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.
3,673
datasets available to search
ShareScore release 0.9.0
Dataset results
3,673 results for “Temporal”
Personality and temporal plasticity in fish populations along a gradient of evolutionary divergence
<p>Personality is now recognized as an ecologically and evolutionarily significant phenomenon because it can affect fitness directly. However, empirical studies specifically tackling the importance of personality in the processes of adaptive divergence and speciation are scarce. Whether selection favours plasticity or canalization of personality traits in specific contexts and how ecological conditions affect the presence and the structure of personalities remain to be elucidated. We used five populations of Arctic charr, <em>Salvelinus</em> <em>alpinus</em>, chosen along a gradient of genetic and phenotypic divergence: an anadromous population supposedly close to the ancestral population and two pairs of sympatric lake morphs. Fish were raised individually from eggs in a common-garden experiment to specifically assess the genetic bases of the boldness trait in these populations. Thirty-two individuals per morph were repeatedly tested at 11 months old in an open field test with a shelter to assess boldness. The repeatability of the boldness trait (i.e. personality), within- and among-individual variation and temporal variation were assessed within each population. Then, the temporal plasticity in boldness and boldness mean score were assessed among populations. The results show differences between populations for the three variables, with the highest repeatability in the most diverged population, an increase in boldness over time and a lower mean value (especially the first time they were exposed to the novel environment) in the anadromous population. This suggests that boldness and temporal plasticity differ between the anadromous population and the more diverged lake populations under a controlled embryonic/juvenile environment. Further research is needed to verify the contribution of temporal plasticity for personality traits in the context of rapid adaptive radiation such as that seen in Arctic charr.</p>
Data from: Functionally analogous body- and animacy-responsive areas in the dog (Canis familiaris) and human occipito-temporal lobe
<p>Comparing the neural correlates of socio-cognitive skills across species provides insights into the evolution of the social brain and has revealed face- and body-sensitive regions in the primate temporal lobe. Although from a different lineage, dogs share convergent visuo-cognitive skills with humans and a temporal lobe which evolved independently in carnivores. We investigated the neural correlates of face and body perception in dogs (<em>N </em>= 15) and humans (<em>N</em> = 40) using functional MRI. Combining univariate and multivariate analysis approaches, we found functionally analogous occipito-temporal regions involved in the perception of animate entities and bodies in both species and face-sensitive regions in humans. Though unpredicted, we also observed neural representations of faces compared to inanimate objects, and dog compared to human bodies in dog olfactory regions. These findings shed light on the evolutionary foundations of human and dog social cognition and the predominant role of the temporal lobe.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 15 dogs</li> <li>structural scans of the same dogs incl. brain masks & skull-stripped versions</li> <li>files containing the condition names, onsets and durations for each dog and task run</li> <li>files containing the motion regressors incl. motion scrubbing for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for detailed sample descriptives and group-level imaging data of both species (<a href="https://osf.io/kzcs2/">https://osf.io/kzcs2/</a>).</p>
Supplementary Tables and Datasets for publication: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream
<p>This is a data dump of the tables, genomes, and .faa files that were too large to submit as part of the publication titled: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream</p> <p> </p> <p>Files here include:</p> <p>-Fasta file containing 1230 vMAGs.</p> <p>-Zip file containing individual fasta files for 125 MAGs</p> <p>-Annotations output for DRAM and DRAM-v for all MAGs and vMAGs</p> <p>-.faa proteins file for the full Freshwater / Wastewater / TARA Oceans dataset that was used for vContact2 biogeography analyses</p>
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics
<p>Datasets associated with https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797</p>
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
<p>Expansion of marine conservation areas (CA) necessitates resource-efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada's static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used eight years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA's regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 hours of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation's full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting.</p>
Identifying temporal dynamics in post-release survival of a restored large ungulate
