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THE CORRELATION BETWEEN MATERNAL MALNUTRITION AND NEONATAL OUTCOMES AT MBAGATHI HOSPITAL KENYA
<p>The data was collected at Mbagathi hospital in Kenya</p>
Malaria disease and grading system dataset from public hospitals reflecting complicated and uncomplicated conditions
<p>Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy.</p>
Characteristics of Respiratory Syncytial Virus infections in children in the Post-COVID seasons: a Northern Italy Hospital experience.
<p>Background: Public health measures for COVID-19 mitigation influenced the circulation of Respiratory Syncytial Virus (RSV) during the 2020-2021 winter season. In the following autumn, an unprecedented resurgence of RSV occurred. In our study we monitored RSV pediatric infections, one and two years after the relaxation of containment measures for COVID-19 pandemics. Methods: we analyzed diagnostic molecular data for SARS-CoV-2, Flu and RSV infections and clinical data from children with respiratory symptoms referring to our hospital during 2021-2022 and 2022-2023 seasons. Results: In 2021-22 season, the number of RSV affected children was very high, especially for babies <1 year. The outbreak appeared in a shorter interval of time, with a high clinical severity. In the 2022-23 season, a reduced number of infected pediatric patients were detected, with a similar hospitalization rate (46% vs 40%) and RSV accounted for 12% of the infections. Coinfections were observed in age <2 years. In RSV patients, symptoms were similar across the two seasons. Conclusions: clinical presentation of RSV in the two post-COVID seasons suggests that the pathophysiology of the virus did not change across these two years. Further studies are needed to continuously monitor RSV, for supporting an effective prevention strategy.</p>
Comorbidities in Psoriasis Among Iraqi Patients Attending al Yaemouk Teaching Hospital
<p><strong><u><span>ABSTRACT</span></u></strong>:</p> <p><strong>Background</strong>: Psoriasis is a chronic dermatological condition that has been shown to exhibit a correlation with a heightened prevalence of comorbidities, such as the metabolic syndrome. It is recommended that individuals diagnosed with psoriasis have regular screening for metabolic syndrome. <strong>Aim of study</strong>: Is to evaluate the rate of metabolic syndrome and depression in patients with psoriasis in comparison with control patients and to assess association of metabolic syndrome and depression with the severity of psoriasis. <strong>Methods</strong>: This is a case control study conducted in department of dermatology and venereology out-patient in Al Yarmouk teaching hospital, Baghdad, Iraq from January 2022 to January 2023. This study involved 100 psoriatic patients (cases) and 100 patients attended outpatient for other dermatological diseases (controls) and matched for same age and gender, two different questionnaires were used for all study. All patients were subjected to detailed History, complete physical examination, work-up for psoriasis disease diagnosis and further follow-up and management, and evaluation of metabolic syndrome and depression which was achieved by different tools mentioned<span> </span>previously. <strong>Results</strong>: 31% of them were overweighed while other 31% had normal BMI, 72% were normotensive and 78% were not depressed. 58% of psoriatic patients were presented as severe pattern. Psoriasis was significantly seen among obese patients (74.5%, P=0.001), depressed patients (72.7%, P=0.001). Also, it was significantly found in patients with high FBS (71%, P=0.011), hi cholesterol level (69.6%, P=0.02), hi TG (78.6, P=0.026), high<span> </span>LDL<span> </span>(69.2%,<span> </span>P=0.035),<span> </span>and<span> </span>low<span> </span>HDL<span> </span>(72.3%,<span> </span>P=0.001).<span> </span>Severe form of psoriasis was significantly associated with long duration of disease (84%, P=0.005), with obese patients (74.3%, P=0.018), with hypertensive patients (81.3%, P=0.004), and with patients had low HDL (82.4%, P=0.001). <strong>Conclusion</strong>: Psoriatic patients in Iraq had higher prevalence of comorbidities in comparison to those who didn’t have psoriasis. Obesity, hypertension, and low s. HDL are positively correlated with severity of psoriasis. Psoriasis is considered as an associated risk factor that may cause depression.</p> <p><strong><em> </em></strong></p> <p><strong><em>Keyword: Psoriasis, depression, metabolic syndrome, Iraq</em></strong></p> <p><strong><em> </em></strong></p>
