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.
5,805
datasets available to search
ShareScore release 0.9.0
Dataset results
5,805 results for “Data model”
Data from: Control of entropy in neural models of environmental state
Humans and animals construct internal models of their environment in order to select appropriate courses of action. The representation of uncertainty about the current state of the environment is a key feature of these models that controls the rate of learning as well as directly affecting choice behaviour. To maintain flexibility, given that uncertainty naturally decreases over time, most theoretical inference models include a dedicated mechanism to drive up model uncertainty. Here we probe the long-standing hypothesis that noradrenaline is involved in determining the entropy, and thus flexibility, of neural models. Pupil diameter, which indexes neuromodulatory state including noradrenaline release, predicted increases (but not decreases) in entropy in a neural state model encoded in human medial orbitofrontal cortex, as measured using multivariate functional MRI. Activity in anterior cingulate cortex predicted pupil diameter. These results provide evidence for top-down, neuromodulatory control of entropy in neural state models.
Data from: Kakusan4 and Aminosan: two programs for comparing nonpartitioned, proportional, and separate models for combined molecular phylogenetic analyses of multilocus sequence data
Proportional and separate models able to apply different combination of substitution rate matrix and among-site rate variation model to each locus are frequently used in phylogenetic studies of multilocus data. However, the selection from among nonpartitioned (i.e., a common combination of models is applied to all-loci concatenated sequences), proportional, and separate models is usually based on the researcher's preference rather than on any information criteria. The present study describes two programs, "Kakusan4" (for DNA sequences) and "Aminosan" (for amino-acid sequences), that allow the selection of evolutionary models based on several types of information criteria. The programs can handle both multilocus and single-locus data, in addition to providing an easy-to-use wizard interface and a non-interactive command line interface. In the case of multilocus data, substitution rate matrices and among-site rate variation models are compared at each locus and at all-loci concatenated sequences, after which nonpartitioned, proportional, and separate models are compared based on information criteria. The programs also provide model configuration files for MrBayes, PAUP*, PHYML, RAxML, and Treefinder to support further phylogenetic analysis using a selected model. The best-fit models were found to differ depending on the data set. Furthermore, differences in the information criteria among nonpartitioned, proportional, and separate models were much larger than those among the nonpartitioned models. These findings suggest that selecting from nonpartitioned, proportional, and separate models results in a better phylogenetic tree. Kakusan4 and Aminosan are available at http://www.fifthdimension.jp/. They are licensed under GNU GPL Ver.2, and are able to run on Windows, MacOS X, and Linux.
Data from: Measuring population differentiation using GST or D? A simulation study with microsatellite DNA markers under a finite island model and nonequilibrium conditions
Genetic differentiation of populations is a key question in population genetic investigations. Wright's FST (and its relatives such as GST) has been a standard measure of differentiation. However, the deficiencies of these indexes and their significance have been increasing realized in recent years, leading to some new measures being proposed, such as Jost's (2008) D. This has also stimulated some considerable debate which, in certain sense, makes empirical biologists even more confused, for example, on statistics which should be used for estimating population differentiation. Here we report a simulation study with neutral microsatellite DNA loci under a finite island model to compare the performance of GST and D, under non-equilibrium conditions, in particular. Our results suggest that there exist fundamental differences between the two statistics and neither GST nor D operate satisfactorily in all situations for quantifying differentiation. D is very sensitive to mutation models but GST noticeably less so ...
