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7,920 results for “implications”
Supplementary videos for "A second fossil species of the enigmatic rove beetle genus Charhyphus in Eocene Baltic amber, with implications on the morphology of the female genitalia (Coleoptera: Staphylinidae: Phloeocharinae)"
<p><strong>Original figures used in this study:</strong></p> <p>The holotype of <em>Charhyphus serratus </em>sp. nov. and four extant <em>Charhyphus </em>species.</p> <p> </p> <p><strong>Supplementary Videos 1–3:</strong></p> <p><strong>Supplementary Videos 1</strong> <em>Charhyphus serratus </em>sp. nov., 001 DUBC, holotype, habitus, movie of X-ray micro-CT volume renderings.</p> <p><strong>Supplementary Videos 2</strong> <em>Charhyphus serratus </em>sp. nov., 001 DUBC, holotype, habitus, movie of X-ray micro-CT volume renderings using different parameters from Supplementary Videos 1.</p> <p><strong>Supplementary Videos 3</strong> <em>Charhyphus serratus </em>sp. nov., 001 DUBC, holotype, female genitalia, movie of X-ray micro-CT volume renderings.</p>
Deciphering the Neurosensory Olfactory Pathway and Associated Neo-Immunometabolic Vulnerabilities Implicated in COVID-Associated Mucormycosis (CAM) and COVID-19 in a Diabetes Backdrop—A Novel Perspective
<p>Raw data files of transcriptomic profiling experiments, which form the basis for our publication (https://www.mdpi.com/2673-4540/3/1/13).</p>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
First genetic data for the Critically Endangered Cuban endemic Zapata Rail Cyanolimnas cerverai, and the taxonomic implications
<p>Data associated with the publication First genetic data for the Critically Endangered Cuban endemic Zapata Rail <em>Cyanolimnas cerverai</em>, and the taxonomic implications.</p>
Implications of Handover Events in commercial 5G Non-Standalone Deployments in Rome
<p>Passive and active network measurements used for analysing the implications of Handover (HO) events inn commercial 5G Non-Standalone (NSA) deployments in Rome.</p> <p> </p> <p> </p>
Supplementary Materials for "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications"
<p>Contained here are the SI files for the article "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications". With all the materials contained here, as well as the openly accessible datasets cited in S1_File, every step of the study can be reproduced. Detailed instructions are contained within. Includes code for Data Analysis.</p> <p>Article DOI: [forthcoming]</p>
Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state (Data Underlying Figures)
<p>Data underlying figures in A. Bain 'Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state' RSC Environmental Science: Nano, 2022. </p> <p>CA = citric acid<br> NaCl = sodium chloride<br> AS = ammonium sulfate</p> <p>rho = difference in density (g/cm^3)<br> Rh = % relative humidity<br> radius is in micrometers<br> Pe0 are the calculated dimensionless Peclet numbers<br> </p>
Characterization of the polyspecific transferase of murine type I fatty acid synthase (FAS) and implications for polyketide synthase (PKS) engineering
<p><strong>Characterization of the polyspecific transferase of murine type I fatty acid synthase (FAS) and implications for polyketide synthase (PKS) engineering</strong></p> <p><a href="https://dx.doi.org/10.1021/acschembio.7b00718">https://dx.doi.org/10.1021/acschembio.7b00718</a></p> <p><strong>Abstract</strong></p> <p>Fatty acid synthases (FASs) and polyketide synthases (PKSs) condense acyl compounds to fatty acids and polyketides, respectively. Both, FASs and PKSs, harbor acyltransferases (ATs), which select substrates for condensation by β-ketoacyl synthases (KSs). Here, we present the structural and functional characterization of the polyspecific malonyl/acetyltransferase (MAT) of murine FAS. We assign kinetic constants for the transacylation of the native substrates, acetyl- and malonyl-CoA, and demonstrate the promiscuity of FAS to accept structurally and chemically diverse CoA-esters. X-ray structural data of the KS-MAT didomain in a malonyl-loaded state suggests a MAT-specific role of an active site arginine in transacylation. Owing to its enzymatic properties and its accessibility as a separate domain, MAT of murine FAS may serve as versatile tool for engineering PKSs to provide custom-tailored access to new polyketides that can be applied in antibiotic and antineoplastic therapy.</p> <p><strong>Raw dataset for protein databank accession code (PDB) 5my0</strong></p> <p><a href="http://dx.doi.org/10.2210/pdb5my0/pdb">http://dx.doi.org/10.2210/pdb5my0/pdb</a></p>
Peak Flow Event Durations in the Mississippi River Basin and Implications for Temporal Sampling of Rivers
