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Dissecting the smell of fear from conspecific and heterospecific prey: Investigating the processes that induce anti-predator defenses.
Prey use chemical cues from predation events to obtain information about predation risk to alter their phenotypes. Though we know how many prey respond to predators, we still have a poor understanding of the processes and chemical cues involved during a predation event. We examined how gray treefrog tadpoles (Hyla verisciolor) altered their behavior and morphology when raised with cues from different stages of predator attack, predators fed different amounts of prey, and predators consuming different combinations of treefrog tadpoles or snails (Helisoma trivolvis). We found that starved predators and predators fed snails induced no defensive responses whereas tadpoles exposed to a predator consuming gray treefrogs induced greater hiding, lower activity, and relatively deeper tails. We also found that the tadpoles did not respond to crushed, chewed, or digested conspecifics, but they did respond to consumed (i.e. chewed + digested) conspecifics. When we increased the treefrog biomass consumed by predators, tadpoles frequently increased their defenses when only tadpoles were consumed and always increased their defenses when the total diet biomass was held constant via the inclusion of snails. When predators experienced temporal variation in diet composition, including cues from snails to cause additional digestive cues or chemical noise, there was no effect on tadpole phenotypes. Our results suggest that amphibian prey rely on cues from both chewing and digestion of conspecifics and that the presence of cues from digested heterospecifics play little or no role in adding chemical noise or increased digestive enzymes and by-products that interfere with induced defenses.
PsPM-VIS: SCR, ECG, respiration and eyetracker measurements in a delay fear conditioning task with visual CS and electrical US
<p>This dataset consists of a three-block experiment conducted with 29 healthy unmedicated participants (17 females and 12 males aged 25.3 +/- 3.7). The experiment contains a classical (Pavlovian) discriminant delay fear conditioning test. CSs are 2 full-screen fractals of approximate brightness, contrast, and spatial frequency. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathod/ring-anode configuration. SOA between the CS and US is 3.5 s. The first 2 blocks are fear acquisition, with 15 CS+US+, 15 CS+US-, and 30 CS- in each block, and the last block is an extinction phase with 20 CS- and 20 CS+ trials without US delivery. The order of trials in each block was randomized. No fixation cross was presented during CS. ITI is randomly determined on each trial to be an integer between 7 - 11 s. During ITI, a black fixation cross was presented in the center of a grey background (RGB 0.7, 0.7, 0.7). The blocks were recorded on the same day with a self-paced break. For all three blocks this dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements.</p>
PsPM-SF: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS (monophones/triads), performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 20 healthy unmedicated participants (10 females and 10 males, age range: 19 - 35 years, mean age: 24.2 +/- 4.9) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less. NOTE: In Staib et al. 2015, this dataset is denoted as SC1F</p>
PsPM-SCB2D: Skin conductance response from a delay fear conditioning task with auditory CS (monophones/triads)
<p>This dataset includes skin conductance response (SCR) for 22 healthy unmedicated participants (15 females and 7 males, age range: 18 - 32 years, mean age: 22.1 +/- 3.4) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less.</p>
Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students
<p>The deposited files contain a database related to the study of the fear of COVID-19 among Polish students and a code book. It is connected with the article titled <em>Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students</em></p>
PsPM-PCF2: PSR, SCR, ECG, respiration and startle-eyeblink EMG measurements in a delay fear conditioning task with 4 CS and different reinforcement rates
<p>This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration, electromyogram (EMG), and auditory startle output (snd, as delivered by sound card) measurements. Also included are CS and US information, keypress responses and keypress response times for 19 healthy unmedicated participants (5 males and 14 females aged 24.68 +/- 3.65 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS were 4 coloured rectangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. In the last learning block, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (100 dB, 50 ms duration with 2ms on- and offset ramp). The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
A striatal circuit balances learned fear in the presence and absence of sensory cues