Data from: Ocean acidification affects acid-base physiology and behaviour in a model invertebrate, the California sea hare (Aplysia californica)
Behavioural impairment following exposure to ocean acidification-relevant CO2 levels has been noted in a broad array of taxa. The underlying cause of these disruptions is thought to stem from alterations of ion gradients (HCO3Cl) across neuronal cell membranes that occur as a consequence of maintaining pH homeostasis via the accumulation of HCO3Cl. While behavioural impacts are widely documented, few studies have measured acid-base parameters in species showing behavioural disruptions. In addition, current studies examining mechanisms lack resolution in targeting specific neural pathways corresponding to a given behaviour. With these considerations in mind, acid-base parameters and behaviour were measured in a model organism utilized for decades as a research model to study learning, the California sea hare (Aplysia californica). Aplysia exposed to CO2 elevated hemolymph HCO3Cl, achieving full and partial pH compensation at 1200 and 3000 μatm CO2, respectively. Increased CO2 did not affect self-righting behaviour. In contrast, both levels of elevated CO2 reduced the time of the tail-withdrawal reflex, suggesting a reduction in antipredator response. Overall, these results confirm that Aplysia are promising models to examine mechanisms underlying CO2-induced behavioural disruptions since they regulate HCO3-Cl- and have behaviours linked to neural networks amenable to electrophysiological testing.
Data from: Vaccination method affects immune response and bacterial growth but not protection in the Salmonella Typhimurium animal model of typhoid
Understanding immune responses elicited by vaccines, together with immune responses required for protection, is fundamental to designing effective vaccines and immunisation programs. This study examines the effects of the route of administration of a live attenuated vaccine on its interactions with, and stimulation of, the murine immune system as well as its ability to increase survival and provide protection from colonisation by a virulent challenge strain. We assess the effect of administration method using the murine model for typhoid, where animals are infected with S. Typhimurium. Mice were vaccinated either intravenously or orally with the same live attenuated S. Typhimurium strain and data were collected on vaccine strain growth, shedding and stimulation of antibodies and cytokines. Following vaccination, mice were challenged with a virulent strain of S. Typhimurium and the protection conferred by the different vaccination routes was measured in terms of challenge suppression and animal survival. The main difference in immune stimulation found in this study was the development of a secretory IgA response in orally-vaccinated mice, which was absent in IV vaccinated mice. While both strains showed similar protection in terms of challenge suppression in systemic organs (spleen and liver) as well as survival, they differed in terms of challenge suppression of virulent pathogens in gut-associated organs. This difference in gut colonisation presents important questions around the ability of vaccines to prevent shedding and transmission. These findings demonstrate that while protection conferred by two vaccines can appear to be the same, the mechanisms controlling the protection can differ and have important implications for infection dynamics within a population.
Data from: The impact of rate heterogeneity on inference of phylogenetic models of trait evolution
Rates of trait evolution are known to vary across phylogenies; however, standard evolutionary models assume a homogeneous process of trait change. These simple methods are widely applied in small-scale phylogenetic studies, whereas models of rate heterogeneity are not, so the prevalence and patterns of potential rate variation in groups up to hundreds of species remain unclear. The extent to which trait evolution is modelled accurately on a given phylogeny is also largely unknown because studies typically lack absolute model fit tests. We investigated these issues by applying both rate-static and variable-rates methods on (i) body mass data for 88 avian clades of 10–318 species, and (ii) data simulated under a range of rate-heterogeneity scenarios. Our results show that rate heterogeneity is present across small-scaled avian clades, and consequently applying only standard single-process models prompts inaccurate inferences about the generating evolutionary process. Specifically, these approaches underestimate rate variation, and systematically mislabel temporal trends in trait evolution. Conversely, variable-rates approaches have superior relative fit (they are the best model) and absolute fit (they describe the data well). We show that rate changes such as single internal branch variations, rate decreases and early bursts are hard to detect, even by variable-rates models. We also use recently developed absolute adequacy tests to highlight misleading conclusions based on relative fit alone (e.g. a consistent preference for constrained evolution when isolated terminal branch rate increases are present). This work highlights the potential for robust inferences about trait evolution when fitting flexible models in conjunction with tests for absolute model fit.
