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63 results for “Longitudinal dataset”
Part of the dataset and trained models in "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media" by Meng et al. in Geophysical Research Letters
<p><strong>This repository is created to contain part of the data, codes and trained models in the research project titled "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media".</strong></p> <p> </p>
A longitudinal MRI dataset of young adult C57BL6J mouse brain
<p>This dataset contains 101 labeled young adult C57BL6J mouse brain MRI scans acquired with a 11.7T MR scanner. As part of the source domain data of Brain Extraction Net (BEN), specific analysis and usage are provided at <a href="https://github.com/yu02019/BEN">github</a> and in the <a href="https://www.biorxiv.org/content/10.1101/2022.05.25.492956v2.abstract">manuscript</a>.</p> <p>Since T2WI is the most commonly used modality for rodent brain imaging and we have accumulated extensive brain scans with high-quality annotations in our previous research studies, we first trained the model on the Mouse-T2WI-11.7T dataset, which served as the source-domain dataset, following the conventional fully supervised training strategy.</p> <p>In particular, we focus on the following features:</p> <ul> <li><strong>Transferability and flexibility</strong>: BEN can adapt to different species, modalities and platforms through its adaptive batch normalization module and semi-supervised learning module.</li> <li><strong>Automatic quality assessment</strong>: Unlike traditional toolboxes, which rely on manual inspection to assess the brain extraction quality, BEN incorporates a quality assessment module to automatically evaluate its brain extraction performance.</li> <li><strong>Speed</strong>: As a DL-based method, BEN can process an MRI volume faster (<1 second) than traditional toolboxes (several minutes or longer).</li> </ul> <p> </p>
Dataset: Longitudinal personal thermal comfort preference datain the wild
<p>Thermal comfort affects the well-being, productivity, and overall satisfaction of building occupants. However, due to economical and practical limitations, the number of longitudinal studies that have been conducted is limited, and only a few of these studies have shared their data publicly. Longitudinal datasets collected indoors are a valuable resource to better understand how people perceive their environment. Moreover, they provide a more realistic scenario to those conducted in thermal chambers. Our objective was to share publicly a longitudinal dataset comprising data collected over a 4-week long experiment. A total of 17 participants completed thermal preferences surveys which accounted for a total of approximately 1400 unique responses across indoor and outdoor 17 spaces. For the whole duration of the study, we monitored environmental variables (e.g., temperature and relative humidity) throughout 3 buildings. Participants completed comfort surveys from the screen of their smartwatches using an open-source application named Cozie. Their indoor location was continuously monitored using a custom-designed smartphone application.<br> Location data were used to time and spatially align environmental measurements to thermal preference responses provided by the participants. Background information of participants, such as physical characteristics and personality traits (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits), was collected using an on-boarding survey administered at the beginning of the experiment.</p> <p>You can find the features descriptions, raw data processing, and raw files in our project repository:</p> <p><a href="https://github.com/buds-lab/longitudinal-personal-thermal-comfort">https://github.com/buds-lab/longitudinal-personal-thermal-comfort</a></p>
A longitudinal RNA sequencing dataset in primary human fibroblasts with mitochondrial genetic, metabolic, and endocrine perturbations
GEO Series GSE179848. Homo sapiens. 345 samples. Type: Expression profiling by high throughput sequencing.
Longitudinal dataset: An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray (I)
GEO Series GSE110530. Homo sapiens. 12 samples. Type: Methylation profiling by genome tiling array.
Dataset for Longitudinal metabolic alterations in plasma of rats exposed to low doses of high linear energy transfer radiation
<p><span>Astronauts embarking on deep space missions are at high risk of long-term exposure to low doses of high linear energy transfer (LET) radiation, which can contribute to the development of cancer and multiple degenerative diseases. However, long term effects of exposure to low doses of high LET radiation in plasma metabolite profiles have not been elucidated. We utilized an untargeted metabolomics and lipidomics approach to analyze plasma obtained from adult male Long Evans rats to determine the longitudinal effects of low-dose proton and low-dose oxygen ion whole-body irradiation on metabolic pathways. Our findings reveal that radiation exposure induced modest changes in the metabolic profiles in plasma, 7 months after exposure. Furthermore, we identified some common metabolite dysregulations between protons and oxygen ions, which may indicate a similar mechanism of action for both radiation types.</span></p>
Two-Phase Model of Habit Change: a Longitudinal Qualitative Study Dataset
Open the record for dataset details and reuse information.
