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Figure 6 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 6. Frequency distributions of the change in observed diagnosability (x-axis) in the simulated data for increasing levels of the probability of misstratification (vertical panels). Figures on the left and right columns are censored by data sets for original diagnosability ≤50% and>50%, respectively.
Figure 5 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 5. Two-dimensional GAM fits of theta (Ɵ), number of migrants (Nem), and divergence time in generations (T) from Model 2 simulated data. From left to right, columns show results from models without migration (m = 0), with migration and Nem <1, and Nem ≥ 1. Colors indicate model prediction of percent correctly classified.
Figure 4 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 4. GAM fit of number of migrants (Nem) from Model 2 parameters. Solid line shows median value of predicted percent correctly classified, and shaded area shows 95% CI. The switch from bimodal distribution to a normal distribution occurs at Nem = 1 (log10Nem = 0).
Figure 3 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 3. Two-dimensional GAM fits of effective population size (Ne), divergence time in generations (T), and mutation rate (µ) from Model 1 simulated data. Results from models without migration to the left and those with migration to the right. Colors indicate model prediction of percent correctly classified.
Figure 1 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 1. (A) Distribution of a hypothetical character for two putative subspecies (red and blue) demonstrating minimum overlap necessary to satisfy 75% rule of Amadon (1949). Character is continuous on the x-axis. Dashed lines indicate the point at which 75% of each distribution is outside of 99%+ of the other. Solid line indicates point of overlap where 97% of both distributions are outside one another. (B) Probability of membership to subspecies for specimens having values along the character axis. Probability is based on the ratio of the distribution frequencies at each point along the x-axis, with a 50:50 probability occurring at the threshold point.
Summary statistics from "Sex-Specific Causal Relations between Steroid Hormones and Obesity—A Mendelian Randomization Study"
<p>GWAMA summary statistics of four steroid hormone levels and one steroid hormone ratio using fixed-effect model.</p> <p>When using this data, please cite: Pott J, Horn K, Zeidler R, et al.. Sex-Specific Causal Relations between Steroid Hormones and Obesity - A Mendelian Randomization Study. <em>Metabolites</em> <strong>2021</strong>, <em>11</em>, 738. https://doi.org/10.3390/metabo11110738</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>ea (effect allele)</li> <li>oa (other allele)</li> <li>eaf (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>
Dataset accompanying the journal article: Bouldering psychotherapy is not inferior to cognitive behavioural therapy in the group treatment of depression: A randomized controlled trial
<p>SPSS-Dataset containing the (not-imputed) raw data for the non-inferiority trial on BPT vs. CBT. All personal data removed. Only data of participants of the CBT or BPT group included.</p>
Random Sample of Open Source Ventilators
<p>This is a random sample of references to open source ventilators based on the data published by the PubInv project (https://github.com/PubInv/covid19-vent-list CC0-1.0). It was used to verify technology and documentation readiness scales introduced in a CIRP design conference paper. This upload shall make the data set used for the conference paper publicly accessible.</p>
Random sample of habitat suitability for wolves in Scotland
<p>A rule-based habitat suitability model was created for wolves (<em>Canis lupus</em>) in mainland Scotland. Six variations of the model were run, in order to test sensitivity to changes in input values. In order to test for difference in the outputs of the six models, 500 random points were sampled from all six models, and then a test for statistical difference performed. This dataset constitutes the values of the 500 random points, where 1 indicates complete suitability and 0 indicates complete unsuitability.</p>
