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467 results for “Trial Data”
RNA sequensing data of a phase 2 clinical trial of pancreatic ductal adenocarcinoma treated with entinostat in combination with nivolumab
GEO Series GSE248014. Homo sapiens. 45 samples. Type: Expression profiling by high throughput sequencing.
Expression data from a malaria vaccine trial (HG-U133A2.0 and U133 Plus 2.0)
GEO Series GSE18323. Homo sapiens. 254 samples. Type: Expression profiling by array.
Expression Data from 226 patients of the REMAGUS02 trial
GEO Series GSE26639. Homo sapiens. 226 samples. Type: Expression profiling by array.
Results from analysis of microarray data from clinical trial NCT00805389
GEO Series GSE116975. Homo sapiens. 924 samples. Type: Expression profiling by array.
Tumor expression data from neoadjuvant trial of cisplatin monotherapy in triple negative breast cancer patients
GEO Series GSE18864. Homo sapiens. 84 samples. Type: Expression profiling by array.
Gene expression data of trial drug Dehydroabietylamine derivative-2 (DAAD-2) for Sensitive (HEP3B) and resistant (SNU449) hepatocellular carcinoma (HCC) cell lines with controls
GEO Series GSE88812. Homo sapiens. 12 samples. Type: Expression profiling by array.
Gene expression data from whole blood of a Phase II randomized clinical trial of the treatment of methamphetamine dependence with topiramate
GEO Series GSE107015. Homo sapiens. 209 samples. Type: Expression profiling by array.
SEALIVE Net Trial Use and Perceptions Raw Data
<div> <p><span><span>Data </span><span>emerging</span><span> from the pilot implementation of the bio-based, industrially </span><span>compostable</span><span> and marine biodegradable fishing nets in Cyprus, over a period of 12 months. </span><span>Includes d</span></span><span><span>ata and information on the durability of the nets (wear, stretch, elongation </span><span>etc</span><span>) and </span></span><span><span>end-user </span><span>perception</span><span> and satisfaction data. </span></span><span><span>Data captured through field work.</span></span></p> </div>
Raw Acoustic data samples from hydrophone SLim Towed Array towed by marine robot acquired during STO-CMRE sea trials
<p>Sample raw acoustic datasets acquired from hydrophone array SLiTA (SLim Towed Array, specific for AUV applications) towed by marine autonomous vehicles (e.g, Autonomous Underwater Vehicles, AUVs) [*] during three STO-CMRE sea trials:</p> <ul> <li>PORTOPALO (2013), at Portopalo di Capo Passero, Sicily, Italy, on 18-19 February 2013</li> <li>COLLAB13, at Palmaria Island, La Spezia, Italy, on 30 June - 7 July 2013</li> <li>COLLAB14, at Massa, Italy, on 29-31 October 2014</li> </ul> <p>Access to this dataset is restricted to NATO and H2020 INFORE consortium. This data set is provided to INFORE consortium partners for the purposes of executing tasks under the INFORE Grant Agreement and may not be used for other purposes or further distributed.</p> <p>The creation of derived products, as well the use in scientific publications must be pre-approved by NATO STO CMRE and acknowledged.</p> <p>These data are available to partners of NATO Nations after the execution of the project on the basis of existing NATO policies and rules for data release, other memorandum of understanding and agreements, and on the basis of CMRE terms and conditions for data reuse.</p> <p>Acoustic raw data from SliTa array header specification is described in the Table below,</p> <table> <thead> <tr> <th> <p><strong>Field Name</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Description</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>headersize</p> </td> <td> <p>int</p> </td> <td> <p>Size of header in bytes</p> </td> </tr> <tr> <td> <p>dataFormat</p> </td> <td> <p>int</p> </td> <td> <p>0 means 2’s complement; 1 means Offset Binary</p> </td> </tr> <tr> <td> <p>fs</p> </td> <td> <p>float</p> </td> <td> <p>Sampling Frequency [Hz]</p> </td> </tr> <tr> <td> <p>inputRange</p> </td> <td> <p>int</p> </td> <td> <p>Flag to determine the max voltage.<br> {3=10V, 2=5V, else 2.5V}</p> </td> </tr> <tr> <td> <p>gainHydPreamp</p> </td> <td> <p>float</p> </td> <td> <p>Preamplifier fain</p> </td> </tr> <tr> <td> <p>gainA2dAmp</p> </td> <td> <p>float</p> </td> <td> <p>A/D gain</p> </td> </tr> <tr> <td> <p>dataWidth</p> </td> <td> <p>int</p> </td> <td> <p>Flag to determine the number of bit per sample<br> {3=24bit, 2=20bit, 1=18bit, else 15bit}</p> </td> </tr> <tr> <td> <p>acqLength</p> </td> <td> <p>float</p> </td> <td> <p>Size (in seconds) on each block of data returned by A/D</p> </td> </tr> <tr> <td> <p>octave</p> </td> <td> <p>int</p> </td> <td> <p>Determine the array spacing:<br> {1=0.21m, 2=0.42, 3=0.84, 4=1.05m}</p> </td> </tr> <tr> <td> <p>pc_day</p> </td> <td> <p>int</p> </td> <td> <p>Day from PC time</p> </td> </tr> <tr> <td> <p>pc_month</p> </td> <td> <p>int</p> </td> <td> <p>Month from PC time</p> </td> </tr> <tr> <td> <p>pc_year</p> </td> <td> <p>int</p> </td> <td> <p>Year from PC time</p> </td> </tr> <tr> <td> <p>pc_hr</p> </td> <td> <p>int</p> </td> <td> <p>Hour from PC time</p> </td> </tr> <tr> <td> <p>pc_min</p> </td> <td> <p>int</p> </td> <td> <p>Minute from PC time</p> </td> </tr> <tr> <td> <p>pc_sec</p> </td> <td> <p>int</p> </td> <td> <p>Seconds from PC time</p> </td> </tr> <tr> <td> <p>gps_month</p> </td> <td> <p>int</p> </td> <td> <p>Month from GPS</p> </td> </tr> <tr> <td> <p>gps_day</p> </td> <td> <p>int</p> </td> <td> <p>Day from GPS</p> </td> </tr> <tr> <td> <p>gps_year</p> </td> <td> <p>int</p> </td> <td> <p>Year from GPS</p> </td> </tr> <tr> <td> <p>oex_hr</p> </td> <td> <p>int</p> </td> <td> <p>Hour from Frontseat PC</p> </td> </tr> <tr> <td> <p>oex_min</p> </td> <td> <p>int</p> </td> <td> <p>Minute from Frontseat PC</p> </td> </tr> <tr> <td> <p>oex_sec</p> </td> <td> <p>double</p> </td> <td> <p>Seconds from Frontseat PC</p> </td> </tr> <tr> <td> <p>lat_deg</p> </td> <td> <p>int</p> </td> <td> <p>Latitude [degrees]</p> </td> </tr> <tr> <td> <p>lat_min</p> </td> <td> <p>double</p> </td> <td> <p>Latitude [minutes]</p> </td> </tr> <tr> <td> <p>lon_deg</p> </td> <td> <p>int</p> </td> <td> <p>Longitude [degrees]</p> </td> </tr> <tr> <td> <p>lon_min</p> </td> <td> <p>double</p> </td> <td> <p>Longitude [minutes]</p> </td> </tr> <tr> <td> <p>heading</p> </td> <td> <p>float</p> </td> <td> <p>Size of header in bytes</p> </td> </tr> <tr> <td> <p>cog</p> </td> <td> <p>float</p> </td> <td> <p>0 means 2’s complement; 1 means Offset Binary</p> </td> </tr> <tr> <td> <p>depth</p> </td> <td> <p>float</p> </td> <td> <p>Sampling Frequency [Hz]</p> </td> </tr> <tr> <td> <p>altitude</p> </td> <td> <p>double</p> </td> <td> <p>Flag to determine the max voltage.<br> {3=10V, 2=5V, else 2.5V}</p> </td> </tr> <tr> <td> <p>sog(dm/s)</p> </td> <td> <p>int</p> </td> <td> <p>Preamplifier fain</p> </td> </tr> <tr> <td> <p>sow(dm/s)</p> </td> <td> <p>int</p> </td> <td> <p>A/D gain</p> </td> </tr> <tr> <td> <p>track_stat</p> </td> <td> <p>int</p> </td> <td> <p>Flag to determine the number of bit per sample<br> {3=24bit, 2=20bit, 1=18bit, else 15bit}</p> </td> </tr> <tr> <td> <p>fix_type</p> </td> <td> <p>int</p> </td> <td> <p>Size (in seconds) on each block of data returned by A/D</p> </td> </tr> <tr> <td> <p>pps_output</p> </td> <td> <p>int</p> </td> <td> <p>Determine the array spacing:<br> {1=0.21m, 2=0.42, 3=0.84, 4=1.05m}</p> </td> </tr> </tbody> </table> <p>[*] Alain Maguer, Rodney Dymond, Piero Guerrini, Luigi Troiano, Vittorio Grandi, Alberto Figoli, Claudio Olivero, Alessandro Sapienza, Stefano Fioravanti, John Potter Receiving and transmitting acoustic systems for AUV/gliders. Proceedings of the 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies and Results, 21-26 June, 2009, Nafplion, Greece. <a href="https://openlibrary.cmre.nato.int/bitstream/handle/20.500.12489/651/NURC-PR-2009-004.pdf">https://openlibrary.cmre.nato.int/bitstream/handle/20.500.12489/651/NURC-PR-2009-004.pdf</a></p>
VA HDL Intervention Trial (VA-HIT) Ancillary Study Data Analysis
ClinicalTrials.gov study NCT00035711. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Life With Covid Since 2020: a Randomized Control Trial of a Real-time Data Collection Smartphone-based App Assessing and Treating the Covid-19 Psychological Impacts
ClinicalTrials.gov study NCT05054101. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Meta-analysis of Orlistat Laboratory Data From Placebo-controlled Clinical Trials
ClinicalTrials.gov study NCT01332448. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Expression data from nonrandomized trial of vitamin D in Barrett's esophagus
GEO Series GSE100843. Homo sapiens. 76 samples. Type: Expression profiling by array.
Data from: Functional connectivity and structural analysis of trial spinal cord stimulation responders in failed back surgery syndrome
Open the record for dataset details and reuse information.
Data from: Scholarly influence of surgical hip randomized controlled trials
Open the record for dataset details and reuse information.
Expression Data from transNOAH breast cancer trial
GEO Series GSE50948. Homo sapiens. 156 samples. Type: Expression profiling by array.
Data for: The gut microbiome and the genetic predisposition are driving factors of the metabolic response to a dietary intervention – an integrative multi-omics analysis of a randomized trial in obese adults
<p><span>A</span><span> plant-based</span><span> diet </span><span>may contribute to </span><span>reducing</span><span> cardiometabolic risk. We conducted a 6-week</span><span>, randomized,</span><span> </span><span>dietary intervention trial</span><span> comparing </span><span>the </span><span>effect</span><span>s</span><span> of </span><span>two plant-based diets on </span><span>lipid </span><span>metabolism and the gut-brain axis. <span>120 obese adults (</span>59 ± 1 years, 70 females) consumed an isoenergetic Nordic (ND) or a vegetarian diet (VD) or maintained their habitual diet (control group). At baseline and after six weeks, deep metabolic characterization was performed, including measurement of glucagon-like peptide 1 (GLP-1), postprandial lipid metabolism, genetic make-up, gut microbiome and peripheral immune system composition. <span>ND</span>, but not VD beneficially altered lipid metabolism, mainly in participants with a specific gut microbiome signature and a high genetic predisposition for hyperlipidemia. <span>T</span>he special microbial signatures and the individual genetic risk have a strong impact on metabolic response to a dietary change, pointing towards a personalized nutritional approach in preventing cardiometabolic diseases.<br></span></p>
H2020 TWEETHER Field Trial Results (Data files)
<p>ZIP file containing the CSV files of the different parameters captured during TWEETHER Field Trial (21/09/2018 to 04/10/2018).</p> <p>Please contact TWEETHER consortium (www.tweether.eu) to request access.</p>
Data and Code for Replication. PM4M trial
<p>This dataset includes all the needed files and syntax for replicating the PM4M trial including: Deidentified Intervention Baseline and Endline data, pre and post intervention data, dosage data and randomization schedule.</p> <p></p>
Data set from Laforgia P, Bandera F, Alfonzetti E, Guazzi M. Exercise chronotropic incompetence phenotypes the level of cardiovascular risk and exercise gas exchange impairment in the general population. An analysis of the Euro-EX prevention trial. Eur J Prev Cardiol. 2020 Mar;27(5):526-535. doi: 10.1177/2047487319863506. Epub 2019 Jul 25. PMID: 31345056.
