cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.hmgqnk9nq
title: Data for: Phenotypic outcomes of predator-prey coevolution are predicted by landscape variation in climate and community composition

description: Landscape patterns of phenotypic coevolution are determined by variation
 in the outcome of predator-prey interactions. These outcomes may depend
 not only on the functional phenotypes that mediate species interactions
 but also on aspects of the environment that enable encounters between
 coevolutionary partners. Exploring the relationship between coevolutionary
 traits and the environment requires extensive sampling across the range of
 the interaction to determine the relationship between local ecological
 variation and coevolution. In this study, we synthesized &gt;30 years
 of data on predator-prey interactions between toxic newts (Taricha
 granulosa) and their snake predators (Thamnophis sirtalis) to explore the
 environmental predictors of arms race escalation. We found that geographic
 variation in phenotypes at the interface of coevolution was best predicted
 by a combination of community and climatic variation. Coevolutionary
 phenotypes were greatest in environments with climate favorable for
 newt-snake overlap. We found prey toxicity was elevated in regions with
 more predator species, and predator resistance was higher in regions with
 more prey species. Our results suggest specific environmental conditions
 reinforce the process of coevolution, signifying the phenotypic outcomes
 of coevolutionary arms races are sensitive to local ecological contexts
 that vary across the landscape. 
description: Data were synthesized from co-authors into a single dataset. Data
 were collected between 1985–2017 on the coevolutionary interaction between
 toxic newts and their snake predators. Data were synthesized in FileMaker
 prior to validation checks and analysis.
DOI: 10.25345/c5610w573
title: MassIVE MSV000100614 - Concurrent Chronic-Plus-Binge Alcohol and Nicotine Vaping Alter Cardiac Ventricular Proteome
title: Label-free LC MS/MS proteomics data from left ventricular free wall tissue of male C57BL/6J mice exposed to alcohol, nicotine vaping, combined exposure, or control conditions. Samples were analyzed using an Evosep One LC system coupled to a CaptiveSpray source on a timsTOF Flex MALDI-2 mass spectrometer (Bruker).
DOI: 10.5061/dryad.ngf1vhj05
title: Complex relationships between climate and reproduction in a resident montane bird

description: Animals use climate-related environmental cues to fine-tune breeding
 timing and investment to match peak food availability. In birds, spring
 temperature is a commonly documented cue used to initiate breeding, but
 with global climate change, organisms are experiencing both directional
 changes in ambient temperatures and extreme year-to-year precipitation
 fluctuations. Montane environments exhibit complex climate patterns where
 temperatures and precipitation change along elevational gradients, and
 where exacerbated annual variation in precipitation has resulted in
 extreme swings between heavy snow and drought. We used 10 years of data to
 investigate how annual variation in climatic conditions is associated with
 differences in breeding phenology and reproductive performance in resident
 mountain chickadees (Poecile gambeli) at two elevations in the northern
 Sierra Nevada mountains, USA. Variation in spring temperature was not
 associated with differences in breeding phenology across elevations in our
 system. Greater snow accumulation was associated with later breeding
 initiation at high, but not low, elevation. Brood size was reduced under
 drought, but only at low elevation. Our data suggest complex relationships
 between climate and avian reproduction and point to fall climate as
 important for reproductive performance, likely via its effect on phenology
 and abundance of invertebrates.
DOI: 10.5068/d19q37
title: Synthetic and reticulated foam solid and velocity data used to train and validate CNN models

