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685 results for “errors”
Representation of model error in convective-scale data assimilation: additive noise, relaxation methods and combinations
<p>ModelError_1_dpsdt.tar.xz for Fig. 11<br> <br> ModelError_1_innovstat.tar.xz for Fig. 4, 10 and 13<br> <br> ModelError_1_KESpectrum.tar.xz for Fig. 1<br> <br> ModelError_1_pre_veri.tar.xz for Fig. 8 and 9<br> <br> ModelError_1_precipobs_tau.tar.xz for Fig. 2<br> <br> ModelError_1_refl_veri.tar.xz for Fig. 6, 12 and 14</p> <p>For plotting, MATLAB (R2017b) and python are used</p> <p> </p>
Dataset used in "What drives the Bitcoin price? A Factor Augmented Error Correction Mechanism investigation"
<p><strong>What drives the </strong><strong>Bitcoin price</strong><strong>? A Factor Augmented Error Correction Mechanism investigation</strong></p> <p>This article aims to determine what drives the price of Bitcoin. To achieve this aim, a large set of data is analysed using VEC models augmented by factors representing unobservable economic forces. They have been obtained by means of principal component analysis. This method enables us to contribute to the existing literature on Bitcoin in two ways. First, we employ the dimension reduction technique to combine variables from several papers. Second, we estimate several unobservable economic concepts instead of utilizing proxy variables as is usually done. We find that the main factor driving the Bitcoin price is its popularity. Hence, our result not only confirms some previous findings but reinforces them by providing a better definition of popularity. Finally, we conclude that the Bitcoin price is not affected by supply and demand factors in the way that is natural for conventional currencies. </p>
Error-corrected HG002 experimental high-accuracy UL reads
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Data for 'Cooperative thalamocortical circuit mechanism for sensory prediction errors'
<p>Data and code for 'Cooperative thalamocortical circuit mechanism for sensory prediction errors'</p> <p>Nature (2024)</p> <p>DOI: 10.1038/s41586-024-07851-w</p>
Means and standard errors of fits for the l=2 spherical harmonic expansion coefficients of the radio brightness distribution on the sky using Parker Solar Probe/FIELDS/RFS data
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Dataset and code for A Multidimensional Error Verification Method for Weather Forecast Based on the Spatial Distribution Structural Similarity of Meteorological Elements
<p>The dataset includes rainfall observations and mesoscale numerical prediction data for 16 July 2019, 13 June 2020 and 2024 rainy season.<br>The code is the algorithm procedure of this paper. Among them, meiyutest.py is the rainfall scoring procedure for the Meiyu period, and 2019case is the forecast scoring and plotting procedure for two individual cases.</p>
Types of refractive errors in a sample of Iraqi children with Intermittent exotropia
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Supporting data for 'Computing with spin qubits at the surface code error threshold'
<p>IMPORTANT:</p> <p>The data must not be reused in other studies or publications without approval of the authors.</p>
TAPER: Pinpointing errors in multiple sequence alignments despite varying rates of evolution
<p>Datasets related to "TAPER: Pinpointing errors in multiple sequence alignments despite varying rates of evolution."</p>
Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality [Dataset]
<p>The calcium imaging data of the telencephalon of head-tethered adult zebrafish during GO/NOGO tasks in the virtual reality environment and the behavior data were deposited.</p> <p>The codes to process the neural activity data by calcium imaging to perform Non-negative Matrix Factorization </p> <p>For details, see "Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality" Torigoe et al., Nature Communications in press.</p>
Refseq Test Subsets for Frame Classification with and without Errors
