Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,064
datasets available to search
ShareScore release 0.9.0
Dataset results
13,064 results for “predictions”
Data for: Roles of the cerebellar vermis in predictive postural controls against external disturbances
<p>The central nervous system predictively controls posture against external disturbances; however, the detailed mechanisms remain unclear. We tested the hypothesis that the cerebellar vermis plays a substantial role in acquiring predictive postural control by using a standing task with floor disturbances in rats. The intact, lesioned, and sham groups of rats sequentially underwent 70 conditioned floor-tilting trials, and kinematics were recorded. Six days before these recordings, only the lesion group underwent focal suction surgery targeting vermal lobules Ⅳ-Ⅷ. In the naïve stage of the sequential trials, the upright postures and fluctuations due to the disturbance were mostly consistent among the groups. Although the pattern of decrease in postural fluctuation due to learning corresponded among the groups, the learning rate estimated from the lumbar displacement was significantly lower in the lesion group than in the intact and sham groups (approximately 35% of the sham group). These results suggest that the cerebellar vermis contributes to predictive postural control.</p>
With or without you: Gut microbiota does not predict aggregation behavior in European earwig females
<p>The reasons why some individuals are solitary and others gregarious are the subject of ongoing debate as we seek to understand the emergence of sociality. Recent studies suggest that the expression of aggregation behaviors may be linked to the gut microbiota of the host. Here, we tested this hypothesis in females of the European earwig. This insect is ideal for addressing this question, as adults both naturally vary in the degree to which they live in groups and show inter-individual variation in their gut microbial communities. We video-tracked 320 field-sampled females to quantify their natural variation in aggregation and then tested whether the most and least gregarious females had different gut microbiota. We also compared the general activity, boldness, body size, and body condition of these females and examined the association between each of these traits and the gut microbiota. Contrary to our predictions, we found no difference in the gut microbiota between the most and least gregarious females. There was also no difference in activity, boldness, and body condition between these two types of females. Independent of aggregation, gut microbiota was overall associated with female body condition, but not with any of our other measurements. Overall, these results demonstrate that a host's gut microbiota is not necessarily a major driver or a consequence of aggregation behavior in species with inter-individual variation in group living and call for future studies to investigate the determinants and role of gut microbiota in earwigs.</p>
Data and scripts for: Airborne DNA reveals predictable spatial and seasonal dynamics of fungi
<p><span>Fungi are among the most diverse and ecologically important kingdoms of life. However, the distributional ranges of fungi remain largely unknown, as do the ecological mechanisms that shape their distributions. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardised aerial sampling of fungal spores. The vast majority of OTUs were detected only within one climatic zone, and the spatio-temporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal, and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating only for a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with microbial lifestyles follow the global biodiversity paradigms known for macro-organisms.</span></p> <p>The analyses presented in the paper can be reproduced with the R-script pipeline provided here. The starting point for the scripts is the datafile allData.RData that was published originally by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). The datafile allData.RData is provided also here for convenience, and it includes the following three objects: metadata, taxonomy, and otu.table (see Ovaskainen et al. for details). The script pipeline consists of the following elements (for deltails, see the Methods of the paper):</p> <ul> <li>Scripts S01: data preparation<br> <ul> <li>S01.1_download_clim_data.R. This script downloads daily climatic data for the entire world.</li> <li>S01.2_select_and_preprocess_clim_data.R. This script selects the data relevant for the study locations and preprocesses it.