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Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity"
<p>Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity".</p> <p>Data and further information at GitHub repository https://github.com/kreutz-lab/dia-benchmarking (DOI: 10.5281/zenodo.6371925)</p>
Metabolic and lipidomic data of patients with idiopathic pulmonary fibrosis and healthy volunteers
<p>The metabolomic / lipidomic datasets used in the manuscript draft entiteld: </p> <p>"Changes in Serum Metabolomics in Idiopathic Pulmonary Fibrosis and effect of approved antifibrotic medication"</p> <p>by</p> <p>Benjamin Seeliger, Alfonso Carleo, Pedro David Wendel-Garcia, Jan Fuge, Ana Montes Worboys, Sven Schuchardt, Maria Molina-Molina and Antje Prasse</p> <p>Data is untransformed and missing data were imputated. All values are in µmol/L.</p>
Mutations in the telomerase reverse transcriptase promoter and PIK3CA gene are common events in penile squamous cell carcinoma of Italian and Ugandan patients
<p>Somatic mutations in the promoter region of TERT (TERTp) gene are highly frequent in penile carcinoma in Italian and Ugandan patients, predominantly in human papillomavirus (HPV) negative cancers (67.6%). TERTp and PIK3CA hotspot changes coexist in 15.8% of cases. The higher mutant allele frequencies (MAFs) of TERT -124A/-146A compared to PIK3CA E545K MAFs suggest an asynchronous mutation timing. The coexistence of TERTp and PIK3CA mutations may represent a novel co-actionable therapeutic target in penile carcinoma patients.</p>
SILICOFCM_Virtual patients repository_clinical features
<p>Generation of a virtual dataset based on one of the integrated virtual generators into the SILICOFCM platform. The dataset contains 1000 virtually generated patients with 20 virtually generated clinical features by the real dataset of SILICOFCM: age, sex, NYHA class, systolic pressure, diastolic pressure, syncope, heart murmurs, left ventricular ejection fraction (LVEF or EFLV), left ventricular internal dimension at end-diastole (LVIDd), left ventricular internal dimension at end systole (LVIDs), intraventricular septal thickness at end-diastole (IVSd), posterior wall thickness at end-diastole (PLWd), endsystolic volume of left ventricle (SVLV), left ventricular outflow tract maximum pressure gradient (maxLVOTPG), Doppler E/E’ ratio (EE), body to mass index (BMI), left atrium size (LA), Alanine aminotransferase (ALT), aorta size (AO), and aortic valve (AV). The dataset can be utilized for Machine Learning modeling.</p>
Caregivers and MDR-TB patients: Dataset for the importance of knowledge and behavior on drug resistance tuberculosis
<p>Multidrug-resistant Tuberculosis (MDR-TB) is a type of Tuberculosis (TB) that is resistant to at least one or more of the main anti-TB drugs, namely Rifampin or Isoniazid, so this infection is more difficult to eliminate. Good knowledge and behavior of caregivers and patients can affect the success of treatment because they tend not to be late in taking treatment. In this data note we provide the details of a research database of 228 MDR-TB caregivers and patients underwent treatment from January 2020 to December 2021 in preferred hospital in West Java, Indonesia. The purposes of this publication are to describe the dataset for external researchers who may be interested in making use of it, and to detail the methods used to obtain the dataset to determine the level of knowledge and behavior of MDR-TB caregivers and patients regarding the disease through a validated questionnaire consisted of the knowledge and behavior distributed to respondents via online and offline.</p>
Small extracellular vesicles from malignant ascites of patients with advanced ovarian cancer provide insights into the dynamics of the extracellular matrix
<p>Representative Atomic Force Microscope images collected in liquid on Extracellular Vesicles from Ascite and Cell cultures. Data were collected by Broker Multimode VIII. Images like these were used in the paper to measure the contact angle and size distribution.</p>
Development and implementation of a tool for measuring the training effectiveness of the patient-centered consultation model
<p><strong>Background:</strong> The patient-centered consultation model comprises four elements: exploring health, illness, and disease experiences, understanding the whole person, finding common ground, and enhancing the patient-doctor relationship. This method is taught at the course in general practice at Copenhagen University. The aim of the study was to develop a simple tool consisting of a questionnaire about the patient-centered elements and a test video consultation. The outcome is the change in the students’ ability to identify these elements. Used as a pre-course and post-course test it can inform the teachers which elements of the patient-centered consultation need intensifying in the teaching.<br> <strong>Methods:</strong> The students from a course in general practice volunteered to participate in all steps of the development. They took part in individual interviews to select items from an already existing questionnaire (DanSCORE). The preliminary questionnaire was tested for face and content validity, pilot-tested and tested for test-retest reliability. All video consultations were transcribed and assessed for patient-centered elements through a conversation analysis. The videos showed medical students seeing real patients.<br> <strong>Results:</strong> The preliminary version of the questionnaire (called DanOBS) <a href="#_msocom_1">[CM1]</a> <a href="#_msocom_2">[MJ2]</a> had 23 items. In the subsequent interviews, items were reduced to 17, each with three response options. After the pilot test, the questionnaire was further reduced to 13 items, all strictly relevant to the model and with two response options. The final questionnaire had acceptable test-retest reliability. The number of test consultation videos underwent a reduction from six videos to one.<br> <strong>Conclusions:</strong> The DanOBS combined with a test video consultation, used as a pre-and post-course test demonstrates for teachers which elements in the patient-centered consultation need to be intensified in the teaching.</p> <p> </p> <p> </p>
The input data set includes 729 objects (patients) and 39 variables (clinical qualitative and quantitative descriptors).
