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392 results for “blood biomarker”
Fluorescent Confocal Laser Scanning Microscopy of White Blood Cells, Cancer Cell Line MCF7, and Mixtures of these Cells: A Model System for Circulating Tumor Cell Biomarker Evaluation V.1
<p>This is a confocal laser scanning microscopy data set of white blood cells (leukocytes), the cancer cell line MCF7, and mixtures of these cells acquired on a Zeiss LSM 780 microscope in the University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core. Cells are fluorescently labeled for DNA with DAPI (Sigma D9542), lipids with Bodipy 495/503 (Thermo Fisher D3922), the filament protein cytokeratin (CK) with pan-cytokertain-alexa555 antibodies (Cell Signaling Technologies 3478S) and the surface membrane antigen CD45 with CD45-alexa647 antibodies (Biolegend 304020). Bodipy was excited with a continuous wave (CW) 488 nm laser, alexa555 was excited with CW 561 nm laser, and alexa647 was excited with a CW 633 nm laser. The acquiring instrument does not have a CW 405 nm source so DAPI was excited by two photon process using a Coherent Cameleon ultrafast pulsed laser tuned to 765 nm. The objective used was a Zeiss Plan-Apochromat 20x, 0.8 NA, air.</p> <p>The data consists of 4 channel 8x8 mosaic z-stacks. The Zeiss software performed stitching of the mosaics. These stitched data images are included and marked with _Stitched at the end. Those interested in performing the stitching themselves can do this with the raw data files (without the _Stitched). The jpeg images are processed from the stitched LSM images. The LSM files contain additional meta data on the experiment including power levels and acquisition settings.</p> <p>The _Stiched .lsm files will load in ImageJ (tested with V.1.49) as 4 channel 3 stack images.</p> <p>This data is a model system for evaluating the DNA/Lipids/CK/CD45 biomarker panel to identify circulating tumor cells (CTCs). The D- population of the model is the WBCs and the D+ population is the MCF7 cancer cell line. The amount of separation the biomarker panel plus analysis algorithm can produce between these populations (D+/D-) is an estimate the sensitivity and specificity of the biomarker panel plus algorithm to CTCs.</p> <p>Experiments generating the data were performed over the course of 15 days. Peripheral blood samples were collected from the Gynecological Tissue and Fluid Bank (COMIRB 07-0935 / COMIRB 05-1081) from consenting patients undergoing surgery at the University of Colorado Hospital. Blood samples were used the same day they were collected. Blood samples were collected from 3 patients with benign conditions, labeled WBBN#, and 3 patients with ovarian cancer, labeled WBCA#. We do not expect there to be any difference in the isolated white blood cells samples prepared from the cancer and benign patients. Samples were stored at room temperature until white blood cells were isolated. Mixed samples were prepared by passaging a MCF7 flask and mixing it with isolated white blood cells before fixation. A schedule showing the time duration between collection, processing and imaging is included as “experimental schedule.gif”.</p> <p>The MCF7 cancer cell line was a kind gift from Dr. Heide Ford. Genomic DNA was isolated from the MCF7 cell line after the experiment and sent for cell line authentication. The gDNA was a match to MCF7. The authentication report and data are included in this submission.</p> <p>CD45 antibodies were exhausted on day 7. New antibody was purchased and received on day 8. The day 7 images only has labels for DAPI and Bodipy. The samples prepared with the old antibodies on days 4 and 7 were relabeled and imaged with the new antibodies on days 14 and 15. This labeling was also done to confirm the pan-CK antibodies remained good since they are dim in the MCF7 cells imaged on days 12 and 13. The pan-CK on days 14 and 15 looks the same as it did on days 5 and 7 confirming the antibodies are good.