cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.2f1vk3p
title: Data from: Impact of the spatial uncertainty of seed dispersal on tree colonization dynamics in a temperate forest

description: An aggregated distribution of dispersed seeds may influence the
 colonization process in tree communities via inflated spatial uncertainty.
 To evaluate this possibility, we studied 10 tree species in a temperate
 forest: one primarily barochorous, six anemochorous and two endozoochorous
 species. A statistical model was developed by combining an empirical seed
 dispersal kernel with a gamma distribution of seedfall density, with
 parameters that vary with distance. In the probability density, the fitted
 models showed that seeds of Fagaceae (primarily barochorous) and
 Betulaceae (anemochorous) were disseminated locally (i.e., within 60 m of
 a mother tree), whereas seeds of Acer (anemochorous) and endozoochorous
 species were transported farther. Greater fecundity compensated for the
 lower probability of seed dispersal over long distances for some species.
 Spatial uncertainty in seedfall density was much greater within 60 m of a
 mother tree than farther away, irrespective of dispersal mode, suggesting
 that seed dispersal is particularly aggregated in the vicinity of mother
 trees. Simulation results suggested that such seed dispersal patterns
 could lead to sites in the vicinity of a tree being occupied by other
 species that disperse seeds from far away. We speculate that this process
 could promote coexistence by making the colonization rates of the species
 more similar on average and equalizing species fitness in this temperate
 forest community.
description: seed trap
 dataThis file includes the
 number of seeds in the traps for 10 species. XY coordinates (shown in
 meter) of the traps are also included.seed_trap_data.csvlocation of seed source treesThis file includes the XY coordinates (shown in meter) of the trees which produced seeds in and around the 6 ha plot for 10 species.mother_tree_locations.csv
DOI: 10.15468/dl.w2v67p
title: Occurrence Download

description: A dataset listing the 1386 species recorded in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Strigiformes"
 ]
}

 The dataset's 1386 records were derived from 5674 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001802-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pjrk9y
title: Occurrence Download

description: A dataset containing 409 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Drepanopus pectinatus Brady, 1883"
 ]
}

 The dataset includes 409 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001531-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qjhax8
title: Occurrence Download

description: A dataset containing 1408 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jubaea Kunth"
 ]
}

 The dataset includes 1408 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001532-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7nujud
title: Occurrence Download

description: A dataset containing 13441 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Morchella esculenta (L.) Pers."
 ]
}

 The dataset includes 13441 records from 326 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001529-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25375/uct.30286885
title: Dataset of Wrongful Conviction Cases in South Africa

description: Wrongful conviction is broadly defined as the conviction of an innocent person for a crime that they did not commit. The overturning of such a conviction is, in the United States of America (USA), referred to as an exoneration. The National Registry of Exonerations (NRE), which is a database of all known exonerations in the USA, contained 3,737 cases as of October 2025. International research has identified numerous factors that contribute to wrongful convictions, with psychological studies focusing particularly on eyewitness misidentification and false confessions. In South Africa (SA), however, wrongful conviction remains a largely unexplored area of enquiry. This research had two primary aims: first, to systematically collect and analyse cases of wrongful conviction in SA, with a specific focus on cases involving eyewitness misidentification and/or false confessions; and second, to establish a South African database of wrongful convictions using these cases. This repository contains the data that was used in this research, which consisted of publicly available court judgements. The coding sheet contained herein, modelled after databases such as the NRE, is intended to be used for future research, subject to approval.
DOI: 10.25375/uct.30286885.v1
title: Dataset of Wrongful Conviction Cases in South Africa

description: Wrongful conviction is broadly defined as the conviction of an innocent person for a crime that they did not commit. The overturning of such a conviction is, in the United States of America (USA), referred to as an exoneration. The National Registry of Exonerations (NRE), which is a database of all known exonerations in the USA, contained 3,737 cases as of October 2025. International research has identified numerous factors that contribute to wrongful convictions, with psychological studies focusing particularly on eyewitness misidentification and false confessions. In South Africa (SA), however, wrongful conviction remains a largely unexplored area of enquiry. This research had two primary aims: first, to systematically collect and analyse cases of wrongful conviction in SA, with a specific focus on cases involving eyewitness misidentification and/or false confessions; and second, to establish a South African database of wrongful convictions using these cases. This repository contains the data that was used in this research, which consisted of publicly available court judgements. The coding sheet contained herein, modelled after databases such as the NRE, is intended to be used for future research, subject to approval.
DOI: 10.5281/zenodo.17604299
title: Analytical base of satellite monitoring of agro-climatic conditions and crop status in 2024 in Ukraine

