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8,038 results for “validation”
EUREKA Task 1.3 validation survey analysis
Curated results and analysis of the WP1 validation survey, including the figures for the corresponding deliverable and any resulting publications.
Translation and validation of the Body-Mind-Spirit Wellness Behavior and Characteristic Inventory in the Greek language
<p>This investigation was guided by Hettler's 1979 Six Dimensions of Wellness Model. Dimensions of physical, emotional, intellectual, occupational, social, and spiritual wellness were identified. The Body-Mind-Spirit Wellness Behavior and Characteristic Inventory was used to assess this (BMS-WBCI). The purpose of this study was to further validate the BMS-WBCI by reporting reliability as the scale's internal consistency when used to measure wellness in a sample of aged Greek people. a descriptive cross-sectional design was used for the sample collection. A random sample (n =520) from the Greek general population participated voluntarily and anonymously in the study including 28,8% males and 71,2% females. Their mean age of the sample was 39.86 years (SD = 10.5), ranging from 16 to 75 years .The BMS-WBCI consists of 44 items which are divided into three subscales (Hey, Calderon, & Carroll, 2006). The first subscale is labeled “<strong>body</strong>” and consists of nine items (items 1 – 9) relating to risk behaviors including physical fitness, personal safety and dietary intake, covering the physical domain of wellness. The second subscale is labeled “<strong>mind</strong>” and consists of 20 items (items 10 – 29) representing the intellectual, social, emotional, and occupational domains of wellness. Finally, the third subscale is labeled “<strong>spirit</strong>” and consists of 15 items (items 30 – 44) covering the spiritual, emotional, and occupational domains of wellness. Each of the 44 items asks the participant to respond on a 3-point Likert scale where 1 indicates “rare/seldom,” 2 “occasionally/sometimes,” and 3 “often/always.” Possible scores for the entire scale range from a minimum of 44 to a maximum of 132. Higher scores designate a higher level of participation in positive health behaviors and agreement with characteristics that contribute to overall well-being. </p>
Data from: Validation of Quality-of-Life assessment tool for Ethiopian old age people
<p><strong>Background</strong>: Reliable quality of life assessment is critical for identifying health issues, evaluating health interventions, and establishing the best health policies and care packages. The World Health Organization Quality of Life-Old Module is a tool for assessing the subjective quality of life in old age people. It's validated and available in more than 20 languages, except Amharic. Hence, this study was intended to translate it into Amharic language and validate it among old age people in Ethiopia.</p> <p><strong>Methods</strong>: A cross-sectional study was conducted among 180 community-dwelling old age people in Ethiopia, from January 16 to March 13, 2021. Psychometric validation was achieved through Cronbach's alpha of the internal consistency reliability test, and construct validity from confirmatory factor analysis.</p> <p><strong>Results</strong>: The study participants aged from 60 to 90 years old with a mean age of 69.44. Females made up 61.7% of the population, and 40% of them could not read and write. The results showed a relatively low level of quality of life, with the total transformed score of 58.58 ± 23.15. The Amharic version of the World Health Organization Quality of Life-Old Module showed a Cronbach's Alpha value of 0.96 and corrected item-total correlations of more than 0.74. Confirmatory factor analysis confirmed the six-factor model with a chi-square (X2) of 341.98 with a p-value less than 0.001. The comparative fit index (CFI) was 0.98, Tucker-Lewis's index (TCL) was 0.97, and the root mean square error of approximation (RMSEA) was 0.046.</p> <p><strong>Conclusion</strong>: The Amharic version of the World Health Organization Quality of Life-Old Module indicated good internal consistency reliability and construct validity. The tool can be utilized to provide care to Ethiopian community-dwelling old age people.</p>
Raw Data for the article: Mortality after transjugular intrahepatic portosystemic shunt in older adult patients with cirrhosis: A validated prediction model
<p><strong>Background and aims: </strong>Implantation of a transjugular intrahepatic portosystemic shunt (TIPS) improves survival in patients with cirrhosis with refractory ascites and portal hypertensive bleeding. However, the indication for TIPS in older adult patients (greater than or equal to 70 years) is debated, and a specific prediction model developed in this particular setting is lacking. The aim of this study was to develop and validate a multivariable model for an accurate prediction of mortality in older adults.</p> <p><strong>Approach and results: </strong>We prospectively enrolled 411 consecutive patients observed at four referral centers with de novo TIPS implantation for refractory ascites or secondary prophylaxis of variceal bleeding (derivation cohort) and an external cohort of 415 patients with similar indications for TIPS (validation cohort). Older adult patients in the two cohorts were 99 and 76, respectively. A cause-specific Cox competing risks model was used to predict liver-related mortality, with orthotopic liver transplant and death for extrahepatic causes as competing events. Age, alcoholic etiology, creatinine levels, and international normalized ratio in the overall cohort, and creatinine and sodium levels in older adults were independent risk factors for liver-related death by multivariable analysis.</p> <p><strong>Conclusions: </strong>After TIPS implantation, mortality is increased by aging, but TIPS placement should not be precluded in patients older than 70 years. In older adults, creatinine and sodium levels are useful predictors for decision making. Further efforts to update the prediction model with larger sample size are warranted.</p>
Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC?
