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126,115 results for “Patients”
SIMpat: a synthetic benchmark for similarity metrics on patient representations
<div> <h2>Introduction</h2> <p>We used Synthea to generate six cohorts of patients with certain specified disease. Please refer to Synthea documentation for the generation process.</p> <p>We selected 6 different diseases that could be generated by Synthea, that were deemed by a medical professional as “different enough”. The goal of this simulation is to find a metric that can differentiate between patients.</p> <p>The six conditions are:</p> <ul> <li>Cerebral Palsy (SNOMED-CT code : 128188000 - Cerebral palsy (disorder))</li> <li>Colorectal Cancer (SNOMED-CT code : 93761005 - Primary malignant neoplasm of colon (disorder))</li> <li>Dialisys (SNOMED-CT code : 265764009 - Renal dialysis (procedure))</li> <li>Hypertension (SNOMED-CT code : 59621000 - Essential hypertension (disorder))</li> <li>Breast Cancer (SNOMED-CT code : 254837009 - Malignant neoplasm of breast (disorder))</li> <li>Prostate Cancer (SNOMED-CT code : 126906006 - Neoplasm of prostate (disorder))</li> </ul> <p>NB:</p> <ul> <li>Dialisys is not a disorder, but a condition, but is used here as a proxy for renal issue</li> <li>Synthea doesn’t have a module to generate prostate cancer in men, but only prostate cancer in veteran, hence this is the module used here (all men with prostate cancer are veterans)</li> </ul> <p>We use those cohort to compare the ability of 12 different distance metrics to separate patients.</p> <p>Those 12 metrics are split in three groups :</p> <p>Sementic based metrics:</p> <ul> <li>AvgEmb* method encodes text by averaging the pre-trained word embeddings of all the words present in it.</li> <li>BERT* uses bidirectional transformer based neural model to solve the task of masked language modeling.</li> <li>Universal Sentence Encoders (USE)* use transformer based encoders to encode sentences into embedding vectors.</li> <li>Embeddings from Language Models (ELMo)* uses bi-directional LSTM based encoders to encode a sentence into a fixed size representation</li> </ul> <p>Graph based metrics:</p> <ul> <li>DeepWalk* uses random walks to generate sequences of vertices (vertex sentences) which are subsequently fed to a skip-gram model to learn the embeddings corresponding to the vertices.</li> <li>Node2Vec* uses biased random walks to optimize a neighborhood preserving objective function such that the nodes which are highly interconnected and the nodes with similar roles in the graph are closer in the embedding space.</li> <li>LINE* tries to directly optimize the vertex embeddings based on one hop and two hop random walk probabilities.</li> <li>HARP* proposes a meta-strategy for embedding vertices of a graph such that they preserve the higher-order structural features.</li> <li>Bags of findings^</li> <li>Average Links^</li> <li>Average Links Weighted by Information Content (IC)^</li> <li>Path Distance weighted by IC^</li> </ul> <p>Concept followed by a * are extracted from <a href="https://proceedings.mlr.press/v116/pattisapu20a/pattisapu20a.pdf">this paper</a> and can be downloaded <a href="../records/3842143">here</a></p> <p>Concept followed by a ^ were develloped by Jean-Virgile Voegeli (SIMED)</p> </div> <div> <div> </div> <h2>Descriptive analysis of the sample</h2> <p>We will first look at the cohort that were created by Synthea. The cohorts were created using the seed 123456789 for reproducibility.</p> <p>For this first experiment, Synthea was asked to generate 100 alive individuals for each specific disease. We asked Synthea to keep only 10 years of history. Each individual was set to be between the age of 18 and 80 years old. Except for specific sex-disease such as breast cancer and prostate cancer, all cohorts contains both male and female individuals. We used the default location, which is Massachussetts.</p> <p>One important note on age. The Synthea modules sometimes specify a minimum age to onset a certain condition / disease. For example, colorectal cancer can only onset after 50 years old, and prostate cancer after 60 years old.</p> <p>Each Synthea run was set to run 10.000 times. If after 10.000 tries, the software didn’t manage to generate a patient that fit the criterion (here, a specific snomed code), the run would fail. Synthea can also generate patients that dies before the “run date”, and if this happens will simulate another patient.</p> <p>This explains why we have cohorts of more than 100 individuals but less than 100 alive individuals. We can also have in certain cases a little above 100 individuals. This is due to the fact that the synthea generator is multicore, and patients are generated simultaneously.</p> </div>
