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1,659 results for “Patient Data”
Data from: Antibacterial activity of graphene oxide nanosheet against multi drug resistant superbugs isolated from infected patients
Graphene oxide (GO) is a derivative of graphene nanosheet which is the most promising material of the decade in biomedical research. In particular, it has been known as an antimicrobial nanomaterial with good biocompatibility. In this study, we have synthesized and characterize GO and checked its antimicrobial property against different Gram-negative and Gram-positive multi drug resistant (MDR) hospital superbugs grown in solid agar-based nutrient plates with and without human serum through the utilization of agar well diffusion method, live/dead fluorescent staining and genotoxicity analysis. The main focus of the antimicrobial activity analysis is to distinguish the killing performance of GO in artificial and blood containing media because after injection into the bloodstream the activity of GO may be modified by adsorption of blood proteins or other biomolecules. No significant changes in antibacterial activity were found in these two different conditions. We also compare the bactericidal capability of GO with some commonly administrated antibiotics and in all cases the degree of inhibition is found to be higher. The data presented here are novel and show that GO is an effective bactericidal agents against different superbugs and can be used as a future antibacterial agent.
Data from: K-13 Propeller gene polymorphisms isolated between 2014 and 2017 from Cameroonian Plasmodium falciparum malaria patients
The emergence of artemisinin-resistant parasites since the late 2000s at the border of Cambodia and Thailand poses serious threats to malaria control globally, particularly in Africa which bears the highest malaria transmission burden. This study aimed to obtain reliable data on the current state of the kelch13 molecular marker for artemisinin resistance in Plasmodium falciparum in Cameroon. DNA was extracted from the dried blood spots collected from epidemiologically distinct endemic areas in the Center, Littoral and North regions of Cameroon. Nested PCR products from the Kelch13-propeller gene were sequenced and analyzed on an ABI 3730XL automatic sequencer. Of 219 dried blood spots, 175 were sequenced successfully. We identified six K13 mutations in 2.9% (5/175) of samples, including 2 non-synonymous, the V589I allele had been reported in Africa already and one new allele E612K had not been reported yet. These two non-synonymous mutations were uniquely found in parasites from the Littoral region. One sample showed two synonymous mutations within the kelch13 gene. We also observed two infected samples with mixed K13 mutant and K13 wild-type infection. Taken together, our data suggested the circulation of the non-synonymous K13 mutations in Cameroon. Albeit no mutations known to be associated with parasite clearance delays in the study population, there is need for continuous surveillance for earlier detection of resistance as long as ACTs are used and scaled up in the community.
Data from: 1970s and 'Patient 0' HIV-1 genomes illuminate early HIV/AIDS history in North America
The emergence of HIV-1 group M subtype B in North American men who have sex with men was a key turning point in the HIV/AIDS pandemic. Phylogenetic studies have suggested cryptic subtype B circulation in the United States (US) throughout the 1970s1, 2 and an even older presence in the Caribbean2. However, these temporal and geographical inferences, based upon partial HIV-1 genomes that postdate the recognition of AIDS in 1981, remain contentious3, 4 and the earliest movements of the virus within the US are unknown. We serologically screened >2,000 1970s serum samples and developed a highly sensitive approach for recovering viral RNA from degraded archival samples. Here, we report eight coding-complete genomes from US serum samples from 1978–1979—eight of the nine oldest HIV-1 group M genomes to date. This early, full-genome 'snapshot' reveals that the US HIV-1 epidemic exhibited extensive genetic diversity in the 1970s but also provides strong evidence for its emergence from a pre-existing Caribbean epidemic. Bayesian phylogenetic analyses estimate the jump to the US at around 1970 and place the ancestral US virus in New York City with 0.99 posterior probability support, strongly suggesting this was the crucial hub of early US HIV/AIDS diversification. Logistic growth coalescent models reveal epidemic doubling times of 0.86 and 1.12 years for the US and Caribbean, respectively, suggesting rapid early expansion in each location3. Comparisons with more recent data reveal many of these insights to be unattainable without archival, full-genome sequences. We also recovered the HIV-1 genome from the individual known as 'Patient 0' (ref. 5) and found neither biological nor historical evidence that he was the primary case in the US or for subtype B as a whole. We discuss the genesis and persistence of this belief in the light of these evolutionary insights.
