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Figure 2. Autogenerative Learning Object Model Definition (1)-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The proposed model is defined using the EBNF meta-language resulting in the concise<br> grammar from figures 2 and 3. We will not present the full XML Schema because of clarity and<br> space reasons.<br> Structurally, the AGLO model has six sections: i) name; ii) scenario; iii) theory; iv) question;<br> v) answers; vi) feedbacks. Next, we will explain each section in details.</p>
Figure 4. AGLO Name and Description-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>In this subsection we describe an AGLO model example. Figure 4 presents in details all the<br> XML elements containing AGLO metadata.</p>
Figure 1. The AGLO Workflow-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>E-learning is a key area of research with a great influence on the developments of several<br> industries. For example, the nowadays ITC industry is in a continuous growth because of its<br> applications in almost all industrial domains. Companies tend to lack qualified human resources and<br> because of that they reject high economical value projects. In response to this lack of human<br> resource problem, universities started to develop several alternative study programs, many of them<br> are based on e-learning technology and namely on electronic learning materials. Learning objects<br> (LO) are considered to be digital resources that support learning and can be delivered across<br> networks in large or small sizes (Wiley, 2000). In order to increase the reusability and<br> interoperability of LOs, standards were developed by several organizations (IEEE Learning<br> Technology Standards Committee) (e.g. LOM http://ltsc.ieee.org/doc/wg12/LOMv4.1.htm.)</p>
Execution and data log: on the effectiveness of random and adaptive random test case prioritization
<p>Execution and data logs automatically generated by the test driver during experimentation. These results are similar (but not identical) to the results summarized in Table 1 of the following paper:</p> <p>Z. Q. Zhou, A. Sinaga, and W. Susilo, "On the fault-detection capabilities of adaptive random test case prioritization: case studies with large test suites," in Proceedings of the 45th Annual Hawaii International Conference on System Sciences (HICSS-45). IEEE, 2012, pp. 5584-5593.<br> https://doi.org/10.1109/HICSS.2012.454</p> <p>Compared with the above paper, the current dataset was collected from a different but similar set of experiments and included faulty versions 13, 23, and 26 of the Replace program---although these three versions were excluded from the experiments reported in the above paper due to their instability.</p> <p>If you find these data useful, please cite the above paper.</p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
The impact of pulsed Electromagnetic Fault Injection on true random number generators
<p>Random number acquisition from HECTOR daughter board and the clock signals of both decimator and Priority Encoder. The acquisition where done with a shift register between the output of Priority Encoder’s output and the scope (the same applies for the decimator’s output).</p> <p>Folder is class by type of faults:</p> <ol> <li>tmp_00_wb means two of Priority Encoder’s bits are stuck at 00.</li> <li>tmp_01_wb means two of Priority Encoder’s bits are stuck at 01.</li> <li>tmp_10_wb means two of Priority Encoder’s bits are stuck at 10.</li> <li>tmp_11_wb means two of Priority Encoder’s bits are stuck at 11.</li> <li>tmp_0_wb means one of Priority Encoder’s bit is stuck at 0.</li> <li>tmp_1_wb means one of Priority Encoder’s bit is stuck at 1.</li> <li>Decim_0_wb means one bit of decimator’s output stuck at 0.</li> <li>Decim_1_wb means one bit of decimator’s output stuck at 1.</li> </ol> <p>Curves are class as follows:</p> <p> (<em>Index</em> means the acquisition number)</p> <ol> <li>CIdecimIndex_0.trc files are the clock signal before decimation, i.e. the clock used to sample Priority encoder’s output.</li> <li>COdecimIndex_0.trc files are the clock signal after decimation, i.e. the clock used to sample decimator’s output.</li> <li>IrandDecim_Index_0.trc is the output of the Priority Encoder.</li> <li>OrandDecim_Index_0.trc is the output of the Priority Encoder.</li> </ol> <p>All trc files are binary files.</p> <p>The file conditions_wb recap all the injection parameter used to obtain the different fault.</p>