The Genetic Background of Hearing Loss in Patients with EVA and Cochlear Malformation
<p>This dataset comprises likely pathogenic variants identified through next-generation sequencing (NGS) using a multigene panel (237 genes associated with hearing loss), as well as clinical exome sequencing (TruSightOne Sequencing Panel). Raw data obtained during variant validation using Sanger sequencing method are also included. Furthermore, audiometric data are attached, from which the average air-conduction thresholds for ears with EVA malformation were determined.</p> <p> </p> <p><strong>Abstract manuscripts: </strong></p> <p>The most frequently observed congenital inner ear malformation is enlarged vestibular aqueduct (EVA). It is often accompanied with incomplete partition type 2 (IP2) of the cochlea and a dilated vestibule, which together constitute Mondini malformation. Pathogenic SLC26A4 variants are considered the major cause of inner ear malformation but the genetics still needs clarification. The aim of this study was to identify the cause of EVA in patients with hearing loss (HL). Genomic DNA was isolated from HL patients with radiologically confirmed bilateral EVA (n = 23) and analyzed by next generation sequencing using a custom HL gene panel encompassing 237 HL-related genes or a clinical exome. The presence and segregation of selected variants and the CEVA haplotype (in the 5′ region of SLC26A4) was verified by Sanger sequencing. Minigene assay was used to evaluate the impact of novel synonymous variant on splicing. Genetic testing identified the cause of EVA in 17/23 individuals (74%). Two pathogenic variants in the SLC26A4 gene were identified as the cause of EVA in 8 of them (35%), and a CEVA haplotype was regarded as the cause of EVA in 6 of 7 patients (86%) who carried only one SLC26A4 genetic variant. In two individuals with a phenotype matching branchio-oto-renal (BOR) spectrum disorder, cochlear hypoplasia resulted from EYA1 pathogenic variants. In one patient, a novel variant in CHD7 was detected. Our study shows that SLC26A4, together with the CEVA haplotype, accounts for more than half of EVA cases. Syndromic forms of HL should also be considered in patients with EVA. We conclude that to better understand inner ear development and the pathogenesis of its malformations, there is a need to look for pathogenic variants in noncoding regions of known HL genes or to link them with novel candidate HL genes.</p>
Prospective with historical control, case-matched cohort study of the tertiary survey beneficial in critically severe trauma patients.
<p>Raw data, Tertiary survey record form, and STROBE checklist of "Prospective with historical control, case-matched cohort study of the tertiary survey beneficial in critically severe trauma patients" study.</p>
A Dataset for Engagement Prediction in Neuromotor Disorder Patients during Rehabilitation
<p>This dataset is related to the work titled "Artificial Intelligence Tools for Engagement Prediction in Neuromotor Disorder Patients undergoing Robot-Assisted Rehabilitation," whose aim is to methodologically explore the performance of artificial intelligence algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during robot-assisted gait rehabilitation (RAGR).</p> <p>It is composed of three Excel files related to the 3-minute windows data augmentation scenario applied to the bimodal dataset made of 14 HRV and 19 EDA features. Specifically:</p> <ul> <li>ds_bimodal_win_3min.xlsx contains the features extracted from 3-minute windows of HRV and EDA signals, recorded during the RAGR activity, and normalized with respect to the reference (baseline) signals. Features are not z-scored.</li> <li>labels_self_win_3min contains one single column with, for each row, the label related to the self-perceived engagement classification target. </li> <li>labels_therapist_win_3min contains one single column with, for each row, the label related to the therapist-perceived engagement classification target.</li> </ul> <p>The coding of classes for both classification targets is:</p> <ul> <li>0: Underchallenged</li> <li>1: Minimally Challenged</li> <li>2: Challenged</li> </ul>
Multiple motor tasks while walking in patients with chronic bilateral and unilateral vestibulopathy