description: Data-driven deep learning models are emerging as a new method to predict
 the flow and transport through porous media with very little
 computational power required. Previous deep learning models,
 however, experience difficulty or require additional computations
 to predict the 3D velocity field which is essential to characterize
 porous media at the pore-scale. We design a deep learning model
 and incorporate a physicsinformed loss function to relate the
 spatial information of the 3D binary image to the 3D velocity
 field of porous media. We demonstrate that our model, trained only
 with synthetic porous media as binary data without additional
 image processing, can predict the 3D velocity field of real
 reticulated foams which have microstructures different
 from porous media that are studied in previous works. We also
 show that our loss function enforces the law of mass conservation
 in incompressible flows. Our study provides deep learning
 framework for predicting the velocity field of porous media
 and conducting subsequent transport analysis for various
 engineering applications. As an example, we conduct heat transfer analysis
 using the predicted velocity fields and demonstrate the accuracy
 and advantage of our deep learning model.
DOI: 10.5068/d16108
title: Periodic and heterogeneous solid and velocity data used to train and validate CNN models

description: Data-driven deep learning models are emerging as a promising method for
 characterizing pore-scale flow through complex porous media while
 requiring minimal computational power. However, previous models often
 require extensive computation to simulate flow through synthetic porous
 media for use as training data. We propose a convolutional neural network
 trained solely on periodic unit cells to predict pore-scale velocity
 fields of complex heterogeneous porous media from binary images without
 the need for further image processing. Our model is trained using a range
 of simple and complex unit cells that can be obtained analytically or
 numerically at a low computational cost. Our results show that the model
 accurately predicts the permeability and pore-scale flow characteristics
 of synthetic porous media and real reticulated foams. We significantly
 improve the convergence of numerical simulations by using the predictions
 from our model as initial guesses. Our approach addresses the limitations
 of previous models and improves computational efficiency, enabling the
 rigorous characterization of large batches of complex heterogeneous porous
 media for a variety of engineering applications.
DOI: 10.5061/dryad.8w9ghx3rx
title: High-resolution 4DVAR-based Gulf Stream data-assimilative model product

description: This dataset contains results from a Regional Ocean Modeling System (ROMS)
 that covers the northwestern Atlantic Ocean, including the U.S. East
 Coast, Caribbean, and Gulf of Mexico. The model uses a horizontal grid
 spacing of 7 kilometers (1/15 degree) and has 36 vertical layers. It
 applies a 4D variational data assimilation algorithm with a strong
 constraint, assimilating satellite-based sea surface height and sea
 surface temperature measurements as well as in situ temperature and
 salinity profiles. The dataset contains model output for the two-year
 study period from 2017 to 2018, including sea surface height, ocean
 current components (u and v), temperature, and salinity.
description: For details on model construction, validation, and data analyses,
 please refer to Mao et al. (Deep Sea Research, in revision).
DOI: 10.7280/d1569b
title: Source data: Native American ataxia medicines rescue ataxia-linked mutant potassium channel activity via binding to the voltage sensing domain

description: There are currently no drugs known to rescue the function of Kv1.1
 voltage-gated potassium channels carrying loss-of-function sequence
 variants underlying the inherited movement disorder, Episodic Ataxia 1
 (EA1). The Kwakwaka'wakw First Nations of the Pacific
 Northwest Coast used Fucus gardneri (bladderwrack kelp), Physocarpus
 capitatus (Pacific ninebark) and Urtica dioica (common nettle) to
 treat locomotor ataxia. Here, extracts of these plants enhanced wild-type
 Kv1.1 current, especially at subthreshold potentials. Screening of their
 constituents revealed that gallic acid and tannic acid similarly augmented
 wild-type Kv1.1 current, with submicromolar potency. Crucially, the
 extracts and their constituents also enhanced activity of Kv1.1 channels
 containing EA1-linked sequence variants. Molecular dynamics simulations
 revealed that gallic acid augments Kv1.1 activity via a previously
 unreported small-molecule binding site in the extracellular S1-S2 linker.
 Thus, traditional Native American ataxia treatments utilize a molecular
 mechanistic foundation that can inform small-molecule approaches to
 therapeutically correcting EA1 and potentially other Kv1.1-linked
 channelopathies.
description: # Source data: Native American ataxia medicines rescue ataxia-linked
 mutant potassium channel activity via binding to the voltage sensing
 domain The Excel files and molecular dynamic simulation files used for
 Figures 1 - 10 have been included as part of this repository. ###
 Description of Data Excel files are organised into:
 Figure&gt;channel&gt;drug/extract&gt;analysis(e.g. Gmax, IV). Each Excel
 file is divided into individual tabs for control and concentration of
 compound/extract. For molecular dynamic simulations, each file includes:
 system_name.psf : topology information and forcefield atom types
 system_name_initial.pdb: initial configuration coordinates
 system_name_final.pdb: final configuration coordinates where system_name
 is one of the following: Simulation System system_name Kv1.1 VSD
 kv11_popc_waters_ions Kv1.1 VSD with gallic acid
 kv11_gala_popc_waters_ions Kv1.1-3M kv11_3M_popc_waters_ions Kv1.1-3M with
 gallic acid kv11_3M_2_gala_popc_waters_ions
DOI: 10.5061/dryad.w0vt4b8x0
title: Connectedness and spillover effect between cryptocurrency and financial assets

