cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/bj2-9l0-ch5
title: Pityrogramma sulphurea (Sw.) Maxon

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/tzh-qsd-tag
title: Campanula L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/852-tnx-k32
title: Pariana Aubl.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/8lt-5ra-ng5
title: Dianella ensifolia (L.) Redouté

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/z7n-86q-ez4
title: Adiantum pyramidale (L.) Willd.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/67b-f6h-6ly
title: Heliotropium L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/f6s-102-wjb
title: Cyrtochilum Kunth

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/mfk-5fv-2ax
title: Vanilla mexicana Mill.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/63z-f0t-ljh
title: Adiantum pyramidale (L.) Willd.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/f7v-4jc-fnx
title: Pityrogramma sulphurea (Sw.) Maxon

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/xr4-vd4-rsc
title: Reynoutria japonica Houtt.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/mc3-1qf-h5x
title: Adiantum pyramidale (L.) Willd.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/nz9-hlc-3z4
title: Salsola collina Pall.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/xld-vs6-af5
title: Adiantum pyramidale (L.) Willd.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/jdt-086-bsv
title: Pityrogramma sulphurea (Sw.) Maxon

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/pbh-4s5-78p
title: Adiantum pyramidale (L.) Willd.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.25574/25516
title: Chandra X-ray Observatory ObsId 25516

description: CHANDRA CYCLES 23-25 SPATIAL AND SPECTRAL MONITORING OF SN 1987A
DOI: 10.25574/27419
title: Chandra X-ray Observatory ObsId 27419

description: Investigating the plasma properties of Big Dipper, 4U 1624-490
DOI: 10.15468/dl.7grruv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cleaveius inglisi",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000702-260129131611470/datasets/export for details.
DOI: 10.25574/30463
title: Chandra X-ray Observatory ObsId 30463

description: Characterizing the Energetics and Dynamics of V750 Ara, a gamma Cas-type Star
DOI: 10.6084/m9.figshare.31198045
title: Banco de dados artigo original

description: Estudo quase-experimental sem grupo controle, do tipo antes e depois. A pesquisa é derivada da dissertação de mestrado do Programa de Pós-Graduação em Gerontologia da Universidade Federal de Pernambuco. O estudo está de acordo com a Resolução nº 466/2012 e a Resolução nº 510/2016 do Conselho Nacional de Saúde e que foi aprovado pelo Comitê de Ética em Pesquisa com Seres Humanos do Centro de Ciências da Saúde da Universidade Federal de Pernambuco (CEP/CCS/UFPE), CAAE: 77124023.7.0000.5208, parecer N° 6.813.185 de 09 de maio de 2024.
DOI: 10.6084/m9.figshare.31198045.v1
title: Banco de dados artigo original

description: Estudo quase-experimental sem grupo controle, do tipo antes e depois. A pesquisa é derivada da dissertação de mestrado do Programa de Pós-Graduação em Gerontologia da Universidade Federal de Pernambuco. O estudo está de acordo com a Resolução nº 466/2012 e a Resolução nº 510/2016 do Conselho Nacional de Saúde e que foi aprovado pelo Comitê de Ética em Pesquisa com Seres Humanos do Centro de Ciências da Saúde da Universidade Federal de Pernambuco (CEP/CCS/UFPE), CAAE: 77124023.7.0000.5208, parecer N° 6.813.185 de 09 de maio de 2024.
DOI: 10.15468/dl.9bmcy4
title: Occurrence Download

description: A dataset containing 947 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Canada",
 "Geometry POLYGON((-81.36871 42.85008,-81.12088 42.86316,-81.13324 43.08918,-81.33818 43.09499,-81.36871 42.85008))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cygnus"
 ]
}

 The dataset includes 947 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000706-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8gkxgc
title: Occurrence Download

description: A dataset containing 1371 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Yucca filifera"
 ]
}

 The dataset includes 1371 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000708-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18419892
title: SuperDARN data in netCDF format (2007-Nov)

description: 2007-Nov SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6591913
title: SuperDARN data in netCDF format (2007-Nov)

description: 2007-Nov SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.18421487
title: Supplemental Data - Mood and sleep outcomes following daily 5-hydroxytryptophan (derived from Griffonia simplicifolia) supplementation in healthy young adults: a randomized, double-blind, placebo-controlled trial

description: The supplementary materials include additional safety and clinical assessment data supporting the findings of this randomized, double-blind, placebo-controlled trial. Supplementary Table S1 presents changes in vital signs (oral temperature, heart rate, systolic and diastolic blood pressure) from baseline (Day 0) to study completion (Day 25). Supplementary Table S2 reports hematological parameters assessed at baseline and Day 25. Supplementary Table S3 provides serum chemistry markers related to liver and kidney function measured at baseline and study completion. Collectively, these data support the short-term tolerability and safety of 5-HTP (from Griffonia simplicifolia) supplementation in the study population.
DOI: 10.5281/zenodo.18421486
title: Supplemental Data - Mood and sleep outcomes following daily 5-hydroxytryptophan (derived from Griffonia simplicifolia) supplementation in healthy young adults: a randomized, double-blind, placebo-controlled trial