<p><em><span>This dataset contains whole body movements (i.e. 3D trajectories) and 3D kinematics from 30 subjects (10 with bilateral vestibulopathy, 10 with unilateral vestibulopathy, and 10 healthy subjects) during multiple motor tasks while walking: change of speed, double task, gait with eyes closed, gait with head horizontal turns, gait with head vertical turn, step over an obstacle, gait and U-turn. Participants were instrumented with 35 reflective placed on the whole body according to the Convention Gait Model 1.0. </span></em><em><span>A 12-camera motion capture system (Oqus 7+, Qualisys, Göteborg, Sweden), set at a 100 Hz sampling frequency, was used to track cutaneous reflective markers. The marker trajectories were labeled using Qualisys Tracking Manager software (QTM 2019.3, Qualisys, Göteborg, Sweden) and exported in the C3D file format. Joint kinematics were calculated from the raw data of the marker trajectories and stored in C3D files.</span></em></p>
ADIPOQ Gene Variants (rs266729, rs2241766, rs1501299) and Acute Myocardial Infarction in Vietnamese Patients with Type 2 Diabetes Mellitus
<p>This data is from a study project about ADIPOQ Gene Variants (rs266729, rs2241766, rs1501299) and Acute Myocardial Infarction in Vietnamese Patients with Type 2 Diabetes Mellitus. The data contains information from 550 patients with their identification removed to ensure confidentiality.</p>
Determining aligner-induced tooth movements in three dimensions using clinical data of two patients: datasets
<h2>Abstract</h2> <p>The effectiveness of a series of optically transparent aligners for orthodontic treatments depends on the anchoring of each tooth. In contrast with the roots, the crowns’ positions and orientations are measurable with intraoral scans, thus avoiding any X-ray dose. Exemplified by two patients, we demonstrate that three-dimensional crown movements could be determined with micrometer precision by registering weekly intraoral scans. The data show the movement and orientation changes in the individual crowns of the upper and lower jaws as a result of the forces generated by the series of aligners. During the first weeks, the canines and incisors were more affected than the premolars and molars. We detected overall tooth movement of up to about 1 mm during a nine-week active treatment. The data on these orthodontic treatments indicate the extent to which actual tooth movement lags behind the treatment plan, as represented by the aligner shapes. The proposed procedure can not only be used to quantify the clinical outcome of the therapy, but also to improve future planning of orthodontic treatments for each specific patient. This study should be treated with caution because only two cases were investigated, and the approach should be applied to a reasonably large cohort to reach strong conclusions regarding the efficiency and efficacy of this therapeutic approach.</p> <h2>Data</h2> <p>The repository contains the data of the intraoral scans and all data where manual interactions were performed to allow reproducing the results. </p> <p>The directory and file names are as follows:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Level, type<br></strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><em>p</em>_Clinical_Trial</td> <td>1st, directory</td> <td>Patient <em>p</em>: <em>p</em>=3485 stands for patient A, <em>p</em>=6457 stands for patient B</td> </tr> <tr> <td>Bottmedical</td> <td>2nd, directory</td> <td>Planning data</td> </tr> <tr> <td>Sirona</td> <td>2nd, directory</td> <td>Intraoral scan data</td> </tr> <tr> <td>T<em>n</em></td> <td>3rd, directory</td> <td>Time step <em>n</em> for <em>n</em>=0: before treatment, <em>n</em>=1-9: after 1-9 weeks of treatment; <em>n</em>=10: end of treatment</td> </tr> <tr> <td>Lower_Aligner_<em>n</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh of planned data, lower jaw for time step <em>n</em></td> </tr> <tr> <td>Upper_Aligner_<em>n</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh of planned data, upper jaw for time step <em>n</em></td> </tr> <tr> <td><em>p</em>_OnyxCeph3_Export_<em>j</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh from intraoral scan, for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw</td> </tr> <tr> <td><em>p</em>_OnyxCeph3_Export_<em>j</em>.stl</td> <td>4th, file</td> <td>Original surface mesh from intraoral scan, for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw</td> </tr> <tr> <td>teethSeg</td> <td>4th, directory</td> <td>Segmented crowns via OnyxCeph3 TM</td> </tr> <tr> <td>teethSeg_noSnap_TolS</td> <td>4th, directory</td> <td>Transferred