TARGET BioBombe Results - Randomly Permuted Data
<p>BioBombe analysis applied to <strong>randomly permuted</strong> gene expression data from The Therapeutically Applicable Research To Generate Effective Treatments (TARGET) program. Method and results described in https://github.com/greenelab/BioBombe</p>
TCGA BioBombe Results - Randomly Permuted Data
<p>BioBombe analysis applied to <strong>randomly permuted</strong> gene expression data from The Cancer Genome Atlas (TCGA) PanCanAtlas. Method and results described in https://github.com/greenelab/BioBombe</p>
Memory Effects in a Random Walk Description of Protein Structure Ensembles
<p>This <a href="https://www.activepapers.org/">ActivePaper </a>file contains all the code and data that was used in generating the figures for the article "Memory Effects in a Random Walk Description of Protein Structure Ensembles" by Gerald R Kneller and Konrad Hinsen, J. Chem. Phys. <strong>150</strong>, 064911 (2019); <a href="https://doi.org/10.1063/1.5054887">https://doi.org/10.1063/1.5054887</a></p>
A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers
<p>This short video shows the results of the application of a classifier for microplastics as described by Hufnagl et al. (2019).</p> <p> </p> <p>If you reuse this video please cite</p> <p> </p> <p>Hufnagl, B., Steiner, D., Renner, Löder, M. G. J., Laforsch, C. and Lohninger, H. <em>A Methodology for the Fast Identification and Monitoring of Microplastics in</em><em> Environmental Samples using Random Decision Forest Classifiers,</em> Analytical Methods, 2019, DOI:10.1039/C9AY00252A</p>
How Confidence in Prior Attitudes, Social Tag Popularity, and Source Credibility Shape Confirmation Bias Toward Antidepressants and Psychotherapy in a Representative German Sample: Randomized Controlled Web-Based Study
<p>ABSTRACT</p> <p>Background: In health-related, Web-based information search, people should select information in line with expert (vs nonexpert) information, independent of their prior attitudes and consequent confirmation bias.</p> <p>Objective: This study aimed to investigate confirmation bias in mental health–related information search, particularly (1) if high confidence worsens confirmation bias, (2) if social tags eliminate the influence of prior attitudes, and (3) if people successfully distinguish high and low source credibility.</p> <p>Methods: In total, 520 participants of a representative sample of the German Web-based population were recruited via a panel company. Among them, 48.1% (250/520) participants completed the fully automated study. Participants provided <em>prior attitudes</em> about antidepressants and psychotherapy. We manipulated (1) <em>confidence</em> in prior attitudes when participants searched for blog posts about the treatment of depression, (2) <em>tag popularity</em> —either psychotherapy or antidepressant tags were more popular, and (3) <em>source credibility</em> with banners indicating high or low expertise of the tagging community. We measured <em>tag</em> and <em>blog post</em> selection, and <em>treatment</em><em>efficacy ratings</em> after navigation.</p> <p>Results: Tag popularity predicted the proportion of selected antidepressant tags (beta=.44, SE 0.11; <em>P</em><.001) and blog posts (beta=.46, SE 0.11; <em>P</em><.001). When confidence was low (−1 SD), participants selected more blog posts consistent with prior attitudes (beta=−.26, SE 0.05; <em>P</em><.001). Moreover, when confidence was low (−1 SD) and source credibility was high (+1 SD), the efficacy ratings of attitude-consistent treatments increased (beta=.34, SE 0.13; <em>P</em>=.01).</p> <p>Conclusions: We found correlational support for defense motivation account underlying confirmation bias in the mental health–related search context. That is, participants tended to select information that supported their prior attitudes, which is not in line with the current scientific evidence. Implications for presenting persuasive Web-based information are also discussed.</p> <p>Trial Registration: ClinicalTrials.gov NCT03899168; https://clinicaltrials.gov/ct2/show/NCT03899168 (Archived by WebCite at http://www.webcitation.org/77Nyot3Do)</p> <p>J Med Internet Res 2019;21(4):e11081</p> <p>doi:10.2196/11081</p>
Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation
<p>ABSTRACT</p> <p>Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective.</p> <p>Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts’ evaluations.</p> <p>Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts’ blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants’ treatment recommendation after information search.</p> <p>Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; <em>t</em><sub>173</sub>=7.67, <em>P</em><.001, <em>d</em>=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (<em>F</em><sub>2,168</sub>=10.61, <em>P</em><.001, partial eta squared=0.112), blog post selection (<em>F</em><sub>2,168</sub>=6.55, <em>P</em>=.002, partial eta squared=0.072), and treatment efficacy ratings (<em>F</em><sub>2,168</sub>=8.48, <em>P</em><.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (<em>F</em><sub>1,168</sub>=4.32, <em>P</em>=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (<em>P</em>s>.33).</p> <p>Conclusions: We conclude that the psychotherapy bias is most effectively attenuated—and even eliminated—when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion.</p>
Spatial distribution of random velocity inhomogeneities in the southern Aegean from inversion of S‐wave peak delay times - Dataset
<p>This dataset contains supplementary files uploaded as part of the above journal article.</p> <p>5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few<br> records contain negative peak delay times due to change in waveform shape from filtering, these<br> were excluded during further calculations. The other 4 sub-datasets contain files as well as the script<br> to generate the results of Δlog <em>t<sub>p</sub></em> , κ, ε<sub>param</sub> and P(<em>m<sub>l </sub></em>) as shown in figures 7, 8, 9 and 10 respectively<br> of the main article.</p> <p>Sub-Dataset S1: File “ds01.csv” contains the list of peak delay times (<em>t<sub>p</sub> </em>) in 2-4 Hz, 4-8 Hz, 8-16 Hz<br> and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent<br> origin time (in year-month-day’T’hour:minute:seconds.microseconds format), event latitude,<br> event longitude, event depth, station code, station latitude, station longitude, <em>t<sub>p</sub></em> in 2-4 Hz, <em>t<sub>p</sub></em> in 4-<br> 8 Hz, <em>t<sub>p</sub></em> in 8-16 Hz and <em>t<sub>p</sub></em> in 16-32 Hz in a sequential manner.</p> <p><br> Sub-Dataset S2: File “ds02.zip” contains four text files (nodes_24e.txt, nodes_48e.txt, and<br> nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt)<br> written in BASH. The text files contain Δlog <em>t<sub>p</sub></em> values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands<br> respectively. The columns in the text files represent node index, node latitude, node longitude,<br> node depth and Δlog <em>t<sub>p</sub></em> value in a sequential manner. The GMT script uses GSHHG coastline<br> data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ .<br> Once downloaded and extracted its path can be added to the variable “GDIR” at the beginning of<br> the script. The GMT script file can be run to see the spatial distribution of Δlog t p using GMT-5<br> (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S3: File “ds03.zip” contains four text files (kappa_f10.txt, kappa_f30.txt,<br> kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The<br> text files contain κ values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively.<br> The columns in the text files represent node latitude, node longitude and κ value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of κ using GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S4: File “ds04.zip” contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt,<br> aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain<br> ε<sub>param</sub> values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in<br> the text files represent node latitude, node longitude and ε<sub>param</sub> value of the node sequentially.<br> This GMT script also uses GSHHG coastline data whose path can be added to the script, same as<br> in data set S2 case. The GMT script file can be run to see the spatial distribution of ε<sub>param</sub> using<br> GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S5: File “ds05.zip” contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt,<br> psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain<br> psdf (P(<em>m<sub>l</sub></em><sub> </sub>)) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The<br> columns in the text files represent node latitude, node longitude and P(<em>m<sub>l</sub></em> ) value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of P(<em>m</em><sub><em>l </em></sub>) using GMT-5 (Wessel et al., 2013) and above.</p>