<p>Wildlife translocation is an important conservation tool for restoring species and reducing global biodiversity decline; however, this practice is challenging for wildlife, and translocated individuals must adjust to their release landscapes for restoration success. The period following release is a vulnerable time for wildlife and determining when animals acclimate following translocation improves long-term estimates of population dynamics and allows researchers to efficiently tailor translocation design and post-release management to each population's needs.</p> <p>We evaluated demographic acclimation and investigated changes in post-release survival of a population of 106 elk (<em>Cervus canadensis</em>) translocated to Missouri, U.S.A., from 2011 to 2013 using generalized survival modeling. We defined the acclimation period as the duration of time prior to stabilization in mortality risk relative to time from release, and we evaluated acclimation duration from flexible plots of estimated mortality hazards across time. We simultaneously investigated factors contributing to post-release survival and compared survival rates and mortality sources observed during acclimation and post-acclimation periods.</p> <p>Elevated mortality risk for translocated elk spanned a 5-month acclimation period following release, with an additional one-year period reflecting reduced, but still elevated, mortality hazards. Baseline hazards during the initial 5-month acclimation period were approximately four times higher than the subsequent 4.4 years following release.</p> <p>Release cohort, but not age class or sex, was associated with post-release mortality risk, and mortality sources within the acclimation period tended to be undetermined with greater frequency relative to mortalities occurring after the acclimation period.</p> <p><em><strong>Synthesis and applications.</strong></em> Our results highlight the utility of flexible modeling approaches to evaluate dynamic post-release responses of translocated populations. Translocated elk demonstrated a pronounced demographic acclimation period that spanned five months following release. Changes in mortality sources and the timing of stabilization in mortality hazards suggest that demographic acclimation in translocated elk was associated with adjustments in physiological stress and movement rates that stabilize over a similar post-release duration. Improving post-release monitoring and assessment of restored populations by evaluating temporal dynamics of manifold biological and ecological responses of translocated animals.</p>
Temporal trends fish size spectra
<p>Analysis of the temporal trends of fish size spectra in stream communities across France. This releases is a brief overview of the main findings of the manuscript "Modulating effects of human pressures on climate warming-induced changes in size spectra of stream fish communities" published in Nature Ecology and Evolution</p>
Spatially and temporally continuous reconstruction of Antarctic Amundsen Sea sector ice sheet surface velocities: 1996-2018
<p>Spatially and temporally continuous reconstruction of ice sheet surface velocities for the Amundson Sea Sector of the Antarctica. The reconstruction is derived from the synthesis of annual published InSAR (R14: Rignot et al. 2014) and optical (G18: Gardner et al., 2018 & Gardner et al., 2022) surface velocities. Data are posted on a uniform 240 m by 240 m grid in Antarctic Polar Stereographic (EPSG:3031) coordinates. The temporal posting is every 2.4 months or 1/5 of a year.</p> <p>R14 and G18 annual velocity data have large errors and data gaps in both space and time that make the data challenging to work with. For this reason, a Spatially and temporally continuous reconstruction was made. These are the preprocessing steps that were applied to create the reconstruction:</p> <ol> <li>R14 component velocities [vx/vy] are mapped to the same 240-m grid as G18 for the Amundson Sea sector.</li> <li>Velocities falling outside of mapped ice extents (see Paolo et al., 2022) are set to no data values.</li> <li>A reference velocity is defined as the 1996 velocity field or the earliest valid measurement thereafter. The average of both velocities is taken if multiple observations exist for the first year of data.</li> <li>For areas moving faster than 200 m/yr., the percentage anomalies are calculated for all years relative the reference velocity. This was done for both G18 and R14 velocities separately.</li> <li>Annual velocity anomalies are then filter with a 5-km windowed moving median.</li> <li>G18 and R14 filtered anomalies are merge by taking the mean of each year. Years with less than 30% coverage for fast moving ice (>= 200 m/yr.) were discarded.</li> <li>If missing annual values were within 25 km of a valid datapoint they are filled using natural neighbor interpolation, otherwise anomalies were set to zero.</li> <li>Outside of fast-moving areas, annual anomalies are tapered to zero using a 10-km cosine taper.</li> <li>Merged and filled annual anomalies are then smoothed one last time using a 5-km windowed moving mean.</li> <li>To create a continuous record of velocity, annual anomalies are interpolated in time to every 1/5 of a year for every 240 m pixel using a spline interpolant and multiplied by the reference velocity.</li> </ol> <p>All x and y component velocities [vx/vy] and velocity magnitudes [v] are stored as individual geotiff files and are contained in the .zip included file. A visualization of the velocity magnitudes is included as an animated gif. </p> <p> </p> <p>References:</p> <p>Gardner, A., M. Fahnestock, and T. Scambos. (2022). MEaSUREs ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/6II6VW8LLWJ7. Date Accessed 04-07-2019.<br> <br> Gardner, A. S., Moholdt, G., Scambos, T., Fahnstock, M., Ligtenberg, S., van den Broeke, M., & Nilsson, J. (2018). Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. <em>The Cryosphere</em>, <em>12</em>(2), 521–547. https://doi.org/10.5194/tc-12-521-2018</p> <p>Paolo, F., Gardner, A., Greene, C., Nilsson, J., Schodlok, M., Schlegel, N., & Fricker, H. (2022). Widespread slowdown in thinning rates of West Antarctic Ice Shelves. <em>EGUsphere</em>, <em>2022</em>, 1–45. https://doi.org/10.5194/egusphere-2022-1128</p> <p>Rignot, E., J. Mouginot, and B. Scheuchl. (2014). MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0545.001. Date Accessed 04-07-2019.</p>
