Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
126,115
datasets available to search
ShareScore release 0.9.0
Dataset results
126,115 results for “Patients”
Aggregated Virtual Patient Model Dataset
<p>The dataset is a collection of aggregated clinical parameters for the participants (such as clinical scores), parameters extracted from the utilized devices (such as average heart rate per day, average gait speed etc.), and coupled events about them (such as falls, loss of orientation etc.). It contains information which was collected during the clinical evaluation of the older people from medical experts.This information represents the clinical status of the older person across different domains, e.g. physical, psychological, cognitive etc.</p> <p>The dataset contains several medical features which are used by clinicians to assess the overall state of the older people.</p> <p>The purpose of the Virtual Patient Model is to assess the overall state of the older people based on their medical parameters, and to find associations between these parameters and frailty status.</p> <p>A list of the recorded clinical parameters and their description is shown below:</p> <p>- <strong> part_id</strong>: The user ID, which should be a 4-digit number</p> <p>-<strong> q_date</strong>: The recording timestamp, which follows the “YYYY-MM-DDTHH:mm:ss.fffZ” format (eg. 14 September 2017 12:23:34.567, is formatted as 2019-09-14T12:23:34.567Z)</p> <p>-<strong> clinical_visit</strong>: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to</p> <p>-<strong> fried</strong>: Ordinal categorization of frailty level according to Fried operational definition of frailty</p> <p>-<strong> hospitalization_one_year</strong>: Number of nonscheduled hospitalizations in the last year</p> <p>- <strong>hospitalization_three_years</strong>: Number of nonscheduled hospitalizations in the last three years</p> <p>- <strong>ortho_hypotension</strong>: Presence of orthostatic hypotension</p> <p>- <strong>vision</strong>: Visual difficulty (qualitative ordinal evaluation)</p> <p>- <strong>audition</strong>: Hearing difficulty (qualitative ordinal evaluation)</p> <p>- <strong>weight_loss</strong>: Unintentional weight loss >4.5 kg in the past year (categorical answer)</p> <p>- <strong>exhaustion_score</strong>: Self-reported exhaustion (categorical answer)</p> <p>- <strong>raise_chair_time</strong>: Time in seconds to perform a lower limb strength clinical test</p> <p>- <strong>balance_single</strong>: Single foot station (Balance) (categorical answer)</p> <p>- <strong>gait_get_up</strong>: Time in seconds to perform the 3meters’ Timed Get Up And Go Test</p> <p>- <strong>gait_speed_4m</strong>: Speed for 4 meters’ straight walk</p> <p>- <strong>gait_optional_binary</strong>: Gait optional evaluation (qualitative evaluation by the investigator)</p> <p>- <strong>gait_speed_slower</strong>: Slowed walking speed (categorical answer)</p> <p>- <strong>grip_strength_abnormal</strong>: Grip strength outside the norms (categorical answer)</p> <p>- <strong>low_physical_activity</strong>: Low physical activity (categorical answer)</p> <p>- <strong>falls_one_year</strong>: Number of falls in the last year</p> <p>- <strong>fractures_three_years</strong>: Number of fractures during the last 3 years</p> <p>- <strong>fried_clinician</strong>: Fried’s categorization according to clinician’s estimation (when missing data for answering the Fried’s operational frailty definition questionnaire)</p> <p>- <strong>bmi_score</strong>: Body Mass Index (in Kg/m²)</p> <p>- <strong>bmi_body_fat</strong>: Body Fat (%)</p> <p>- <strong>waist</strong>: Waist circumference (in cm)</p> <p>- <strong>lean_body_mass</strong>: Lean Body Mass (%)</p> <p>- <strong>screening_score</strong>: Mini Nutritional Assessment (MNA) screening score</p> <p>- <strong>cognitive_total_score</strong>: Montreal Cognitive Assessment (MoCA) test score</p> <p>- <strong>memory_complain</strong>: Memory complain (categorical answer)</p> <p>- <strong>mmse_total_score</strong>: Folstein Mini-Mental State Exam score</p> <p>- <strong>sleep</strong>: Reported sleeping problems (qualitative ordinal evaluation)</p> <p>- <strong>depression_total_score</strong>: 15-item Geriatric Depression Scale (GDS-15)</p> <p>- <strong>anxiety_perception</strong>: Anxiety auto-evaluation (visual analogue scale 0-10)</p> <p>- <strong>living_alone</strong>: Living Conditions (categorical answer)</p> <p>- <strong>leisure_out</strong>: Leisure activities (number of leisure activities per week)</p> <p>- <strong>leisure_club</strong>: Membership of a club (categorical answer)</p> <p>- <strong>social_visits</strong>: Number of visits and social interactions per week</p> <p>- <strong>social_calls</strong>: Number of telephone calls exchanged per week</p> <p>- <strong>social_phone</strong>: Approximate time spent on phone per week</p> <p>- <strong>social_skype</strong>: Approximate time spent on videoconference per week</p> <p>- <strong>social_text</strong>: Number