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.
10,006
datasets available to search
ShareScore release 0.9.0
Dataset results
10,006 results for “randomness”
Dataset for "A randomized benchmarking suite for mid-circuit measurements"
<p>Dataset and simulation code to generate the results of "A randomized benchmarking suite for mid-circuit measurements".</p>
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 1000 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed.</p>
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 100 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed</p>
THREE-DIMENSIONAL VOLUMETRIC EVALUATION OF ROOT RESORPTION IN MAXILLARY ANTERIORS FOLLOWING EN-MASSE RETRACTION WITH VARYING FORCE VECTORS - A RANDOMIZED CONTROL TRIAL
<p>To assess the severity of root resorption (RR) during retraction of maxillary anteriors with and without skeletal anchorage (three different force systems); and analyze it comprehensively using cone-beam computed tomography (CBCT) superimpositions.</p>
Gastric Residual Volume, Safety and Effectiveness of Drinking 250ml Glucose Solution 2-3 Hours before Surgery in Gastric Cancer Patients: A Multicenter, Single-blind, Randomized Controlled Trial
<p><strong>Supplementary Figure 1</strong></p> <p> </p> <p>A. Change of QUICKI level after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that the effect of time ( p=0.004) was significant, but no significant differences were noted for group(P=0.600) or group*time(P=0.963).</p> <p>B. Change of Recovery Level of QUICKI after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that no significant differences were noted for group(P=0.600) ,time (P=0.235) or group×time(P=0.963).</p> <p>C. Change of WBC level after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that the effect of time ( p<0.001) was significant, and no significant differences were noted for group(P=0.392) or group×time(P=0.314).</p> <p>D. Change of CRP level after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that the effect of time ( p<0.001) was significant, but no significant differences were noted for group(P=0.312) or group × time(P=0.826).</p>
Comparing mixed models and Random Forest association tests using naturalGWAS and a Striped Bass SNP dataset
<p>In this study, we used the phenotype simulation package naturalGWAS to test the performance of Zhao's Random Forest method in comparison to an uncorrected Random Forest test, latent factor mixed models (LFMM), genome-wide efficient mixed models (GEMMA), and confounder adjusted linear regression (CATE). We created 400 sets of phenotypes, corresponding to five effect sizes and 2, 5, 15, or 30 causal loci, simulated from two empirical datasets containing SNPs from Striped Bass representing three and 13 populations. All association methods were evaluated for their ability to detect genotype-phenotype associations based on power, false discovery rates, and number of false positives. Genomic inflation was highest for uncorrected Random Forest and LFMM tests and lowest for Gemma and Zhao's Random Forest. All association tests had similar power to detect causal loci, and Zhao's Random Forest had the lowest false discovery rate in all scenarios. To measure the performance of association tests in small datasets with few loci surrounding a causal gene we also ran analyses again after removing causal loci from each dataset. All association tests were only able to find true positives, defined as loci located within 30k bp of a causal locus, in 3%–18% of simulations. In contrast, at least one false positive was found in 17%–44% of simulations. Zhao's Random Forest again identified the fewest false positives of all association tests studied. The ability to test the power of association tests for individual empirical datasets can be an extremely useful first step when designing a GWAS study.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Dataset for "Definition and rationale for placebo composition: Cross-sectional analysis of randomized trials and protocols published in high-impact medical journals"
<p>Contains our data extraction sheets</p>
Numerical analysis of working paper "Joint integrated production-maintenance policy of a deteriorating equipment considering random yield and maintenance delay"
<p>This is the numerical analysis code using in the working paper "Joint integrated production-maintenance policy of a</p> <p>deteriorating equipment considering random yield and maintenance delay"</p>
Recent HIV infections among newly diagnosed individuals living with HIV in rural Lesotho: Secondary data from the VIBRA cluster-randomized trial
<p>These are pseudo-anonymised data from a secondary analysis of the VIBRA randomised trial.</p> <p>HIV recency assays are used to distinguish recently acquired infection from long-term infection among individuals newly diagnosed with HIV. Since 2015, the World Health Organisation recommends the use of an algorithm to assess recency of infections which is based on an HIV recency assay and viral load (VL) quantification. We determined the proportion of recent HIV infections among participants of the VIBRA (Village-Based Refill of Antiretroviral therapy) cluster-randomized trial in Lesotho and assessed risk factors for these recent infections.</p> <p>The VIBRA trial recruited individuals living with HIV and not taking antiretroviral therapy during a door-to-door HIV testing campaign in two rural districts (Butha-Buthe and Mokhotlong). Samples were collected from participants newly diagnosed and tested for HIV recency using the Asanté HIV-1 Rapid Recency Assay and VL using the Roche Cobas System. Clinical and socio-demographic data were extracted from the trial database. Univariate analysis was conducted to determine factors associated with recent compared to long-term infection.</p> <p>There is one dataset containing all data presented in this dtuy. The data codebook explains the data available in the dataset.</p>
Eurasian lynx GLCs' characteristics for classification with random forest algorithm
<p><span>Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill site identification, such as failing to detect GLCs that are kill sites and misclassifying the generated GLCs (e.g. kill for non-kill) that were not field-checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx, an apex predator of conservation concern in Europe, in three multi-prey systems, with different combinations of wild, semi-domestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affect the detection of GLCs indicating kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as non-kills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced to from 7 to 3 fixes/night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24-h decreased the probability of detecting GLCs connected with kill sites, particularly those of semi-domestic or domestic prey, and small prey. Random forest successfully predicted between 73%-90% of ungulate kills but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm's overall accuracy. We provide a set of recommendations for studies focusing on kill site detection, which can be considered for other large carnivore species besides the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.</span></p>
9gag logo [Random]
This is the 9gag logo, created using Blender. The .zip archive contains the .blend file and two .png renders (one for the Blender internal motor, the other one for the Cycles render motor). Enjoy ! :) Source: Objaverse 1.0 / Sketchfab
Random Street
This i my first scenary design. Just a random japanese-korean street? It is made in Maya using modules. Source: Objaverse 1.0 / Sketchfab
iterative Random Forests data and analyses
<p>This repository contains scripts to run the simulations and case studies described in: <em>iterative Random Forests to discover predictive and stable high-order interactions</em>.</p>
Random catalog of stars from LSST Simulations
<p>A random catalog of stars drawn from the LSST Simulations database. Columns are:</p> <p>RA (degrees)</p> <p>Dec (degrees)</p> <p>u (extincted magnitudes)</p> <p>g (extincted magnitudes)</p> <p>r (extincted magnitudes)</p> <p>i (extincted magnitudes)</p> <p>z (extincted magnitudes)</p> <p>y (extincted magnitudes)</p> <p>E(B-V) (magnitudes)</p> <p> </p> <p>Note: because this simulation was done for LSST, there are no stars north of Dec~35 degrees</p>
Data for: Inter-rater reliability of risk of bias tools for non-randomized studies
<p><strong>PURPOSE:</strong> Currently, there is limited knowledge about the reliability of risk of bias (ROB) tools for assessing internal validity in systematic reviews of exposure and frequency studies. We aimed to identify and then compare the inter-rater reliability (IRR) of six commonly used tools for frequency (Loney scale, Gyorkos checklist, American Academy of Neurology [AAN] tool) and exposure (Newcastle-Ottawa scale, SIGN50 checklist, AAN tool) studies.</p> <p><strong>METHODS:</strong> Six raters independently assessed the ROB of 30 frequency and 30 exposure studies using the 3 respective ROB tools. Articles were rated on a 3-level summary measure of ROB (low, intermediate, or high). We calculated an intraclass correlation coefficient (ICC) for each tool and category of ROB tool. We compared the IRR between ROB tools and tool type by inspection of overlapping ICC 95% CIs and by comparing their coefficients after transformation to Fisher Z values. We assessed criterion validity of the AAN ROB tools by calculating an ICC for each rater in comparison with the original ratings from the AAN.</p> <p><strong>RESULTS:</strong> All individual ROB tools had an IRR in the substantial range or higher (ICC point estimate = 0.61-0.80). The IRR was almost perfect (ICC point estimate > 0.80) for the AAN frequency tool and the SIGN50 checklist. All tools were comparable in IRR, except for the AAN frequency tool which had a significantly higher ICC than the Gyorkos checklist (p=0.021) and trended towards a higher ICC when compared to the Loney scale (p=0.085). When examined by category of ROB tool, scales and checklists had a substantial IRR, whereas the AAN tools had an almost perfect IRR. For the criterion validity of the AAN ROB tools, the average agreement between our raters and the original AAN ratings was moderate.</p> <p><strong>CONCLUSION:</strong> All tools had substantial IRR except for the AAN frequency tool and the SIGN50 checklist, which both had an almost perfect IRR. The AAN ROB tools were the only category of ROB tool to demonstrate an almost perfect IRR. This category of ROB tool had fewer and more simple criteria. Overall, parsimonious tools with clear instructions, such as those from the AAN, may provide more reliable ROB assessments.</p>
Effect Of Pulsed Electromagnetic Field And Microwave Therapy On Pain And Physical Function In Older Adults With Knee Osteoarthritis: A Randomized Clinical Trial.
<p><strong><span>Background and purpose:</span></strong><span> </span><span>Pulsed electromagnetic field (PEMF) therapy and microwaves (MW) are two electrotherapy modalities that have shown to improve pain and function in patients with knee osteoarthritis (KOA). Nevertheless, the effectiveness of these therapies is controversial due to diversity in the application parameters and treatment protocols in these patients. The objective is to compare the effectiveness of active PEMF versus MW, as well as sham PEMF, in addressing pain and improving functionality for treating KOA.</span></p> <p><strong><span>Methods:</span></strong><span> </span><span>Double-blind, placebo-controlled, randomized clinical trial. Participants diagnosed with KOA were assigned to an intervention combining an exercise program with active PEMF, MW, or sham PEMF, delivered in three weekly sessions over four weeks. The main outcomes were pain, reported on a 0–10 cm visual analogue scale (VAS), and functionality, as evaluated with the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire, and the Timed Up and Go test. The outcomes were measured at pre-intervention, immediately post-intervention, and one and four months after the intervention.</span></p>
Evaluation of comparative efficacy of Babool Vati versus Tablet Metformin in the Management of Type 2 Diabetes Mellitus (Prameha)- a randomized controlled trial protocol
Open the record for dataset details and reuse information.
Can ingredients based forecasting be learned? Disentangling a random forest's severe weather predictions
<p>Machine learning (ML)-based models have been rapidly integrated into forecast practices across the weather forecasting community in recent years. While ML tools introduce additional data to forecasting operations, there is a need for explainability to be available alongside the model output, such that the guidance can be transparent and trustworthy for the forecaster. This work makes use of the algorithm tree interpreter (TI) to disaggregate the contributions of meteorological features used in the Colorado State University Machine Learning Probabilities (CSU-MLP) system, a random forest-based ML tool that produces real-time probabilistic forecasts for severe weather using inputs from the Global Ensemble Forecast System v12. TI feature contributions are analyzed in time and space for CSU-MLP day-2 and 3 individual hazard (tornado, wind, and hail) forecasts and day-4 aggregate severe forecasts over a 2-yr period. For individual forecast periods, this work demonstrates that feature contributions derived from TI can be interpreted in an ingredients-based sense, effectively making the CSU-MLP probabilities physically interpretable. When investigated in an aggregate sense, TI illustrates that the CSU-MLP system's predictions use meteorological inputs in ways that are consistent with the spatiotemporal patterns seen in meteorological fields that pertain to severe storms climatology. This work concludes with a discussion on how these insights could be beneficial for model development, real-time forecast operations, and retrospective event analysis.</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.