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Computing a correlation length scale from MFLL-OCO2 CO2 differences, and accounting for correlated errors when assimilating OCO-2 data
<p>This dataset contains code and data used in 'A new exponentially-decaying error correlation model for assimilating OCO-2 column-average CO<sub>2</sub> data, using a length scale computed from airborne lidar measurements' by David F. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler, submitted to Geoscientific Model Development.</p> <p>In particular, the MATLAB script used to compute the autocorrelation spectrum of Multi-functional Fiber Laser LiDAR (MFLL) and Orbiting Carbon Observatory (OCO-2) column CO<sub>2</sub> differences (in Section 2.2 of the paper) is given here as file</p> <p>comp_MFLL_OCO2_autocorrl_spectrum.m</p> <p>along with the needed MFLL and OCO-2 CO<sub>2</sub> data for each of the six flights analyzed (as described in Section 2.1 of the paper) in files:</p> <p>20160727_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20160805_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20170215_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20170308_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20171022_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20171027_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20160727_oco_averaged_B9_GMAO_ACTadj.h5<br> 20160805_oco_averaged_B9_GMAO_ACTadj.h5<br> 20170215_oco_averaged_B9_GMAO_ACTadj.h5<br> 20170308_oco_averaged_B9_GMAO_ACTadj.h5<br> 20171022_oco_averaged_B9_GMAO_ACTadj.h5<br> 20171027_oco_averaged_B9_GMAO_ACTadj.h5</p> <p>The MFLL data given here was downloaded in late 2018 in the form of L1b files (calibrated radiances), as described in Bell et al (2020).</p> <p>In the second part of the paper, different error correlation models are presented and applied to the averaging of OCO-2 column CO<sub>2</sub> data. The original bias-corrected OCO-2 data, in the form of daily OCO-2 version 10 "Lite" files, have been from obtained from NASA's GES DISC data repository, here:<br> https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary?keywords=OCO2_L2_Lite_FP</p> <p>The bias-corrected column CO<sub>2</sub> retrievals, their uncertainties, and other parameters needed for this analysis were extracted from these<br> "Lite" files and saved to daily files, which have been packaged up in the following compressed tarball:<br> OCO2_XCO2_2014_2020.tar.gz</p> <p>These daily files are read in and averaged across 2-second and 10-second spans (as described in Section 3.5 of the paper), using the different error correlation models outlined in the paper. The code that implements these averages is given in the following two FORTRAN programs:</p> <p>Make_OCO2_2sec_averages.f90<br> Make_OCO2_10sec_averages.f90</p> <p>which need the following list of days having good OCO-2 data:</p> <p>OCO2_dates.txt</p> <p>Program "Make_OCO2_2sec_averages.f90" averages the OCO-2 data across a 2-second (~13.5 km long) span along the groundtrack, collapsing the relatively thin data swath into a one-dimensional data record, upon which the one-dimensional averaging models describe in Sections 3.1 and 3.2 of the paper may be applied. Program "Make_OCO2_10sec_averages.f90" implements these averaging models, which average the 2-second averages across longer, 10-second (~67.5 km) spans. Please see the manuscript for more information on the data and methods provided here.</p>
Data from: Error-robust modes of the retinal population code
Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina's output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells' collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina.
Data from: Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola
As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
False positive detections, such as species misidentifications, occur in ecological data, although many models do not account for them. Consequently, these models are expected to generate biased inference. The main challenge in an analysis of data with false positives is to distinguish false positive and false negative processes while modeling realistic levels of heterogeneity in occupancy and detection probabilities without restrictive assumptions about parameter spaces. Building on previous attempts to account for false positive and false negative detections in occupancy models, we present hierarchical Bayesian models that utilize a subset of data with either confirmed detections of a species' presence (CP model) or both confirmed presences and confirmed absences (CACP model). We demonstrate that our models overcome the challenges associated with false positive data by evaluating model performance in Monte Carlo simulations of a variety of scenarios. Our models also have the ability to improve inference by incorporating previous knowledge through informative priors. We describe an example application of the CP model to quantify the relationship between songbird occupancy and residential development, plus we provide instructions for ecologists to use the CACP and CP models in their own research. Monte Carlo simulation results indicated that, when data contained false positive detections, the CACP and CP models generated more accurate and precise posterior probability distributions than a model that assumed data did not have false positive errors. For the scenarios we expect to be most generally applicable, those with heterogeneity in occupancy and detection, the CACP and CP models generated essentially unbiased posterior occupancy probabilities. The CACP model with vague priors generated unbiased posterior distributions for covariate coefficients. The CP model generated unbiased posterior distributions for covariate coefficients with vague or informative priors, depending on the function relating covariates to occupancy probabilities. We conclude that the CACP and CP models generate accurate inference in situations with false positive data for which previous models were not suitable.
