cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.p4h6hd
title: Occurrence Download

description: A dataset containing 363703 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-10-06",
 "EventDate is less than or equal to 2025-11-05",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 363703 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010206-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17496861
title: Favorite and most difficult school subjects among STEM and non-STEM students: A comparative study of students in Serbia and the USA
DOI: 10.5281/zenodo.17496862
title: Favorite and most difficult school subjects among STEM and non-STEM students: A comparative study of students in Serbia and the USA
DOI: 10.7298/7mve-5139
title: Data and scripts from : Ultrablack Wool Textiles Inspired by Hierarchical Avian Structure
DOI: 10.5281/zenodo.17496913
title: Dataset for "Parker Solar Probe Observations of Proton Core-Beam Distributions in Heliospheric Current Sheet Region: Quasilinear Instability Analysis Combined With Collisional Relaxation"

description: Dataset for Figures 3 and 4.
DOI: 10.15468/dl.wbktv9
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Micrathyria venezuelae"
 ]
}

 The dataset includes 103 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010217-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17496912
title: Dataset for "Parker Solar Probe Observations of Proton Core-Beam Distributions in Heliospheric Current Sheet Region: Quasilinear Instability Analysis Combined With Collisional Relaxation"

description: Dataset for Figures 3 and 4.
DOI: 10.5281/zenodo.17358327
title: snRNAseq of olfactory epithelium with AAV serotypes for transduction of olfactory sensory neurons in Mus musculus

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "snRNAseq of olfactory epithelium with AAV serotypes for transduction of olfactory sensory neurons in Mus musculus".

Description: Single-nucleus RNA sequencing data of 4 mice, each inoculated with 4 AAV serotypes. The mice were all male and 8-10 weeks of age at when olfactory epithelium tissue was harvested. Samples were paired-end sequences using v3.1 – Dual Index using Illumina Novaseq 6000 sequencer.





This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset
DOI: 10.5281/zenodo.17402344
title: snRNAseq of olfactory epithelium with AAV serotypes for transduction of olfactory sensory neurons in Mus musculus

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "snRNAseq of olfactory epithelium with AAV serotypes for transduction of olfactory sensory neurons in Mus musculus".

Description: Single-nucleus RNA sequencing data of 4 mice, each inoculated with 4 AAV serotypes. The mice were all male and 8-10 weeks of age at when olfactory epithelium tissue was harvested. Samples were paired-end sequences using v3.1 – Dual Index using Illumina Novaseq 6000 sequencer.





This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset
DOI: 10.15468/dl.medwrv
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen)",
 "Geometry POLYGON((-89.17202 -32.17767,-0.82798 -32.17767,-0.82798 3.17767,-89.17202 3.17767,-89.17202 -32.17767))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hylopezus ochroleucus"
 ]
}

 The dataset includes 16 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010218-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17496863
title: Predicting Power Conversion Efficiency in Donor-Acceptor Pairs for Organic Solar Cells Using Machine Learning Ensemble Models

description: This study explores the application of machine learning (ML) models, specifically tree-based and ensemble models, for accurately predicting the power conversion efficiency (PCE) of organic solar cells (OSCs). The employed ML models were examined on 319 donor-acceptor (D-A) records containing important physical/chemical descriptors of organic solar cells. This research aims to develop suitable D-A material to achieve high-performing OSC under any given material conditions. To achieve this goal, multiple ML-based regression models, including Fine Tree (FT), Medium Tree (MT), Coarse Tree (CT), Bagged Tree (BGT), and Boosted Tree (BST), were studied on the dataset. These employed models were evaluated using standard performance metrics: mean absolute error (MAE), root mean squared error (RMSE), coefficient of correlation (R), and coefficient of determination (R²). Among the models examined, the BST ensemble outperformed the others, achieving an R² of 88.75%, a lowest MAE of 0.522, and an RMSE of 0.725 for validation; it also achieved an R² of 85.26%, a lowest MAE of 0.549, and an RMSE of 0.734 for testing. These results indicate that ensemble methods (BST) offer robust generalization and superior performance in complex PCE predictions. This work demonstrates that ML can serve as a reliable and time-efficient alternative to traditional computational methods for estimating OSC physical/chemical properties. The outcomes are expected to assist researchers in accelerating the discovery of the photovoltaic cell in the laboratory.
DOI: 10.5281/zenodo.17496864
title: Predicting Power Conversion Efficiency in Donor-Acceptor Pairs for Organic Solar Cells Using Machine Learning Ensemble Models