description: This analytical base presents an assessment of agroclimatic conditions and crop status across Ukraine during the 2024 growing season, based on satellite-derived data. The analysis covers temperature regimes, moisture availability, and vegetation dynamics as indicated by the Normalized Difference Vegetation Index (NDVI), with comparisons to long-term climatic averages and data from the previous year. Relevant proposals and recommendations have been formulated based on the observed trends.

The information was submitted to experts at the Ministry of Agrarian Policy and Food of Ukraine, as well as the Department of Agriculture, Land Reclamation, and Mechanization of the National Academy of Agrarian Sciences of Ukraine.

The analytical information may be utilized by agricultural professionals to support the development and implementation of management decisions at various hierarchical levels of national, regional, and central governmental and administrative bodies.
DOI: 10.5281/zenodo.17604300
title: Analytical base of satellite monitoring of agro-climatic conditions and crop status in 2024 in Ukraine

description: This analytical base presents an assessment of agroclimatic conditions and crop status across Ukraine during the 2024 growing season, based on satellite-derived data. The analysis covers temperature regimes, moisture availability, and vegetation dynamics as indicated by the Normalized Difference Vegetation Index (NDVI), with comparisons to long-term climatic averages and data from the previous year. Relevant proposals and recommendations have been formulated based on the observed trends.

The information was submitted to experts at the Ministry of Agrarian Policy and Food of Ukraine, as well as the Department of Agriculture, Land Reclamation, and Mechanization of the National Academy of Agrarian Sciences of Ukraine.

The analytical information may be utilized by agricultural professionals to support the development and implementation of management decisions at various hierarchical levels of national, regional, and central governmental and administrative bodies.
DOI: 10.6084/m9.figshare.28082252.v4
title: MHIT36: A phase-field code for GPU simulations of multiphase homogeneous isotropic turbulence

description: Validation data reported in the paper "MHIT36: A GPU-tailored code for phase-field simulations of homogenous isotropic turbulence"XXX identifies a certain grid (e.g., 256, 512, etc.)XXX_leonardo.dat -&gt; Strong scaling results using grid XXX^3 on LeonardoXXX_Marenostrum5.dat -&gt; Strong scaling results using grid XXX^3 on MN5-ACC.XXX_sbs.txt -&gt; Comulative scale by scale budget (obtained from MHIT36)XXX_spectrum -&gt; Spectra of kinetic energy (obtained from MHIT36)vtkwrite - &gt; Matlab script to create vtk file from matrixu_iter.dat -&gt; x-component of the velocity field (3D field, 200k and 300k)v_iter.dat -&gt; y-component of the velocity field (3D field, 200k and 300k)w_iter.dat -&gt; z-component of the velocity field (3D field, 200k and 300k)phi_iter.dat -&gt; Phase-field (3D field, 200k and 300k)loadfields.m -&gt; Script to load the fields in Matlab (1 GB each), also perform interpolation at cell center (can be commented if not needed).
DOI: 10.4121/6b9f4a02-f691-4d49-a5c5-6daae318d3fe
title: Code for evaluating the theoretical model and data for figures in the publication "A predictive model for bubble-particle collisions in turbulence"

description: Computer code which calculates the prediction of the model presented in "A predictive model for bubble–particle collisions in turbulence", Chem. Eng Sci. Statistics from simulations of non-deformable finite-size spherical bubbles and point-particles in homogeneous isotropic turbulence at Taylor Reynolds number of 64.
DOI: 10.4121/6b9f4a02-f691-4d49-a5c5-6daae318d3fe.v1
title: Code for evaluating the theoretical model and data for figures in the publication "A predictive model for bubble-particle collisions in turbulence"

description: Computer code which calculates the prediction of the model presented in "A predictive model for bubble–particle collisions in turbulence", Chem. Eng Sci. Statistics from simulations of non-deformable finite-size spherical bubbles and point-particles in homogeneous isotropic turbulence at Taylor Reynolds number of 64.
DOI: 10.5281/zenodo.17604368
title: Analytical base of satellite monitoring of agro-climatic conditions and crop status in  2025 in Ukraine.

