cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.huh3te
title: Occurrence Download

description: A dataset containing 429 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lathyrus blepharicarpus Boiss.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 429 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154383-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hukvpr
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Limonium cosyrense (Guss.) Kuntze",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0300593-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.huzdav
title: Occurrence Download

description: A dataset containing 194940 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pistacia lentiscus L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 194940 records from 181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0262307-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hv3bzx
title: Occurrence Download

description: A dataset containing 21376 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reichardia picroides (L.) Roth",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 21376 records from 124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0261476-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hvvzqw
title: Occurrence Download

description: A dataset containing 1310 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Potentilla alchimilloides Lapeyr.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1310 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0293482-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hvxjfr
title: Occurrence Download

description: A dataset containing 51963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Orchis anthropophora (L.) All.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 51963 records from 245 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0298418-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/9n9d-2581
title: GATC27 pollen surface sample dataset

description: Raw data for the GATC27 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.14956640
title: Exothermal data from thermal safety assessment of type 18650 lithium-ion batteries with NMC and LFP cathodes and sodium-ion batteries by means of Accelerating Rate Calorimetry (ARC)

description: Data of safety investigation and thermal abuse behavior of commercial type 18650 LIB and SIB cells is provided.

It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.

Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at state of charge (SOC) of 100.

Data of different cathode chemistries (NMC and LFP) and cell type (Na-Ion vs Li-Ion) is provided.

For each cell, data includes the exothermal behavior of the cells, which is recorded only when the cell is behaving exothermally in the ARC, above a threshold of 0.02 °C / min.

Hence, in the files, time in minutes, temperature on the surface at the center of the cell in °C and the registered temperature rate in °C / min is provided. Cell type and for LIB cathode chemistry is indicated in the file name, each set of parameters has been tested twice with two different cells, which is indicated with cell and consecutive numbering of the test number.

This data is supporting this article:

https://doi.org/10.1016/j.jpowsour.2025.237240

More exothermal ARC date can be found here:

Type 21700 cells: https://doi.org/10.5281/zenodo.7707929

Type 4680 cells: https://doi.org/10.5281/zenodo.14956635
DOI: 10.5281/zenodo.14956641
title: Exothermal data from thermal safety assessment of type 18650 lithium-ion batteries with NMC and LFP cathodes and sodium-ion batteries by means of Accelerating Rate Calorimetry (ARC)

description: Data of safety investigation and thermal abuse behavior of commercial type 18650 LIB and SIB cells is provided.

It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.

Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at state of charge (SOC) of 100.

Data of different cathode chemistries (NMC and LFP) and cell type (Na-Ion vs Li-Ion) is provided.

For each cell, data includes the exothermal behavior of the cells, which is recorded only when the cell is behaving exothermally in the ARC, above a threshold of 0.02 °C / min.

Hence, in the files, time in minutes, temperature on the surface at the center of the cell in °C and the registered temperature rate in °C / min is provided. Cell type and for LIB cathode chemistry is indicated in the file name, each set of parameters has been tested twice with two different cells, which is indicated with cell and consecutive numbering of the test number.

This data is supporting this article:

https://doi.org/10.1016/j.jpowsour.2025.237240

More exothermal ARC date can be found here:

Type 21700 cells: https://doi.org/10.5281/zenodo.7707929

Type 4680 cells: https://doi.org/10.5281/zenodo.14956635
DOI: 10.21233/6j6k-4358
title: East Lily Peatland testate amoebae surface sample dataset

description: Raw data for the East Lily Peatland testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/4q30-w383
title: Issyk-Kul Lake pollen dataset

description: Raw data for the Issyk-Kul Lake pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.14956634
title: Exothermal data from thermal safety assessment of type 4680 lithium-ion batteries with NMC and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)

description: Data of safety investigation and thermal abuse behavior of commercial type 4680 LIB cells is provided.

It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type EVx ARC.

Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at state of charge (SOC) of 100.