Data for "Random forest-based modeling of stream nutrients at national level in a data-scarce region"
<p>The aim of the study was to model annual total nitrogen (TN) and total phosphorus (TP) concentrations at national level using an ML approach. We used water quality data originating from the Environmental Monitoring Database KESE to train RF models for nutrient concentration prediction in 242 catchments across Estonia. A total of 82 environmental variables were used as predictors in the models. In order to yield the best results, a feature selection strategy along with hyperparameter optimization was performed when building the models. The models are applicable for predicting nutrient loads on an annual level, e.g. for the purpose of reporting national level water quality statistics in regional projects, such as HELCOM. The results showed that this relatively basic RF modeling approach can have a performance similar to process-based models. Moreover, these models are easier to reuse and apply on a larger scale, since the required inputs can be derived from freely available datasets (e.g. satellite imagery)</p> <p>This repository contains the input data used for building the RF models and the files describing the modeling results.</p> <p>The description of the files is given in the README.txt file.</p> <p>Virro, H., Kmoch, A., Vainu, M. and Uuemaa, E., 2022. Random forest-based modeling of stream nutrients at national level in a data-scarce region. Science of The Total Environment, 840, p.156613.</p> <p><a href="https://doi.org/10.1016/j.scitotenv.2022.156613">https://doi.org/10.1016/j.scitotenv.2022.156613</a></p>
Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model
<p>This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy.</p> <p>Wouter Meijninger<sup>1</sup>, Berien Elbersen<sup>1</sup>, Michiel van Eupen<sup>1</sup>, Stephan Mantel<sup>2</sup>, Pilar Ciria Ciria<sup>3</sup>, Andrea Parenti<sup>4</sup>, Marina Sanz Gallego<sup>3</sup> and Paloma Perez Ortiz<sup>3</sup>, Marco Acciai<sup>4</sup>,and Andrea Monti<sup>4</sup><br> Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University,</p> <p><strong>Abstract (Manuscript)</strong></p> <p>In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect.</p> <p><strong>Readme data files</strong></p> <p><em>Coherence_quarterly_statisitcs_2017_to_2020.zip</em></p> <p>Radar coherence quarterly statistics - Albacete (Spain)</p> <p>Radar coherence data is based on Sentinel-1B<br> Period: 2017 to 2020</p> <p>File naming (.tif files) per year (<em>YYYY</em>):</p> <ul> <li>Mean coherence: <em>mean_YYYY_1to4.tif</em></li> <li>Standard deviation coherence: <em>std_YYYY_1to4.tif</em></li> <li>Range coherence: <em>range_YYYY_1to4.tif</em></li> <li>Mean delta coherence: <em>mean_delta_YYYY_1to4.tif</em></li> <li>Standard deviation delta coherence: <em>std_delta_YYYY_1to4.tif</em></li> <li>Maximum delta coherence: <em>max_delta_YYYY_1to4.tif</em></li> </ul> <p>Each file consists of 4 bands:</p> <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> <p>Statistics are based on radar coherence data, which is scaled between >0 and 1<br> No data: 0-values</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>SIGPAC_data_Albacete_2018_to_2020.zip</em></p> <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> <p>More than 5 year fallow (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> <p>Pixel values:<br> 0: Not fallow<br> 1: Fallow more than 5 years</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p>Land Use Land Cover LULC (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> <p>Pixel values:</p> <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Annual_unused_used_land_maps_Albacete_2017_to_2020.zip</em></p> <p>Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model<br> Period: 2017-2020<br> File naming (ENVI