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1,659 results for “Patient Data”
Validation of transpulmonary thermodilution variables in hemodynamically stable patients with heart diseases - Individual patient data
<p>This dataset contains individual subject data for hemodynamic measurements assessed in the present study.</p>
Supplementary data to "High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global Clustering Approach"
<p>Compressed ".feather" files including the entire dataset of 319 Raman maps of the same number of cells from 19 patients affected by nine distinct leukemia subtypes.<br>Raw data have been pre-processed as follows using custom software (LabVIEW, National Instruments Corp., TX): a) cosmic rays removal by singular value decomposition (SVD); b) camera offset subtraction; c) CCD response correction (intensity and etaloning) using a tungsten halogen light with known emission (Avalight-HAL, Avantes BV, NL)); d) wavenumber calibration using the zero-wavenumber laser line, toluene and argon-mercury emission (CAL-2000, Ocean Optics, Germany); e) denoising by SVD.<br>More details in the open access published article and supplementary material (10.1021/acs.analchem.4c00787).</p>
Peripheral blood TCRseq data in AIRR-C format for cancer patients who received either photon or proton based radiation therapy
<p>These are the AIRR-C format converted data from the original Adaptive ImmunoSEQ v2 data format.</p> <p>See the analysis repo for more information: <a href="https://github.com/JamieHeather/radiation-induced-lymphopenia-paper-analysis" target="_blank" rel="noopener">https://github.com/JamieHeather/radiation-induced-lymphopenia-paper-analysis</a>.</p>
Data from: Bristol stool scale: Patient versus expert score data
<p>The Bristol Stool Scale (BSS) is one of the most commonly used tools for evaluation of stool consistency.<strong> </strong>BSS ranges from 1-7 and each score is assigned to a given consistency of the feces. Self-reported characterizations can differ from an expert evaluation, and the reliability of BSS is unclear. The dataset consists of BSS scores by patients with inflammatory bowel disease collected throughout a 3-year follow-up, matched with scores assessed by experienced bioengineers (experts). The purpose of the study where data was collected was to compare patient scores to expert scores and hence determine the reliability of the BSS.</p>
FHIRed MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data has been standardized in Fast Healthcare Interoperability Resources (FHIR) data standard. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2).</p> <p>Authors express their gratitude to all the AlmaHealthDB Team.</p> <h2>Instruction MOTU-to-FHIR Importer</h2> <div> <div> <p>The repository includes a Docker Compose setup for importing the MOTU dataset into a HAPI FHIR server, formatted as NDJSON following the HL7 FHIR R4 standards.</p> <h3>Prerequisites</h3> <p>Before you begin, ensure you have the following installed:</p> <ul> <li><a href="https://www.docker.com/get-started/">Docker </a></li> <li><a href="https://docs.docker.com/compose/install/">Docker Compose</a></li> <li><a href="https://www.python.org/downloads/">Python >=3.7</a> </li> <li><a href="https://pypi.org/project/requests/">Requests Python Library</a></li> </ul> </div> <div> <h3>How to run</h3> <ol> <li>First, unzip the <code>dataset</code> directory containing the NDJSON files.</li> <li>Open a terminal or command prompt in the root directory of this repository.</li> <li>Run the command <code>docker-compose up</code> in the terminal to start the Docker containers.</li> <li>Once the containers are up and running, open another terminal window in the root directory of this repository.</li> <li>Run the command <code>python main.py</code> in the terminal to start the data import process.</li> <li>After the import process is complete, you can access the HAPI FHIR server by opening a web browser and navigating to <a href="http://localhost:8082" target="_blank" rel="nofollow noreferrer noopener">http://localhost:8082</a>.</li> </ol> <p> </p> </div> </div>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
single-nucleus RNA sequencing data of Minimal Change Disease and Focal Segmental Glomerulosclerosis patients
<p>single-nucleus RNA sequencing data of Minimal Change Disease and Focal Segmental Glomerulosclerosis patients</p>
Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk
<p><strong>Paper Title: </strong>Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk</p> <p><strong>Paper: </strong><a href="https://doi.org/10.1007/s13755-024-00332-4">https://doi.org/10.1007/s13755-024-00332-4</a> (<span>full-text view-only version: <a title="URL d'origine : https://rdcu.be/eccmN. Cliquez ou appuyez si vous faites confiance à ce lien." href="https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Frdcu.be%2FeccmN&data=05%7C02%7Chakima.laribi%40usherbrooke.ca%7C23870f4657634d7a102908dd5b986df8%7C3a5a8744593545f99423b32c3a5de082%7C0%7C0%7C638767432425362186%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=d9ieen5mU9pPFXGv8hJNF%2Bf5UlJNyhjDuk%2F8MWEKo28%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://rdcu.be/eccmN</a></span>)</p> <p><strong>GitHub Link: </strong><a href="https://github.com/MEDomics-UdeS/POYM" target="_blank" rel="noopener">https://github.com/MEDomics-UdeS/POYM </a></p> <p><strong>Description:</strong></p> <p>This dataset accompanies <a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a> and contains synthetic data generated using the <a href="https://doi.org/10.1038/s41746-023-00771-5" target="_blank" rel="noopener">AVATAR method</a> in partnership with <a href="https://www.octopize.io/" target="_blank" rel="noopener">Octopize</a>.</p> <p><strong>Files:</strong></p> <ul> <li><strong>dataset.csv:</strong> This file contains 248,485 rows and 247 columns, representing 248,485 synthetic visits from 123,646 synthetic patients. Detailed descriptions of each column can be found in <a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a>. To preserve patient's privacy, we did not save admission and discharge dates. Consequently, it is not possible to split the dataset temporally as done with the original dataset or to identify admissions with same-day discharge.</li> </ul> <p><strong>Comparison of synthetic and original data: </strong><a href="https://doi.org/10.21203/rs.3.rs-5363467/v1">https://doi.org/10.21203/rs.3.rs-5363467/v1</a></p> <p> </p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </p>
Data from "Exploring Gut Microbiota Profile Induced by Antipsychotics in Schizophrenic Patients: Insights from an Eastern European Pilot Study", Nita (Ilie) et al. 2025
<p>Dataset containing raw demultiplexed FASTQ files of the sequenced samples, generated by the Illumina MiSeq platform. </p> <p>MiSeq_demultiplexed-V3_V4-HC_SCZ.zip - MiSeq raw sequences of the V3-V4 region 16S rRNA gene from subject fecal material. This ZIP file contains the FASTQ files of the paired-end reads (R1: forward reads; R2: reverse reads) produced for each sample using the MiSeq platform.</p> <p>metadata-HC_SCZ.csv - The list of sequenced samples and associated metadata.</p>
Data from: Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression
<p>Supporting data for the article "Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression", published in Frontiers in Neuroscience (DOI: 10.3389/fnins.2023.1260409).</p>
Genomic data from a pharmacogenetic study based on the efficacy of Tocilizumab in severe COVID-19 patients
<p>These data correspond to a pharmacogenetic study aimed to explore genetic associations related to the efficacy of Tocilizumab in severe COVID-19 patients. The study comprised two sets of patients, an initial subset consisted of 425 patients to whom a in-dpeth sequencing of a thermofisher sequencing panel of immune-related genes was performed, as well as a second set of 245 patients with the information of the genotyping of three selected SNPs</p>
Data and Code from: Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients
<div> <div> <div> <p>Abstract</p> <p>Spinocerebellar ataxia type 7 (SCA7) is a genetic neurodegenerative disorder caused by a CAG- polyglutamine repeat expansion. Purkinje cells (PCs) are central to the pathology of ataxias, but their low abundance in the cerebellum underrepresents their transcriptomes in sequencing assays. To address this issue, we developed a PC enrichment protocol and sequenced individual nuclei from mice and patients with SCA7. Single-nucleus RNA sequencing in SCA7-266Q mice revealed dysregulation of cell identity genes affecting glia and PCs. Specifically, genes marking zebrin-II PC subtypes accounted for the highest proportion of DEGs in symptomatic SCA7-266Q mice. These transcriptomic changes in SCA7-266Q mice were associated with increased numbers of inhibitory synapses as quantified by immunohistochemistry and reduced spiking of PCs in acute brain slices. Dysregulation of zebrin-II cell subtypes was the predominant signal in PCs of SCA7-266Q mice and was associated with the loss of zebrin-II striping in the cerebellum at motor symptom onset. We furthermore demonstrated zebrin-II stripe degradation in additional mouse models of polyglutamine ataxia and observed decreased zebrin-II expression in cerebellum of patients with SCA7. Our results suggest that a breakdown of zebrin subtype regulation is a shared pathological feature of polyglutamine ataxias.