Skip to main content
Powered by ShareScore

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.

96

datasets available to search

ShareScore release 0.7.1

Reset

Dataset results

96 results for “signaling behavior”

Learn how ShareScore rates datasets ↗
zenodo44/100

Data set for "Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior"

<p>Data set for: Le Merre P, Esmaeili V, Charri&egrave;re E, Galan K, Salin P-A, Petersen CCH, Crochet S (2018) Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron, https://doi.org/10.1016/j.neuron.2017.11.031</p> <p>There are 44 files in this data upload:<br> 1.&nbsp;&nbsp; &nbsp;&#39;2018_LeMerre_Neuron.pdf&#39; - this is a pdf version of the online publication.<br> 2.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_data.mat&#39; - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_data.mat&#39; - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4.&nbsp;&nbsp; &nbsp;&#39;Opto_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5.&nbsp;&nbsp; &nbsp;&#39;Mus_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6.&nbsp;&nbsp; &nbsp;&#39;Learning_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7.&nbsp;&nbsp; &nbsp;&#39;Exposed_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&rsquo;.<br> 9.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap2.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&rsquo;; &rsquo;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 10.&nbsp;&nbsp; &nbsp;&#39;scatterplot_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39;.<br> 11.&nbsp;&nbsp; &nbsp;&#39;SEP_colormtrx.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39;; &#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39;.<br> 12.&nbsp;&nbsp; &nbsp;&#39;zscore_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 13.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Chronic_LFP_dataViewer.m&#39;.<br> 14.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Chronic_LFP_data.mat&#39;.<br> 15.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Silicon_Probe_dataViewer.m&#39;.<br> 16.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Silicon_Probe_data.mat&#39;.<br> 17.&nbsp;&nbsp; &nbsp;&#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18.&nbsp;&nbsp; &nbsp;&#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19.&nbsp;&nbsp; &nbsp;&#39;plot_fig2A_SEP_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20.&nbsp;&nbsp; &nbsp;&#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21.&nbsp;&nbsp; &nbsp;&#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22.&nbsp;&nbsp; &nbsp;&#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23.&nbsp;&nbsp; &nbsp;&#39;plot_fig3B_Amplitude_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Randomization.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26.&nbsp;&nbsp; &nbsp;&#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27.&nbsp;&nbsp; &nbsp;&#39;plot_fig4A_SEP_H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28.&nbsp;&nbsp; &nbsp;&#39;plot_fig4B_Amplitude_ H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31.&nbsp;&nbsp; &nbsp;&#39;plot_fig4D_Photoinhibitions.m&#39; - this is a Matlab code, which analyses the data in &#39;Opto_Inactivation_data.mat&#39;, and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32.&nbsp;&nbsp; &nbsp;&#39;plot_figS2D_Performance_DetectionTask_NeutralExposition.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33.&nbsp;&nbsp; &nbsp;&#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34.&nbsp;&nbsp; &nbsp;&#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35.&nbsp;&nbsp; &nbsp;&#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36.&nbsp;&nbsp; &nbsp;&#39;plot_figS4B_Pharmacological_Inactivations.m&#39; - this is a Matlab code, which analyses the data in &#39;Mus_Inactivation_data.mat&#39;, and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37.&nbsp;&nbsp; &nbsp;&#39;Load_LFP_Multisite_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Chronic_LFP_data.mat&#39;.<br> 38.&nbsp;&nbsp; &nbsp;&#39;Load_Silicon_Probe_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Silicon_Probe_data.mat&#39;.<br> 39.&nbsp;&nbsp; &nbsp;&#39;Load_Optogenetic_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Opto_Inactivation_data.mat&#39;.<br> 40.&nbsp;&nbsp; &nbsp;&#39;Load_Pharmacological_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Mus_Inactivation_data.mat&#39;.<br> 41.&nbsp;&nbsp; &nbsp;&#39;bonf_holm.m&#39; - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code &#39;plot_figS4B_Pharmacological_Inactivations.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42.&nbsp;&nbsp; &nbsp;&#39;boundedline.m&#39; - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&rsquo;; &#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43.&nbsp;&nbsp; &nbsp;&#39;inpaint_nans.m&#39; - this is a Matlab code, which is called in the Matlab code &#39;boundedline.m&#39;.<br> 44.&nbsp;&nbsp; &nbsp;&#39;PSTH_Simple.m&#39; - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.</p>

opencc-by-4.0Dec 2017View details →
zenodo40/100

Data for "Exemplifying parametric timed specifications over signals with bounded behavior"

<p>Data for &quot;<strong>Exemplifying parametric timed specifications over signals with bounded behavior</strong>&quot; by &Eacute;tienne Andr&eacute;, Masaki Waga, Natuski Urabe, and Ichiro Hasuo, published in the proceedings of the 14th NASA Formal Methods Symposium (NFM 2022)</p> <p>See README.md in the zipped file for explanations and instructions for reproductibility.</p>

opencc-by-4.0Jan 2022View details →
zenodo40/100

Figure 4 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 4 Spotted towhee predictors from the AICc top model for PC1approach (a), PC2fly (b), and PC3song (c). (a) Towhees approach more slowly and maintain a greater distance from the speaker as sound level increases. Shading represents 95% CI and rug plot denotes sound level of individual trials. California spotted towhees fly more frequently on control trials (b) and display a weaker song response during treatment-off trials (c) than on all other trials, within and among populations (see [a] for color legend). (b–c) Violins denote kernel density probabilities differentiated by trial type (x axis) and population (color), with means (±1 SE) connected by dotted line; boxplots denote median and quartiles, and whiskers show 1.5 times the interquartile range; points represent individual trials; and asterisks denote significant contrasts corresponding to 95% CIs (see Supplementary Appendix Table A3 for values). Data displayed as untransformed components.

opencc-by-4.0Jul 2021View details →
zenodo40/100

Figure 2 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 2 Site design schematic of Idaho and California study areas. Tripods represent loudspeaker setups. aAny trial with chorusing cicadas was counted as cicada treatment, regardless of the trial type (treatment-on, treatment-off, control). bControl sites had the same layout as treatment sites, but with mock loudspeaker setups. cWe excluded cicada trials from analysis for spotted towhees due to low sample size.

opencc-by-4.0Jul 2021View details →
zenodo40/100

Figure 3 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 3 Lazuli bunting predictors from the AICc top model for PC1fly (a) and PC3approach (b) response variables. (a) Buntings fly more as sound level increases in the presence of cicada noise (dashed line) and fly less as sound level increases in the absence of cicada noise (solid line). Rug plot indicates sound level for individual trials differentiated by presence/absence of cicadas (see [b] for color legend). (b) As sound level increases, buntings approach the conspecific speaker more slowly and maintain a greater distance from it. (a &amp; b) Shaded bands denote 95% CIs. Data displayed as untransformed components.

opencc-by-4.0Jul 2021View details →
zenodo40/100

Figure 1 in Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

Figure 1 Spectrograms (a) of spotted towhee song (left) and lazuli bunting song (right) in quiet control conditions. (b) Power spectra of the three treatments are overlayed with bunting and towhee song power spectra. Power spectra are normalized to a relative peak amplitude of 70 dB (re 1 dimensionless sample units). Treatment noise and song spectra overlap substantially, suggesting high masking potential.

opencc-by-4.0Jul 2021View details →
dryad36/100

Cross-context behavioral correlations and signals of aggression in females of a livebearing fish

<p>Behaviors may be adaptively correlated with each other and with other aspects of phenotype. We investigated behavior across foraging, mating, and risk contexts in females of the poeciliid fish, <em>Girardinus metallicus</em>. We quantified relationships between these behaviors and aggression signals, body size, and reproductive output. Behaviors describing aggression and boldness, some of which were repeatable, were correlated in females. Aggression was signaled by the darkening of a black spot on the extended dorsal fin or by fin flaring. Spot darkening occurred during intra- and intersexual interactions and was positively correlated with inter-female aggression and negatively correlated with time females spent following males, suggesting that it honestly indicates motivational state. In contrast, fin flaring was only directed at females and occurred more frequently in the foraging context. Larger females had fewer offspring and received fewer courtship displays, possibly because they were more aggressive to males. In contrast to studies of males, we found a negative relationship between rank order of boldness and aggression, consistent with either a tradeoff or with selection favoring the negative relationship. Our results highlight the importance of studying females in model systems to uncover novel patterns and potentially meaningful departures from what is typically seen in males.</p>

opencc-zeroFeb 2024View details →
dryad36/100

Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds

<p>Recent research suggests that anthropogenic noise can substantially alter animal behavior. Although there are many sources of natural background noise, the relative influence of these sounds on behavior has received much less attention. Using landscape-scale playbacks of rushing rivers and crashing ocean surf, we investigated how habitat appropriate natural noise alters territorial defense behaviors in lazuli buntings (<i>Passerina amoena</i>) occupying riparian areas and spotted towhees (<i>Pipilo maculatus</i>) in riparian and coastal areas when exposed to simulated intruder song. We also incorporated naturally occurring cicada noise as an acoustic source influencing lazuli bunting behavior. Both songbird species possess songs that share substantial spectral overlap with low-frequency, water-generated noise, and lazuli bunting song shares an additional high-frequency overlap with cicada calls. Thus, there is potential for background acoustic conditions to mask conspecific signals. We found that detection and discrimination of conspecific playback occurred more slowly for both species as background sound level increased. Lazuli buntings also exhibited complex flight behavior in noise, suggesting they respond differently depending on the amplitude and type (with versus without cicada calls) of background noise. Our results suggest natural noise can impair territorial defense behaviors in songbirds, highlighting natural soundscapes as an under-appreciated axis of the environment.</p>

opencc-zeroOct 2021View details →
dryad36/100

Varied female and male courtship behavior facilitated the evolution of a novel sexual signal

<p><span><span>Sexual selection can contribute to speciation when signals and preferences expressed during mate choice are coupled within groups, but come to differ across groups (generating assortative mating). When new  sexual signals evolve, it is important to investigate their roles in both  mate location and courtship contexts, as both signaling functions are critical in mate choice. In previous work, researchers identified two new male morphs (silent and purring) in Hawaiian populations of the Pacific field cricket, </span><span>Teleogryllus oceanicus.</span><span> These morphs likely evolved because they protect males from an acoustically orienting parasitoid, yet still obtain some reproductive success. But, it remains unknown how purring morphs function in close courtship encounters. We compared the relative success of the very recently evolved purring morph to that of the ancestral and silent morphs during courtship encounters. Purring males produce a novel courtship song and were not as successful in courtship as the ancestral type, but were mounted by females as often and as quickly as the obligately silent morph that arose and spread ~20 years ago. Purring males initiate courtship more quickly than other morphs, and females from populations where purring is common exhibit higher overall mounting rates. Thus, differences in the behavior of purring males and of females from populations where purring is common may have facilitated the origin of this novel sexual signal. We found no assortative mating between males of a given morph and females from their own population,  and so we hypothesize that multiple male types will be maintained within the species because each achieves fitness in different ways. </span></span></p>

opencc-zeroMar 2022View details →
dryad36/100

Short-range C-signaling restricts cheating behavior during Myxococcus xanthus development

<p>Starving <em>Myxococcus xanthus</em> bacteria use short-range C-signaling to coordinate building of multicellular mounds with differentiation from rods into spores during fruiting body development. A <em>csgA</em> mutant deficient in C-signaling can cheat on wild type (WT) in mixtures and form spores disproportionately, but our understanding of cheating behavior is incomplete. We report that cheating requires excess WT cells in the initial mixture and occurs during the mound-building phase of development. We subjected mixtures of WT and <em>csgA</em> cells at different ratios to co-development, and used confocal microscopy and image analysis to quantify the arrangement and morphology of cells near the bottom of nascent fruiting bodies (NFBs). At a ratio of one WT to four <em>csgA</em> cells (1:4), NFBs failed to form. At 1:2, broad mounds formed with half the normal cell density and very few spores. At 1:1, NFBs formed normally with a similar number of WT and <em>csgA</em> rods early in development and a similar number of spores later, so C-signaling by WT rescued <em>csgA </em>development efficiently, but the mutant<em> </em>did not cheat. In contrast, at 2:1 and 4:1 excess WT starting ratios, <em>csgA</em> rods were more abundant than expected in early NFBs, indicative of cheating during mound formation. As NFBs matured, <em>csgA</em> and WT eventually formed spores with similar efficiency, although <em>csgA</em> began sporulation earlier and closer to the radial center. Our results reveal restrictions on cheating behavior, which may have selected C-signaling evolutionarily, and may explain the prevalence of short-range signaling in bacterial biofilm and multicellular animal development.</p>

opencc-zeroJun 2022View details →
zenodo36/100

Statistical behavior of Laser-Induced Plasma and its complementary characteristic signals

<p>The dataset provided contains measurements for an article in The Journal of Analytical Atomic Spectrometry (JAAS). Further details are available in the .txt file included in the folder.</p>

opencc-by-4.0Jun 2024View details →
zenodo36/100

Dopamine signaling in the dorsomedial striatum promotes compulsive behavior -- Western Blot Supplement

<p>Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals&rsquo; transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

Action Prediction Error: a value-free dopaminergic teaching signal that drives stable learning - Behavioral dataset

<p>Behavioral data to reproduce figures of this paper: https://doi.org/10.1101/2022.09.12.507572</p> <p>See Github repository:&nbsp;https://github.com/HernandoMV/APE_paper</p>

opencc-by-4.0Oct 2022View details →
dryad36/100

Data and code for: Somatostatin peptide signaling dampens cortical circuits and promotes exploratory behavior

<p>Our dataset reflects the data and code used in Brockway et al., Cell Reports. We sought to characterize the unique role of somatostatin (SST) in the prelimbic (PL) cortex. We perform slice electrophysiology in pyramidal and GABAergic neurons to characterize the pharmacological mechanism of SST signaling, and fiber photometry of GCaMP6f fluorescent calcium signals from SST neurons to characterize the activity profile of SST neurons during exploration of an elevated plus maze (EPM) and open field test (OFT). We used local delivery of a broad SST receptor (SSTR) agonist and antagonist to test causal effects of SST signaling. SSTR activation hyperpolarizes layer 2/3 pyramidal neurons, an effect that is recapitulated with optogenetic stimulation of SST neurons. SST neurons in PL are activated during EPM and OFT exploration, and SSTR agonist administration directly into the PL enhances open-arm exploration in the EPM. This work describes a broad ability for SST peptide signaling to modulate microcircuits within the prefrontal cortex and related exploratory behaviors.</p>

opencc-zeroAug 2023View details →
dryad36/100

Data from: Behavioral threat and appeasement signals take precedence over static colors in lizard contests

<p>The interplay between morphological (structures) and behavioral (acts) signals in contest assessment is still poorly understood. During contests, males of the common wall lizard (<em>Podarcis muralis</em>) display both morphological (i.e. static color patches) and behavioral (i.e. raised-body display, foot shakes) traits. We set out to evaluate the role of these putative signals in determining the outcome and intensity of contests by recording agonistic behavior in ten mesocosm enclosures. We find that contests are typically won by males with relatively more black coloration, which are also more aggressive. However, black coloration does not seem to play a role in rival assessment, and behavioral traits are stronger predictors of contest outcome and winner aggression than prior experience, morphology, and coloration. Contest intensity is mainly driven by resource- and self-assessment, with males probably using behavioral threat (raised-body displays) and de-escalation signals (foot shakes) to communicate their willingness to engage/persist in a fight. Our results agree with the view that agonistic signals used during contests are not associated with mutual evaluation of developmentally-fixed attributes, and instead animals monitor each other to ensure that their motivation is matched by their rival. We emphasize the importance of testing the effect of signals on receiver behavior and discuss that social recognition in territorial species may select receivers to neglect potential morphological signals conveying static information on sex, age, or intrinsic quality.</p>

opencc-zeroMay 2024View details →
dryad36/100

Zona incerta distributes a broad movement signal that modulates behavior

Open the record for dataset details and reuse information.

publicDec 2023View details →
dryad36/100

Varied female and male courtship behavior facilitated the evolution of a novel sexual signal

Open the record for dataset details and reuse information.

publicMar 2022View details →
dryad36/100

Data from: Behavioral threat and appeasement signals take precedence over static colors in lizard contests

Open the record for dataset details and reuse information.

publicMay 2024View details →
dryad36/100

Cross-context behavioral correlations and signals of aggression in females of a livebearing fish

Open the record for dataset details and reuse information.

publicFeb 2024View details →
dryad36/100

Data and code for: Somatostatin peptide signaling dampens cortical circuits and promotes exploratory behavior

Open the record for dataset details and reuse information.

publicAug 2023View 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