<p>This repository stores the raw data that gave rise to the study by Kintscher et al. 2023, published by eLife (eLife2023;12:e75703; DOI:https://doi.org/10.7554/eLife.75703), as well as a preprint at bioRxiv (doi: 10.1101/2021.12.09.471922). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis. Below we supply information on the provided metadata files which, in turn, refer to individual raw data files.</p> <p><strong>General structure of the repository:</strong></p> <ul> <li>the raw data is organized in 22 subsets defined by the figures or supplementary figures they contribute to. Each subset is documented by its own metadata file;</li> <li>the metadata files listing names of the individual data files were made in “.csv” format, one per data subset. Field separator: comma;</li> <li>all metadata are summarized in the main metadata file “metadata_master.csv”. Field separator: comma;</li> <li>the dataset expands into the second repository accessible at the following doi: 10.5281/zenodo.7522958</li> </ul> <p> </p> <p><strong>Description of the non-textual data formats:</strong></p> <ul> <li>due to its excessive volume, the raw data for in-vivo microendoscopic Ca<sup>2+</sup> recordings is not present in the current repository but will be made available upon request from the corresponding author;</li> <li>certain blocks of data, such as the output of CaImAn package, or slide scanner imaging projects, contained multiple smaller files and thus were compressed into encryption- and password-free .zip archives;</li> <li>video recordings of animal behavior during the fear conditioning protocol were provided as unmodified “.wmv” files created by the VideoFreeze acquisition software (Med Associates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, 30 fps, bitrate 300 kb/s. Alternatively (Figure 4 – figure supplement 3), videos were stored by EthoVisionXT software as “.mpg” files with the following parameters: AVI container, mpeg4 codec, color space yuv420p, 1280x1024 pixels, 30 fps, bitrate 1206 kb/s;</li> <li>widefield fluorescent images of single coronal sections were either uploaded as original unmodified data in a proprietary .vsi format of Olympus slide scanning microscope (for Figure 1 – figure supplement 1, Figure 3 – figure supplement 1, Figure 7, Figure 7 – figure supplement 1), or were first binned and saved as a composite TIFF format at a resolution ~10.3 micrometers/pixel (for all the post-hoc images showing the virus expression and the fiber/lens placement). Both “.vsi” and TIFF formats are readable by Bioformats plugin working under open-source free software packages FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> , <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>), or QuPath (<a href="https://qupath.github.io/">https://qupath.github.io/</a>). Imaging metadata such as pixel resolution is embedded inside the individual .vsi or TIFF files. Correspondence between the fluorescent signals and image color channels is provided in the .csv metadata files;</li> <li>confocal fluorescent image stack underlying images in Figure 7B is provided in original proprietary “.lsm” format (Carl Zeiss). The imaging metadata is embedded in .lsm format, which can be read by the Bioformats plugin under FIJI/ImageJ or QuPath;</li> <li>data for patch clamp recordings (Figure 5 – figure supplement 2, Figure 8, Figure 8 – figure supplement 1, Figure 9) are provided as original .dat files from the acquisition software PatchMaster (HEKA Elektronik, Germany). These files can be imported using an Igor Pro extension “bpc_ReadHeka.xop” (for 32-bit Igor Pro versions 5.xx - 6.37) by Holger Taschenberger (<a href="https://www.wavemetrics.com/project/bpc_ReadHeka">https://www.wavemetrics.com/project/bpc_ReadHeka</a>), or using a Python script by Luke Campagnola (<a href="https://github.com/campagnola/heka_reader">https://github.com/campagnola/heka_reader</a>), or using a Matlab script “HEKA PatchMaster Importer” by Christian Keine (<a href="https://github.com/ChristianKeine/HEKA_Patchmaster_Importer">https://github.com/ChristianKeine/HEKA_Patchmaster_Importer</a>).</li> </ul>
Database Fear of COVID-19 and Vaccine Attitudes Examination Scale (VAX) in Spain
<p>This dataset contains data collected between November 15, 2021 and March 7, 2022, and between December 1, 2022 and February 6, 2023. It contains demographic variables, data on vaccinated people, responses to the items on the Fear of COVID-19 Scale, and to the items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels are written in English to facilitate their understanding.<br> Data and codebooks are provided in csv format, following the FAIR principles.<br> Three files are provided:<br> 1. Database Fear of COVID-19 and VAX, with the data related to sample characteristics and the answers to the items of the questionnaires.<br> 2. Database codebook of variables, with information of the labels of the variables of the Database file.<br> 3. Variable values codebook, with the labels of the values of the variables in the Database file.</p>
PsPM-trSP4: SCR measurement in response to face photographs withangry, neutral, and fearful expression while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) measurements for each of 42 healthy unmedicated participants (21 males and 21 females aged 25.2 +/- 4.0 years) in response to 38 face photographs (modified from the Karolinska Directed Emotional Faces set, KDEF), each presented once with angry, neutral, and fearful expression for 1 s each. Meanwhile, participants were listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was selected randomly on each trial from 7.5 s, 9.0 s, or 10.5 s (misprinted in the publications), plus a variable delay of around 0.1 s for image loading. The experiment was preceded by a 2-minute resting period and divided into 3 blocks, separated by resting periods. Each resting period begins and ends with an event marker in the SCR recordings.</p>
A VTA to basal amygdala dopamine projection contributes to signal salient somatosensory events during fear learning
