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.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “state-dependent”
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
Data from: Whole-organism behavioral profiling reveals a role for dopamine in state-dependent motor program coupling in C. elegans.
<p>Animal behaviors are commonly organized into long-lasting states that coordinately impact the generation of diverse motor outputs such as feeding, locomotion, and grooming. However, the neural mechanisms that coordinate these diverse motor programs remain poorly understood. Here, we examine how the distinct motor programs of the nematode <i>C. elegans </i>are coupled together across behavioral states. We describe a new imaging platform that permits automated, simultaneous quantification of each of the main <i>C. elegans</i> motor programs over hours or days. Analysis of these whole-organism behavioral profiles shows that the motor programs coordinately change as animals switch behavioral states. Utilizing genetics, optogenetics, and calcium imaging, we identify a new role for dopamine in coupling locomotion and egg-laying together across states. These results provide new insights into how the diverse motor programs throughout an organism are coordinated and suggest that neuromodulators like dopamine can couple motor circuits together in a state-dependent manner. </p>
Dataset accompanying publication: "Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation is State-Dependent"
<p>Dataset accompanying publication:</p> <p>"Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation is State-Dependent", Alagapan, Schmidt, Lefebvre, Hadar, Shin and Frohlich</p> <p>For questions, contact flavio_frohlich@med.unc.edu</p> <p>The mat file consists of the following Matlab variables</p> <ol> <li><strong>Electrode Distance</strong>: 3 x 1 cell array containing the arrays (trial x electrode) of distance from stimulating electrode to recording electrode for the three ECoG participants. (First array corresponds to P001, Second array corresponds to P005 and Third array corresponds to P008)</li> <li><strong>Spectra_Electrode_EC</strong>: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-closed experiment. nTrial corresponds to number of trials, nFreq corresponds to frequencies at which spectral power is calculated, nChannels corresponds to number of electrodes in the analysis and nEpochs corresponds to “Before Stimulation”, “During Stimulation” and “After Stimulation” epochs.</li> <li><strong>Spectra_Electrode_EO</strong>: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-open experiment. The dimensions are the same as above. The first array consists of task-engaged dataset from Participant P001.</li> <li><strong>MI_Summary</strong>: 8 x 1 cell array consisting of 3 x 1 cell arrays of modulation indexes for the three participants. The 8 arrays stand for the modulation indexes in different epochs and different frequencies. Refer <strong>MI_Summary_Names</strong></li> <li><strong>MI_Summary_Names</strong>: 8 x 1 cell array consisting of strings denoting the arrays in <strong>MI_Summary</strong>. <strong>During</strong> in text corresponds to “During Stimulation” epoch and <strong>After</strong> corresponds to “After Stimulation” epoch.</li> <li><strong>f</strong>: Frequencies at which spectral power was estimated.</li> <li><strong>NetworkModel</strong>: Matlab struct containing the time series generated by the network model and corresponding spectra. The <strong>timeseries</strong> consists of 4 columns – 1<sup>st</sup> column corresponds to time, 2<sup>nd</sup> column corresponds to task-engaged state data, 3<sup>rd</sup> column corresponds to eyes-open state data and 4<sup>th</sup> column corresponds to eyes-closed state data. The <strong>spectra </strong>struct consists of spectral powers estimated in the different epochs. 1<sup>st</sup> column of each epoch array corresponds to task-engaged state, 2<sup>nd</sup> column corresponds to eyes-open state and the 3<sup>rd</sup> column corresponds to eyes-closed state.</li> <li><strong>SummationModel</strong>: Matlab struct containing the time series generated by the summation model and the peak values in spectra before and during stimulation by varying the two strength parameters. The columns correspond to stimulation strength while rows correspond to oscillation strength. The oscillation strength parameter was varied from 0.5 to 50 in steps of 0.5 and the stimulation strength parameter was varied from 0.1 to 10 in steps of 0.1. </li> </ol> <p> </p>
Sex- and state-dependent covariation of risk-averse and escape behavior in a widespread lizard