Data from: Sucrose and starch intake contribute to reduced alveolar bone height in a rodent model of naturally occurring periodontitis
While there is a burgeoning interest in the effects of nutrition on systemic inflammatory diseases, how dietary macronutrient balance impacts local chronic inflammatory diseases in the mouth has been largely overlooked. Here, we used the Geometric Framework for Nutrition to test how the amounts of dietary macronutrients and their interactions, as well as carbohydrate type (starch vs sucrose vs resistant starch) influenced periodontitis-associated alveolar bone height in mice. Increasing intake of carbohydrates reduced alveolar bone height, while dietary protein had no effect. Whether carbohydrate came from sugar or starch did not influence the extent of alveolar bone height. In summary, the amount of carbohydrate in the diet modulated periodontitis-associated alveolar bone height independent of the source of carbohydrates.
Data from: Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance
Non-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stochastic processes with stationary increments which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of beta-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://github.com/mrc-ide/skygrowth.
Data from: Modeling strategic sperm allocation: tailoring the predictions to the species
Two major challenges exist when empirically testing the predictions of sperm allocation theory. First, the study species must adhere to the assumptions of the model being tested. Unfortunately, the common assumption of sperm allocation models that females mate a maximum of once or twice does not hold for many, if not most, multiply and sequentially mating animals. Second, a model's parameters, which dictate its predictions, must be measured in the study species. Common examples of such parameters, female mating frequency and sperm precedence patterns, are unknown for many species used in empirical tests. Here, we present a broadly applicable model, appropriate for multiply, sequentially mating animals, and test it in three species for which data on all the relevant parameter values are available. The model predicts that relative allocation to virgin females, compared to non-virgins, depends on the interaction between female mating rate and the sperm precedence pattern: relative allocation to virgins increases with female mating rate under first-male precedence, while the opposite is true under later-male precedence. Our model is moderately successful in predicting actual allocation patterns in the three species, including a cricket in which we measured the parameter values and performed an empirical test of allocation.
Data from: Predictions of response to temperature are contingent on model choice and data quality
The equations used to account for the temperature dependence of biological processes, including growth and metabolic rates are the foundations of our predictions of how global biogeochemistry and biogeography change in response to global climate change. We review and test the use of 12 equations used to model the temperature dependence of biological processes across the full range of their temperature response, including supra- and sub-optimal temperatures. We focus on fitting these equations to thermal response curves for phytoplankton growth, but also tested the equations on a variety of traits across a wide diversity of organisms. We found that many of the surveyed equations have comparable abilities to fit data and equally high requirements for data quality (number of test temperatures and range of response captured), but lead to different estimates of cardinal temperatures and of the biological rates at these temperatures. When these rate estimates are used for biogeographic predictions, differences between the estimates of even the best fitting models can exceed the global biological change predicted for a decade of global warming. As a result, studies of the biological response to global changes in temperature must make careful consideration of model selection and of the quality of the data used for parametrizing these models.
Data from: Correlations of behavioral deficits with brain pathology assessed through longitudinal MRI and histopathology in the HDHQ150/Q150 mouse model of Huntington's disease
A variety of mouse models have been developed that express mutant huntingtin (mHTT) leading to aggregates and inclusions that model the molecular pathology observed in Huntington's disease. Here we show that although homozygous HdhQ150 knock-in mice developed motor impairments (rotarod, locomotor activity, grip strength) by 36 weeks of age, cognitive dysfunction (swimming T maze, fear conditioning, odor discrimination, social interaction) was not evident by 94 weeks. Concomitant to behavioral assessments, T2-weighted MRI volume measurements indicated a slower striatal growth with a significant difference between wild type (WT) and HdhQ150 mice being present even at 15 weeks. Indeed, MRI indicated significant volumetric changes prior to the emergence of the "clinical horizon" of motor impairments at 36 weeks of age. A striatal decrease of 27% was observed over 94 weeks with cortex (12%) and hippocampus (21%) also indicating significant atrophy. A hypothesis-free analysis using tensor-based morphometry highlighted further regions undergoing atrophy by contrasting brain growth and regional neurodegeneration. Histology revealed the widespread presence of mHTT aggregates and cellular inclusions. However, there was little evidence of correlations between these outcome measures, potentially indicating that other factors are important in the causal cascade linking the molecular pathology to the emergence of behavioral impairments. In conclusion, the HdhQ150 mouse model replicates many aspects of the human condition, including an extended pre-manifest period prior to the emergence of motor impairments.