Temporal variation in translocated Isle Royale wolf diet
<p>Wolves (<em>Canis lupus</em>) can exert top-down pressure and shape ecological communities through predation of ungulates and beavers (<em>Castor</em> spp.). Therefore, understanding wolf foraging is critical to estimating their ecosystem-level effects. Specifically, if wolves are consumers that optimize tradeoffs between costs and benefits of prey acquisition, changes in these factors may lead to prey switching or negative-density dependent selection with potential consequences for community stability. For wolves, factors affecting cost and benefits include prey vulnerability, risk, reward, and availability which can vary temporally. We described wolf diet by frequency of occurrence and percent biomass and characterized diet using prey remains found in wolf scats on Isle Royale National Park, Michigan, USA during May–October 2019 and 2020. We used logistic regression to estimate prey consumption over time. We predicted prey with temporal variation in cost (availability and/or vulnerability) such as adult moose (<em>Alces</em> <em>alces</em>), calf moose, and beaver (Castor canadensis) to vary in wolf diets. We analyzed 206 scats and identified 62% of remains as beaver, 26% as moose, and 12% as other species (birds, smaller mammals, and wolves). Adult moose were more likely to occur in wolf scats in May, when moose are in poor condition following winter. The occurrence of moose calves peaked during June–mid July following birth but before calf vulnerability declined as they matured. In contrast, beaver occurrence in wolf scat did not change over time, reflecting the importance of low-handling cost prey items for recently introduced lone or paired wolves. Our results demonstrate that wolf diet is responsive to temporal changes in prey costs. Temporal fluctuation in diet may influence wolves' ecological role if prey respond to increased predation risk by altering foraging or breeding behavior.</p>
Temporal variation in selection influences microgeographic local adaptation
<p>Ecological heterogeneity can lead to local adaptation when populations exhibit fitness trade-offs among habitats. However, the degree to which local adaptation is affected by the spatial and temporal scale of environmental variation is poorly understood. A multi-year reciprocal transplant experiment was performed with populations of the annual plant <em>Leptosiphon parviflorus</em> living on adjacent serpentine and non-serpentine soil. Local adaptation over this small geographic scale was observed, but there were differences in the temporal variability of selection across habitats. On serpentine soil, the local population had a consistently large survival advantage, presumably due to the temporal stability in selection imposed by soil cation content. In contrast, a fecundity advantage was observed for the sandstone population on its native soil type, but only in the two years of study with the highest rainfall. A manipulative greenhouse experiment demonstrated that the fitness advantage of the sandstone population in its native soil type depends critically on water availability. The temporal variability in local adaptation driven by variation in precipitation suggests that continued drought conditions have the potential to erode local adaptation in these populations. These results show how different selective factors can influence spatial and temporal patterns of variation in fitness trade-offs.</p>
Nitrogen trade between plants that differ in their temporal demand of nutrients
<p>Primary data and to reproduce the analyses reported in the publication: </p> <p>Data S1 to S12 [Land_sp_feno2_18, Land_sp_feno2_19, Mypatches, Res2, PhenoWC2, INtran, INenrich, Npheno, Temp_var_pheno_N, land, phenoind, avgWC].</p> <p>A description of each item is provided in the file Metadata_Readme.doc included in the zip.file deposited</p> <p><br> </p>
Beyond 'greening' and 'browning': trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation
<p class="MsoNormal">Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long‑term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modelling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally-uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non‑photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote‑sensing‑based monitoring of trends in grasslands so that underlying processes can be discerned.</p>
Ecological and life-history traits predict temporal trends in biomass of boreal moths