Abandoned Reid Hospital -RAWscan
The Old Reid Hospital is a building in Richmond, Indiana that previously housed Reid Hospital and Health Care Services. The hospital was founded in 1905 by Daniel Grey Reid. The building is currently abandoned with plans in place for demolition. wikipedia photogrammetry from video Source: Objaverse 1.0 / Sketchfab
Hospital de sant Julià. Besalú
Laia Domingo Source: Objaverse 1.0 / Sketchfab
Base de datos de Gonadotropina coriónica en lesiones premalignas gástricas en pacientes del Hospital General Docente "Dr. Agostinho Neto"
<p>Base de datos empleada en un estudio realizado en pacientes del Hospital General Docente "Dr. Agostinho Neto" realizado en el periodo enero 2021 a enero 2023, con el objetivo de evaluar el valor diagnóstico de la gonadotropina coriónica como marcador tumoral en lesiones premalignas gástricas en pacientes atendidos en el servicio de Gastroenterología.</p>
Disease trajectories in hospitalized COVID-19 patients are predicted by clinical and peripheral blood signatures representing distinct lung pathologies
<p><span>COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology over the course of acute COVID-19 is necessary to understand its diverse pathogenesis and inform more precise and effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood biomarkers, and lung pathology in COVID-19 patients from the Brazilian Amazon. We identified core clinical and peripheral blood signatures differentiating disease progression between recovered patients from severe disease and fatal cases. Signatures were heterogenous among fatal cases yet clustered into two patient groups: “early death” (< 15 days of disease until death) and “late death” (> 15 days). Progression to early death was characterized systemically and in lung histopathology by rapid, intense endothelial and myeloid activation/chemoattraction and presence of thrombi, associated with SARS-CoV-2<sup>+</sup> macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis and abundant SARS-CoV-2<sup>+</sup> epithelial cells in post-mortem lung, with cytotoxicity, interferon and Th17 signatures only detectable in the peripheral blood 2 weeks into hospitalization. Progression to recovery was associated with higher lymphocyte counts, Th2 and anti-inflammatory-mediated responses. By integrating ante-mortem longitudinal systemic and spatial single-cell lung signatures, we defined an enhanced set of prognostic clinical parameters predicting disease outcome for guiding more precise and optimal treatments.</span><span> Finally, this study represents a major advance in the investigation of acute respiratory infections by integrating serial clinical data and peripheral blood samples with histopathological and </span><span>spatially-resolved single-cell </span><span>analyses of post-mortem lung samples.</span></p>
Colonoscopy anonymized data, University Hospital of Bern, 2020-2021
<p>Anonymized colonoscopy video, University Hospital for Visceral Surgery and Medicine, University of Bern. 2020-2021</p>
Clinical data from cervical precancerous lesions, Hospital de la Mujer
<p>Se muestran los datos de mujeres con lesiones precursoras cervicales del Hospital de la Mujer en CDMX</p>
TIMCI Hospital Cost Data Kenya
<p>Survey data on costs of selected hospital services in 3 hospitals in 3 counties in Kenya</p>
Patient experience with nursing care and patient satisfaction with overall hospital services
<p><span><b>Objective: </b>To determine how patient experience with nursing care influence patient satisfaction with overall hospital services.</span></p> <p><span><b>Design:</b> This was a cross-sectional study.</span></p> <p><span><b>Setting:</b> Inpatients were consecutively recruited at the national hospital (with 2000 beds) in Shanghai, China.</span></p> <p><span><b>Participants: </b>The inclusion criteria were as follows: (1) hospitalized for 2 days or more; (2) able to read and understand Chinese; and (3) aged 18 years old or above. Patients with mental health problems were excluded. 