cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18773714
title: DEVELOPMENT OF HORTICULTURE AND VITICULTURE SECTORS IN TASHKENT REGION

description:  This article provides a historical and analytical overview of the formation and development stages of horticulture and viticulture in the Tashkent region. The study examines the influence of the region’s natural and climatic conditions, irrigation systems, and agrarian policies on fruit and grape production. In particular, it analyzes the expansion of orchards and vineyards within the framework of the collective farm (kolkhoz) and state farm (sovkhoz) system during the Soviet period, the establishment of specialized agricultural enterprises, and the implementation of agrotechnical measures aimed at increasing productivity.
DOI: 10.5281/zenodo.18773726
title: Directions for Creating Civil Disorder in Government Media Coverage

description: The table was elaborated for the research project Civil Disorder in Pandemic-ridden European Union. The latter was financially supported by the National Science Centre, Poland [grant number 2021/43/B/HS5/00290].
DOI: 10.5281/zenodo.18773725
title: Directions for Creating Civil Disorder in Government Media Coverage

description: The table was elaborated for the research project Civil Disorder in Pandemic-ridden European Union. The latter was financially supported by the National Science Centre, Poland [grant number 2021/43/B/HS5/00290].
DOI: 10.5281/zenodo.18768337
title: Beyond Afforestation: Habitat heterogeneity rather than forest cover drive bird diversity in a flyway megacity

description: This repository contains the dataset and R scripts for the study: “Beyond Afforestation: Habitat heterogeneity rather than forest cover drive bird diversity in a flyway megacity”. The files provide the data and code necessary to replicate the statistical analyses and figures presented in the manuscript.
DOI: 10.5281/zenodo.18768336
title: Beyond Afforestation: Habitat heterogeneity rather than forest cover drive bird diversity in a flyway megacity

description: This repository contains the dataset and R scripts for the study: “Beyond Afforestation: Habitat heterogeneity rather than forest cover drive bird diversity in a flyway megacity”. The files provide the data and code necessary to replicate the statistical analyses and figures presented in the manuscript.
DOI: 10.5281/zenodo.18773759
title: AGING AND SELF-UNDERSTANDING: PSYCHOLOGICAL CHANGES AND ADAPTATION STRATEGIES

description: Aging is a complex and multidimensional process that affects cognitive, emotional, social, and self-perception domains. This paper explores the psychological changes that occur during aging and emphasizes the role of self-understanding in adapting to these changes. Key adaptation strategies are examined, including cognitive engagement, physical activity, social involvement, emotional regulation, and technological literacy. The integration of these strategies promotes resilience, autonomy, life satisfaction, and overall psychological well-being in older adults. The study underscores that aging is not merely a period of decline but an opportunity for continued growth, self-reflection, and meaningful participation in life.
DOI: 10.5281/zenodo.18773758
title: AGING AND SELF-UNDERSTANDING: PSYCHOLOGICAL CHANGES AND ADAPTATION STRATEGIES

description: Aging is a complex and multidimensional process that affects cognitive, emotional, social, and self-perception domains. This paper explores the psychological changes that occur during aging and emphasizes the role of self-understanding in adapting to these changes. Key adaptation strategies are examined, including cognitive engagement, physical activity, social involvement, emotional regulation, and technological literacy. The integration of these strategies promotes resilience, autonomy, life satisfaction, and overall psychological well-being in older adults. The study underscores that aging is not merely a period of decline but an opportunity for continued growth, self-reflection, and meaningful participation in life.
DOI: 10.5281/zenodo.18773733
title: COMPLEX INFLUENCE OF ABIOTIC FACTORS ON ALGAE BLOOMS IN NATURAL WATER BODYS

description: Currently, providing the world's population with ecologically clean food products is becoming an important issue. Therefore, it is important to study the complex influence of abiotic factors in water during the reproduction of algae species used as food in water bodies. Because abiotic factors are factors that determine the life of living organisms in water, metabolic processes occurring in them.
DOI: 10.5281/zenodo.18773732
title: COMPLEX INFLUENCE OF ABIOTIC FACTORS ON ALGAE BLOOMS IN NATURAL WATER BODYS

description: Currently, providing the world's population with ecologically clean food products is becoming an important issue. Therefore, it is important to study the complex influence of abiotic factors in water during the reproduction of algae species used as food in water bodies. Because abiotic factors are factors that determine the life of living organisms in water, metabolic processes occurring in them.
DOI: 10.15468/dl.xzr7ku
title: Occurrence Download

description: A dataset containing 930 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Sphallerocarpus"
 ]
}

 The dataset includes 930 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000259-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18773752
title: gekko_gecko_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.18773753
title: gekko_gecko_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.6efmbk
title: Occurrence Download

description: A dataset listing the 2771 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is one of (North America, South America)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Sphingidae, Trochilidae)",
 "Year 1925-2025"
 ]
}

