cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sgvb7sh4yn.5
title: Pharmacotherapy in Autism Spectrum Disorder

description: We present psychotropic medication use in children with ASD from low-income families who lacked specialized behavioral therapy. Most children received psychopharmacological intervention. One-third underwent a polypharmacy regimen. Polytherapy regimen was associate with ASD level 3 and epilepsy. Age and epilepsy increased the odds of a polytherapy regimen. The most used classes were antipsychotics, followed by antiseizure and antidepressants. The most used psychotropic medications were risperidone and periciazine. The combined use of valproic acid and risperidone was the most frequent combination
DOI: 10.15468/dl.5697k2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius horstockii (Schlegel, 1837)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1332 -20.158,25.5395 -23.2724,25.5395 -24.0715,30.5701 -24.0715,30.5701 -20.3186,29.6496 -19.6123,28.947 -19.4003,28.1821 -19.4609,27.6072 -19.7214,27.1332 -20.158))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033609-250525065834625/datasets/export for details.
DOI: 10.6073/pasta/a29bbd899695189f3cab163bdc9a625f
title: Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Soil Temperature, Soil Frost, and Snow Depth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025

description: Data associated with the publication:
 Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025.
 
 This dataset includes soil temperature (winter 2021-2022) and snow depth and frost depth (winter 2022-2023) at the Climate Change Across Seasons Experiment.
 
 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
DOI: 10.5281/zenodo.13935317
title: Ecotoxicological assessment of MWCNT-reinforced MOC composites: impacts on model bacteria and eukaryotes with environmental relevance
DOI: 10.5281/zenodo.13935318
title: Ecotoxicological assessment of MWCNT-reinforced MOC composites: impacts on model bacteria and eukaryotes with environmental relevance
DOI: 10.17632/9vv482b6h7
title: Computer Applications for Qualitative Exposuremetrics Analysis

description: This dataset includes computer software applications developed based on the Qualitative Exposuremetrics methodology—a comprehensive, sensitive, and efficient approach for analyzing organismal resistance and susceptibility dynamics using qualitative variables. These applications were designed to improve user accessibility and facilitate the practical implementation of the methodology.

Two software tools are provided in a separate zip folder:
• Qualitative_exposuremetrics_A-1.0: This standalone application enables users to input a single dataset and view the corresponding analysis results directly within the interface.
• Qualitative_exposuremetrics_B-1.0: This versatile application allows users to upload or manually enter one or multiple datasets in CSV or Excel format. It processes the data and provides options to either save the results as a new CSV file or copy them for use in external applications.

These tools support researchers and practitioners in applying the qualitative exposuremetrics framework with ease and consistency.
DOI: 10.17632/9vv482b6h7.1
title: Computer Applications for Qualitative Exposuremetrics Analysis

description: This dataset includes computer software applications developed based on the Qualitative Exposuremetrics methodology—a comprehensive, sensitive, and efficient approach for analyzing organismal resistance and susceptibility dynamics using qualitative variables. These applications were designed to improve user accessibility and facilitate the practical implementation of the methodology.

Two software tools are provided in a separate zip folder:
• Qualitative_exposuremetrics_A-1.0: This standalone application enables users to input a single dataset and view the corresponding analysis results directly within the interface.
• Qualitative_exposuremetrics_B-1.0: This versatile application allows users to upload or manually enter one or multiple datasets in CSV or Excel format. It processes the data and provides options to either save the results as a new CSV file or copy them for use in external applications.

These tools support researchers and practitioners in applying the qualitative exposuremetrics framework with ease and consistency.
DOI: 10.15468/dl.yc54jg
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Melanothamnus ferulaceus (Suhr ex J.Agardh) Díaz-Tapia & Maggs"
 ]
}