<p> Dramatic insect declines, and their consequences for ecosystems globally, have received considerable attention recently. Yet, it is still poorly known if ecological and life-history traits can explain declines and whether insect decline occurs also at high latitudes. Insects' diversity and abundance are dramatically lower at high latitudes compared to the tropics, and insects might benefit from climate warming in high-latitude environments. </p> <p>We adopted a trait- and biomass-based approach to estimate temporal change between 1993 and 2019 in Finnish macro-moth communities by using data from 85 long-running light traps. We analysed spatio-temporal variation in biomass of moth functional groups with Joint Dynamic Species Distribution Models while accounting for environmental variables. </p> <p>We did not detect any declining trends in total moth biomass of moth functional groups, and most groups were stable over time. Moreover, biomass increased for species using coniferous trees, lichens, or mushrooms as hosts, multivoltine species, as well as monophagous and oligophagous species feeding on trees. We found that length and temperature of the growing season, winter climatic conditions, and habitat structure all partially explained variation in moth biomass.</p> <p>Although boreal moth communities are rapidly changing due to species turnover, in terms of total biomass they seem to contradict the trend of dramatic insect declines observed globally. This may lessen the immediate possibility of negative bottom-up trophic cascades in boreal food webs.</p>
A dynamic modelling approach to quantify pollution contributions from critical source areas within watersheds at fine temporal resolutions
<p>The support data for <em>A dynamic modelling approach to quantify pollution contributions from critical source areas within watersheds at fine temporal resolutions</em></p>
Temporal correlations among demographic parameters are ubiquitous but highly variable across species
<p><span>Temporal correlations among demographic parameters can strongly influence </span><span>population dynamics. Our empirical knowledge, however, is very limited regarding the direction and the magnitude of these correlations and how they vary among demographic parameters and species' life-histories</span><span>. Here, we</span><span> use long-term demographic data from 15 bird and mammal species with contrasting pace of life to quantify correlation patterns among five key demographic parameters: juvenile and adult survival, reproductive probability, reproductive success and productivity.</span><span> Correlations among demographic parameters were ubiquitous, more frequently positive than negative, but strongly differed across species. Correlations did not markedly change along the slow-fast continuum of life-histories, suggesting that they were more strongly driven by ecological than evolutionary factors. </span><span>As</span><span> positive temporal demographic correlations decrease the mean of the long-run population growth rate, the common practice of ignoring temporal correlations in population models could lead to the underestimation of extinction risks in most species.</span></p>
Aridity and soil fertility, not species richness, interact to affect temporal stability along a large natural gradient in Northern China
<p><span>There is mounting evidence from experimental studies that drought and nutrient enrichment can interact to impact the biodiversity and productivity of terrestrial ecosystems. Whether such interactive effect influence plant diversity and the temporal stability of community productivity of natural ecosystems is unknown. To fill this knowledge gap, we combined a field survey of plant diversity and soil conditions with remote sensing temporal estimates of primary productivity in grasslands along a natural gradient in northern China. We found that aridity and soil ammonium (NH<sub>4</sub><sup>+</sup>-N) interacted to influence temporal stability of NDVI. That is, the relationship between ammonium and temporal stability of NDVI shifted from positive to negative due to increased </span><span>standard deviation</span> <span>of NDVI with increasing aridity. Species richness was not related to temporal stability because it influenced the mean and </span><span>standard deviation</span> <span>of NDVI proportionally. As a result, soil fertility outweighed the contribution of species richness to temporal stability. Our study demonstrates the synergistic effect of aridity and soil fertility, but not species richness, on temporal stability along a large natural gradient. Predicting how environmental drivers affect diversity and the stable provisioning of ecosystem services in real-world ecosystems therefore requires a better understanding of the complex interactions among environmental drivers.</span></p>
Data from: Fission-fusion dynamics in sheep: The influence of resource distribution and temporal activity patterns
<p><span>Fission-fusion events, i.e. changes to the size and composition of animal social groups, are a mechanism to adjust the social environment in response to short-term changes in the cost-benefit ratio of group living. Furthermore, the time and location of fission-fusion events provide insight into the underlying drivers of these dynamics. Here, we describe a method for identifying group membership over time and for extracting fission-fusion events from animal tracking data. We applied this method to high-resolution GPS data of free-ranging sheep (<em>Ovis aries</em>). Group size was highest during times when sheep typically rest (mid-day and at night), and when anti-predator benefits of grouping are high while costs of competition are low. Consistent with this, fission and fusion frequencies were highest during early morning and late evening, suggesting that social restructuring occurs during periods of high activity. However, fission and fusion events were not more frequent near food patches and water resources when adjusted for overall space use. This suggests a limited role of resource competition. Our results elucidate the dynamics of grouping in response to social and ecological drivers, and we provide a tool for investigating these dynamics in other species.</span></p>
Workflow-Based Spatio-Temporal Data Analytics