of written messages (SMS and emails) sent by the participant per week</p> <p>- <strong>house_suitable_participant</strong>: Subjective suitability of the housing environment according to participant’s evaluation (categorical answer)</p> <p>- <strong>house_suitable_professional</strong>: Subjective suitability of the housing environment according to investigator’s evaluation (categorical answer)</p> <p>- <strong>stairs_number</strong>: Number of steps to access house (without possibility to use elevator)</p> <p>- <strong>life_quality</strong>: Quality of life self-rating (visual analogue scale 0-10)</p> <p>- <strong>health_rate</strong>: Self-rated health status (qualitative ordinal evaluation)</p> <p>- <strong>health_rate_comparison</strong>: Self-assessed change since last year (qualitative ordinal evaluation)</p> <p>- <strong>pain_perception</strong>: Self-rated pain (visual analogue scale 0-10)</p> <p>- <strong>activity_regular</strong>: Regular physical activity (ordinal answer)</p> <p>- <strong>smoking</strong>: Smoking (categorical answer)</p> <p>- <strong>alcohol_units</strong>: Alcohol Use (average alcohol units consumption per week)</p> <p>- <strong>katz_index</strong>: Katz Index of ADL score</p> <p>- <strong>iadl_grade</strong>: Instrumental Activities of Daily Living score</p> <p>- <strong>comorbidities_count</strong>: Number of comorbidities</p> <p>- <strong>comorbidities_significant_count</strong>: Number of comorbidities which affect significantly the person’s functional status</p> <p>- <strong>medication_count</strong>: Number of active substances taken on a regular basis</p>
Multi-study reanalysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures
<p>Supplemental Information, Supplementary Tables, and Supplementary Files, as well as accompanying data for the manuscript "Stratified computational meta-analysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures" by Raeuf Roushangar and George I. Mias. </p>
Graph topological features extracted from expression profiles of neuroblastoma patients
<p><strong>Introduction</strong></p> <p>This dataset contains the data described in the paper titled "A deep neural network approach to predicting clinical outcomes of neuroblastoma patients." by Tranchevent, Azuaje and Rajapakse. More precisely, this dataset contains the topological features extracted from graphs built from publicly available expression data (see details below). This dataset does not contain the original expression data, which are available elsewhere. We thank the scientists who did generate and share these data (please see below the relevant links and publications).</p> <p> </p> <p><strong>Content</strong></p> <p>File names start with the name of the publicly available dataset they are built on (among "Fischer", "Maris" and "Versteeg"). This name is followed by a tag representing whether they contain raw data ("raw", which means, in this case, the raw topological features) or TF formatted data ("TF", which stands for TensorFlow). This tag is then followed by a unique identifier representing a unique configuration. The configuration file "Global_configuration.tsv" contains details about these configurations such as which topological features are present and which clinical outcome is considered.</p> <p>The code associated to the same manuscript that uses these data is at <a href="https://gitlab.com/biomodlih/SingalunDeep">https://gitlab.com/biomodlih/SingalunDeep</a>. The procedure by which the raw data are transformed into the TensorFlow ready data is described in the paper.</p> <p> </p> <p><strong>File format</strong></p> <p>All files are TSV files that correspond to matrices with samples as rows and features as columns (or clinical data as columns for clinical data files). The data files contain various sets of topological features that were extracted from the sample graphs (or Patient Similarity Networks - PSN). The clinical files contain relevant clinical outcomes.</p> <p>The raw data files only contain the topological data. For instance, the file "Fischer_raw_2d0000_data_tsv" contains 24 values for each sample corresponding to the 12 centralities computed for both the microarray (<em>Fischer-M</em>) and RNA-seq (<em>Fischer-R</em>) datasets. The TensorFlow ready files do not contain the sample identifiers in the first column. However, they contain two extra columns at the end. The first extra column is the sample weights (for the classifiers and because we very often have a dominant class). The second extra column is the class labels (binary), based on the clinical outcome of interest.</p> <p> </p> <p><strong>Dataset details</strong></p> <p>The <em>Fischer</em> dataset is used to train, evaluate and validate the models, so the dataset is split into train / eval / valid files, which contains respectively 249, 125 and 124 rows (samples) of the original 498 samples. In contrast, the other two datasets (<em>Maris</em> and <em>Versteeg</em>) are smaller and are only used for validation (and therefore have no training or evaluation file).