<p>These test files extend the<a href="https://zenodo.org/record/4306248"> 'Refseq datasets for training frame classification</a>' dataset. It provided the original test file and three variations containing erroneous sequences to simulate realistic data.</p> <p>The data is based on randomly selected viral and bacterial genomes and the human193(GRCh38.p13) reference genome which was downloaded from GenBank. From each original nucleic acid sequence, we created multiple patches of length 300 in all possible reading frames using a sliding window on the initial sequence and its reversed complement. The data is stored in the FASTA format according to the following convention:<br> <br> >{ID}_subsequence{patch index}_frame{frame index}|{class marker}|{frame index}<br> sequence<br> <br> with<br> <br> ID - denotes the ReSeq accession of the original sequence in the Refseq dataset.<br> sequence - nucleic acid sequence patch of length 300 or 250<br> patch_index - denotes the starting triplet of the given patch within the original sequence or reverse complemented sequence (i.e. 3*patch_index is the starting index of frame 0 in the original sequence)<br> class marker - indicates the taxonomic domain<br> 0 - virus<br> 1 - bacteria<br> 2 - human / mammal<br> frame index - indicates the reading frame<br> 0 - on-frame<br> 1 - shifted by one<br> 2 - shifted by two<br> 3 - reverse complemented<br> 4 - shifted by one and reverse complemented<br> 5 - shifted by two and reverse complemented</p> <p>Each file contains 212.618 patches per frame.</p>
Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors
<p>Data for Hollon et al. (2021) Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors. <em>Current Biology</em></p>
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules
<div> </div> <h3>Data</h3> <p> This folder contains the raw data used during this work. `out_seq_total.txt` contains information on the sequences used (mutations, number of mutations, etc.). `output_energies_total.txt` contains the response variables, which include: a) unrelaxed EvoEF energies (peptides); b) relaxed EvoEF energies (peptides, `*_repaired.txt`); and c) solvation energies (molecules). 3D structures are provided in `.xyz` format in the subfolder `XYZ` (molecules).</p> <div> <div><strong>GB1 dataset</strong></div> <br> <div>The GB1 dataset was <strong>not</strong> generated by us (https://doi.org/10.48550/arXiv.2405.05167). If you use the GB1 dataset, please cite the original paper:</div> <br> <div>Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., & Sun, R. (2016). <em>Adaptation in protein fitness landscapes is facilitated by indirect paths. </em><strong>eLife</strong>, 5:e16965. doi:10.7554/eLife.16965</div> </div> <h3>Results</h3> <p>This folder contains the results (outputs) of the ML models trained using the provided scripts (see github repository). Such results are incuded in the form of `.npy` files. To load the files please include the option `allow_pickle=True`.</p> <p> Each `.npy` file contains the following keys:<br> * `initial_parameters`: script inputs.<br> * `d_encoder`: encoder used (not always included).<br> * `ns_train`: number of training points used for the LCs (rounded, integers).<br> * `ns_train_float`: number of training points used for the LCs (not rounded, float).<br> * `ns_train_norm`: number of training points used for the LCs (normalized, float).<br> * `res`: test MAEs.<br> * `res_tot`: (train,validation,test) MAEs.<br> * `res_tot_mut`: (train,validation,test) MAEs sorted by mutation number.<br> * `l_opt`: optimal kernel length used during the test.<br> * `ls`: kernel lengths used for grid search.<br> * `idx_seeds`: indices used to reshuffle the data. If one want to rebild the initial order use `np.argsort(idx_seeds)`.<br> * `alpha_opt`: optimal regression parameters used to calculate the test error. To sort the data use `alpha_opt[i][ii][np.argsort(idx_seeds[ii,0:arg_train_max].astype(int)[:ns_train[i]]`. Where `i` is the replicate number (0-99) and ii is the idex in the LC.<br> * `valid_errs`: validation error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.<br> * `test_errs`: test error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.</p>
Error curves of finite completely monotonic functions associated with inverse power functions by exponential sum approximations