Dataset for Longitudinal metabolic alterations in plasma of rats exposed to low doses of high linear energy transfer radiation
Open the record for dataset details and reuse information.
A longitudinal DNA methylation dataset in primary human fibroblasts with mitochondrial genetic, metabolic, and endocrine perturbations
GEO Series GSE179847. Homo sapiens. 479 samples. Type: Methylation profiling by array.
longitudinal expression profiling in whole blood of multiple sclerosis patients and controls [IFN_replication dataset]
GEO Series GSE41847. Homo sapiens. 183 samples. Type: Expression profiling by array.
longitudinal expression profiling in whole blood of multiple sclerosis patients and controls [MS vs Ctrl Discovery dataset]
GEO Series GSE41848. Homo sapiens. 212 samples. Type: Expression profiling by array.
Longitudinal DNA methylation and RNA-seq datasets in primary human fibroblasts with mitochondrial genetic, metabolic, and endocrine perturbations
GEO Series GSE179849. Homo sapiens. 824 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by array.
longitudinal expression profiling in whole blood of multiple sclerosis patients and controls [IFN_discovery dataset]
GEO Series GSE41846. Homo sapiens. 318 samples. Type: Expression profiling by array.
longitudinal expression profiling in whole blood of multiple sclerosis patients and controls [MS vs Ctrl Replication dataset]
GEO Series GSE41849. Homo sapiens. 102 samples. Type: Expression profiling by array.
Insight MS Longitudinal MRI dataset
<p>Multiple Sclerosis (MS) is an idiopathic chronic inflammatory demyelinating disease of the central nervous system. Timely and accurate diagnosis via the McDonald criteria improves outcome but relies heavily on radiologist interpretation of MRI studies. A growing body of research is focused on identifying new MS lesions on MRI by comparing one time point with another but only limited longitudinal data are available.</p> <p>We present a dataset of which contains 496 scans of 172 patients with MS each with at least 2 time points including T1, Flair and T2 sequences. There are 110 stable scans and 214 instances of change. We also include demographic information. Data has been anonymised, processed and segmented with three stages of expert opinion contribute to ground truth.</p> <p>Future research using this dataset could include new lesion identification, radiomic characterisation of new lesions, relationship with disease activity and brain atrophy and time series applications including prediction of new lesions and active learning for efficient segmentation.</p>
Longitudinal multimodal MRI dataset of experimental ischemic stroke in male and female rats
<p>Preclinical MRI dataset (rat) accompanying https://doi.org/10.1177/0271678X231208993</p>
Dataset related to article "Morphometric vertebral fractures in patients with castration-resistant prostate cancer undergoing treatment with radium-223: a longitudinal study in the real-life clinical practice"
<p>This record contains data related to article "Morphometric vertebral fractures in patients with castration-resistant prostate cancer undergoing treatment with radium-223: a longitudinal study in the real-life clinical practice"</p> <p><strong>Abstract</strong></p> <p><strong>Purpose: </strong>Radium-223 was associated with high incidence of non-vertebral fractures in patients with castration-resistant prostate cancer (CRPC). However, it is still unclear whether radium-223 may induce skeletal fragility regardless of other therapies for CRPC. We aimed at evaluating the prevalence, incidence, and determinants of vertebral fractures (VFs), i.e., the most frequent complication of skeletal fragility, in CRCP patients undergoing radium-223 therapy in the real-life clinical practice.</p> <p><strong>Methods: </strong>We retrospectively reviewed 49 CRPC patients with symptomatic bone metastases treated with radium-223. Patients received median number of four radium-223 doses (range: 2-6) and were followed-up for a median period of 11 months (range: 6-44). VFs were assessed by a quantitative morphometry using lateral images of spine 11C-Choline PET/CT, excluding from the analysis the vertebral bodies affected by bone metastases.</p> <p><strong>Results: </strong>Before radium-223 administration, 24 patients (49%) had VFs significantly associated with duration of androgen deprivation therapy (ADT; odds ratio 1.29) and previous abiraterone therapy (odds ratio 3.80). During radium-223 therapy, incident VFs occurred in 25% of patients, in relationship with prevalent VFs (hazard ratio 6.89) and change in serum total alkaline phosphatase values (hazard ratio 0.97), whereas the correlations with ADT and abiraterone therapy were lost. Noteworthy, the risk of VFs did not correlate with the therapeutic end points of radium-223.</p> <p><strong>Conclusions: </strong>This study provides a first evidence that in real-life clinical practice, radium-223 therapy may induce skeletal fragility with high risk of VFs, likely by inhibition of bone formation and independently of ADT and abiraterone therapy.</p>