Data of different cathode chemistries (NMC811 and LFP) is provided.

For each cell, data includes the exothermal behavior of the cells, which is recorded only when the cell is behaving exothermally in the ARC, above a threshold of 0.02 °C / min.

Hence, in the files, time in minutes, temperature on the surface at the center of the cell in °C and the registered temperature rate in °C / min is provided. Cathode chemistry is indicated in the file name, each set of parameters has been tested at least twice with two different cells, which is indicated with cell and consecutive numbering of the test number.

This data is supporting this article:

https://doi.org/10.1016/j.jpowsour.2025.237240

More exothermal ARC date can be found here:

Type 21700 cells: https://doi.org/10.5281/zenodo.7707929

Type 18650 cells: https://doi.org/10.5281/zenodo.14956641
DOI: 10.5281/zenodo.14956635
title: Exothermal data from thermal safety assessment of type 4680 lithium-ion batteries with NMC and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)

description: Data of safety investigation and thermal abuse behavior of commercial type 4680 LIB cells is provided.

It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type EVx ARC.

Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at state of charge (SOC) of 100.

Data of different cathode chemistries (NMC811 and LFP) is provided.

For each cell, data includes the exothermal behavior of the cells, which is recorded only when the cell is behaving exothermally in the ARC, above a threshold of 0.02 °C / min.

Hence, in the files, time in minutes, temperature on the surface at the center of the cell in °C and the registered temperature rate in °C / min is provided. Cathode chemistry is indicated in the file name, each set of parameters has been tested at least twice with two different cells, which is indicated with cell and consecutive numbering of the test number.

This data is supporting this article:

https://doi.org/10.1016/j.jpowsour.2025.237240

More exothermal ARC date can be found here:

Type 21700 cells: https://doi.org/10.5281/zenodo.7707929

Type 18650 cells: https://doi.org/10.5281/zenodo.14956641
DOI: 10.21233/tzv1-xs73
title: Potato Lake diatom dataset

description: Raw data for the Potato Lake diatom dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.6084/m9.figshare.29042309
title: Pre-post English Data copy.sav

description: Language intervention study in EFL among Arabic L1 speaking children in 6th grade
DOI: 10.6084/m9.figshare.29042309.v1
title: Pre-post English Data copy.sav

description: Language intervention study in EFL among Arabic L1 speaking children in 6th grade
DOI: 10.21233/g7w9-tn85
title: Butternut Lake diatom dataset

description: Raw data for the Butternut Lake diatom dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15390542
title: Mitosis Subtyping Dataset

description: Overview

This dataset is a curated subset of the final dataset developed for atypical mitosis detection in histopathology whole slide images (WSIs). It contains 8,236 image patches of size 64×64 pixels, categorized into four classes:





Artifact




Mimicker




Mitosis




Atypical Mitosis



The dataset is split into three cross-validation folds, allowing for standardized evaluation across multiple training/testing splits.

 

Dataset Generation Process

To construct a high-quality dataset for training and evaluating atypical mitosis detection algorithms, we employed an AI-assisted labeling pipeline:





Initial Model TrainingAn initial Atypical Mitosis Detection (AMD) model was trained using a combination of publicly available datasets — MIDOG21 and TUPAC — as described by Fick, Bertram, and Aubreville (2024), along with an internal dataset containing artifacts. These training sources provided approximately 9,500 patches of 64×64 pixels at 40x magnification, covering four target categories: artifact, mimicker, mitosis, and atypical mitosis. To access this dataset, please directly contact Marc Aubreville. 




Diverse Patch Sampling from TCGATo enhance diversity and representativeness, 6,000 image patches were sampled from The Cancer Genome Atlas (TCGA).





For 40x WSIs, 64×64 patches were directly extracted.




For 20x WSIs, 32×32 patches were extracted and then resized to 64×64 pixels.Feature embeddings of these patches were generated using the UNI foundation model.