crown segmentations and occlusion plane points</td> </tr> <tr> <td><em>p</em>_Z<em>i</em>.stl</td> <td>5th, file</td> <td> <p>Surface mesh of crown segmentation for tooth number <em>i </em>of patient<em> p<br></em></p> </td> </tr> <tr> <td><em>p</em>_<em>j</em>.stl</td> <td>5th, file</td> <td> <p>Surface mesh of all crown segmentations for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw of patient <em>p</em></p> </td> </tr> <tr> <td><em>p</em>_<em>j</em>_occPlane.mat</td> <td>5th, file</td> <td> <p>Binary Matlab file of saved variable occPlane, which defines transferred occlusion plane points for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw of patient <em>p</em></p> </td> </tr> </tbody> </table> <p>File formats:</p> <p>stl files describe an unstructured triangulates surface by vertices and triangles. These files can be read by the open source software freecad or the MATLAB function stlread.</p> <p>mat files are MATLAB files and can be read via MATLAB function load.</p>
Data for "Impact of early cleft lip and palate surgery on maxillary growth in 5- and 10-Year-old patients with unilateral cleft lip and palate: a cross-sectional study"
<p>Relative frequency in % (absolute frequency is shown above each bar). Frequency of 5YO indices in cleft patients and frequency of GOSLON indices in cleft patients.</p>
Electrophoresis Images of Acute Myeloid Leukemia Patients
<p>The database consists of a set of 22 2DGE images obtained from the peripheral blood samples of 11 patients with acute myeloid leukemia. Of these, 11 images correspond to samples taken at the time of diagnosis, and the other 11 correspond to samples taken from the same patients after induction therapy (approximately 21–28 days after starting treatment). Images named with the suffix BEFORE refer to 2DGE images of samples taken at the time of diagnosis (before treatment), while images named with the suffix AFTER correspond to 2DGE images of samples taken after treatment. These 22 images are also made available with the preprocessing stage applied, to which the prefix PREPROC has been applied. Each image in the database is in tagged image file format (TIFF) format with a resolution of 300 dots per inch (DPI). In total, the database, which can be found in the Supplementary Materials, contains 44 images (22 raw 2DGE images and 22 pre-processed 2DGE images).</p>
A non-parametric model-free analysis of actigraphic recordings of acute insomnia patients
Both parametric and non-parametric approaches to time series analysis have advantages and disadvantages. Parametric methods, although powerful and widely used, can yield inconsistent results due to the oversimplification of the observed phenomena, they require the setting of arbitrary constants for their creation and refinement, and, although these constants relate to assumptions about the observed systems, it can lead to erroneous results when treating a very complex problem with a sizable list of unknowns. Their non-parametric counterparts, instead, are more widely applicable but present a higher detrimental sensitivity to noise and low density in the data. For the case of approximately periodic phenomena, such as human actigraphic time series, parametric methods are widely used and concepts such as acrophase are staple in chronobiology; in this work we present a non-parametric method of analysis of actigraphic time series from insomniac patients and healthy age-matched controls, the method is fully data-driven, reproduces previous results in the context of sleep-onset delay and, crucially, extends the concept of acrophase not only to circadian but also for ultradian spectral components.
Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"
<p>Processed data and code for reproducing the main results and figures of the paper "<strong>Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions</strong>".</p> <p>We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019).</p> <p>Reference<br> A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, <strong>‘‘</strong>Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms<strong>’’</strong> <em>in proceedings of the</em> <em>ACM/IEEE Design, Automation, and Test in Europe Conference (DATE)</em>, Florence, Italy, March 25-29, 2019. </p>
Bevacizumab plus erlotinib versus erlotinib alone as first line treatment of patients with EGFR-mutated advanced nonsquamous non-small cell lung cancer. BEVacizumab plus ERLotinib studY (BEVERLY): an academic, multicenter, randomised phase III trial.