<p>This dataset represents the raw data that gave rise to the study by Tang et al., J. Neuroscience 2020 (DOI: 10.1523/JNEUROSCI.1796-19.2020). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis. Below we supply information on the provided metadata files (which, in turn, refer to individual raw data files), and essential details on the specific data formats.</p> <ol> <li>raw data is organized in the datasets related to the Figs. 1H-L, 1M, 3B-C, 2, 4G-H, 5D-E, 5H-I of the paper (Tang et al., 2020);</li> <li>the metadata for each individual dataset, which lists individual data filenames from the respective dataset, are stored in separate “.csv” files, one per each dataset. Field separator: comma;</li> <li>individual metadata files for each dataset are described in the master metadata file “metadata_master.csv”. Field separator: comma;</li> <li>widefield fluorescent images of single coronal slices containing VTA (Fig. 1H-M) were converted from the proprietary format of Olympus slide scanning microscope into composite TIFF format readable by FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> or <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>). Image stacks covering the injection area of CTB into BA are provided in single multi-plane TIFF files, one per animal. Unless specified in the metadata file, the information on pixel resolution is embedded inside the individual image files as TIFF metadata. Attribution of fluorescent probes to the color channels is given in the corresponding metadata files; AP positions refer to Franklin KB, Paxinos G (2016) The mouse brain in stereotaxic coordinates, Ed 4. San Diego: Elsevier/Academic;</li> <li>confocal fluorescent image stacks acquired from single coronal slices containing VTA (Fig. 3B-C) are provided in the original format “.lsm” written by a confocal software Zen (Carl Zeiss). Besides the original Zen software, this format can be readily imported into FIJI/ImageJ using built-in converters “LSM…” or “Bio-Formats”. All the metadata containing imaging parameters is embedded in this format and can be accessed from FIJI/ImageJ after importing the stack. Attribution of fluorescent probes to the color channels is given in the corresponding “.csv” metadata file; AP positions refer to Franklin and Paxinos (2016);</li> <li>video recordings of animal behavior are provided as “.wmv” files, unmodified from the original version created by the acquisition software VideoFreeze (MedAssociates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, bitrate 300 kb/s, 30 fps;</li> <li>timing patterns of the sound (CS) and of the footshock (US) applications during each day of fear conditioning protocol are provided in the respective “.csv” files: “day1_CS_timing.csv”, “day2_CS_timing.csv”, “day2_US_timing.csv”, “day3_CS_timing.csv”. The timestamps in these files are expressed in seconds relative to the start of the VideoFreeze video recordings (see p. 4 above). These patterns are in common for all the video recordings done on respective training days in every data subset (Figs. 2, 4-5);</li> <li>extracellular optrode recording data (Fig. 2) were converted from the original proprietary “.mcd” format of the MC_Rack software (Multichannel Systems) into the open HDF5 format “.h5” using the Multi Channel DataManager software (Multichannel Systems). We provide both the continuous recording data acquired during behavior sessions on days 1-3 of the fear learning protocol, as well as recordings of light-evoked spiking acquired during the opto-tagging sessions on each experimental day. In the latter, each of 8-10 consequently recorded data files contains individual triggered sweeps (from -50 to +50 ms), each centered around a single laser pulse (t=0 ms);</li> <li>the raw unfiltered electrode data is stored as a 32-bit integer matrix 16xN (16 - number of electrodes, N - number of sampling points @ 40 kHz) in the container Data->Recording_0->AnalogStream->Stream_1->ChannelData of the HDF5 files. Conversion factor to the units of volts for the raw values is 1.25e-6. Correspondence of the rows of the data matrix to the electrodes “E1”-“E16” is indexed by the string array Data->Recording_0->AnalogStream->Stream_1->I_Label. The electrodes were physically arranged into four tetrodes in following groups: E1-E4, E5-E8, E9-E12, E13-E16;</li> <li>timestamps for the continuous recordings, or for each of the triggered sweeps in case of opto-tagging, are stored in the 2D floating point array Data->Recording_0->AnalogStream->Stream_1->ChannelDataTimeStamps; the timestamp values are in microseconds;</li> <li>the timing of CS and US stimuli produced by the VideoFreeze software (see p. 5 above) was sampled as input digital triggers by the amplifier for extracellular recordings for precise synchronization between the optrode- and video recordings. These signals are stored as single bit changes in the 32-bits integer N-samples array Data->Recording_0->AnalogStream->Stream_0->ChannelData of the corresponding HDF5 files.</li> </ol>
PsPM-SC2F: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS, performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 18 healthy unmedicated participants (9 females and 9 males, age range: 18 - 34 years, mean age: 24.8 +/- 4.2) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tone (4 s), and up or down frequency sweep sounds, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less.</p>
