cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.zs7h44j4q
title: Plant traits shape soil legacy effects on individual plant-insect interactions

description: Plant-mediated soil legacy effects can be important determinants of the
 performance of plants and their aboveground insect herbivores, but so far,
 soil legacy effects on plant-insect interactions have been tested for only
 a limited number of host plant species and soils. Here, we tested the
 performance of a polyphagous aboveground herbivore, caterpillars of the
 cabbage moth, Mamestra brassicae on twelve host plant species that were
 grown on a set of soils conditioned by each of these twelve species. We
 tested how growth rate (fast- or slow-growing) and functional type (grass
 or forb) of the plant species that conditioned the soil and of the
 responding host plant species growing in those soils affect the response
 of insect herbivores to conditioned soils. Our results show that plants
 and insect herbivores had lower biomass in soils that were conditioned by
 fast-growing forbs than in soils conditioned by slow-growing forbs. In
 soils conditioned by grasses, growth rate of the conditioning plant had
 the opposite effect, i.e., plants and herbivores had higher biomass in
 soils conditioned by fast-growing grasses, than in soils conditioned by
 slow-growing grasses. We show that the response of aboveground insects to
 soil legacy effects is strongly positively correlated with the response of
 the host plant species, indicating that plant vigour may explain these
 relationships. We provide evidence that soil communities can play an
 important role in shaping plant-insect interactions aboveground. Our
 results further emphasize the important and interactive role of the
 conditioning and the response plant in mediating soil-plant-insect
 interactions.
description: Data presented are plant shoot (fresh weight) and root
 (dryweight) data for two sets of plants that were given soil legacy
 treatments. One set was exposed to herbivory by Mamestra brassicae, the
 other was untouched and served as a control. From the herbivore treatment
 we collected fresh biomass of the caterpillars and consumed leaf area for
 each plant. 
description: Notes are detailed in the file
DOI: 10.5061/dryad.6m905qfvx
title: Predation risk constrains herbivores’ adaptive capacity to warming

description: Global warming compels larger endothermic animals to adapt either
 physiologically or behaviourally to avoid thermal stress, especially in
 tropical ecosystems. Their adaptive responses may however be compromised
 by other constraints, such as predation risk or starvation. Using an
 exceptional camera trap dataset spanning 32 protected areas across
 Southern Africa, we find that intermediate-sized herbivores (100-550kg)
 switch activity to hotter times of the day when exposed to predation by
 lions. These herbivores thus face a tight window for foraging activity
 being exposed to nocturnal predation and to heat during the day,
 suggesting a trade-off between predation risk and thermoregulation
 mediated by body size. These findings stress the importance of
 incorporating trophic interactions into climate change predictions.
description: 73 camera-trap surveys were run for 50 consecutive days on
 average (s.d. 8) between 2013 and 2017 at 1339 locations (39 per survey on
 average) in 33 protected areas across South Africa. These surveys captured
 a total of 386,815 passages of animals (detections) where species could be
 identified. From this total dataset, we selected all passages from
 herbivores (&gt;2kg) and larger carnivores (&gt;20kg). In order to
 reduce the bias obtained by groups or curious individuals triggering the
 cameras multiple times in a short time period, we defined an independent
 detection as one detection per species per hour per day at each camera
 station, yielding a total of 169,771 and 21,259 independent detections for
 herbivores and carnivores, respectively. We made further selections of
 these data depending on the analyses as described in the
 manuscript.
description: Please use the readRDS() function to load the RData files.
DOI: 10.5061/dryad.nvx0k6dn6
title: Collective decision-making appears more egalitarian in populations where group fission costs are higher

description: Collective decision-making is predicted to be more egalitarian in
 conditions where the costs of group fission are higher. Here we ask
 whether Trinidadian guppies (Poecilia reticulata) living in high or low
 predation environments, and thereby facing differential group fission
 costs, make collective decisions in line with this prediction. Using a
 classic decision-making scenario, we found that fish from high predation
 environments switched their positions within groups more frequently than
 fish from low predation environments. Because the relative positions
 individuals adopt in moving groups can influence their contribution
 towards group decisions, increased positional switching appears to support
 the prediction of more evenly distributed decision-making in populations
 where group fission costs are higher. In an agent-based model, we further
 identified that more frequent, asynchronous updating of individuals'
 positions could explain increased positional switching, as was observed in
 fish from high predation environments. Our results are consistent
 with  theoretical predictions about the structure of
 collective decision-making and the adaptability of social decision-rules
 in the face of different environmental contexts.
DOI: 10.5061/dryad.bg79cnp71
title: ATN-classification and clinical progression in subjective cognitive decline: the SCIENCe project