 The dataset's 2771 records were derived from 1338 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000217-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18612237
title: MD trajectories of KempTIM designs

description: Transition state analogue-bound and -unbound simulations of KempTIM enzymes. Ten replicate 100 ns simulations were run and combined into a single trajectory (.dcd) file.
DOI: 10.5281/zenodo.18773436
title: MD trajectories of KempTIM designs

description: Transition state analogue-bound and -unbound simulations of KempTIM enzymes. Ten replicate 100 ns simulations were run and combined into a single trajectory (.dcd) file.
DOI: 10.15468/dl.psyafh
title: Occurrence Download

description: A dataset containing 308 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "GadmGid is one of (ESP.2_1, ESP.2.2_1, ESP.2.3_1, ESP.2.1_1)",
 "Geometry POLYGON((-2.07113 41.09753,-1.9758 41.10573,-1.84576 41.05046,-1.69342 40.93173,-1.68315 40.85054,-1.61312 40.79567,-1.61629 40.64257,-1.69906 40.64353,-1.74686 40.61703,-1.76051 40.51808,-1.86824 40.4291,-1.85767 40.35693,-1.78768 40.32018,-1.7888 40.25713,-1.75403 40.22973,-1.68352 40.22895,-1.55617 40.13734,-1.50903 40.14575,-1.50962 40.11873,-1.46331 40.0911,-1.29914 40.08881,-1.19434 40.06019,-1.23043 40.02471,-1.23159 39.97969,-1.19765 39.93412,-1.16304 39.91556,-0.97568 39.92144,-0.97779 39.84941,-0.92074 39.80336,-0.85098 39.79307,-0.75615 39.83632,-0.73071 39.89889,-0.77601 39.94481,-0.69232 39.99723,-0.57432 40.02184,-0.49814 40.18237,-0.3321 40.2237,-0.28154 40.3126,-0.22165 40.33819,-0.21869 40.41018,-0.2415 40.42874,-0.20506 40.45489,-0.20319 40.49988,-0.23559 40.57272,-0.23262 40.64471,-0.1728 40.66127,-0.17051 40.71526,0.06725 40.64062,0.10152 40.66851,0.20749 40.68013,0.34469 40.80046,0.31701 40.8989,0.3623 40.95399,0.36086 40.98998,0.34682 41.0437,0.28555 41.08731,0.44717 41.2171,0.4563 41.28935,0.43138 41.31582,0.4412 41.37008,0.4166 41.38754,0.41445 41.44153,0.46159 41.4606,0.51865 41.53389,0.49087 41.63236,0.41819 41.64876,0.40511 41.67548,0.45169 41.71255,0.49973 41.71361,0.53369 41.76838,0.59346 41.77865,0.67502 41.85238,0.67175 41.94238,0.73042 41.98858,0.78511 42.15175,0.77175 42.18752,0.83945 42.3329,0.78587 42.47597,0.82078 42.52166,0.82889 42.63889,0.67862 42.744,0.32542 42.71828,0.32349 42.76326,0.23723 42.77922,0.06673 42.76597,0.0558 42.73868,0 42.71922,0 42.6922,-0.04573 42.69339,-0.15176 42.77712,-0.15052 42.80411,-0.19979 42.79632,-0.2711 42.84306,-0.35667 42.84507,-0.38264 42.80965,-0.43189 42.80176,-0.51633 42.83064,-0.52999 42.79492,-0.55442 42.79544,-0.66099 42.88772,-0.72217 42.88895,-0.71985 42.95194,-0.74369 42.97042,-0.86716 42.94577,-0.91858 42.87468,-0.9207 42.81169,-0.99607 42.75,-1.05714 42.75105,-1.10763 42.69786,-1.18084 42.69905,-1.24338 42.64599,-1.24414 42.61899,-1.30557 42.6019,-1.35573 42.5486,-1.35738 42.48559,-1.40695 42.45027,-1.42159 42.35141,-1.45866 42.32489,-1.4602 42.26187,-1.48487 42.24419,-1.46413 42.09982,-1.40503 42.04497,-1.44303 41.97344,-1.55182 41.96586,-1.61117 42.01163,-1.69568 42.01262,-1.71929 42.0399,-1.81559 42.05895,-1.87601 42.05956,-1.91272 42.03289,-1.92722 41.88892,-1.89184 41.84355,-1.91729 41.76274,-1.86966 41.73526,-1.87013 41.70824,-1.95513 41.655,-2.05146 41.63782,-2.06451 41.56587,-2.04079 41.54766,-2.04172 41.48462,-2.13742 41.49439,-2.23484 41.35996,-2.21331 41.16165,-2.07113 41.09753),(-0.73855 40.01615,-0.7387 40.01167,-0.73853 40.01678,-0.73855 40.01615))"
 ]
 },
 "EventDate 2026-01-26-2026-02-25"
 ]
}