Data from: Characterization of gut microbiota composition in hemodialysis patients with normal weight obesity
<p><b>Background:</b> Normal weight obesity (NWO), defined by a normal body mass index (BMI) but increased body fat percentage (BF%), is associated with an increased risk of cardiovascular disease and mortality. NWO is characterized by inflammation and muscle wasting in chronic kidney disease (CKD), but the underlying mechanisms remain largely unknown. Gut microbiota has been implicated in the regulation of host metabolism and may play important roles in the development of NWO in CKD.</p> <p><b>Methods:</b> In this case-control study, we examined the gut microbial diversity and taxonomy in 96 hemodialysis patients with normal weight (BMI <25 kg/m<sup>2</sup> and BF% ≤25% for men or ≤35% for women, n = 32), NWO (BMI <25 kg/m<sup>2</sup> and BF% >25% for men or >35% for women, n = 32), and overweight/obesity (BMI ≥25 kg/m<sup>2</sup>, n = 32), matched for age, gender, and diabetes. BF% was measured using bioimpedance spectroscopy device. Gut microbiota was determined by 16S rRNA sequencing.</p> <p><b>Results:</b> We found that α-diversity was significantly different among the 3 adiposity phenotypes, with NWO being the least diverse. α-diversity was positively correlated with BMI, subjective global assessment score, and physical activity, but negatively correlated with interleukin-6 and tumor necrosis factor-α. Patients with or without NWO were distinguished with respect to principal coordinate analysis of β-diversity. Notably, the relative abundance of butyrate-producing bacteria, such as <i>Faecalibacterium </i><i>prausnitzii</i> and<i> Coprococcus</i>, was markedly reduced in patients with NWO.</p> <p><b>Conclusion:</b> Our findings support associations between gut dysbiosis and a proinflammatory and catabolic state in hemodialysis patients with NWO.</p>
Accompanying data to "Empowerment or concealed compliance? A review of literature on mobile ICT solutions for patient empowerment"
<p>This file contains the details of the systematic literature review that underlies the article "Empowerment or concealed compliance? A review of literature on mobile ICT solutions for patient empowerment". The article will be published in the proceedings of ACM PETRA 2016. This file contains all the coding tables, and the list of included primary studies.</p>
Study protocol and data dictionary: Effectiveness of a GP delivered medication review in reducing polypharmacy and potentially inappropriate prescribing in older patients with multimorbidity in Irish primary care: a cluster randomised controlled trial (SPPiRE study)
<p><strong>Methods</strong></p> <p><strong>Study design and participants</strong></p> <p>The methods for the SPPiRE cluster RCT have been described in the trial protocol (21). This study is reported in line with the CONSORT 2010 cluster RCT checklist (22), see Appendix 1, and was approved by the Irish College of General Practitioners Research Ethics Committee. In brief, SPPiRE was a pragmatic two arm cluster RCT, with the intervention delivered to GP clusters and analysis of outcomes at the patient level. Information about the trial was publicised through a variety of GP research, teaching and training networks throughout Ireland. Eligible practices expressing an interest were formally invited. Practices were eligible to participate if they had at least 300 registered patients aged ≥65 years (based on the need to identify a sufficient number of eligible participants) and used either of the two Irish GP practice management systems (PMS) with over 80% national cover; this enabled use of a SPPiRE patient finder tool which was developed and embedded into these systems. Practices were excluded if they were currently involved in a medication management or prescribing trial or if they were unable to recruit at least five participants.</p> <p>Eligible patients were aged ≥65 years and prescribed ≥15 repeat medicines. A repeat medicine was defined as any unique item with a World Health Organisation Anatomical Therapeutic Chemical code on the patient’s current repeat prescription. Patients were excluded if they had been recruited into a practice that was unable to recruit at least four other participants, they were judged by their GP as unable to give informed consent or they were unable to attend the practice for a face to face medication review, (e.g. nursing home residents and house bound patients). Recruited GPs ran the SPPiRE patient finder tool and screened the generated list to ensure only eligible patients were invited. Practices who identified more than 40 eligible patients were supported in selecting a random sample of 30 patients to invite. All recruited practices and patients gave fully informed consent and baseline data was collected prior to practice allocation, to reduce the likelihood of selection bias.</p> <p><strong>Randomisation and masking</strong></p> <p>Recruited practices were allocated to intervention or control groups by minimisation using Minimpy software (23) by the trial statistician (FB) who had no knowledge of participating practices. Minimisation variables included practice size (number of GP sessions per week, 0-14, 14-28 and 28 or more) and location (urban, rural or mixed). Considering the nature of the intervention, it was not possible to blind GPs or patients to the intervention, however to reduce the risk of detection bias the two primary outcome measures; the number of repeat medicines and whether a PIP was present were assessed by an independent blinded pharmacist (MF).