description: The data of the analysis of agroclimatic conditions and crop status during the 2025 growing season, derived from satellite-based observations is presented. The assessment includes temperature dynamics, crop condition, moisture availability, and vegetation health as indicated by the Normalized Difference Vegetation Index (NDVI), benchmarked against long-term averages and data from the previous year. Relevant proposals and recommendations have been formulated based on the findings.

The information was provided to experts at the Ministry of Economy, Environmental Protection and Agriculture of Ukraine, the All-Ukrainian Environmental League, and the Department of Agriculture, Land Reclamation, and Mechanization of the National Academy of Agrarian Sciences of Ukraine.

The analytical information may be utilized by agricultural specialists to support the preparation and implementation of management decisions across various hierarchical levels of national, regional, and central governmental and administrative bodies.
DOI: 10.5281/zenodo.17604367
title: Analytical base of satellite monitoring of agro-climatic conditions and crop status in  2025 in Ukraine.

description: The data of the analysis of agroclimatic conditions and crop status during the 2025 growing season, derived from satellite-based observations is presented. The assessment includes temperature dynamics, crop condition, moisture availability, and vegetation health as indicated by the Normalized Difference Vegetation Index (NDVI), benchmarked against long-term averages and data from the previous year. Relevant proposals and recommendations have been formulated based on the findings.

The information was provided to experts at the Ministry of Economy, Environmental Protection and Agriculture of Ukraine, the All-Ukrainian Environmental League, and the Department of Agriculture, Land Reclamation, and Mechanization of the National Academy of Agrarian Sciences of Ukraine.

The analytical information may be utilized by agricultural specialists to support the preparation and implementation of management decisions across various hierarchical levels of national, regional, and central governmental and administrative bodies.
DOI: 10.15468/dl.wpyz9x
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , , , , , , , , Phoebe excelsa, , Ocotea excelsa, Laurus geminiflora, , , , , , Persea excelsa, Laurus geminiflora, , , Alseodaphne excelsa, , , , , , , , , )",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 38 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032723-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zfhbhm
title: Occurrence Download

description: A dataset containing 311 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biodiversity of Kalimantan, Indonesia"
 ]
}

 The dataset includes 311 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032724-251025141854904/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.30487070
title: AIMD Trajectories

description: All data are trajectory files generated from ab initio molecular dynamics (AIMD) calculations using PWmat. AIMD_data contains trajectories of various molecules (including TTE, DMC, EC, EA, DME, EGDME, PF6, and FSI) at different temperatures. These trajectory files include atomic positions, total energy, and atomic forces, and can be directly used as training data for DBMLFF. For details on the DBMLFF code and tutorials, refer to https://github.com/Sj-ai-ikahgi/DBMLFF.git. <br><br>MOVEMENT_5LiFSI_10EA_15TTE is a trajectory file generated from 5 ps AIMD calculations at 500 K, with the XYZ format.
DOI: 10.6084/m9.figshare.30487070.v1
title: AIMD Trajectories

description: AIMD and MLFF trajectories for DBMLFF simulation package.
DOI: 10.6084/m9.figshare.30487070.v2
title: AIMD Trajectories

description: All data are trajectory files generated from ab initio molecular dynamics (AIMD) calculations using PWmat. AIMD_data contains trajectories of various molecules (including TTE, DMC, EC, EA, DME, EGDME, PF6, and FSI) at different temperatures. These trajectory files include atomic positions, total energy, and atomic forces, and can be directly used as training data for DBMLFF. For details on the DBMLFF code and tutorials, refer to https://github.com/Sj-ai-ikahgi/DBMLFF.git. <br><br>MOVEMENT_5LiFSI_10EA_15TTE is a trajectory file generated from 5 ps AIMD calculations at 500 K, with the XYZ format.
DOI: 10.34740/kaggle/dsv/13728635
title: World Weather Repository ( Daily Updating )
DOI: 10.6084/m9.figshare.28082252
title: MHIT36: A phase-field code for GPU simulations of multiphase homogeneous isotropic turbulence