description: Objective: To investigate the relationship between the ATN-model and risk
 of dementia and cognitive decline in individuals with subjective cognitive
 decline (SCD). Methods: We classified 693 participants with SCD (60±9yr,
 41%F, MMSE 28±2) from the Amsterdam Dementia Cohort and SCIENCe project
 according to the ATN-model, as determined by amyloid PET or CSF Abeta (A),
 CSF p-tau (T) and MRI-based medial temporal lobe atrophy (N). All
 underwent extensive neuropsychological assessment. For 342 participants
 follow-up was available (3±2yr). As a control population, we included 124
 participants without SCD. Results: 56% (n=385) participants had normal AD
 biomarkers (A-T-N-), 27% (n=186) had non-AD pathologic change (A-T-N+,
 A-T+N-, A-T+N+), 18% (n=122) fell within the Alzheimer’s continuum
 (A+T-N-, A+T-N+, A+T+N-, A+T+N+). ATN profiles were unevenly distributed,
 with A-T+N+, A+T-N+ and A+T+N+ containing very few participants. Cox
 regression showed that compared to A-T-N-, participants in A+ profiles had
 a higher risk of dementia with a dose-response pattern for number of
 biomarkers affected. Linear mixed models showed participants in A+
 profiles showed a steeper decline on tests addressing memory, attention,
 language, and executive functions. In the control group, there was no
 association between ATN and cognition. Conclusions: Among individuals
 presenting with SCD at a memory clinic, those with a biomarker profile
 A-T+N+, A+T-N-, A+T+N- and A+T+N+ were at increased risk of dementia, and
 showed steeper cognitive decline compared to A-T-N- individuals. These
 results suggest a future where biomarker results could be used for
 individualized risk profiling in cognitively normal individuals presenting
 at a memory clinic.
DOI: 10.15468/dl.c9g9tk
title: Occurrence Download

description: A dataset containing 17522335 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bryophyta"
 ]
}

 The dataset includes 17522335 records from 2749 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001899-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jtxfcvt39k.1
title: Appendix of Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach

description: This file, provided as an appendix to the paper “Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach,” contains two main parts that offer detailed background information on the prediction models for the Coal Hardgrove Grindability Index (HGI) and related coal sample statistics.

1. Summary of Regression Models (Appendix A):
 This section summarizes various regression models from multiple publications used for HGI prediction, including datasets from different countries (such as Serbia, Poland, India, China, Turkey, and Kentucky).
2. Statistical Description of the CAER Dataset (Appendix B):
 This part provides a detailed statistical description of 902 coal samples from the CAER (China Coal Research Institution).
 The variables include moisture, ash, volatile matter, carbon, hydrogen, nitrogen, oxygen, sulfur, various mineral components, and other coal properties (such as different organic matter contents and reflectance indices).
 The table presents the minimum value, maximum value, mean, and standard deviation for each variable, enabling users to gain a comprehensive understanding of the physical and chemical properties of the coal samples. This information is crucial for developing regression models related to coal grindability prediction.

Additional Information:
In addition to the tabular data, related literature references are provided, allowing users to trace the original studies and methods, thereby enhancing the reliability and reproducibility of the dataset.
DOI: 10.17632/jtxfcvt39k.2
title: Appendix of Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach

description: This file, provided as an appendix to the paper “Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach,” contains two main parts that offer detailed background information on the prediction models for the Coal Hardgrove Grindability Index (HGI) and related coal sample statistics.

1. Summary of Regression Models (Appendix A):
 This section summarizes various regression models from multiple publications used for HGI prediction, including datasets from different countries (such as Serbia, Poland, India, China, Turkey, and Kentucky).
2. Statistical Description of the CAER Dataset (Appendix B):
 This part provides a detailed statistical description of 902 coal samples from the CAER (University of Kentucky Center for Applied Energy Research (CAER) dataset).
 The variables include moisture, ash, volatile matter, carbon, hydrogen, nitrogen, oxygen, sulfur, various mineral components, and other coal properties (such as different organic matter contents and reflectance indices).
 The table presents the minimum value, maximum value, mean, and standard deviation for each variable, enabling users to gain a comprehensive understanding of the physical and chemical properties of the coal samples. This information is crucial for developing regression models related to coal grindability prediction.

