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1,659 results for “Patient Data”
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera 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 list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields:</p> <p>1. subject_id: scalar<br> A numerical value that uniquely identifies the subject.</p> <p>2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions.</p> <p>3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field.</p> <p>4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<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>* 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.<br> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"
<p>These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded).</p> <p>These files provide insight into the AI-supported screening process and the choices made by the human reviewer.</p>
CyTOF data of PBMC samples of patients with metastatic pancreatic ductal adenocarcinoma
<p>These two CyTOF datasets are a part of the manuscript by M. Baretti "E<span>ntinostat in combination with nivolumab in metastatic pancreatic ductal adenocarcinoma: a phase 2 clinical trial" accepted in Nature Communications. The datasets contain FCS files of PBMCs samples of patients with metastatic pancreatic ductal adenocarcinoma treated with entinostat and nivolumab. PBMC samples were run with myeloid- and lymphoid-oriented panels.<br></span></p>
Keystroke timing and pressure data captured during touchscreen typing by early Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The present dataset comprises keystroke timing and pressure data that correspond to short text excerpts typed by early Parkinson’s disease (PD) patients (n=18) and healthy controls (n=15) on a common touchscreen-equipped smartphone (LG Nexus 5X with a screen of 5.2 inches in diagonal and a resolution of 1080 × 1920 pixels, running native Android 7.0). Subjects were asked to transcribe up to 11 short text excerpts, with the initial one being 200 characters-long and common for all subjects, while the rest were 40-115 characters-long, pseudorandomly drawn from the fairy tale 'The Little Prince'. Data were recorded using a custom Android Operating System input method (keyboard), developed for the purposes of the study. Additional details on exepriment design, material and methods can be found in the related research article mentioned below. </p> <p>Data consist of sequences of raw press and release timestamps (in milliseconds), as well as of values of normalized pressure (0.000-1.000) applied to initiate keystrokes, corresponding to the consecutive keys tapped during the transcription of each text excerpt. Data included in the 'Data' folder are organised in sub-folders per subject. Each sub-folder contains a number of .txt files with each one corresponding to a text excerpt typed by the particular subject. Files are named using the format S##_TEX##.txt, with S## denoting the subject's coded ID and TEX## the serial number of the transcribed text excerpt. For all subjects, file S##_TEX01.txt corresponds to the initial and common 200 characters-long text excerpt. Each file contains the sequences of raw key press/release timestamps (Tp#, Tp#) and normalized pressure (NP#), applied to initiate each keystroke, in the following format:</p> <p>{<br> Press, Tp1, Release, Tr1, NP1<br> Press, Tp2, Release, Tr2, NP2<br> .<br> .<br> . <br> Press, Tpn, Release, Trn, NPn<br> }</p> <p>where 1,2,...,n denote the serial index of the key tapped during typing.</p> <p><em>Note:</em> Out of 33 subjects, 32 managed to transcribe 8 to 11 text excerpts, while the remaining one (Subject ID: 16) typed only 5. Ten subjects (Subject IDs: 6, 14, 16, 17, 25, 27, 29, 31, 32, 33) did not manage to type the initial 200 characters-long excerpt in its entirety.</p> <p>The dataset also includes a record, in Microsoft Excel format (Demographics_Clinical_Characteristics.xlsx), of the demographic and clinical characteristics (with respect to PD) of subjects. Entries of the Excel file are linked to subjects' sub-folders and individual keystroke data text files via the coded ID of the subject.</p> <p>Demographic characteristics included:</p> <p>Age; Gender; Education level; Years of smartphone usage; Dominant hand<sup>1</sup></p> <p>Clinical characteristics included:</p> <p>Group (PD, Control); Years from diagnosis; Hoehn-Yahr disease stage; Most affected side<sup>2</sup>; Levodopa Equivalent Daily Dose; UPDRS_III<sup>3</sup> total score; UPDRS_III Item 21 Tremor-Right hand; UPDRS_III Item 21 Tremor-Left hand; UPDRS_III Item 22 Rigidity-Right hand; UPDRS_III Item 22 Rigidity-Left hand; UPDRS_III Item 23 Finger taps-Right hand; UPDRS_III Item 23 Finger taps-Left hand; UPDRS_III Item 31 Body bradykinesia/ Hypokinesia</p> <p><sup>1</sup>Dominant hand: (Relating to handedness) the operant hand generally used for performing fine motor-skills tasks.<br> <sup>2</sup>Most affected body side by Parkinson's disease<br> <sup>3</sup>UPDRS_III: Unified Parkinson's Disease Rating Scale Part III (Motor section)</p> <p> </p> <p><strong>RELATED RESEARCH</strong></p> <p>This dataset was originally used and described in the OPEN ACCESS publication: </p> <p>[1] Iakovakis, D., Hadjidimitriou, S., Charisis, V., Bostantzopoulou, S., Katsarou, Z., & Hadjileontiadis, L. J. (2018). Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease. Scientific reports, 8(1), 7663. <a href="http://doi.org/10.1038/s41598-018-25999-0">https://doi.org/10.1038/s41598-018-25999-0</a> </p> <p>All documents and papers that report on research that uses this dataset will acknowledge this by citing the above publication.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected was approved by the Aristotle University of Thessaloniki Bioethics Committee of Medical School (approval no. 359/3.4.17), Thessaloniki, Greece. Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Dimitrios Iakovakis (Electrical & Computer Engineer, PhD candidate)</p> <p>Signal Processing & Biomedical Technology Unit<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building D, 6th floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996319<br> Fax: +30 2310 996312<br> E-mail: dimiiako12@gmail.com</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </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>
