Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

2,353

datasets available to search

ShareScore release 0.9.0

Dataset results

2,353 results for “channel”

Learn how ShareScore rates datasets ↗

zenodo36/100

Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels - full trajectories of SaFabI and EcFabI tetramers

The following IDs are related to trajectories: v1 = EcFabI apo, tetramer v2 = EcFabI + TCL complex, tetramer v3 = EcFabI + MUT complex, tetramer v39 = EcFabI + AFN complex, tetramer v16 = SaFabI apo, tetramer v17 = SaFabI + TCL complex, tetramer v18 = SaFabI + MUT complex, tetramer v40 = SaFabI + AFN complex, tetramer

opencc-by-4.0Jul 2022View details →

zenodo36/100

injection of PVA MBs via microcatheter in microfluid channel

jinjection tests operating the syringe manually, also in reverse flux, to verify the absence of obstructions and damages.Thereafter, the injection syringe was mounted on a programmable, step-by-step motor driven syringe pump system which was set at different constant volumetric flow rates, ranging from 0.05 ml/min to 1.12 ml/min according to material and methods, section "Handling of microcatheters with PVA MBs for insertion in microfluidic channels"

opencc-by-4.0Jul 2022View details →

zenodo36/100

Source data for "Ca2+ channels couple spiking to mitochondrial metabolism in substantia nigra dopaminergic neurons"

Fig.1Aa-c.tif 2PLSM images (Fura-2 filled neuron) used for the reconstruction in Fig.1A   Fig1CDJ.xlsx Numerical data for the charts in Fig. 1C, Fig.1D, Fig.1J   Fig.1F_GCEPIA1er.tif Confocal image (green channel, G-CEPIA1er) for Fig. 1F   Fig.1F_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 1F   Fig.1G_GCEPIA1er.tif Confocal image (green channel, G-CEPIA1er) for Fig. 1G   Fig.1G_CRT.tif Confocal image (magenta channel, anti-CRT immunostaining) for Fig. 1G   Fig.1H_GCEPIA1er.tif Confocal image (green channel, G-CEPIA1er) for Fig. 1H   Fig.1H_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 1H   Fig. 2B_mitoGCaMP6.tif Confocal image (green channel, mito-GCaMP6) for Fig. 2B   Fig.2B_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 2B   Fig. 2C_mitoGCaMP6.tif Confocal image (green channel, mito-GCaMP6) for Fig. 2C   Fig.2C_COXIV.tif Confocal image (magenta channel, anti-COXIV immunostaining) for Fig. 2C   Fig. 2D_mitoGCaMP6.tif Confocal image (green channel, mito-GCaMP6) for Fig. 2D   Fig.2D_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 2D   Fig.2FGJL.xlsx Numerical data for the charts in Fig. 2F, Fig.2G, Fig.2J, Fig.2L   Fig.3BDE.xlsx Numerical data for the charts in Fig. 3B, Fig.3D, Fig.3E   Fig.3C_Alexa.tif MAX Projection of z-stack of 2PLSM images (magenta channel, Alexa 594 dye) used to generate Fig.3C top panel   Fig.3C_mitoGCaMP6.tif MAX Projection of z-stack of 2PLSM images (green channel, mito-GCaMP6) used to generate Fig.3C top panel   Fig.3C_inset_dendritic_mitoGCaMP6.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom left panel   Fig.3C_inset_soma_mitoGCaMP6.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom right panel   Fig. 4A_PercevalHR.tif Confocal image (green channel, PercevalHR) for Fig.4A   Fig.4A_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig.4A   Fig. 4B_PercevalHR.tif Confocal image (green channel, PercevalHR) for Fig.4B   Fig.4B_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig.4B   Fig.4G.xlsx Numerical data for the charts in Fig.4G   Fig.5BDFHI.xlsx Numerical data for the charts in Fig.5B, Fig.5D, Fig.5F, Fig.5H, Fig.5I   Fig.6ADEFJKLN.xlsx Numerical data for the charts in Fig.6A, Fig.6D, Fig.6E, Fig.6F, Fig.6J, Fig.6K, Fig.6L, Fig.6N   Fig.6H_mitoroGFP.tif 2PLSM image (green channel, mito-roGFP) for Fig.6H   Fig.6M_MCU-KO.tif Combined EM micrographs used to generate Fig. 6M right side   Fig.6M_wildtype.tif Combined EM micrographs used to generate Fig. 6M left side   Fig.7CEFG.xlsx Numerical data for the charts in Fig.7C, Fig.7E, Fig.7F, Fig.7G   Fig.8CEHJK.xlsx Numerical data for the charts in Fig.8C, Fig.8E, Fig.8H, Fig.8J, Fig.8K   Fig.S1A_bottom.tif 2PLSM image (green channel, G-CEPIA1er) for Fig.S1A bottom panel (low Ca2+)   Fig.S1A_top.tif 2PLSM image (green channel, G-CEPIA1er) for Fig.S1A top panel (high Ca2+)   Fig.S1B.xlsx Numerical data for the chart in Fig.S1B   Fig.S2A_baseline.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2A middle panel (baseline)   Fig.S2A_MAX.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2A top panel (high Ca2+)   Fig.S2A_min.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2A bottom panel (low Ca2+)   Fig.S2C_baseline.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2C middle panel (baseline)   Fig.S2C_MAX.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2C top panel (high Ca2+)   Fig.S2C_min.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2C bottom panel (low Ca2+)   Fig.S2E_baseline.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom left panel (baseline)   Fig.S2E_end.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom right panel   Fig.S2E_peak.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom center panel   Fig.S2F.xlsx Numerical data for the chart in Fig.S2F   Fig.S3ABC.xlsx Numerical data for the charts in Fig.S3A, Fig.S3B, Fig.S3C   Fig.S4CD.xlsx Numerical data for the charts in Fig.S4C, Fig.S4D   Fig.S5ABC.xlsx Numerical data for the charts in Fig.S5A, Fig.S5B, Fig.S5C   Fig.S7A.tif 2PLSM image (green channel, GCaMP6) for Fig.S7A   Fig.S7CEFGH.xlsx Numerical data for the charts in Fig.S7C, Fig.S7E, Fig.S7F, Fig.S7G, Fig.S7H   Fig.S8ABCDEF.xlsx Numerical data for the charts in Fig.S8A, Fig.S8B, Fig.S8C, Fig.S8D, Fig.S8E, Fig.S8F   Fig.S9AHIJK.xlsx Numerical data for the charts in Fig.S9A, Fig.S9H, Fig.S9I, Fig.S9J, Fig.S9K   Fig.S9B_wildtype_DLStr.tif Confocal image of dorso-laterateral striatum in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9B   Fig.S9C_MCU-KO_DLStr.tif Confocal image of dorso-laterateral striatum in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9C   Fig.S9D_wildtype_SN.tif Confocal image of midbrain in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9D   Fig.S9E_MCU-KO_SN.tif Confocal image of midbrain in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9E   Fig.S9F_wildtype_openfield.png Open field path tracked for wildtype mouse for Fig.S9F   Fig.S9G_MCU-KO_openfield.png Open field path tracked for MCU-KO mouse for Fig.S9G   Fig.S10A.xlsx Numerical data for the charts in Fig.S10A