Monthly word embeddings for Twitter random sample (English, 2012-2018)
<p>This dataset contains monthly word embeddings created from the tweets available via the statuses/sample endpoint of the Twitter Streaming API from 2012 to 2018. Full details of the creation of the dataset are given in <a href="https://www.aclweb.org/anthology/D19-1007/">Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings</a>. </p> <p>The md5sum of the gzipped tarball file is a76888ffec8cc7aebba09d365ca55ace .</p>
Figs. 21-31. Omotropis terrenus. 21 in Random studies upon Histerini (Coleoptera: Histeridae)
Figs. 21-31. Omotropis terrenus. 21 – head, 22 – body, upper side, 23 – pronotum, 24 – elytron, 25 – pro- and pygidium, 26 – body, under side, 27-31. genital structure of the male. 27-29 – edeagus, 30 – 8th segment, 31 – 9th and 10th tergites, 23, 24, 29 0 laterally, 27 – dorsally, 28 – ventrally.
Statistical Identification of Coherent and Incoherent Random Lasing from Carbon Dots
<p><span>Here we demonstrate RL emission at ~565 nm from green emitting CDs (gCDs), and we use the emitted light as a light source for speckle-free microscopy of biological tissues and microparticles. The emission of the CD-based RL is thoroughly studied as a function of experimental conditions, and well-established mathematical tools in the field are used to perform a detailed statistical study of the lasing output. The CD-based RL displays ultra-narrow (~ 0.70 nm) emission lines over a comparatively broader (~10 nm) background. These two emissions are due to, so-called, coherent and incoherent RL. Their relative weight can be controlled by an appropriate choice of experimental conditions, allowing to tune the characteristics of RL light. The results demonstrate the potential of gCDs as a viable alternative to environmental unfriendly, scarce, or chemically unstable nanomaterials as gain media for RL with customizable emissions.</span></p>
Figure 1 in A new policy to implement CONSORT guidelines for surgical randomized controlled trials
Figure 1. – Smilosicyopus species (males). A: S. chloe (P. Gaucher); B: S. fehlmanni (P. Keith); C: S. nigriradiatus (P. Keith); D: S. sasali (P. Keith); E: S. pentecost (C. Lord); F: S. bitaeniatus (E. Vigneux); G: S. leprurus (P. Keith).
Data from: Wasted efforts impair random search efficiency and reduce choosiness in mate-pairing termites. American Naturalist
<div> <h1>README</h1> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#readme"></a></div> <div> <h2>Article Information</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#article-information"></a></div> <p>This repository provides access to the data and source code used for the manuscript</p> <div> <h3><strong>Wasted efforts impair random search efficiency and reduce the level of choosiness in mate-pairing termites</strong></h3> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#wasted-efforts-impair-random-search-efficiency-and-reduce-the-level-of-choosiness-in-mate-pairing-termites"></a></div> <div> <h4>Nobuaki Mizumoto, Naohisa Nataya, Ryusuke Fujisawa</h4> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#nobuaki-mizumoto-naohisa-nataya-ryusuke-fujisawa"></a></div> <p>Contact, Nobuaki Mizumoto: <a href="mailto:nzm0095@auburn.edu">nzm0095@auburn.edu</a></p> <p>This paper is accepted at The American Naturalist.<br>Preprint is available at <a href="http://img.shields.io/badge/DOI-10.1101/2024.02.01.578198.svg" rel="nofollow">bioRxiv</a>.</p> <p>This study examines how movement patterns of mate searchers of a termite <em>Reticulitermes speratus</em> changes according to time. Then investigated how this change in movement patterns affect random search efficiency and mate choice behavior.<br>This includes tracking data, R codes to analyze it, and Cpp code for simulations.