 The dataset includes 89 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033610-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15632604
title: Gut microbially produced phenylacetic acid reduces atherosclerosis burden and impacts host lipid homeostasis
DOI: 10.17632/crj2sw5xc7
title: Automatic Tumor Infiltration Detection Algorithm

description: These are the pieces necessary to replicate our automatic tumor detection algorithm as described in my Bachelor's Thesis, High-Throughput Automated Cell Classification for Measuring the Connection Between Tumor Infiltration and Radiation Response (June 2025).
DOI: 10.17632/crj2sw5xc7.1
title: Automatic Tumor Infiltration Detection Algorithm

description: These are the pieces necessary to replicate our automatic tumor detection algorithm as described in my Bachelor's Thesis, High-Throughput Automated Cell Classification for Measuring the Connection Between Tumor Infiltration and Radiation Response (June 2025).
DOI: 10.6073/pasta/d670a96a08b790f4d4ec7f231d6cba2d
title: Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Tree Growth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025

description: Data associated with the publication:
 Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025.
 
 This dataset includes cumulative stem biomass carbon data (from pre-treatment in 2012 until 2022) and annual stem biomass growth rates (not cumulative) for 2015-2022 for the red maple trees at the Climate Change Across Seasons Experiment.
 
 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
DOI: 10.15468/dl.43cn9w
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Lophuromys brunneus Thomas, 1906",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033612-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c5u2yn
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius horstockii (Schlegel, 1837)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.0214 -34.0501,25.5395 -34.1098,25.5395 -32.9007,20.509 -32.9007,20.509 -34.5676,20.524 -34.555,20.8859 -34.5419,20.9606 -34.4398,21.2995 -34.5131,21.5296 -34.4338,21.8696 -34.4439,22.0323 -34.2835,22.221 -34.2428,22.2566 -34.1304,22.5993 -34.0741,22.9652 -34.1674,23.4069 -34.1858,23.4955 -34.0792,23.6056 -34.0563,24.8471 -34.2877,24.9447 -34.2397,25.0214 -34.0501))"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033613-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f2reca
title: Occurrence Download

description: A dataset containing 554 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Myanmar, Nepal, Bhutan, Bangladesh, Pakistan, Afghanistan)",
 "TaxonKey is Colotis aurora (Cramer, 1780)"
 ]
}

 The dataset includes 554 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033616-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3hxxcj
title: Occurrence Download

description: A dataset containing 585 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Angelica dahurica (Hoffm.) Benth. & Hook.fil. ex Franch. & Sav."
 ]
}

 The dataset includes 585 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033615-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wz2ry6dc5w
title: Python Script for Simulating, Analyzing, and Evaluating Qualitative Exposuremetrics Estimators

description: This document presents a comprehensive validation study of the Qualitative Exposuremetrics estimators—novel tools designed for analyzing organismal resistance and susceptibility dynamics using qualitative variables. The validation was conducted using datasets derived from Monte Carlo simulation experiments and real-world case scenarios.

The study assesses and compares the statistical robustness, efficiency, and behavior of the qualitative exposuremetrics estimators against classical methods commonly used in exposure-response data analysis. By providing both simulated and empirical datasets, this work enhances transparency, reproducibility, and practical understanding of the estimators, thereby supporting wider adoption and usability in relevant research contexts.
DOI: 10.17632/wz2ry6dc5w.1
title: Python Script for Simulating, Analyzing, and Evaluating Qualitative Exposuremetrics Estimators

description: This document presents a comprehensive validation study of the Qualitative Exposuremetrics estimators—novel tools designed for analyzing organismal resistance and susceptibility dynamics using qualitative variables. The validation was conducted using datasets derived from Monte Carlo simulation experiments and real-world case scenarios.

The study assesses and compares the statistical robustness, efficiency, and behavior of the qualitative exposuremetrics estimators against classical methods commonly used in exposure-response data analysis. By providing both simulated and empirical datasets, this work enhances transparency, reproducibility, and practical understanding of the estimators, thereby supporting wider adoption and usability in relevant research contexts.
DOI: 10.15468/dl.qeyz2m
title: Occurrence Download

description: A dataset containing 10428 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Malus baccata (L.) Borkh."
 ]
}

 The dataset includes 10428 records from 284 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033614-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29281346
title: Online Appendix for Devine and Bertsch 2025

description: Online Appendix for Devine and Bertsch 2025
DOI: 10.6084/m9.figshare.29281346.v1
title: Online Appendix for Devine and Bertsch 2025

description: Online Appendix for Devine and Bertsch 2025
DOI: 10.17632/k25jgp3fmm
title: ANNPF

description: A code that automatically estimates missing precipitation data in any set of meteorological stations using the most consistent station as a reference, called Artificial Neural Network for Precipitation Filling (ANNPF).