Data from: Experimental heatwaves compromise sperm function and cause transgenerational damage in a model insect
Climate change is affecting biodiversity, but proximate drivers remain poorly understood. Here, we examine how experimental heatwaves impact on reproduction in an insect system. Male sensitivity to heat is recognised in endotherms, but ectotherms have received limited attention, despite comprising most of biodiversity and being more influenced by temperature variation. Using a flour beetle model system, we find that heatwave conditions (5 to 7 °C above optimum for 5 days) damaged male, but not female, reproduction. Heatwaves reduce male fertility and sperm competitiveness, and successive heatwaves almost sterilise males. Heatwaves reduce sperm production, viability, and migration through the female. Inseminated sperm in female storage are also damaged by heatwaves. Finally, we discover transgenerational impacts, with reduced reproductive potential and lifespan of offspring when fathered by males, or sperm, that had experienced heatwaves. This male reproductive damage under heatwave conditions provides one potential driver behind biodiversity declines and contractions through global warming.
Data from: Effect of sensor location on continuous intraperitoneal glucose sensing in an animal model
In diabetes research, the development of the artificial pancreas has been a major topic since continuous glucose monitoring became available in the early 2000's. A prerequisite for an artificial pancreas is fast and reliable glucose sensing. However, subcutaneous continuous glucose monitoring carries the disadvantage of slow dynamics. As an alternative, we explored continuous glucose sensing in the peritoneal space, and investigated potential spatial differences in glucose dynamics within the peritoneal cavity. As a secondary outcome, we compared the glucose dynamics in the peritoneal space to the subcutaneous tissue. Eight-hour experiments were conducted on 12 anesthetised non-diabetic pigs. Four commercially available amperometric glucose sensors (FreeStyle Libre, Abbott Diabetes Care Ltd., Witney, UK) were inserted in four different locations of the peritoneal cavity and two sensors were inserted in the subcutaneous tissue. Meals were simulated by intravenous infusions of glucose, and frequent arterial blood and intraperitoneal fluid samples were collected for glucose reference. No significant differences were discovered in glucose dynamics between the four quadrants of the peritoneal cavity. The intraperitoneal sensors responded faster to the glucose excursions than the subcutaneous sensors, and the time delay was significantly smaller for the intraperitoneal sensors, but we did not find significant results when comparing the other dynamic parameters.
Data from: Wolf in sheep's clothing: model misspecification undermines tests of the neutral theory for life histories
Understanding the processes behind change in reproductive state along life-history trajectories is a salient research program in evolutionary ecology. Two processes, state dependence and heterogeneity, can drive the dynamics of change among states. Both processes can operate simultaneously, begging the difficult question of how to tease them apart in practice. The Neutral Theory for Life Histories (NTLH) holds that the bulk of variations in life-history trajectories is due to state dependence and is hence neutral: Once previous (breeding) state is taken into account, variations are mostly random. Lifetime reproductive success (LRS), the number of descendants produced over an individual's reproductive life span, has been used to infer support for NTLH in natura. Support stemmed from accurate prediction of the population-level distribution of LRS with parameters estimated from a state dependence model. We show with Monte Carlo simulations that the current reliance of NTLH on LRS prediction in a null hypothesis framework easily leads to selecting a misspecified model, biased estimates and flawed inferences. Support for the NTLH can be spurious because of a systematic positive bias in estimated state dependence when heterogeneity is present in the data but ignored in the analysis. This bias can lead to spurious positive covariance between fitness components when there is in fact an underlying trade-off. Furthermore, neutrality implied by NTLH needs a clarification because of a probable disjunction between its common understanding by evolutionary ecologists and its translation into statistical models of life-history trajectories. Irrespective of what neutrality entails, testing hypotheses about the dynamics of change among states in life histories requires a multimodel framework because state dependence and heterogeneity can easily be mistaken for each other.