<p>In the biodiversity domain, researchers often have to combine a large variety of heterogeneous spatio-temporal data sources. For example, the loss of biodiversity can be quantified by analyzing occurrence observations of various species across time. To find the root cause of that loss, occurrence data may need to be combined with satellite images to find possible correlations with climate variables. To facilitate an exploratory approach for this combination of data sources, it is essential to provide researchers with workflow-based tools such that each step during the formulation of a research hypothesis can be tracked. In this presentation, we will discuss Geo Engine, a workflow-based analysis platform for spatio-temporal data analytics, and its place within FAIR data spaces.</p>
Temporal trends in untreated stormwater quality
<p>This repository contains the excel file (NSQD+.xlsx) which has data from the National Stormwater Quality Database (NSQD) and more recent sources as well as two R files. These R files were used to analyze the NSQD+ in section 3.1 of the manuscript and create and analyze the NSQD++ in section 3.2 of the manuscript.</p>
Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation
<p>Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation</p> <p>Megan E. Young, Camille Spencer-Salmon, Clayton Mosher, Sarita Tamang, Kanaka Rajan, and Peter H. Rudebeck</p> <p>This dataset contains peripheral physiology (heart rate) and single neuron activity data from the paper entitled “Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation” by Young, Spencer-Salmon and colleagues.</p> <p>The study investigated how neurons in macaque subcallosal anterior cingulate cortex, basolateral amygdala, and rostromedial striatum encoded anticipated reward during Pavlovian and instrumental tasks.</p> <p>Heart rate data were pre-processed using methods described in the paper and were downsampled to 50 Hz for analysis. Neural activity data were pre-processed using steps as described in the paper.</p> <p>The dataset is saved as .MAT files.</p> <p>Files included:</p> <p>‘Pavlovian_task_neurons.mat’ – single neuron data from the Pavlovian task. Each of the 656 rows represents a single neuron and its associated information.</p> <p>‘Instrumental_task_neurons.mat’ – single neuron data from the instrumental task. Each of the 425 rows represents a single neuron and its associated information.</p> <p>“heart_rate.mat” – heart rate data from monkeys D and H.</p> <p><br> File structure and information:</p> <p>Pavlovian_task_neurons.mat</p> <p>Structure “Pavlovian_task_neurons”<br> - “Pavlovian_task_neurons.unit_name” – neuron specific identifier<br> - “Pavlovian_task_neurons.monkeynumber” – subject specific #<br> - “Pavlovian_task_neurons.monkeyname” – subject specific name<br> - “Pavlovian_task_neurons.date” – date on which data were recorded<br> - “Pavlovian_task_neurons.session” – session identifier from date (a-d)<br> - “Pavlovian_task_neurons.channel” – recording channel data recorded from<br> - “Pavlovian_task_neurons.wavemark” – waveform number (a-e)<br> - “Pavlovian_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “Pavlovian_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “Pavlovian_task_neurons.condition” – condition # from Monkey Logic for each of the trials (1 by n trials)<br> - “Pavlovian_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “Pavlovian_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)<br> - “Pavlovian_task_neurons.stimID” – stimulus shown on that trial (1 = neutral, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).</p> <p><br> Instrumental_task_neurons.mat</p> <p>Structure “Instrumental_task_neurons”<br> - “instrumental_task_neurons.unit_name” – neuron specific identifier<br> - “instrumental_task_neurons.monkeynumber” – subject specific #<br> - “instrumental_task_neurons.monkeyname” – subject specific name<br> - “instrumental_task_neurons.date” – date on which data were recorded<br> - “instrumental_task_neurons.session” – session identifier from date (a-d)<br> - “instrumental_task_neurons.channel” – recording channel data recorded from<br> - “instrumental_task_neurons.wavemark” – waveform number (a-e)<br> - “instrumental_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “instrumental_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “instrumental_task_neurons.condition” – condition 7-18 from Monkey Logic for each of the trials (1 by n trials). CNDs 7,8,13,14= CS+ juice vs CS+ water; CNDs 9,10,15,16= CS+ juice vs CS-; CNDs 11,12,17,18= CS+ water vs CS-.<br> - “instrumental_task_neurons.choice” – outcome associated with chosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.unchosen” - outcome associated with unchosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.chosenside” – side of the screen chosen (left [0] or right [1]) (1 by n trials)<br> - “instrumental_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “instrumental_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)</p> <p>heart_rate.mat</p> <p>Structures – “monkey_d_hr” – monkey D heart rate data<br> – “monkey_h_hr” – monkey H heart rate data</p> <p>Structure of monkey_d/h_hr<br> - “monkey_d/h_hr.trial_type” – trial type presented (1 = neutral, 2 = unsignaled, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).<br> - “monkey_d/h_hr.trials” – number of trials in each session by trial type<br> - “monkey_d/h_hr.session – percent change in heart rate for each trial from -200 ms to 3500ms after stimulus onset. Column 1 = session; Column 2 = trial type; Column 3 = trial number; Columns 4 – 3703 = percent change in heart rate.</p> <p> </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.