</li> <li>S01.3_add_climatic_data_to_metadata.R. This script adds the preprocessed climatic data to the metadata.</li> <li>S01.4_otu_guild_assignment.R. This script performs the guild assignment to the OTUs. It utilizes the datafiles Fung_LifeStyle_Data.RDS and funguild_db.rds provided here, and it utilizes the taxonomy of ProtaxFungi provided by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). Note that while the paper presents analyses and results only for the trait database of Aguilar-Trugueros et al., the scipts repeat the trait analyses also for the FunGuild database. The reason for not showing the results for the FunGuild database in the paper was that the database of Aguilar-Trugueros et al. contains FunGuild as one of the data sources, and that the results were highly coherent between the two databases.</li> <li>S01.5_add_trait_data_to_taxonomy_and_metadata.R. This script adds the guild data and spore size data to taxonomy (taxon-specific traits) as well as to metadata (community-weighted mean traits). It utilizes the datafile Spore_data_12Nov21.RDS provided here. This script can also be used to generated simulated contamination to the OTU table by setting contaminate=TRUE.</li> </ul> </li> <li>Scripts S02: exploratory analyses <ul> <li>S02.1_show_descriptive_statistics.R. This script outputs basic desriptive statistics from the data.</li> <li>S02.2_make_study_design_maps.R. This script plots the study design map shown in the paper.</li> <li>S02.3_compute_site_and_biome_profiles.R. This script computes site_profiles (needed in ordinations) and biome_profiles (needed to create Venn diagrams).</li> <li>S02.4_make_venns.R. This script produces Venn diagrams.</li> </ul> </li> <li>Scripts S03: ordination analyses <ul> <li>S03.1_make_ordination_maps.R. This script makes the ordination analyses.</li> </ul> </li> <li>Scripts S04: univariate analyses <ul> <li>S04.1_conceptualize_univariate_models.R. This script produces a figure that illustrates conceptually the differenent model variants. </li> <li>S04.2_make_univariate_analysis.R. This script implements the univariate analyses.</li> <li>S04.3_show_univariate_results.R. This script summarizes the results of the univariate analyses by producing tables of AIC and R2.</li> <li>S04.4_plot_univariate_results.R. This script plots the univariate models.</li> <li>S04.5_compute_temporal_turnover.R. This script computes site-specific indices of temporal turnover.</li> <li>S04.6_show_temporal_turnover.R. This script generates a plot illustrating temporal turnover.</li> </ul> </li> <li>Scripts S05: Hmsc analyses <ul> <li>S05.1_define_Hmsc_models.R. This script defines the Hmsc models. It utilizes the R-function as.phylo.formula provided here.</li> <li>S05.2_export_Hmsc_models_for_fitting.R. This script exports the unfitted Hmsc-models for fitting with Hmsc-HPC that operates on python/tensorflow.</li> <li>S05.3_import_fitted_Hmsc_models.R. This script imports the fitted Hmsc-models back to Hmsc-R.</li> <li>S05.4_postprocess_Hmsc_results.R. This script postprocesses the results of the fitted Hmsc model.</li> <li>S05.5_show_Hmsc_results.R. This script generates a plot that illustrates the postprocessed results.</li> </ul> </li> </ul>
Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction
<p>Dataset produced for the Project PRELUDIUM19 2020/37/N/NZ2/00967 entitled: "Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction"</p> <p>The dataset counts with the three families of enzymes used: dehalogenase, aldehyde reductase and nitrilase.</p> <p>For each enzyme, the docked structures, docked parameters and scripts to analyze them further are present. Moreover, the protocol that derives geometric descriptors from docked structures is also present.</p> <p>This work was supported by the National Science Centre, Poland (grant no. 2020/37/N/NZ2/00967)</p>
Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
<p>String-like motions (SLMs) cooperative, "snake"-like movements of particles—are crucial for dynamics in diverse glass formers. Despite their ubiquity, questions persist: do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our iso-configurational ensemble simulations reveal that SLMs indeed follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways solely based on the static structure. GNN gauges a structural measure to assess each path's propensity to engage in SLMs, akin to a "softness" metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along paths in steering SLMs. By monitoring "path-softness", we elucidate SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: "paths".</p>
Dataset, splits, models, and scripts for the QM descriptors prediction