<p>Background. Adding bevacizumab to erlotinib prolonged PFS of patients with EGFR-mutated advanced NSCLC in the Japanese NEJ026 trial, but limited data were available in non-Asian patients. BEVERLY is an Italian, multicenter, randomized phase III trial of bevacizumab plus erlotinib versus erlotinib alone as first-line treatment of advanced EGFR-mutated NSCLC.</p> <p>Methods. Eligible patients were randomized 1:1 to erlotinib (150mg daily) plus bevacizumab (15mg/kg iv q3w) or erlotinib alone, until disease progression or unacceptable toxicity. Center, ECOG PS and type of mutation (ex19 deletion vs ex21 L858R vs others) were stratification variables. Investigator-assessed PFS (IA-PFS) and blinded-independent centrally-reviewed PFS (BICR-PFS) were co-primary endpoints. With 80% power in detecting a 0·60 HR and 2–sided α error 0·05, 126 events out of 160 patients were needed. The trial was registered as NCT02633189 and EudraCT 2015-002235-17.</p> <p>Findings. From Apr 11, 2016 to Feb 27, 2019, 160 pts were randomized to erlotinib pus bevacizumab (80) or erlotinib alone (80). Baseline characteristics were balanced between arms; 34 (42·5%) patients in erlotinib plus bevacizumab arm and 43 (53·8%) in erlotinib arm were former or current smokers. At a median follow-up of 36·3 months, 140 PFS events (87·5%) were reported, 68 with erlotinib plus bevacizumab and 72 with erlotinib. Median IA-PFS was 15·4 months (95% CI 12·2–18·6) with erlotinib plus bevacizumab and 9·6 months (95% CI 8·2–10·6) with erlotinib (HR 0·66; 95%CI: 0·47–0·92). BICR-PFS analysis confirmed this result. A significant interaction with treatment effect was found for smoking habit (P=0·0323): former or current smokers receiving erlotinib plus bevacizumab had a longer PFS (16·9 months [95% CI 10·2–21·8] versus 8·8 months [95% CI 5·6–9·6]) than those receiving erlotinib alone.</p> <p>Hypertension (grade≥3: 24% vs 5%), skin rash (grade≥3: 31% vs 14%), thromboembolic events (any grade: 11% vs 4%), and proteinuria (any grade: 23% vs 6%) were more frequent with the combination treatment.</p> <p>Interpretation. The addition of bevacizumab to first-line erlotinib significantly prolonged PFS in Italian patients with EGFR-mutated NSCLC, without unexpected safety issues.</p>
Dataset related to aticle "Additive Fabrication of a Vascular 3D Phantom for Stereotactic Radiosurgery of Arteriovenous Malformations"The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.