</p> <p><strong>Procedures</strong></p> <p>Intervention GPs received unique login details to the SPPiRE website where they had access to five training videos and a template for performing the SPPiRE medication review. The training videos provided background information on multimorbidity and polypharmacy, PIP, eliciting patient treatment priorities and conducting a brown bag medication review. GPs were instructed to book a double appointment and to ask their patients to bring all their medicines in to the medication review visit with them. The SPPiRE medication review process had two main components; gather and record information and then to discuss and agree changes with their patient based on the recorded information, with a focus on deprescribing medicines that were potentially inappropriate, figure 1. The website provided suggested treatment alternatives for identified PIP but all treatment decisions were ultimately at the discretion of the individual GP, based on their clinical judgement and their patients’ individual priorities.</p> <p>Control GPs delivered usual care during the six to twelve month study period. At the time of intervention delivery there was no structured chronic disease management programme in Irish primary care and many patients with multimorbidity attended multiple hospital specialists. In Ireland, the majority of people aged ≥70 years of age have access to free GP visits and medicines with some prescription charge co-payments. In the 65 – 69 year old age category a lower proportion have access to both free GP visits and prescription medicines. Access to specialists and diagnostics in secondary care is free for the entire population.</p> <p><strong>Outcomes</strong></p> <p>The two primary outcomes were the number of repeat medicines and the proportion of patients with any PIP, from a list of 34 pre-specified indicators (see Appendix 2). A series of secondary prescribing related outcome measure were pre-specified to allow a more in depth analysis of the effect of the intervention on prescribing. These were:</p> <ul> <li>The number of medicines stopped and started</li> <li>The proportion of patients with a reduction in significant polypharmacy (defined as ≥15 repeat medicines)</li> <li>The number of PIP</li> <li>The proportion of patients with a high risk PIP (see Appendix 2)</li> <li>The proportion of patients with any reduction in PIP</li> </ul> <p>Secondary patient reported outcomes measures were included to capture the effectiveness of the intervention from the patients’ perspective. These were:</p> <ul> <li>Health related Quality of life (EQ5D-5L)(24)</li> <li>Revised Patients' attitudes towards deprescribing (rPATD) (25)</li> <li>Multimorbidity Treatment Burden Questionnaire (MTBQ) (26)</li> </ul> <p>Health care utilisation data was collected to assess the effect of the intervention on health care usage and for the trial’s economic evaluation.</p> <p>Outcomes were collected at baseline and at six months after intervention delivery. Patient reported measures were collected by postal questionnaires. Data for all other measures including prescribed medicines, medical and investigations history and healthcare utilisation were collected by participating GPs and submitted to the study manager (CMC). This was a deviation from the original protocol, which indicated this data would be collected by the research team. This deviation related to changes in data protection and national health research regulations during the study period, which precluded research team access to the patients’ full clinical record.</p> <p><strong>Adverse events</strong></p> <p>Information on adverse events such as mortality, ED presentations and hospital admissions was collected at follow up. Given the deprescribing approach of the intervention a safety protocol for identifying and reporting any suspected adverse drug withdrawal events (ADWEs) was developed. An ADWE is defined as either recurrence of the condition for which the drug was prescribed (e.g. recurrence of angina after stopping a beta blocker) or a physiologic reaction to drug withdrawal (e.g. SSRI withdrawal syndrome) (27, 28). Although discontinuing medicines in older people has been demonstrated to be safe (29), given the paramount importance of the principle of “do no harm” in research ethics a vigorous and detailed method was established to ensure that any potential ADWEs precipitated by deprescribing in a SPPiRE medication review were captured. Intervention GPs were asked to report any possible ADWE following the SPPiRE medication review. The Naranjo ADR probability scale (30) has been adapted in other studies to assess the likelihood a reaction is related to drug withdrawal (27, 28). This tool was further adapted for SPPiRE and used to make an assessment on the causality of the ADWE. To ensure the patient perspective was included, self-reported possible ADWEs were also collected from patient follow up questionnaires. </p> <p> </p> <p><strong>Sample size</strong></p> <p>As outlined in the trial protocol (21), the study was designed with 90% power to detect a 20% reduction in the proportion with PIP and a mean difference of one medicine between intervention and control groups (based on a mean of 17.4 medicines SD (2.6)) and the sample size inflated to incorporate the effects of clustering (using an ICC of 0.025). The sample size was recalculated when it became apparent during early recruitment that it would not be possible to recruit clusters with an average of 15 participants, as was initially planned in the protocol. An average cluster size of eight was anticipated which inflated the original sample size from 30 practices (450 patients) to 50 practices (400 patients).</p> <p><strong>Statistical analysis</strong></p> <p>Descriptive statistics were used to describe baseline characteristics of recruited practices and participants. All analyses were conducted under the intention-to-treat principle and those lost to follow up had their baseline data carried forward. The primary analysis was carried out using multi-level modelling. The first primary outcome measure, number of repeat medications, was assessed using mixed effects Poisson regression with the individual as the unit of analysis and the practice included as the random effect to control for the effects of clustering and results presented using incidence rate ratios (IRR) and 95% confidence intervals (CI). The baseline number of medicines, GP size (number of GP sessions per week) and GP location (urban/rural) were included in the analysis as fixed effects. The second outcome measure, proportion of patients with a PIP, was analysed in a similar manner using mixed effects logistic regression, including PIP at baseline, GP size and location, and results presented using odd ratios (OR) and 95% CIs. A number of pre-specified sensitivity analyses were conducted; complete case analysis, per protocol analysis and including “presence of a repeat prescribing policy” as a covariate. All secondary outcomes were analysed in a similar manner to the primary outcomes, using appropriate mixed effects regression methods (i.e. linear, logistic, Poisson).</p> <p> </p> <p>Note: Version 3 (published 28 April 2025) updates Version 2 by removing Participant GP1P4 following consent withdrawal. This version should be used for all future analyses.</p> <p> </p>