<p>There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, cross-validation represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard MCMC runs under the posterior distribution.</p>
Multiplexed long-read plasmid validation and analysis using OnRamp
<p>Plasmid read data and references from "Multiplexed long-read plasmid validation and analysis using OnRamp "</p> <p>Experiments:</p> <ol> <li>AAZ605 - 7 plasmids</li> <li>AFQ178 - 9 plasmids</li> <li>ACK577 - 30 plasmids</li> <li>AEZ576 - 15 plasmids</li> <li>plasmids_ref_7.fasta</li> <li>plasmids_ref_9.fasta</li> <li>plasmids_ref_15.fasta</li> <li>plasmids_ref_30.fasta</li> </ol> <p> </p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Training, Validation and Test Sets for paper 'A Little Data goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning'
<p>Training, Validation and Test Data for model presented in paper 'A Little Data Goes A Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning', submitted to Journal of Geophysical Research: Solid Earth.</p> <p>Files:</p> <p>- train_events_2498.h5 = training set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- train_events_2498.pkl = event training set metadata (UTC P-/S-wave phase arrival times)</p> <p>- train_noise_2498.h5 = training set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- train_noise_2498.pkl = noise training set metadata (UTC time for training noise waveforms)</p> <p>- val_events.h5 = validation set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- val_events.pkl = event validation set metadata (UTC P-/S-wave phase arrival times)</p> <p>- val_noise.h5 = validation set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- val_noise.pkl = noise validation set metadata (UTC time for validation noise waveforms)</p> <p>- test.h5 = test set of seismic waveforms (events and noise)</p> <p>- test_events.pkl = event test set metadata (UTC P-/S-wave phase arrival times for test event waveforms)</p> <p>- test_noise.pkl = noise test set metadata (UTC time for test noise waveforms)</p> <p>- nabro_2011-247.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-04), saved in mseed format (e.g., can be read with obspy)</p> <p>- nabro_2011-269.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-26), saved in mseed format (e.g., can be read with obspy)</p> <p> </p> <p>Further details and code for reading and using these files can be found at the GitHub repo for this paper: <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a></p> <p> </p>
Sample, test, and validation data for findmycells
<p>findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells</p> <p><strong>Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.</strong></p> <p>For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here:</p> <blockquote> <p>Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.4b8gtht9d">https://doi.org/10.5061/dryad.4b8gtht9d</a></p> </blockquote> <p>The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7)</p> <blockquote> <p>Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. <em>arXiv preprint arXiv:2111.06693</em>.</p> </blockquote> <p>The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".</p>
Machine-guided path sampling to discover mechanisms of molecular self-organization (Training and validation data)
<p>Training and validation data for the Nature Computational Science manuscript "Machine-guided path sampling to discover mechanisms of molecular self-organization"</p>
Use of geolocators for investigating breeding ecology of a rock crevice-nesting seabird: method validation and impact assessment
<p>1: Investigating ecology of marine animals, imposes a continuous challenge due to their temporal and/or spatial unavailability. Light-based geolocators (GLS) are animal-borne devices that provide relatively cheap and efficient method to track seabird movement and are commonly used to study migration. Here we explore the potential of GLS data to establish individual behaviour during the breeding period in a rock crevice-nesting seabird, the Little Auk, Alle alle. 2: By deploying GLS on 12 breeding pairs, we developed a methodological workflow to extract birds' behaviour from GLS data (nest attendance, colony attendance and foraging activity), and validated its accuracy using behaviour extracted from a well-established method based on video recordings. We also compared breeding outcome, as well as behavioural patterns of logged individuals with a control group treated similarly in all aspects except for the deployment of a logger, to assess short-term logger effects on fitness and behaviour. 3: We found a high accuracy of GLS-established behavioural patterns, especially during the incubation and early chick rearing period (when birds spend relatively long time in the nest). We observed no apparent effect of logger deployment on breeding outcome of logged pairs, but recorded some behavioural changes in logged individuals (longer incubation bouts and shorter foraging trips). 4: Our study provides a useful framework for establishing behavioural patterns (nest attendance and foraging) of a crevice-nesting seabird from GLS data (light and conductivity), especially during incubation and early chick rearing period. Given that GLS deployment does not seem to affect the breeding outcome of logged individuals but does affect fine-scale behaviour, our framework is likely to be applicable to a variety of crevice/burrow nesting seabirds, even though precautions should be taken to reduce deployment effect. Finally, because each species may have its own behavioural and ecological specificity, we recommend performing a pilot study before implementing the method in a new study system.</p>