opencc-by-4.0Aug 2022View details →

zenodo36/100

Data for "Estimation of Return Stroke Velocity by Time Reversal Reconstruction of Channel Feature Points"

In the manuscript entitled “Estimation of Return Stroke Velocity by Time Reversal Reconstruction of Channel Feature Points”, station coordinates of the location system, simulation data, and experimental data can be obtained through the following attachment. These files can be opened by Matlab 2018(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citations.   'IniationParameters_center.mat' is the coordinates of LFLLS stations and strike point (simulation). a) simulation_strike_point: coordinates of the return point (simulation). b) x0, y0, and z0: coordinates of LFLLS stations in x, y, and z directions.   1. Simulation data (EE_zd: E-field waveforms (Unit V/m); Ee_zd: Electrostatic component of the E-field waveforms (Unit V/m); Ei_zd: Induction component of the E-field waveforms (Unit V/m); Er_zd: Radiation component of the E-field waveforms (Unit V/m); T: times corresponding to the E-field waveforms; vv: RS velocity condition; tort_x, tort_y, and tort_z: Coordinates of segmented channels). 'Simulation_Vertical_channel_Ez_V1.mat' is the vertical channel E-field waveforms calculated under the RS velocity condition V1. 'Simulation_Vertical_channel_Ez_V2.mat' is the vertical channel E-field waveforms calculated under the RS velocity condition V2. 'Simulation_Vertical_channel_Ez_V3.mat' is the vertical channel E-field waveforms calculated under the RS velocity condition V3. 'Simulation_Inclined_channel_Ez.mat' is the inclined channel E-field waveforms calculated under the RS velocity condition V1. 'Simulation_Tortuous(randomly)_channel_Ez.mat' is the tortuous channel (randomly) E-field waveforms calculated under the condition of RS velocity constant. 'Simulation_Tortuous_channel_Ez.mat' is the tortuous channel E-field waveforms calculated under the RS velocity condition V1.   2. Experimental data '2020-08-09-002418-905.5ms(0.4)-siteidx(12346).mat' is the E-field original waveforms of -CG002418.RS2. a) wave: E-field original waveforms of -CG002418.RS2 (D.U.). b) time: times corresponding to the E-field waveforms.   3. Figure data This folder contains the .fig format files of Figure 3 ~ 10 in the paper.

opencc-by-4.0Aug 2022View details →

zenodo36/100

Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels - trajectories employed in Markov State Models and water analyses

The following trajectories were employed in the generation of MSM models and water analyses: SaFabI_60us_align.zip EcFabI_60us_align.zip waters_SaFabI.tar.gz waters_EcFabI.tar.gz

opencc-by-4.0Aug 2022View details →

dryad36/100

Functional constraints channel mandible shape ontogenies in rodents

In mammals, postnatal growth plays an essential role in the acquisition of the adult shape. During this period, the mandible undergoes many changing functional constraints, leading to spatialization of bone formation and remodelling to accommodate various dietary and behavioural changes. The interactions between the bone, muscles and teeth drive this developmental plasticity, which, in turn, could lead to convergences in the developmental processes constraining the directionality of ontogenies, their evolution and thus the adult shape variation. To test the importance of the interactions between tissues in shaping the ontogenetic trajectories, we compared the mandible shape at five postnatal stages on three rodents: the house mouse, the Mongolian gerbil and the golden hamster, using geometric morphometrics. After an early shape differentiation, either by longer gestation and allometric scaling in gerbils or early divergence of postnatal ontogeny in hamsters in comparison to the mouse, the ontogenetic trajectories appear more similar around weaning. The changes in muscle load associated to new food processing and new behaviours at weaning seem to impose similar physical constraints on the mandible driving the convergences of the ontogeny at that stage despite an early anatomical differentiation. Nonetheless, mice present a rather different timing compared to gerbils or hamsters.

opencc-zeroSep 2022View details →

zenodo36/100

Effect of freshwater discharge from estuary dam on residual circulation in bifurcated channel

Zip-files contain matlab m-files and raw data of transects data in Yeoungsan Rver estuary. The code was designed to analysis the changes from the raw data. During freshwater discharge.zip file contains data set with a freshwater impact from a estuary dam, Whereas during non-freshwater discharge.zip file contains data set without a freshwater impacts period.

opencc-by-4.0Sep 2022View details →

dryad36/100

Cytosolic peptides encoding CaV1 C-termini downregulate the calcium channel activity-neuritogenesis coupling

L-type Ca2+ (CaV1) channels transduce channel activities into nuclear signals critical to neuritogenesis. Also, standalone peptides encoded by CaV1 DCT (distal carboxyl-terminus) act as nuclear transcription factors reportedly promoting neuritogenesis. Here, by focusing on exemplary CaV1.3 and cortical neurons under basal conditions, we discover that cytosolic DCT peptides downregulate neurite outgrowth by the interactions with CaV1's apo-calmodulin binding motif. Distinct from nuclear DCT, various cytosolic peptides exert a gradient of inhibitory effects on Ca2+ influx via CaV1 channels and neurite extension and arborization, and also the intermediate events including CREB activation and c-Fos expression. The inhibition efficacies of DCT are quantitatively correlated with its binding affinities. Meanwhile, cytosolic inhibition tends to facilitate neuritogenesis indirectly by favoring Ca2+-sensitive nuclear retention of DCT. In summary, DCT peptides as a class of CaV1 inhibitors specifically regulate the channel activity-neuritogenesis coupling in a variant-, affinity-, and localization-dependent manner.

opencc-zeroSep 2022View details →

zenodo36/100

Dataset of "Giant gate-controlled odd-parity magnetoresistance in one-dimensional channels with a magnetic proximity effect"

According to Onsager’s principle, electrical resistance R of general conductors behaves as an even function of external magnetic field B. Only in special circumstances, which involve time reversal symmetry (TRS) broken by ferromagnetism, the odd component of R against B is observed. This unusual phenomenon, called odd-parity magnetoresistance (OMR), was hitherto subtle (< 2%) and hard to control by external means. Here, we report a giant OMR as large as 27% in edge transport channels of an InAs quantum well, which is magnetized by a proximity effect from an underlying ferromagnetic semiconductor (Ga,Fe)Sb layer. Combining experimental results and theoretical analysis using the linearized Boltzmann’s equation, we found that simultaneous breaking of both the TRS by the magnetic proximity effect (MPE) and spatial inversion symmetry (SIS) in the one-dimensional (1D) InAs edge channels is the origin of this giant OMR. We also demonstrated the ability to turn on and off the OMR using electrical gating of either TRS or SIS in the edge channels. These findings provide a deep insight into the 1D semiconducting system with a strong magnetic coupling.

