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10,006 results for “randomness”
Input data files for Dietrich et al. Chl-a and nutrient random forest modeling
<p>Input data for the models originally from:</p> <p>EPA, U. S. <em>WSIO Indicator Data Library</em>, <<a href="https://www.epa.gov/wsio/wsio-indicator-data-library">https://www.epa.gov/wsio/wsio-indicator-data-library</a>> (2023).</p> <p>Platt, L. R., Spaulding, S.A., Covert, A., Murphy, J.C., and Raynor, N. A national harmonized dataset of discrete chlorophyll from lakes and streams (2005-2022). (2023). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/P9J0ZIOF</a></p> <p>Saad, D. A., Argue, D.M., Schwarz, G.E., Anning, D.W., Ator, S.W., Hoos, A.B., Preston, S.D., Robertson, D.M., and Wise, D.R., 2019. Water-quality and streamflow datasets used for estimating long-term mean daily streamflow and annual loads to be considered for use in regional streamflow, nutrient and sediment SPARROW models, United States, 1999-2014. (2019). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/F7DN436B</a></p> <p> </p>
Pinata AES Random Key Dataset
<p>~230k power traces collected from riscure pinata running an unprotected software implemetation of AES.</p> <p>The three traceset are collected from 3 different devices during the first sbox operation. The sampling rate was 625MHz using a Tektronic MSO58 with 8 bit vertical resolution and then resampled at 168MHz.</p> <p>Each trace is an execution of the sub bytes operation with random key and plaintext, which are saved in the trace metadata</p>
Effect of Informative Cesarean Delivery Operative Steps Video to Maternal Anxiety Level: a Randomized Controlled Trial
<p>This is a SPSS data for analysis in "Effect of Informative Cesarean Delivery Operative Steps Video to Maternal Anxiety Level: a Randomized Controlled Trial" research, and video is used for intervention group.</p>
Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Data for the project 'Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial'
<p>Data for the project 'Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial' including four complete datasets with all variables collected in this project and a corresponding README file ('README-File_Data_Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial.txt'). The latter provides (1) general information, (2) sharing and access information, (3) data and file overview, (4) methodological information, and (5) data-specific information.</p>
Dataset for replication of Randomly Sampling HP-Protein Conformations: Mission Impossible?
<p>This is the dataset used for our paper into the complexity of HP-model protein folding titled "Randomly Sampling HP-Protein Conformations: Mission Impossible?".</p>
Polyandry and non-random fertilisation maintain long-term genetic diversity in an isolated island population of adders (Vipera berus)
<p>Conservation genetic theory suggests that small and isolated populations should be subjected to reduced genetic diversity i.e., heterozygosity and allelic diversity. Our 34 years study of an isolated island population of adders (<em>Vipera berus</em>) in southern Sweden challenges this notion. Despite a lack of gene flow and a yearly mean estimated reproductive adult population size of only 65 adult adders (range 12 to 171), the population has been able to maintain high levels of heterozygosity and allelic diversity similar to that observed in two mainland populations. Even a 14-year major "bottleneck" i.e., a reduction in adult adder numbers, encompassing at least four adder generations, did not result in any reduction in the island adders' heterozygosity and allelic diversity. Female adders are polyandrous, and fertilisation is non-random, which our empirical data and modelling suggest underpinning the island adders' ability to maintain a high level of heterozygosity. Our empirical results and subsequent modelling suggest that the positive genetic effects of polyandry in combination with non-random fertilisation, often overlooked in conservation genetic analyses, deserve greater consideration when predicting long-term survival of small and isolated populations.</p>
Model for random atmospheric inhomogeneities in engine noise auralization: Audio files for validation
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: A. Prescher, A. Moreau, S. Schade, "<a href="https://doi.org/10.1007/s13272-024-00764-4" target="_blank" rel="noopener"><em>Model for random atmospheric inhomogeneities in engine noise auralization</em></a>", CEAS Aeronautical Journal, 2024.</p> <p>Selected binaural audio files to illustrate the impact of random atmospheric inhomogenities on the noise characteristics of a turbofan engine.</p> <p>The corresponding time signals and spectrograms are available in the associated paper in Figure 7.</p>
Trained Random Forest Model for PNW Seismic Event Classification Trained on 150s waveforms (P-50, P+100), 50 Hz, and 1-10 Hz BP Filtered
<p>This dataset contains three trained random forest models named as following - </p> <ul> <li>P_10_100_F_1_10_50.joblib - This is a model trained on 110s long waveforms (origin time - 10, origin time +100) in case of earthquakes and explosions and (first arrival pick -10, first arrival pick + 100) in case of surface events, the waveforms are tapered using 10% cosine taper, bandpass filtered between 1-10 Hz using Butterworth four corner filter, normalized and resampled to 50 Hz. </li> <li>P_50_100_F_1_10_50.joblib </li> <li>P_10_30_F_1_15_50.joblib. </li> </ul> <p>And also the standard scaler parameters for each features that will be used to normalize them. </p>
Medical publications with information as to whether a publication reports a randomized controlled trial and/or if it covers an oncology topic
<p><strong>Background:</strong></p> <p>Most tools trying to automatically extract information from medical publications are domain agnostic and process publications from any field. However, only retrieving trials from dedicated fields could have advantages for further processing of the data.</p> <p><strong>Dataset collection:</strong></p> <p>A random sample of 900 publications from seven major journals (British Medical Journal, JAMA, JAMA Oncology, Journal of Clinical Oncology, Lancet, Lancet Oncology, New England Journal of Medicine) published between 2010 and 2022 were annotated. Publications that described randomized controlled trials (RCTs) received the label "RCT". Publications that covered oncological topics received the label "ONCOLGY". Trials that fulfilled both criteria were assigned both labels. Trials that were neither RCTs nor covered oncology topics were assigned no label. 100 randomly sampled trials from the New England Journal of Medicine were used as the unseen test set as the journal publishes both oncology and non-oncology articles. </p> <p><strong>Data properties:</strong></p> <p>Each trial is a row in the CSV file. For each trial, there is a doi, a publication date, a title, an abstract, the abstract sections (introduction, methods, results, conclusion), several tags associated with the annotation process (text, _input_hash, _task_hash, options, _view_id, config, accept, answer, _timestamp, _annotator_id,_session_id), and the assigned labels (answer).</p>
10 Mio randomly selected PubChem SMILES strings with maximum length of 128
<p>This dataset contains SMILES strings of maximum 128 token randomly selected from the PubChem database.</p>
The Efficacy of Daily versus Weekly Ferrous Sulfate in Maintaining Normal Serum Ferritin Levels During Pregnancy: A Randomized Controlled Trial
<p>Serum ferritin is the most reliable indication of stored iron in pregnancy, offering a<br>noninvasive way to detect iron deficiency anemia before it occurs. Therefore, this study aimed to<br>determine serum ferritin levels among women receiving daily versus weekly iron supplementation,<br>with a secondary focus on comparing the proportion developing iron deficiency anemia and compli-<br>ance rates between the two groups.<br>Methods: This non-blinded randomized control trial involved non-anaemic pregnant women attend-<br>ing antenatal clinics at two Teaching Hospitals in Osun State. One hundred twenty-five subjects were<br>recruited to receive 65mg in the control group, while another 125 subjects in the active group re-<br>ceived three tablets (195mg) of ferrous sulfate (Fesulf) once weekly for 17 weeks from the 20th to<br>37th weeks of gestation. The primary outcome measure was comparing mean serum ferritin levels in<br>both groups at 37 weeks.<br>Results: Among the 240 subjects analyzed, the 37-week serum ferritin level was higher in the daily<br>group (73.26±26.67µg/L) compared to the weekly group (63.04±30.71 µg/L), p value=0.006. Four<br>(3.36%) and 10 (8.26%) of our subjects had Iron deficiency anaemia. Nine subjects (3.75%) reported<br>dyspepsia as a side effect. Daily 65 mg of Felsulf proved more effective than weekly 195mg in main-<br>taining normal blood ferritin levels during pregnancy.<br>Conclusions: Daily iron supplementation with 65mg ferrous sulfate was more effective at main-<br>taining adequate maternal iron concentration in this group of non-anaemic pregnant women. This<br>dosage is recommended for routine iron supplementation in our environment.</p>
Open Science Project SPP1516: Random Reasoning is fun
<p>This is the unified data set for the paper "Random Reasoning is fun".</p> <p>Please refer to the Codebook and Readme-file for more information</p>
Supplementary Data: iterative Random Forests to discover predictive and stable high-order interactions
<p>This repository contains scripts and data for the simulations and case studies described in <em>iterative Random Forests to discover predictive and stable high-order interactions.</em></p>
Embeddings Augmented by Random Permutations (EARP)