Data from: Integrated population modelling reveals a perceived source to be a cryptic sink
Demographic links among fragmented populations are commonly studied as source-sink dynamics, whereby source populations exhibit net recruitment and net emigration, while sinks suffer net mortality but enjoy net immigration. It is commonly assumed that large, persistent aggregations of individuals must be sources, but this ignores the possibility that they are sinks instead, buoyed demographically by immigration. We tested this assumption using Bayesian integrated population modelling of Greenland white-fronted geese (Anser albifrons flavirostris) at their largest wintering site (Wexford, Ireland), combining capture–mark–recapture, census and recruitment data collected from 1982 to 2010. Management for this subspecies occurs largely on wintering areas; thus, study of source-sink dynamics of discrete regular wintering units provides unprecedented insights into population regulation and enables identification of likely processes influencing population dynamics at Wexford and among 70 other Greenland white-fronted goose wintering subpopulations. Using results from integrated population modelling, we parameterized an age-structured population projection matrix to determine the contribution of movement rates (emigration and immigration), recruitment and mortality to the dynamics of the Wexford subpopulation. Survival estimates for juvenile and adult birds at Wexford and adult birds elsewhere fluctuated over the 29-year study period, but were not identifiably different. However, per capita recruitment rates at Wexford in later years (post-1995) were identifiably lower than in earlier years (pre-1995). The observed persistence of the Wexford subpopulation was only possible with high rates of immigration, which exceeded emigration in each year. Thus, despite its apparent stability, Wexford has functioned as a sink over the entire study period. These results demonstrate that even large subpopulations can potentially be sinks, and that movement dynamics (e.g. immigration) among winters can dramatically obscure key processes driving subpopulation size. Further, novel population models which integrate capture–mark–recapture, census and recruitment data are essential to correctly ascribing source-sink status and accurately informing development of site-safeguard networks.
Data from: Learning relevance models for patient cohort retrieval
OBJECTIVE We explored how judgements provided by physicians can be used to learn relevance models that enhance the quality of patient cohorts retrieved from Electronic Health Records (EHR) collections. METHODS A very large number of features were extracted from patient cohort descriptions as well as electronic health record collections. Specifically, we investigated retrieving (1) neurology-specific patient cohorts from the Temple University Hospital EEG Corpus as well as (2) the more general cohorts evaluated in the TREC Medical Records Track (TRECMed) from the de-identified hospital records provided by the University of Pittsburgh Medical Center. The features informed a Learning Relevance Model (LRM) that took advantage of relevance judgements provided by physicians. The LRM implements a pairwise learning-to-rank framework, which enables our learning patient cohort retrieval (L-PCR) system to learn from physicians' feedback. RESULTS AND DISCUSSION We evaluated the L-PCR system against state-of-the-art traditional patient cohort retrieval systems, and observed a 27% improvement when operating on EEGs and a 53% improvement when operating on TRECMed EHRs, showing the promise of the L-PCR system. We also performed extensive feature analyses to reveal the most effective strategies for representing cohort descriptions as queries, encoding EHRs, and measuring relevance. CONCLUSION The learning patient cohort retrieval system has significant promise for reliably retrieving patient cohorts from EHRs in multiple settings when trained with relevance judgments. When provided with additional cohort descriptions, the L-PCR will continue to learn, thus offering a potential solution to the performance barriers of current cohort retrieval systems.