Clustering for DiversityThe extracted embeddings were clustered into six distinct groups using the K-Nearest Neighbors algorithm, with the number of clusters chosen using the Elbow Method. From each cluster, 1,000 patches were randomly selected, totaling 6,000 diverse candidates.




AI-Assisted LabelingThese 6,000 patches were pre-classified using the initially trained AMD model. The predictions were then manually reviewed and corrected by three board-certified pathologists to ensure high labeling accuracy.




Final Dataset CompositionThe validated 6,000 TCGA-derived patches were merged with some previously labelled patches across the four diagnostic categories, resulting in this release (8,236 samples).



Use Cases and Applications

This dataset is intended for use in developing and benchmarking machine learning models for mitosis detection and classification in digital pathology. Potential applications include:





Training deep learning models for mitotic figure classification.




Evaluating performance on atypical mitosis detection.
DOI: 10.5281/zenodo.15390543
title: Mitosis Subtyping Dataset

description: Overview

This dataset is a curated subset of the final dataset developed for atypical mitosis detection in histopathology whole slide images (WSIs). It contains 8,236 image patches of size 64×64 pixels, categorized into four classes:





Artifact




Mimicker




Mitosis




Atypical Mitosis



The dataset is split into three cross-validation folds, allowing for standardized evaluation across multiple training/testing splits.

 

Dataset Generation Process

To construct a high-quality dataset for training and evaluating atypical mitosis detection algorithms, we employed an AI-assisted labeling pipeline:





Initial Model TrainingAn initial Atypical Mitosis Detection (AMD) model was trained using a combination of publicly available datasets — MIDOG21 and TUPAC — as described by Fick, Bertram, and Aubreville (2024), along with an internal dataset containing artifacts. These training sources provided approximately 9,500 patches of 64×64 pixels at 40x magnification, covering four target categories: artifact, mimicker, mitosis, and atypical mitosis. To access this dataset, please directly contact Marc Aubreville. 




Diverse Patch Sampling from TCGATo enhance diversity and representativeness, 6,000 image patches were sampled from The Cancer Genome Atlas (TCGA).





For 40x WSIs, 64×64 patches were directly extracted.




For 20x WSIs, 32×32 patches were extracted and then resized to 64×64 pixels.Feature embeddings of these patches were generated using the UNI foundation model.






Clustering for DiversityThe extracted embeddings were clustered into six distinct groups using the K-Nearest Neighbors algorithm, with the number of clusters chosen using the Elbow Method. From each cluster, 1,000 patches were randomly selected, totaling 6,000 diverse candidates.




AI-Assisted LabelingThese 6,000 patches were pre-classified using the initially trained AMD model. The predictions were then manually reviewed and corrected by three board-certified pathologists to ensure high labeling accuracy.




Final Dataset CompositionThe validated 6,000 TCGA-derived patches were merged with some previously labelled patches across the four diagnostic categories, resulting in this release (8,236 samples).



Use Cases and Applications

This dataset is intended for use in developing and benchmarking machine learning models for mitosis detection and classification in digital pathology. Potential applications include:





Training deep learning models for mitotic figure classification.




Evaluating performance on atypical mitosis detection.
DOI: 10.21233/b8th-nw62
title: Strathcona [FjPi-29] vertebrate fauna dataset

description: Raw data for the Strathcona [FjPi-29] vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.6084/m9.figshare.19114218
title: Exploring the role of power during debriefing of interprofessional simulations