<p>Mounting evidence has show<span>n</span> that personality and behavioral syndromes have a substantial influence on <span>interspecific interactions</span> and <span>individual </span>fitness. However, the stability of <span>covariation among multiple behavioral traits involved in antipredator responses</span> <span>has seldom been tested</span>. Here, we <span>investigate whether </span>sex, <span>gravidity</span>, and parasit<span>e infestations influence</span> <span>the covariation</span> <span>between</span> <span>risk-aversion</span> <span>(hiding time within a refuge) </span>and <span>escape</span> <span>response (immobility, escape distance)</span> using a viviparous lizard<span>,</span> <em>Zootoca vivipara</em><span> as a model system. Our results</span> <span>demonstrated a correlation between risk-averse and escape behavior at the among-individual level, but </span>only <span>in</span> <span>gravid</span> females<span>. We found no significant correlations in either males or neonates</span>. <span>A striking result was the loss of the association in post-parturition females. </span>Th<span>is</span> <span>suggests</span> that the <span>'risk-averse</span> – <span>e</span>scape<span>'</span> syndrome <span>is ephemeral and only emerges in response to constraints on locomotion driven by reproductive burden.</span> <span>Moreover</span>, parasites have the potential to disassociate the<span> correlations between</span> <span>risk-aversion and </span>escape <span>response</span> in <span>gravid</span> females, <span>yet the causal chain requires further examination</span>. <span>Overall, o</span>ur findings provide evidence of differences in the association <span>between</span> <span>behaviors within the life-time of an individual</span> and <span>indicate</span> that <span>individual states, sex and life stages can together</span> influence the stability of behavioral syndromes.</p>
Dataset for "Impacts and state-dependence of AMOC weakening in a warming climate" by Bellomo & Mehling
<p>This dataset allows the user to reproduce figures from the journal article "Impacts and state-dependence of AMOC weakening in a warming climate" by Bellomo & Mehling.</p>
Fish resist temptation from junk food: State-dependent diet choice in reproductive Atlantic cod (Gadus morhua) facing seasonal fluxes of lipid-rich prey
<p>In ecological sciences, animal diets are often simplified to "resources" or "caloric quantities". However, in the present study, we investigated the optimal foraging strategy of Atlantic cod (Gadus morhua) when both macro- and micro-nutritional requirements are accounted for. Proteins cannot be synthesized from fatty acids, so the proteins for gonad development must come from other dietary sources. In addition, micronutrients are required in smaller quantities. For example, for cod, arachidonic acid (ARA) acts as a micronutrient precursor for prostaglandins, which is important for reproduction. We formulated a dynamic state-dependent model to make predictions about optimal diet choice and foraging behavior. We applied the model to a case study in the strait between Denmark and Sweden. The model predicted that energy acquired from dietary protein should be twice that acquired from lipids, with a small increase in the lipid requirements when gonads are growing. The model also predicted that the "energy sparing effect of lipids" made it beneficial to engage in risky foraging activity to supplement a lean diet with a little bit of fat. When we re-constructing the model to also optimize ARA uptake, the cod consumed relatively more ARA-rich crabs in the months prior to spawning, despite the otherwise poor energetic value of this prey. In support of the model predictions, field observations indicated that lipid stores reached a peak shortly after the arrival of the lipid-rich migrating herring and the fatty acid signal of these herring were evident in the liver of nearly all cod. Three month later, only half of the cod contained the herring-derived fatty acid signal, supporting the predicted shift in prey type prior to spawning. From these model predictions and field observations, we conclude that, also in the wild, nutritional requirements can be at least as important as pure energy acquisition.</p>
Raw experimental data and matlab codes for the paper "State-dependent driving : A route to non-equilibrium stationary states"
<p>Raw experimental data and matlab codes used for numerical simulation in the paper - "State-dependent driving : A route to non-equilibrium stationary states". The data and codes to generate figures 2, 3 and 5 are available in the folders named "Fig 2", "Fig 3" and "Fig 5" respectively.</p>
Dataset for "State-dependence of the equilibrium climate sensitivity in a clear-sky GCM" by Matthew Henry et al. (2023).
<p>Dataset for "State-dependence of the equilibrium climate sensitivity in a clear-sky GCM" by Matthew Henry, Geoffrey K. Vallis, Nicholas J. Lutsko, Jacob T. Seeley, and Brett A. McKim.</p>
Data from: Causes and consequences of individual variation: Linking state-dependent life histories to population performance
Open the record for dataset details and reuse information.
Data from: Temperament, state-dependent behaviors, and their interplay in large herbivores: Lessons from a long-term study on mule deer, elk, and bighorn sheep
Open the record for dataset details and reuse information.
Data from: Charge state-dependent ion condensation near conjugated polymer backbones
Open the record for dataset details and reuse information.
Sex- and state-dependent covariation of risk-averse and escape behavior in a widespread lizard
Open the record for dataset details and reuse information.
Fish resist temptation from junk food: State-dependent diet choice in reproductive Atlantic cod (Gadus morhua) facing seasonal fluxes of lipid-rich prey
Open the record for dataset details and reuse information.
Data from: Whole-organism behavioral profiling reveals a role for dopamine in state-dependent motor program coupling in C. elegans.
Open the record for dataset details and reuse information.