<p>For reliable data treatment and interpretation qualitative descriptors were omitted and only numerical clinical indicators were included in the data matrix. Finally, the data set dimension was [729 x 18].</p> <p> The data were treated by hierarchical cluster analysis and factor analysis. The major goal of the data mining was to reach statistically significant partitioning of the objects and variables into similarity patterns (clusters) which helps to better understand the data structure, to assess the meaning of the partitioning achieved, thus promoting the evaluation of the health status of the patients and the role of specific descriptors for the formation of the partitioning patterns.</p> <p>3D classification Python tool.</p>
All-atom molecular dynamics simulations of phenylalanine-4-hydroxylase (PAH) tetramer to investigate the impact of two novel heterozygous mutations, p.Y198N and p.Y204F, observed in a classical phenylketonuria patient
<p>Phenylalanine-4-hydroxylase (PAH) tetramer system (Robetta modelling to complete the structure with template PDB ID: 6hyc) with parametrised BH<sub>4</sub> ligand (parameters are available in the dataset) and Fe(II) metal ions in a TIP3P water box ionised with 0.15 M KCl were presented as wild-type and carrying two novel mutations as Y198N on dimeric chains A and B, and Y204F on dimeric chains C and D. In addition, E353 and E422 are protonated as predicted by PROPKA. BH<sub>4</sub> molecule parametrization was performed by using GAFF, Antechamber and “amb2chm_par.py” program of Amber2018.</p> <p>5,000-step minimization and 1 ns equilibration were performed by fixing the protein to relax the system. Then, another 5,000-step minimization and 1 ns equilibration were performed without any constraints, except the SHAKE algorithm on water molecules, to relax the protein and system. The production simulations were performed along 100 ns trajectory at 310 K collected under NpT ensemble.</p> <p>All system preparation and simulation details for this dataset is available with the related background, results and conclusions in the following article:</p> <p>Tolga Aslan, Aslı Yenenler-Kutlu, Umut Gerlevik, Ayşe Çiğdem Aktuğlu Zeybek, Ertuğrul Kıykım, Osman Uğur Sezerman & Necla Birgul Iyison (2021) Identifying and elucidating the roles of Y198N and Y204F mutations in the PAH enzyme through molecular dynamic simulations, Journal of Biomolecular Structure and Dynamics, DOI: <a href="https://doi.org/10.1080/07391102.2021.1921619">10.1080/07391102.2021.1921619</a></p>
A dataset of anonymised hospitalised COVID-19 patient data: outcomes, demographics and biomarker measurements for two New York hospitals
<p>These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State<br> University of New York (SUNY) Downstate Health Sciences University and Maimonides<br> Medical Center.</p> <p>The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.</p> <p>The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.</p> <p><strong>Study approval and data collection</strong></p> <p>Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.</p>
Patient and GP experiences of implementing a medication review intervention in older people with multimorbidity: process evaluation of the SPPiRE trial
<p>Abstract</p> <p><strong>Background: </strong>The SPPiRE cluster randomised controlled trial (RCT) found that a GP delivered medication review that incorporated screening potentially inappropriate prescriptions (PIP), a brown bag review and a patient priority assessment, resulted in a significant but small reduction in the number of medicines and no significant reduction in PIP.</p> <p><strong>Objective: </strong>To explore the experiences of GPs and patients engaged in the SPPiRE intervention and the potential for system wide implementation.</p> <p><strong>Design: </strong>Mixed methods process evaluation; quantitative data was collected from the SPPiRE intervention website and qualitative data via semi-structured interviews.</p> <p><strong>Setting and participants:</strong> 51 general practices throughout Ireland, and 404 participants with multimorbidity aged ≥65 years, prescribed ≥15 medicines participated in the RCT. Qualitative data was collected with purposive samples of intervention GPs (18/26) and patients (27/208). </p> <p><strong>Methods: </strong>Quantitative data was analysed descriptively, qualitative data thematically and both were integrated using a triangulation protocol.