BD_Delphi-validation_NbS-CoBAs-tool
<p>2 BBDD of the Delphi validation of tool for NbS psychosocial benefits assessment in urban regeneration processes (<em>NbS-CoBAs tool</em>): A panel of 10 experts in different fields (environmental and social psychology, urbanism, urban regeneration, and Nature-based Solutions) were involved in the validation process with Delphi. The two data bases are from the first and the second steps of the Delphi method.</p>
Data for: Validation of a nutria (Myocastor coypus) environmental DNA assay highlights considerations for sampling methodology
<p>Nutria (<em>Myocastor coypus</em>) is a semi-aquatic rodent species that is invasive across multiple regions within the United States. Here we evaluated a qPCR assay previously described for use in Japan for application across invasive populations in the United States. We also compared two environmental DNA sampling methodologies for this assay: field filtration of large volumes of water passed through filters versus direct sampling of small volumes of water. We validated assay specificity, generality, and sensitivity, compared assay performance between two independent laboratories, and successfully tested the assay<em> in situ </em>on a known wild population. The filtration method required fewer samples for environmental DNA detection than direct sampling, but the choice of methods should be assessed based on specific field conditions and time and budget considerations. Our extensive assay validation and comparison across laboratories suggests that the assay is ready to be applied in environmental DNA monitoring of nutria throughout the United States.</p>
Dataset for the Indonesian Newly Validated Collaborative Practice Assessment Tool
<p>The dataset is used for the analysis of an article titled: A psychometric evaluation of the Indonesian version of the Collaborative Practice Assessment Tool (CPAT) for assessing interprofessional collaborative practice in health practitioners and students.</p>
Mini Nutritional Assessment tool validation project
<p><strong>Background</strong>: The health status of older people is usually overlooked in many low-income countries like Ethiopia. Appropriate nutritional assessment improves the health of old age people. The Mini Nutritional Assessment tool is a noninvasive and cheap practical evaluation tool that provides a simple and quick method of evaluating the nutritional status of old age people. The tool has multiple versions of confirmed validity in diverse languages, except in Amharic. Furthermore, the tool has still not been properly validated for Ethiopian old age people.</p> <p><strong>Objective</strong>: The purpose of this study was to translate the Mini Nutritional Assessment tool into the Amharic language and validate it among old age people in Bahir Dar City.</p> <p><strong>Methods</strong>: This tool translation and validation study followed normal COSMIN Study design and reporting guidelines. The study was conducted in three stepwise phases from January 16 to March 13, 2021. The first phase was reviewing and selecting nutritional assessment tools for old age people. In the second phase, the selected Mini Nutritional Assessment tool was translated and reviewed by experts. Using the heterogenous purposive sample, ten healthcare specialists with professional experience in the care of old age people were chosen for this step. The experts examined the face and content validity of the Amharic version of the instrument in two rounds of the Delphi technique. Finally, after incorporating the experts' suggestions and comments, a cross-sectional study was conducted among old age people for psychometric validation. The participant-to-variables ratio of the 10:1 rule of thumb was followed for the minimum sample size. Since the Mini Nutritional Assessment tool has 18 items, 180 community-dwelling old-age people were selected in multistage cluster sampling from Belay Zeleke, one of the sub-cities of Bahir Dar City. Principal component analysis was used to measure construct validity while Cronbach's alpha was employed to assess internal consistency reliability.</p> <p><strong>Results</strong>: As experts reviewed, all items in the translated tool are socially acceptable and have no taboo or sensitive words. The translated tool's content validity ratio was 0.93, and its scale validities (S-CVI/Ave and S-CVI/AU) were 0.97 and 0.83, respectively. Moreover, 180 community-dwelling old age people aged 60 to 90 years old participated in a psychometric evaluation study. Construct validity of the tool was confirmed with factor loadings ranging from 0.47 to 0.89 with a Cronbach's alpha of 0.65. The tool had a sensitivity and specificity of 97% and 83%, respectively.</p> <p><strong>Conclusion</strong>: The Amharic version of the Mini Nutritional Assessment tool showed good cross-cultural adaption, internal consistency reliability, and construct validity in Bahir Dar community-dwelling old age people. The tool can be used in regular care activities for aged people.</p>