description: Interprofessional simulation aims to improve teamwork and patient care by bringing participants from multiple professions together to practice simulated patient care scenarios. Yet, power dynamics may influence interprofessional learning during simulation, which typically occurs during the debriefing. This issue has received limited attention to date but may explain why communication breakdowns and conflicts among healthcare teams persist despite widespread adoption of interprofessional simulation. This study explores the role of power during interprofessional simulation debriefings. We collected data through observations of seven interprofessional simulation sessions and debriefings, four focus groups with simulation participants, and four interviews with simulation facilitators. We identified ways in which power dynamics influenced discussions during debriefing and sometimes limited participants’ willingness to share feedback and speak up. We also found that issues related to power that arose during interprofessional simulations often went unacknowledged during the debriefing, leaving healthcare professionals unprepared to navigate power discrepancies with other members of healthcare teams in practice. Given that the goal of interprofessional simulation is to allow professionals to learn together about each other, explicitly addressing power in debriefing after interprofessional simulation may enhance learning.
DOI: 10.6084/m9.figshare.19114218.v1
title: Exploring the role of power during debriefing of interprofessional simulations

description: Interprofessional simulation aims to improve teamwork and patient care by bringing participants from multiple professions together to practice simulated patient care scenarios. Yet, power dynamics may influence interprofessional learning during simulation, which typically occurs during the debriefing. This issue has received limited attention to date but may explain why communication breakdowns and conflicts among healthcare teams persist despite widespread adoption of interprofessional simulation. This study explores the role of power during interprofessional simulation debriefings. We collected data through observations of seven interprofessional simulation sessions and debriefings, four focus groups with simulation participants, and four interviews with simulation facilitators. We identified ways in which power dynamics influenced discussions during debriefing and sometimes limited participants’ willingness to share feedback and speak up. We also found that issues related to power that arose during interprofessional simulations often went unacknowledged during the debriefing, leaving healthcare professionals unprepared to navigate power discrepancies with other members of healthcare teams in practice. Given that the goal of interprofessional simulation is to allow professionals to learn together about each other, explicitly addressing power in debriefing after interprofessional simulation may enhance learning.
DOI: 10.3886/e229064
title: Data and Code for: Global Evidence on Children's Living Arrangements

description: This paper provides global evidence on the living arrangements of children, using harmonized census data from 95 countries, encompassing 85 percent of the world population. While residing with two parents is the most common living arrangement in nearly every country, there is substantial cross-country heterogeneity. When children do not live with two parents, they tend to live with their mother only or with no parent. Living with only one’s father remains rare. We document that countries with greater economic resources, lower income inequality, and higher human development scores consistently exhibit larger shares of children living with two parents.<br><br>These data/code accompany this article.
DOI: 10.3886/e229064v1
title: Data and Code for: Global Evidence on Children's Living Arrangements

description: This paper provides global evidence on the living arrangements of children, using harmonized census data from 95 countries, encompassing 85 percent of the world population. While residing with two parents is the most common living arrangement in nearly every country, there is substantial cross-country heterogeneity. When children do not live with two parents, they tend to live with their mother only or with no parent. Living with only one’s father remains rare. We document that countries with greater economic resources, lower income inequality, and higher human development scores consistently exhibit larger shares of children living with two parents.<br><br>These data/code accompany this article.
DOI: 10.6084/m9.figshare.11852037
title: Professional and interprofessional identities: a scoping review

description: Identity development within the interprofessional field is an emerging area of research. This scoping review aims to establish how professional and interprofessional identities are defined, conceptualized, theorized and measured within the interprofessional literature. Six databases were systematically searched for papers focusing on professional and/or interprofessional identities in interprofessional healthcare and education using a scoping review methodology. A total of 84 papers were included. Most papers discussed professional identity only; the minority discussed both identities. There were three key findings. First, no universal definition of interprofessional identity exists. Second, there is no shared understanding of interprofessional identity and its relationship with professional identity. Third, poor alignment between definitions, conceptualizations, theories and measures of interprofessional identity exists. The absence of a psychometrically robust instrument that specifically measures interprofessional identity and the short-term focus of current interprofessional identity research further limits understanding. Research that critically examines professional and interprofessional identity development should be underpinned by clear definitions, concepts, theories and measures of both identities. High-quality research will allow greater understanding of interprofessional identity development and its impact on interprofessional practice.