<p>Dataset, splits, models, and scripts from the manuscript "When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?" are provided. The curated dataset includes 37 QM descriptors for 64,921 unique molecules across six levels of theory: wB97XD, B3LYP, M06-2X, PBE0, TPSS, and BP86. This dataset is stored in the data.tar.gz file, which also contains a file for multitask constraints applied to various atomic and bond properties. The data splits (training, validation, and test splits) for both random and scaffold-based divisions are saved as separate index files in splits.tar.gz. The trained D-MPNN models for predicting QM descriptors are saved in the models.tar.gz file. The scripts.tar.gz file contains ready-to-use scripts for training machine learning models to predict QM descriptors, as well as scripts for predicting QM descriptors using our trained models on unseen molecules and for applying radial basis function (RBF) expansion to QM atom and bond features.</p> <p>Below are descriptions of the available scripts:</p> <ol> <li><code>atom_bond_descriptors.sh</code>: Trains atom/bond targets.</li> <li><code>atom_bond_descriptors_predict.sh</code>: Predicts atom/bond targets from pre-trained model.</li> <li><code>dipole_quadrupole_moments.sh</code>: Trains dipole and quadrupole moments.</li> <li><code>dipole_quadrupole_moments_predict.sh</code>: Predicts dipole and quadrupole moments from pre-trained model.</li> <li><code>energy_gaps_IP_EA.sh</code>: Trains energy gaps, ionization potential (IP), and electron affinity (EA).</li> <li><code>energy_gaps_IP_EA_predict.sh</code>: Predicts energy gaps, IP, and EA from pre-trained model.</li> <li><code>get_constraints.py</code>: Generates constraints file for testing dataset. This generated file needs to be provided before using our trained models to predict the atom/bond QM descriptors of your testing data.</li> <li><code>csv2pkl.py</code>: Converts QM atom and bond features to .pkl files using RBF expansion for use with Chemprop software.</li> </ol> <p>Below is the procedure for running the ml-QM-GNN on your own dataset:</p> <ol> <li>Use <code>get_constraints.py</code> to generate a constraint file required for predicting atom/bond QM descriptors with the trained ML models.</li> <li>Execute <code>atom_bond_descriptors_predict.sh</code> to predict atom and bond properties. Run <code>dipole_quadrupole_moments_predict.sh</code> and <code>energy_gaps_IP_EA_predict.sh</code> to calculate molecular QM descriptors.</li> <li>Utilize <code>csv2pkl.py</code> to convert the data from predicted atom/bond descriptors .csv file into separate atom and bond feature files (which are saved as .pkl files here).</li> <li>Run <a href="https://github.com/chemprop/chemprop">Chemprop</a> to train your models using the additional predicted features supported here.</li> </ol>
Integrative processing in artificial and biological vision predicts the perceived beauty of natural images
<p><em>Data, code, and materials for Nara & Kaiser (2023). </em></p> <p><em>Preprint: </em><a href="https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1">https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1</a></p> <p>Paper: <a href="https://doi.org/10.1126/sciadv.adi9294">https://doi.org/10.1126/sciadv.adi9294</a></p> <p>In this version (v2) of the repository, we:</p> <ul> <li>fixed an error in the fMRI data, where only the data from one participant, instead of all participants was uploaded previously - now all data are available,</li> <li>added brain masks (extracted by SPM) for each participant, and</li> <li>added realignment parameter text files (created by SPM) to the functional data for each participant.</li> </ul>
On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models
<p>In this article, a local Linear Parameter Varying (LPV) model identification approach is exploited to analyze the dynamic behaviour of a structure whose dynamics varies over time. This structure is composed by two aluminum crosses connected by a rubber mount. To observe time-dependent variations on the dynamics of this assembly, it is placed in a climate chamber and submitted to a six minute temperature run-up. During this run-up the structure is continuously excited by a shaker. The load provided by this device is measured by a load cell, while six accelerometers are measuring the responses of the system. The temperatures of the air inside the climate chamber and at the surface of the mount are also continuously measured. It is found that during the performed temperature run-up, the rubber mount temperature increased from, roughly, 14℃ to, approximately, 35.2℃. By using the measured load provided by the shaker and the measured