<p><em>The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.</em></p>
dataset related to article "On the evaluation of edgeless diode detectors for patient-specific QA in high-dose stereotactic radiosurgery"
<p>The database contains the comparison table between calculated doses and values actually measured by the edgeless detectors and an example of dose planar distribution as exported from the radiotherapy Treatment Planning System.</p> <p> </p>
ATOPE+Breast, Continuous Monitoring of Training Load in Patients with Breast Cancer during Therapeutic Exercise Intervention
<p>The ATOPE+Breast dataset (ATOPE+ for patients with breast cancer) describes the daily status of 23 patients with breast cancer during therapeutic exercise intervention with daily measures of HRV, self-reported wellness, physical activity, and sleep. Besides, the ATOPE+Breast dataset contains information about training sessions, such as intensity recorded, demographic data, treatment details, initial evaluations of quality of life, physical activity levels, previous medical conditions, and risk factors. ATOPE+Breast was recorded using the ATOPE+ mHealth system, whose usability [1] and reliability [2] were successfully validated.</p> <p>References</p> <ul> <li>[1] S. Moreno-Gutierrez et al., “ATOPE+: An mHealth System to Support Personalized Therapeutic Exercise Interventions in Patients With Cancer,” IEEE Access, vol. 9, pp. 16878–16898, 2021, doi: 10.1109/ACCESS.2021.3049398.</li> <li>[2] P. Postigo-Martin et al., “mHealth system (ATOPE+) to support exercise prescription in breast cancer survivors: A validity and reliability, cross-sectional observational study (ATOPE study) (Preprint),” JMIR mHealth and uHealth, Preprint, Jan. 2022. doi: 10.2196/preprints.36733.</li> </ul>
Experiences of family caregivers of caring for terminally ill patients in Lithuania
<p>The dataset are the sample of anonymized transcripts of interviews with family caregivers who shared experiences of caring for terminally ill patients in Lithuania in 2020.</p>
CSV files and R script: writing process data of typed picture description by 15 cognitively impaired patients and 15 healthy controls
<p>Writing process data of 15 cognitively impaired patients and 15 age- and gender-matched healthy controls were obtained. Each of them completed two typed picture description tasks that were logged with Inputlog, a keystroke logging tool. Variables included time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceeding specific word categories was analyzed.</p> <p>The data were used to explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). This data set contains the CSV files that were used for the analyses and the corresponding R script.</p>
Dataset of The Relationship Between Postoperative Opioid Analgesia and Sleep Apnea Severity in Patients Undergoing Hip Arthroplasty: A Randomized, Controlled, Triple-Blinded Trial
<p>This the dataset related to the article entitled "he Relationship Between Postoperative Opioid Analgesia and Sleep Apnea Severity in Patients Undergoing Hip Arthroplasty: A Randomized, Controlled, Triple-Blinded Trial".</p> <p>Purpose: Residual postoperative pain after hip arthroplasty is usually treated with oral opioids. While classic opioids are associated with respiratory depression and worsening of sleep apnea, tramadol has been reported to preserve respiratory function. However, this has not been investigated in a prospective trial using respiratory polygraphy. This randomized controlled triple-blinded trial tested the hypothesis that postoperative treatment with oral opioids such as oxycodone would increase sleep apnea severity, measured with a respiratory polygraphy, compared with oral tramadol.<br> Patients and Methods: Sixty patients undergoing hip arthroplasty under spinal anesthesia with 15 mg isobaric bupivacaine 0.5% were randomized to receive postoperative pain treatment with either oral oxycodone (controlled-release 10 mg every 12 hours and immediate-release 5 mg every 4 hours as needed) or oral tramadol (controlled-release 100 mg every 8 hours and immediate-release 50 mg every 4 hours as needed). Respiratory polygraphy was performed on the first postoperative night. The primary outcome was the apnea-hypopnea index in the supine position. Secondary outcomes included the oxygen desaturation index, postoperative pain scores and intravenous morphine consumption.<br> Results: Mean supine apnea-hypopnea index on postoperative night 1 was 11.3 events.h−1 (95% confidence interval, 4.8–17.7) in the oxycodone group and 10.7 (4.6–16.8) events.h−1 in the tramadol group (p=0.89). There were no significant differences between the oxycodone and tramadol groups with respect to any secondary sleep-related or pain-related outcomes.<br> Conclusion: Oral oxycodone did not increase sleep apnea severity measured using respiratory polygraphy compared with oral tramadol on the first postoperative night after hip arthroplasty.</p>
Dataset Related to article "NEURODEVELOPMENTAL DISORDER AND LATE-ONSET DEGENERATIVE PARKINSONISM IN A PATIENT WITH A WDR45 DEFECT"
<p>NGS data (.vcf; .bam; .bam.bai; .csv; .fastq) of patient analysed and sanger sequencing (.abi) for confirm the mutation.</p>
Treating anti-vax patients, a new stressor for COVID-19 center doctors. Data from a 2-year prospective study.
<p>Dataset of the study "Treating anti-vax patients, a new stressor for COVID-19 center doctors. Data from a 2-year prospective study."</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.