cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30581129.v8
title: DeepEvo

description: The identification of adaptively driven genes underlying human evolutionary traits remains a key challenge in evolutionary genomics. Here we present DeepEvo, an interpretable Siamese neural network that predicts cross-species expression differences from orthologous sequences while pinpointing evolutionary regulatory variants. DeepEvo outperforms existing methods in cross-species modeling, with validation through documented annotations and single-base perturbation assays (MPRA/Perturb-seq). We discover that unlike population-level variations that predominantly disrupt existing regulatory motifs, evolutionary regulatory variants frequently enhance existing motifs over longer timescales. These variants are enriched in disease-associated regions, indicating their shared functions underlying human evolution and disease. Furthermore, analysis of their combinatorial cis-regulation revealed an ‘additive effect’ model. Within this framework, genes can maintain global stability through compensatory changes, while simultaneously achieving precise, cell-type-specific expression changes. This insight led to a ‘concerted drive’ strategy, prioritizing four adaptively driven genes that function across multiple systems, based on coordinated pushes from multiple evolutionary regulatory elements. As validation, we focused on PRKD2, upregulated in humans through concordant cis-regulatory changes. PRKD2-depleted rhesus macaques exhibited multi-system alterations—including reduced neuronal activity, decreased dendritic complexity, altered functional connectivity of brain, elevated insulin levels and lymphocyte counts—recapitulating key human-rhesus phenotypic differences. Our framework deciphers the cis-regulatory grammar of human transcriptome evolution and provides an effective strategy for identifying adaptively driven genes, generalizable to other cross-species comparisons.
DOI: 10.6084/m9.figshare.30581129.v7
title: DeepEvo

description: The identification of adaptively driven genes underlying human evolutionary traits remains a key challenge in evolutionary genomics. Here we present DeepEvo, an interpretable Siamese neural network that predicts cross-species expression differences from orthologous sequences while pinpointing evolutionary regulatory variants. DeepEvo outperforms existing methods in cross-species modeling, with validation through documented annotations and single-base perturbation assays (MPRA/Perturb-seq). We discover that unlike population-level variations that predominantly disrupt existing regulatory motifs, evolutionary regulatory variants frequently enhance existing motifs over longer timescales. These variants are enriched in disease-associated regions, indicating their shared functions underlying human evolution and disease. Furthermore, analysis of their combinatorial cis-regulation revealed an ‘additive effect’ model. Within this framework, genes can maintain global stability through compensatory changes, while simultaneously achieving precise, cell-type-specific expression changes. This insight led to a ‘concerted drive’ strategy, prioritizing four adaptively driven genes that function across multiple systems, based on coordinated pushes from multiple evolutionary regulatory elements. As validation, we focused on PRKD2, upregulated in humans through concordant cis-regulatory changes. PRKD2-depleted rhesus macaques exhibited multi-system alterations—including reduced neuronal activity, decreased dendritic complexity, altered functional connectivity of brain, elevated insulin levels and lymphocyte counts—recapitulating key human-rhesus phenotypic differences. Our framework deciphers the cis-regulatory grammar of human transcriptome evolution and provides an effective strategy for identifying adaptively driven genes, generalizable to other cross-species comparisons.
DOI: 10.5281/zenodo.18629366
title: Global Deposit Dollarization Panel Dataset (1980–2019)

description: A panel dataset of deposit dollarization ratios (Foreign Currency Deposits / Total Deposits) covering 128 countries from 1980 to 2019, with 2,646 observations. Data were collected from central bank publications, IMF Staff Reports, and CEIC.
DOI: 10.5281/zenodo.18629367
title: Global Deposit Dollarization Panel Dataset (1980–2019)

description: A panel dataset of deposit dollarization ratios (Foreign Currency Deposits / Total Deposits) covering 128 countries from 1980 to 2019, with 2,646 observations. Data were collected from central bank publications, IMF Staff Reports, and CEIC.
DOI: 10.5281/zenodo.18313842
title: VENI VINDy VICI Resource Data

description: Overview

This dataset contains preprocessed simulation data for two exemplary reduced-order modeling applications presented in the paper “VENI, VINDy, VICI: A generative reduced-order modeling framework with uncertainty quantification” (Neural Networks). Both datasets include high-dimensional state variables transformed via principal component analysis (PCA), their derivatives, and associated metadata for training and testing machine learning models.The data are suitable for applications in reduced-order modeling, system identification, surrogate modeling, and machine learning-based dynamics prediction. Both files are provided as NumPy binary arrays (`.npy` format) and can be loaded directly into Python-based scientific computing frameworks.

