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13,064 results for “predictions”
Michael Deem's PCOD database of Predicted Zeolitic Structures
<p>These are the SLC 'good' sections of version 2.1 of <a href="https://mwdeem.org">Michael Deem</a>'s database of predicted zeolite structures. Version 1 was published in M. W. Deem, R. Pophale, P. A. Cheeseman, and D. J. Earl, ``Computational Discovery of New Zeolite-Like Materials,'' with cover image, <em>J. Phys. Chem. C</em> <strong>113</strong> (2009) 21353-21360. Version 2 was published in R. Pophale, P. A. Cheeseman, and M. W. Deem, "A Database of New Zeolite-Like Materials," <em>Phys. Chem. Chem. Phys.</em> <strong>13</strong> (2011) 12407-12412:</p> <p>"We here describe a database of computationally predicted zeolite-like materials. These crystals were discovered by a Monte Carlo search for zeolite-like materials. Positions of Si atoms as well as unit cell, space group, density, and number of crystallographically unique atoms were explored in the construction of this database. The database contains over 2.6 M unique structures. Roughly 15% of these are within +30 kJ mol<sup>−1</sup>Si of α-quartz, the band in which most of the known zeolites lie. These structures have topological, geometrical, and diffraction characteristics that are similar to those of known zeolites. The database is the result of refinement by two interatomic potentials that both satisfy the Pauli exclusion principle." <a href="https://doi.org/10.1039/C0CP02255A">https://doi.org/10.1039/C0CP02255A</a></p> <p>These files are also available at <a href="https://mwdeem.org/PCOD">https://mwdeem.org/PCOD</a></p> <p> </p>
PREDICT drug repurposing dataset
<p>/!\ LATEST VERSION IS LOCATED at the following page (v2.0.1): https://zenodo.org/record/7983090</p> <p>Version 1.0.3 (06/27/2023)</p> <p>----</p> <p>CHANGELOG:</p> <p>- Same as v1.0.0, except for the drug similarity matrices, which are no longer empty and ratings_mat.csv is present.</p> <p>---</p> <p>This is a drug repurposing dataset, compiled by Dr. Clémence Réda <clemence.reda@uni-rostock.de>, comprising a drug-disease association matrix, and several drug-drug and disease-disease similarity matrices. It uses the same type of data than the dataset compiled by Gottlieb et al., 2011 [PMID: 21654673]. The sparsity number is the percentage of nonzero values in the association matrix.</p> <p># drugs | # diseases | Sparsity number | # positive associations | # negative associations<br> ------- | ---------- | --------------- | ----------------------- | -----------------------<br> 1,395 | 1,501 | 0.38% | 8,240 | 295</p> <p>Note that not all drugs (resp., diseases) might have an associated feature vector (that is, all drugs and diseases in the similarity matrix appear in the association matrix, but not necessarily the other way around).</p> <p>----------</p> <p>This dataset consists of five .CSV files:</p> <p>* Drug-Disease Association Matrix</p> <p>1. "ratings_mat.csv"</p> <p>This matrix contains values in {-1,0,1} where -1 stands for a negative association (i.e., the drug failed for some reason to treat the considered disease: e.g., lack of accrual in the associated clinical trial, or proven toxicity), 1 for a positive association (i.e., the drug was shown to treat the disease), and 0 for unknown associated status. The columns are diseases, identified by their MedGen Concept ID, whereas rows are drugs, identified by their DrugBank IDs or PubChem CIDs.</p> <p>* Drug-Drug Similarity Matrix</p> <p>All drug-drug similarity matrices have drugs in their columns and rows, identified by their DrugBank IDs or PubChem CIDs.</p> <p>1. "se_PREDICT_matrix.csv"</p> <p>Jaccard score similarity between one-hot encodings of the side effects reported for drugs.</p> <p>2. "signature_PREDICT_matrix.csv"</p> <p>Jaccard score similarity between drug signatures (from the CREEDS or the LINCS L1000 databases), that is, vectors reporting the genewise change in activity due to treatment.</p> <p>Other types of similarity matrices and further information about the generation of those matrices are available by running the Jupyter notebook PREDICT_dataset_v1.0.0.ipynb on the following GitHub repository: https://github.com/RECeSS-EU-Project/drug-repurposing-datasets.</p> <p>* Disease-Disease Similarity Matrix</p> <p>All disease-disease similarity matrices have diseases in their columns and rows, identified by their MedGen Concept IDs.</p> <p>1. "disease_semantic_PREDICT_matrix.csv"</p> <p>Resnik semantic similarity between onthology nodes associated with diseases (from the HPO database).</p> <p>2. "disease_phenotype_PREDICT_matrix.csv"</p> <p>Jaccard score similarity between disease phenotypes (from the CREEDS database), that is, vectors reporting the genewise change in activity due to the disease.</p> <p>Other types of similarity matrices and further information about the generation of those matrices are available by running the Jupyter notebook PREDICT_dataset_v1.0.0.ipynb on the following GitHub repository: https://github.com/RECeSS-EU-Project/drug-repurposing-datasets.</p> <p>----------</p> <p>For any questions, please contact the author at <clemence.reda@uni-rostock.de> or the RECeSS project contributors at <recess-project@proton.me>.</p>
Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"
<p>Supplementary datasets accompanying the manuscript "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds" published in the Metabolic Engineering Journal (<a href="https://doi.org/10.1016/j.ymben.2022.03.013">https://doi.org/10.1016/j.ymben.2022.03.013). </a>In line with the standards of open science, the ARBRE toolbox is freely available to the scientific community on gitHub (<a href="https://github.com/EPFL-LCSB/ARBRE">https://github.com/EPFL-LCSB/ARBRE</a>) and we also provide the web-version at <a href="http://lcsb-databases.epfl.ch/arbre/">http://lcsb-databases.epfl.ch/arbre/</a></p> <p>ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer is a new computational resource consisting of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 33′000 known and 390′000 novel reactions predicted with generalized enzymatic reactions rules and over 74′000 compounds, of which 19′000 are known to biochemical databases and 55′000 only to PubChem. Over 1′000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included in the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways and products of lignin degradation to predict valuable compound derivations.</p> <p>Supplementary files are organized as follows:</p> <p>- 1-s2.0-S1096717622000490-mmc4.docx contains Supplementary Figures 1-4 and Tables 1, 2, and 4.</p> <p>- 1-s2.0-S1096717622000490-mmc2.xlsx contains Supplementary Table 3.</p> <p>- 1-s2.0-S1096717622000490-mmc1.xlsx contains Supplementary Table 5</p> <p>- 1-s2.0-S1096717622000490-mmc3.xlsx contains Supplementary Table 6</p> <p> </p> <p> </p> <p> </p> <p> </p>
A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model
<p>A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model</p>
Block-group level predicted mode share for New York City and New York State
<p>We provide two datasets of predicted mode share, one for New York City and another for New York State. Each row contains the mode proportion of trips along a census block group-level OD pair made by one of the four population segments: low-income, not low-income, students, and senior population. Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool.</p> <p>The prediction is based on GLAM logit model calibrated with Replica's statewide synthetic population dataset. The in-sample prediction accuracy is quite competitive, with an overall accuracy of 90.28% in New York State and 88.63% in New York City. For more details of the model, please refer to our Github repository: <a href="https://github.com/BUILTNYU/GLAM-Logit">BUILTNYU/GLAM-Logit (github.com)</a></p>
Dataset for the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest"
<p>Datasets to accompany the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest". Written by J. Elgy and P. D. Ledger (Keele University, 2023).</p> <p>The datasets include data files, meshes, and source code for generating figures from the paper. This requires the open source MPT-Calculator software available at <a href="https://github.com/MPT-Calculator/MPT-Calculator%7D">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch).</p> <p>The authors gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1</p> <p> </p>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction (Supplementary Data)
<p>This dataset contains supplementary replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". In particular, it contains a new version of our `final_raw_dips.tar.gz` protein pair representations which now contain (1) residue-level annotations for intrinsic disorder regions (IDRs) as well as (2) a copy of each protein pair representation in the HDF5 file format for programming language-agnostic read capabilities. In addition, this record also contains (3) raw MSAs (in HDF5 file format) generated for each protein pair using Jackhmmer and AlphaFold's small version of the Big Fantastic Database (BFD). Lastly, this record contains (4) PDB metadata derived for each DIPS-Plus complex using Graphein's PDBManager API as well as (5) structure-based (i.e., FoldSeek-based) training and validation splits of the dataset's complexes in the form of respective text files containing the file paths of complexes assigned to each split.</p>
Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers
<p>Raw data (occurrences and environmental predictors) used in the manuscript "Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers"</p>
An Expanded Dataset for Improved Prediction of Chemical Biodegradability
<p>Biodegradability is a crucial factor in assessing the long-term impact of chemicals on the environment. However, experimental testing to determine biodegradability is time-consuming and laborious. To address this issue, <em>in silico </em>approaches such as quantitative structure-activity relationship (QSAR) models are highly encouraged by legislators.</p> <p> </p> <p>European legislators have incorporated chemical persistency in the Registration, Evaluation, and Authorization of Chemicals (REACH) for the assessment of chemicals. However, only 61% of chemicals produced or imported in quantities of over 1000 tons per year have information on biodegradability. As a potential solution, REACH encourages the use of QSAR models to predict the biodegradability of compounds.</p> <p> </p> <p>To encourage the development of QSAR models to predict the biodegradability of compounds, this work extends the "All-Public set," which is an aggregated dataset with information on 2830 compounds from various sources. In this study, we contribute to this dataset by adding information on the biodegradability of 3707 new compounds from the ECHA database, resulting in a larger dataset with the biodegradability information of 6537 compounds.</p> <p> </p> <p>By providing a larger dataset with biodegradability information, we aim to promote the development of more accurate QSAR models for predicting the biodegradability of compounds. This will enable more efficient and effective assessments of the potential impact of chemicals on the environment, facilitating the development of more sustainable and environmentally friendly products.</p>
Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials
<p>Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation.</p> <p>Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.</p>
Temporal Validity Change Prediction - Dataset
<p>This dataset contains data for <em>temporal validity change prediction</em>, an NLP task that will be defined in an upcoming publication. The dataset consists of five columns. </p> <ul> <li>target - A Tweet ID. This column must be manually rehydrated via the Twitter API to obtain the tweet text.</li> <li>follow_up - A synthetic follow-up tweet that semantically relates to the target tweet.</li> <li>context_only_tv - The expected temporal validity duration of the <strong>target </strong>tweet, when read in isolation.</li> <li>combined_tv - The expected temporal validity duration of the <strong>target </strong>tweet, when read <strong>together with the follow-up tweet</strong>.</li> <li>change - The TVCP task label, i.e., whether the temporal validity duration of the target tweet is <em>decreased</em>, unchanged (<em>neutral</em>), or <em>increased </em>by the information in the follow-up tweet.</li> </ul> <p>The duration labels (context_only_tv, combined_tv) are class indices of the following class distribution:<br> [no time-sensitive information, less than one minute, 1-5 minutes, 5-15 minutes, 15-45 minutes, 45 minutes - 2 hours, 2-6 hours, more than 6 hours, 1-3 days, 3-7 days, 1-4 weeks, more than one month]</p> <p>Different dataset splits are provided.</p> <ul> <li>"dataset.csv" contains the full dataset.</li> <li>"train.csv", "val.csv", "test.csv" contain an 80-10-10 train-val-test split.</li> <li>"train[0-4].csv" and "test[0-4].csv" respectively contain training and test data for one of 5 folds for 5-fold cross-validation. The train file contains 80% of the data, while the test file contains 20%. To replicate the original experiments, the train file should be sorted by the preprocessed target tweet text, then the first 12.5% of target tweets should be sampled to generate validation data, leading to a 70-10-20 train-val-test split. </li> </ul>
Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset
<p><strong>Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the undesirable effects of wildfires increases with the understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the contiguous United States (CONUS). FPA FOD v6 contains information on the location, jurisdiction, discovery time, cause, and final size of >2.2 million wildfires from 1992-2020 in CONUS. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.</strong></p>
Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