T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus
<h2><strong>T1DiabetesGranada</strong></h2> <p>A longitudinal multi-modal dataset of type 1 diabetes mellitus</p> <p>Documented by:</p> <p><strong>Rodriguez-Leon, C., Aviles-Perez, M. D., Banos, O., Quesada-Charneco, M., Lopez-Ibarra, P. J., Villalonga, C., & Munoz-Torres, M. (2023). T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus.<em> Scientific Data, 10(1), 916.</em> <a href="https://doi.org/10.1038/s41597-023-02737-4">https://doi.org/10.1038/s41597-023-02737-4</a></strong></p> <p> </p> <h3><strong>Background</strong></h3> <p>Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. The main problem for the generation of these models is the scarcity of data, as most published works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, a open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257780 days of measurements over four years from 736 T1D patients from the province of Granada, Spain. This dataset progresses significantly beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.</p> <p> </p> <h3><strong>Data Records</strong></h3> <p>The data are stored in four comma-separated values (CSV) files which are available in <em><strong>T1DiabetesGranada.zip</strong></em>. These files are described in detail below.</p> <p> </p> <h4><strong>Patient_info.csv</strong></h4> <p><em>Patient_info.csv</em> is the file containing information about the patients, such as demographic data, start and end dates of blood glucose level measurements and biochemical parameters, number of biochemical parameters or number of diagnostics. This file is composed of 736 records, one for each patient in the dataset, and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Sex</strong> – Sex of the patient. Values: F (for female), masculine (for male) </p> <p><strong>Birth_year</strong> – Year of birth of the patient. Format: YYYY.</p> <p><strong>Initial_measurement_date</strong> – Date of the first blood glucose level measurement of the patient in the <em>Glucose_measurements.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Final_measurement_date</strong> – Date of the last blood glucose level measurement of the patient in the <em>Glucose_measurements.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Number_of_days_with_measures</strong> – Number of days with blood glucose level measurements of the patient, extracted from the <em>Glucose_measurements.csv</em> file. Values: ranging from 8 to 1463.</p> <p><strong>Number_of_measurements</strong> – Number of blood glucose level measurements of the patient, extracted from the <em>Glucose_measurements.csv</em> file. Values: ranging from 400 to 137292.</p> <p><strong>Initial_biochemical_parameters_date</strong> – Date of the first biochemical test to measure some biochemical parameter of the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Final_biochemical_parameters_date</strong> – Date of the last biochemical test to measure some biochemical parameter of the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Number_of_biochemical_parameters</strong> – Number of biochemical parameters measured on the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Values: ranging from 4 to 846.</p> <p><strong>Number_of_diagnostics</strong> – Number of diagnoses realized to the patient, extracted from the <em>Diagnostics.csv</em> file. Values: ranging from 1 to 24.</p> <p> </p> <h4><strong>Glucose_measurements.csv</strong></h4> <p><em>Glucose_measurements.csv</em> is the file containing the continuous blood glucose level measurements of the patients. The file is composed of more than 22.6 million records that constitute the time series of continuous blood glucose level measurements. It includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Measurement_date</strong> – Date of the blood glucose level measurement. Format: YYYY-MM-DD. </p> <p><strong>Measurement_time</strong> – Time of the blood glucose level measurement. Format: HH:MM:SS.</p> <p><strong>Measurement</strong> – Value of the blood glucose level measurement in mg/dL. Values: ranging from 40 to 500.</p> <p> </p> <h4><strong>Biochemical_parameters.csv</strong></h4> <p><em>Biochemical_parameters.csv</em> is the file containing data of the biochemical tests performed on patients to measure their biochemical parameters. This file is composed of 87482 records and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Reception_date</strong> – Date of receipt in the laboratory of the sample to measure the biochemical parameter. Format: YYYY-MM-DD.