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10,006 results for “Random”
Fluxes project at North Temperate Lakes LTER: Random lake survey 2004
The overarching goal of this project is to understand carbon and nutrient cycles for a landscape on which terrestrial and freshwater systems are intimately connected in multiple and reciprocal ways. In the Northern Highlands region of Wisconsin, they are studying a spatially complex landscape in which water features make up almost half of the land area, with wetlands (27% of land surface) and lakes (13%) both prevalent throughout the region, interspersed in upland forests. Weather and limnological data from a set of 170 lakes in the NHLD samples summer 2004. The sampled lakes were from a random stratified subsample (N=300 of 7588 total) of all the lakes in the NHLD.
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
<p>The dataset consists of particulate matter pollution concentration, measured in three localities - Hermsdorf, Charlottenburg and Adlershof, in Berlin, Germany.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer_rd_30s.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer_rd_30s.geojson</a> shows the observed PM2.5 concentration in a 30 second interval.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> shows the concentrations shown is the local concentration (observed concentration - background concentration) in a 30 second interval. The background concentration is calculated as the lowest 5 percentile of the measured concentration for each measurement round. </p> <p><a href="../api/records/10076056/draft/files/PM2.5_lc_max.geojson/content" target="_blank" rel="noopener noreferrer">PM2.5_lc_max.geojson</a> contains the information from <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> in a 25m resolution. Additionally, it contains the land use information for each coordinate.</p> <p>The original publication providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany. MDPI Sensors, 24(13), 4193, DOI: 10.3390/s24134193. The paper is fully open access and can be downloaded at <a href="https://doi.org/10.3390/s24134193">https://doi.org/10.3390/s24134193</a>.</p> <p>Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .</p>
Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets
<p>The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms – AdaBoost and Random Forests – on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.</p>
Code for Random Forest models that predict pharmaceutical and water chemistry measurements in Baltimore Ecosystem Study streams
This file contains code to model the relationship between the water chemistry measurements and discharge measured as part of BES routine sampling and the pharmaceuticals measured in WY 2018. We use Random Forest models to predict 1) total (i.e., summed) concentration of the pharmaceuticals for which we screened, 2) total nutrient concentrations (TN & TP), 3) whether or not the antibiotic trimethoprim was detected in a given sample, and 4) whether or not nitrate and TP were above or below environmentally-relevant threshold concentrations. We also use RF models to predict N and P concentrations over a longer period, in order to compare models for nutrients to pharma. Code and analyses here rely on data processed in the file "BESPharma_WY2018.Rmd", published on EDI (doi:10.6073/pasta/610cb67fcbc8982c2af8ed946dce8ea5) and BES water chemistry data published on EDI (doi:10.6073/pasta/ce7f30e6013e003bfe28c5fd7d4aed23 )
Atlantic ghost crab (Ocypode quadrata) burrow counts at shorebird nests and randomly available sites on Metompkin Island, Virginia, 2022
Atlantic ghost crabs (Ocypode quadrata) are predators of beach-nesting shorebirds, their nests, and their chicks on the United States' Atlantic and Gulf coasts. Ghost crabs may also disturb birds, altering their foraging, habitat use, or nest and brood attendance patterns. Shorebird conservation strategies often involve predator and disturbance management to improve reproductive success, but efforts rarely target ghost crabs. Despite the threat to shorebird reproductive success, ghost crabs are a poorly understood part of the beach ecosystem and additional knowledge about ghost crab habitat selection is needed to inform shorebird conservation. We monitored ghost crab activity, defined as burrow abundance, throughout the shorebird breeding season on Metompkin Island, Virginia, an important breeding site for piping plovers (Charadrius melodus) and American oystercatchers (Haematopus palliatus). We counted burrows at shorebird nests and random points throughout the shorebird breeding season and tested whether ghost crab activity was greater at shorebird nest sites than random sites. We observed burrows at all nest sites in our study area (n = 63 nests), but found fewer burrows at nest sites than random sites. Ghost crabs may avoid shorebird nest sites due to aggressive defensive behaviors from incubating adults or differences in microhabitat characteristics selected by shorebirds versus ghost crabs. We also tested the effects of date, air temperature, habitat type, and shell cover on the abundance of ghost crab burrows. We found that while burrows were present across the barrier island landscape, there were more burrows in sandy habitats with sparse to little shell cover and in and behind the dunes relative to the beach and beach-front berm. Ghost crab activity increased later in the shorebird breeding season and as air temperature increased. Understanding when and where ghost crabs are most likely to be active in the landscape can aid decision-making to benefit imperi
Random Simple Undirected Weighted Graphs
<div> <div> <div> <div> <div> <div dir="auto"> <div> <div> <h3>Dataset Description</h3> <p>We introduce a dataset consisting of <em><strong>over 60 flow matrices</strong></em> representing <strong>simple</strong>, <strong>undirected</strong>, <strong>weighted</strong> <strong>graphs</strong>. This dataset is designed to support empirical studies in graph algorithms, clustering, and network analysis.</p> <p>Each graph is characterized by</p> <ul> <li> <p><strong>Order (|V|):</strong><br><span><span>{20, 50, 100, 300, 500, 700, 800, 900, 1000, 2000, 3000}</span></span></p> </li> <li> <p><strong>Density:</strong><br>For each graph order, 10 instances are generated with edge densities from the following set:<br><span><span>{0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0}</span></span></p> </li> <li> <p><strong>Clustered Structure:</strong><br>Nodes are partitioned into predefined clusters, where intra-cluster edges have significantly higher weights, while all inter-cluster edges are uniformly weighted (weight = 1). This structure simulates modular graphs commonly encountered in real-world networks.</p> </li> <li> <p><strong>Number of Clusters:</strong><br>The number of clusters varies according to graph order:<br><span><span>{5, 5, 10, 30, 50, 70, 80, 90, 100, 400, 500}</span></span></p> </li> </ul> <p>This dataset enables systematic testing of algorithms under varying structural conditions, including <strong>scale</strong>, <strong>sparsity</strong>, and <strong>community strength.</strong></p> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div dir="auto"> <div> <div> <p> </p> </div> </div> </div> </div> </div> </div> </div> </div>
Dataset: Effective T-matrix of a cylinder filled with a random 2D particulate
<p>This data is the one used in the paper "Effective T-matrix of a cylinder filled with a random 2D particulate", currently submitted to the Proceedings A of the Royal Society. A preprint version of this paper can be found at: https://arxiv.org/abs/2308.13338</p> <p>This dataset contains the numerically computed effective T-matrix of a cylinder filled with a random 2D particulate. Since the T-matrix is diagonal, only the diagonal elements T_n are computed. Values of T_n are provided for various set of parameters (frequency, particle type and volume fraction). Furthermore, for each set of parameters, T_n is computed with three different methods:</p> <p>1) The Monte Carlo method (MC), which requires computing the scattered field for several configurations of particles.</p> <p>2) The Effective Waves Method (EWM), based on results on random particulate materials. It provides a formula of the effective T-matrix with an effective wavenumber.</p> <p>3) A simplified version of the EWM when only monopole scattering is accounted for (EWM-MA). </p> <p>This dataset includes the following files: metadata_MC.csv and per each parameter one csv file with data records. The notations used in the headers of the files MCx.csv are described in the file header_notations.pdf.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Code and Data for "Anticoncentration and state design of random tensor networks"
<p>We investigate quantum random tensor network states where the bond dimensions scale polynomially with the system size, N. Specifically, we examine the delocalization properties of random Matrix Product States (RMPS) in the computational basis by deriving an exact analytical expression for the Inverse Participation Ratio (IPR) of any degree, applicable to both open and closed boundary conditions. For bond dimensions χ∼γN, we determine the leading order of the associated overlaps probability distribution and demonstrate its convergence to the Porter-Thomas distribution, characteristic of Haar-random states, as γ increases. Additionally, we provide numerical evidence for the frame potential, measuring the 2-distance from the Haar ensemble, which confirms the convergence of random MPS to Haar-like behavior for χ≫\sqrt{N}. We extend this analysis to two-dimensional systems using random Projected Entangled Pair States (PEPS), where we similarly observe the convergence of IPRs to their Haar values for χ≫\sqrt{N}. These findings demonstrate that random tensor networks with bond dimensions scaling polynomially in the system size are fully Haar-anticoncentrated and approximate unitary designs, regardless of the spatial dimension.</p>
LMD Inconel 718 Random Tracks 12 versions 2021-03-03
<p>Description of dataset 10.5281/zenodo.5607321</p> <p>12 versions of deposition of 4 "random pattern" tracks<br> Inconel 718 tracks with process parameters:<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Machine: Laserdyne 430<br> Power and velocity used in each experiment are described in the CNC file name (and g-code)</p> <p>The dataset is constituted by 12 .zip files (exp01.zip to exp12.zip), each one containing:</p> <p>- G-code part program, with name code Exp[n]_RandomTrack_4_rot_False_V[v]_P[p], where:<br> - [n] is the experiment index (1-12)<br> - [v] is the used deposition velocity (in mm/min)<br> - [p] is the used laser power (in W)<br> - trAll_ONEms.csv containing:<br> - t: timestamp in ms.<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power</p>
LMD Inconel 718 Random Tracks 2021-03-03 r2
<p>Description of dataset 10.5281/zenodo.5607279</p> <p>Deposition of 4 Inconel 718 random tracks with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocity = 600 (mm/min)<br> - Powder flux = 0.0825 (g/s)<br> - Spiral size: 60 mm side, 3 mm step<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Machine: Laserdyne 430</p> <p>The dataset is constituted by:<br> - Melt pool images, in file Deposition_2021_03_03__15_42_36.zip, acquired at 200fps with 850 nm narrow band filter, 1ms exposure time. 400x400 px size<br> - trAll.csv containing:<br> - t: timestamp in ms. Synchronized with imAll.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imAll.csv containing:<br> - t: timestamp in ms. Synchronized with trAll.csv<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)</p> <p>Additional source files:<br> - G-Code part program RandomTrack_0_rot_False.cnc<br> - Machine log file Scope 20210303-154513 RANDOMTRACK_r2.mos<br> - 3D scan of sample geometry randomTrack_r2_keyence_2_skip3_surface<br> - Sample photo 20210303_RandomTracks_r2.jpeg</p>
Chemical and Random Additive Noise Elimination (CRANE)
<p><strong>Improved identification and quantification of peptides in mass spectrometry data via chemical and random additive noise elimination (CRANE)</strong></p> <p><strong>Availability and implementation</strong></p> <p>The software is available on Github (<a href="https://github.com/CMRI-ProCan/CRANE">https://github.com/CMRI-ProCan/CRANE</a>). The datasets were obtained from ProteomeXchange (Identifiers—PXD002952 and PXD008651). Preliminary data and intermediate files are available via ProteomeXchange (Identifiers—PXD020529 and PXD025103).</p>
Stiffness of randomly sampled stainless steel frames under gravity and gravity plus wind load scenarios
<p>Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of vertical and lateral system stiffness values corresponding to different random samples of six different nominal stainless steel frames under gravity and gravity plus wind load combinations. The values of the random variable assignments are given for each case. </p> <p>The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states. Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425</p> <p>The data included in the dataset corresponds to the vertical & lateral stiffness of each frame under different load conditions.</p> <p>Although the data has been generated using the finite element software ABAQUS, no special software is required to read or interpret the data.</p>
Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data
<p>Live fuel moisture content (LFMC), defined as the mass of water in the foliage and small twigs relative to its total dry mass, is a key factor affecting fire potential and determining wildfire danger and activity. Fuel moisture is directly related to the amount of energy needed to evaporate water before ignition. Consequently, high moisture values reduce, or even inhibit, ignitability and subsequent fire spread.</p> <p>To cover the absence of a specific model to estimate LFMC for the Mediterranean Basin at the sub-continental scale, we built an empirical model based on Random Forests (LFMC<sub>RF</sub>) and combining MODIS spectral bands, vegetation indices, land surface temperature, and the day of year as predictors. The details on the modeling and validation methods, and the accuracy of the estimates are in the related publication <strong><a href="https://doi.org/10.3390/rs14133162">Cunill Camprubí et al., 2022</a></strong>.</p> <p>This dataset contains a collection of weekly LFMC maps from February 2000 to December 2021. The maps cover the Mediterranean and part of the Temperate biomes of the Mediterranean Basin. File <em>mapping_area_LFMC-RF_W-1.0.png</em> shows the target mapping areas.</p> <p>Metadata:</p> <ul> <li>Spectral Information: MODIS MCD43A4 C.6</li> <li>Land Surface Temperature: MODIS MOD11A2 C.6</li> <li>Land Cover Mask: MODIS MCD12Q1 C.6</li> <li>Coordinate Reference System: Native MODIS Sinusoidal</li> <li>Temporal Resolution: Weekly (W)</li> <li>Spatial Resolution: ~500 m</li> <li>File Format: NetCDF v.4</li> <li>Scale Factor: 0.01</li> </ul> <p>Fundings:</p> <p>The study was funded by the MICINN (RTI2018-094691-B-C31), European Union’s Horizon 2020-Research and Innovation Framework Programme under grant agreement no. 101003890 project FirEUrisk, the National Natural Science Foundation of China (U20A20179, 31850410483), and the talent proposals in Sichuan Province (2020JDRC0065) from Southwest University of Science and Technology (18ZX7131).</p>
Supplementary Report for the paper "A Preliminary Analysis on the Effect of Randomness in a CEGAR Framework"
<p> A supplementary report for the paper "A Preliminary Analysis on the Effect of Randomness in a CEGAR Framework" by Ákos Hajdu and Zoltán Micskei, presented at the 25th PhD Mini-Symposium (2018), organized by the Department of Measurement and Information Systems at the Budapest University of Technology and Economics.</p>
Data in: Aging power spectrum of membrane protein transport and other subordinated random walks
<p>Datasets generated in the report "Aging power spectrum of membrane protein transport and other subordinated random walks". Included data are:</p> <p><strong>Numerical simulations </strong><br> RWdata1.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata3.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata8.mat: 5,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8.<br> RWdataCTRW.mat: 10,000 realizations, continuous time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7.</p> <p><strong>Spectra of simulations</strong><br> PSDdata1.mat: Power spectral density (PSD) of a subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata3.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata8.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8. Four different realization times are used to compute the PDS: 2^15, 2^16, 2^17, and 2^18.<br> PSDs_CTRW.mat: PSD of a continuous-time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.</p> <p><strong>Experimental data of Nav1.6 channels in the soma of hippocampal neurons</strong><br> NavMSDtimes.csv: ensemble-averaged (EA) MSD and time-averaged (TA) MSD. The TA-MSD is measured for three observation times, 64, 128, and 256 frames (3.2, 6.4, and 12.8 s).<br> NavPSD.csv: Power spectral density (PSD) measured for three observation times, 64, 128, and 256 frames.</p>
TomoBreast randomized clinical trial's lung-heart outcomes and mortality through the 2020 COVID-19 pandemic: data and software
<p>Dataset and R script to reproduce the analyses of the manuscript:</p> <p>Vinh-Hung V, Gorobets O, Adriaenssens N, Van Parijs H, Storme G, Verellen D, Nguyen NP, Magne N, De Ridder M.</p> <p><strong>Lung-heart outcomes and mortality through the 2020 COVID-19 pandemic in a prospective cohort of breast cancer radiotherapy patients.</strong></p> <p>Cancers 2022; 14(24):6241. https:// doi.org/10.3390/cancers14246241</p> <p>https://www.mdpi.com/2072-6694/14/24/6241</p> <p>PubMed: PMID: 36551726</p> <p>PMCID: PMC9777311</p> <p>Info on the variables in file "aelq6_public.R"</p> <p>reproduced in "aelq_2_3_readme.txt":</p> <p>"aelq2_base2.txt" = baseline characteristics.</p> <p>"aelq3.txt" = longitudinal maesurements.</p> <p>Variables in "aelq2_base2.txt":</p> <p>"<strong>aelq2_base2.txt</strong>" = baseline characteristics. <br># Age at randomization, years. <br># RTdose: cf TomoBreast papers. <br># 51 Gy = hypofractionated, simultaneous integrated boost<br># 42 Gy = hypofractionated, no boost, mastectomy cases only<br># 50 Gy = conventional, no boost, mastectomy cases only<br># 66 Gy = conventional, sequential boost<br># Weight kg, Height cm, <br># Detection 1=found by screening (senology follow-up/controle)<br># 2=found by symptoms (pain, palpable)<br># 9=unknown<br># Smoker 0= Not smoker<br># 1= Smoker<br># 2=ex-smoker<br># Mastectomy (and other binary coded) 1= yes<br># chemosched 0=none<br># 1= planned after RT (sequential)<br># 2= prior to RT and is finished (sequential)<br># 3= chemo is on-going or is planned to start with RT (concomitant)<br># hormonetherapy 0=no<br># 1=tamoxifen (nolvadex)<br># 2=Femara (Letrozole)<br># 3=zoladex<br># 4=tamoxifen + zoladex<br># Laterality 1,=Right, 2=Left, 3=Bilateral<br># LengthFU: length of follow-up, days from randomization</p> <p>"<strong>aelq3.txt</strong>" = longitudinal maesurements.<br># "Nr" = Case ID<br># "Time" in days from origin (origin =date of randomization), <br># if negative =before randomization<br># "KPS" "Weight" <br># "Died" "LocalRec" "Metast" "NewPrim" = binary code, 0=no, 1=yes<br># "fAEBreast" "fAEHeart" "fAELung" "fAEOther" <br># fAE = freedom from breast, heart, lung, other adverse event score<br># "LVEF2" = ejection fraction, %<br># "MacIver" = estimated cardiac strain</p> <p># the following are pulmonary function tests, untransformed units<br># "FVC", "FEV1", "PEF", "VC", "TLC", "RV", "FRC", "Raw", "sRaw", "DLCO",<br># "VA", "PF"</p> <p># "fDY", "fFA", "fPA" = freedom from dyspnea, from fatigue, from pain<br># range 0 to 100 (best)<br># see papers:</p> <p># Van Parijs, H.; Vinh-Hung, V.; Fontaine, C.; Storme, G.; Verschraegen, C.;<br># Nguyen, D.M.; Adriaenssens, N.; Nguyen, N.P.; Gorobets, O.; De Ridder, M.<br># Cardiopulmonary-related patient-reported outcomes in a randomized clinical<br># trial of radiation therapy for breast cancer. BMC Cancer 2021, 21, 1177,<br># doi:10.1186/s12885-021-08916-z.</p> <p># preprint:<br># Van Parijs, H.; Cecilia-Joseph, E.; Gorobets, O.; Storme, G.; <br># Adriaenssens, N.; Heyndrickx, B.; Verschraegen, C.; Nguyen, N.P.;<br># De Ridder, M.; Vinh-Hung, V. Lung-heart toxicity in a randomized <br># clinical trial of hypofractionated image guided radiation therapy for<br># breast cancer. Preprints 2022, 202212, 0214.<br># https://doi.org/10.20944/preprints202212.0214.v1</p> <p># <br># "Year" = year of the observation<br># example: randomized 1/1/2011, measurement done 1/31/2011, time = 30 days,<br># Year =2011<br>#<br> </p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
Dataset for Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator
<p>The dataset contains all gathered data from the experiment from Wednesday, March 16, 2022 11:58:41.929 AM UTC+0 (1647431921929) until Sunday, April 3, 2022 1:08:35.353 PM UTC+0 (1648991315353). The experiment was executed during physical presence within the Arctic Circle in Tromsø, Norway 69° 40' 53.117'' N 18° 58' 36.027'' E at 35m elevation above sea level. The dataset was gathered with a prototype [1] based on the CREDO android application [2]. The main research is to use Ultra High Energy Cosmic Rays (UHECR) as an entropy source for a Random Bit Generator (RBG). </p> <p>The associated publication will probably have the title "Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator"</p> <p>In order to reproduce the results the SQLite3 database "mrng_arctic_experiment_2022.db" is needed. To get the visual representations of the detections use "image_decoding_and_codesnippets.py" to generate the cleaned (414 detections / ~15MB) or the uncleaned (5567 detections / ~195 MB) dataset. The compressed folder "raw_data_incl_space_weather.7z" contains all raw data as gathered with the MRNG prototype, unprocessed, uncleaned, and unmerged. </p> <p> </p> <p>[1] https://github.com/StefanKutschera/mrng-prototype, visited on 27.03.2023</p> <p>[2] https://github.com/credo-science/credo-detector-android, visited on 27.03.2023</p>
First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials
<p><strong>Abstract</strong></p> <p>Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML.</p> <p> </p> <p><strong>Methods</strong></p> <p>The upload contains two zip files and a .json file.</p> <ul> <li>plain.html.zip</li> </ul> <p>Original corpus (n = 10) in .html format. The corpus was generated using the methodology described in the paper. Each .html file could be opened in any default text editor in any operating system or browser. A .html contains full text divided into several annotatable text parts. </p> <p> </p> <ul> <li>ann.json.zip</li> </ul> <p>The .zip contains RoB annotations conducted by the authors (R.H., M.S., K.G., R.C.). The annotation files are in .json format. Each .json is divided into two JSON objects and three JSON arrays. </p> <ol> <li>annotatable (object): Parts from the full-text document corresponding to the text parts from the plain .html files. </li> <li>metas (object): full-text document label</li> <li>entities (array): contains labelled entities. Each entity is linked to which part of the full-text it is linked to.</li> <li>relations (array)</li> <li>sources (array)</li> </ol> <p> </p> <ul> <li>annotations-legend.json</li> </ul> <p>This .json file contains entity and entity labels encoded to text legends. For example, entity class label "1_2_Yes_Good" is encoded as "e_113".</p> <p> </p> <p><strong>Resources</strong></p> <p>The code to parse annotations can be found on <a href="http:// https://github.com/anjani-dhrangadhariya">GitHub</a>.</p> <p> </p> <p><strong>Funding</strong></p> <p>HES-SO Valais-Wallis, Sierre, Switzerland</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.