<p>Each figure of page <span class="math-tex">\(M=1,\ldots,17\)</span> in each PDF file represents the error curve of a finite completely monotonic function <span class="math-tex">\(f(x) = \int_a^b \mathrm{e}^{-xt} \frac{t^{\eta - 1}}{\Gamma(\eta)} \mathrm{d}t\)</span> by an <span class="math-tex">\(M\)</span>-term exponential sum approximation. Each PDF file corresponds to the following parameters:</p> <p>inverse_power0.5_a0.5_b1_EMx.pdf for <span class="math-tex">\(\eta = 0.5, a = 2^{-1}, b = 1\)</span><br> inverse_power0.5_a0.0009765625_b1_EMx.pdf for <span class="math-tex">\(\eta = 0.5, a = 2^{-10}, b = 1\)</span><br> inverse_power1_a0.5_b1_EMx.pdf for <span class="math-tex">\(\eta = 1, a = 2^{-1}, b = 1\)</span><br> inverse_power1_a0.0009765625_b1_EMx.pdf for <span class="math-tex">\(\eta = 1, a = 2^{-10}, b = 1\)</span><br> inverse_power2_a0.5_b1_EMx.pdf for <span class="math-tex">\(\eta = 2, a = 2^{-1}, b = 1\)</span><br> inverse_power2_a0.0009765625_b1_EMx.pdf for <span class="math-tex">\(\eta = 2, a = 2^{-10}, b = 1\)</span></p> <p> </p> <p>Each page includes 6 row figures:<br> Row=1 Best exponential sum approximation<br> Row=2 Approximation by the Gaussian quadrature with the variable transformation <span class="math-tex">\(\Phi_{a/b}(u)\)</span><br> Row=3 Approximation by the Gaussian quadrature with the variable transformation <span class="math-tex">\(\varphi_{\exp, a/b}(u)\)</span><br> Row=4 Approximation by the Gaussian quadrature with the variable transformation <span class="math-tex">\(\varphi_{\mathcal{P}_2, a/b}(u)\)</span><br> Row=5 Approximation by the Gaussian quadrature with the variable transformation <span class="math-tex">\(\varphi_{\mathcal{P}_1, a/b}(u)\)</span><br> Row=6 Approximation by the Gaussian quadrature with the variable transformation <span class="math-tex">\(\varphi_{\mathcal{R}_{0, 1}, a/b}(u)\)</span></p> <p> </p> <p>The figures were generated by</p> <p>https://github.com/ymkoyama/fcmf, commit 17342bf3974eee9b4d6b3d21b06f13b87929a85b on January 21, 2023.</p>
Prediction error, prior certainty, or belief updating: P3a component function in temporal Bayesian inference
<p>Data for "<strong>Prediction error</strong><strong>, prior certainty, or belief updating: P3a component function in temporal Bayesian inference</strong>"</p>
SEER: Estimating the sampling size in performance models considering errors
<p>Generating a performance model to optimize or to reason about variability-intensive systems is costly and the user never knows (unless she or he is a seer) when to stop sampling. The performance measurement can take from several seconds to several days, depending on the measurement and the noise, so it is very important to know if new measurements are profit. Here is where SEER can support users, by estimating the sampling size to generate performance models below a relative prediction error.</p> <p>There are three folders their files that can help you to start using SEER. The folder CODES include all the source code needed to use it. SEER has been validated with ten configurable systems. Folders Ground Truth and Results include the measurements done to validate SEER.</p>
Maximally Error-Tolerant MZI Mesh-based Optical Neural Networks
<p>Codes.zip contains code that trains and tests maximally error-tolerant Mach-Zehnder mesh optical neural networks on MNIST, FashionMNIST, and KMNIST.</p> <p>models.zip contains all the trained model parameters.</p>
Data for the paper Evaluation of error components in rainfall retrieval from collocated commercial microwave links
<p>The published data for the paper includes rain-induced attenuation and rainfall intensities for commercial microwave links.</p> <p>The data are stored in text files. Timestamps are in UTC time in format yyyy-mm-dd HH:MM:SS. The data are at 1-min temporal resolution.</p> <p>Metadata can be found in the paper (Appendix A: Metadata table of CMLs): Špačková, A., Fencl, M., and Bareš, V.: Evaluation of error components in rainfall retrieval from collocated commercial microwave links, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-340, in review, 2023.</p> <p>The repository contains 2 folders (rain-induced attenuation and rainfall intensities) and Read_me file.</p>
Radar rainfall errors
<p>Example simulated radar rainfall errors</p>
Minimising statistical errors in calibration of quantum-gate sets
<p>Data used to conduct the research documented in the paper https://arxiv.org/pdf/2206.03417.pdf.</p> <p>Organisation of files:</p> <ol> <li>Data used for the statistical analysis <ul> <li>data_statistical_analysis.zip</li> </ul> </li> <li>Data used for experimental considerations <ul> <li>GSC Retuning.zip</li> <li>Initial Gate Optimization: <ul> <li>Xtheta_20_B1__7__-1_Ec0_n1e+03.zip</li> <li>Ytheta_20_B1__7__-1_Ec0_n1e+03.zip</li> </ul> </li> </ul> </li> </ol>
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.