files):</p> <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> <p>Pixel values:<br> 0 - Used (and/or Nan)<br> 1 - Unused</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Four_year_abandoned_land_Albacete_2017_to_2020.zip</em></p> <p>Four-year abandonment map is based on the 4 annual unused/used land maps<br> File naming (ENVI):</p> <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> <p>Pixel values:<br> 0 - (Nan)<br> 1 - Used (1 year unused in period 2017 - 2020)<br> 2 - Used (2 year unused in a row in period 2017 - 2020)<br> 3 - Abandoned (3 year unused in a row in period 2017 - 2020)<br> 4 - Abandoned (4 year unused in a row in period 2017 - 2020)</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</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>
Early Virtual-Reality-Based Home Rehabilitation after Total Hip Arthroplasty: A Randomized Controlled Trial
<p>The benefits of early virtual-reality-based home rehabilitation following total hip arthroplasty (THA) have not yet been assessed. The aim of this randomized controlled study was to compare the efficacy of early rehabilitation via the Virtual Reality Rehabilitation System (VRRS) versus traditional rehabilitation in improving functional outcomes after THA. Subjects were randomized either to an experimental (VRRS; n = 21) or a control group (control; n = 22). All participants were invited to perform a daily home exercise program for rehabilitation after THA with different administration methods—namely, an illustrated booklet for the control group and a tablet with wearable sensors for the VRRS group. The primary outcome was the hip disability (HOOS JR). Secondary outcomes were the level of independence and the degree of global perceived effect of the rehabilitation program (GPE). Outcomes were measured before surgery (T0) and at the 4th (T1), 7th (T2), and 15th (T3) day after surgery. Mixed-model ANOVA showed no significant group effect but a significant effect of time for all variables (<em>p </em>< 0.001); no differences were observed in HOOS JR between VRRS and the control at T0, T1, T2, or T3. Further, no differences in the level of independence were found between VRRS and the control, whereas the GPE was higher at T3 in VRSS compared to the control (4.76 ± 0.43 vs. 3.96 ± 0.65; <em>p </em>< 0.001). Virtual-reality-based home rehabilitation resulted in similar improvements in functional outcomes with a better GPE compared to the traditional rehabilitation program following THA. The application of new technologies could offer novel possibilities for service delivery in rehabilitation.</p>
Fig. 1 in Molecular Characterization Of Lates Niloticus (Perciformes, Latidae) Populations From Three Nigerian Waterbodies Using Random Amplified Polymorphic Dna And Microsatellite Markers
Fig. 1. Map showing the sample locations of L. niloticus (Linnaeus, 1758). Population 1 — Kainji lake, Population 2 — River Benue, Makurdi and Population 3 — Ikere-Gorge reservoir, Iseyin, Oyo state.
Video-based learning of coping strategies for common errors improves laparoscopy training - a randomized study
<p>Instructional video material from the doctoral thesis "Introducing a coping role model to increase the learning efficiency in learning laparoscopic knot tying - a randomised controlled trial" by Ms Annabelle Gerhäuser at Heidelberg University, Medical School.</p> <p>Developing the skills needed to perform minimally invasive procedures requires intensive training of trainee surgeons. Laparoscopic knot tying is a particularly challenging technique. Subjects of the study learned this technique with the help of instructional videos developed specifically for this purpose. These "coping videos" show typical mistakes made by beginners and the corresponding solution strategies. They have been validated before use in the above-mentioned study.</p> <p>Coping Video 1 - Needle Load</p> <p>Coping Video 2 - Handling & Tissue</p> <p>Coping Video 3 - Tail length</p> <p>Coping Video 4 - Difficulties in knot tying</p> <p>Coping Video 5 - Lift & drift</p> <p> </p>