description: This study explores the application of machine learning (ML) models, specifically tree-based and ensemble models, for accurately predicting the power conversion efficiency (PCE) of organic solar cells (OSCs). The employed ML models were examined on 319 donor-acceptor (D-A) records containing important physical/chemical descriptors of organic solar cells. This research aims to develop suitable D-A material to achieve high-performing OSC under any given material conditions. To achieve this goal, multiple ML-based regression models, including Fine Tree (FT), Medium Tree (MT), Coarse Tree (CT), Bagged Tree (BGT), and Boosted Tree (BST), were studied on the dataset. These employed models were evaluated using standard performance metrics: mean absolute error (MAE), root mean squared error (RMSE), coefficient of correlation (R), and coefficient of determination (R²). Among the models examined, the BST ensemble outperformed the others, achieving an R² of 88.75%, a lowest MAE of 0.522, and an RMSE of 0.725 for validation; it also achieved an R² of 85.26%, a lowest MAE of 0.549, and an RMSE of 0.734 for testing. These results indicate that ensemble methods (BST) offer robust generalization and superior performance in complex PCE predictions. This work demonstrates that ML can serve as a reliable and time-efficient alternative to traditional computational methods for estimating OSC physical/chemical properties. The outcomes are expected to assist researchers in accelerating the discovery of the photovoltaic cell in the laboratory.
DOI: 10.15468/dl.c24hby
title: Occurrence Download

description: A dataset containing 3546828 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae, Undaria pinnatifida, Lindavia intermedia)",
 "Country is New Zealand",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 0",
 "DecimalLongitude is greater than or equal to -180",
 "DecimalLatitude is greater than or equal to -90",
 {
 "not" : {
 "or" : [
 "Issue is Zero coordinate",
 "Issue is Coordinate out of range",
 "Issue is Country coordinate mismatch"
 ]
 }
 }
 ]
}

 The dataset includes 3546828 records from 294 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010199-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5jhncx
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Micrathyria wasscheri"
 ]
}

 The dataset includes 8 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010219-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17242295
title: PD5D_MTG_MEGA_Chip_Genotype

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "PD5D_MTG_MEGA_Chip_Genotype".

Description: This dataset represents the PLINK bed format genotype data of PD5D  subjects (PD5D_MTG_snRNAseq). All subjects were genotyped on the  Infinium Multi-Ethnic Global-8 v1 chip. Raw genotypes were processed  with PLINK (v1.90) and basic quality control, including removal of low quality genotypes and low-quality variants, was performed. Please note  that the position information in the file was based on human GRC37/hg19  reference genome.





This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset
DOI: 10.5281/zenodo.17242299
title: PD5D_MTG_MEGA_Chip_Genotype

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "PD5D_MTG_MEGA_Chip_Genotype".

Description: This dataset represents the PLINK bed format genotype data of PD5D  subjects (PD5D_MTG_snRNAseq). All subjects were genotyped on the  Infinium Multi-Ethnic Global-8 v1 chip. Raw genotypes were processed  with PLINK (v1.90) and basic quality control, including removal of low quality genotypes and low-quality variants, was performed. Please note  that the position information in the file was based on human GRC37/hg19  reference genome.





This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset
DOI: 10.5281/zenodo.17242087
title: PD5D_MTG_Visium10x_ST

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "PD5D_MTG_Visium10x_ST".

Description: Visium 10x spatial transcriptomic raw data in FASTQ format of the middle  temporal gyrus of 96 human subjects. Individuals with incidental Lewy body  disease, clinical PD, and healthy control were included. In addition to subject level metadata, Visium spatial metadata such as H&E images and spot  coordinates are included.   These data are one component of the PD5D cohort, which intends to build a  multi-dimensional and multi-omic atlas of PD pathology. For the same subjects, other omics assays have been collected including genotyping,  snRNA-seq, and scATAC-seq across two brain tissues: middle temporal gyrus  and substantia nigra.





This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset
DOI: 10.5281/zenodo.17242099
title: PD5D_MTG_Visium10x_ST

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "PD5D_MTG_Visium10x_ST".

Description: Visium 10x spatial transcriptomic raw data in FASTQ format of the middle  temporal gyrus of 96 human subjects. Individuals with incidental Lewy body  disease, clinical PD, and healthy control were included. In addition to subject level metadata, Visium spatial metadata such as H&E images and spot  coordinates are included.   These data are one component of the PD5D cohort, which intends to build a  multi-dimensional and multi-omic atlas of PD pathology. For the same subjects, other omics assays have been collected including genotyping,  snRNA-seq, and scATAC-seq across two brain tissues: middle temporal gyrus  and substantia nigra.