opencc-by-4.0Oct 2022View details →

zenodo36/100

Interaction of the inhibitory peptides ShK and HmK with the voltage-gated potassium channel KV1.3: Role of conformational dynamics

ABSTRACT: Peptide toxins that adopt the ShK fold can inhibit the voltage-gated potassium channel KV1.3 with IC50 values in the pM range, and are therefore potential leads for drugs targeting autoimmune and neuroinflammatory diseases. NMR relaxation measurements and pressure-dependent NMR have shown that, despite being cross-linked by disulfide bonds, ShK itself is flexible in solution. This flexibility affects the local structure around the pharmacophore for KV1.3 channel blockade and, in particular, the relative orientation of the key Lys and Tyr side chains (Lys22 and Tyr23 in ShK), and has implications for the design of KV1.3 inhibitors. In this study, we have performed molecular dynamics (MD) simulations on ShK and a close homolog, HmK, in order to probe the conformational space occupied by the Lys and Tyr residues, and docked the different conformations with a recently determined cryo-EM structure of the KV1.3 channel. Although ShK and HmK have 60% sequence identity, their dynamic behaviors are quite different, with ShK sampling a broad range of conformations over the course of a 5 μs MD simulation, while HmK is relatively rigid. We also investigated the importance of conformational dynamics, in particular the distance between the side chains of the key dyad Lys22 and Tyr23, for binding to KV1.3. Although these peptides have quite different dynamics, the dyad in both adopts a similar configuration upon binding, revealing a conformational selection upon binding to KV1.3 in the case of ShK. Intriguingly, the more flexible peptide, ShK, binds with nearly 300-fold higher affinity than HmK.

opencc-by-4.0Oct 2022View details →

zenodo36/100

Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution

This is the dataset for the work titled and authored by: Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles. Software can be found under this DOI: 10.5281/zenodo.846448

openbsd-3-clauseAug 2017View details →

zenodo36/100

Initiation of downward positive leader beneath the negative leader channel

The data supports the manuscript entitled "Initiation of downward positive leader beneath the negative leader channel" and MATLAB can open these *.fig and *.mat files. MATLAB Code ReadTdms.m can open these *.tdms. The data of *.cine can be opened by  Phantom Camera Control software that can be download from this link:   https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware   The data can be used freely for scientific purposes with the appropriate citation.

opencc-by-4.0Apr 2024View details →

dryad36/100

Data from: Towards high-resolution modeling of small molecule - ion channel interactions

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two Rosetta ligand docking methods, RosettaLigand and GALigandDock, on structures of known ligand - cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods were able to frequently sample a ligand binding pose within 1-2 Å root mean square deviation (RMSD) relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand - ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state specific interactions of the ion channel and inherent receptor site flexibility that could influence ligand binding.

opencc-zeroMay 2024View details →

zenodo36/100

Antijamming Schemes for Generalized MIMO Y Channel

The following dataset contains the Block Error Rate (BLER) results from the link-level simulator, obtained for the proposed anti-jamming schemes (AJ-SSA, JIC), which were compared to those of the iterative beamforming optimization algorithm (labeled as SSA and SSA no jammer, when the jammer is absent). Simulations were conducted for two antenna configurations: 4x4 and 8x8.

opencc-by-4.0Mar 2024View details →

zenodo36/100

Side-channel analysis on masked-AES implementation on ARM M3

A data set of power traces that was acquired from executing a two-shares masked AES SubBytes implementation (written in Thumb Assembly Language) on an ARM Cortex M3 processor core from NXP (LPC1313). This implies that no single point leaks information about the unshared intermediate value, further confirmed via leakage detection.   We use a custom measurement board (Picoscope 5243D), which provides good measurements (at 250 MSa/s, and the working frequency is set to 2 MHz). We use our scope in a basic setting to avoid any trace processing (de-noising) and extract discrete traces (5 million), where each point is represented by 8 bits. We have uploaded a compressed version of .trs file. We have used the data for our paper "Leakage Certification Made Simple"

opencc-by-4.0May 2024View details →

dryad36/100

Constructing visualization tools and training resources to assess climate impacts on the channel islands national marine sanctuary NetCDF files