description: The supplementary materials include additional safety and clinical assessment data supporting the findings of this randomized, double-blind, placebo-controlled trial. Supplementary Table S1 presents changes in vital signs (oral temperature, heart rate, systolic and diastolic blood pressure) from baseline (Day 0) to study completion (Day 25). Supplementary Table S2 reports hematological parameters assessed at baseline and Day 25. Supplementary Table S3 provides serum chemistry markers related to liver and kidney function measured at baseline and study completion. Collectively, these data support the short-term tolerability and safety of 5-HTP (from Griffonia simplicifolia) supplementation in the study population.
DOI: 10.15468/dl.vcfuef
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aphelandra espirito-santensis)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 13 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000710-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18421604
title: FUNDAMENTALTHEORIESOFLANGUAGEACQUISITIONANDREADINGCOMPREHENSIONATTHEB1LEVEL

description: The article analyzes key concepts of foreign language acquisition and the development of reading comprehension skills at the B1 level according to the Common European Framework of Reference for Languages (CEFR). It examines behaviorist, nativist, cognitive, and sociocultural approaches, as well as major reading models - bottom-up, top-down, and interactive. Special attention is given to their practical significance in teaching reading at secondary and higher education institutions. The study’s novelty lies in systematizing these approaches for B1-level learners and providing methodological recommendations for developing cognitive and metacognitive reading strategies
DOI: 10.5281/zenodo.18421605
title: FUNDAMENTALTHEORIESOFLANGUAGEACQUISITIONANDREADINGCOMPREHENSIONATTHEB1LEVEL

description: The article analyzes key concepts of foreign language acquisition and the development of reading comprehension skills at the B1 level according to the Common European Framework of Reference for Languages (CEFR). It examines behaviorist, nativist, cognitive, and sociocultural approaches, as well as major reading models - bottom-up, top-down, and interactive. Special attention is given to their practical significance in teaching reading at secondary and higher education institutions. The study’s novelty lies in systematizing these approaches for B1-level learners and providing methodological recommendations for developing cognitive and metacognitive reading strategies
DOI: 10.15468/dl.d83s38
title: Occurrence Download

description: A dataset containing 1367 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Gentianopsis thermalis",
 "Year 1997-2026"
 ]
}

 The dataset includes 1367 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000713-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uah9cw
title: Occurrence Download

description: A dataset containing 1699 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-189.78922 -43.32305,-189.4194 -43.32305,-189.4194 -43.11914,-189.78922 -43.11914,-189.78922 -43.32305))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1699 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000712-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26030/mjvk-v435
title: PH-04 Chile Peppers in Space

description: Growing plants in spaceflight presents challenges to achieve optimal plant growth and productivity. One of NASA's goals is growing edible crops in spaceflight to supplement the astronauts' diet with fresh vegetables and assess food safety of the crops. It is important to understand the optimal growing conditions for crops grown in spaceflight conditions. Leafy greens have been grown on the International Space Station in the Veggie plant chamber and were safe for the astronauts to eat. The Advanced Plant Habitat (APH), an enclosed, controlled plant growth facility on the ISS has also been used to grow edible plants. The technology demonstration in the APH grew chile peppers, Capsicum annum c.v. NuMex Espanola Improved. This was one of the longest continuous plant growth experiments conducted on ISS (137 days), producing 26 pepper fruit.
DOI: 10.5281/zenodo.17404710
title: Data for Vision Foundation DINOv2-based models outperform multi-modal CLIP-based models for classification task in a long-tail distribution

description: This dataset was used to train and test the models presented in the paper Vision Foundation DINOv2-based models outperform multi-modal CLIP-based models for classification task in a long-tail distribution.
DOI: 10.5281/zenodo.17404709
title: Data for Vision Foundation DINOv2-based models outperform multi-modal CLIP-based models for classification task in a long-tail distribution

description: This dataset was used to train and test the models presented in the paper Vision Foundation DINOv2-based models outperform multi-modal CLIP-based models for classification task in a long-tail distribution.
DOI: 10.6084/m9.figshare.31198123
title: Bitcoin_final_dataset_15m_prepared

description: data set prepared at 15-minute intervals
DOI: 10.6084/m9.figshare.31198123.v1
title: Bitcoin_final_dataset_15m_prepared

description: data set prepared at 15-minute intervals
DOI: 10.5281/zenodo.18420757
title: Deep Learning with Airborne Laser Scanning Data for Forest Inventories

description: This tutorial provides an introductory, hands-on guide to applying deep learning techniques to Enhanced Forest Inventories (EFIs) using airborne laser scanning (ALS) data. Developed for the Canadian Cross-Country EFI Checkup, the workshop covers the complete workflow: from understanding deep learning concepts, preparing and reading data, to training, evaluating, and deploying deep learning models for forest inventory tasks such as species classification and biomass prediction.