Data from: Pre- and post- treatment fold change plasma proteomics in metastatic NSCLC patients
<p>Blood plasma samples were collected from advanced-stage NSCLC patients as part of a clinical study (PROPHETIC; NCT04056247). All clinical sites received IRB approval for the study protocol. Patient blood samples were drawn at baseline (referred to as T0) and, on average, 4 weeks after the treatment commenced, prior to the second dose of treatment (referred to as T1). Blood samples were drawn into tubes containing EDTA as an anticoagulant, and plasma was separated from the whole blood. The protocol adheres to the Clinical and Laboratory Standards Institute (CLSI) guidelines.</p>
Processed OLINK serum proteomics data MIS-C patients versus healthy controls
<p>This dataset contains processed OLINK serum proteomics data MIS-C patients versus healthy controls. Data was generated by Diorio et al. (Diorio, C., Shraim, R., Vella, L.A. <em>et al.</em> Proteomic profiling of MIS-C patients indicates heterogeneity relating to interferon gamma dysregulation and vascular endothelial dysfunction. <em>Nat Commun</em> <strong>12</strong>, 7222 (2021). https://doi.org/10.1038/s41467-021-27544-6). Processing in format provided here was done by dr. Levi Hoste. This table is used in the MultiNicheNet package (https://github.com/saeyslab/multinichenetr) and mentioned in the updated corresponding manuscript. </p>
Raw data for Figures 1-4 for journal article: "Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"
<p>This is a data set containing the raw data for figures 1-4 from the journal article:</p> <p>"Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"</p> <p>Original article DOI: 10.1080/14992027.2021.1903586</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/34097554/</p> <p> </p> <p>The data is contained within plots in word files, created with Microsofft Office (v18).</p>
The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome
<p>Data associated with my PhD thesis titled "The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome".</p>
DATA BASE Effectiveness of an educational intervention in the integral care of patients with DM2
<p>the database of the article is presented without sensitive data of the participants.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 12
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 12 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 and C07 which were acquired by scanning electron microscopy. The images show alveolae with various degree of epithelial damage.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 11
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 11 contains a stitched image montage of a thin section (selected area) through the lung of patient C05 which was acquired by scanning electron microscopy. The image shows a lung area with a dissolved alveolar architecture and a massive type-2-cell hyperplasia.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 10
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 10 contains stitched image montages of thin sections (selected areas) through the lung of patient C08 which were acquired by transmission electron microscopy. Cells, infected with SARS-CoV-2 particles, are shown in overview (A, C) and detail (B, C).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 14
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 14 contains stitched image montages of a thin section through the lung of patient C04 which were acquired by scanning electron microscopy. The file “Data_set_14.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 09
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 09 contains stitched image montages of thin sections (selected areas) through the lung of patient C03 which were acquired by scanning electron microscopy (C03_A & C) or transmission electron microscopy (C03_B). The images show accumulation of cells and debris in the alveolar cavity.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 08
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 08 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 and C08 which were acquired by scanning electron microscopy (C04) or transmission electron microscopy (C08_A & B). The images show the pathological changes of the alveolar epithelium: Type-1-cells detachment from the basal membrane (C08_A & B) and type-2-cell hyperplasia (C04).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 07
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 07 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 to C06 which were acquired by scanning electron microscopy. The images show alveolae with different degree of structural modification: Intact alveolar septum (C06); alveolar septum with detached alveolar epithelium (C04); dissolved alveolar organization (C05).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 06
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 06 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C08, which was acquired by bright-field light microscopy.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 05
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 05 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C07, which was acquired by bright-field light microscopy.</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.