PsPM-SCRV4: Skin conductance responses in fear conditioning with visual CS and auditory US
<p>This dataset includes skin conductance response (SCR) measurements, CS and US information, keypress responses and keypress response times for each of 32 healthy unmedicated participants (16 males and 16 females aged 22.4+/-4.5 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS is a visual stimulus with variation in position on screen and color. Us is a 1s long white noise burst at 95dB presented over headphones. SOA between the CS and US is varied between participants to be 4, 10, or 16 s. The ITI is selected randomly on each trial from 14, 19, or 23 s</p>
PsPM-HRA1: Skin conductance responses in fear conditioning with visual CS and electrical US
<p>This dataset includes skin conductance response (SCR) measurements, CS and US information, keypress responses, keypress response times, key correctness and shock ratings for each of 20 healthy unmedicated participants (10 males and 10 females aged 22.2+/-4.0 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS is a visual stimulus appearing in the middle of the screen with variation in color. US is an electric shock as a 500 Hz current pulses train (individual pulse width: 0.5ms, varying current amplitudes (0.90+/-0.63 mA) train width:500 ms). SOA between the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 8, 9, 10 or 11 s. (This was correctly stated in Staib et al. (2015) but wrongly described in Bach et al. (2010).)</p>
Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory
<p>Eye tracking data and statistical analysis of:</p> <p>Koenig, S., Uengoer, M., & Lachnit, H. (2017). Attentional bias for uncertain cues of shock in human fear conditioning: Evidence for attentional learning theory. Frontiers in Human Neuroscience. doi: 10.3389/fnhum.2017.00266.</p> <p>Abstract: We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L), partial (P), and high (H) shock expectancy respectively. The cues differed with respect to the strength of their shock association (L < P < H) and the uncertainty of their prediction (L < P > H). During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues.<br> After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically.<br> Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early<br> attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention.</p>
When fear meets anger: Attitudes toward positively versus negatively evaluated pandemic policy proposals when negative emotions are competing in society
<p>Since the outbreak of the COVID‐19 pandemic, citizens of many countries have been faced with health‐related fear, as well as anti‐establishment and anti‐governmental anger. This emotional landscape colored the ongoing efforts by the authorities to convince citizens to accept various public policy proposals. In two studies (total <em>N</em> = 528, one preregistered) conducted in Poland in two different situations, we focused on the role of the simultaneously evoked pandemic fear and anti‐government anger in shaping attitudes toward the pandemic regulations. For negatively evaluated proposals, both of these emotions worked in opposite directions: fear was associated with increasing support, while anger was associated with increasing rejection. However, for positively evaluated policy proposals, fear and anger worked in consonance, and both were associated with increasing acceptance of the proposed regulations. Thus, while fear seems to motivate the acceptance of even negatively evaluated proposals that are seen as protective ones, anger works to amplify or polarize the proposals’ basic evaluations. Our findings could help plan the implementation of public policies in societies in times of turbulent emotional landscapes.</p>
Data from: Ecology of fear alters behaviour of grizzly bears exposed to bear-viewing ecotourism
<p>Humans are perceived as predators by many species and may generate landscapes of fear, influencing the spatiotemporal activity of wildlife. Additionally, wildlife might seek out human activity when faced with predation risks (human shield hypothesis). We used the Anthropause, a decrease in human activity resulting from the COVID-19 pandemic, to test the ecology of fear and human shield hypotheses and quantify the effects of bear-viewing ecotourism on grizzly bear (<em>Ursus arctos</em>) activity. We deployed camera traps in the Khutze watershed in Kitasoo Xai'xais Territory in the absence of humans in 2020 and with experimental treatments of variable human activity when ecotourism resumed in 2021. Daily bear detection rates decreased with more people present and increased with days since people were present. Human activity was also associated with more bear detections at forested sheltered sites, and less at exposed sites, likely due to the influence of habitat on bear perception of safety. The number of people negatively influenced adult male detection rates, but we found no influence on females with young detections, providing no evidence that females responded behaviourally to a human shield effect from reduced male activity. We also observed apparent trade-offs of risk avoidance and foraging. When salmon levels were moderate to high, detected bears were more likely to be females with young than adult males on days with more people present. Should managers want to minimize human impacts on bear activity and maintain baseline age-sex class composition at ecotourism sites, multi-day closures and daily occupancy limits may be effective. More broadly, this work revealed that antipredator responses can vary with the intensity of risk cues, habitat structure, and forage trade-offs, as well as manifest as the altered age-sex class composition of individuals using human-influenced areas, highlighting that wildlife avoids people across multiple spatiotemporal scales.</p>