</p> <p>Data and Code Availability</p> <p>Here you will find data and code associated with our manuscript "Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients", Bartelt et al., <em>Sci. Trans. Med. </em>16, eadn5449 (2024).</p> <p>The data file labeled "HuCb_filtered.rds" is a processed and annotated single-nucleus RNA-seq Seurat object, containing the gene-level count data for the multiplexed snRNA-seq experiment performed on post-mortem human cerebellar tissues from patients with SCA7 and unaffected controls. Data obtained from WT and SCA7-266Q mice as described in our paper can be accessed in the NIH Gene Expression Omnibus under accession number GSE269430.</p> <p>There are three code files numbered 00 through 02 which contain analysis code for snRNA-seq data applied to both the mouse and human datasets. These files are sequential and will take the user from CellRanger output, to filtered and annotated Seurat objects, and include details for subclustering analysis as well as our pseudobulk DEseq2 differential expression approach. There are places where the user may need to modify the code based on their computer system, version of R or Seurat, and whether they are processing the 5 week, 8 week, or human data sets; these locations in the code are marked with comments.</p> <ul> <li>The first file, 00_Preprocessing_MULTIseq, begins with CellRanger filtered_feature_barcode_matrix output, extracts cell barcodes, utilizes the MULTIseq deMULTIplex software to match cell barcodes to oligo barcodes from MULTIseq fastq files, and annotates the Seurat file with metadata. Cell type identification and annotation also takes place in this file. Note: the deMULTIplex step will likely need to be run on a high performance compute cluster.</li> <li>The second file, 01_Seurat_Analysis, uses the filtered and annotated Seurat file to calculate useful QC metrics, investigate disease signals, and perform cell type subclustering analyses.</li> <li>The third file, 02_Pseudobulk_DEseq2, contains custom analysis code to extract raw counts for each cell type and each animal from the Seurat file, and uses the DEseq2 package to calculate DEGs, taking into account biological replicates, and raw read count differences between control and SCA7 animals.</li> </ul> </div> </div> </div>
Data from: Discharge communication for chronic disease patients in three hospitals in India
OBJECTIVES <p>Poor discharge communication is associated with negative health outcomes in high-income countries. However, quality of discharge communication has received little attention in India and many other low and middle-income countries. Primary Objective To investigate verbal and documented discharge communication for chronic non-communicable disease (NCD) patients. Secondary objective To explore the relationship between quality of discharge communication and health outcomes.</p> METHODS: <p>Design Prospective study.</p> <p><strong>Setting:</strong> Three public hospitals in Himachal Pradesh and Kerala states, India.</p> <p><strong>Participants:</strong> 546 chronic NCD (chronic respiratory disease, cardiovascular disease or diabetes) patients. Piloted questionnaires were completed at admission, discharge and Five and eighteen-week follow-up covering health status, health-seeking behaviour and healthcare information exchange practices. Logistic regression was used to explore the relationship between quality of discharge communication and health outcomes.</p> <p><strong>Outcome Measures:</strong></p> <p><strong>Primary:</strong> Patient recall and experiences of verbal and documented discharge communication.</p> <p><strong>Secondary:</strong> Death, hospital readmission and self-reported deterioration of NCD/s.</p> RESULTS <p>All patients received discharge notes, which were predominantly on minimally structured sheets of paper (71%); 31% of notes contained all of the following information required for facilitating continuity of care: diagnosis, medication information, lifestyle advice, and follow-up instructions. Patient reports indicated notable variations in verbal information provided during discharge consultations; 50% received ongoing treatment/management information and 23% received lifestyle advice. Within 18 weeks of follow-up, 25 (5%) patients had died, 69 (13%) had been readmitted and 62 (11%) reported that their chronic NCD/s had deteriorated. Significant associations were found between low-quality documented discharge communication and death (AOR=3.00; 95% CI 1.27,7.06) and low-quality verbal discharge communication and self-reported deterioration of chronic NCD/s (AOR=0.46; 95% CI 0.25,0.83) within 18-weeks of follow-up.</p> CONCLUSIONS <p>Sub-optimal discharge practices may be compromising the continuity and safety of chronic NCD patient care. Structured protocols, documents and training are required to improve discharge communication, healthcare integration and overall NCD management.</p>
Phase-contrast MRI data of 18 Chiari-I malformation patients and 21 controls
<p>We collected phase-contrast MRI data (5-mm midline sagittal section, rostro-caudal direction) and obtained average velocity in certain Regions-0f-Interest (ROIs). The measured ROIs are cerebellar tonsil, ventral spinal subarachnoid space, dorsal spinal subarachnoid space, upper portion of syrinxes, upper cervical cord, and medulla. Data obtained from 18 Chiari-I patients (preoperative and postoperative) and 21 controls are included. Because the preoperative MRI data was missing in one patient, the preoperative studies included 17 MRI sessions. The unit of the data is cm/sec. The data are synchronized with pre-processing so that the rise of the caudal CSF movement be placed at the center of the cardiac cycle.</p>
Raw data fort the article: FLUOROSCOPY-GUIDED BILIARY PROCEDURES IN A PREGNANT, LIVER TRANSPLANT PATIENT: FETUS RADIATION PROTECTION
<p>We report three cases of clinically necessary, fluoroscopy-guided, percutaneous biliary procedures performed safely in a pregnant, liver transplant recipient using three different angiography suites. The uterine cumulative equivalent dose was 0.25 mSv, a value obtained by adding the doses of the three procedures described above, and which is relatively low when compared with the naturally occurring background radiation exposure for a 9-month pregnancy (~0.5-1 mSv). Our experience shows that staff knowledge, awareness and liaison promote the application of all dose reduction strategies possible while still achieving the clinical aim despite using different angiographic equipment.</p>
Raw data for the article: A commentary on "Simultaneous versus staged resection for synchronous colorectal liver metastases: A population-based cohort study". Importance of avoiding any other additional risk in selected patients with synchronous colorectal liver metastases
<p>We read with great interest the article of Dr. Bogach and Colleagues, in which they have evaluated trends of resection for synchronous colorectal cancer liver metastases (CRLM) and associated patient outcomes with a retrospective cohort study from 2006 to 2015 in the province of Ontario, Canada.</p>
Pre-processed IgH receptor repertoire data from MS patients after aHSCT from BioProject PRJNA763367
<p><strong>Data Processing</strong></p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p> </p> <p><strong>software_versions</strong> pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p><strong>quality_thresholds</strong> FilterSeq.py pRESTO Q>20</p> <p><strong>paired_reads_assembly</strong> AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong> MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p><strong>consensus_building</strong> BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong> CollapseSeq.py pRESTO</p> <p><strong>germline_database </strong>IMGT</p> <p> </p> <p><strong>Format</strong></p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>ISOTYPE_SUBCLASS </strong>Isotype subclass</p> <p><strong>SEQUENCE_ID </strong>Sequence identifier</p> <p><strong>JUNCTION_LENGTH </strong>Junction length</p> <p><strong>CONSCOUNT </strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT </strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE </strong>Constant region primer (isotype)</p> <p><strong>MUT_TOTAL </strong>Total number of mutations in V gene </p> <p><strong>SAMPLE </strong>Sample identifier, linking back to raw data</p> <p><strong>JUNCTION </strong>Junction nucleotide sequence</p> <p><strong>Protein_seq </strong>Amino acid sequence</p> <p><strong>CDR3_AA_GRAVY </strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_BULK </strong>CDR3 bulkiness</p> <p><strong>CDR3_AA_ALIPHATIC </strong>CDR3 aliphatic