Datasets

1. Reaction-Diffusion (reaction_diffusion_data.npy)

Description: Simulation results from a two-dimensional reaction-diffusion system that generates spiral wave dynamics. 

Contents:  



x: state variables  (n_sims, n_timesteps, Nx, Ny, channels)

t: Time steps (n_timesteps)


2. MEMS Beam Resonator (mems.npy)

Description: Simulation results for a straight double-clamped MEMS (micro-electro-mechanical systems) beam resonator excited at resonance. The model describes the nonlinear dynamical response of a polysilicon beam device.

This dataset contains preprocessed and reduced-order representations of beam dynamics data. The data was generated and processed for use in reduced-order modeling, system identification, and machine learning applications. It includes principal component analysis (PCA)-transformed state variables, their derivatives, and reference coordinates. The dataset is suitable for tasks such as dynamic system analysis, surrogate modeling, and control design.  

Contents:  



x: PCA-transformed state variables (n_samples, n_pod)

dxdt: First derivatives of the PCA-transformed state variables (n_samples, n_pod)

dxddt: Second derivatives of the PCA-transformed state variables (n_samples, n_pod)

x_test, dxdt_test, dxddt_test: Test set equivalents of the above variables (n_samples_test, n_pod)

V: PCA components (n_features, n_pod).

ref_coords: Reference coordinates of the beam (n_nodes, 3) 

params, params_test: Simulation parameters for training and testing (n_samples, n_params), (n_samples_test, n_params)

t, t_test: Time steps for training and testing. (n_timesteps), (n_timesteps_test)

n_sims, n_timesteps: Number of simulations and timesteps in the dataset.


with n_samples=n_sims*n_timesteps, n_features=n_nodes*3.

Usage: The dataset is stored as a Python pickle file and can be loaded using the pickle module. It is compatible with Python-based machine learning frameworks such as PyTorch and TensorFlow.  

License: Please refer to the accompanying license file for terms of use.  

Citation: If you use this dataset in your research, please cite the associated publication.
DOI: 10.5281/zenodo.18313843
title: VENI VINDy VICI Resource Data

description: Overview

This dataset contains preprocessed simulation data for two exemplary reduced-order modeling applications presented in the paper “VENI, VINDy, VICI: A generative reduced-order modeling framework with uncertainty quantification” (Neural Networks). Both datasets include high-dimensional state variables transformed via principal component analysis (PCA), their derivatives, and associated metadata for training and testing machine learning models.The data are suitable for applications in reduced-order modeling, system identification, surrogate modeling, and machine learning-based dynamics prediction. Both files are provided as NumPy binary arrays (`.npy` format) and can be loaded directly into Python-based scientific computing frameworks.

Datasets

1. Reaction-Diffusion (reaction_diffusion_data.npy)

Description: Simulation results from a two-dimensional reaction-diffusion system that generates spiral wave dynamics. 

Contents:  



x: state variables  (n_sims, n_timesteps, Nx, Ny, channels)

t: Time steps (n_timesteps)


2. MEMS Beam Resonator (mems.npy)

Description: Simulation results for a straight double-clamped MEMS (micro-electro-mechanical systems) beam resonator excited at resonance. The model describes the nonlinear dynamical response of a polysilicon beam device.

This dataset contains preprocessed and reduced-order representations of beam dynamics data. The data was generated and processed for use in reduced-order modeling, system identification, and machine learning applications. It includes principal component analysis (PCA)-transformed state variables, their derivatives, and reference coordinates. The dataset is suitable for tasks such as dynamic system analysis, surrogate modeling, and control design.  

Contents:  



x: PCA-transformed state variables (n_samples, n_pod)

dxdt: First derivatives of the PCA-transformed state variables (n_samples, n_pod)

dxddt: Second derivatives of the PCA-transformed state variables (n_samples, n_pod)

x_test, dxdt_test, dxddt_test: Test set equivalents of the above variables (n_samples_test, n_pod)

V: PCA components (n_features, n_pod).

ref_coords: Reference coordinates of the beam (n_nodes, 3) 

params, params_test: Simulation parameters for training and testing (n_samples, n_params), (n_samples_test, n_params)

t, t_test: Time steps for training and testing. (n_timesteps), (n_timesteps_test)

n_sims, n_timesteps: Number of simulations and timesteps in the dataset.


with n_samples=n_sims*n_timesteps, n_features=n_nodes*3.