Data from: A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error
Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes towards conservation. Importantly, few approaches account for bias arising from reporting errors; that is, reporting a positive attitude towards conservation when the respondent actually does not have one (a false positive error), or not reporting a positive attitude when the respondent is positive towards conservation (a false negative error). We borrow from developments in applied conservation science to use a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude towards wildlife, notionally (or in abstract terms) and at localized scales. The model allows us to assess stakeholder attitudes, and factors influencing these attitudes, while accounting for false negative and false positive reporting errors. We show through simulations that this method has lower bias than naïve estimates of the proportion of respondents who are positive towards wildlife, or Likert‐scores. We demonstrate the utility of the model by applying it to questionnaire surveys on Asian elephants Elephas maximus in the Kaziranga–Karbi Anglong landscape, Northeast India. After accounting for reporting errors, we estimated the probability of being positive towards elephants notionally as 0.85; at a localized scale, however, the proportion of respondents that were positive towards elephants was 50%. In comparison, without accounting for reporting errors, the proportion of respondents professing positive attitudes towards elephants in at least one of the certain questions, was 0.69 and 0.23, notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently non‐zero (0.22–0.68). We submit that regular and reliable assessment of stakeholder attitudes––combined with an understanding of factors contributing to variation in attitudes––can feed into participatory conservation monitoring programs, help assess the success of initiatives aimed at facilitating human behavioral change, and inform conservation decision‐making.
Data from: Variation across mitochondrial gene trees provides evidence for systematic error: how much gene tree variation is biological?
The use of large genomic datasets in phylogenetics has highlighted extensive topological variation across genes. Much of this discordance is assumed to result from biological processes. However, variation among gene trees can also be a consequence of systematic error driven by poor model fit, and the relative importance of biological versus methodological factors in explaining gene tree variation is a major unresolved question. Using mitochondrial genomes to control for biological causes of gene tree variation, we estimate the extent of gene tree discordance driven by systematic error and employ posterior prediction to highlight the role of model fit in producing this discordance. We find that the amount of discordance among mitochondrial gene trees is similar to the amount of discordance found in other studies that assume only biological causes of variation. This similarity suggests that the role of systematic error in generating gene tree variation is underappreciated and critical evaluation of fit between assumed models and the data used for inference is important for the resolution of unresolved phylogenetic questions.
Data from: Finding the right coverage: The impact of coverage and sequence quality on SNP genotyping error rates
Restriction-enzyme-based sequencing methods enable the genotyping of thousands of single nucleotide polymorphism (SNP) loci in non-model organisms. However, in contrast to traditional genetic markers, genotyping error rates in SNPs derived from restriction-enzyme-based methods remain largely unknown. Here, we estimated genotyping error rates in SNPs genotyped with double digest RAD sequencing from Mendelian incompatibilities in known mother-offspring dyads of Hoffman's two-toed sloth (Choloepus hoffmanni) across a range of coverage and sequence quality criteria, for both reference-aligned and de novo-assembled datasets. Genotyping error rates were more sensitive to coverage than sequence quality and low coverage yielded high error rates, particularly in de novo-assembled datasets. For example, coverage ≥5 yielded median genotyping error rates of ≥0.03 and ≥0.11 in reference-aligned- and de novo-assembled datasets, respectively. Genotyping error rates declined to ≤0.01 in reference-aligned datasets with a coverage >30, but remained >0.04 in the de novo-assembled datasets. We observed approximately 10- and 13-fold declines in the number of loci sampled in the reference-aligned and de novo-assembled datasets when coverage was increased from >5 to >30 at quality score ≥30, respectively. Finally, we assessed the effects of genotyping coverage on a common population genetic application, parentage assignments, and showed that the proportion of incorrectly assigned maternities was relatively high at low coverage. Overall, our results suggest that the tradeoff between sample size and genotyping error rates be considered prior to building sequencing libraries, reporting genotyping error rates become standard practice, and that effects of genotyping errors on inference be evaluated in restriction-enzyme-based SNP studies.
Data from: EMG versus torque control of human-machine systems: equalizing control signal variability does not equalize error or uncertainty
In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors. Second, we quantified uncertainty by measuring the just-noticeable difference of a visual perturbation. We found that for equal errors, EMG resulted in higher movement uncertainty than both torque and torque+noise. The differences suggest that performance and confidence are influenced by more than just the noisiness of the control signal, and suggest that other factors, such as the user's ability to incorporate feedback and develop accurate internal models, also have significant impacts on the performance and confidence of a person's actions. We theorize that users have difficulty distinguishing between random and systematic errors for EMG control, and future work should examine in more detail the types of errors made with EMG control.