</p> <p>The <em>Fischer</em> dataset also has more data files because various configurations were tested (see manuscript). In contrast, the validation, using the <em>Maris</em> and <em>Versteeg</em> datasets is only done for a single configuration and there are therefore less files.</p> <p>For <em>Fischer</em>, a few configurations are listed in the global configuration file but there is no corresponding raw data. This is because these items are derived from concatenations of the original raw data (see global configuration file and manuscript for details).</p> <p> </p> <p><strong>References</strong></p> <p>This dataset is associated with Tranchevent L., Azuaje F.. Rajapakse J.C., A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.</p> <p>If you use these data in your research, please do not forget to also cite the researchers who have generated the original expression datasets.</p> <p><em>Fischer</em> dataset:</p> <ul> <li>Zhang W. et al., Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biology 16(1) (2015). doi:10.1186/s13059-015-0694-1</li> <li>Wang C. et al., The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32(9), 926–932. doi:10.1038/nbt.3001</li> </ul> <p><em>Versteeg</em> dataset:</p> <ul> <li>Molenaar J.J. et al., Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483(7391), 589–593. doi:10.1038/nature10910</li> </ul> <p><em>Maris</em> dataset:</p> <ul> <li>Wang Q. et al., Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Cancer Res. 66(12), 6050–6062. doi:10.1158/0008-5472.CAN-05-4618</li> </ul>
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
Data from: External validation of prognostic and predictive gene signatures in 1097 European head and neck squamous cell carcinoma patients
<p><span>Anonymized data containing survival endpoints and gene signature scores for head and neck cancer patients.</span></p> <p><span>File <strong>data_os_gs.csv</strong> : data linking overall survival and gene signature scores</span></p> <p><span>File <strong>data_dfs_gs.csv</strong> : data linking disease-free survival and gene signature scores</span></p> <p><span><strong>Variables</strong>:</span></p> <ul> <li><span><em>supertreat_id</em>: patient ID</span></li> <li><span><em>GS_score_172GS</em>: gene signature score for the <em>172-GS</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_3clustersHPV</em>: gene signature score for the <em>3 clusters HPV</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_RSI</em>: gene signature score for the <em>radiosenstivity index (RSI) </em>signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_pancancerCisplatin</em>: gene signature score for the <em>pancancer-cisplatin</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_cl3Hypoxia</em>: gene signature score for the <em>Cl3-hypoxia</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span>Variables only available in <strong>data_os_gs.csv: </strong></span> <ul> <li><span><em>overall_survival_days_2years</em>: Overall survival censored at 2 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_days_5years</em>: Overall survival censored at 5 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_status_2years</em>: Overall survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> <li><span><em>overall_survival_status_5years</em>: Overall survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> </ul> </li> </ul> <ul> <li><span>Variables only available in <strong>data_dfs_gs.csv:</strong></span> <ul> <li><span><em>disease_free_survival_days_2years</em>: Disease-free survival censored at 2 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_days_5years</em>: Disease-free survival censored at 5 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_status_2years</em>: Disease-free survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> <li><span><em>disease_free_survival_status_5years</em>: Disease-free survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> </ul> </li> </ul>
Dataset on preferences of rheumatic patients towards EQ-5D-5L health states in Germany
<p>The dataset includes preferences of rheumatic patients towards EQ-5D-5L health states in Germany, adjacent codebooks.</p>
Dataset on preferences of patients with diabetes towards EQ-5D-5L health states in Germany
<p>The dataset includes data on preferences of patients with diabetes toward EQ-5D-5L health states in Germany and adjacent codebooks. </p>
Metabolic shift of chronic myeloid leukemia patients under Imatinib-Pioglitazone regimen and discontinuation_dataset