Ehrhart series coefficients and quasi-period for random rational polytopes
<p><strong>Ehrhart series coefficients and quasi-period for random rational polytopes</strong></p> <p>A dataset of Ehrhart data for 84000 randomly generated rational polytopes, in dimensions 2 to 4, with quasi-periods 2 to 15.</p> <p>The polytopes used to generate this data were produced by the following algorithm:</p> <ol> <li>Fix <span class="math-tex">\(d\)</span> a positive integer in <span class="math-tex">\(\{2,3,4\}\)</span>.</li> <li>Choose <span class="math-tex">\(r\in\{2,\ldots,15\}\)</span> uniformly at random.</li> <li>Choose <span class="math-tex">\(d + k\)</span> lattice points <span class="math-tex">\(\{v_1,\ldots,v_{d+k}\}\)</span> uniformly at random in a box <span class="math-tex">\([-5r,5r]^d\)</span>, where <span class="math-tex">\(k\)</span> is chosen uniformly at random in <span class="math-tex">\(\{1,\ldots,5\}\)</span>.</li> <li>Set <span class="math-tex">\(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\)</span>. If <span class="math-tex">\(\mathrm{dim}(P)\)</span> is not equal to <span class="math-tex">\(d\)</span> then return to step 3.</li> <li>Choose a lattice point <span class="math-tex">\(v\in P \cap \mathbb{Z}^d\)</span> uniformly at random and replace <span class="math-tex">\(P\)</span> with the translation <span class="math-tex">\(P-v\)</span>.</li> <li>Replace <span class="math-tex">\(P\)</span> with the dilation <span class="math-tex">\(P/r\)</span>.</li> </ol> <p>The final dataset was produced by first removing duplicate records, and then downsampling to a subset with 2000 datapoints for each pair <span class="math-tex">\((d,q)\)</span>, where <span class="math-tex">\(d\)</span> is the dimension of <span class="math-tex">\(P\)</span> and <span class="math-tex">\(q\)</span> is the quasi-period of <span class="math-tex">\(P\)</span>, with <span class="math-tex">\(d\in\{2,3,4\}\)</span> and <span class="math-tex">\(q\in\{2,\ldots,15\}\)</span>.</p> <p>For details, see the paper:</p> <p> <em>Machine Learning the Dimension of a Polytope</em>, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p> doi:10.5281/zenodo.6614829</p> <p><strong>quasiperiod.txt.gz</strong><br> The file "quasiperiod.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 84000 records in the file.</p> <p><strong>Example record</strong><br> ULID: 01G57JBYP2ZW825E0NT4Q9JQNQ<br> Dimension: 2<br> Quasiperiod: 2<br> Volume: 97<br> EhrhartDelta: [1,50,195,289,192,49]<br> Ehrhart: [1,50,198,...]<br> LogEhrhart: [0.000000000000000000000000000000,3.91202300542814605861875078791,5.28826703069453523626966617327,...]</p> <p>(The values for Ehrhart and LogEhrhart in the example have been truncated.)</p> <p>For each polytope <span class="math-tex">\(P\)</span> of dimension <span class="math-tex">\(d\)</span> and quasi-period <span class="math-tex">\(q\)</span> we record the following keys and values in the dataset:</p> <p>ULID: A randomly generated string identifier for this record.<br> Dimension: A positive integer. The dimension <span class="math-tex">\(2 \leq d \leq 4\)</span> of the polytope <span class="math-tex">\(P\)</span>.<br> Quasiperiod: A positive integer. The quasi-period <span class="math-tex">\(2 \leq q \leq 15\)</span> of the polytope <span class="math-tex">\(P\)</span>.<br> Volume: A positive rational number. The lattice-normalised volume <span class="math-tex">\(\mathrm{Vol}(P)\)</span> of the polytope <span class="math-tex">\(P\)</span>.<br> EhrhartDelta: A sequence <span class="math-tex">\([1,a_1,a_2,\ldots,a_N]\)</span> of integers of length <span class="math-tex">\(N + 1\)</span>, where <span class="math-tex">\(N := q(d + 1) - 1\)</span>. This is the Ehrhart <span class="math-tex">\(\delta\)</span>-vector (or <span class="math-tex">\(h^*\)</span>-vector) of <span class="math-tex">\(P\)</span>. The Ehrhart series <span class="math-tex">\(\mathrm{Ehr}(P)\)</span> of <span class="math-tex">\(P\)</span> is given by the power-series expansion of <span class="math-tex">\((1 + a_1t + a_2t^2 + \ldots + a_Nt^N) / (1 - t^q)^{d+1}\)</span>.