cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17617/3.igdd44
title: d2Smell - Black Pepper

description: Data collected as part of the D2Smell project. The data includes the following fields&lt;br&gt;Collection method: Sample in glass vial&lt;br&gt;Collection volume: 20 ml&lt;br&gt;Mass of object sample: 0.04 g&lt;br&gt;Sampling method: static_headspace&lt;br&gt;Notes: Trap heating rate: Max
DOI: 10.5281/zenodo.18095963
title: Le régime juridique du paiement des cotisations sociales au Cameroun

description: Université d'Etat
DOI: 10.5281/zenodo.18095962
title: Le régime juridique du paiement des cotisations sociales au Cameroun

description: Université d'Etat
DOI: 10.6084/m9.figshare.29827361
title: Dataset_Space_Teacher_Creates.csv

description: README – Student Motivation Dataset<br><br>Title: Cleaned dataset from a study on student motivation in mathematics classrooms.<br><br>Source:<br>The dataset was collected via an anonymous online Google Form completed by adult participants. The survey measured aspects of motivation and classroom climate in mathematics learning environments.<br><br>Format:<br>- File format: CSV (UTF-8 encoded)<br>- Decimal separator: dot (e.g., 3.14)<br>- Each row represents one participant.<br>- Columns include raw survey responses, recoded item scores, computed factor scores, and clustering results.<br><br>Data structure:<br>- Columns labeled with “Fi” refer to questionnaire items. Some items were reverse-coded according to the scoring key „Fi-” their value is substracted in Fi score, i=1,2,3,4.<br>- Factor scores were calculated based and standardized, next PCA was performed.<br>- The dataset includes variables representing hard cluster assignments (e.g., "Cluster") resulting from k-means clustering procedure (k=4).<br><br>Data cleaning:<br>- All responses are anonymous.<br>- No sensitive or personally identifying information is included.<br>- One statistical outlier was removed based on Mahalanobis distance during data preparation.<br>- Few missing or incomplete responses (about 1%) were replaced with median score.<br><br>Use:<br>These data are shared for the purpose of academic review, replication, or further methodological development. For questions, please contact the corresponding author.malnow@kul.pl, gosianmk@wp.pl<br><br>License:<br>This dataset is shared under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br><br>
DOI: 10.6084/m9.figshare.29827361.v1
title: Dataset_Space_Teacher_Creates.csv

description: README – Student Motivation Dataset<br><br>Title: Cleaned dataset from a study on student motivation in mathematics classrooms.<br><br>Source:<br>The dataset was collected via an anonymous online Google Form completed by adult participants. The survey measured aspects of motivation and classroom climate in mathematics learning environments.<br><br>Format:<br>- File format: CSV (UTF-8 encoded)<br>- Decimal separator: dot (e.g., 3.14)<br>- Each row represents one participant.<br>- Columns include raw survey responses, recoded item scores, computed factor scores, and clustering results.<br><br>Data structure:<br>- Columns labeled with “Fi” refer to questionnaire items. Some items were reverse-coded according to the scoring key „Fi-” their value is substracted in Fi score, i=1,2,3,4.<br>- Factor scores were calculated based and standardized, next PCA was performed.<br>- The dataset includes variables representing hard cluster assignments (e.g., "Cluster") resulting from k-means clustering procedure (k=4).<br><br>Data cleaning:<br>- All responses are anonymous.<br>- No sensitive or personally identifying information is included.<br>- One statistical outlier was removed based on Mahalanobis distance during data preparation.<br>- Few missing or incomplete responses (about 1%) were replaced with median score.<br><br>Use:<br>These data are shared for the purpose of academic review, replication, or further methodological development. For questions, please contact the corresponding author.malnow@kul.pl, gosianmk@wp.pl<br><br>License:<br>This dataset is shared under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br><br>
DOI: 10.6084/m9.figshare.29827361.v2
title: Dataset_Space_Teacher_Creates.csv