<p>This data set includes the word embeddings used for our CoNLL 2018 paper, "<em>Bringing Order to Neural Word Embeddings with Embeddings Augmented By Random Permutations"</em>.</p> <p>As described in the paper, encoding word order with random permutations leads to substantive improvements in performance across multiple analogical retrieval tasks, most notably for "syntactic" analogies that involve mapping between morphological derivatives. Smaller improvements are also evident in downstream sequence labeling tasks. </p> <p>Both baseline Skipgram-with-negative-sampling and permutation-based vectors are provided in two: formats - the Semantic Vectors (https://github.com/semanticvectors/semanticvectors) binary format (.bin) and the word2vec binary format (<em>.w2v.bin</em>).</p> <p>All models were created with two different sliding window configurations - radius 2 (<em>r2</em>) and radius (<em>r5</em>); as well as with and without subword embeddings (<em>sw</em>). The models are as follows:</p> <p><em>basic, fastText</em>: standard Skipgram-with-negative-sampling, implemented in Semantic Vectors and fastText respectively</p> <p><em>earp_directional</em>: these models use random permutations (EARP) to distinguish between positions before and after the focus term in a sliding window</p> <p><em>earp_positional</em>: these models use random permutations (EARP) to distinguish between each position in a sliding window</p> <p><em>earp_proximity</em>: these models use random permutations (EARP) to generate encodings that are different, but not orthogonal, for neighboring positions in a sliding window. </p> <p><em>earpx variants</em>: these models use exact sliding window position without replacing subsampled terms, as occurs in other models. </p> <p>The word2vec binary editions include random vectors for some characters and terms that were eliminated by Semantic Vectors' Lucene-based tokenization procedure - in particular punctuation marks - as these are useful for certain downstream machine learning tasks.</p>
Raw Experimental Data for work presented in 'Optimally diverse communication channels in disordered environments with tuned randomness'
<p>This is the raw experimental data for the work presented in 'Optimally diverse communication channels in disordered environments with tuned randomness', published in Nature Electronics.</p> <p> </p> <p><a href="https://doi.org/10.1038/s41928-018-0190-1">https://doi.org/10.1038/s41928-018-0190-1</a> </p> <p> </p> <p>See the README file for an explanation of the data.</p>
Broccoli sprout beverage is safe for thyroid hormonal and autoimmune status: Results of a 12-week randomized trial.
<p>The .csv file contains all the primary data for publication: <br> Chartoumpekis DV, Ziros PG, Chen JG, Groopman JD, Kensler TW, Sykiotis GP.<br> Broccoli sprout beverage is safe for thyroid hormonal and autoimmune status: Results of a 12-week randomized trial.<br> Food Chem Toxicol. 2019 Feb 5;126:1-6.<br> [Epub ahead of print] PubMed PMID: 30735751<br> doi: 10.1016/j.fct.2019.02.004.</p> <p>These data are used in all figures of the paper (Figure 1 and Figure 2).</p> <p>The data are tabular. The headers are the following:</p> <p>sample ID: It comprises 4 digits. The first 3 digits are the unique identifier of the subject in the trial, e.g., "613".<br> The last digit indicates whether the sample was collected at the start of the trial "0", or at the end of the trial "1".<br> E.g., "6130" is the sample collected from participant 613 at the beginning of the trial,<br> and 6131 is the sample collected from the same participant at the end of the trial.</p> <p>Autoimmunity: "0" means that both anti-TG and anti-TPO antibodies were within the reference range.<br> "1" means that either anti-TG, or anti-TPO antibodies, or both, were above the reference range.</p> <p>anti-TG (IU/ml): antibodies against thyroglobuline. The units are given in the parenthesis.</p> <p>anti-TPO (IU/ml): antibodies against thyropreoxidase. The units are given in the parenthesis.</p> <p>Treatment: "1"=placebo and "2"=broccoli sprouts extract beverage.</p> <p>Gender: "F" for female and "M" for male. Data from males were not included in the analyses.</p> <p>Age: In years.</p> <p><br> For any questions, contact: gerasimos.sykiotis@chuv.ch</p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 0
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images </strong>of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. <strong>This data set</strong> (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. The second part (part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 1
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images</strong> of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. The first part (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. <strong>This data set </strong>(part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Spectra of random Non-commutative Geometries
<p>The data in this upload was generated using the code in https://github.com/LisaGlaser/MCMCv4 using initialisation files generated using the scripts iniprep which are also supplied here.<br> The data was analysed in the articles 1612.00713 and 1902.03590.</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.