</p> <p><strong>Results: </strong>The analysis generated three themes, intervention implementation, mechanisms of action, and both were underpinned by the theme of context. One fifth of patients had no review, primarily due to insufficient GP time. The brown bag review component resulted in the most deprescription of medications. GPs felt it easier to change medicines if the patient was well known to them, and patients were generally receptive to change. GPs identified lack of integration into practice software systems and resources as barriers to future implementation.</p> <p><strong>Conclusion: </strong>Consideration of implementation of successful interventions is key to informing policy and integration into clinical practice. GPs and patients viewed the intervention positively, but implementation will depend on resourcing and integration into practice software systems.</p> <p>Trial registration number: <a href="https://doi.org/10.1186/ISRCTN12752680">ISRCTN12752680</a></p>
Brain functional connectivity data in anesthetized participants and patients with neuropathological or psychiatric diagnoses
<p>Five fMRI datasets were collected from independent research sites including: propofol deep sedation (PDS; drug effect site concentration= ~2.4 μg/ml) in Dataset-1, propofol general anesthesia (PGA; drug effect site concentration= 4.0 μg/ml) in Dataset-2, ketamine anesthesia (KA) in Dataset-3, unresponsive wakefulness syndrome (UWS) in Dataset-4, and schizophrenia (SCHZ), bipolar disorder (BD), and attentional deficit hyperactivity disorder (ADHD) in Dataset-5. Following fMRI data preprocessing, the fMRI time courses were extracted from 400 cortical areas according to a well-established brain parcellation scheme (Schaefer's 400 ROIs). A connectivity matrix was then calculated using Pearson correlation resulting in a 400x400 connectivity matrix for each participant and each condition.</p>
Incidence and predictor of diabetic foot ulcer and its association with change in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia, 2021
<p>Abstract</p> <p> </p> <p><strong>Background</strong></p> <p>Diabetes mellitus is one of the global public health problems and fasting blood sugar is an important indicator of diabetes management. Uncontrolled diabetes can lead to diabetic foot ulcers, which is a common and disabling complication. The association between fasting blood glucose level and the incidence of diabetic foot ulcers is rarely considered, and knowing its predictors is good for clinical decision-making. Therefore, the aim of this study was to determine the incidence and predictors of diabetic foot ulcers and its association with changes in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia.</p> <p><strong>Methods</strong></p> <p>A multicenter retrospective follow-up study was conducted at a referral hospital in Northwest Ethiopia. A total of 539 newly diagnosed DM patients who had follow-up from 2010 to 2020 were selected using a computer-generated simple random sampling technique. Data was entered using Epi-Data 4.6 and analyzed in R software version 4.1. A Cox proportional hazard with a linear mixed effect model was jointly modeled and 95% Cl was used to select significant variables. AIC and BIC were used for model comparison.</p> <p><strong>Result</strong></p> <p>A total of 539 diabetes patients were followed for a total of 28727.53 person-month observations. Overall, 65 (12.1%) patients developed diabetic foot ulcers with incidence rate of 2.26/1000-person month observation with a 95% CI of [1.77, 2.88]. Being rural (AHR= 2.30, 95%CI: [1.23, 4.29]), being a DM patient with Diabetic Neuropathy (AHR= 2.61, 95%CI: [1.12, 6.06]), and having peripheral arterial disease(PAD) (AHR= 2.96, 95%CI: [1.37, 6.40]) were significant predictors of DFU. The time-dependent lagged value of fasting blood sugar change was significantly associated to the incident of DFU (α = 1.85, AHR=6.35, 95%CI [2.40, 16.79]).</p> <p><strong>Conclusion and recommendation</strong></p> <p>In this study, the incidence of DFU was higher than in previous studies and was influenced by multiple factors like rural residence, having neuropathy, and PAD were significant predictors of the incidence of DFU. In addition, longitudinal changes in fasting blood sugar were associated with an increased risk of DFU. Health professionals and DM patients should give greater attention to the identified risk factors for DFU were recommended.</p>
Antifungal activity of propolis extract against Candida albicans in patients with vulvovaginal candidiasis