</p> <p>Four of the filters containing cells were not sufficiently flat to be acquired with a 3 slice z-stack so a 5 slice z-stack was used. These files have been zipped to compress them under the 2 GB limit permitted by zenodo.org</p> <p>Further information on how these samples were prepared, processed, and analyzed can be found in our associated 2016 SPIE Photonics West BIOS conference proceeding titled, “Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system”, http://dx.doi.org/10.1117/12.2222317.</p> <p>This work was supported by funding provided to the University of Colorado Cancer Center by the American Cancer Society and awarded as Institutional Research Grant Number 57-001-53, by funding provided by the Defense Advanced Research Projects Agency under grant number N66001-10-4035, and by funding provided by NIH/NCATS Colorado CTSI Grant Number TL1 TR001081. The University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core is also supported in part by NIH/NCATS Colorado CTSI Grant Number UL1 TR001082. The funders had no role in the study design, data collection, analysis, or decision to publish.</p>
A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells
<p>Annotated WES VCF file and phenotype file used in Gene burden analysis of Naaldijk et al., "A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells", 2023. </p><p> </p>
APOE4 is associated with elevated blood lipids and lower levels of innate immune biomarkers in a tropical Amerindian subsistence population
<p>In post-industrial settings, <i>APOE4</i> is associated with increased cardiovascular and neurological disease risk. However, the majority of human evolutionary history occurred in environments with higher pathogenic diversity and low cardiovascular risk. We hypothesize that in high-pathogen and energy-limited contexts, the <i>APOE4</i> allele confers benefits by reducing innate inflammation when uninfected, while maintaining higher lipid levels that buffer costs of immune activation during infection. Among Tsimane forager-farmers of Bolivia (N=1266), <i>APOE4</i> is associated with 30% lower C-reactive protein, and higher total cholesterol and oxidized-LDL. Blood lipids were either not associated, or negatively associated with inflammatory biomarkers, except for associations of oxidized-LDL and inflammation which were limited to high BMI adults. Further, <i>APOE4</i> carriers maintain higher levels of total and LDL cholesterol at low BMIs. These results suggest the relationship between <i>APOE4</i> and lipids may be beneficial for pathogen-driven immune responses, and unlikely to increase cardiovascular risk in an active subsistence population.</p>
Effects of Real vs. Soundless Acoustic Stimulation During Deep Sleep on Brain Activity, Memory, and Blood Biomarkers in Older Adults (60-85) With Mild Memory Impairment
ClinicalTrials.gov study NCT06669546. IPD Sharing: NO. Countries: 1. Publications: 4.
A Study to Evaluate the Safety, Efficacy and Changes in Induced Sputum and Blood Biomarkers Following Daily Repeat Doses of Inhaled GSK2269557 in Chronic Obstructive Pulmonary Disease (COPD) Subjects
ClinicalTrials.gov study NCT02522299. IPD Sharing: YES. Countries: 2. Publications: 1.
Effect of Flutamide on Biomarkers in Blood and Tissue Samples From Patients at High Risk of Ovarian Cancer
ClinicalTrials.gov study NCT00699907. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study to Collect Blood Biomarker Samples From Participants With Chronic Hepatitis B (CHB) Who Received Treatment With Pegasys (Peginterferon Alfa-2a) ± Nucleoside/Nucleotide Analogue
ClinicalTrials.gov study NCT01855997. IPD Sharing: Not stated. Countries: 15. Publications: 1.
Blood Sample Collection to Evaluate Biomarkers for Hepatocellular Carcinoma
ClinicalTrials.gov study NCT03628651. IPD Sharing: YES. Countries: 7. Publications: 2.
Study on Novel Peripheral Blood Diagnostic Biomarkers for MCI Due to Alzheimer's Disease
ClinicalTrials.gov study NCT04509271. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Pilot Study of the Effects of BCG Immunization on CSF and Blood-based Biomarkers in Older Adults.
ClinicalTrials.gov study NCT04507126. IPD Sharing: NO. Countries: 1. Publications: 0.