accelerations, Frequency Response Functions (FRFs) at five different rubber mount temperatures are computed. From each of these sets of FRFs, state-space models are estimated. Afterwards, these models are used to define an interpolating LPV model, which enables the computation of interpolated state-space models representative of the dynamics of the system at each time sample. It is found that by feeding the interpolated state-space models with the measured load, an accurate simulation of the measured accelerations is obtained. Moreover, by exploiting a joint input state estimation algorithm with the interpolated state-space models and with the measured accelerations, a very good prediction of the applied load can be obtained. It is also shown that if the time dependency of the dynamics of the system is ignored, the results are less accurate.</p>
NEON Tree Species Predictions
<p># Individual Tree Predictions for 100 million trees in the National Ecological Observatory Network</p> <p>Preprint: https://www.biorxiv.org/content/10.1101/2023.10.25.563626v1</p> <p>## Manuscript Abstract</p> <p>The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales allows an unprecedented view of forest ecosystems, forest restoration and responses to disturbance. To create detailed maps of tree species, airborne remote sensing can cover areas containing millions of trees at high spatial resolution. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual tree species using ground truthed data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees for 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1km^2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species. All predictions were uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. These data can be used to study forest macro-ecology, functional ecology, and responses to anthropogenic change.</p> <p>## Data Summary</p> <p>Each NEON site is a single zip archive with tree predictions for all available data. For site abbreviations see: https://www.neonscience.org/field-sites/explore-field-sites. For each site, there is a .zip and .csv. The .zip is a set 1km .shp tiles. The .csv is all trees in a single file.</p> <p>## Prediction metadata</p> <p>*Geometry*</p> <p>A four pointed bounding box location in utm coordinates.</p> <p>*indiv_id*</p> <p>A unique crown identifier that combines the year, site and geoindex of the NEON airborne tile (e.g. 732000_4707000) is the utm coordinate of the top left of the tile. </p> <p>*sci_name*</p> <p>The full latin name of predicted species aligned with NEON's taxonomic nomenclature. </p> <p>*ens_score*</p> <p>The confidence score of the species prediction. This score is the output of the multi-temporal model for the ensemble hierarchical model. </p> <p>*bleaf_taxa*</p> <p>Highest predicted category for the broadleaf submodel</p> <p>*bleaf_score*</p> <p>The confidence score for the broadleaf taxa submodel </p> <p>*oak_taxa*</p> <p>Highest predicted category for the oak model </p> <p>*dead_label*</p> <p>A two class alive/dead classification based on the RGB data. 0=Alive/1=Dead.</p> <p>*dead_score*</p> <p>The confidence score of the Alive/Dead prediction. </p> <p>*site_id*</p> <p>The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations.</p> <p>*conif_taxa*</p> <p>Highest predicted category for the conifer model</p> <p>*conif_score*</p> <p>The confidence score for the conifer taxa submodel</p> <p>*dom_taxa*</p> <p>Highest predicted category for the dominant taxa mode submodel</p> <p>*dom_score*</p> <p>The confidence score for the dominant taxa submodel</p> <p>## Training data</p> <p>The crops.zip contains pre-cropped files. 369 band hyperspectral files are numpy arrays. RGB crops are .tif files. Naming format is <br><individualID>_<year>_<sensor>, for example. "NEON.PLA.D07.GRSM.00583_2022_RGB.tif" is RGB crop of the predicted crown of NEON data from Great Smoky Mountain National Park (GRSM), flown in 2022.<br>Along with the crops are .csv files for various train-test split experiments for the manuscript.</p> <p>### Crop metadata</p> <p>There are 30,042 individuals in the annotations.csv file. We keep all data, but we recommend a filtering step of atleast 20 records per species to reduce chance of taxonomic or data cleaning errors. This leaves 132 species.</p> <p>*score*</p> <p>This was the DeepForest crown score for the crop.</p> <p>*taxonID*<br>For letter species code, see NEON plant taxonomy for scientific name: https://data.neonscience.org/taxonomic-lists</p> <p>*individual*<br>unique individual identifier for a given field record and crown crop</p> <p>*siteID*<br>The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations.</p> <p>*plotID*</p> <p>NEON plot ID within the site. For more information on NEON sampling see: https://www.neonscience.org/data-samples/data-collection/observational-sampling/site-level-sampling-design</p> <p>*CHM_height*</p> <p>The LiDAR derived height for the field sampling point.</p> <p>*image_path*</p> <p>Relative pathname for the hyperspectral array, can be read by numpy.load -> format of 369 bands * Height * Weight</p> <p>*tile_year* </p> <p>Flight year of the sensor data</p> <p>*RGB_image_path*</p> <p>Relative pathname for the RGB array, can be read by rasterio.open()</p> <p># Code repository</p> <p>The predictions were made using the DeepTreeAttention repo: https://github.com/weecology/DeepTreeAttention<br>Key files include model definition for a [single year model](https://github.com/weecology/DeepTreeAttention/blob/main/src/models/Hang2020.py) and [Data preprocessing](https://github.com/weecology/DeepTreeAttention/blob/cae13f1e4271b5386e2379068f8239de3033ec40/src/utils.py#L59).</p>
Uncertainties of Predictions from Temperature Replica Exchange Simulations
<p>Parallel tempering molecular dynamics simulation, also known as temperature replica exchange simulation, is a popular enhanced sampling method used to study biomolecular systems. This method makes it possible to calculate the free energy differences between states of the system for a series of temperatures. We developed a method to easily calculate errors (standard errors or confidence intervals) of these predictions using a modified version of our recently introduced JumpCount method. The number of transitions between states (e.g. protein folding events) is counted for each temperature. This number of transitions, together with the temperature, fully determines the value of standard error or the confidence interval of the free energy difference. We also address the issue of convergence in the situation where all replicas start from one state by developing an estimator of the equilibrium constant from simulations that are not fully equilibrated. The prerequisite of the method is the Markovianity of the process studied.</p>
Data from: Climate change is predicted to impact the global distribution and richness of pines (genus Pinus) by 2070
<p>Aim: Climate change is altering habitat suitability for many organisms and modifying species ranges at a global scale. Here we explored the impact of climate change on 112 pine species (<em>Pinus</em>), fundamental elements of Northern terrestrial ecosystems.</p> <p>Location: Global.</p> <p>Methods: We applied a novel methodology for species distribution modelling that considers uncertainty in climatic projections and taxon sampling, and incorporates elements of species' recent evolutionary history. We based our niche calculations on climate and soil data and computed projections across multiple algorithms and IPCC scenarios, which were ensembled into one single suitability map. We then used phylogenetic methods to account for recent evolution in climatic requirements by estimating the evolution of climatic niche. Edaphoclimatic and evolutionary analyses were then combined to calibrate the projections in areas showing high uncertainty. We validated our models using naturalized occurrences of invasive pine species.</p> <p>Results: Our models predicted that by 2070 most pine species (58%) might face important reductions of habitat suitability, potentially leading to range losses and a decrease in species richness, particularly in some regions such as the Mediterranean Basin and South North America, albeit migration might mitigate these shifts in some cases. In contrast, our projections showed increased habitat suitability for approx. 20% of species, which may undergo range expansions under climate change. Moreover, the consideration of recent evolutionary trends modified projected scenarios, decreasing range loss and increasing range expansion for some species. The independent validation endorsed our models for many species and the influence of recent evolution in some cases.</p> <p>Conclusions: We predict that climate change will impose drastic changes in pine distribution and diversity across biogeographical regions, but the magnitude and direction of change will vary significantly across regions and taxa. Species-level responses are likely to be influenced by regional conditions and the recent evolutionary history of each taxon.</p>
Supplemental Material to Doctoral Thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses"
<p>This set supplements the figure data to the doctoral thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses", DOI: <a href="https://doi.org/10.14279/depositonce-19144">10.14279/depositonce-19144</a></p>
NordicDat: A Cross-Border Predictive QoS Dataset
<p>NordicDat is a network QoS and mobility dataset, collected during our dedicated measurement campaign across three European countries i.e., Finland, Sweden and Norway. It comprises of a rich set of features that are related to a) physical layer properties e.g., signal strength, signal to noise ratio, b) network characteristics e.g., band, operator, c) user kinematics e.g., location, speed and d) (application-level) QoS value e.g., throughput, delay. The measurement area is set near each country’s border, to specifically capture the effect of roaming on QoS values. Our measurements capture traces from both LTE and 5G RAN. The dataset comprises of over 90K samples that are collected during driving runs with diverse speed patterns (speed, acceleration, direction), typically categorized under the speed profiles of highway driving. All sensitive information has been anonymized.<br><br>Features included:</p> <p>timestamp (s)<br>RSRQ (dB)<br>RSRP (dB)<br>RSSI (dB)<br>SINR (dB)<br>band (string)<br>RAN (string)<br>serving cell ID (integer)<br>delay/network ping) (ms)<br>service status (boolean)<br>operator_anonymized (integer)<br>DL throughput/ifstat (kilobytes per sec)<br>UL throughput/ifstat out (kilobytes per sec)<br>latitude (degrees)<br>longitude (degrees)<br>elevation (m)<br>GNSS mode (integer)<br>heading (degrees)<br>lateral acceleration (meters per second squared)<br>longitudinal acceleration (meters per second squared)<br>absolute acceleration (meters per second squared)<br>absolute velocity (meters per second)<br>longitudinal velocity (meters per second)</p>
Adaptive sampling-based structural prediction reveals opening of a GABAA receptor through the αβ interface
<p>These are the simulation of an open state of α1β2<span>γ</span><span>2 GABAA receptor. There are five replicates, each for 200 ns, saved with every ns freequency. The prot_masses.pdb is the desenstized starting state model and the .dcd files are simualtion frames of the open state. </span></p>
Decrease in MJO predictability following Indo–Pacific Warm Pool Expansion
<p>Data:</p> <p>CTRL.clim.SST.nc: climatology SST for control simulation</p> <p>CTRL.clim.TMQ.nc: climatology TMQ for control simulation</p> <p>CTRL.tot.OLR.nc: 10yr daily OLR for control simulation</p> <p>CTRL.tot.U850.nc: 10yr daily U850for control simulation</p> <p>WPE.clim.SST.nc: climatology SST for WPEsimulation</p> <p>WPE.clim.TMQ.nc: climatology TMQ for WPEsimulation</p> <p>WPE.tot.OLR.nc: 10yr daily OLR for WPEsimulation</p> <p>WPE.tot.U850.nc: 10yr daily U850for WPEsimulation</p> <p>Figure:</p> <p>All figure generation files</p> <p>Program: </p> <p>Figure3.LREG.f90: lregression program</p> <p> </p> <p> </p>
The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome
<p>Data associated with my PhD thesis titled "The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome".</p>
Predicted time‐to‐recover (days) for southeast Australia forested regions affected by the 2019–2020 bushfires.
<p>Model prediction of time-to-recover of southeast Australian eucalypt forests that burned during the megafires of 2019-2020. See the following publication for details regarding its creation.</p> <p>Rifai, S. W., De Kauwe, M. G., Gallagher, R. V., Cernusak, L. A., Meir, P., & Pitman, A. J. (2024). Burn severity and post‐fire<br>weather are key to predicting time‐to‐recover from Australian forest fires. Earth's Future, 12, e2023EF003780. https://doi.org/10.1029/2023EF003780</p>
Predicted metabolites and their MS/MS spectra
<p>Chemical structures and sampling frequencies of metabolites generated by DeepMet: `Generated molecules.csv.gz.' This file contains the canonical SMILES, exact mass, chemical formula, and sampling frequency for all of the generated molecules.</p> <p>MS/MS spectra for generated metabolites sampled 5 or more times, predicted by CFM-ID 4.0: `CFM-ID predicted MS2 spectra.zip`. This file contains MS/MS spectra predicted in positive and negative ionization modes at three different collision energies.</p>
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis
<p>The research that created this data is described in:</p> <ul> <li>Mossbridge, J., Tressoldi, P.E. and Utts, J. (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis. Frontiers in Psychology 3:390. doi: 10.3389/fpsyg.2012.00390.</li> </ul> <p>Two files are included:</p> <ul> <li>PPAA_MA.xlsx - The original file, Excel format</li> <li>PPAA_MA.csv - The file converted to CSV format by Adrian Ryan; comments converted to columns.</li> </ul>
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.