LiftWEC deliverable 3.6 - Part I: Dataset from 3D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from 3D-validation studies of the high-fidelity RANS model employed in the LiftWEC project. The case identifiers (ID) correspond to the case numbering employed in the experimental reference cases defined by École Centrale de Nantes. It is highly recommended to read the corresponding project reports on numerical modelling (D3.6) and on experimental modelling (D4.5, D4.6, D4.7, D4.8) which are also available in the LiftWEC community on zenodo (https://zenodo.org/communities/liftwec/).</p> <p>The cases comprise simulations of a rotor at constant velocity in calm water and regular waves in full 3D simulations. It further includes 2D simulation results of a rotor at constant rotational velocity in irregular waves and at variable velocity in monochromatic waves.</p> <p>All loads in the data set are given in force per unit span length (N/m), torque and power output is given as values per unit span as well. Wave elevation data up and down-wave of the rotor is given in (m).</p> <p> </p> <p> </p> <p> </p>
LiftWEC deliverable 3.3 - Dataset from 2D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains results obtained from numerical simulations of the LiftWEC model scale device in a two-dimensional setting. The simulations were done based on the experimental validation campaign conducted in the scope of the LiftWEC project and documented in deliverables D4.2, D4.3 and D.4. The corresponding experimental datasets are also available within the LiftWEC community on zenodo.</p> <p>The numerical setup as well as a presentation and discussion of obtained results is available in LiftWEC deliverable D3.3 Tool Validation and Extension report, which also contains information on the potential flow model. All forces presented in this document are given as forces per unit span length. As the 2D RANS model was found to be rather sensitive to high fluctuations at this preliminary investigation stage, results are presented as mean forces and force fluctuations at rotation period, analysed by means of an FFT post-processing routine. The case identifiers correspond to the case numbering employed in the experimental model tests.</p>
Global reference data set for validating ESA WorldCereal temporary cropland extent
<p>IIASA team has created a new validation data set, which is completely independent from all other existing maps or reference data sets, and which is in line with the cropland definitions and mapping period of the WorldCereal products. To decide if this is an active cropland in each period, the experts looked at very high-resolution Google historical imagery and Google Street level images, Microsoft Bing images, ESRI imagery, Planet historical data, Sentinel-2 time series, and Modis NDVI time series. The experts were asked to label 5 by 5 Sentinel pixels (each pixel 10m by 10m) either as winter crops, or as summer crops, or as maize (if this was possible), or as active crops (where it was not possible to confirm a growing season, e.g. overlap between seasons was too big or crop fields were too small in size), or as no crops, or as not sure where was too little information available for 2021. There was additional question on irrigation system, either circle, or other irrigation, or rainfed, or not sure. </p> <p>Fields:</p> <p>"rowid" -unique row identifier</p> <p>"submissionid" – unique submission id, which consists of 25 single pixels (rows)</p> <p>"timestamp" – time stamp of each submission</p> <p>"sampleid" – unique sample site, which could have a few submission ids</p> <p>"submission_itemid" – unique single pixel submission id</p> <p>"enhancement" – unique legend identifier in the Geo-Wiki database</p> <p>"question" – question asked (either on crop presence or irrigation)</p> <p>"answer" – answer to a question asked</p> <p>"sub_pixel_x","sub_pixel_y" – centroids of a single pixel in WGS84</p>
Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients
<p>Supplementary materials for the article titled “Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients”</p>
The validity of the bioaccumulation index versus the bioaccumulation factor for assessment of element accumulation in black soldier fly larvae
<p>Dataset for the input-output calculations to evaluate bioaccumulation factor (BAF) and bioaccumulation index (BAI) in comparison with true element retention rate in black soldier fly larvae (BSFL). The results suggest a higher agreement of true element retention rate with BAF than with BAI. https://doi.org/10.3920/JIFF2023.0021</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.