APOE4 is associated with elevated blood lipids and lower levels of innate immune biomarkers in a tropical Amerindian subsistence population
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Data from: Blood NfL: a biomarker for disease severity and progression in Parkinson's disease
Objective: To examine whether plasma neurofilament light chain (NfL) levels were associated with motor and cognitive progression in Parkinson's disease (PD). Methods: This prospective follow-up study enrolled 178 participants, including 116 with PD, 22 with multiple system atrophy (MSA), and 40 healthy controls. We measured plasma NfL levels with electrochemiluminescence immunoassay. Patients with PD received evaluations of motor and cognition, at baseline and at a mean follow-up interval of 3 years. Changes in the unified Parkinson's disease rating scale (UPDRS) part III motor score and Mini-Mental State Examination (MMSE) score were used to assess motor and cognition progression. Results: Plasma fL levels were significantly higher in MSA than in PD and healthy groups (35.8±6.2 pg/ml, 17.6±2.8 pg/ml, and 10.6±2.3 pg/ml, respectively; P<0.001). In the PD group, NfL levels were significantly elevated in patients with advanced Hoehn-Yahr (H-Y) stage and patients with dementia (PDD) (P<0.001). NfL levels were modestly correlated with UPDRS part III scores (r=0.42, 95% CI: 0.46-0.56, P<0.001). After a mean follow-up of 3.4±1.2 years, a Cox regression analysis adjusted for age, sex, disease duration and baseline motor or cognitive status showed that higher baseline NfL levels were associated with higher risks for either motor or cognition progression (P=0.029 and P=0.015, respectively). Conclusions: Plasma NfL levels correlated with disease severity and progression in terms of both motor and cognitive functions in PD. Classification of evidence: This study provides Class III evidence that plasma NfL levels distinguish PD and MSA, and is a surrogate biomarker for PD progression.
Dataset BD "Inflammation Biomarkers in Blood as Mortality Predictors in Community-Acquired Pneumonia Admitted Patients: Importance of comparison with Neutrophil Count Percentage or Neutrophil-Lymphocyte Ratio."
<p>Dataset of the article tittled: <strong>Inflammation Biomarkers in Blood as Mortality Predictors in Community-Acquired Pneumonia Admitted Patients: Importance of comparison with Neutrophil Count Percentage or Neutrophil-Lymphocyte Ratio.</strong></p> <p> </p>
Immature Granulocytes As Biomarker In Peripheral Blood For Sub-acute Inflammation In Early Diagnosis of Lower Extremity Arterial Thrombosis
<p>Early diagnosis and treatment are critically important in terms of prognosis in subacute arterial thrombosis. The aim of our study is to investigate whether immature granulocytes are useful in the early diagnosis of subacute artery thrombosis. This retrospective study was conducted in a single center between 2019 and 2021 A total of 99 patients with lower extremity chronic peripheral arterial disease were included in the study. Among these patients, 27 patients with subacute artery thrombosis were included in SAT group. The remaining 72 patients were included in control group. The blood samples of the patients in both groups, belonging to the first application and before receiving any treatment, were analyzed. CBC parameters calculated with automatic hematological analyzer between groups were compared statistically. Our study showed that immature granulocytes can be very useful in the diagnosis of SAT, with a sensitivity of 81% and a specificity of 90%.</p>
Data from: Analysis of altered level of blood-based biomarkers in the prognosis of COVID-19 patients
<p><strong>Introduction:</strong> Immune and inflammatory responses developed by the patients with Coronavirus Disease 2019 (COVID-19) during rapid disease progression result in an altered level of biomarkers. Therefore, this study aimed to analyze levels of blood-based biomarkers that are significantly altered in patients with COVID–19.</p> <p><strong>Methods: </strong>A cross-sectional study was conducted among COVID-19 diagnosed patients admitted to the tertiary care hospital. Several biomarkers – biochemical, hematological, inflammatory, cardiac, and coagulatory – were analyzed and subsequently tested for statistical significance at P<0.01 by using SPSS version 17.0.</p> <p><strong>Results:</strong> A total of 1,780 samples were analyzed from 1,232 COVID-19 patients (median age 45 years [IQR 33-57]; 788 [63.96%] male). The COVID-19 patients had significantly (99% CI, p<0.001) elevated glucose, urea (p=0.001), alanine transaminase (ALT), aspartate aminotransaminase (AST), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) levels, total white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), ferritin, D-Dimer, and creatinine phosphokinase-MB (CPK-MB, p=0.004) as compared to control group. However, the levels of total protein, albumin, and platelets were significantly lowered in COVID-19 patients as compared to control group. The elevated levels of glucose, urea, direct bilirubin, WBC, CRP, prothrombin time (PT), D-Dimer, and LDH were significantly associated with in-hospital mortality among COVID-19 patients.