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.
9,153
datasets available to search
ShareScore release 0.7.1
Dataset results
9,153 results for “behavior”
Dataset for "Large scale patterns and drivers of the diving behavior of large pelagic predators"
<p>This dataset comprises 694 independent diving depth estimates of large pelagic predators extracted from 101 tagging studies. For both daytime and nighttime observations, two unambiguous quantities were reported: 1) the preferred diving depth (D<sub>pref</sub>), representative of the approximate depth at which individuals spend most of the time, or in other words a mean representative depth; and (2) the preferred diving depth range (ΔD<sub>pref</sub>), representative of the portion of the water column where individuals are most commonly recorded by the tags, or, in other words, the vertical range over which they are most commonly observed. Alongside the diving depth data, three additional types of information were extracted to co-locate diving depth observations with environmental variables, and to account for potential ontogenetic behavioral effects. These additional pieces of information include the location and period of the observations, and a representative size of the tagged individual or group of individuals. The extraction process was replicated by two separate analysts to ensure accuracy and reliability. For further details on the dataset and extraction procedure, refer to the manuscript "<em>Large scale patterns and drivers of the diving behavior of large pelagic predators"</em>.</p>
Figures 1–8 in Breeding behavior of the Helmeted Manakin Antilophia galeata (Passeriformes: Pipridae) in a gallery forest from São Paulo state, Brazil
Figures 1–8. Egg color patterns, hatchlings and nestlings of the Helmeted Manakin, Antilophia galeata, in different developmental stages observed in a gallery forest from southeastern Brazil: (1) eggs with light background color and streaks distributed throughout their surfaces; (2) eggs with pale beige background and blotches; (3) eggs with reddish-brown blotches and scratches forming a crown; (4) hatchlings; (5) three days old nestlings evidencing the bright-yellow mouth lining; (6) seven days old nestlings; (7) 10 ten days old nestlings; (8) nestling in the day before fledging.
Data from: Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala
<p>This dataset accompanies our publication titled: "Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala."</p> <p><strong>Files Included:</strong></p> <p><strong>1. geno.N26.vcf.gz</strong><br> - Description: Contains genotypes for 26 Heterogeneous Stock rats whose gene expression was predicted.</p> <p><strong>2. pred_expr.Brain.N26.tsv</strong><br> - Description: This tab-delimited table contains predicted relative gene expression in the brain for 26 Heterogeneous Stock rats. <br> - Details: Predictions were made for 8,997 genes from linear models based on cis-eQTLs from whole brain hemisphere tissue downloaded from the <a href="https://ratgtex.org/download/">RatGTEx Portal</a>. A gene is included in the table if it had at least one significant cis-eQTL, and if its predicted expression in these 26 animals had nonzero variance. The values in the table give the predicted log2(relative expression), where log2(2) = 1 is the baseline expression from the two haplotypes of the gene if it had only reference alleles at all its regulatory loci.<br> - Additional Info: Predictions were generated using <strong>gene_expr_pred.py</strong> available at https://github.com/PejLab/gene_expr_pred<br> An explanation of the prediction model is given in https://doi.org/10.1101/2022.01.28.478116</p> <p><strong>3. Behavioral data.xlsx</strong><br> - Description: Contains behavioral data for the Heterogeneous Stock (HS) rats.<br> - Organization: Each sheet in the file corresponds to data for a specific figure.</p> <p><strong>Additional Dataset Locations:</strong></p> <p>The primary datasets generated during this study can be found on the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE212417">GSE212417</a></p> <p><strong>Publicly Available Datasets Utilized:</strong></p> <p>- Rattus norvegicus Ensembl v98 reference genome and genome assembly: <a href="http://useast.ensembl.org/Rattus_norvegicus/Info/Index">Rnor_6.0 </a><br> - JASPAR2022 transcription factor binding profiles for vertebrates: <a href="https://jaspar.genereg.net/">JASPAR</a><br> - ENCODE Honeybadger 2 ChIP-seq: <a href="https://personal.broadinstitute.org/meuleman/reg2map/">Broad Institute</a><br> - Liu et al. 2019106 GWAS for tobacco and nicotine addiction summary statistics: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358542/">PubMed</a><br> - RatGTEx Portal tissue-specific cis-eQTLs: <a href="https://ratgtex.org/download/">RatGTEx Portal </a><br> - 1000 Genomes European reference panel: <a href="https://alkesgroup.broadinstitute.org/LDSCORE/">Alkes Group</a><br> - KEGG pathways: <a href="https://www.kegg.jp/kegg/rest/keggapi.html">KEGG API</a></p>
Comparing travel behavior and opportunities to increase transportation sustainability in small cities, towns, and rural communities
<p>The vast majority of travel behavior and sustainable transportation research has focused on urban areas. A rural perspective is lacking. In this study, we aim to dive deeper into understanding how people travel and their perceptions and opinions about various components of travel in a majority rural state. By speaking directly with Vermonters through in-person interviews, we obtain uniquely personal points of view and analyze them for commonalities and differences between urban, suburban, and rural Vermonters. We ask questions on day-to-day challenges of traveling, suggestions for reducing greenhouse gas (GHG) emissions, responses to fuel prices, and opinions on electric vehicles. Some of our key findings include that rural areas struggle most with traveling long distances to reach services, urban areas are more concerned with traffic, and opinions on electric vehicle (EV) ownership are consistent across the state, with people being likely to consider owning an EV if costs were to decrease. Our interviews identify additional questions that should be evaluated further to help states develop practical and effective policies aimed at reducing GHG emissions in rural areas. We also recommend further in-depth survey research to provide a more complete picture of the potential to shift travel behavior, particularly in rural areas. This research adds to the body of knowledge in a historically understudied population, enabling the research community to better understand and work more closely with small and rural communities to address climate change and achieve deeper GHG emission reductions.</p>
Fig. 2 in Notes on the defensive behavior and activity of Ablepharus kitaibelii (Bibron & Bory de Saint-Vincent, 1833) in Bulgaria
Fig. 2. Spirally wound adult individual of A. kitaibelii from "Sinite Kamani" area, Sliven town. Photography: A. Dyugmedzhiev.
