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3,484 results for “Plastics”
Plastic additive qualitative analysis - Plastic reference materials
<p><span> Clusters of tabular data (with metadata including variable labels, code labels and definition of missing value) reporting plastic additive profile in the reference materials with ID P3, P4, P5, P6, P8 and P9. </span><span>Quality assurance measures were in place during analysis including calibration curves, blank measurements and use of validated methods.</span></p>
Figure 4 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 4. Length frequency distribution of the fibers.
Figure 2 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 2. Plastic fibres from fish intestines.
Plasticity of the selectivity filter is essential for permeation in lysosomal TPC2 channels
<p>Molecular dynamics raw data (input files and skipped example trajectories) for the associated manuscript "Plasticity of the selectivity filter is essential for permeation in lysosomal TPC2 channels" in PNAS.</p>
Despite plasticity, heatwaves are costly for a coral reef fish
<p>Climate change is intensifying extreme weather events, including marine heatwaves, which are prolonged periods of anomalously high sea surface temperature that pose a novel threat to aquatic animals. Tropical animals may be especially vulnerable to marine heatwaves because they are adapted to a narrow temperature range. If these animals cannot acclimate to marine heat waves, the extreme heat could impair their behavior and fitness. Here, we investigated how marine heatwave conditions affected the performance and thermal tolerance of a tropical predatory fish, arceye hawkfish (<em>Paracirrhites arcatu</em>s), across two seasons in Mo'orea, French Polynesia. We found that the fish's daily activities, including recovery from burst swimming and digestion, were more energetically costly in fish exposed to marine heatwave conditions across both seasons, while their aerobic capacity remained the same. Given their constrained energy budget, these rising costs associated with warming may impact how hawkfish prioritize activities. Additionally, hawkfish that were exposed to hotter temperatures exhibited cardiac plasticity by increasing their maximum heart rate but were still operating within a few degrees of their thermal limits. With more frequent and intense heatwaves, hawkfish, and other tropical fishes must rapidly acclimate, or they may suffer physiological consequences that alter their role in the ecosystem. <strong><br></strong></p>
Supporting data and code for: Individual plasticity in response to rising sea temperatures contributes to an advancement in green turtle nesting phenology.
Open the record for dataset details and reuse information.
Structural Plasticity as a Driver of the Maturation of Pro-Interleukin-18
<p>A series of NMR datasets characterizing the dynamics of pro-interleukin-18 on a range of timescales are provided. These include HSQC spectra (wild type and P93T pro-interleukin-18), 15N relaxation (R1rho, R1, 15N-1H heteronuclear NOE, HxNx, HzNx, HxNz, HzNz), methyl 3Q forbidden, CPMG (15N, 1HN, 13CO, methyl 13C MQ, methyl 1H SQ), CEST (15N, 1HN, 13CO, 13Calpha, 13Cbeta, 1Halpha, methyl 13C, methyl 1H), and hydrogen-deuterium exchange (HDX). 15N CPMG and CEST datasets were recorded at 25C, 29C, 32.5C, 35C, and 40C. For additional details refer to the associated publication. </p>
Virulence and antibiotic resistance plasticity of Arcobacter butzleri: insights on the genomic diversity of an emerging human pathogen (genome assembly, annotation dataset, core- and pan-genome loci)
<p>This dataset refers to the analysis of 49 <em>Arcobacter butzleri</em> genomes and includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the predicted transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files), the respective amino acid sequences of the translated CDS sequences (.faa files), the nucleotide alignments of all the 1165 core-genome loci, the nucleotide alignments of the genes <em>hecA</em>, <em>tetR </em>and <em>porA</em>, the categorized amino acid sequences of the six hypervariable regions of PorA, and the nucleotide sequences of the first allele of each of the 7474 pan-genome loci with the respective complete allelic profile matrix.</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB34441).</p>
Data and R code used in Baudson et al (2019) Developmental plasticity of Brachypodium distachyon in response to P deficiency: modulation by inoculation with phosphate-solubilizing bacteria
<p>This repository contains the raw data and R code used for the following paper: Baudson et al (2019) Developmental plasticity of <em>Brachypodium distachyon</em> in response to P deficiency: modulation by inoculation with phosphate-solubilizing bacteria</p>
Fig. 6 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 6. Detail of the calculation model: layer III of Miraflores I tracksite.