description: Validation data reported in the paper "MHIT36: A GPU-tailored code for phase-field simulations of homogenous isotropic turbulence" as well as for the NVA paper "MHIT36: Extension to wall-bounded turbulence and scalar transport equation".XXX identifies a certain grid (e.g., 256, 512, etc.)XXX_leonardo.dat -&gt; Strong scaling results using grid XXX^3 on LeonardoXXX_Marenostrum5.dat -&gt; Strong scaling results using grid XXX^3 on MN5-ACC.XXX_sbs.txt -&gt; Comulative scale by scale budget (obtained from MHIT36, HIT configuration)XXX_spectrum -&gt; Spectra of kinetic energy (obtained from MHIT36, HIT configuration)tempstats_550_pr1.dat -&gt; Temperature statistics for channel with uniform internal heating (channel, Pr=1)flowstats_re590.dat -&gt; Flow statistics of turbulent channel flow at re=590 (mean, rms, skw, flt)mhit_tcf_dsd.dat -&gt; Drop size distribution, turbulent channel flow, We=6, Re=180nusselt.dat -&gt; Nusselt number for the Rayleigh-Benard setup as a function of the Rayleigh numbervtkwrite - &gt; Matlab script to create vtk file from matrixu_iter.dat -&gt; x-component of the velocity field (3D field, 200k and 300k)v_iter.dat -&gt; y-component of the velocity field (3D field, 200k and 300k)w_iter.dat -&gt; z-component of the velocity field (3D field, 200k and 300k)phi_iter.dat -&gt; Phase-field (3D field, 200k and 300k)loadfields.m -&gt; Script to load the fields in Matlab (1 GB each), also perform interpolation at cell center (can be commented if not needed).
DOI: 10.6084/m9.figshare.28082252.v6
title: MHIT36: A phase-field code for GPU simulations of multiphase homogeneous isotropic turbulence

description: Validation data reported in the paper "MHIT36: A GPU-tailored code for phase-field simulations of homogenous isotropic turbulence" as well as for the NVA paper "MHIT36: Extension to wall-bounded turbulence and scalar transport equation".XXX identifies a certain grid (e.g., 256, 512, etc.)XXX_leonardo.dat -&gt; Strong scaling results using grid XXX^3 on LeonardoXXX_Marenostrum5.dat -&gt; Strong scaling results using grid XXX^3 on MN5-ACC.XXX_sbs.txt -&gt; Comulative scale by scale budget (obtained from MHIT36, HIT configuration)XXX_spectrum -&gt; Spectra of kinetic energy (obtained from MHIT36, HIT configuration)tempstats_550_pr1.dat -&gt; Temperature statistics for channel with uniform internal heating (channel, Pr=1)flowstats_re590.dat -&gt; Flow statistics of turbulent channel flow at re=590 (mean, rms, skw, flt)mhit_tcf_dsd.dat -&gt; Drop size distribution, turbulent channel flow, We=6, Re=180nusselt.dat -&gt; Nusselt number for the Rayleigh-Benard setup as a function of the Rayleigh numbervtkwrite - &gt; Matlab script to create vtk file from matrixu_iter.dat -&gt; x-component of the velocity field (3D field, 200k and 300k)v_iter.dat -&gt; y-component of the velocity field (3D field, 200k and 300k)w_iter.dat -&gt; z-component of the velocity field (3D field, 200k and 300k)phi_iter.dat -&gt; Phase-field (3D field, 200k and 300k)loadfields.m -&gt; Script to load the fields in Matlab (1 GB each), also perform interpolation at cell center (can be commented if not needed).
DOI: 10.6084/m9.figshare.28082252.v5
title: MHIT36: A phase-field code for GPU simulations of multiphase homogeneous isotropic turbulence