Fig. 1. Juvenile A in Notes on the defensive behavior and activity of Ablepharus kitaibelii (Bibron & Bory de Saint-Vincent, 1833) in Bulgaria
Fig. 1. Juvenile A. kitaibelii from Pancharevo, Sofia with orange-reddish tail. Photography: N. Tzankov.
Data from: Novel approaches for assessing acclimatization in birds reveal seasonal changes in peripheral heat exchange and thermoregulatory behaviors
<p>Using thermography and behavioral analyses, we found that heat exchange and thermoregulatory behaviors changed seasonally in chipping sparrows (<em>Spizella passerina</em>). Studies on seasonal acclimatization in birds have primarily involved metabolic measurements, few of which have investigated behaviors, and none have investigated changes in peripheral heat exchange. We captured chipping sparrows in the winter and summer of 2022 in Wilmington, North Carolina, and we collected thermal images of these birds at 15.0°, 27.5°, and 40.0°C. We found that heat dissipation through the bill and legs changed seasonally, but surprisingly both were higher in winter than in summer. We found that heat dissipating behaviors were more common in winter, whereas heat conserving behaviors were more common in summer, and that behaviors associated with resource costs (e.g., panting) or predation risk (e.g., bill tucking) showed the most distinct differences between seasons. Meanwhile, low-cost and low-risk postural adjustments (e.g., feather adjustments and tarsus exposure) did not vary as strongly between seasons but followed similar trends. The seasonal adjustments to behaviors suggest that non-acclimatized birds must use costly thermoregulatory behaviors more frequently than acclimatized birds. The use of thermography catalyzed the discovery of one completely novel behavior, and the first detection of a known behavior in a new species. Both novel behaviors aided in evaporative heat loss and occurred more commonly in winter, supporting the presence of seasonal acclimatization as evidenced by behavioral adjustments. These results provide novel insights to the process of acclimatization and suggest a role of behavioral adjustments in seasonal acclimatization.</p>
Illuminating the mechanism and allosteric behavior of NanoLuc luciferase
<p>NanoLuc, a superior β-barrel fold luciferase, was engineered 10 years ago but the nature of its catalysis<br> remains puzzling. Here experimental and computational techniques were combined, revealing that<br> imidazopyrazinone luciferins bind to an intra-barrel catalytic site but also to an allosteric site shaped on<br> the enzyme surface. Binding to the allosteric site prevents simultaneous binding to the catalytic site, and<br> vice versa, through concerted conformational changes. We demonstrate that restructuration of the<br> allosteric site can boost the luminescent reaction in the remote active site. Mechanistically, an intra-barrel<br> arginine coordinates the imidazopyrazinone component of luciferin which then react with O 2 via a radical<br> charge-transfer mechanism, and it also protonates the resulting excited amide product to form a light-<br> emitting neutral species. Concomitantly, an aspartate, supported by two tyrosines, is fine-tuning the blue<br> color emitter to secure a high emission intensity. This information is critical to engineering the next-<br> generation of ultrasensitive bioluminescent reporters.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages:<br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Data from: Prospection of potential actions during visual working memory starts early, is flexible, and predicts behavior.