Usage: The dataset is stored as a Python pickle file and can be loaded using the pickle module. It is compatible with Python-based machine learning frameworks such as PyTorch and TensorFlow.  

License: Please refer to the accompanying license file for terms of use.  

Citation: If you use this dataset in your research, please cite the associated publication.
DOI: 10.57760/sciencedb.j00207.00058
title: Appendix of VarDim-Transformer: A Unified Framework for Variable-Dimension Time Series with Unknown Missing Patterns
title: Appendix of VarDim-Transformer: A Unified Framework for Variable-Dimension Time Series with Unknown Missing Patterns

description: This dataset contains the complete appendix of the paper "VarDim-Transformer: A Unified Framework for Variable-Dimension Time Series with Unknown Missing Patterns" (JSSC-2025-0546). It provides rigorous mathematical proofs (Theorem 4.2 on degenerate equivalence), engineering implementation details (Algorithm B.1 with PiRegistry lookups and soft gating), comprehensive complexity analysis, training hyperparameters, and extensive experimental results.The included materials support the reproducibility of the VarDim-Transformer model, including:Full derivation of the model’s reduction to standard Transformer under fixed-dimension conditions.Optimized pseudocode for dynamic projection and VarDim attention.Time complexity analysis and empirical training time comparison.Detailed statistical reports (pairwise p-values, Mann–Whitney U tests, seed-level performance).Supplementary figures and tables (ranking heatmaps, sensitivity analysis, top‑K score distributions).These data are essential for researchers wishing to understand, implement, or extend the VarDim-Transformer for variable-dimension time series forecasting with unknown missing patterns.
description: This dataset contains the complete appendix of the paper "VarDim-Transformer: A Unified Framework for Variable-Dimension Time Series with Unknown Missing Patterns" (JSSC-2025-0546). It provides rigorous mathematical proofs (Theorem 4.2 on degenerate equivalence), engineering implementation details (Algorithm B.1 with PiRegistry lookups and soft gating), comprehensive complexity analysis, training hyperparameters, and extensive experimental results.The included materials support the reproducibility of the VarDim-Transformer model, including:Full derivation of the model’s reduction to standard Transformer under fixed-dimension conditions.Optimized pseudocode for dynamic projection and VarDim attention.Time complexity analysis and empirical training time comparison.Detailed statistical reports (pairwise p-values, Mann–Whitney U tests, seed-level performance).Supplementary figures and tables (ranking heatmaps, sensitivity analysis, top‑K score distributions).These data are essential for researchers wishing to understand, implement, or extend the VarDim-Transformer for variable-dimension time series forecasting with unknown missing patterns.
DOI: 10.5281/zenodo.18493594
title: Dataset related to article "Validating Numerical Simulations to Support Experimental Testing of MRI Gradient-Induced Heating of Passive Implants"

description: Dataset related to simulations described in article:



Validating Numerical Simulations to Support Experimental Testing of MRI Gradient-Induced Heating of Passive Implants

Magnetic Resonance in Medicine

Digital Object Identifier: 10.1002/mrm.70306
DOI: 10.5281/zenodo.18493595
title: Dataset related to article "Validating Numerical Simulations to Support Experimental Testing of MRI Gradient-Induced Heating of Passive Implants"

description: Dataset related to simulations described in article:



Validating Numerical Simulations to Support Experimental Testing of MRI Gradient-Induced Heating of Passive Implants