<br> Ehrhart: A sequence <span class="math-tex">\([1,c_1,c_2,\ldots,c_{1100}]\)</span> of positive integers. The value <span class="math-tex">\(c_i\)</span> is equal to the number of lattice points in the <span class="math-tex">\(i\)</span>-th dilation of <span class="math-tex">\(P\)</span>, that is, <span class="math-tex">\(c_i = \#(iP \cap \mathbb{Z}^d)\)</span>. Equivalently, <span class="math-tex">\(c_i\)</span> is the coefficient of <span class="math-tex">\(t^i\)</span> in <span class="math-tex">\(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_Nt^N) / (1 - t^q)^{d+1}\)</span>.<br> LogEhrhart: A sequence <span class="math-tex">\([0,y_1,y_2,\ldots,y_{1100}]\)</span> of non-negative floating point numbers. Here <span class="math-tex">\(y_i := \log c_i\)</span>.</p>
Ehrhart series coefficients for random lattice polytopes
<p><strong>Ehrhart series coefficients for random lattice polytopes</strong></p> <p>A dataset of Ehrhart data for 2918 randomly generated lattice polytopes, in dimensions 2 to 8.</p> <p>The polytopes used to generate this data were produced by the following algorithm:</p> <ol> <li>Fix <span class="math-tex">\(d\)</span> a positive integer in <span class="math-tex">\(\{2,\ldots,8\}\)</span>.</li> <li>Choose <span class="math-tex">\(d + k\)</span> lattice points <span class="math-tex">\(\{v_1,\ldots,v_{d+k}\}\)</span> uniformly at random in a box <span class="math-tex">\([-5,5]^d\)</span>, where <span class="math-tex">\(k\)</span> is chosen uniformly at random in <span class="math-tex">\(\{1,\ldots,5\}\)</span>.</li> <li>Set <span class="math-tex">\(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\)</span>. If <span class="math-tex">\(\mathrm{dim}(P)\neq d\)</span> then return to step 2.</li> </ol> <p>The final dataset has duplicate records removed. The data is distributed by dimension <span class="math-tex">\(d\)</span> as follows:</p> <table> <tbody> <tr> <th scope="row">d</th> <td>2</td> <td>3</td> <td>4</td> <td>5</td> <td>6</td> <td>7</td> <td>8</td> </tr> <tr> <th scope="row">#</th> <td>431</td> <td>787</td> <td>812</td> <td>399</td> <td>181</td> <td>195</td> <td>113</td> </tr> </tbody> </table> <p>For details, see the paper:</p> <p> <em>Machine Learning the Dimension of a Polytope</em>, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p> doi:10.5281/zenodo.6614821</p> <p><strong>dimension.txt.gz</strong><br> The file "dimension.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 2918 records in the file.</p> <p><strong>Example record</strong><br> ULID: 1FTU9VGPXXU82CTDGD6WYMBF9<br> Dimension: 3<br> Volume: 342<br> EhrhartDelta: [1,70,223,48]<br> Ehrhart: [1,74,513,...]<br> LogEhrhart: [0.000000000000000000000000000000,4.30406509320416975378532779249,6.24027584517076953419476314266,...]</p> <p>(The values for Ehrhart and LogEhrhart in the example have been truncated.)</p> <p>For each polytope <span class="math-tex">\(P\)</span> of dimension <span class="math-tex">\(d\)</span> we record the following keys and values in the dataset:</p> <p>ULID: A randomly generated string identifier for this record.<br> Dimension: A positive integer. The dimension <span class="math-tex">\(2 \leq d \leq 8\)</span> of the polytope <span class="math-tex">\(P\)</span>.<br> Volume: A positive integer. The lattice-normalised volume <span class="math-tex">\(\mathrm{Vol}(P)\)</span> of the polytope <span class="math-tex">\(P\)</span>.<br> EhrhartDelta: A sequence <span class="math-tex">\([1,a_1,a_2,\ldots,a_d]\)</span> of integers of length <span class="math-tex">\(d + 1\)</span>. This is the Ehrhart <span class="math-tex">\(\delta\)</span>-vector (or <span class="math-tex">\(h^*\)</span>-vector) of <span class="math-tex">\(P\)</span>. The Ehrhart series <span class="math-tex">\(\mathrm{Ehr}(P)\)</span> of <span class="math-tex">\(P\)</span> is given by the power-series expansion of <span class="math-tex">\((1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\)</span>. In particular, <span class="math-tex">\(\mathrm{Vol}(P) = 1 + a_1 + a_2 + \ldots + a_d\)</span>.