<p>Data set from Laforgia P, Bandera F, Alfonzetti E, Guazzi M. Exercise chronotropic incompetence phenotypes the level of cardiovascular risk and exercise gas exchange impairment in the general population. An analysis of the Euro-EX prevention trial. Eur J Prev Cardiol. 2020 Mar;27(5):526-535. doi: 10.1177/2047487319863506. Epub 2019 Jul 25. PMID: 31345056.</p> <p> </p> <p>This is the article:</p> <p><strong>Background: </strong> Chronotropic insufficiency (CI) is defined as the inability of the heart to increase its rate commensurate with increased demand. Exercise CI is an established predictor of major adverse cardiovascular events in patients with cardiovascular diseases.</p> <p><strong>Aim: </strong> The aim of this study was to evaluate how exercise CI phenotypes different levels of cardiovascular risk and how it may better perform in defining cardiovascular risk when analysed in the context of cardiopulmonary exercise test (CPET)-derived measures and standard echocardiography in a healthy population with variable cardiovascular risk profile.</p> <p><strong>Methods: </strong> Apparently healthy individuals (<em>N</em> = 702, 53.8% females) with at least one major cardiovascular risk factor (MCVRF; hypertension, diabetes, tabagism, dyslipidaemia, body mass index > 25), enrolled in the Euro-EX prevention trial, underwent CPET. CI was defined as the inability to reach 80% of the chronotropic index, that is, the ratio of peak heart rate - rest heart rate/peak heart rate - age predicted maximal heart rate (AMPHR: 220 - age), they were divided into four groups according to the heart rate reserve (<80%>) and respiratory gas exchange ratio (RER; < 1.05>) as a marker of achieved maximal performance. Subjects with a RER < 1.05 (<em>n</em> = 103) were excluded and the final population (<em>n</em> = 599) was divided into CI group (<em>n</em> = 472) and no-CI group (<em>n</em> = 177).</p> <p><strong>Results: </strong> Compared with no-CI, CI subjects were more frequently females with a history of hypertension in a high rate. CI subjects also exhibited a significantly lower peak oxygen uptake (VO<sub>2</sub>) and circulatory power and an echocardiographic pattern indicative of higher left atrial volume index and left ventricular mass index. An inverse stepwise relationship between heart rate reserve and number of MCVRFs was observed (one MCVRF: 0.71 ± 0.23; two MCVRFs: 0.68 ± 0.24, three MCVRFs: 0.64 ± 0.20; four MCVRFs: 0.64 ± 0.23; five MCVRFs: 0.57 ± 18; <em>p</em> < 0.01). In multivariate analysis the only variable found predicting CI was peak VO<sub>2</sub> (<em>p</em> < 0.05; odds ratio 0.91; confidence interval 0.85-0.97).</p> <p><strong>Conclusions: </strong> In a population of apparently healthy subjects, exercise CI is common and phenotypes the progressive level of cardiovascular risk by a tight relationship with MCVRFs. CI patients exhibit some peculiar abnormal exercise gas exchange patterns (lower peak VO<sub>2</sub> and exercise oscillatory ventilation) and echo-derived measures (higher left atrium size and left ventricle mass) that may well anticipate evolution toward heart failure.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.