<p><strong>General Description</strong></p> <p>The dataset contains composites at 250 m spatial resolution of (1) monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) the yearly average of the difference between actual and potential (actual minus potential) FAPAR. The dataset is based on the <a href="https://zenodo.org/record/8392976">95th percentile of the monthly aggregated FAPAR</a> derived from <a href="http://glass.umd.edu/Overview.html">250 m 8 d GLASS V6 FAPAR</a>. Potential FAPAR was predicted by fitting an ensemble ML model using globally distributed training points (cca 3 Mio) and a set of 52 biophysical covariates including several layers related to human pressure. The code for modeling potential FAPAR is openly available at <a href="http://github.com/Open-Earth-Monitor/Global_FAPAR_250m">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m</a>. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. </p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2021 - December 2021</li> <li><strong>Type of data: </strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR</li> <li><strong>Statistical methods used: </strong>Ensemble machine learning</li> <li><strong>Limitations or exclusions in the data: </strong>The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size: </strong>172,800 x 71,698</li> <li><strong>File format: </strong>Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p> </p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> pot.fapar = Potential Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination: </strong>eml = ensemble machine learning</li> <li><strong>Position in the probability distribution / variable type:</strong> m = mean</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference: </strong>s = surface</li> <li><strong>Time reference begin time:</strong> 20210101 = 2021-01-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2021-12-31</li> <li><strong>Bounding box: </strong>go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230924 = 2023-09-24 (creation date)</li> </ol>
AlphaFold structures reported in "AlphaFold2 Can Predict Single-Mutation Effects"
<p>This contains AlphaFold predictions for X proteins that are found in the Protein Data Bank (PDB), that were used to evalluate AlphaFold's predictions of mutation effects. This includes one set of structures predicted by AlphaFold2.0, using default settings, and one structure for each of 5 models. This also includes structures predicted by the ColabFold version of AlphaFold (6 recycles, 5 models, no template, amber minimization, 4 repeats).</p><p>There are also additional predicted structures that are found in the PDB that were not analyzed in the paper.</p><p>There are AlphaFold predictions for three proteins (BFP / RFP, GFP, and PafA), covering either all (BFP/RFP, PafA) or a subset (GFP) of the sequences in three datasets of phenotype measurements from high-throughput experiments.</p><p>Results are separated into tar files based on whether DeepMind (AF2.0) or ColabFold implementation was used.</p><p>Folders under "ColabFold/PDB" are labelled according to a sequence ID, since multiple PDB structures can exist for a single sequence. These sequence IDs can be mapped back to PDB IDs using the information in "seq_id_pdb_id.json".</p><p>All PDB files have been compressed using Foldcomp (<a href="https://github.com/steineggerlab/foldcomp">https://github.com/steineggerlab/foldcomp</a>). Foldcomp is required to decompress the ".fcz" files in order to recover the ".pdb" files.</p>
Code and data for manuscript: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.
<p>This is the source code and data required to reproduce data analysis and figures from the manuscript, "Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir". </p>
Predicted Surficial Blue Carbon Maps for Blackbird Creek and St. Jones River Tidal Salt Marshes using 2014-2023 Landsat-8 OLI records
These data tables reflect the predictions of a gradient boosted trees model for predicting soil organic matter (SOM) in the surficial layer of tidal marsh soils. The model was trained on soil core data related to its corresponding spectral characteristics from decadal Landsat-8 Operational Land Imager data (see associated publication Warner et al. "Leveraging a decade of Landsat-8 spectral records for mapping blue carbon storage in tidal salt marshes"). This is a modeled data product designed to illustrate spatial patterns of organic matter storage as predicted by satellite data.
Relyea, R. A., and K. L. Yurewicz. 2002. Predicting community outcomes from pairwise interactions: Integrating density- and trait-mediated effects. Oecologia 131:569-579.