Data from: A data-driven model for influenza transmission incorporating media effects
Numerous studies have attempted to model the effect of mass media on the transmission of diseases such as influenza, however quantitative data on media engagement has until recently been difficult to obtain. With the recent explosion of "big data" coming from online social media and the like, large volumes of data on a population's engagement with mass media during an epidemic are becoming available to researchers. In this study we combine an online data set comprising millions of shared messages relating to influenza with traditional surveillance data on flu activity to suggest a functional form for the relationship between the two. Using this data we present a simple deterministic model for influenza dynamics incorporating media effects, and show that such a model helps explain the dynamics of historical influenza outbreaks. Furthermore, through model selection we show that the proposed media function fits historical data better than other media functions proposed in earlier studies.
Data from: Integrated population models poorly estimate the demographic contribution of immigration
<p class="BodyA">Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated Population Models (IPMs) offer a possibility to estimate contributions of additional demographic parameters, for which no data have been explicitly collected: typically immigration. Such parametersare often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated.</p> <p class="BodyA">To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using Northern Wheatear <i>Oenanthe oenanthe </i>population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration, and sample size. We also used empirical data on populations with known rates of immigration: Soay Sheep <i>Ovis aries</i> and Mauritius kestrel <i>Falco punctatus</i> with zero immigration and<i> </i>grey wolf <i>Canis lupus</i> in Scandinavia with<i> </i>near-zero immigration.</p> <p class="Default">IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near-zero (Kestrel 19.1-98.2%, Sheep 4.2-36.1%, Wolf 84.0-99.2%). Although the estimation of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically large sample sizes may be required to estimate the contribution of immigration well.</p> <p class="Body">To minimise the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (i) look for evidence of variation in immigration before investigating its contribution to population growth, (ii) simulate and model data for comparison to the real data, and (iii) use explicit data on immigration when possible.</p>
Data from: Mainland size variation informs predictive models of exceptional insular body size change in rodents
The tendency for island populations of mammalian taxa to diverge in body size from their mainland counterparts consistently in particular directions is both impressive for its regularity and, especially among rodents, troublesome for its exceptions. However, previous studies have largely ignored mainland body size variation, treating size differences of any magnitude as equally noteworthy. Here, we use distributions of mainland population body sizes to identify island populations as 'extremely' big or small, and we compare traits of extreme populations and their islands with those of island populations more typical in body size. We find that although insular rodents vary in the directions of body size change, 'extreme' populations tend towards gigantism. With classification tree methods, we develop a predictive model, which points to resource limitations as major drivers in the few cases of insular dwarfism. Highly successful in classifying our dataset, our model also successfully predicts change in untested cases.
Data from: Application of benchmark concentration (BMC) analysis on zebrafish data – a new perspective for quantifying toxicity in alternative animal models
Over the past decade, the zebrafish is increasingly being used as a model to screen for chemical-mediated toxicities including developmental toxicity (DT) and neurotoxicity (NT). One of the major challenges is lack of harmonization in data analysis approaches, thereby posing difficulty in comparing findings across laboratories. To address this, we sought to establish a unified data analysis strategy for both DT and NT data, by adopting the benchmark concentration (BMC) analysis. There are two critical aspects in the BMC analysis: having a toxicity endpoint amenable for BMC and selecting a proper benchmark response (BMR) for the endpoint. For the former, in addition to the typical endpoints in NT assay (e.g., hyper/hypo- response quantified by distance moved), we also used endpoints that assess the differences in movement patterns between chemical-treated embryos and control embryos. For the latter, we standardized the selection of BMR, which is analogous to minimum activity threshold, based on intrinsic response variations in the endpoint. When comparing our BMC results with a traditionally used LOAEL method (Lowest-Observed-Adverse-Effect Level), we found high active compound concordance (100% for DT vs 74% for NT); generally, the BMC was more sensitive than LOAEL (# of BMC more sensitive/# of concordant active compounds, 43/50 for DT vs 16/26 for NT). Using the BMC with standardized toxicity endpoints and an appropriate BMR, we may now have a unified data-analysis approach to comparing results across different zebrafish datasets, for a better understanding of strengths and challenges when using the zebrafish as a screening tool.
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.