Data from: Pedigree error due to extra-pair reproduction substantially biases estimates of inbreeding depression
Understanding the evolutionary dynamics of inbreeding and inbreeding depression requires unbiased estimation of inbreeding depression across diverse mating systems. However, studies estimating inbreeding depression often measure inbreeding with error, for example, based on pedigree data derived from observed parental behavior that ignore paternity error stemming from multiple mating. Such paternity error causes error in estimated coefficients of inbreeding (f) and reproductive success and could bias estimates of inbreeding depression. We used complete "apparent" pedigree data compiled from observed parental behavior and analogous "actual" pedigree data comprising genetic parentage to quantify effects of paternity error stemming from extra-pair reproduction on estimates of f, reproductive success, and inbreeding depression in free-living song sparrows (Melospiza melodia). Paternity error caused widespread error in estimates of f and male reproductive success, causing inbreeding depression in male and female annual and lifetime reproductive success and juvenile male survival to be substantially underestimated. Conversely, inbreeding depression in adult male survival tended to be overestimated when paternity error was ignored. Pedigree error stemming from extra-pair reproduction therefore caused substantial and divergent bias in estimates of inbreeding depression that could bias tests of evolutionary theories regarding inbreeding and inbreeding depression and their links to variation in mating system.
Data from: Estimation of genotyping error rate from repeat genotyping, unintentional recaptures and known parent-offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus)
Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent–offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop-out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent–offspring pairs and unintentionally recaptured individuals, respectively. By direct-count error estimates, the recapture and known parent–offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent–offspring comparisons. Furthermore, there was no correlation in locus-specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus-specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent–offspring comparisons in parentage studies).
Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America. We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection. We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence. Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters. Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
Data from: Correction of location errors for presence-only species distribution models
1. Species distribution models (SDMs) for presence-only data depend on accurate and precise measurements of geographic and environmental covariates that influence presence and abundance of the species. Some data sets, however, may contain both systematic and random errors in the recorded location of the species. Environmental covariates at the recorded location may differ from those at the true location and result in biased parameter estimates and predictions from SDMs. 2. Regression calibration is a well-developed statistical method that can be used to correct the bias in estimated coefficients and predictions from SDMs when the recorded geographic location differ from the true location for some, but not all locations. We expand the application of regression calibration methods to SDMs and provide illustrative examples using simulated data and opportunistic records of whooping cranes (Grus americana). 3. We found we were able to successfully correct the bias in our SDM parameters estimated from simulated data and opportunistic records of whooping cranes using regression calibration. 4. When modeling species distributions with data that have geographic location errors, we recommend researchers consider the effect of location errors. Correcting for location errors requires that at least a portion of the data have locations recorded without error. Bias correction can result in an increase in variance; this increase in variance should be considered when evaluating the utility of bias correction.
Supplementary dataset for journal article "High-accuracy current measurement with low-cost shunts by means of dynamic error correction"
<p>Supplementary material for the journal article "High-accuracy current measurement with low-cost shunts by means of dynamic error correction".</p> <p>Measurements include</p> <ul> <li>Temperature dependence of a single 10mOhm shunt resistor in TO-247 package.</li> <li>Calibration sequence measurements for automated 600A test equipment for studies on automotive Li-Ion cells.</li> <li>Obtained dynamic and steady-state calibration parameters</li> <li>Parameter distribution for 100 test systems.</li> <li>Parameter drift for a single tester after one year of operation</li> </ul> <p>All data has been combined into a single binary Matlab-File.</p>
FIGURE 3 in Error cascade in taxonomy: The case of Cardiomya perrostrata (Mollusca: Bivalvia: Cuspidariidae) in Brazilian waters
FIGURE 3. Species most often confused with Cardiomya perrostrata (Dall, 1881). A–B. Cardiomya ornatissima (Orbigny, 1853 in 1842–1853), USNM 35362, holotype of Cardiomya glypta Bush in Verrill & Bush, 1898, a junior synonym. C–D. Cardiomya cleryana (Orbigny, 1842 in 1834–1847), syntypes of Cuspidaria (Cardiomya) simillima E.A. Smith, 1915, a junior synonym; C. NHMUK 1915.4.18.512; D. NHMUK 1915.4.18.513–515. E–F. Cardiomya striata (Jeffreys, 1876), USNM 63965, syntype. Scale bars: A–B = 1 mm; C–D = 3 mm; E–F = 5 mm.