Initial preparation:
*Install Python:

If you don't have Python installed, download it from python.org and install it.
*Install the necessary libraries:

Open the terminal or command prompt (depending on your operating system) and run the following command to install the required libraries: pip install pandas numpy scikit-learn matplotlib openpyxl
#You can run this code from the terminal or command prompt:

Steps to run the code from the terminal or command prompt:
*Prepare the files:

Save the code in a text file with .py extension.
Make sure you have an Excel file called Input.xlsx in the same directory as the .py file. This file should have a structure similar to what the code expects.
*Execute the code:

Open the terminal or command prompt.
Navigate to the directory where your .py and Input.xlsx file are located.
Type python filename.py (replace filename.py with the actual name of your Python file) and press Enter.
*Interact with the program:

The program will ask you to enter a number, either 1 or 2, to determine the number of base stations to use.
Follow the instructions that appear in the terminal or command prompt depending on the questions the program asks.
Or You can run this code from Jupyter Lab:
Steps to run the code in Jupyter Lab:
Initial preparation:
*Install Jupyter Lab:
If you don't already have Jupyter Lab installed, you can do so by using pip in your terminal or command prompt with the command: pip install jupyterlab
*Open Jupyter Lab:
Open the terminal or command prompt.
Navigate to the directory where your .ipynb and Input.xlsx file is located.
Run the jupyter lab command. This will open Jupyter Lab in your default web browser.
Code execution:
*Create a new notebook:
In Jupyter Lab, click the "New" button and select "Notebook: Python" to create a new notebook.
*Copy and paste the code:
In a cell of the notebook, copy and paste the provided code.
*Execute the code in sections:
Divide the code into logical sections (cells). You can do this by separating each block of code with comments indicating sections #0, #1, #2, etc.
Click on a cell and press Shift + Enter to run that section of code.
*Interact with the program:
When the program prompts for input (as in section #6), enter the answer in the appropriate cell and press Enter.
*Observe the results:
If there are parts of the code that generate graphs, these will be displayed directly below the corresponding code cells.
*Save results (optional):
You can save the generated results to files if the part of the code that saves the graphs or data to CSV files has been uncommented.
*End execution:
Continue executing the cells until you have finished using the code.
Once finished, close Jupyter Lab or save and close the notebook if you want to resume work later.
DOI: 10.17632/k25jgp3fmm.1
title: ANNPF

description: A code that automatically estimates missing precipitation data in any set of meteorological stations using the most consistent station as a reference, called Artificial Neural Network for Precipitation Filling (ANNPF).

Initial preparation:
*Install Python:

If you don't have Python installed, download it from python.org and install it.
*Install the necessary libraries:

Open the terminal or command prompt (depending on your operating system) and run the following command to install the required libraries: pip install pandas numpy scikit-learn matplotlib openpyxl
#You can run this code from the terminal or command prompt:

Steps to run the code from the terminal or command prompt:
*Prepare the files:

Save the code in a text file with .py extension.
Make sure you have an Excel file called Input.xlsx in the same directory as the .py file. This file should have a structure similar to what the code expects.
*Execute the code:

Open the terminal or command prompt.
Navigate to the directory where your .py and Input.xlsx file are located.
Type python filename.py (replace filename.py with the actual name of your Python file) and press Enter.
*Interact with the program:

The program will ask you to enter a number, either 1 or 2, to determine the number of base stations to use.
Follow the instructions that appear in the terminal or command prompt depending on the questions the program asks.
Or You can run this code from Jupyter Lab:
Steps to run the code in Jupyter Lab:
Initial preparation:
*Install Jupyter Lab:
If you don't already have Jupyter Lab installed, you can do so by using pip in your terminal or command prompt with the command: pip install jupyterlab
*Open Jupyter Lab:
Open the terminal or command prompt.
Navigate to the directory where your .ipynb and Input.xlsx file is located.
Run the jupyter lab command. This will open Jupyter Lab in your default web browser.
Code execution:
*Create a new notebook:
In Jupyter Lab, click the "New" button and select "Notebook: Python" to create a new notebook.
*Copy and paste the code:
In a cell of the notebook, copy and paste the provided code.
*Execute the code in sections:
Divide the code into logical sections (cells). You can do this by separating each block of code with comments indicating sections #0, #1, #2, etc.
Click on a cell and press Shift + Enter to run that section of code.
*Interact with the program:
When the program prompts for input (as in section #6), enter the answer in the appropriate cell and press Enter.
*Observe the results:
If there are parts of the code that generate graphs, these will be displayed directly below the corresponding code cells.
*Save results (optional):
You can save the generated results to files if the part of the code that saves the graphs or data to CSV files has been uncommented.
*End execution:
Continue executing the cells until you have finished using the code.
Once finished, close Jupyter Lab or save and close the notebook if you want to resume work later.
DOI: 10.15468/dl.w5d8v6
title: Occurrence Download

description: A dataset containing 650434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is LACM Entomology Collection",
 "TaxonKey is one of (Animalia, Phoridae)"
 ]
}

 The dataset includes 650434 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033603-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pun9mr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius horstockii (Schlegel, 1837)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((32.064 -28.9416,32.4847 -28.5786,30.5701 -28.5786,30.5701 -30.7329,30.6252 -30.6659,31.4179 -29.4257,31.8178 -29.0299,32.064 -28.9416))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033618-250525065834625/datasets/export for details.
DOI: 10.15468/dl.47y8qt
title: Occurrence Download

description: A dataset containing 2222820473 records in GBIF matching the query:
SELECT gbifid, bibliographiccitation, license, "references", institutionid, collectionid, institutioncode, collectioncode, basisofrecord, occurrenceid, catalognumber, recordnumber, recordedby, associatedoccurrences, eventdate, "year", countrycode, locality, typestatus, scientificnameid, originalnameusageid, scientificname, originalnameusage, family, datasetkey, mediatype, typifiedname, v_typestatus FROM occurrence WHERE typestatus IS NOT NULL

 The dataset's 2222820473 records were derived from 58338 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033488-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dnsfyd
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Strombus pugilis Linnaeus, 1758",
 "HasGeospatialIssue is true",
 "HasCoordinate is true"
 ]
}

 The dataset includes 17 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014619-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ozxsq
title: Occurrence Download

description: A dataset containing 191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-108.69873 25.6019,-109.00635 25.20494,-108.52295 24.36711,-106.50146 22.43134,-106.54541 21.0845,-105.97412 19.43551,-104.61182 18.35453,-102.19482 17.18278,-99.11865 15.74996,-98.50342 15.96133,-98.06396 16.93071,-98.76709 17.51834,-99.38232 17.6859,-100.26123 17.93693,-100.70068 18.27109,-101.05225 18.77112,-101.84326 18.89589,-102.4585 19.35261,-103.64502 19.7667,-104.12842 20.2622,-104.17236 20.7972,-103.99658 21.33032,-103.90869 22.39071,-104.83154 23.16056,-105.49072 23.64452,-106.01807 24.44715,-106.58936 25.28444,-106.72119 25.79989,-107.24854 26.19488,-108.25928 25.95804,-108.69873 25.6019))",
 {
 "or" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "DatasetKey is EOD – eBird Observation Dataset",
 "DatasetKey is Digitalización de la Biblioteca de Sonidos Naturales del Museo de Zoología, Facultad de Ciencias, UNAM"
 ]
 },
 "TaxonKey is Crypturellus cinnamomeus (Lesson, 1842)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 191 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048001-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ztum4
title: Occurrence Download

description: A dataset containing 149 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Clinopodium multiflorum (Ruiz & Pav.) Kuntze"
 ]
}

 The dataset includes 149 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0150141-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.