The entorhinal cortex modulates trace fear memory formation and neuroplasticity in the lateral amygdala via cholecystokinin
<p>Although the neural circuitry underlying fear memory formation is important in fear-related mental disorders, it is incompletely understood. Here, we utilized trace fear conditioning to study the formation of trace fear memory. We identified the entorhinal cortex (EC) as a critical component of sensory signaling to the amygdala. Moreover, we used the loss of function and rescue experiments to demonstrate that release of the neuropeptide cholecystokinin (CCK) from the EC is required for trace fear memory formation. We discovered that CCK-positive neurons extend from the EC to the lateral nuclei of the amygdala (LA), and inhibition of CCK-dependent signaling in the EC prevented long-term potentiation of sensory signals to the LA and formation of trace fear memory. Altogether, we suggest a model where sensory stimuli trigger the release of CCK from EC neurons, which potentiates sensory signals to the LA, ultimately influencing neural plasticity and trace fear memory formation.</p>
raw data for Optogenetic Stimulation of Prelimbic Pyramidal Neurons Maintains Fear Memories and Modulates Amygdala Pyramidal Neuron Transcriptome
<p>Figure Legend </p> <p>Modulation of cellular excitability of Prelimbic (PrL) pyramidal neurons by optogenetic stimulation. (<strong>A</strong>) Representative traces in current-clamp configuration reporting evoked firing activity triggered by a series of depolarizing current steps (0 to 400 pA) applied to PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. The cumulative plot shows the changes in firing activity. (<strong>B</strong>) Representative traces of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups showing the firing activity triggered by linear depolarization from 0 to 800 pA. Graph (on the right) reports the effects of optogenetic stimulation on rheobase value. Namely, PrL pyramidal neurons of OPTO FEAR and No-EX groups recorded after optogenetic stimulation showed a clear reduction in the rheobase value in comparison to neurons of SHAM FEAR group (* at least <em>p</em>= 0.01). (<strong>C</strong>) Representative traces of Excitatory Post-Synaptic Currents (EPSC) of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. Graph plot (in the middle) and cumulative curve (on the right) depict the clear increase in firing frequency in PrL pyramidal neurons of OPTO FEAR and No-EX groups (* at least <em>p</em> = 0.01). (<strong>D</strong>) Graphs and cumulative curves report no significant differences in cellular excitability in PrL pyramidal neurons of SHAM NOT FEAR (black) and OPTO NOT FEAR (blue) groups. Data are reported as median with interquartile range.</p>
Data and code of "Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats"
<p>This dataset contains behavioral and transcriptomic data, and the original code related to the following article:</p> <p>Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats. Fanny Demars, Ralitsa Todorova, Gabriel Makdah, Antonin Forestier, Marie-Odile Krebs, Bill P Godsil, Thérèse M Jay, Sidney I Wiener, & Marco N Pompili (2022) Current Biology <em>32. https://doi.org/10.1016/j.cub.2022.05.050</em></p>
Data from: Flying without fear: shooting disturbance has little effect on site preferences in a conflict goose species
<p>Human-modified landscapes have created opportunities for numerous taxa. Agricultural expansion has proven advantageous for several Arctic-breeding goose species, leading to increased abundance and intensified conflict with farmers. Shooting is frequently implemented as a mitigation strategy to control population and via scaring to alter the spatial distribution of conflict species. However, the efficacy of such regimes in manipulating the fear landscape is not always investigated.</p> <p>We developed resource selection functions using GPS-tracking data for Greenland barnacle geese (<em>Branta leucopsis</em>) wintering on Islay, Scotland to assess foraging site choice. We assessed overall foraging site preference and evaluated the influence of shooting management on foraging site selection of key habitats.</p> <p>Barnacle geese selected for improved grassland areas and the likelihood of utilisation varied between these fields according to field-specific management. Protected areas were strongly selected for along with newly reseeded grassland. Field-level exposure to shooting disturbance did not cause a notable change in site selection.</p> <p><strong><em>Synthesis and Applications: </em></strong>Our results demonstrate the importance of providing refuges within managed agricultural landscapes to encourage site use and minimise conflict. We highlight how low intensity shooting disturbance may be ineffective in altering winter habitat selection of high-value foraging sites (especially near roosts). If future management aimed to stimulate redistribution higher intensity shooting disturbance along with the spatial and temporal coordination of shooting effort would likely be required to create a stronger perceived gradient of disturbance risk.</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.