</p> <p><strong>Name</strong> – Name of the measured biochemical parameter. Values: 'Potassium', 'HDL cholesterol', 'Gammaglutamyl Transferase (GGT)', 'Creatinine', 'Glucose', 'Uric acid', 'Triglycerides', 'Alanine transaminase (GPT)', 'Chlorine', 'Thyrotropin (TSH)', 'Sodium', 'Glycated hemoglobin (Ac)', 'Total cholesterol', 'Albumin (urine)', 'Creatinine (urine)', 'Insulin', 'IA ANTIBODIES'.</p> <p><strong>Value</strong> – Value of the biochemical parameter. Values: ranging from -4.0 to 6446.74.</p> <p> </p> <h4><strong>Diagnostics.csv</strong></h4> <p><em>Diagnostics.csv</em> is the file containing diagnoses of diabetes mellitus complications or other diseases that patients have in addition to type 1 diabetes mellitus. This file is composed of 1757 records and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Code</strong> – ICD-9-CM diagnosis code. Values: subset of 594 of the ICD-9-CM codes (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).</p> <p><strong>Description</strong> – ICD-9-CM long description. Values: subset of 594 of the ICD-9-CM long description (<a href="https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes">https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes</a>).</p> <p> </p> <h3><strong>Technical Validation</strong></h3> <p>Blood glucose level measurements are collected using FreeStyle Libre devices, which are widely used for healthcare in patients with T1D. Abbott Diabetes Care, Inc., Alameda, CA, USA, the manufacturer company, has conducted validation studies of these devices concluding that the measurements made by their sensors compare to YSI analyzer devices (Xylem Inc.), the gold standard, yielding results of 99.9% of the time within zones A and B of the consensus error grid. In addition, other studies external to the company concluded that the accuracy of the measurements is adequate.</p> <p>Moreover, it was also checked in most cases the blood glucose level measurements per patient were continuous (i.e. a sample at least every 15 minutes) in the <em>Glucose_measurements.csv </em>file as they should be.</p> <p> </p> <h3><strong>Usage Notes</strong></h3> <p>For data downloading, it is necessary to be authenticated on the Zenodo platform, accept the Data Usage Agreement and send a request specifying full name, email, and the justification of the data use. This request will be processed by the Secretary of the Department of Computer Engineering, Automatics, and Robotics of the University of Granada and access to the dataset will be granted.</p> <p>The files that compose the dataset are CSV type files delimited by commas and are available in <em><strong>T1DiabetesGranada.zip</strong></em>. A Jupyter Notebook (Python v. 3.8) with code that may help to a better understanding of the dataset, with graphics and statistics, is available in <em><strong>UsageNotes.zip</strong></em>.</p> <p> </p> <h4><strong>Graphs_and_stats.ipynb</strong></h4> <p>The Jupyter Notebook generates tables, graphs and statistics for a better understanding of the dataset. It has four main sections, one dedicated to each file in the dataset. In addition, it has useful functions such as calculating the patient age, deleting a patient list from a dataset file and leaving only a patient list in a dataset file.</p> <p> </p> <h3><strong>Code Availability</strong></h3> <p>The dataset was generated using some custom code located in <em><strong>CodeAvailability.zip</strong></em>. The code is provided as Jupyter Notebooks created with Python v. 3.8. The code was used to conduct tasks such as data curation and transformation, and variables extraction.</p> <p> </p> <h4><strong>Original_patient_info_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data. Mainly irrelevant rows and columns are removed, and the sex variable is recoded.</p> <p> </p> <h4><strong>Glucose_measurements_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with the continuous glucose level measurements of the patients. Principally rows without information or duplicated rows are removed and the variable with the timestamp is transformed into two new variables, measurement date and measurement time.</p> <p> </p> <h4><strong>Biochemical_parameters_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data of the biochemical tests performed on patients to measure their biochemical parameters. Mainly irrelevant rows and columns are removed and the variable with the name of the measured biochemical parameter is translated.</p> <p> </p> <h4><strong>Diagnostic_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data of the diagnoses of diabetes mellitus complications or other diseases that patients have in addition to T1D.