description: Validation data reported in the paper "MHIT36: A GPU-tailored code for phase-field simulations of homogenous isotropic turbulence" as well as for the NVA paper "MHIT36: Extension to wall-bounded turbulence and scalar transport equation".XXX identifies a certain grid (e.g., 256, 512, etc.)XXX_leonardo.dat -&gt; Strong scaling results using grid XXX^3 on LeonardoXXX_Marenostrum5.dat -&gt; Strong scaling results using grid XXX^3 on MN5-ACC.XXX_sbs.txt -&gt; Comulative scale by scale budget (obtained from MHIT36, HIT configuration)XXX_spectrum -&gt; Spectra of kinetic energy (obtained from MHIT36, HIT configuration)tempstats_550_pr1.dat -&gt; Temperature statistics for channel with uniform internal heating (channel, Pr=1)flowstats_re590.dat -&gt; Flow statistics of turbulent channel flow at re=590 (mean, rms, skw, flt)mhit_tcf_dsd.dat -&gt; Drop size distribution, turbulent channel flow, We=6, Re=180nusselt.dat -&gt; Nusselt number for the Rayleigh-Benard setup as a function of the Rayleigh numbervtkwrite - &gt; Matlab script to create vtk file from matrixu_iter.dat -&gt; x-component of the velocity field (3D field, 200k and 300k)v_iter.dat -&gt; y-component of the velocity field (3D field, 200k and 300k)w_iter.dat -&gt; z-component of the velocity field (3D field, 200k and 300k)phi_iter.dat -&gt; Phase-field (3D field, 200k and 300k)loadfields.m -&gt; Script to load the fields in Matlab (1 GB each), also perform interpolation at cell center (can be commented if not needed).
DOI: 10.5281/zenodo.17570419
title: Data files for "Constraining the nature of the most extreme Galactic particle accelerator: H.E.S.S. observations of the microquasar V4641 Sgr"

description: In this repository, we provide some auxiliary material in connection to our paper "Constraining the nature of the most extreme Galactic particle accelerator: H.E.S.S. observations of the microquasar V4641 Sgr" accepted for publication in the Astronomy and Astrophysics (A&A) Journal. For the full list of authors, please refer to the paper.

 

List of files

Significance and flux maps



SignificanceMap.fits.gz: Significance map above 0.8 TeV (Fig 1 in the paper)

FluxMap.fits.gz: Flux map above 0.8 TeV

FluxErrMap.fits.gz: Error on the flux map above 0.8 TeV

SignificanceMapEnergyBands.fits.gz: Significance maps between 0.8-5 TeV and 5-22 TeV (Fig 5 in the paper)

SignificanceMapEnergyBandsLargeSmoothing.fits.gz: Significance maps between 0.8-5 TeV and 5-22 TeV using the larger smoothing (Fig 5 in the paper)


Models and flux points



Model A (single elliptical Gaussian, see Table 1 in the paper): model parameters (model_A.yaml), covariance (model_A_covariance.dat) and flux points (model_A_flux_points.fits.gz)

Models B (two elliptical Gaussians which share spectral index and angle, B1&B2, see Table 1 in paper): model parameters (models_B.yaml), covariance (models_B_covariance.dat) and flux points (model_B1_flux_points.fits.gz and model_B2_flux_points.fits.gz )

Models C (two elliptical Gaussians which share angle, C1&C2, see Table A.1 in paper): model parameters (models_C.yaml), and covariance (models_C_covariance.dat),

Models D (two elliptical Gaussians with no shared parameters, D1&D2, see Table A.1 in paper): model parameters (models_D.yaml), and covariance (models_D_covariance.dat),

Central source: Upper limits derived for a point source with position fixed at that of V4641 Sgr derived by including models B1 and B2 in the fit (upper_limits_central_source_model_B.fits.gz)


Spatial profiles



Flux profile measured along the major axis of the emission, as a function of distance to V4641 Sgr (FluxProfileMajorAxis.fits.gz)

Index profile measured along the major axis of the emission, as a function of distance to V4641 Sgr (IndexProfileMajorAxis.fits.gz)

Flux profile measured along the minor axis of the emission, as a function of distance to V4641 Sgr (FluxProfileMinorAxis.fits.gz)

Index profile measured along the minor axis of the emission, as a function of distance to V4641 Sgr (IndexProfileMinorAxis.fits.gz)