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This repository corresponds to all the input and output files that were used in the study reported in:</p> <ul> <li>Cerbelaud, A., David, C. H., Biancamaria, S., Wade, J., Tom, M., Prata de Moraes Frasson, R., & Blumstein, D. (2024). Peak flow event durations in the Mississippi River basin and implications for temporal sampling of rivers. Geophysical Research Letters, 51, e2024GL109220. <a href="https://doi.org/10.1029/2024GL109220" target="_blank" rel="noopener">https://doi.org/10.1029/2024GL109220</a>.</li> </ul> <p>When making use of any of the output files of this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Main goals of the publication</strong></p> <p>The corresponding work aims to quantify peak flow event durations at an hourly time scale and their impact on high-frequency river sampling requirements using sampling ratios. The analysis is performed over the Mississippi basin using hourly USGS gages over 2010-2022.</p> <p>The findings derived from these output files have direct implications for future satellite missions concerned with capturing high-frequency dynamics in rivers, including flood events.</p>
RNA-seq dataset for Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism
<p>Data to be used along with <a href="https://github.com/neelroop/asd-development-coexpression-2013">code</a> from 2013 paper that was originally on a site hosted at UCLA, but may no longer be accessible.</p>
Data for Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation
<p>Numerical dataset for the data presented in Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation, manuscript submitted to Icarus journal.</p>
Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data
<p>This dataset contains valuable information on earthquake events, including their location, magnitude, depth, and focal mechanism solutions. This README file provides detailed explanations of each header in the dataset, as well as information about the files included in the repository.<br><br><em>"Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data"</em> <strong>(Under Review)</strong><br> </p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall
<h1>Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon.</h1>
Supplementary Files for "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals"
<p>This repository hosts supplemental files for the Manuscript "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals" by Ruperti, et al., 2024.</p> <ul> <li><strong>Suppl_File_wreath_domain_model.pdb</strong>: AlphaFold3 model for the <em>C. prolifera</em> MAFp3 wreath domain (aa 33 - 317)</li> <li><strong>Suppl_File_MAFAP1_Cterm_model.cif</strong>: AlphaFold3 model for the <em>C. prolifera</em> MAFAP1 C-terminal domain, region 1 and 2</li> <li><strong>Suppl_File_AFInteracting_hmm.hmm</strong>: HMM sequence profile of AF-interacting region of C. prolifera proteins</li> <li><strong>XXX_Foldseek.zip</strong>: Foldseek raw search results, separated by target databases (Swissprot, AFDB, CATH50)</li> </ul>
Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications
<p>This is the data supporting the manuscript "Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications".</p> <p>Abstract</p> <p>InP/ZnS core/shell quantum dots (QDs), recognized as highly promising heavy-metal-free emitters, are increasingly utilized in lighting and display applications. Their synthesis in a tubular flow reactor enables production in a highly efficient, scalable, and reproducible manner, particularly when combined with a single-source shell precursor, such as zinc diethyldithiocarbamate (Zn(S2CNEt2)2). However, the photoluminescence quantum yield (PLQY) of QDs synthesized with this route remains significantly lower compared to those synthesized in batch reactors involving multiple steps for the shell growth. Our study identifies the formation of absorbing, yet non-emissive ZnS nanoparticles during the ZnS shell formation process as a main contributing factor to this discrepancy. By varying the shelling conditions, especially the shelling reaction temperature and InP core concentration, we investigated the formation of pure ZnS nanoparticles and their impact on the optical properties, particularly PLQY, of the resultant InP/ZnS QDs through UV-vis absorption, steady-state and time-resolved photoluminescence (PL) spectroscopy, scanning transmission electron microscopy (STEM) and analytical ultracentrifugation (AUC) measurements. Our results suggest that process conditions, such as lower shelling temperatures or reduced InP core concentrations (resulting in a lower external surface area), encourage the homogeneous nucleation of ZnS. This reduces the availability of shell precursors necessary for an effective passivation of the InP core surfaces, ultimately resulting in lower PLQYs. These findings explain the origin of persistently underperformed PLQY of InP/ZnS QDs synthesized from this synthesis route and suggest further optimization strategies to improve their emission for lighting and display applications.</p> <p>The data are sorted per techniques used for characterization. Information about the measurement details can be found in README files attached to each technique folder.</p>
Codes and test datasets developed for Mapping paleolacustrine deposits with a UAV-borne multispectral camera: Implications for future drone mapping on Mars.