<p>Abstract</p> <p><strong>Objective:</strong> To evaluate the antifungal activity of propolis extract against <em>Candida Albicans</em> (or <em>C. Albicans</em>) in patients with vulvovaginal candidiasis.</p> <p><strong>Method:</strong> The research presents a quantitative approach with an experimental design, with a population of <em>C. Albicans</em> strains isolated from patients diagnosed with vulvovaginal candidiasis who were admitted to the Gynecology Service of the Maria del Socorro Clinic, district of Ate - Lima, from which a sample of 34 strains was chosen in total, considering three repetitions per strain, obtaining 102 experimental units.</p> <p>The antifungal susceptibility test was carried out by the disc diffusion plate method using a single concentration of propolis extract, with fluconazole as a positive control.</p> <p><strong>Resultados:</strong> Propolis extract was shown to inhibit all strains of <em>C. albicans</em>, as was fluconazole, with the effect of the latter being significantly greater than that of the product under investigation.</p> <p><strong>Conclusion:</strong> The conclusion is that propolis extract does show antifungal activity against <em>C. albicans</em> in patients with vulvovaginal candidiasis.</p> <p><strong>Keywords:</strong> Propolis extract, <em>Candida Albicans</em>, Antifungal activity, Vulvovaginal candidiasis.</p> <p> </p>
Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)
<p>This video is the fifth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)</p> <p>Bio: Beth Harvey is currently a master’s student in digital health and data analytics at the Henley Business School. Having completed her bachelor’s in biomedical science, Beth has then gone on to work in the medical device and IVD regulatory sector with a consultancy firm in Vancouver. After moving back to the UK in 2019 Beth worked with multiple UK manufacturers in the digital health space which spurred her interest in the field she is now studying. Beth’s research interests are in healthcare innovation remote patient monitoring, and data analysis. She is currently finishing her dissertation on the opportunities and challenges for remote blood monitoring in oncology in collaboration with the Future Blood Network.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/8pbO3N9IQKM</p>
The Supplementary Material for the article entitled "Comparative analysis of global transcriptomes in nontyphoidal Salmonella clinical isolates from pediatric patients with and without bacteremia after infecting human intestinal epithelium in vitro"
<p>The Supplementary Material (Additional files 1-5, including Table S1-S4 and Figure S1) for this article.</p> <p> </p> <p><strong>Table S1.</strong> Upregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S2.</strong> Downregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S3. </strong>The enriched GO terms in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S4. </strong>The enriched KEGG pathways in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Figure S1. </strong>The enriched GO terms and KEGG pathways in Group B relative to Group A. Bar charts show the enriched GO terms (A) and the enriched KEGG pathways (B) by significance power. Color of bars indicate power of significance and length in x axes of bar indicate number of annotated genes in the particular term of pathway. Cnetplots show the relationship between GO term (C) and KEGG pathways (D). Dot size representing GO terms and KEGG pathways indicates number of significantly changed and its annotated genes. The GO terms or KEGG pathways connected through their common and annotated genes. </p>
Data set - What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study
<p><strong>Data set from- What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study</strong></p> <p><strong>Abstract of the study: </strong>The treatment of cancer can have a significant impact on quality of life in older patients and this needs to be taken into account in decision making. However, quality of life can consist of many different components with varying importance between individuals. We set out to assess how older patients with cancer define quality of life and the components that are most significant to them. This was a single-centre, qualitative interview study. Patients aged 70 years or older with cancer were asked to answer open-ended questions: What makes life worthwhile? What does quality of life mean to you? What could affect your quality of life? Subsequently, they were asked to choose the five most important determinants of quality of life from a predefined list: cognition, contact with family or with community, independence, staying in your own home, helping others, having enough energy, emotional well-being, life satisfaction, religion and leisure activities. Afterwards, answers to the open-ended questions were independently categorized by two authors. The proportion of patients mentioning each category in the open-ended questions were compared to the predefined questions. Overall, 63 patients (median age 76 years) were included. When asked, “What makes life worthwhile?”, patients identified social functioning (86%) most frequently. Moreover, to define quality of life, patients most frequently mentioned categories in the domains of physical functioning (70%) and physical health (48%). Maintaining cognition was mentioned in 17% of the open-ended questions and it was the most commonly chosen option from the list of determinants (72% of respondents). In conclusion, physical functioning, social functioning, physical health and cognition are important components in quality of life. When discussing treatment options, the impact of treatment on these aspects should be taken into consideration.