It also contains the necessary files to only plot the main and supplemental figures in python (only Step 3 in the README.txt file). 

The "StudyDatasets.zip" contains all input and output files used in the full workflow. These files are in compressed .npz files, which are typically agile if mainly using python and numpy (as these scripts here primarily use). However, "StudyDatasets_netcdf_PartX.zip" provide these same datasets as .nc files, which is accessible for a wider range of programming applications.

Please consult the README.txt file for more information about python and names of files and scripts being used. 

For any use of the datasets and scripts, please cite (1) the Feldman et al "Widespread co-location of less frequent and more intense daily precipitation over land" publication in Geophysical Research Letters. Additionally, (2) please acknowledge the use of these scripts and/or datasets in this Zenodo repository in the acknowledgements section of your publication. The use of any scripts or data are not allowed unless this paper is officially in print. For questions, please contact Andrew Feldman at andrew.feldman@nasa.gov or afeld24@umd.edu.

The authors thank the following funding sources: the NASA Postdoctoral Program (NPP) as well as a NASA ROSES A.63 Ecohydrology Grant (80NSSC25K0156). Both grants were acquired and led by PI A. Feldman.
DOI: 10.5281/zenodo.18759556
title: IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data fusion and sensing technology_ICECS2025_Data

description: The dataset consists of tactile signals acquired from an 8-sensor PVDF (P(VDF-TrFE)) array embedded in a soft finger cap worn on the fingertip during touch interactions. Data were collected while subjects performed press, slide, and release actions on two textured surfaces (smooth and rough), with a 6-axis force/torque sensor used as ground truth for contact and slippage events. Two subjects were involved in this experiment, with each one collecting 150 trials from each texture type, and the dataset was used to develop and evaluate a threshold-based real-time algorithm for detecting contact onset, slippage, and release events.