FIGURE 2 in Error cascade in taxonomy: The case of Cardiomya perrostrata (Mollusca: Bivalvia: Cuspidariidae) in Brazilian waters
FIGURE 2. Cardiomya perrostrata (Dall, 1881). A. USNM 95393, left valve measuring 25 mm in length; B. IBUFRJ 20950, microsculpture of the dissoconch; C–D. IBUFRJ 18247, internal views of left and right valves; E–F. detail of the hinges of C and D, respectively. Scale bars: A = 10 mm; B = 50 µm; C–D = 500 µm; E–F = 200 µm.
FIGURE 1 in Error cascade in taxonomy: The case of Cardiomya perrostrata (Mollusca: Bivalvia: Cuspidariidae) in Brazilian waters
FIGURE 1. Cardiomya perrostrata (Dall, 1881). Growth series. A–L. Right view. M. Dorsal view. A and B. IBUFRJ 20951; C. IBUFRJ 20950; D and F. IBUFRJ 14985; E. IBUFRJ 20949; G. IBUFRJ 18247; H. IBUFRJ 14985; I. MCZ 8100, syntype; J. USNM 63119, syntype; K. IBUFRJ 15625; L. USNM 63119, syntype; M. IBUFRJ 20052. Scale bar = 3 mm.
FIGURE 4. A in Error cascade in taxonomy: The case of Cardiomya perrostrata (Mollusca: Bivalvia: Cuspidariidae) in Brazilian waters
FIGURE 4. A. Boxplot of depth of records of Cardiomya perrostrata (Dall, 1881) considered as a whole (all), or grouped according to whether or not the taxonomic identification was verified herein. B. Geographic and bathymetric distribution of C. perrostrata. Coordinates were taken from literature sources whenever available, or translated from geographic names using the online services by Claus et al. (2014) and GeoNames (2016). Raw data, including sources of records, are provided in Appendix 1.
Simulated T-cells Rep-Seq datasets (called SIMTCR) from "Reconstructing antibody repertoires from error-prone immunosequencing datasets" paper
<p>Simulated test datasets used for benchmarking of immunorepertoire construction tools</p>
Simulated B-cells Rep-Seq datasets (called SIMULATED) from "Reconstructing antibody repertoires from error-prone immunosequencing datasets" paper
<p>Simulated test datasets used for benchmarking of immunorepertoire construction tools</p>
Text generated by OPUS-MT and T5 models with single-bit errors in the parameters
<h2>Description</h2> <p>The dataset contains text generated using T5 and OPUS-MT model with and with single-bit errors in the parameters of the LLM. The T5 LLM used the <a href="https://huggingface.co/datasets/cnn_dailymail/viewer/3.0.0/test">CNN Daily Mail</a> dataset for summarization and OPUS-MT used the <a href="https://aclanthology.org/2017.iwslt-1.1/">IWSLT2017</a> dataset for Chinese-to-English translation.</p> <p> </p> <p>Folders:</p> <ul> <li>t5_fp32: T5 model with a quantified version of FP32</li> <li>t5_fp16: T5 model with a quantified version of FP16</li> <li>opus_fp32: OPUS-MT model with a quantified version of FP32</li> <li>opus_fp16: OPUS-MT model with a quantified version of FP16</li> </ul> <p>Files:</p> <ul> <li><strong>{cnn/iwslt2017}_input_text.txt</strong>: Input text, that is, text to summarize (cnn and T5) or Chinese text to translate (iwslt2017 and OPUS-MT). For each dataset in total there are <em>number_input_texts.</em></li> <li><strong>{cnn/iwslt2017}_output_reference.txt:</strong> Example of result expected for CNN (T5) and IWSLT2017 (OPUS-MT). For each dataset in total there are <em>number_input_texts.</em></li> <li><strong>{cnn/iwslt2017}_output_predict_fault_free:</strong> Example of predictions without single-bit errors. For each dataset in total there are <em>number_input_texts.</em></li> <li><strong>{cnn/iwslt2017}_output_predict_single_fi_bit_100times:</strong> Example of predictions with 100 different single-bit error. In each dataset in total there are <em>100*number input texts</em>.</li> </ul> <h2>Paper</h2> <ul> <li>Paper: <a href="https://doi.org/10.48550/arXiv.2403.16393">Concurrent Linguistic Error Detection (CLED) for Large Language Models</a></li> <li>Cite:</li> </ul> <p><code>@misc{zhu2024concurrent,</code><br><code> title={Concurrent Linguistic Error Detection (CLED) for Large Language Models}, </code><br><code> author={Jinhua Zhu and Javier Conde and Zhen Gao and Pedro Reviriego and Shanshan Liu and Fabrizio Lombardi},</code><br><code> year={2024},</code><br><code> eprint={2403.16393},</code><br><code> archivePrefix={arXiv},</code><br><code> primaryClass={cs.AI}</code><br><code>}</code></p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.