756 patient surveys distributed among 36 wards were analyzed. The mean age of participants in<b> </b>the study was 57.7 (SD=14.5) and ranged from 18-80 years. Most participants were male (61.5%) and ever married (94.6%). </span></p> <p><span><b><span>Primary and secondary outcome measures:</span></b> Patient experience with nursing care, meaning the sum of all interactions between patients and nurses, was measured using the self-designed questionnaire, which was developed by patient interviews, literature analysis and expert consultation. The overall patient satisfaction question was measured with a ten-point response option ranging from 1-10. </span></p> <p><span><b>Results: </b>A linear relationship between the patient experience with nursing care and overall patient satisfaction was observed. The patient experience with nursing care was significantly associated with overall satisfaction in the crude model and in the adjusted models. Even after adjusting for 6 sociodemographic and 3 disease-related factors, the patient experience with nursing care explained 34.9% of the variation in overall patient satisfaction.</span></p> <p><span><b>Conclusions: </b>This study showed that patient experience with nursing care was an important predictor for overall patient satisfaction.</span></p>
Linkage of hospital records and death certificates by a search engine and machine learning: training and test set data
<p>INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information.</p> <p>MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons.</p> <p>RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively.</p> <p>CONCLUSION: In this article, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning.</p>
Characteristics of COVID-19 patients predicting hospital readmission after ED discharge
<p><strong>Objective:</strong> We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalization/death, in COVID-19 patients previously discharged from the ED. We also developed a disease progression velocity index.</p> <p><strong>Design and setting:</strong> Retrospective cohort study of prospectively collected data. The charts of consecutive COVID-19 patients discharged from the Reggio Emilia (Italy) ED (March 2 - March 31, 2020) were retrospectively examined. Clinical, laboratory, and computed tomography (CT) findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.</p> <p><strong>Participants: </strong>450 patients discharged from the ED with diagnosis of COVID-19.</p> <p><strong>Main outcome measure:</strong> ED readmission within 14 days, followed by hospitalization/death.</p> <p><strong>Results:</strong> Of the discharged patients, 84 (18.7%) were readmitted to the ED and 61 (13.6%) were hospitalized and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03-1.08), Charlson Comorbidity Index 3 vs 0 (OR=11.61; 95% CI 1.76-76.58), days from symptom onset (OR for one day increase=0.81; 95% CI 0.73-0.90), and CT extension (OR for 1% increase=1.03; 95% CI 1.01-1.06) were associated in a multivariable model for readmission with hospitalization/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08-1.36); the model including this index resulted in less information loss.</p> <p><strong>Conclusions:</strong> A velocity index combining CT extension and days from symptom onset predicts disease progression in COVID-19 patients. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.</p>
Attitudes of university hospital staff towards in-house assisted suicide
<p><span><strong><span>Objective</span></strong></span><span><span><strong>:</strong></span> To investigate staff attitudes toward assisted suicide in the hospital and factors influencing its practices in Switzerland.</span></p> <p><span><strong><span>Design:</span></strong></span><span> Cross-sectional study</span></p> <p><span><strong><span>Setting:</span></strong></span><span> Two University Hospitals in French speaking regions of Switzerland.</span></p> <p><span><strong><span>Participants:</span></strong></span><span> 13'834 health care professionals including all personal caring for patients were invited to participate. </span></p> <p><span><strong><span>Main outcome measures and other variables: </span></strong></span><span>Attitudes towards the participation of hospital health care professionals in assisted suicide were investigated with an online questionnaire.</span></p> <p><span><strong><span>Results</span><span>:</span></strong></span> <span>Among all invited professionals, 5'127 responded by filling in the survey at least partially (response rate 37.0%) and 3'683 completed the entire survey (26.6%).</span><span> 72.6% of participants approved that this practice should be authorized in their hospital and saw more positive than negative effects. 57.6% would consider assisted suicide for themselves. </span><span>Non-medical professionals were 1.23 to 5.23 times more likely to approve assisted suicide than physicians (p<0.001). 68.8% of respondents indicated that each professional should have the choice whether or not to assist in suicide. </span></p> <p><span><strong><span>Conclusions:</span></strong></span><span> This multi-professional survey sheds light on hospital staff perceptions of assisted suicide happening within hospital walls, which may inform the development of rules considering their wishes but also their reluctance. Further research using a mixed-methods approach could help reaching an in-depth understanding of staff's attitudes and considerations towards assisted suicide practices.</span></p>