Magnetic Resonance in Medicine

Digital Object Identifier: 10.1002/mrm.70306
DOI: 10.15468/dl.vzas6s
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Absconditella pauxilla",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014688-260208012135463/datasets/export for details.
DOI: 10.15468/dl.rdnc8x
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.29_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cymbidium elegans"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014689-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.auwy9y
title: Occurrence Download

description: A dataset containing 62231 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cyperus gymnocaulos, Cyperus hamulosus, Cyperus haspan, Cyperus hortensis, Cyperus hyalinus, Cyperus hystricoides, Cyperus imbricatus, Cyperus alternifolius flabelliformis, Cyperus iria, Cyperus javanicus, Cyperus laevigatus, Cyperus lanceolatus, Cyperus lentiginosus, Cyperus leptocarpus, Cyperus ligularis, Cyperus lipomexicanus, Cyperus longus, Cyperus longus badius, Cyperus longus longus, Cyperus lupulinus, Cyperus macrostachyos, Cyperus malaccensis, Cyperus malaccensis monophyllus, Cyperus melanospermus, Cyperus metzii, Cyperus meyenianus, Cyperus michelianus, Cyperus michelianus pygmaeus, Cyperus microbolbos, Cyperus microiria, Cyperus mindorensis, Cyperus mitis, Cyperus mundii, Cyperus neotropicalis, Cyperus nutans eleusinoides, Cyperus ochraceus, Cyperus odoratus, Cyperus odoratus odoratus, Cyperus echinatus, Cyperus owanii, Cyperus oxylepis, Cyperus papyrus, Cyperus papyrus madagascariensis, Cyperus pectinatus, Cyperus phaeolepis, Cyperus pilosus, Cyperus polystachyos, Cyperus polystachys, Cyperus procerus, Cyperus prolifer)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 },
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 62231 records from 852 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014644-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gr26v6
title: Occurrence Download

description: A dataset containing 27583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cyanthillium cinereum",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 27583 records from 390 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014687-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.me7f97
title: Occurrence Download

description: A dataset containing 251 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Monodon monoceros",
 "Year 2005-2026"
 ]
}

 The dataset includes 251 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014690-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18629389
title: LINF_330010800

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18629390
title: LINF_330010800

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.16317874
title: Data from street-level Automatic Weather Stations (AWS) in Freiburg from 2024-01-01 to 2024-12-31 [L1]

description: Converted and hamonized data files from a street-level automatic weather station (AWS) network in Freiburg im Breisgau, Germany, for the period 2024-01-01 to 2024-12-31. The street-level network provides localized weather data in urban context from 42 weather stations and resolves intra-urban and urban-rural differences across the City of Freiburg and adjacent municipalities. Details on the network are documented here:

https://doi.org/10.5281/zenodo.12732551

All stations provide data on air temperature, atmospheric humidiy and precipitation. Selected stations in addition provide data on solar irradiance, wind speed, wind direction, pressure, black globe temperature and lightning. The network was operated throughout the year 2024.

The production comprises [L1] computations and conversion to a common vocabulary, derived from [L0] conversion to a common data structure, in turn derived from [RAW] the original data.
description: COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project

LICENSE URI

Creative Commons Attribution 4.0 International License
DOI: 10.5281/zenodo.16317873
title: Data from street-level Automatic Weather Stations (AWS) in Freiburg from 2024-01-01 to 2024-12-31 [L1]

description: Converted and hamonized data files from a street-level automatic weather station (AWS) network in Freiburg im Breisgau, Germany, for the period 2024-01-01 to 2024-12-31. The street-level network provides localized weather data in urban context from 42 weather stations and resolves intra-urban and urban-rural differences across the City of Freiburg and adjacent municipalities. Details on the network are documented here:

https://doi.org/10.5281/zenodo.12732551

All stations provide data on air temperature, atmospheric humidiy and precipitation. Selected stations in addition provide data on solar irradiance, wind speed, wind direction, pressure, black globe temperature and lightning. The network was operated throughout the year 2024.

The production comprises [L1] computations and conversion to a common vocabulary, derived from [L0] conversion to a common data structure, in turn derived from [RAW] the original data.
description: COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project

LICENSE URI

Creative Commons Attribution 4.0 International License
DOI: 10.15468/dl.u272ad
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Absconditella delutula",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014692-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18629354
title: Dataset supporting the manuscript Thermo-humid aging standardizes Allium macrostemon extract and reveals CI-defined synergy with Opuntia ficus-indica mucilage against acetaminophen hepatotoxicity
DOI: 10.5281/zenodo.18629353
title: Dataset supporting the manuscript Thermo-humid aging standardizes Allium macrostemon extract and reveals CI-defined synergy with Opuntia ficus-indica mucilage against acetaminophen hepatotoxicity