The Channel Islands Marine Sanctuary (CINMS) comprises 1,470 square miles surrounding the Northern Channel Islands: Anacapa, Santa Cruz, Santa Rosa, San Miguel, and Santa Barbara, protecting various species and habitats. However, these sensitive habitats are highly susceptible to climate-driven 'shock' events which are associated with extreme values of temperature, pH, or ocean nutrient levels. A particularly devastating example was seen in 2014-16, when extreme temperatures and changes in nutrient conditions off the California coast led to large-scale die-offs of marine organisms. Global climate models are the best tool available to predict how these shocks may respond to climate change. To better understand the drivers and statistics of climate-driven ecosystem shocks, a 'large ensemble' of simulations run with multiple climate models will be used. The objective of this project is to develop a Python-based web application to visualize ecologically significant climate variables near the CINMS. The web application will be used by researchers from the University of California, Santa Barbara (UCSB) to analyze climate model output, and by CINMS staff to develop new indicators of shocks to marine ecosystems.

opencc-zeroJun 2024View details →

zenodo36/100

Comparison of measurement protocols for internal channels of transparent microfluidic devices - datasets

Supplementary material to "Comparison of measurement protocols for internal channels of transparent microfluidic devices" submitted to Micromachines. Data accompanying comparison of measurement protocols.   Further readings can be found at <a href="https://mfmet.eu/publications">https://mfmet.eu/publications</a> The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.

opencc-by-4.0Jun 2024View details →

zenodo36/100

A Mid-crustal Channel of Positive Radial Anisotropy Beneath the Eastern South China Block From F-J Multimodal Ambient Noise Tomography

It contains the CCAB-FJpy, the Python code package for calculating the multi-component cross-correlation functions (CCFs) and extracting multimodal Love dispersions with multi-component frequency-Bessel (F-J) transform method. . Also, it contains multi-component CCFs (i.e., RR and TT), Vsh model and 3-D crustal radial anisotropy model of the eastern South China Block (ESCB).

opencc-by-4.0Jun 2024View details →

zenodo36/100

Raw numerical data and cleavage assay blot for Figures found in the article: "The ion channel Anoctamin 10/TMEM16K coordinates organ morphogenesis across scales in the urochordate notochord"

In relation to our publication: "The ion channel Anoctamin 10/TMEM16K coordinates organ morphogenesis across scales in the urochordate notochord" we provide all the individual quantitative observations that underlie the data summarized in the figures and results of our paper. More specifically we provide the data for Fig. 1G, H, I, P, Q; Fig. 2D-G, R-U; Fig. 3E-H; Fig. 4D-F, Q-S; Fig. 5D-G, K-N; R-U; Fig. 6D-F, S-U; Fig. 7G, H, J-T; Fig. 8G-I, M, N, S, X, Y; Fig. 9D-G, R, S; Fig. 10D-Q, K-R; Fig. 11F, G; Fig. S2T-X; Fig. S3G; Fig. S4K, O, P and Fig. S6E-J. In addition we provide a non-annotated and an annotated version of the original, uncropped gel picture shown in Fig. S4A

opencc-by-4.0Jun 2024View details →

dryad36/100

Relationship between Channel Island Melospiza melodia (Song Sparrow) bill size and vegetation, seed extraction time, bite force, and climate (2014-2016)

Inferring the environmental selection pressures responsible for phenotypic variation is a challenge in adaptation studies as traits often have multiple functions and are shaped by complex selection regimes. We provide indirect evidence that morphology of the multifunctional avian bill is primarily shaped by climate and thermoregulatory ability in Melospiza melodia (Song Sparrows) on the California Channel Islands. Our research builds on a study in Song Sparrow museum specimens that demonstrated a positive correlation between bill surface area and maximum temperature, suggesting a greater demand for dry heat dissipation in hotter, xeric environments. We sampled contemporary sparrow populations across three climatically distinct islands to test the hypotheses that bill morphology is influenced by habitat differences with functional consequences for foraging efficiency and is related to maximum temperature and, consequently, important for thermoregulation. Measurements of >500 live individuals indicated a significant, positive relationship between maximum temperature and bill surface area when correcting for body size. In contrast, maximum bite force, seed extraction time, and vegetation on breeding territories (a proxy for food resources) were not significantly associated with bill dimensions. While we cannot exclude the influence of foraging ability and diet on bill morphology, our results are consistent with the hypothesis that variation in Song Sparrows' need for thermoregulatory capacity across the northern Channel Islands selects for divergence in bill surface area.

opencc-zeroJun 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record