<br> Ehrhart: A sequence <span class="math-tex">\([1,c_1,c_2,\ldots,c_{1100}]\)</span> of positive integers. The value <span class="math-tex">\(c_i\)</span> is equal to the number of lattice points in the <span class="math-tex">\(i\)</span>-th dilation of <span class="math-tex">\(P\)</span>, that is, <span class="math-tex">\(c_i = \#(iP \cap \mathbb{Z}^d)\)</span>. Equivalently, <span class="math-tex">\(c_i\)</span> is the coefficient of <span class="math-tex">\(t^i\)</span> in <span class="math-tex">\(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\)</span>.<br> LogEhrhart: A sequence <span class="math-tex">\([0,y_1,y_2,\ldots,y_{1100}]\)</span> of non-negative floating point numbers. Here <span class="math-tex">\(y_i := \log c_i\)</span></p>
Data and code for Scale-invariant random geometry from mating of trees: a numerical study
<p>This entry contains the source code, simulation data and data analysis notebooks for the paper</p> <p>T. Budd, A. Castro, "Scale-invariant random geometry from mating of trees: a numerical study." <a href="https://arxiv.org/abs/2207.05355">arXiv:2207.05355</a>.</p> <p>This work is part of START-UP 2018 programme with project number 740.018.017, which is financed by the Dutch Research Council (NWO).</p>
A multicenter randomized phase 4 trial comparing Sodium Picosulphate plus Magnesium Citrate vs Polyethylene Glycol plus Ascorbic Acid for bowel PREparation before COLonoscopy. The PRECOL trial.
<p>This is the database for final analysis of the PRECOL clinical trial, whose abstract follows</p> <p>Background<strong>.</strong> Adequate bowel preparation before colonoscopy is crucial. Unfortunately, up to 25% of all colonoscopies have inadequate bowel cleansing. From a patient perspective, bowel preparation is a main obstacle for colonoscopy. Several low-volume bowel preparations have been formulated to provide more tolerable purgative solutions without loss of efficacy.</p> <p>Methods: In this phase 4, randomized, multicenter, two-arm trial, adult outpatients undergoing colonoscopy received either Sodium Picosulphate plus Magnesium Citrate (SPMC) or Polyethylene Glycol plus Ascorbic Acid (PEG-ASC) for bowel preparation. The primary aims were to test quality of bowel cleansing (primary endpoint, scored according the Boston Bowel Preparation Scale) and patient’s acceptance (measured with 6 visual analogue scales). The study was open as for treatment assignment, and blinded for primary endpoint assessment that was done independently on videotaped colonoscopies by 2 endoscopists not aware of study arm. A sample size of 525 patients was calculated to recognize a difference of 10% in the proportion of successes between the arms with a two-sided alpha error of 0·05 and 90% statistical power.</p> <p>Findings: overall 550 subjects (279 assigned to PEG-ASC and 271 assigned to SPMC) represented the analysis population. There was no statistically significant difference in the success rate according to BBPS: 94·4% with PEG-ASC and 95·7% with SPMC (P=0·49). Acceptance and willing to repeat were significantly better for SPMC with all the scales. Compliance was less than full in 6·6% and 9·9% of cases with PEG-ASC and SPMC, respectively (P=0·17). Nausea and meteorism were significantly more bothersome with PEG-ASC than SPMC. There were no serious adverse events in either group.</p> <p>Interpretation. SPMC and PEG-ASC are not different in terms of efficacy, but SPMC is better tolerated than PEG-ASC. SPMC could be used as alternative to low-volume PEG based purgative solutions for bowel preparation.</p> <p>Funding. This research had no financial support.</p> <p>ClinicalTrials.gov NCT01649674; EudraCT 2011—000587—10.</p>
Pre- and post-intervention responses to a knowledge, attitudes, and practices survey for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial"
<p>This dataset contains responses to the pre- and post-intervention knowledge, attitudes, and practices surveys utilized for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial."</p>
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