This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset
DOI: 10.5281/zenodo.17496888
title: Control of encounter kinetics by chemically active droplets

description: Data and codes to run the LAMMPS numerical simulations presented in the article "Control of encounter kinetics by chemically active droplets". The systems are composed of scaffold and client particles in a crowded environment. All the particles are either represented as spheres (pylamps_reacting_clients_and_scaffolds.py) or as spheres and patchy particles, which corresponds to spheres to which small spheres (patches) are attached with harmonic bonds (pylamps_reacting_patchy_clients_and_scaffolds).
DOI: 10.25824/redu/xlzkzg
title: Viva Bem Wheelchair sprint and slalom dataset

description: This dataset was obtained in a controlled experiment with wheelchair users, aiming to record physiological and biomechanical information during a standardized motor protocol. Two Samsung Galaxy Watch 4 (GW4) smartwatches—one on each arm—were synchronized with the COLA app and video recordings. The protocol included phases such as rest, elbow flexions, the waiting position, sprints over varying distances, and return to the starting position, with continuous recording and event marking. The data collected included accelerometry, gyroscope, heart rate, synchronized videos, and contextual annotations, using units such as m/s², rad/s, BPM, and seconds. Metadata and variables were standardized in a dedicated dictionary, with precise time stamps. Due to privacy and ethical considerations, only a portion of the dataset may be publicly available. Researchers interested in accessing the full dataset must contact the authors and submit a formal request, which will be reviewed on a case-by-case basis to ensure compliance with privacy regulations and ethical standards. The PGD is made available here.
DOI: 10.5281/zenodo.16885839
title: PD5D_MTG_snRNAseq_hybsel

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "PD5D_MTG_snRNAseq_hybsel".

Description: This dataset contains the raw data in FASTQ format for single-nucleus RNA sequencing (snRNAseq) enriched with Agilent SureSelect for reads from a list of targeted transcripts for human middle temporal gyrus (MTG) samples from Parkinson's patients and healthy controls. This data comes from the same 10X libraries as PD5D_MTG_snRNAseq, except with the additional enrichment step before sequencing. It's part of the PD5D cohort, where the same subjects were also profiled with other omics assays incl. genotyping, single-cell ATACseq, and spatial transcriptomics.



This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset

Dataset Metadata was updated for the v1.1 update
DOI: 10.5281/zenodo.17241221
title: PD5D_MTG_snRNAseq_hybsel

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "PD5D_MTG_snRNAseq_hybsel".

Description: This dataset contains the raw data in FASTQ format for single-nucleus RNA sequencing (snRNAseq) enriched with Agilent SureSelect for reads from a list of targeted transcripts for human middle temporal gyrus (MTG) samples from Parkinson's patients and healthy controls. This data comes from the same 10X libraries as PD5D_MTG_snRNAseq, except with the additional enrichment step before sequencing. It's part of the PD5D cohort, where the same subjects were also profiled with other omics assays incl. genotyping, single-cell ATACseq, and spatial transcriptomics.



This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset

Dataset Metadata was updated for the v1.1 update
DOI: 10.34973/31ee-x555
title: Follow-up infant learning progress project

description: This repository includes data and analysis scripts for the article "Individual differences in infants’ curiosity are linked to cognitive capacity in early childhood".


Abstract:
Research has shown that infants are curious and actively seek situations from which they can learn. For instance, a recent eye-tracking study demonstrates that babies tend to allocate their attention to stimuli that offer opportunities for learning new information. Interestingly, however, the degree to which attention is guided by information gain varies among individual infants. This longitudinal study provides the first empirical evidence suggesting that these early individual differences in infants’ sensitivity to information gain are linked to later cognitive development. Specifically, we found that the extent to which infants’ attention was guided by information gain at 8 months was related to their IQ scores at 3½ years of age (n=60, 50% female): Especially children who displayed the greatest curiosity as infants tended to have a more favourable cognitive development. These findings demonstrate the lasting consequences of early existing differences in curiosity-driven exploration for later childhood cognitive development.
DOI: 10.34973/h1d5-5j25
title: Dealing with the heterogeneous presentations of freezing of gait: how reliable are the freezing index and heart rate for freezing detection?

description: ! IN VERSION 1, ALL SUBJECT FOLDERS CONTAINED COPIES OF THE DATA OF SUB-01. THIS IS FIXED BY VERSION 2! 

This is the raw data associated with the article of Cockx et al., Journal of NeuroEngineering and Rehabilitation. (2023). https://doi.org/10.1186/s12984-023-01175-y

This dataset includes the raw data of sixteen people with Parkinson’s disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task (see Figure_FOGcourse.pdf, Task_instructions.pdf).

3-lead ECG data and 3-axis accelerometer data of the left and right shin are shared. Events were obtained from video annotations (videos are not shared for privacy issues) and stimulus presentation files.
DOI: 10.5281/zenodo.17355407
title: Golgi-IP, a tool for multimodal analysis of Golgi molecular content

description: This Zenodo deposit contains a publicly available description of the Dataset:

Title: "Golgi-IP, a tool for multimodal analysis of Golgi molecular content".

Description: Quantitative DIA-based proteomic analysis of Golgi-IP in HEK293 cells. The data set contains six replicates of GolgiTAG-IP, Control-IP, GolgiTAG whole cell extract and Control cells whole cell extract. The mass spectrometry data is acquired using variable data independent acquisition (vDIA) on an Orbitrap Exploris 480 mass spectrometer. Database searches performed against Human Uniprot database using Spectronaut search algorithm.





This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.

This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset