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13,064 results for “Prediction”
The effects of solar cycle variability on nanodust dynamics in the inner heliosphere: Predictions for future STEREO A/WAVES measurements
<p>This dataset contains results from the associated manuscript in JGR Space Physics. The dataset consists of two-dimensional nanodust grain fluxes in the HEEQ equatorial plane for various specified Carrington Rotations (CRs), as specified in the parent manuscript.</p>
data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation
<p>This is supporting data and software code for the following preprint in bioRxiv</p> <p><strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong></p> <p>https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1</p>
A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation
<p>Thermocatalytic CO<sub>2</sub> hydrogenation to methanol is an attractive decarbonization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In<sub>2</sub>O<sub>3</sub>-, and ZnO-ZrO<sub>2</sub>-based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models (<em>R</em><sup>2</sup> > 0.85) are developed to predict the methanol space-time yield (<em>STY</em>) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 g<sub>MeOH</sub> h<sup>−1</sup> g<sub>cat</sub><sup>−1 </sup>between the actual and predicted methanol<em> STY</em>. The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization.</p>
Dataset for Surface waves prediction based on acoustic backscattering
<p>Underwater acoustic measurements dataset is represented by three types of files “.raw", “.mat", ".dat" as follows:<br> * “.raw" format also represented by three types of data.<br> - “...search.raw" files contain complex envelop from all hydrophones calculated at four emitted frequencies<br> - “...chan.raw" files is a signal in a wide band from one of the hydrophones - for control and noise analysis.<br> - the largest files are the raw wideband signal from all hydrophones. One such file was saved per eight-hour sound emission cycle.<br> * Spectrogram files are saved in MATLAB format “.mat” v7 . Phasing of the antenna array (all-round view) and calculation of window spectra near each emitted pulse was carried out.<br> * ".dat" files contain features of the average spectrum of the backscattered signal.<br> * Direct measurements of surface wave characteristics, made by a Datawell DWR-G4 wave-rider buoy, accompanied the acoustic measurements. This data is included too.</p> <p>In this archive, we upload all available files of the "dat" and "mat" type and a limited number of "raw" files. You may unpack all ".tar.gz" files into one folder, preserving the directory tree, existing in the archives.</p> <p>Users should refer to the included “.pdf” file for the data format description and to a published preprint for a description of the experimental conditions and instrumentation characteristics. See [arXiv:arXiv:2204.10153] via <a href="https://arxiv.org/abs/2204.10153">https://arxiv.org/abs/2204.10153</a> (Also check when the link is updated to the journal paper) </p> <p>The authors are grateful to their colleges, who helped during the expedition. Data acquisition would be impossible without their contribution. This research was supported by the Russian Science Foundation, grant number 20-77-10081 (the expedition and motivation for study) and the State Contract with the Ministry of Education and Science of the Russian Federation, grant number 0030-2021-0017 (the instruments for underwater acoustic measurements).</p>
Predicted 13C NMR Chemical Shifts of Natural Products
<p>The Natural Product structures are those from the <a href="https://zenodo.org/record/5336220">COCONUTv5 database</a> .</p> <p>Predictions were obtained by means of the "Check Chemical Shifts" method from <a href="https://www.acdlabs.com/">ACD/Labs</a> C+H NMR Predictors and DB software, version 2020.1.0.</p> <p>The acd_coconut.zip archive contains a single file, acd_coconut.sdf, a collection of 2D structures from COCONUT supplemented by <sup>13</sup>C NMR chemical shifts values from ACD/Labs CNMR Predictor in verification mode.</p> <p>The file mol1.sdf describes the first compound in acd_coconut.sdf and indicates how chemical shift values are encoded.</p> <p>SDF tags related to NMR:</p> <ul> <li><CNMR_SHIFTS> for ACD/Labs DB software</li> <li><Predicted 13C shifts>, <Quaternaries>, <Tertiaries>, <Secondaries>, <Primaries> for <a href="https://sourceforge.net/projects/mixonat/">MixONat</a></li> <li><NMREDATA_ASSIGNMENT>, <NMREDATA_ORIGIN> in the style of <a href="https://nmredata.org/">NMReDATA</a></li> </ul> <p>The calculation workflow is based on tools developped <a href="https://github.com/nuzillard/KnapsackSearch/">here</a>.</p> <p>No attempt was made to change unlikely tautomers (like aliphatic iminols standing for aliphatic amides). Unlikely structures are likely associated to unlikely predicted chemical shift value sets.</p>
Predicted stress development during cure and subsequent cooling
<p>Predicted stress development during cure and subsequent cool-down to room temperature. Stress-time curves for a path dependent model and the visco-elastic VisCoR model.<em> </em></p>
Catalogue of Bayesian SZNet's spectroscopic redshift predictions
<p>The "dr16q_superset_redshift.csv" file provides a catalogue of spectroscopic redshift predictions for spectra from the <a href="https://www.sdss.org/dr16/algorithms/qso_catalog/">16th data release of the Sloan Digital Sky Survey (SDSS) quasar superset catalogue</a> <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>. Redshifts are predicted by a Bayesian convolutional neural network named Bayesian SZNet with associated predictive uncertainties in the form of predictive variances. The catalogue is released in the CSV format with the following columns:</p> <ul> <li><em>plate</em>: spectroscopic plate number;</li> <li><em>mjd</em>: modified Julian day of the spectroscopic observation;</li> <li><em>fiberid</em>: fiber identification number;</li> <li><em>z_pred</em>: redshift from Bayesian SZNet;</li> <li><em>variance</em>: predictive variance associated with redshift from Bayesian SZNet;</li> <li><em>z</em>: primary redshift;</li> <li><em>source</em><em>_z</em>: origin of the reported redshift in <em>z;</em></li> <li><em>is_qso_final</em>: flag indicating quasars included in the DR16Q <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>;</li> <li><em>z_vi</em>: redshift from visual inspection;</li> <li><em>z_pipe</em>: redshift from the SDSS pipeline;</li> <li><em>zwarning</em>: quality flag on the redshift from the SDSS pipeline;</li> <li><em>z_dr12q</em>: redshift from the DR12Q catalogue <a href="http://ui.adsabs.harvard.edu/abs/2017A%26A...597A..79P/abstract">(Pâris et al., 2017)</a>;</li> <li><em>z_dr7q_sch</em>: redshift from the DR7Q catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010AJ....139.2360S/abstract">(Schneider et al., 2010)</a>;</li> <li><em>z_dr6q_hw</em>: redshift from the DR6 catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010MNRAS.405.2302H/abstract">(Hewett and Wild, 2010)</a>;</li> <li><em>z_10k</em>: redshift from the random visual inspection of 10000 spectra in the DR16Q superset;</li> <li><em>z_pca</em>: redshift from the <a href="https://ascl.net/2106.017">redvsblue algorithm</a>;</li> <li><em>z_qn</em>: redshift from QuasarNET <a href="https://arxiv.org/abs/1808.09955">(Busca and Balland, 2018)</a>;</li> <li><em>z_pred_1</em> to <em>z_pred_256</em>: sampled redshifts from Bayesian SZNet;</li> </ul> <p>where columns <em>z</em>, <em>source_z</em>, <em>is_qso_final</em>, <em>z_vi</em>, <em>z_pipe</em>, <em>zwarning</em>, <em>z_dr12q</em>, <em>z_dr7q_sch</em>, <em>z_dr6q_hw</em>, <em>z_10k</em>, <em>z_pca</em>, and <em>z_qn</em> are taken from the 16th data release of the SDSS quasar superset catalogue.</p>
Semi-empirical methods SPT inputs for bearing capacity prediction
<p>These datasets presents inputs for bearing capacity prediction methods. These methods are four well-known semi-empirical models for predicting bearing capacity of piles. The data was collected from the works of Lobo (2005), Vianna (2000) and Jr. (1988) and includes 168 load tests and SPT measures taken from severam Brazilian regions. . The file Data.csv is composed only with the numeric values used in each method and the file Data_with_soils.csv includes the soil types for the piles.</p> <p>The suffix 'Dq', 'Mey', 'Av' and 'Tx' represents which method this input was obtained from, corresponding respectively to Decourt and Quaresma (1978), Meyerhof (1983), Aoki and Velloso (1975) and Teixeira (1996).</p> <p>The columns indexes represents:</p> <p>N_pile - Pile number (for reference);<br> SPT_L - SPT result for the pile lenght;<br> SPT_P - SPT result for the pile tip<br> Soil_L - Predominant soil type along the pile lenght;<br> Soil_P - Predominant soil type in the pile tip;<br> L - pile lenght;<br> D - pile diameter;<br> Qu - Pile bearing capacity, obtained through NBR 6122 load test.<br> <br> When using this dataset, please cite the following paper:</p> <p>//<a href="http://soilsandrocks.com/sr-2021-074921">soilsandrocks.com/sr-2021-074921</a></p> <p>DOI: 10.28927/SR.2021.074921</p>
Replication Data for: Geometric Transformers for Protein Interface Contact Prediction
<p>This dataset contains replication data for the paper titled "Geometric Transformers for Protein Interface Contact Prediction". The dataset consists of pickled Python dictionaries containing pairs of DGLGraphs that can be used to train and validate protein interface contact prediction models. It also contains our best model checkpoints saved as PyTorch LightningModules. Our GitHub repository, DeepInteract, linked in the "Additional notes" metadata section below provides more details on how we use these files as examples for cross-validation.</p>
Event-Based Velocity Prediction for Spiking Neural Networks
<p>This dataset is intended to be used to predict the velocity based on the event pixels present in the data, given in (t, x, y, p) format alongside a ground truth velocity reading. A novel dataset using people and various objects moving in front of an RGB video camera was created. The positions of each entity and the associated times were captured with a Vicon motion tracking system. These two types of data were calibrated so that the movement in the video matched the measurements recorded by the Vicon system. The types of data collected include two different people carrying a calibrated Vicon Active Wand and moving around the room, a Lambda aerial robot with motion tracking markers that flew around the room, a box with motion tracking markers that were tossed back and forth in the air, and the same box was slid across the floor. The video recording was simulated as event camera data. Each pixel changes state independently of all the other pixels. The Open Event Camera Simulator (ESIM) from the Robotics and Perception Group at the University of Zurich and ETH Zurich is used. The event camera simulator used, ESIM, allows for accurate event simulation data. The ground truth velocities were calculated from the position and timestamps recorded by the Vicon system.</p>
Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore
<p>Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore; see https://github.com/crisprVerse/crisprScore for more detail.</p> <p> </p>
Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.
<p>NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2 seasonal forecasting system described in Lin et al. (2020) https://doi.org/10.1175/WAF-D-19-0259.1 </p> <ul> <li>2 models, CanCM4i and GEM-NEMO</li> <li>10 ensemble members for each model, each in separate files as indicated by suffixes _1 to _10</li> <li>initialized May 1, 1980 to 2021</li> <li>840 files total (42 predicted years x 10 ensemble members x 2 models)</li> <li>model outputs interpolated to common 1-degree grid</li> </ul> <p>The calibrated probabilistic forecast map shown in Figure 3a is based on the nonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021) https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available at https://github.com/adirkson/sea-ice-timing </p> <p>The procedure uses as inputs</p> <ul> <li>freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016) https://doi.org/10.1002/2016GL071396</li> <li> <p>NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3</p> </li> </ul>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
Russo-Ukrainian War: Prediction and explanation of Twitter suspension
<p>Dataset used in research paper : "Russo-Ukrainian War: Prediction and explanation of Twitter<br> suspension". Contain extracted features for Twitter users, correlated to discussion of Russo-Ukrainian war. Contain multiple feature categories. Source code of dataset usage in Machine Learning approach is available at GitHub: https://github.com/alexdrk14/TwitterSuspension .</p>
Input features of E. coli proteome for predicting and modeling protein-protein interactions with AF2Complex
<p>Input features to be used with AF2Complex for predicting protein-protein interactions among ~4400 E. coli proteins. A pickled feature file was generated by the feature data pipeline of AF2Complex for each E. coli protein. To reduce storage size, we limited up to 10,000 MSA sequences and up to 10 structural templates from the Protein Data Bank. The cutoff date for sequence libraries and the Protein Data Bank releases used for feature generation is no later than 11-30-2021.</p> <ul> <li>ecoli_af2c_fea.txt -- A list of all E coli protein with pre-generated input features</li> <li>af2c_fea_ecoli_220331_msa10ktem10.tar -- Input features named after the UniProt ID of each proteins. Note that after untar the tarball, you may use the gzipped feature pickle files directly with AF2Complex w/o gunzip.</li> </ul>
Factors to predict above-ground biomass carbon carrying capacity
<p>The climate data (Mean annual temperature (°C, MAT), mean annual precipitation (mm, MAP), annually accumulated temperature with days ≥ 0°C (°C-days, AAT0), annually accumulated temperature with days ≥ 10°C (°C-days, AAT10), aridity index, and humidity index ), soil properties (soil texture and soil types) and DEM are available from the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/); The geological elements and hydrological elements data can be found at http://dcc.ngac.org.cn/geologicalData/rest/geologicalData/geologicalDataDetail/402881f75d9bc077015d9bc084160000and https://www.webmap.cn/commres.do?method=result25W; The geomorphology data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/zh-hans/data/63e290d7-7087-462a-acac-50195fba530b/). All data were resampled at 500m resolution.</p>
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
Data from article "Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?"
<p>Data from the article Zanchettin D., et al.: Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?, Earth and Space Science, article number 2022EA002494</p> <p>The dataset contains:</p> <p>- annual time series of October-March average of relative sea level in Venice corrected for vertical land movement (VLMcorrectedRSL) with associated standard error of the mean (LMcorrectedRSL_SEM) for the period 1873-2019;</p> <p>- annual time series of estimate of subsidence in Venice (Subsidence) for the period 1873-2019</p> <p>- modeled state of Venice sea level with associated uncertainty, provided as mean (delta_mean), 1st percentile (delta_1_percentile) and 99th percentile (delta_99_percentile), for the period 1873-2019</p> <p>- modeled local stochastic trend of Venice sea level with associated uncertainty, provided as mean (beta_mean), 1st percentile (beta_1_percentile) and 99th percentile (beta_99_percentile), for the period 1873-2019</p>
Data and code accompanying: A quantitative synthesis of and predictive framework for studying winter warming effects in reptiles
<p>This data and code were used to generate the publication "A quantitative synthesis of and predictive framework for studying winter warming effects in reptiles", doi: 10.1007/s00442-022-05251-3</p> <p>Please direct any queries or requests to use these datasets/code to: k.macleod@bangor.ac.uk</p> <p>Two datasets are presented in separate excel files: one contains meta-analytical data from experimental studies on winter warming effects on reptiles, and the other contains the same type of data from observational studies on the same.</p> <p>R code for analysis is in an R file; this should be openable in any text editing application.</p> <p>Manuscript abstract below:</p> <p><em>Increases in temperature related to global warming have important implications for organismal fitness. For ectotherms inhabiting temperate regions, ‘winter warming’ is likely to be a key source of the thermal variation experienced in future years. Studies focusing on the active season predict largely positive responses to warming in the reptiles; however, overlooking potentially deleterious consequences of warming during the inactive season could lead to biased assessments of climate change vulnerability. Here, we review the overwinter ecology of reptiles, and test specific predictions about the effects of warming winters, by performing a meta-analysis of all studies testing winter warming effects on reptile traits to date. We collated information from observational studies measuring responses to natural variation in temperature in more than one winter season, and experimental studies which manipulated ambient temperature during the winter season. Available evidence supports that most reptiles will advance phenologies with rising winter temperatures, which could positively affect fitness by prolonging the active season although effects of these shifts are poorly understood. Conversely, evidence for shifts in survivorship and body condition in response to warming winters was equivocal, with disruptions to biological rhythms potentially leading to unforeseen fitness ramifications. Our results suggest that the effects of warming winters on reptile species are likely to be important but highlight the need for more data and greater integration of experimental and observational approaches. To improve future understanding, we recap major knowledge gaps in the published literature of winter warming effects in reptiles and outline a framework for future research.</em></p>
Normal mode splitting function predictions for mantle anisotropy
<p>Predictions for normal mode splitting functions for 6 models of mantle anisotropy, accompanying the paper published in Geophysical Journal International by Restelli, Koelemeijer & Ferreira (2023). This is version 2 related to the revised manuscript. </p> <p>More details can be found in the README. </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.