description: Cryptocurrencies have quickly become one type of important financial
 asset. Accordingly, it is important to understand the interaction between
 cryptocurrency and other financial asset markets. However, previous
 literature paid less attention to the correlation between the price trend
 of cryptocurrencies and other financial assets. Using the vector
 autoregression model, we analyzed price correlation and spillover effect
 between cryptocurrencies and financial assets between November 2017 and
 February 2022. The study concludes that stock price has a spillover effect
 on cryptocurrencies, government bonds, and precious metals. The research
 results are useful while allocating portfolios or hedge strategies that
 include cryptocurrencies and financial assets such as stocks, government
 bonds, and precious metals.
description: The study downloads cryptocurrencies and other financial assets
 data through the price information website investing.com. We wrote a
 letter to the source website to inquire whether it is possible to use the
 data for analysis and writing a paper, and received a positive
 response. We collect price data of the daily
 transaction. The data period is from November 2017 to February 2022.
 Cryptocurrencies are traded 24 hours a day. However, stock, bonds, and
 other financial assets are traded only on business days. We exclude the
 transaction data of the stock market close day. As a result, we obtain
 1087 price data of cryptocurrency and other assets. The return rate is
 calculated by taking the price to one order difference. Volatility is
 calculated in the same way as studied by Diebold and Yilmaz (2012),
 estimated by subtracting the maximum and minimum prices of the
 day.
DOI: 10.5061/dryad.bg79cnpfs
title: Dataset for: Immediate and carry-over effects of late-spring frost and growing season drought on forest gross primary productivity capacity in the Northern Hemisphere

description: Forests are increasingly exposed to extreme global warming-induced
 climatic events. However, the immediate and carry-over effects of extreme
 events on forests are still poorly understood. Gross primary productivity
 (GPP) capacity is regarded as a good proxy of the ecosystem’s functional
 stability, reflecting its physiological response to its surroundings.
 Using eddy covariance data from 34 forest sites in the Northern
 Hemisphere, we analyzed the immediate and carry-over effects of
 late-spring frost (LSF) and growing season drought on needle-leaf and
 broadleaf forests. Path analysis was applied to reveal the plausible
 reasons behind the varied responses of forests to extreme events. The
 results show that LSF had clear immediate effects on the GPP capacity of
 both needle-leaf and broadleaf forests. However, GPP capacity in
 needle-leaf forests was more sensitive to drought than in broadleaf
 forests. There was no interaction between LSF and drought in either
 needle-leaf or broadleaf forests. Drought effects were still visible when
 LSF and drought coexisted in needle-leaf forests. Path analysis further
 showed that the response of GPP capacity to drought differed between
 needle-leaf and broadleaf forests, mainly due to the difference in the
 sensitivity of canopy conductance. Moreover, LSF had a more severe and
 long-lasting carry-over effect on forests than drought. These results
 enrich our understanding of the mechanisms of forest response to extreme
 events across forest types.
DOI: 10.5061/dryad.m0cfxpp87
title: Data from: Direct evidence of permeability evolution of Nanan granite after two cooling paths under different confining pressures: A comparative study