description: README – Student Motivation Dataset<br><br>Title: Cleaned dataset from a study on student motivation in mathematics classrooms.<br><br>Source:<br>The dataset was collected via an anonymous online Google Form completed by adult participants. The survey measured aspects of motivation and classroom climate in mathematics learning environments.<br><br>Format:<br>- File format: CSV (UTF-8 encoded)<br>- Decimal separator: dot (e.g., 3.14)<br>- Each row represents one participant.<br>- Columns include raw survey responses, recoded item scores, computed factor scores, and clustering results.<br><br>Data structure:<br>- Columns labeled with “Fi” refer to questionnaire items. Some items were reverse-coded according to the scoring key „Fi-” their value is substracted in Fi score, i=1,2,3,4.<br>- Factor scores were calculated based and standardized, next PCA was performed.<br>- The dataset includes variables representing hard cluster assignments (e.g., "Cluster") resulting from k-means clustering procedure (k=4).<br><br>Data cleaning:<br>- All responses are anonymous.<br>- No sensitive or personally identifying information is included.<br>- One statistical outlier was removed based on Mahalanobis distance during data preparation.<br>- Few missing or incomplete responses (about 1%) were replaced with median score.<br><br>Use:<br>These data are shared for the purpose of academic review, replication, or further methodological development. For questions, please contact the corresponding author.malnow@kul.pl, gosianmk@wp.pl<br><br>License:<br>This dataset is shared under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br><br>
DOI: 10.6084/m9.figshare.30971284
title: Data of paper: "Neural responses to emotional displays by politicians"

description: Paper: Neural responses to emotional displays by politicians: differential mu and alpha suppression patterns in response to in-party and out?party leaders Authors: Maaike D. Homan, Mohammad Hamdan, Karlijn Hendriks & Diamantis Petropoulos Petalas.<br>OSF page: https://osf.io/dsfgv/?view_only=57f9c76b170f4e7f8fac0be8eebf89b2<br><br>Abstract: The high levels of polarization raise concerns about individuals’ decreased ability to empathize and understand the representatives of political out-groups. As such, our political biases may lead us to misunderstand out-group politicians. In the current study, we examine the mu rhythm, a neural oscillation in the sensorimotor cortex related to the processing and understanding of other people’s actions, intentions and emotions. The mu rhythm is particularly responsive towards the emotional expressions of others and sensitive to social biases. Hence, we examine (1) whether the emotions displayed by politicians lead to more mu event-related-desynchronization (mu-ERD), (2) whether it matters which emotion (angry, happy, neutral) is displayed, and (3) whether neural responses differ when emotions are displayed by politicians we support (in-party politician) compared to politicians we do not support (out-party politician). To test this, we recorded electroencephalogram (EEG) responses during a preregistered Go/No Go mimicry experiment (N=47, Obs=1104), in which participants are presented with dynamic morphed emotional displays of Dutch politicians (in- and out-party) and non-politicians. We find that politicians emotional displays increase participants’ mu-ERD compared to static neutral displays. Most mu-ERD is found for out-party politicians, especially when angry. In addition, we explored alpha oscillations (related to visual attention), where we find the strongest alpha-ERD for the out-party happy condition. Overall our results suggest that our brain is specifically attuned to process the emotions of out-party politicians.
DOI: 10.6084/m9.figshare.30971284.v1
title: Data of paper: "Neural responses to emotional displays by politicians"

description: Paper: Neural responses to emotional displays by politicians: differential mu and alpha suppression patterns in response to in-party and out?party leaders Authors: Maaike D. Homan, Mohammad Hamdan, Karlijn Hendriks & Diamantis Petropoulos Petalas.<br>OSF page: https://osf.io/dsfgv/?view_only=57f9c76b170f4e7f8fac0be8eebf89b2<br><br>Abstract: The high levels of polarization raise concerns about individuals’ decreased ability to empathize and understand the representatives of political out-groups. As such, our political biases may lead us to misunderstand out-group politicians. In the current study, we examine the mu rhythm, a neural oscillation in the sensorimotor cortex related to the processing and understanding of other people’s actions, intentions and emotions. The mu rhythm is particularly responsive towards the emotional expressions of others and sensitive to social biases. Hence, we examine (1) whether the emotions displayed by politicians lead to more mu event-related-desynchronization (mu-ERD), (2) whether it matters which emotion (angry, happy, neutral) is displayed, and (3) whether neural responses differ when emotions are displayed by politicians we support (in-party politician) compared to politicians we do not support (out-party politician). To test this, we recorded electroencephalogram (EEG) responses during a preregistered Go/No Go mimicry experiment (N=47, Obs=1104), in which participants are presented with dynamic morphed emotional displays of Dutch politicians (in- and out-party) and non-politicians. We find that politicians emotional displays increase participants’ mu-ERD compared to static neutral displays. Most mu-ERD is found for out-party politicians, especially when angry. In addition, we explored alpha oscillations (related to visual attention), where we find the strongest alpha-ERD for the out-party happy condition. Overall our results suggest that our brain is specifically attuned to process the emotions of out-party politicians.
DOI: 10.5281/zenodo.18095892
title: Vector and raster dataset, PNG (graphic) file for project 2024/08/X/HS3/01523 (MINIATURA 8)

description: Dataset description

The dataset includes spatial data related to the archaeological site no. 81 in Lipsk (Poland). It consists of a polygon layer defining a study area of approximately 77 ares, representing the spatial extent of the research area surrounding the archaeological site. Dataset was collected in National Science Centre project Miniatura 8 "Rekonstrukcja przestrzeni osadniczej społeczności łowiecko-zbierackich oraz wybranych elementów paleokrajobrazu na stanowisku archeologicznym w Lipsku (Basen Górnej Biebrzy)".