 The dataset includes 308 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000216-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18773788
title: EXPERIENCE OF INTENSIVE DEVELOPMENT AND MECHANIZATION OF COTTON PRODUCTION IN THE ANDIJAN REGION IN THE PERIOD AFTER THE SECOND WORLD WAR

description: The "Savay" state farm, as we have already said, for a long time in the post-war years could not overcome the backwardness of production indicators. True, a continuous expansion of cotton fields was achieved - it exceeded 4000 hectares. However, state plans were not fulfilled, both in terms of productivity and in terms of product cost, except for 1955.

After the 20th Party Congress, state material and technical assistance to backward farms, including backward state farms, was strengthened. The main fund of the "Savay" state farm also increased significantly.
DOI: 10.5281/zenodo.18773787
title: EXPERIENCE OF INTENSIVE DEVELOPMENT AND MECHANIZATION OF COTTON PRODUCTION IN THE ANDIJAN REGION IN THE PERIOD AFTER THE SECOND WORLD WAR

description: The "Savay" state farm, as we have already said, for a long time in the post-war years could not overcome the backwardness of production indicators. True, a continuous expansion of cotton fields was achieved - it exceeded 4000 hectares. However, state plans were not fulfilled, both in terms of productivity and in terms of product cost, except for 1955.

After the 20th Party Congress, state material and technical assistance to backward farms, including backward state farms, was strengthened. The main fund of the "Savay" state farm also increased significantly.
DOI: 10.11588/duerer.online.699f17ce1a2fc
title: Zwei livländische Frauen, https://d-nb.info/gnd/1199021210
DOI: 10.6084/m9.figshare.31411378
title: A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems

description: Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can be computationally prohibitive. To address this, we present a novel physics-informed spatiotemporal surrogate model for Rayleigh-Bénard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks, for spatial dimension reduction, with an innovative recurrent architecture, inspired by large language models, to model long-range temporal dynamics. Inference is penalized with respect to the governing partial differential equations to ensure physical interpretability. Since RBC exhibits turbulent behavior, we quantify uncertainty using a conformal prediction framework. This model replicates key <i>physical</i> features of RBC dynamics while significantly reducing computational cost, offering a scalable alternative to DNS for long-term simulations.
DOI: 10.6084/m9.figshare.31411378.v1
title: A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems

description: Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can be computationally prohibitive. To address this, we present a novel physics-informed spatiotemporal surrogate model for Rayleigh-Bénard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks, for spatial dimension reduction, with an innovative recurrent architecture, inspired by large language models, to model long-range temporal dynamics. Inference is penalized with respect to the governing partial differential equations to ensure physical interpretability. Since RBC exhibits turbulent behavior, we quantify uncertainty using a conformal prediction framework. This model replicates key <i>physical</i> features of RBC dynamics while significantly reducing computational cost, offering a scalable alternative to DNS for long-term simulations.
DOI: 10.5281/zenodo.14720508
title: MORE-QX, an extended dataset version for "MORE-Q, Dataset for molecular olfactorial receptor engineering by quantum mechanics"

description: We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23, 838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1, 836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices.

The original MORE-Q dataset is provided in 3 HDF5 based files. One can refer to the manuscript: https://doi.org/10.1038/s41597-025-04616-6

######### Splitting line for MORE-QX, an extended version of MORE-Q ###########

To approximate the thermodynamic ensemble and the conformer effects, we expanded the MORE-Q dataset into MORE-QX by sampling multiple low-energy BOV–receptor dimer (DM) conformers adsorbed on graphene. This process increased the number of BOV–receptor–graphene complexes from 1,836 to 10,441, leading to exMORE-G-Q1, exMORE-G-Q2, and exMORE-G-Q3 subsets. 

The only difference in the HDF5 structure between the MORE-Q and MORE-QX subsets is that we added 'DM' keys after the 'REC' and 'OM' keys as indicators for the low-energy conformers taken from the dimer subset exMORE-G-Q2. 

Usage: python ex_createDF.py -ds all

 

 
description: @article{chen2026interpretable,
  title={Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials},
  author={Chen, Li and Sandonas, Leonardo Medrano and Huang, Shirong and Croy, Alexander and Cuniberti, Gianaurelio},
  journal={arXiv preprint arXiv:2601.00503},
  year={2026}
DOI:




https://doi.org/10.48550/arXiv.2601.00503




Github:

https://github.com/LiC1117/MORE-Q
DOI: 10.5281/zenodo.13741196
title: MORE-QX, an extended dataset version for "MORE-Q, Dataset for molecular olfactorial receptor engineering by quantum mechanics"

description: We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23, 838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1, 836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices.