Datasets used for the publication: State-dependence explains individual variation in nest defence behaviour in a long-lived bird
<p>The uploaded datasets were used to test if variation in states predicts nest defence behaviour (a 'risky' behaviour) in a long-lived species, the barnacle goose (<em>Branta leucopsis</em>). Repeated measures of nest defence towards a human intruder (flight initiation distance or FID) of females of known age were collected during 15 breeding seasons. Increasing values of FID represent increasing shyness. Adaptive models have predicted that an individual's residual reproductive value or 'asset' is an important state variable underlying variation in such risk-taking behaviour. Hence, we used this data to investigate how nest defence varies as a function of time of the season and individual age, two state variables that can vary between and within individuals and determine asset.<br> <br> </p>
Data from: When should I be aggressive? A state-dependent foraging game between competitors
More often than not, animals forage with other foragers present. A foraging game may take place when the outcome of a forager's actions depends on both its own and other foragers' strategies. Previous studies on predator–prey systems have verified that complex state-dependent foraging games exist between predators and prey. In this study, we looked for evidence of such a state-dependent foraging game between intra-guild competitors. We studied a desert rodent system featuring 2 coexisting species known to compete with each other: midday gerbils (Meriones meridianus, the dominant competitor) and 3-toed jerboas (Dipus sagitta, the subordinate competitor). We simultaneously manipulated the energetic states of both species and allowed them to forage and interact in arenas with artificial food patches. We found that both species responded to their own energetic states, whereas hungry jerboas also significantly responded to gerbils' energetic state in terms of food harvest. Gerbils preferred to carry food items away when foraging alone but switched to on-tray consumption when jerboas were present. Jerboas harvested more food when gerbils were hungry and the most intensive interference occurred when hungry jerboas encountered well-fed gerbils. A plausible explanation for these results is that the future rather than current value of cacheable food is more important to well-fed gerbils. In contrast, hungry gerbils prefer immediate consumption to completely excluding jerboas from resource patches.
Data from: State-dependent decision-making by predators and its consequences for mimicry
The mimicry of one species by another provides one of the most celebrated examples of evolution by natural selection. Edible Batesian mimics deceive predators into believing they may be defended, whereas defended Müllerian mimics have evolved a shared warning signal, more rapidly educating predators to avoid them. However, it may benefit hungry predators to attack defended prey, while the benefits of learning about unfamiliar prey depends on the future value of this information. Previous energetic state-dependent models of predator foraging behaviour have assumed complete knowledge, while informational state-dependent models have assumed fixed levels of hunger. Here, we identify the optimal decision rules of predators accounting for both energetic and informational states. We show that the nature of mimicry is qualitatively and quantitatively affected by both sources of state dependence. Associative learning weakens the extent of parasitic mimicry by edible prey because naive predators often attack defended models. More importantly, mimicry among equally highly defended prey may be parasitic or mutualistic depending on the ecological context. Finally, mimicry by prey with intermediate defences corresponds to Batesian or Müllerian mimicry depending on whether the mimic is profitable to attack by hungry predators, but it is not a special case of mimicry.
Data from: State-dependent behavior alters endocrine-energy relationship: implications for conservation and management
Glucocorticoids (GC) and triiodothyronine (T3) are two endocrine markers commonly used to quantify resource limitation, yet the relationships between these markers and the energetic state of animals has been studied primarily in small-bodied species in captivity. Free-ranging animals, however, adjust energy intake in accordance with their energy reserves, a behavior known as state-dependent foraging. Further, links between life-history strategies and metabolic allometries cause energy intake and energy reserves to be more strongly coupled in small animals relative to large animals. Because GC and T3 may reflect energy intake or energy reserves, state-dependent foraging and body size may cause endocrine-energy relationships to vary among taxa and environments. To extend the utility of endocrine markers to large-bodied, free-ranging animals, we evaluated how state-dependent foraging, energy reserves, and energy intake influenced fecal GC and fecal T3 concentrations in free-ranging moose (Alces alces). Compared with individuals possessing abundant energy reserves, individuals with few energy reserves had higher energy intake and high fecal T3 concentrations, thereby supporting state-dependent foraging. Although fecal GC did not vary strongly with energy reserves, individuals with higher fecal GC tended to have fewer energy reserves and substantially greater energy intake than those with low fecal GC. Consequently, individuals with greater energy intake had both high fecal T3 and high fecal GC concentrations, a pattern inconsistent with previous documentation from captive animal studies. We posit that a positive relationship between GC and T3 may be expected in animals exhibiting state-dependent foraging if GC is associated with increased foraging and energy intake. Thus, we recommend that additional investigations of GC- and T3-energy relationships be conducted in free-ranging animals across a diversity of body size and life-history strategies before these endocrine markers are applied broadly to wildlife conservation and management.
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p><span>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (</span><em><span>λ</span></em><sub><span>1</span></sub><span> / <em>λ</em><sub>0 </sub></span><span>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</span></p>
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (<em>λ</em><sub>1</sub> / <em>λ</em><sub>0 </sub>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</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.