The dataset also contains vector point layers representing geological probe soundings. These vector files preserve full attribute tables, including the attribute “glebokosc_2”, which records the depth of mineral sediment occurrence and enables raster generation and spatial interpolation based on sediment depth.

Additionally, contour lines (isolines) generated from raster data are included as vector layers. One example raster file ( 77_glebokosc_2_[Triangulation].tif ) is provided, representing sediment depth interpolation created using SAGA GIS, executed within the same GIS environment as the vector data (e.g., QGIS/SAGA GIS).

The dataset is complemented by PNG image files, including:





a map showing the location of the research area at archaeological site no. 81 in Lipsk,




figures reproduced from the scientific and popular-science reports of the project,




a visualization of the raster 77_glebokosc_2_[Triangulation].tif generated in QGIS.
DOI: 10.5281/zenodo.18095891
title: Vector and raster dataset, PNG (graphic) file for project 2024/08/X/HS3/01523 (MINIATURA 8)

description: Dataset description

The dataset includes spatial data related to the archaeological site no. 81 in Lipsk (Poland). It consists of a polygon layer defining a study area of approximately 77 ares, representing the spatial extent of the research area surrounding the archaeological site. Dataset was collected in National Science Centre project Miniatura 8 "Rekonstrukcja przestrzeni osadniczej społeczności łowiecko-zbierackich oraz wybranych elementów paleokrajobrazu na stanowisku archeologicznym w Lipsku (Basen Górnej Biebrzy)".

The dataset also contains vector point layers representing geological probe soundings. These vector files preserve full attribute tables, including the attribute “glebokosc_2”, which records the depth of mineral sediment occurrence and enables raster generation and spatial interpolation based on sediment depth.

Additionally, contour lines (isolines) generated from raster data are included as vector layers. One example raster file ( 77_glebokosc_2_[Triangulation].tif ) is provided, representing sediment depth interpolation created using SAGA GIS, executed within the same GIS environment as the vector data (e.g., QGIS/SAGA GIS).

The dataset is complemented by PNG image files, including:





a map showing the location of the research area at archaeological site no. 81 in Lipsk,




figures reproduced from the scientific and popular-science reports of the project,




a visualization of the raster 77_glebokosc_2_[Triangulation].tif generated in QGIS.
DOI: 10.5281/zenodo.13896451
title: Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)

description: 1.     State-dependent speciation and extinction (SSE) models are a popular framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. 

2.     Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates from phylogenetic trees in BiSSE (binary state dependent speciation and extinction), GeoSSE (geographic state dependent speciation and extinction) and MuSSE (multiple-state dependent speciation and extinction) models. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods, to identify the combinations that best capture the complex relationships between rates of diversification and species traits.

3.     Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate (λ) is highly asymmetric between states in all three models. 

4.     We suggest that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal constitute efficient summary statistics for the ABC method.

5.     By providing an efficient algorithm and a set of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex SSE models, for which a likelihood is not available.
DOI: 10.5281/zenodo.18080831
title: Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)

description: 1.     State-dependent speciation and extinction (SSE) models are a popular framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. 

2.     Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates from phylogenetic trees in BiSSE (binary state dependent speciation and extinction), GeoSSE (geographic state dependent speciation and extinction) and MuSSE (multiple-state dependent speciation and extinction) models. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods, to identify the combinations that best capture the complex relationships between rates of diversification and species traits.

3.     Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate (λ) is highly asymmetric between states in all three models. 

4.     We suggest that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal constitute efficient summary statistics for the ABC method.