Fig. 10 in Ecophenotypic plasticity versus evolutionary trends-morphological variability in Upper Jurassic bivalve shells from Portugal
Fig. 10. Box plot of rib numbers in Arcomytilus. Numbers in squared brackets refer to Fig. 2.
PMF-LP: the first 10 m plastic-mulched farmland distribution map (2019-2021) in the Loess Plateau of China generated using training sample generation and classifier transfer method
<p>This dataset provides 10-m resolution plastic-mulched farmland distribution map in the Loess Plateau of China from 2019 to 2021</p> <p>*** The data file is in ".tif" format</p> <p>*** Temporal resolution: Annually</p> <p>*** Temporal coverage: 2019-2021</p> <p>*** Pixel size: 10 m</p> <p>*** Projection information: EPSG: 4326</p> <p>*** Values: 1 denotes plastic-mulched farmland (PMF) and 0 denotes non-plastic-mulched farmland (non-PMF)</p>
Figure 2 in Predator-induced plasticity in tadpoles of Polypedates cruciger (Anura: Rhacophoridae)
Figure 2. The morphology of early tadpole stages: A, control; B, "open." Scale bar 1 mm.
HRDIC and EBSD data for the study of early stage plasticity of a Ni-base superalloy
<p>We used High-Resolution Digital Image Correlation (HRDIC) to study the strain localisation and slip activity of a Ni-base superalloy sample, which was subjected to an in-situ tensile test inside a scanning electron microscope (SEM). </p> <p>Displacement data for HRDIC were calculated using two methods: the integrated method, which correlates with the initial reference frame, and the differential method, which correlates with the previous step. Both datasets are available and saved in separate folders in the .zip file named 'HRDIC and EBSD data'.</p> <p>EBSD data for the region of interest (ROI) can be found in the same .zip file along with a Jupyter Notebook to read and visualise to both datasets. </p>
RGB and RGNIR Plastic Waste Database
RGB and RGNIR Plastic Waste Database
Sequential Bayesian Inference of Finite-strain Visco-elastic Visco-plastic model parameters of 22-month aged PA12 bulk material printed along different directions
<p>These are the data related to aged PA12 (22 months) following the methodology described in the following publication in which non-aged PA12 has been tested:</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br>journal = "International Journal of Solids and Structures",<br>year = "2023",<br>volume = "283",<br>pages = "112470",<br>doi = "10.1016/j.ijsolstr.2023.112470",<br>author = "Wu, Ling and Anglade, Cyrielle and Cobian, Lucia and Monclus, Miguel and Segurado, Javier and Karayagiz, Fatma and Freitas, Ubiratan and Noels Ludovic"</p> <p>Contrarily to the non-aged material, since high-strain-rate tests are not available, only 5 Maxwell's branches are considered herein.</p> <h1>Description</h1> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a></p> <p>The sequential BI is described in [WU23] The experimental protocol is reported in [COB22,COB22b] but is herein applied on aged PA12</p> <p>To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a></p> <p>If you use these data or model, we would be grateful if you could cite the related papers:</p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: <a href="https://doi.org/10.1016/j.ijsolstr.2023.112470" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.ijsolstr.2023.112470</a></li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: <a href="https://doi.org/10.1016/j.polymertesting.2022.107556" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.polymertesting.2022.107556</a> (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” <a href="http://dx.doi.org/10.5281/zenodo.6136935" target="_blank" rel="nofollow noreferrer noopener">http://dx.doi.org/10.5281/zenodo.6136935</a> (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; <a href="https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008" target="_blank" rel="nofollow noreferrer noopener">https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008</a>, Open access: <a href="https://orbi.uliege.be/handle/2268/197898" target="_blank" rel="nofollow noreferrer noopener">https://orbi.uliege.be/handle/2268/197898</a></li> </ul> <h1>Directories</h1> <p>All the codes and experimental results are in three directories:</p> <ol> <li>Experiment_PA12_AGED: experimental data of aged material, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters<br>2.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range<br>2.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.<br>2.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>2.1.3. Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI<br>2.2. VE_V and VE_H: BI for viscoelastic properties of "V" specimen (VE_V) and "H" specimen (VE_H)<br>2.