The original MORE-Q dataset is provided in 3 HDF5 based files. One can refer to the manuscript: https://doi.org/10.1038/s41597-025-04616-6

######### Splitting line for MORE-QX, an extended version of MORE-Q ###########

To approximate the thermodynamic ensemble and the conformer effects, we expanded the MORE-Q dataset into MORE-QX by sampling multiple low-energy BOV–receptor dimer (DM) conformers adsorbed on graphene. This process increased the number of BOV–receptor–graphene complexes from 1,836 to 10,441, leading to exMORE-G-Q1, exMORE-G-Q2, and exMORE-G-Q3 subsets. 

The only difference in the HDF5 structure between the MORE-Q and MORE-QX subsets is that we added 'DM' keys after the 'REC' and 'OM' keys as indicators for the low-energy conformers taken from the dimer subset exMORE-G-Q2. 

Usage: python ex_createDF.py -ds all

 

 
description: @article{chen2026interpretable,
  title={Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials},
  author={Chen, Li and Sandonas, Leonardo Medrano and Huang, Shirong and Croy, Alexander and Cuniberti, Gianaurelio},
  journal={arXiv preprint arXiv:2601.00503},
  year={2026}
DOI:




https://doi.org/10.48550/arXiv.2601.00503




Github:

https://github.com/LiC1117/MORE-Q
DOI: 10.5281/zenodo.18773804
title: glaucilla_marginata_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.18773803
title: glaucilla_marginata_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.6rx9cn
title: Occurrence Download

description: A dataset containing 82859 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Patagioenas fasciata",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2026",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 82859 records from 194 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000212-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18773512
title: Tropical Pacific SST anomalies generated by a Non-Gaussian Linear Inverse Model (NG-LIM)

description: Tropical Pacific (20S–20N; 120E–50W) sea surface temperature anomalies (ssta) generated by a Non-Gaussian Linear Inverse Model (Martinez-Villalobos et al., 2025; https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025GL118649).

The files are provided in two bundles:

1. Observational bundle (ERSSTv5-based):





Tropical_Pacific_SSTA_ERSSTv5.nc. Sea surface temperature anomalies calculated from the NOAA Extended Reconstruction SST v5 reanalysis (1948–2022) (Huang et al., 2017).




Tropical_Pacific_SSTA_ERSSTv5_10PCs.nc. Same as the previous dataset but filtered to retain only the first 10 SSTA Principal Components and Empirical Orthogonal Functions, accounting for ~90% of the SSTA variance.




Tropical_Pacific_SSTA_LIM.nc. 10,000-yr integration of a standard LIM (Penland and Sardeshmukh, 1995) trained on dataset 2.




Tropical_Pacific_SSTA_NGLIM.nc. 10,000-yr integration of a Non-Gaussian LIM (Martinez-Villalobos et al., 2025) trained on dataset 2.



2. CMIP6 model emulations ([Model name].zip):One .zip file for each of 30 CMIP6 historical models. Inside each .zip file, four files are provided in direct analogy to the observational bundle above:





Tropical_Pacific_SSTA_[MODEL].nc. Historical SST anomalies from the given CMIP6 model.




Tropical_Pacific_SSTA_[MODEL]_10PCs.nc. Same anomalies, reduced to the first 10 PCs/EOFs (variance explained depends on the model, but on average around 75%).




Tropical_Pacific_SSTA_[MODEL]_LIM.nc. 10,000-yr LIM integration trained on the 10-PC dataset.




Tropical_Pacific_SSTA_[MODEL]_NGLIM.nc. 10,000-yr NG-LIM integration trained on the 10-PC dataset.



In addition, the basic code used to calculate and integrate an NG-LIM (and a standard LIM) is provided (NG-LIM_Basic_Code.ipynb), together with full field SST data that can be used to test the code (SST from the NOAA Extended Reconstruction SST v5 reanalysis, regridded bilinearly to a 2.5x2.5 horizontal resolution)

When using the data please cite https://doi.org/10.5281/zenodo.14775714 (the data) and Martinez-Villalobos et al., 2025 (for the methodology).

Any question, please contact Cristian Martinez-Villalobos at cristian.martinez.v@uai.cl.

References





Huang, B. et al. (2017). Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, 30, 8179–8205.




Penland, C., & Sardeshmukh, P. D. (1995). The Optimal Growth of Tropical Sea Surface Temperature Anomalies. Journal of Climate, 8, 1999–2024.




Martinez-Villalobos, C., Capotondi, A., Deser, C., Dewitte, B., Holbrook, N. J., Newman, M., et al. (2025). A low-order data-driven model of ENSO diversity. Geophysical Research Letters, 52, e2025GL118649. https://doi.org/10.1029/2025GL118649