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>
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>
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>
Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation"
<p>Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation". All nuclei passed QC and were corrected for background noise using cellBender. Files are in R objects saved in RDS (R Data Serialization) format. This repo contains one Seurat v4 object and one gene-by-cell raw RNA count matrix in sparse matrix format (dgCMatrix).</p>
QC and WGS data on compound heterozygous PRKN-mutant (R275W/dEx8) PD patient
<p>Primary/raw data for QC on the characterisation of an iPSC line from a PD patient (Stem Cell Research)</p>
Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials
<p>Data extraction table for scoping review on best practices for co-creating patient-facing educational materials</p>
Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors
<p>This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file. </p>
Raw EEG-EOG data used in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State"
<p>The dataset includes raw EEG and EOG recordings during BCI experiments for three patients: p11, p13, p15, and p16. The structure of the dataset is the following: patient/visit/day.</p> <p>The experiment is described in detail in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State". The correspondence between raw file and BCI session is reported in the attached pdf file "Supplementary Table S5 Session to Raw File Recordings Correspondence".</p> <p>The datasets include EEG and EOG channels. The data are raw (i.e. non filtered and non processed). Data have been acquired with a sampling rate of 500Hz using active electrodes and the amplifier V-Amp DC (Brain Products, Germany). EOG channels are labeled EOGU, EOGD, EOGR, EOGL namely for EOG up, down, right, left; the location in the 10-20 system are respectively SO1, IO1, LO1, LO2.</p> <p>The data are marked with triggers: for each session two markers indicate start and end of the session; for each trial markers indicate start of baseline, start of presentation of question, start of response time, start of feedback. Each trial was marked in a different way if it was a yes trial belonging to a training or feedback session, a no trial belonging to a training or feedback session, or a trial belonging to a speller session. The markers that have been used are the following:<br> <strong>start</strong> 9<br> <em> yes no speller</em><br> <strong>baseline</strong> 10 11 12<br> <strong>presentation</strong> 5 6 7<br> <strong>response</strong> 4 8 13<br> <strong>feedback</strong> 1 2 3</p> <p><strong>end</strong><strong> </strong> 15</p>
Data to "Choroidal changes in intermediate age-related macular degeneration patients with drusen or pseudodrusen."
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Rosa R., Corazza P., Musolino M., Mochi C., Maiello G., Traverso C. E., & Nicolò M. Choroidal changes in intermediate age-related macular degeneration patients with drusen or pseudodrusen. European Journal of Ophthalmology. (in press)</p> <p> </p>
Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH): outcome data
<p>The datasets reports baseline and outcome data from the 'Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH)' experimental study. The study evaluated the effectiveness of a geriatric co-management programme on the cardiac care units of the University Hospitals Leuven. Sample included patients aged 75 years or older. Measurements included: demographic, functional status, cognitive status, depressive symptoms, anxiety symptoms, quality of life, physical performance, readmission rates, survival.</p> <p>Please see Word document for more information.</p> <p>Please see protocols for more information:</p> <p><a href="https://clinicaltrials.gov/ct2/show/NCT02890927">https://clinicaltrials.gov/ct2/show/NCT02890927</a></p> <p><a href="https://bmjopen.bmj.com/content/8/10/e023593">https://bmjopen.bmj.com/content/8/10/e023593</a></p> <p>The evaluation study is available at https://agsjournals.onlinelibrary.wiley.com/doi/full/10.1111/jgs.17093 </p>
Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
A protocol to assess the risk of dementia among patients with coronary artery diseases using CAIDE score-Extended Data
<p>The contents of this extended data file are-<br> 01. Consent form (English & Bengali Version)<br> 02. Interview Questionnaire (English & Bengali Version)<br> These contents will help to address the objectives of the study that attempted to identify the risk of long-term dementia among coronary artery disease patients in Bangladesh.</p>
Digitized patient level time to event data of overall survival
<p>This dataset contains digitized patient level time to event data for overall survival of patients with locally advanced and metastatic (stage IIIB/IV) Non-small cell lung cancer (NSCLC). The data can be used to recreate the original Kaplan-Meier survival curves that were published in randomized controlled trials, in order to perform secondary analysis on the survival data. In order to recreate a survival curve, you need two csv.files per trial arm that are in this <br> database: (1) starting with 'surv_', containing the individual patient level time to event data, and (2) starting with 'natrisk_', containing the corresponding numbers at risk table. For the methodology and r-code that can be used for this purpose we refer to article that is linked to this dataset. </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.