<p>NASA’s Ingenuity Mars Helicopter has ushered in a new era in planetary exploration by utilizing Unmanned Aerial Vehicles (UAVs) to enhance our understanding of planetary surfaces. This project evaluates the potential of UAVs for mapping Martian environments, using Lake Natron, Tanzania, as an analog for Martian paleolakes.</p> <p>During two field seasons (January and July 2023), we employed a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera and a TerraSpec Halo VNIR-SWIR spectrometer to capture high-resolution imagery and spectral data. Almost all image processing and analysis were performed using Python scripting, except for image mosaic and Digital Elevation Model (DEM) generation.</p> <p>We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. </p> <p>In this repository, we share all the code developed during our study. Processing steps include,<br>1. DN to radiance conversion<br>2. Panel radiance extraction<br>3. Calculate reflectance factors using DLS data<br>4. Calculate reflectance at MicaSense band<br>5. Convert radiance to reflectance using 1 point empirical line method (1p ELM)<br>6. Convert radiance to reflectance using 2 point empirical line method (2p ELM)<br>7. Atmospheric correction using 6SV method<br>8. Convert radiance to reflectance using DLS data<br>9. Calculate Band indices<br>10. Weighted Kmean clustering<br>11. Finding the optimal number of clusters using the elbow method<br>12. Cmean clustering</p> <p>We also included sample image data used in the study. Feel free to contact us for more information/data.</p>
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis
<p>This repository contain datasets and results for the paper:</p> <p><strong>Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis</strong></p> <p> </p> <p><strong>Github repository for the code: </strong></p> <p><a href="https://github.com/siebeniris/QuantifyingLanguageConfusion/tree/main">Quantifying Language Confusion GitHub repo</a></p> <p> </p> <p><strong>DATA</strong> include the following datasets:</p> <p>i) raw language graphs and</p> <p>ii) the calculated language similarities from the language graphs,</p> <p>iii) <strong>MTEI</strong>: the files from the <a href="https://github.com/siebeniris/vec2text_exp/tree/aaai">experimental results of multilingual inversion attacks</a>, and calculated language confusion entropy from the data;</p> <p>iv) <strong>LCB</strong>: the files from the <a href="https://github.com/for-ai/language-confusion?tab=Apache-2.0-1-ov-file#readme">language confusion benchmark</a> and calculated language confusion entropy from the data </p> <p> </p> <p><strong>Results</strong> include aggregated results for further analysis:</p> <p>i) <strong>inversion_language_confusion</strong>: results from MTEI</p> <p>ii) <strong>prompting_language_confusion</strong>: results from LCB</p> <p> </p> <p> </p>
Data from: Species delimitation in endangered groundwater salamanders: implications for aquifer management and biodiversity conservation
Groundwater-dependent species are among the least-known components of global biodiversity, as well as some of the most vulnerable because of rapid groundwater depletion at regional and global scales. The karstic Edwards–Trinity aquifer system of west-central Texas is one of the most species-rich groundwater systems in the world, represented by dozens of endemic groundwater-obligate species with narrow, naturally fragmented distributions. Here, we examine how geomorphological and hydrogeological processes have driven population divergence and speciation in a radiation of salamanders (Eurycea) endemic to the Edwards–Trinity system using phylogenetic and population genetic analysis of genome-wide DNA sequence data. Results revealed complex patterns of isolation and reconnection driven by surface and subsurface hydrology, resulting in both adaptive and non-adaptive population divergence and speciation. Our results uncover new cryptic species diversity and refine the borders of several threatened and endangered species. The U.S. Endangered Species Act has been used to bring state regulation to unrestricted groundwater withdrawals in the Edwards (Balcones Fault Zone) Aquifer, where listed species are found. However, the Trinity and Edwards–Trinity (Plateau) aquifers harbor additional species with similarly small ranges that currently receive no protection from regulatory programs designed to prevent groundwater depletion. Based on regional climate models that predict increased air temperature, together with hydrologic models that project decreased springflow, we conclude that Edwards–Trinity salamanders and other co-distributed groundwater-dependent organisms are highly vulnerable to extinction within the next century.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.