</p> <p><strong>Conclusions:</strong> Assessing and monitoring the elevated levels of glucose, urea, ALT, AST, ALP, ferritin, DB, WBC, CRP, PCT, LDH, D-Dimer, PT, and CPK-MB and the lowered levels of total protein, albumin, and platelet could provide a basis for evaluation of improved prognosis and effective treatment in patients with COVID-19.</p>
Identification of a human blood biomarker of pharmacological 11β-hydroxysteroid dehydrogenase 1 inhibition
<p>Data-Sets of, “Identification of a human blood biomarker of pharmacological 11β-hydroxysteroid dehydrogenase 1 inhibition”</p> <p>The Dataset (Derived from <a href="https://doi.org/10.1111/bph.16251">https://doi.org/10.1111/bph.16251</a>) contains the original figures and tables as PNG-format (10.1111_bph.16251_Figure 1-4.PNG; 10.1111_bph.16251_Table1-2.PNG and supplemental information 10.1111_bph.16251_FigS1-S2.PNG; 10.1111_bph.16251_TableS1-S6.PNG), as well as the graphical abstract.PNG</p> <p>Corresponding raw data and subsequent data analysis obtained from LC-MS/MS analysis and reused data on THF, THE and allo-THE and clinical parameters as well as the statistical evaluation of obtained data are provided as raw-files and corresponding meta data-files (FAIR-Principle).</p> <p>Fig 2:</p> <p>Two files in CSV format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_4_3.csv and 310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_28_1.csv). Detailed description of the LC-MS/MS method is provided as pdf-Format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_4_M_2.pdf). All further experiment related information provided as one meta-data-file (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_4_3_M .txt) in txt format and two files containing further related information (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_M_A1-2 .pdf) PDF format.</p> <p>Fig3:</p> <p>Two files in CSV format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_4_4.csv; 310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_28_2.csv). One meta data file as pdf-format with detailed LC-MS/MS method description 310030-(214978_10.1111_bph.16251_CGC_Human_Biomarker_4_M_2.pdf). All further related information are provided as one meta-data-file (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_4_4_M .txt) in txt format. Cohort B related information is provided as three files in pdf- format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_M_B1-3).</p> <p>Fig 4:</p> <p>Six files in CSV format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_4_3-5 .csv, 310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_26_1.csv and 214978_10.1111_bph.16251_CGC_Human_Biomarker_28_1-2.csv). Two meta data file as pdf-format with detailed LC-MS/MS method descriptions (214978_10.1111_bph.16251_CGC_Human_Biomarker_4_M_2-3 .pdf ). All further related information are provided as one meta-data-file (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_4_3-5_M.txt) in txt format. Cohort related information is provided as five files in pdf- format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_M_A1-2 and B1-3.pdf) Statistical analysis is provided as R-File (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_26_M_1_1.R).</p> <p>Tab1:</p> <p>One file in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_1 .csv). One meta file as pdf-format with detailed LC-MS/MS method description (214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_1.pdf ). All further related information are provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_1_M.txt) in txt format. Cohort A related information is provided as two files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_A1-2).</p> <p>Tab2:</p> <p>One file in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_2.csv). One meta data file as pdf-format with detailed LC-MS/MS method description (214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_1.pdf ). All further related information are provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_2_M.txt) in txt format. Cohort B related information is provided as three files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_B1-3).</p> <p>Fig S1:</p> <p>Four files in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_3-4.csv and 214978_10.1111_BPH.16251_CGC_Human_Biomarker_28_1-2.csv). One meta data file as pdf-format with detailed LC-MS/MS method description (214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_2.pdf ). All further related information are provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_3-4_M.txt) in txt format. Cohort A and B related information is provided as five files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_A1-2 and-B1-3).