description: Permeability decides the mass transfer behavior and plays a key role in
 the heat extraction of enhanced geothermal systems (EGSs), and the aim of
 the injection of cooling water is to enhance permeability within the
 geothermal reservoirs. Direct evidence of how cool water affecting
 permeability evolution is still limited. Therefore, we explored the
 permeability evolution of Nanan granite after air and water cooling under
 different confining pressures combined with optical microscopy and X-ray
 CT observation. We found that there are lots of microdefects observed in
 Nanan granite after two cooling paths, which dominantly drives the
 evolution of permeability from a microscopic scale. The permeabilities of
 granite under water-cooling condition are always larger than those under
 air-cooling condition, and water-cooling treatment further enhances the
 permeability of specimens. The permeabilities of granite specimens after
 two cooling paths decrease with confining stress. More microcracks and
 better connectivity among microcracks produce a larger permeability within
 the specimen after water cooling. The observed microcracks are regarded as
 the seepage channels and direct microscale evidence of permeability
 evolution of granite after two cooling paths. Our results may suggest that
 water injection is an efficient way for permeability enhancement due to
 thermal microcracks propagation in thermal simulation.
description: A micro-CT instrument
 with an X-ray energy of
 150 kV/60 mA (nano
 voxel-3000) was used to
 scan the small cylindrical specimens (F25×50 mm) after air and water cooling to
 increase the observed resolution.
DOI: 10.5061/dryad.qnk98sfn7
title: Grazing in a megagrazer-dominated savanna does not reduce soil carbon stocks, even at high intensities

description: Recent studies suggest that wild animals can promote ecosystem carbon
 sinks through their impacts on vegetation and soils. However, livestock
 studies show that intense levels of grazing reduce soil organic carbon
 (SOC), leading to concerns that rewilding with large grazers may
 compromise ecosystem carbon storage. Furthermore, wild grazers can both
 limit and promote woody plant recruitment and survival on savanna
 grasslands, with both positive and negative impacts on SOC, depending on
 the rainfall and soil texture contexts. We used grazing lawns in one of
 the few African protected savannas that are still dominated by megagrazers
 (&gt;1000 kg), namely white rhinoceros (Ceratotherium simum), as a
 model to study the impact of prolonged and intense wild grazing on SOC
 stocks. We contrasted SOC stocks between patches of varying grazing
 intensity and woody plant encroachment in sites across different rhino
 habitat types. We found no differences in SOC stocks between the most- and
 least-grazed plots in any of the habitats. Intermediately grazed plots,
 however, had higher SOC stocks in the top 5 cm compared to most and least
 grazed plots, but only in the closed-canopy woodland habitat and not in
 the open habitats. Importantly, we found no evidence to support the
 hypothesis that wild grazing reduces SOC, even at high grazing intensities
 by the world’s largest megagrazer. Compared to the non-encroached
 reference plots, woody encroached plots had higher SOC stocks in soils
 with low clay content and lower SOC stocks in soils with high clay
 content, although only in the top 5 cm. Accordingly, our study highlights
 that wild grazers may influence SOC indirectly through their impact on
 tree-grass ratios in grassy ecosystems. Our study thus provides important
 insights for future nature-based climate solutions that focus on wild
 grazer conservation and restoration.
description: This dataset consists of two documents: 1)
 data_hyvarinen_et.al_oikos.csv consists of soil carbon and other
 attributes data from all sampling plots. 2)
 grasscommunity_data.csv consists of the grass species composition data
 from all sampling plots.
DOI: 10.5061/dryad.pk0p2ngt1
title: Data from: Comparative genetic and demographic responses to climate change in three peatland butterflies in the Jura massif