<p>The EDI-PIO (<strong>E</strong>studo de <strong>D</strong>escontinuação de <strong>I</strong>matinibe após<strong> Pio</strong>glitazona) is a single-center, longitudinal, prospective, phase 2, non-randomized, open, clinical trial (NCT02852486, August 2, 2016 retrospectively registered) for the discontinuation of imatinib after concomitant use of pioglitazone, being the first of its kind in a Brazilian population with chronic myeloid leukemia. Due to remaining of leukemic quiescent cells that are not affected by tyrosine kinase inhibitors, it has been suggested the use of pioglitazone, a PPARγ agonist, together with imatinib as a strategy for the maintenance of deep molecular response. The clinical benefit to this association is still controversial, and the metabolic alteration along this process remains unclear. Therefore, we applied a metabolomic protocol using high-resolution mass spectrometry to profile plasmatic metabolic response of a prospective cohort of 10 individuals under discontinuation of imatinib and pioglitazone protocol. By comparing patients under pioglitazone and imatinib treatment with imatinib monotherapy and discontinuation phase, we were able to annotate 41 and 36 metabolites, respectively. The metabolic alterations observed during Imatinib-Pioglitazone combined therapy are associated with an extensive lipid remodelling, with activation of β-oxidation pathway, in addition to the presence of markers that suggest mitochondrial dysfunction.</p>
Dataset from: Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications
<p>This record includes the data associated with the study "Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications". In this study, we evaluated the extent and quality of patient involvement reporting in examples of current practice in health research. We used a targeted search strategy to identify publications that report on patient involvement using the following three samples:</p> <ul> <li>Publications published in 2019 in <em>The BMJ, </em>which requires reporting on patient involvement in research articles</li> <li>Publications listed in the PCORI database. We filtered for topic: example of engagement in health research; stakeholder involvement: patients; year: 2019</li> <li>Publications citing the <a href="https://www.bmj.com/content/358/bmj.j3453">GRIPP2 reporting checklist</a> or a <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/j.1369-7625.2010.00607.x">critical appraisal guideline to assess the quality and impact of user involvement in research</a></li> </ul> <p>After applying our inclusion and exclusion criteria, the final sample consisted of 87 publications that reported on patient involvement. Included publications were coded according to three coding schemes.</p> <p>This deposit includes the following:</p> <ul> <li>Overview of publications that did not meet our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Overview of publications and additional documents (if applicable) that met our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Coded segments and analysis for the coding scheme "Phase of involvement" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table. </li> <li>Coded segments and analysis for the coding scheme "GRIPP2-SF reporting guidelines according to Staniszewska et al., 2017" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> <li>Coded segments and analysis for the coding scheme "Critical appraisal tool according to Wright et al., (2010)" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> </ul>
Predicting Survival of Tongue Cancer Patients by Machine Learning Models
<p>This repository contains the dataset used in the paper "Predicting Survival of Tongue Cancer Patients by Machine Learning Models." The dataset contains information on 1712 tongue cancer curative surgery recipients. Each row represents one patient. The meaning of each variable is summarized here:</p> <ul> <li>id: patient identifier</li> <li>gender: patient sex</li> <li>survival: patient survival status at follow-up; 0: survival, 1: death</li> <li>follow_time: length of follow-up period in days</li> <li>part: site of operation</li> <li>stage: tumor stage; 0: very small, no spreading, 1: small, no spreading, 2: some growth, spreading, 3: large, spreading to surrounding tissue or lymph nodes, 4A/4B/4C: larger, metastasis to at least one other organ</li> <li>op: operation status; 1: complete</li> <li>rt: radiation therapy status; 0: not received, 1: received</li> <li>ct: chemotherapy status; 0: not received, 1: received</li> <li>t_stage: tumor size; 1 (small) to 4 (large)</li> <li>n_stage: metastasis to lymph nodes; 0 (no metastasis) to 3 (metastasis to multiple lymph nodes)</li> <li>grade: tumor grade; 1 (no proliferation) to 3 (aggressive proliferation)</li> </ul>
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
Data from Time since liver transplantation and immunosuppression withdrawal outcomes: a systematic review with individual patient data meta-analysis
<p>This record provides one CSV file containing anonymized individual patient data (IPD) of pre-withdrawal times (in days) of liver transplant recipients that underwent immunosuppression (IS) withdrawal. Collection and publication of anonymized data was approved by the Ethics Committee Northwest and Central Switzerland. Patients of 15 primary studies are stratified by successfully reaching the state of IS-free operational tolerance (OT) or by developing signs of immunological rejection (non-OT).</p>
A modified decontamination and storage method for sputum from patients with tuberculosis
<p>Sputum sampling is a cheap and non-invasive method for diagnosis Tuberculosis. A modified method is devised to enhance handling capacity and minimize risk of contamination in culture.</p> <p>"22_samples_TTP_GU_method_comparison_dataset.csv" is a dataset for comparing standard method and modified method of sputum handling and storing procedures before being cultured in MGIT. MGIT is used in BD BACTEC 960 MGIT system which generates "Time to positive" hours for a culture to growth and "Growth Unit" for estimating the amount of growth.</p> <p>"348_samples_TTP_GU_modified method_dataset.csv " is a dataset for applying modified method on selected 348 sputum samples for culture in MGIT. The dataset contains "Time to positive", "Growth Unit", "ZN smear grade", and "Duration of frozen:.</p>
Data for patient-specific solution of the electrocorticography forward problem in deforming brain
<p>This dataset contains magnetic resonance (MR) and computed tomography (CT) images of a patient undergoing intracranial electrical monitoring using electrocorticography grid electrodes, together with patient-specific geometry and computational grids created from these images applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain”. The images were acquired at Boston Children’s Hospital and provided to The University of Western Australia’s Intelligent Systems for Medicine Laboratory for analysis. The analysis was conducted using our open-source SlicerCBM software extension for the 3D Slicer medical imaging platform. The analysis steps include image processing to obtain the patient-specific brain geometry, construction of computational grids (tetrahedral grid for meshless solution of biomechanical model and regular hexahedral grid for finite element solution of the electrocorticography forward problem), biomechanics-based image warping to predict the postoperative images corresponding to the brain configuration deformed by placement of subdural electrodes, and patient-specific solution of the electrocorticography forward problem to compute the electric potential distribution within the patient’s head. We use well-established open-source data file formats including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry and Visualization Toolkit (VTK) files for computational grids. This facilitates the re-use of this dataset in a range of studies that rely on medical image analysis, and computational biomechanics and electrostatics to solve the electrocorticography forward problem for electrical source imaging.</p>
Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad
<p>Here we are uploading a data sheet of the<strong> "Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad." </strong></p>
Data on eye movements of glaucoma patients with asymmetrical visual field loss during free viewing.
<p>Raw eye tracking data and processed eye movement data were recorded from fifteen participants with assymmetrical visual field loss (visual field worse in one eye) while they freely viewed 270 images of nature with each eye monocularly.</p>
Repository of Raw Datasets for the Study of Anticoagulation and the Incidence of Stroke and Other Outcomes in Patients with Left Ventricular Thrombus
<p>The optimal duration of anticoagulation in patients with left ventricular thrombus (LVT) is unknown. The data package herein presented contains the data used to assess the effect of duration of anticoagulation in the incidence of stroke in patients with left ventricular thrombus (LVT) in a tertiary hospital. These data includes clinical and demographic information, treatment choices (vitamin K antagonists [VKA] versus direct oral anticoagulants [DOAC]), duration of treatment, reason for interruption of treatment, occurrence of stroke, acute myocardial infarction, bleeding events, thrombus resolution and recurrence, and death.<br> The raw dataset is available upon request to the corresponding author.</p>
Cardiac Patient Bed Head Ticket Dataset
<p>This data set comprises the data which are manually extracted from the Bed Head Tickets(BHT) of patients who transferred from the Emergency Treatment Unit (ETU) to the cardiac ward in Teaching Hospital Karapitiya Galle. The data set contains 19 features as follows</p> <ol> <li>Systolic Blood Pressure (SBP) </li> <li>Diastolic Blood Pressure (DBP) </li> <li>Heart Rate (HR) </li> <li>Respiratory Rate (RR) </li> <li>Body temperature (BT) </li> <li>SpO<sub>2</sub> </li> <li>Age </li> <li>Gender </li> <li>Glasgow Coma Scale (GCS) </li> <li>Sodium Level (Na) </li> <li>Potassium Level (K) </li> <li>Chloride Level (Cl) </li> <li>Urea </li> <li>Creatinine </li> <li>Consumption of alcohol </li> <li>Smoking status</li> <li>Family History of Ischemic Hear Diseases (FHIHD) </li> <li>Triage Score </li> <li>Outcome</li> </ol> <p> </p>
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</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.