</p> <p>Fig S2:</p> <p>Five files in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_3-4.csv and 310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_26_2.csv, 310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_28_1-2.csv) and one file as R-File (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_26_2_M1.R). One meta data file as pdf-format provides detailed LC-MS/MS method description (214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_2.pdf). All further related information are provided as one meta-data-file (310030 214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_26_2_M.txt) in txt format. Cohort A and B related information is provided as five files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_A1-2 and-B1-3).</p> <p>Tab S1:</p> <p>One file in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_28_3.csv). All further related information are provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_28_3_M.txt) in txt format. Cohort A related information is provided as two files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_A1-2). </p> <p>TabS2:</p> <p>One file in CSV format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_28_4.csv). All further related information are provided as one meta-data-file (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_28_4_M.txt) in txt format. Cohort B related information is provided as three files in pdf- format (310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_M_B1-3).</p> <p>TabS3:</p> <p>One file in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_3.csv). Detailed description of the LC-MS/MS method is provided as pdf-Formate (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_2.pdf) All further experiment related information provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_26_3_M.txt) in txt format and two files containing further related information (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_A1-2 .pdf) PDF format.</p> <p>Tab S4:</p> <p>Three file in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_5.csv, 310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_26_1-2.csv). Detailed description of the LC-MS/MS methods is provided two files pdf-Format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_2-3.pdf) All further experiment related information provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_26_4_M.txt) in txt format. Cohort related information is provided as two files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_A1-2. Statistical analysis is provided as R-File (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_26_M_1.R).</p> <p>TabS5:</p> <p>One files in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_4.csv One meta data file as pdf-format with detailed LC-MS/MS method description (214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_2.pdf ). All further related information are provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_26_5_M.txt) in txt format. Cohort B related information is provided as three files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_B1-3).</p> <p>TabS6:</p> <p>Four files in CSV format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_4-5.csv 310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_28_4.csv; 310030-214978_10.1111_bph.16251_CGC_Human_Biomarker_26_1.csv). One meta data file as pdf-format with detailed LC-MS/MS method description (214978_10.1111_BPH.16251_CGC_Human_Biomarker_4_M_2-3.pdf ). All further related information are provided as one meta-data-file (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_26_6_M.txt) in txt format. Cohort B related information is provided as three files in pdf- format (310030-214978_10.1111_BPH.16251_CGC_Human_Biomarker_M_B1-3).</p>
Rapid, Accurate, Cost-effective Assessment of Blood Biomarkers for Diagnosis of Concussion
ClinicalTrials.gov study NCT05588115. IPD Sharing: NO. Countries: 1. Publications: 13.
A Study for Identification of Predictive Immune Biomarker in Peripheral Blood for Nivolumab Therapy in NSCLC Patients
ClinicalTrials.gov study NCT03486119. IPD Sharing: NO. Countries: 1. Publications: 1.
Towards the Validation of a New Blood Biomarker for the Early Diagnosis of Parkinson's Disease
ClinicalTrials.gov study NCT05385315. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Biomarker Database to Investigate Blood-Based and Digital Biomarkers in Participants Screened for Alzheimer's Disease (Bio-Hermes)
ClinicalTrials.gov study NCT04733989. IPD Sharing: UNDECIDED. Countries: 1. Publications: 16.
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.