description: Climate is a main driver of species distributions, but all species are not
 equally affected by climate change, and the differential responses of
 species to similar climatic constraints might dramatically affect the
 local species composition. In the context of climate warming, a better
 knowledge of the ability of dispersal-limited and habitat specialist
 species to track climate change at local scale is urgently needed.
 Comparing the population genetic and demographic impacts of past climate
 cycles in multiple co-distributed species with similar ecological
 requirements can help predicting their future response to climate warming,
 but such comparative studies remain rare. Here, we studied the
 relationship between population demographic history and past changes in
 spatial distribution of three protected peatbog butterfly species (Boloria
 aquilonaris, Coenonympha tullia, Lycaena helle) in the Jura massif
 (France), using a genomic approach (ddRAD sequencing) and species
 distribution modeling (SDM). We found that the three species have a
 similar and narrow thermal niche, and share a common demographic history
 of recent post-glacial decline and fragmentation of populations. Each
 species functions as a single metapopulation more or less structured at
 the regional scale, with a North-South gradient of decreasing genetic
 diversity that fits the local dynamics of the ice cover overtime. However,
 we found no correlation between changes in the quantity or the quality of
 suitable areas and effective population size over time, suggesting that
 species ranges moved beyond the Jura massif during the less favorable
 climatic periods, and that other factors than climate are at play in the
 dramatic decline currently observed in the three species.
description: # Data from: Comparative genetic and demographic responses to climate
 change in three peatland butterflies in the Jura massif Three VCF files
 containing SNP genotypes of sampled (1) *Boloria aquilonaris*, (2)
 *Coenonympha tullia* and, (3) *Lycaena helle* individuals in
 Franche-Comté, France. Snps genotypes were obtained through a ddRAD
 sequencing procedure and the STACKS de novo pipeline. ## Description of
 the data and file structure INFO columns of the VCF files are blank. Data
 were filtered as described in the article. ## Sharing/Access information
 In accordance with the French regulation regarding protected species
 (article L.124-4 of the environment code), sample names do not give any
 clue on their precise localization. More precision are available from
 authors upon request.
DOI: 10.5281/zenodo.18382443
title: Numerical Analysis for RMS Vacuum Phase Transition: MCMC Chains and Power Spectrum Codes

description: This repository contains the Python scripts (Google Colab) and resulting matter power spectrum data used to resolve the $H_0$ and $S_8$ tensions via a geometric phase transition.
DOI: 10.5061/dryad.pzgmsbcrt
title: Data for: The start of frozen dates over northern permafrost regions with the changing climate

description: The soil freeze-thaw cycle in the permafrost regions has a significant
 impact on regional surface energy and water balance. Although increasing
 efforts have been made to understand the responses of spring thawing to
 climate change, the mechanisms controlling the global interannual
 variability of the start date of permafrost frozen (SOF) remain unclear.
 Using long-term SOF from the combinations of multiple satellite microwave
 sensors between 1979–2020, and analytical techniques, including partial
 correlation, ridge regression, path analysis, and machine learning, we
 explored the responses of SOF to multiple climate change factors,
 including warming (surface and air temperature), start date of permafrost
 thawing (SOT), soil properties (soil temperature and volume of water), and
 the snow depth water equivalent (SDWE). Overall, climate warming exhibited
 the maximum control on SOF, but SOT in spring was also an important driver
 of SOF variability; among the 65.9% significant SOT and SOF correlations,
 79.3% were positive, indicating an overall earlier thawing would
 contribute to an earlier frozen in winter. The machine learning analysis
 also suggested that apart from warming, SOT ranked as the second most
 important determinant of SOF. Therefore, we identified the mechanism
 responsible for the SOT-SOF relationship using the SEM analysis, which
 revealed that soil temperature change exhibited the maximum effect on this
 relationship, irrespective of the permafrost type. Finally, we analyzed
 the temporal changes in these responses using the moving window approach
 and found an increased effect of soil warming on SOF. Therefore, these
 results provide important insights into understanding and predicting SOF
 variations with future climate change.