5.     By providing an efficient algorithm and a set of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex SSE models, for which a likelihood is not available.
DOI: 10.15468/dl.xvvkdj
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Allium tuberosum",
 "Year 1970-2000"
 ]
}

 The dataset includes 66 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069242-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15855204
title: Neural crest cell biology shapes lizard skull evolution across evolutionary time scales

description: Landmark files, segmented models and metadata related to the paper "Neural crest cell biology shapes lizard skull evolution across evolutionary time scales"

Horta-Lacueva, Q., Uller, T., Flecks, M., Gabelaia, M., Hipsley, C.  Kirchner, M., Müller, J., Feiner, M. (2025) Neural crest cell biology shapes lizard skull evolution across evolutionary time scales, Evolution Letters, 2025;, qraf050, https://doi.org/10.1093/evlett/qraf050
DOI: 10.5281/zenodo.15855203
title: Neural crest cell biology shapes lizard skull evolution across evolutionary time scales

description: Landmark files, segmented models and metadata related to the paper "Neural crest cell biology shapes lizard skull evolution across evolutionary time scales"

Horta-Lacueva, Q., Uller, T., Flecks, M., Gabelaia, M., Hipsley, C.  Kirchner, M., Müller, J., Feiner, M. (2025) Neural crest cell biology shapes lizard skull evolution across evolutionary time scales, Evolution Letters, 2025;, qraf050, https://doi.org/10.1093/evlett/qraf050
DOI: 10.5281/zenodo.17883797
title: Notas de leitura do processo "Santa Casa da Misericórdia da Murtosa - Operações - Aquisição de terreno para novas instalações de bloco assistencial [1966-1977]"

description: Notas de leitura do processo "Santa Casa da Misericórdia da Murtosa - Operações - Aquisição de terreno para novas instalações de bloco assistencial [1966-1977]" existente no Arquivo do Ministério do Trabalho, Solidariedade e Segurança Social.

Registo efetuado em outubro de 2024 no âmbito do projeto de investigação ReARQ.IB – Built Environment Knowledge for Resilient, Sustainable Communities: Understanding Everyday Modern Architecture and Urban Design in the Iberian Peninsula (1939-1985) (ERC GA949686) e publicado online em https://arquitecturaaqui.eu/pt/documentacao/processos/50172/santa-casa-da-misericordia-da-murtosa-operacoes-aquisicao-de-terreno-para-novas-instalacoes-de-bloco-assistencial-1966-1977.

Dados exportados da plataforma Arquitectura Aqui (https://arquitecturaaqui.eu), repositório do referido projeto, em dezembro de 2025.
DOI: 10.15468/dl.4h4fy9
title: Occurrence Download

description: A dataset listing the 79611 species recorded in GBIF matching the query:
{
 "and" : [
 "PublishingCountry is South Africa",
 "TaxonKey is one of (Animalia [GBIF Backbone Taxonomy])"
 ]
}

 The dataset's 79611 records were derived from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069240-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17883796
title: Notas de leitura do processo "Santa Casa da Misericórdia da Murtosa - Operações - Aquisição de terreno para novas instalações de bloco assistencial [1966-1977]"

description: Notas de leitura do processo "Santa Casa da Misericórdia da Murtosa - Operações - Aquisição de terreno para novas instalações de bloco assistencial [1966-1977]" existente no Arquivo do Ministério do Trabalho, Solidariedade e Segurança Social.

Registo efetuado em outubro de 2024 no âmbito do projeto de investigação ReARQ.IB – Built Environment Knowledge for Resilient, Sustainable Communities: Understanding Everyday Modern Architecture and Urban Design in the Iberian Peninsula (1939-1985) (ERC GA949686) e publicado online em https://arquitecturaaqui.eu/pt/documentacao/processos/50172/santa-casa-da-misericordia-da-murtosa-operacoes-aquisicao-de-terreno-para-novas-instalacoes-de-bloco-assistencial-1966-1977.

Dados exportados da plataforma Arquitectura Aqui (https://arquitecturaaqui.eu), repositório do referido projeto, em dezembro de 2025.
DOI: 10.5281/zenodo.17084485
title: Isomer- and state-dependent ion--molecule reactions between Coulomb-crystallised Ca$^+$ ions and 1,2-dichloroethene
DOI: 10.5281/zenodo.17084486
title: Isomer- and state-dependent ion--molecule reactions between Coulomb-crystallised Ca$^+$ ions and 1,2-dichloroethene
DOI: 10.15468/dl.az7548
title: Occurrence Download

description: A dataset containing 25539 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chenopodium hybridum"
 ]
}

 The dataset includes 25539 records from 479 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069244-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5vrtmd
title: Occurrence Download

description: A dataset containing 82 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Allium victorialis",
 "Year 1970-2000"
 ]
}

 The dataset includes 82 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069245-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.adv.30781208
title: Supplement Number 1

description: Longer and more detailed description of this file
DOI: 10.60893/figshare.adv.30781208.v1
title: Supplement Number 1

description: Longer and more detailed description of this file
DOI: 10.60893/figshare.adv.30781211
title: Supplement Number 2

description: Longer and more detailed description of this file
DOI: 10.60893/figshare.adv.30781211.v1
title: Supplement Number 2

description: Longer and more detailed description of this file