index</p> <p><strong>CDR3_AA_POLARITY </strong>CDR3 polarity</p> <p><strong>CDR3_AA_CHARGE </strong>CDR3 normalized net charge</p> <p><strong>CDR3_AA_BASIC </strong>CDR3 basic side chain residue content</p> <p><strong>CDR3_AA_ACIDIC </strong>CDR3 acidic side chain residue content</p> <p><strong>CDR3_AA_AROMATIC </strong>CDR3 aromatic side chain content</p> <p><strong>Subset </strong>Defined B cell subset </p> <p><strong>Repertoire </strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR </strong>R/S ratio in CDR region</p> <p><strong>R_SFWR </strong>R/S ratio in FWR region</p> <p><strong>V_GENE </strong>V segment gene</p> <p><strong>D_GENE </strong>D segment gene</p> <p><strong>J_GENE </strong>J segment gene</p> <p><strong>V_FAM </strong>V family gene</p> <p><strong>Clust_REPRES </strong>Cluster representative</p> <p><strong>Clust_SIZE </strong>Cluster size</p> <p><strong>Sex </strong>Sex of the Subject</p> <p><strong>UNIQUE_ID </strong>Sample identifier </p> <p><strong>Bcellno </strong>Input B cell number</p> <p><strong>Days_posttx </strong>Sampling time point relative to transplantation</p> <p><strong>Age_at_tx </strong>Age of the subject (at aHSCT)</p> <p><strong>Disease </strong>MS subtype</p> <p><strong>Last_therapy </strong>Last therapy prior to aHSCT</p> <p><strong>Disease_duration </strong>Disease duration</p> <p><strong>CMV_reactivation </strong>Cytomegalovirus reactivation</p> <p><strong>Month_label </strong>Month post-aHSCT inverval bin</p> <p><strong>Patient_label </strong>Subject identifier</p> <pre> </pre> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. <em>Genome Res.</em>21: 936–939.</p>
Labelled and unlabelled hand acceleration data captured unobtrusively from PD patients and Healthy Controls
<p>The dataset contains acceleration signals captured in-the-wild via the IMU sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). It contains two different groups of subjects:</p> <ul> <li>tremor_sdata.pickle --> A group of 45 subjects that have been subjected to neurological examination (the same dataset as https://zenodo.org/record/3519213)</li> <li>tremor_gdata.pickle --> A group of 454 subjects who just self-reported their PD status</li> </ul> <p>All subjects contributed accelerometer data using their personal smartphones, for a period spanning many months. Tri-axial acceleration values were recorded automatically whenever a phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a python dictionary, indexed by the subject ids. Each element of the dictionary is a list with the following significance:</p> <table> <thead> <tr> <th scope="col">Index</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>List of np.arrays, Each array contains the power spectral density for an acceleration segment of 5s duration</td> </tr> <tr> <td>1</td> <td>Dictionary, Denotes subject updrs</td> </tr> <tr> <td>2</td> <td>List of str containing a unique identifier of the acceleration session that each segment in the other lists belongs to</td> </tr> <tr> <td>3</td> <td>List of np.arrays, Each array contains the pre-processed acceleration values for a 5s segment</td> </tr> </tbody> </table> <p> </p><p>The subject updrs is represented as dictionary containing the following tremor-related annotation values (FOR THE FIRST GROUP ONLY):<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p></p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.</p>
Microelectrode register (MER) data from Deep Brain Stimulation (DBS) surgery in Parkinson's disease patients
<p>MER data consist of brain signal in different depths when DBS surgery is being done. In each depth, a data file is created, with different duration depending on the depth, and up to three channels.</p> <p>Data come from 14 patients (9 males and 5 females), they are anonymized and labelled from P1 to P14. They correspond to patients in age 65.1 +- 5.6 years.</p> <p>Data are organized in STN-IN and STN-OUT (different depths in each folder), subthalamus-in, and subthalamus-out since the STN area is the target area when implanting a DBS. Classification in STN-IN and STN-OUT was made by the neurophysiologists and surgeons.</p> <p>Data were recorded for left and right lobes, 8 patients in left and right lobe, 1 patient in right lobe, and 5 patients in left lobe.</p> <p>The format is mat file (MATLAB file)</p> <p>Data sampling frequency is 12kHz.</p> <p>No filtering or data processing was made, they are directly obtained from the MER acquisition system.</p> <p> </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.