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat<br>2.2.2. WarmStart = True is used to restart an inference<br>2.2.3. MCMC_VE_....dat in the VE_V and VE_H directories are the BI results<br>2.3. CheckBayRes: to visualize predictions of a BI sample and experimental curves 2.3.1. MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line 7)<br>2.3.2. ResKGEmu.py plots the evolution of elastic properties with time<br>2.3.3. uses as input VE_V/MCMC_VE_....dat or VE_H/MCMC_VE_....dat<br>2.3.4. uses local ViscoElasticTest.py, line.geo, line. msh as interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code<br>2.3.5. uses local functions plotExp.py<br>2.4. ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V and VE_H to call the VEVP model</li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters<br>3.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges<br>3.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.<br>3.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>3.1.3. Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI<br>3.2. VP_V and VP_H: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V) and "H" specimen (VP_H)<br>3.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat<br>3.2.2. WarmStart = True is used to restart an inference 3.2.3. It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3. CheckBayRes: visualize predictions of a BI sample and experimental curves<br>3.3.1. MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples (inclusing MAP, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.2. plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.3. Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?_1Step.dat (? being H or V) 3.3.4. ResKGEmu.py plots the evolution of elastic properties with time<br>3.4. VEVPTest.py: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> <h1>Figures (reference to the number in [WU23] but for aged PA12)</h1> <ul> <li>Fig. 5 (Selected observations): From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,14,18,22,23,24,25]</li> <li>Fig. 8 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 15B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 16B: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 19B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> </ul> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015.</p>
Stronger transgenerational plasticity in clonal compared to sexual offspring of Fragaria vesca: effects of drought, elevated temperature and CO2 conditions
<div>The dataset contains phenotypic data of woodland strawberry (<em>Fragaria vesca</em>) regarding its reproductive strategy, as well as climate and water availability for both parental and offspring generations.</div> <div> </div> <div><u>F1StatGPT.csv</u>: Table containing Ramet Number at Harvest, Fruit Number at Harvest, Ramet Biomass, Offspring Ramet Biomass, and Total Biomass.</div> <div><u>StomataStatGPT.csv:</u> Table containing Number of Stomata per mm² and Stomata Size.</div>
2D visco-elasto-plastic subduction models in the presence of a high density and viscosity continental block
<p>Three main models with different rheological conditions and density are incorporated for an imposed intrusion between the upper and lower continental crust. Output files for a model with a crust without intrusions are also incorporated.</p> <p>The file contains data on the conditions of plate motion velocity, plate age, plate thickness in Excel (Input_Data_Age_Vel.xlsx), a table with the rheology used to reproduce different numerical models incorporating the intrusion (Table_S3.pdf), and a file High_density_materials.m showing the construction of the intrusion within the continental crust.</p> <p>High_density_materials.m m should be imposed on the original I2ELVIS code provided by Taras Gerya - ETH Zürich, Institut für Geophysik, Switzerland - email: taras.gerya@erdw.ethz.ch</p> <p>The file has the main time steps as a function of temperature, viscosity, density and rock composition into the folders. If you want to visualize them please run the following Script PLOT_OUTPUT_DATA.m where,</p> <p>str=string(2040), represent the time_step</p> <p>Files are organized in a matrix form, each folder has the</p> <p>grid_x :coordinate matrix, <br>gridy_: coordinate matrix, </p> <p>matrix_temperature_, density and viscosity.</p> <p>To plot the Rock composition.</p> <p>mx and my, tracer coordinates in x-direction and y-direction<br>matrix_markcom is matrix tracers.</p> <p> </p> <p>Model 1</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 310= 12 Ma, 670 = 9 Ma, 1120 = 6 Ma, 1620 = 3 Ma, 2040 = 0 Ma)</p> <p> </p> <p>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 1. <br>This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. <br>The high density material between the upper and lower crust in the vicinity of the slab is not included in Model 1. <br>The model evolves over a period of the last 15 Myr and shows a shallow subduction. </p> <p><br>Model 2</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 380= 12 Ma, 850 = 9 Ma, 1490 = 6 Ma, 2090 = 3 Ma, 2540 = 0 Ma)</p> <p>With high density block<br>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 2. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The high density material between the upper and lower crust in the vicinity of the slab is included in Model 2. The model evolves over a period of the last 15 Myr and shows a steep subduction.</p> <p><br>Model 3</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 310= 12 Ma, 670 = 9 Ma, 1120 = 6 Ma, 1690 = 3 Ma, 2040 = 0 Ma)</p> <p>Without high density block and high viscosity</p> <p>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 3. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The initial high viscosity material and without high density between the upper and lower crust in the vicinity of the slab is included in Model 3. The model evolves over a period of the last 15 Myr and shows subduction with a high dip angle. </p> <p><br>Model4</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 330= 12 Ma, 770 = 9 Ma, 1190 = 6 Ma, 1640 = 3 Ma, 2040 = 0 Ma)<br> <br>With high density block and high viscosity<br>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 4. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The high density and high viscosity material between the upper and lower crust in the vicinity of the slab is included in Model 4. The model evolves over a period of the last 15 Myr and shows a steep subduction</p>
The mark of captivity: plastic responses in the ankle bone of a wild ungulate (Sus scrofa)
Deciphering the plastic (non-heritable) changes induced by human control over wild animals in the archaeological record is challenging. We hypothesized that changes in locomotor behaviour in a wild ungulate due to mobility control could be quantified in the bone anatomy. To test this, we experimented the effect of mobility reduction on the skeleton of wild boar (Sus scrofa), using the calcaneus shape as a possible phenotypic marker. We first assessed differences in shape variation and covariation in captive reared and wild caught wild boars, taking into account differences in sex, body mass, available space for movement, and muscle force. This plastic signal was then con-trasted with the phenotypic changes induced by selective breeding in domestic pigs. We found that mobility reduction induces a plastic response beyond the shape variation of wild boars in their natural habitat, associated with a reduction in the range of locomotor behaviors and muscle loads. This plastic signal of captivity in the calcaneus shape differ from the main changes induced by selective breeding for larger muscle and earlier development that impacted the pigs' calcaneus shape in a much greater extent than the mobility reduction during the domestication process of their wild ancestors.
Experimental manipulation of perceived predation risk and cortisol generates contrasting trait trajectories in plastic crucian carp
<p>Most animals constitute potential prey and must respond appropriately to predator-mediated stress in order to survive. Numerous prey also adaptively tailor their response to the prevailing level of risk and stress imposed by their natural enemies, i.e. they adopt an inducible defence strategy. Predator exposure may activate the stress axis, and drive the expression of anti-predator traits that facilitate survival in a high-risk environment (the predation–stress hypothesis). Here, we quantified two key morphological anti-predator traits, body morphology and coloration in crucian carp following exposure (or not) to a predator (pike) as well as to experimental manipulation of physiological stress via implants containing either cortisol or a cortisol inhibitor. We found that predator-exposed fish expressed a deeper-bodied phenotype and darker body coloration as compared with non-exposed individuals. Skin analyses revealed that an increase in the amount of melanophores caused the dramatic colour change in predatorexposed fish. Increased melanization is costly, and the darker body coloration may act as an inducible defence against predation, via a conspicuous signal of the morphological defence or by crypsis towards dark environments and a nocturnal lifestyle. By contrast, the phenotype of individuals carrying cortisol implants did not mirror the phenotype of predator-exposed fish but instead exhibited opposite trajectories of trait change: a shallow-bodied morphology with a lighter body coloration as compared with sham-treated fish. The cortisol inhibitor did not influence the phenotype of fish i.e. neither body depth nor body coloration differed between this group and predator-exposed fish having a sham implant. However, our results illuminate a potential link between stress physiology and morphological defence expression.</p>
ScienceDex guides
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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.