</p> <p> </p> <h4><strong>Get_patient_info_variables.ipynb</strong></h4> <p>In the Jupyter Notebook it is coded the feature extraction process from the files <em>Glucose_measurements.csv</em>, <em>Biochemical_parameters.csv</em> and <em>Diagnostics.csv</em> to complete the file <em>Patient_info.csv</em>. It is divided into six sections, the first three to extract the features from each of the mentioned files and the next three to add the extracted features to the resulting new file.</p> <p> </p> <h3><strong>Data Usage Agreement</strong></h3> <p>The conditions for use are as follows:</p> <ol> <li>You confirm that you will not attempt to re-identify research participants for any reason, including for re-identification theory research.</li> <li>You commit to keeping the T1DiabetesGranada dataset confidential and secure and will not redistribute data or Zenodo account credentials.</li> <li>You will require anyone on your team who utilizes these data to comply with this Data Use Agreement by accessing the data themselves through Zenodo. Each user wishing to access controlled data must individually agree to the Conditions for Use.</li> <li>You understand that these data may not be used for commercial use or to re-contact research participants.</li> <li>You agree to report any misuse, unauthorized access, or data release, intentional or inadvertent within 5 business days. For this purpose, please write an email to icarsecretaria@ugr.es explaining the detected data breach.</li> <li>You agree to cite the research paper under which T1DiabetesGranada dataset was published on all publications or presentations which result from using the T1DiabetesGranada dataset.</li> </ol> <p> </p> <p><strong>Please note that in order to request access to the dataset you need to be authenticated on Zenodo, accept the Data Usage Agreement and specify in the request your full name, email, and the justification of the data use.</strong> </p>
Longitudinal study of hospitalized COVID-19 patients: RNA-seq dataset
<p>Upon request, RNA-seq data are available (n = 82). Longitudinal design: (1) samples collected on 1-2 days of hospitalization; (2) samples collected 4-5 weeks post-hospitalization; (3) samples collected 3-4 months post-hospitalization. Readily available files are 1) read counts for genes in each sample; 2) total read counts per library, QC metrics, etc., and phenotype (sex, age, bmi) in the metadata file. Raw data will be made available through EGA-archive.org in December 2025, ensuring the safe sharing of human genetic RNA sequences and promoting the principles of FAIR data.<br> </p>
Dataset related to article "Xpert Bladder Cancer Monitor May Avoid Cystoscopies in Patients Under "Active Surveillance" for Recurrent Bladder Cancer (BIAS Project): Longitudinal Cohort Study "
<p>This record contains raw data related to article “Xpert Bladder Cancer Monitor May Avoid Cystoscopies in Patients Under "Active Surveillance" for Recurrent Bladder Cancer (BIAS Project): Longitudinal Cohort Study"</p> <p>Abstract</p> <p><strong>Objectives: </strong> To test the hypothesis that patients under active surveillance (AS) for Non-muscle Invasive Bladder Cancer (NMIBC) who were negative on longitudinal re-testing by the Xpert<sup>®</sup> Bladder Cancer Monitor (Xpert BC Monitor) assay may avoid unnecessary cystoscopies and urine cytology (UC).</p> <p><strong>Subjects/patients or materials and methods: </strong> This is a prospective cohort study of patients enrolled in the AS protocol for recurrent NMIBC (Bladder Cancer Italian Active Surveillance, BIAS project), whose urine samples were analyzed by Xpert BC Monitor upon entry in the study (T0). Patients who had a negative Xpert test and did not fail AS, underwent additional Xpert tests after 4 (T1), 8 (T2), and 12 (T3) months. The clinical utility of Xpert was assessed by determining the number of cystoscopies and UC that could be avoided within 1 year.</p> <p><strong>Results: </strong> Overall, 139 patients were tested with Xpert at T0. Median follow-up was 23 (IQR 17-27) months. Sixty-eight (48.9%) patients failed AS, 65 (46.7%) are currently on AS, and 6 (4.3%) were lost at follow-up. At T0 57 (41.0%) patients had a negative test and 36 (63.2%) are still in AS. In patients with 2 consecutives negative Xpert tests, we could have avoided 73.9% of unnecessary cystoscopies, missing 26.4% failure, up to avoid all cystoscopies with 4 negative tests missing only 12% of failure. All the patients with negative Xpert had negative UC. Failure-free-survival at median follow-up (23 month) stratified for having 0, 1, or ≥2 negative tests was 67.0, 55.1. and 84.1, respectively.</p> <p><strong>Conclusion: </strong> Our findings suggest that Xpert BC Monitor assay, when it is longitudinally repeated, could significantly reduce the number of unnecessary cystoscopies and UC during their follow-up.</p> <p> </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.