Key Features



Intro to Deep Learning for EFIs: Learn the fundamentals of deep learning and its applications in forest inventory.

Data Preparation: Step-by-step instructions for preparing ALS data for deep learning workflows.

PyTorch Lightning: Practical examples using the PyTorch Lightning library for efficient model development.

Model Training & Evaluation: Guided exercises on training, testing, and evaluating deep learning models.

Real-World Applications: Case studies demonstrating deep learning solutions for forest inventory challenges.


Data & Resources

All datasets, training outputs, and model evaluations used in this tutorial are precomputed and provided for download to ensure a smooth learning experience. This allows participants to focus on the workflow without long training times.



Workshop Codebase: GitHub Repository

Tutorial Website: Workshop Site


Original Dataset

This tutorial uses a modified version of the Petawawa Research Forest (PRF) dataset hosted by the Canadian National Forest Information System. This dataset can be found here. For more information about the PRF dataset, see White et al.  (2019) below.

White, Joanne C., et al. "The Petawawa Research Forest: Establishment of a remote sensing supersite." The Forestry Chronicle 95.3 (2019): 149-156.
DOI: 10.5281/zenodo.18420758
title: Deep Learning with Airborne Laser Scanning Data for Forest Inventories

description: This tutorial provides an introductory, hands-on guide to applying deep learning techniques to Enhanced Forest Inventories (EFIs) using airborne laser scanning (ALS) data. Developed for the Canadian Cross-Country EFI Checkup, the workshop covers the complete workflow: from understanding deep learning concepts, preparing and reading data, to training, evaluating, and deploying deep learning models for forest inventory tasks such as species classification and biomass prediction.

Key Features



Intro to Deep Learning for EFIs: Learn the fundamentals of deep learning and its applications in forest inventory.

Data Preparation: Step-by-step instructions for preparing ALS data for deep learning workflows.

PyTorch Lightning: Practical examples using the PyTorch Lightning library for efficient model development.

Model Training & Evaluation: Guided exercises on training, testing, and evaluating deep learning models.

Real-World Applications: Case studies demonstrating deep learning solutions for forest inventory challenges.


Data & Resources

All datasets, training outputs, and model evaluations used in this tutorial are precomputed and provided for download to ensure a smooth learning experience. This allows participants to focus on the workflow without long training times.



Workshop Codebase: GitHub Repository

Tutorial Website: Workshop Site


Original Dataset

This tutorial uses a modified version of the Petawawa Research Forest (PRF) dataset hosted by the Canadian National Forest Information System. This dataset can be found here. For more information about the PRF dataset, see White et al.  (2019) below.

White, Joanne C., et al. "The Petawawa Research Forest: Establishment of a remote sensing supersite." The Forestry Chronicle 95.3 (2019): 149-156.
DOI: 10.5281/zenodo.18421639
title: Flaky Test Issues Analysis From Stack Overflow
DOI: 10.5281/zenodo.18421640
title: Flaky Test Issues Analysis From Stack Overflow
DOI: 10.15468/dl.afhgu2
title: Occurrence Download

description: A dataset containing 29115 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is University of Manitoba Herbarium (WIN) - Vascular Plant Collection"
 ]
}

 The dataset includes 29115 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000715-260129131611470/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.30667267
title: Development and validation of a pre-screening tool for early detection of low bone mineral density in female athletes (PreST-LBMD)

description: The prevalence of low bone mineral density (BMD) among female athletes is higher than that among their male counterparts up to 40%. Low BMD increases the risk of stress fractures. To examine factors associated with low BMD in female athletes, and to develop, test, and evaluate a Pre-screening Tool for Early Detection of Low BMD in Female Athletes (PreST-LBMD). The development and validation groups (381 and 233 subjects, respectively) underwent a medical interview and a BMD test at the lumbar spine using dual-energy X-ray absorptiometry (DXA). A Z-score &lt; −1 was classified as low BMD. After extracting factors associated with low BMD from univariate logistic regression and multivariate least absolute shrinkage and selection operator (LASSO) regression analyses, each variable was scored based on the β coefficients from LASSO regression to create the PreST-LBMD. The development model was validated using receiver operating characteristic (ROC) curves. Furthermore, using external validation data, sensitivity, negative predictive value (NPV), and area under the curve (AUC) were calculated to evaluate the performance of PreST-LBMD. Factors associated with low BMD included age at menarche, amenorrhea for more than 1 year during the teenage years, current duration of amenorrhea, body mass index, history of stress fractures, and exercise load. The calculated cutoff value for a high likelihood of low BMD was 3 points, with a sensitivity of 0.83 (0.79–0.88), a negative predictive value of 0.85 (0.81–0.90), and an area under the curve of 0.74 (0.67–0.81).  We successfully developed the PreST-LBMD, which showed promising results identifying female athletes with low BMD. Those scoring ≥3 should be assessed by DXA and evaluated for the likelihood of low BMD by the athlete’s health and performance team.