Additional Information:
In addition to the tabular data, related literature references are provided, allowing users to trace the original studies and methods, thereby enhancing the reliability and reproducibility of the dataset.
DOI: 10.17632/jtxfcvt39k.3
title: Appendix of Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach

description: This file, provided as an appendix to the paper “Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach,” contains two main parts that offer detailed background information on the prediction models for the Coal Hardgrove Grindability Index (HGI) and related coal sample statistics.

1. Summary of Regression Models (Appendix A):
 This section summarizes various regression models from multiple publications used for HGI prediction, including information about the datasets from different countries (such as Serbia, Poland, India, China, Turkey, and Kentucky).
2. Statistical Analysis and Visualization of the CAER Dataset (Appendix B):
 This part provides a detailed statistical description of 902 coal samples from the CAER (University of Kentucky Center for Applied Energy Research (CAER) dataset).
 The variables include moisture, ash, volatile matter, carbon, hydrogen, nitrogen, oxygen, sulfur, various mineral components, and other coal properties (such as different organic matter contents and reflectance indices).
 The table presents the minimum value, maximum value, mean, and standard deviation for each variable, enabling users to gain a comprehensive understanding of the physical and chemical properties of the coal samples. This information is crucial for developing regression models related to coal grindability prediction.
 In addition to the statistical table, we have also included boxplots for the CAER dataset to visually illustrate the distribution and variability of key variables.

Additional Information:
In addition to the tabular data and boxplot figures, related literature references are provided, allowing users to trace the original studies and methods, thereby enhancing the reliability and reproducibility of the dataset.
DOI: 10.17632/jtxfcvt39k.4
title: Appendix of Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach

description: This file, provided as an appendix to the paper “Explainable AI for Coal Hardgrove Grindability Index Prediction: A Genetic Programming and Linear Regression Approach,” contains two main parts that offer detailed background information on the prediction models for the Coal Hardgrove Grindability Index (HGI) and related coal sample statistics.

1. Summary of Regression Models (Appendix A):
 This section summarizes various regression models from multiple publications used for HGI prediction, including information about the datasets from different countries (such as Serbia, Poland, India, China, Turkey, and Kentucky).
2. Statistical Analysis and Visualization of the CAER Dataset (Appendix B):
 This part provides a detailed statistical description of 902 coal samples from the CAER (University of Kentucky Center for Applied Energy Research (CAER) dataset).
 The variables include moisture, ash, volatile matter, carbon, hydrogen, nitrogen, oxygen, sulfur, various mineral components, and other coal properties (such as different organic matter contents and reflectance indices).
 The table presents the minimum value, maximum value, mean, and standard deviation for each variable, enabling users to gain a comprehensive understanding of the physical and chemical properties of the coal samples. This information is crucial for developing regression models related to coal grindability prediction.
 In addition to the statistical table, we have also included boxplots for the CAER dataset to visually illustrate the distribution and variability of key variables.

Additional Information:
In addition to the tabular data and boxplot figures, related literature references are provided, allowing users to trace the original studies and methods, thereby enhancing the reliability and reproducibility of the dataset.
DOI: 10.5281/zenodo.15276196
title: [Data] In-situ neutron diffraction revealing microstructure changes during laser powder bed fusion and in-situ laser heat treatments of 316L and 316L-Al1

description: This dataset includes the experimental data for the paper: "In-situ neutron diffraction revealing microstructure changes during laser powder bed fusion and in-situ laser heat treatments of 316L and 316L-Al1" by C. Navarre, S. Sumarli, F. Malamud, E. Polatidis, M. Strobl, R. Logé.
DOI: 10.5281/zenodo.15276197
title: [Data] In-situ neutron diffraction revealing microstructure changes during laser powder bed fusion and in-situ laser heat treatments of 316L and 316L-Al1