</p> <p><strong>Reference of research paper: </strong>Seghers PAL, Kregting JA, van Huis-Tanja LH, Soubeyran P, O'Hanlon S, Rostoft S, Hamaker ME, Portielje JEA. What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study. <em>Cancers</em>. 2022; 14(5):1123. https://doi.org/10.3390/cancers14051123</p> <p><strong>Content of the data set: </strong>The first Tab describes what questions were asked, the second tab shows all individual anonymised answers to the open questions, the fourth shows the definitions that were used to classify all answers. Q1-Q4 show how the answers were categorised. </p>
Processed data for FOXA2 analysis in TCGA KIRP and KIRC patients
<pre># Data and code to test whether FOXA2 is changed in KIRP patients with low FH ## Code https://github.com/ArianeMora/foxa2_kirp_kirc ## Datasets RNA count data were downloaded from TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) using scidat (https://github.com/ArianeMora/scidat) for patients with kidney cancers (rna_df.csv). ## Processing The kidney cancer patient count data were split into KIRC and KIRP, and only the tumour data was used for this analysis, see notebook FOXA2.ipynb in the code folder. Samples were split by their expression of FH in their tumour samples, with several annotations used to separate patients for completeness: 1. Low-High: Comparing the bottom 25% (< Q1) of patients by FH vs “high” FH (i.e. top 25%, > Q3): p.adj 0.00004 2. Low-Normal: Comparing the bottom 25% of patients by FH to the patients with “normal” range FH (between Q1 and Q3): p.adj 0.053 3. Outlier-High: Comparing outlier FH to “high” (i.e. top 25%): p.adj 0.067 4. Outlier-Normal: Comparing the outlier FH (Q1 – 1.5*IQR) to all “normal” FH patients: p.adj 0.169 We did the same for KIRC patients – we don’t see FOXA2 as expected 1. Comparing the bottom 25% of patients by FH vs the top 25% of patients with FH: 0.14 2. Comparing the bottom 25% of patients by FH to the patients with “normal” range FH: 0.25 3. Comparing the outlier FH to all “normal” FH patients: 0.31 4. Comparing outlier FH to “high” (i.e. top 25%): 0.32 Each of these groups were used to also perform DE analysis between the two groups, see respective RMD files in code for details. ### References If you use this work please cite TCGA: ``` Creighton, C. J., Morgan, M., Gunaratne, P. H., Wheeler, D. A., Gibbs, R. A., Gordon Robertson, A., Chu, A., Beroukhim, R., Cibulskis, K., Signoretti, S., Vandin Hsin-Ta Wu, F., Raphael, B. J., Verhaak, R. G. W., Tamboli, P., Torres-Garcia, W., Akbani, R., Weinstein, J. N., Reuter, V., Hsieh, J. J., … University of North Carolina at Chapel Hill. (2013). Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature, 499(7456), Article 7456. https://doi.org/10.1038/nature12222 ``` </pre>
Figure 2. Patient protection in relation to the parasite signals of electric lines-EKG Through Sound-Card
<p>The recorder module applies the signal to the preamplifier’s non-inverted input and then<br> passes through the limiter which establishes the maximum left or right limits of the stylus, to avoid<br> breaking the recording tape. The power supply of the device contains mainly a convertor with an<br> output transformer and a reaction transformer powered either from a network through a downward<br> transformer followed by a rectifier and filter, or from an accumulator battery.<br> To comply with the rules of patient protection, the supply for the electrocardiograph’s<br> preamplifier is done floatingly according to the grounding null (Figure 2).<br> Figure 2.</p>
Figure 1: Frequency distribution & bar diagram of the combined score of ventilated patients
<p>In order to obtain the range of scores that represent 95% of the observations that ended in<br> respiratory failure, we used the frequency distribution curve (fig 9) with (2SD) above and below the<br> calculated mean. This gives a value of (16-24) as the limits of interval including the score of<br> patients at risk of developing respiratory failure</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.