Data from: Early prediction of in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational cohort study
<p class="MsoNormal">Objective: Congestive heart failure (CHF) is a clinical syndrome in which heart disease progresses to a severe stage. Risk assessment and early diagnosis of death in patients with CHF are critical to patient prognosis and treatment. The purpose of this study was to establish a nomogram predicting in-hospital death for CHF patients in the ICU.</p> <p>Design: A retrospective observational cohort study.</p> <p>Setting and participants: The data of study from 30,411 CHF patients in the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD).</p> <p>Primary outcome: In-hospital mortality.</p> <p>Results: The inclusion criteria were met by 15983 subjects, whose in-hospital mortality rate was 12.4%. Multivariate analysis determined that the independent risk factors were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The C-index of the nomogram (0.767, 95%CI: 0.759–0.779) was <a>superior</a> to that of the traditional SOFA, APSIII and GWTGHF score, indicating its discrimination power. Calibration plots demonstrated that the predicted results are in good agreement with the observed results. The decision curves of the derivation and validation sets both had net benefits.</p> <p>Conclusion: The twenty independent risk factors for in-hospital mortality of CHF patients were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The nomogram that included these factors accurately predicted the in-hospital mortality of CHF patients. The novel nomogram has the potential to be a clinical practice aided predictive tool for predicting and assessing mortality in CHF patients in the ICU.</p>
Dataset for a cross-sectional study collated from June 2019 to July 2022 among patients visiting Kalkaal Hospital, Mogadishu, Somalia
<p>This is a dataset for a cross-sectional study collated from June 2019 to July 2022 among patients visiting Kalkaal Hospital, Mogadishu, Somalia. All individuals were evaluated for the presence of Hepatitis B surface antigen (HBsAg) within one hour of sample collection using fast chromatographic immunoassays. Using ELISA equipment for quantitative testing, positive individuals were confirmed. Similarly, a Hepatitis C blood test was performed using a rapid and a PCR test to validate the result. The Statistical Package for Social Science (SPSS) version 20 was used as the analysis tool.</p>
Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil
<p><strong>Data Repository </strong></p> <p><strong>Dataset name:</strong> covid19_rn-br.csv </p> <p><strong>Version: </strong>1.0 </p> <p><strong>Data collection period:</strong> 04/2020 - 08/2021 </p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances:</strong> 12,635</p> <p><strong>Number of Attributes: </strong>16</p> <p><strong>Missing Values:</strong> Yes </p> <p><strong>Area(s): </strong>Health</p> <p><strong>Sources:</strong> </p> <p>- <em>Primary</em>: </p> <ul> <li>RegulaRN (<a href="https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/">https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/</a>).</li> </ul> <p>- <em>Secondary</em>: </p> <ul> <li>RN Mais Vacina (<a href="https://rnmaisvacina.lais.ufrn.br/cidadao/">https://rnmaisvacina.lais.ufrn.br/cidadao/</a>).</li> </ul> <p><strong>Description:</strong> The covid19_rn-br.csv dataset is composed of data from individuals who were hospitalized due to the Sars-CoV-2 virus. The data comes from the ecosystem of services that includes the regulatory system for clinical and critical beds related to Covid-19 (RegulaRN) and the vaccination system against Covid-19 that records the data of the general population (RN Mais Vacina) from Rio Grande do Norte state, Brazil. This dataset provides elementary data to analyze the impact of vaccination on patients hospitalized in the state. Table 1 presents the dictionary used during the data analysis.