</p> <div> <h2>Table of Contents</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#table-of-contents"></a></div> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/README.md">README</a></li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts">scripts</a> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/output.R">output.R</a> - output all results</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/processing.R">processing.R</a> - data processing of coordinates obtained from servosphere</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/simulations.R">simulations.R</a> - for data-based simulations</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/onesim.cpp">onesim.cpp</a> - functions for simulations</li> </ul> </li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/output">output</a> - all outputs are stored</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data">data</a> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/raw">raw</a> - raw data in .csv <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/raw/ANTAM_4day">ANTAM_4day</a> - directory includes raw data obtained from servosphere. Ignore the first four columns. col4: x, col5: y, col6: time</li> <li><strong>colonyfoundation.csv</strong> - data for colony foundation experiments.</li> <li><strong>tandem_sum.csv</strong> - the number of observations of individual units in each experiment. units include solomale (single male), solofemale (single female), heterotandem (female-male tandem), male tandem (male-male tandem), female tandem (female-female tandem), and tandem3 (tandem run with >= 3 individuals).</li> <li><strong>tandem_timedevelopment.csv</strong> - breakdown of tandem_sum.csv. The measurement was not summarized but for each observation bouts (5: 0-5 min, 10: 5-10 min, ...).</li> <li><strong>termite_weight.csv</strong> - Termite weight measurement. fresh: fresh weight in mg.</li> </ul> </li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/fmt">fmt</a> - formatted data created in processing.R and simulations.R. The formatted data will be used for output.R. The all formatted data are in .rda files. The .csv files with the same contents are also generated for reviewing purpose. <ul> <li><strong>df_all.rda</strong> - The processed data of trajectories for furthur analysis. It has three datafrmaes named df_all (trajectories with traveled distance information for each frame), df_MSD (MSD data for each individual), df_pause (duration of pauses for each pausing events for each individual). The corresponding csv files are df_all.csv, df_MSD.csv, and df_pause.csv</li> <li><strong>df_sum.rda</strong> - The file further processed df_all.rda to summarize all parameters for each individual. df_sum.csv is corresponding.</li> <li><strong>df_sim.rda</strong> - The simulation results that record encounter time for each searching attempts. encounter_time = 1501 indicates the failure to encounter. df_sim.csv is corresponding.</li> </ul> </li> </ul> </li> </ul> <div> <h2>Session information</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#session-information"></a></div> <div> <pre><code>R version 4.3.1 (2023-06-16 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 11 x64 (build 22621) Matrix products: default locale: [1] LC_COLLATE=English_United States.utf8 [2] LC_CTYPE=English_United States.utf8 [3] LC_MONETARY=English_United States.utf8 [4] LC_NUMERIC=C [5] LC_TIME=English_United States.utf8 time zone: Asia/Tokyo tzcode source: internal attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] CircStats_0.2-6 boot_1.3-28.1 MASS_7.3-60 [4] stringr_1.5.0 survival_3.5-5 survminer_0.4.9 [7] ggpubr_0.6.0 Rcpp_1.0.10 PupillometryR_0.0.5 [10] rlang_1.1.1 dplyr_1.1.2 viridis_0.6.3 [13] viridisLite_0.4.2 ggplot2_3.4.2 Rmisc_1.5.1 [16] plyr_1.8.8 lattice_0.21-8 exactRankTests_0.8-35 [19] car_3.1-2 carData_3.0-5 lme4_1.1-34 [22] Matrix_1.6-1 data.table_1.14.8 </code></pre> </div>
Vitamin B12 deficiency anaemia and gestational diabetes mellitus: a two-sample Mendelian randomization study
Open the record for dataset details and reuse information.
Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072
<p>Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072.</p> <p>VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs </p> <p>EC_LCG_bits.txt: bits from the linear congruential generator on elliptic curves</p> <p>LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits</p> <p>VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.