description: This dataset includes the experimental data for the paper: "In-situ neutron diffraction revealing microstructure changes during laser powder bed fusion and in-situ laser heat treatments of 316L and 316L-Al1" by C. Navarre, S. Sumarli, F. Malamud, E. Polatidis, M. Strobl, R. Logé.
DOI: 10.15468/dl.h8xfky
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((22.642066539359455 36.839824546518216, 22.64253988016374 36.8423775066416, 22.642384463046305 36.84495565841912, 22.64160618072002 36.847459921138935, 22.640234871296208 36.84979403899193, 22.638323182413497 36.851868282927384, 22.635944555793266 36.85360290348889, 22.63319040848744 36.85493120123962, 22.630166618595442 36.85580209591472, 22.62698945067962 36.8561820946661, 22.623781078337984 36.856056582959724, 22.620664877439097 36.85543038793973, 22.617760672681257 36.85432759234522, 22.615180122004627 36.8527906062189, 22.613022417875932 36.8508785325088, 22.611370471825406 36.84866489007404, 22.61028772940194 36.8462347824668, 22.609815737761227 36.8436816221958, 22.60997255848241 36.841103537156236, 22.61075208516671 36.838599597899616, 22.61212429026061 36.83626601099084, 22.61403638976779 36.83419242464907, 22.616414879453018 36.832458488218144, 22.619168363111456 36.83113079698213, 22.622191063677935 36.83026033886956, 22.625366882430082 36.8298805402658, 22.628573851167822 36.83000598523425, 22.6316888077086 36.83063185679099, 22.634592116796398 36.83173412144129, 22.637172256848658 36.83327044997387, 22.639330097912495 36.835181839553385, 22.640982707610508 36.83739487547347, 22.642066539359455 36.839824546518216))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 45 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007873-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qdcx4h
title: Occurrence Download

description: A dataset containing 1533 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "DatasetKey is one of (Long-term monitoring of small mammals in Doñana 2011-2021, Estación Biológica de Doñana - CSIC, Mammal Collection, Banco de Datos de la Biodiversidad de la Comunitat Valenciana, Mammals in MZNA-VERT: pellet sampling, Mammals in MZNA-VERT: project Human impacts in rivers of Navarra)",
 "TaxonKey is Mus spretus",
 "Year 2001-2025"
 ]
}

 The dataset includes 1533 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007877-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qsasy5
title: Occurrence Download

description: A dataset containing 50239 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phengaris alcon"
 ]
}

 The dataset includes 50239 records from 241 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007835-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t3xy59
title: Occurrence Download

description: A dataset containing 1046 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Agrilus anxius"
 ]
}

 The dataset includes 1046 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007875-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mzkg9b
title: Occurrence Download

description: A dataset containing 6647 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acisanthera"
 ]
}

 The dataset includes 6647 records from 255 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007861-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.98gjca
title: Occurrence Download

description: A dataset containing 3926337 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is North America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lepidoptera"
 ]
}

 The dataset includes 3926337 records from 276 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007866-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17712777
title: Hydrogeochemical and isotopic observations in the Baltic Sea system

description: Data products are organized into the following files:



Ferrybox surface water survey (1-minute resolution): 1_Cruise_Ferrybox_1min.csv

Meteorological survey (2-minute resolution): 2_Cruise_meteo_2min.csv

HydroFIA pH survey with matched and down-sampled Ferrybox data (10-minute resolution): 3_Cruise_HydroFIA_pH_10min.csv

222Rn surface water survey with matched and down-sampled Ferrybox and meteorological data (30-minute resolution): 4_Cruise_RAD7_30min.csv

Processed CTD profiles: 5_Cruise_CTD.csv

Cruise campaign discrete sample data: 6_Land_discrete.csv

Land campaign discrete sample data: 7_Land_discrete.csv


Additional description of the files, variables, and metadata can be found in the file "Baltic_data_metadata.txt".

 

V4 Update: Corrected typo in 5_Cruise_CTD.csv column names

V3 Updates: Corrected the number of significant figures in the DOC and TDN data, corrected analytical uncertainty values for δ13C-CH4 and δ13C-CO2.
DOI: 10.15468/dl.gdmb8q
title: Occurrence Download

description: A dataset containing 792134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "GadmGid is PRT.15_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 792134 records from 267 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007837-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15792010
title: Hydrogeochemical and isotopic observations in the Baltic Sea system

description: Data products are organized into the following files:



Ferrybox surface water survey (1-minute resolution): 1_Cruise_Ferrybox_1min.csv

Meteorological survey (2-minute resolution): 2_Cruise_meteo_2min.csv

HydroFIA pH survey with matched and down-sampled Ferrybox data (10-minute resolution): 3_Cruise_HydroFIA_pH_10min.csv

222Rn surface water survey with matched and down-sampled Ferrybox and meteorological data (30-minute resolution): 4_Cruise_RAD7_30min.csv

Processed CTD profiles: 5_Cruise_CTD.csv

Cruise campaign discrete sample data: 6_Land_discrete.csv

Land campaign discrete sample data: 7_Land_discrete.csv


Additional description of the files, variables, and metadata can be found in the file "Baltic_data_metadata.txt".

 

V4 Update: Corrected typo in 5_Cruise_CTD.csv column names

V3 Updates: Corrected the number of significant figures in the DOC and TDN data, corrected analytical uncertainty values for δ13C-CH4 and δ13C-CO2.