</p> <p> </p> <p><strong>Table 1: </strong>Description of Dataset Features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>usp</strong></p> </td> <td> <p>Unified Score for Prioritization scale, which combines the parameters described in the quick Sequential Organ Failure Assessment (qSOFA), the Charlson Comorbidity Index (CCI), the Clinical Frailty Scale (CFS) and The Karnofsky Performance Status scores</p> </td> <td> <p>Numerical</p> </td> <td> <p>2.0. 3.0, 4.0, 5.0, 6.0+</p> </td> </tr> <tr> <td> <p><strong>age</strong></p> </td> <td> <p>Informs the patient's age</p> </td> <td> <p>Numerical.</p> </td> <td> <p>integer value for age</p> </td> </tr> <tr> <td> <p><strong>outcome</strong></p> </td> <td> <p>Informs the outcome of the hospitalized patient after leaving the hospital</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Discharge” or “Death"</p> </td> </tr> <tr> <td> <p><strong>comorbidities</strong></p> </td> <td> <p>Informs if the patient has comorbidities</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“Yes” or “No”</p> </td> </tr> <tr> <td> <p><strong>vaccine</strong></p> </td> <td> <p>Informs which type of vaccine was applied to the patient</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Vaccine #1”, “Vaccine #2” or NaN</p> </td> </tr> <tr> <td> <p><strong>bed_date_admission</strong> </p> </td> <td> <p>Informs the date the patient was hospitalized</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>bed_date_outcome</strong></p> </td> <td> <p>Informs the date that the patient left the hospital bed</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>length_hospitalization</strong></p> </td> <td> <p>Informs the number of days that the patient was hospitalized</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days</p> </td> </tr> <tr> <td> <p><strong>interval_d1_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the first dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>interval_d2_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the second dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d1</strong></p> </td> <td> <p>Informs the date of application of the patient's first dose</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d2</strong></p> </td> <td> <p>Informs the patient's second dose application date</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>comorbidities_txt</strong></p> </td> <td> <p>Informs patients' comorbidities</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN</p> </td> </tr> <tr> <td> <p><strong>immunization</strong></p> </td> <td> <p>It informs the patient's immunization level according to the number of doses received and the interval (in days) of application of these doses</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Partially”, “Fully” or “Not vaccinated”</p> </td> </tr> <tr> <td> <p><strong>health_professionals</strong></p> </td> <td> <p>Informs if the patient is a health professional</p> </td> <td> <p>Boolean</p> </td> <td> <p>0 or 1</p> </td> </tr> <tr> <td> <p><strong>age_group</strong></p> </td> <td> <p>Informs the age group of the hospitalized patient according to their age</p> </td> <td> <p>Categorical</p> </td> <td> <p>0-19, 20-49, 50-59, 60-69, 70-79, 80-89, 90+</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Article</strong>: Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil</p> <p><br> <strong>Authors</strong>: Ana Isabela L. Sales-Moioli, Leonardo J. Galvão-Lima, Talita K. B. Pinto, Pablo H. Cardoso, Rodrigo D. Silva, Felipe Fernandes, Ingridy M. P. Barbalho, Fernando L. O. Farias, Nicolas V. R. Veras, Gustavo F. Souza, Agnaldo S. Cruz, Ion G. M. Andrade, Lúcio Gama, Ricardo A. M. Valentim</p>
Essex County Hospital Site
Roman Kiln/Oven Colchester Essex Source: Objaverse 1.0 / Sketchfab
Figure 1 in Self-reported headache among the employees of a Swiss university hospital: prevalence, disability, current treatment, and economic impact
Figure 1 Comparison of age (a), gender (b) and occupation (c) among the 1192 respondents and all university hospital employees (in %). (b) Comparison of gender distribution among the 1192 respondents With those of University hospital employees (in %). (c) Comparison of distribution of occupation in the sample of 1192 respondents With the distribution of occupation of the university hospital employees in total (in %).
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.