<p>Raw data (EEG and behavior) reported in the manuscript "Prospection of potential actions in visual working memory starts early, is flexible, and predicts behavior" by Rose Nasrawi, Sage E.P. Boettcher, and Freek van Ede</p>
Data from: Genomic signatures of convergent shifts to plunge-diving behavior in birds
<p>Understanding the genetic basis of convergence at broad phylogenetic scales remains a key challenge in biology. Kingfishers (Aves: Alcedinidae) are a cosmopolitan avian radiation with diverse colors, diets, and feeding behaviors—including the archetypal plunge-dive into water. Transitioning from air to water poses major sensory and locomotor challenges that might affect the evolution of both sensory genes and morphological structures involved in these functions. Kingfishers therefore offer a powerful opportunity to explore the effects of convergent behaviors on the evolution of genomes and phenotypes, as well as direct comparisons between continental and island lineages. Here, we use whole-genome sequencing of 31 diverse kingfisher species to identify the genomic signatures associated with convergent feeding behaviors. We show that species with smaller ranges (i.e., on islands) have experienced stronger demographic fluctuations than those on continents, and that these differences have influenced the dynamics of molecular evolution. Comparative genomic analyses reveal positive selection and genomic convergence in brain and dietary genes in plunge-divers. These findings enhance our understanding of the connections between genotype and phenotype in a diverse avian radiation.</p>
figure 3 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 3 Variation in body pigmentation between different background coloration treatments during ethe xperimental time in H. arborea tadpoles. dl – dark-light treatment; d – dark treatment; dd – darkdark treatment; ld – light-dark treatment; l – light treatment; ll – light-light treatment
figure 4 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 4 The tadpole body coloration by treatment: day 0 – the start of the experiment, average pigmentation 69% of dark pixels, no treatment groups; day 20 of the experiment (day 20) – two treatment groups, Dark and Light, average pigmentation d – 93% and l – 62% of dark pixels; day 36 – the end of the experiment (day 36) – four treatments, dd – dark-dark treatment, ld – light-dark treatment, dl – dark-light treatment, ll – light-light treatment, average pigmentation dd – 90%, dl – 60%, ld – 91%, ll – 70% of dark pixels.
figure 1 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 1 Experimental design of the study. n – sample size; gs – developmental stage by Gosner, 1960
figure 6 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 6 Mean body shape of each treatment in two time points (after 20 days of the experiment – two treatments, and after 36 days/at the end of the experiment – four treatments) visualized in the canonical variate space (cv1 vs. cv 2). 20 d – dark treatment after 20 days; 20 l – light treatment after 20 days; 36 dd – dark-dark treatment after 36 days; 36 dl – dark-light treatment after 36 days; 36 ll – light-light treatment after 36 days; 36 ld – light-dark treatment after 36 days.
figure 8 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 8 How many times on average (with standard error) H. arborea tadpoles from different treatments were detected in the dark background: without predator chemical cues (black bars), with predator chemical cues (grey bars). dl – dark-light treatment; dd – dark-dark treatment; ld – light-dark treatment; ll – light-light treatment.
figure 2 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 2 Position of landmarks (l) and semi-landmarks (sl): l 1 – the tip of the snout, l 2 & 3 – dorsal and ventral points of anterior eye edge, l 4 – the intersection of head-body and dorsal edge of the tail fin, l 7 – the intersection of head-body and the ventral edge of the tail muscle, sl 5, 6 & 8 – the dorsal side of the tail fin, the dorsal side of the tail muscle, the ventral side of the tail muscle, the ventral side of the tail fin, all in the same vertical line as l 7, sl 9–12 – the dorsal side of the tail fin, the dorsal side of the tail muscle, the ventral side of the tail muscle, the ventral side of the tail fin ¼ the distance between l 7 and l 21, sl 13–16 – the dorsal side of the tail fin, the dorsal side of the tail muscle, the ventral side of the tail muscle, the ventral side of the tail fin ½ the distance between l 7 and l 21, sl 17–20 – the dorsal side of the tail fin, the dorsal side of the tail muscle, the ventral side of the tail muscle, the ventral side of the tail fin ¾ the distance between l 7 and l 21, l 21 – the tip of the tail
figure 7 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 7 Ontogenetic trajectories of each treatment in two time points (after 20 days of the experiment – two treatments, and after 36 days/at the end of the experiment – four treatments) visualized in the space of principal components (pc1 vs. pc2). 20 d – dark treatment after 20 days; 20 l – light treatment after 20 days; 36 dd – dark-dark treatment after 36 days; 36 dl – dark-light treatment after 36 days; 36 ll – light-light treatment after 36 days; 36 ld – light-dark treatment after 36 days.
figure 5 in EFfects of background color on pigmentation, morphological traits, and behavior in the European tree frog (Hyla arborea, Hylidae, Anura) tadpoles
figure 5 Body length variation between different background coloration treatments during experimental time in H. arborea tadpoles. dl – dark-light treatment; d – dark treatment; dd – dark-dark treatment; ld – light-dark treatment; l – light treatment; ll – light-light treatment.
Dataset for the collected responses for the items measuring constructs affecting eHS non-acceptance behavior in Nigeria
<p>This dataset is a collection of responses from the questionnaire distributed to study the e-health service non-acceptance behavior prominent in Nigeria. A total of 543 valid responses were collected. This research used an integration model based on TPB and SOR theory The dataset were analysed using PLS-SEM. Refer to the article for the results of this study.<br><br></p> <p><strong>Note:</strong> CO = Communication overload; CHO = Choice overload; PR = Perceived oisk; HL = Health literacy; NA = Negative attitude; SN = Subjective norms; PBC = Perceived behavioral control; INTU = Intention not to use eHS; NAB = Non-acceptance behavior</p>
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.