Understanding how species interactions shape the structure of ecological communities based on pairwise comparisons has been a difficult undertaking for ecologists because effects in reassembled communities can be different than simple density-mediated interactions would suggest. Part of this complexity occurs because many species change their behavior and morphology with different predators and competitors and, thus, change their per-capita interaction rates (i.e. trait-mediated interactions). Our objective was to use a simple experimental community of two predators (larval dragonflies, Anax longipes, and larval salamanders, Ambystoma tigrinum), two prey (larval green frogs, Rana clamitans, and larval bullfrogs, R. catesbeiana), and a shared prey resource to determine whether we can predict interactions in a reassembled community by combining our knowledge of density- and trait-mediated interactions,. We combined pairwise laboratory experiments on predation rates and predator-induced behaviors with a mesocosm experiment to examine density- and trait-mediated effects. We used a factorial combination of no predators, caged Anax (to induce anti-predator traits without changing prey density), and lethal Anax crossed with no predators, caged Ambystoma, and lethal Ambystoma. The species interactions in the reassembled community were qualitatively predictable based on the pairwise experiments. Lethal Anax preyed upon Ambystoma and green frogs while lethal Ambystoma only preyed upon green frogs. Anax also reduced the activity of the green frogs; this caused a decrease in salamander predation on green frogs, a decrease in green frog acquisition of resources, and an increase in bullfrog acquisition of resources. Ambystoma had no effect on green frog activity, no effect on resource acquisition by green frogs, and no effect on resource acquisition by bullfrogs. These results suggest that we can better understand how species interact in natural communities if we have a more deta
Arthropod pitfall trap biomass captured (weekly) and pitfall biomass model predictions (daily) near Toolik Field Station, Alaska, summers 2012-2016.
This data set contains information about the per pitfall trap arthropod biomass captured (or modeled using GAM modelling approaches) near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It is associated with publication DOI: 10.1111/jav.01712.
Experiment output data from a seamless sea ice prediction system based on AWI-CM 1.1
<p>The data provide experiment output of the seamless sea ice prediction system based on AWI-CM 1.1.</p> <p>Exp_C_T_SIT_2007-2012.nc is the monthly sea ice thickness analysis averaged over from 2007 to 2012 for Exp_C_T.</p> <p>Exp_twin_SIC_SIT.tar.bz is the sea ice concentration and sea ice thickness analysis for Exp_twin.</p> <p>Exp_CTD_T_atm_T2m_n63grid.nc is the averaged 2m atmosphere temperature from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_atm_u10_n63grid.nc is the averaged 10m wind velocity (u-component) from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_atm_v10_n63grid.nc is the averaged 10m wind velocity (v-component) from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_SALT_2007-2018_monmean.nc is the averaged ocean salinity from 2007-2018 for Exp_CTD_T. </p> <p>Exp_CTD_T_TEMP_2007-2018_monmean.nc is the averaged ocean temperature from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_SIC_SIT_SIV.tar.bz is the analysis of sea ice concentration, sea ice thickness, and sea ice drift from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_SIV_INC.nc is the sea ice drift increment from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_TEMP_FCST_2007-2018_monmean.nc is the monthly mean ocean temperature forecast averaged over 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_UEL_2014-2018_mean.nc is the mean oceanic velocity (u-component) averaged over 2014-2018 for Exp_CTD_T.<br> Exp_CTD_T_VEL_2014-2018_mean.nc is the mean oceanic velocity (v-component) averaged over 2014-2018 for Exp_CTD_T.</p> <p><br> Exp_CTRL_atm_T2m_n63grid.nc is the averaged 2m atmosphere temperature from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_atm_u10_n63grid.nc is the averaged 10m wind velocity (u-component) from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_atm_v10_n63grid.nc is the averaged 10m wind velocity (v-component) from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_SALT_2007-2018_monmean.nc is the averaged ocean salinity from 2007-2018 for Exp_CTRL. <br> Exp_CTRL_SIC_SIT.tar.bz is the sea ice concentration and sea ice thickness simulation from 1997-2018 for Exp_CTRL.<br> Exp_CTRL_SIV.tar.bz is sea ice drift simulation from 1997-2018 for Exp_CTRL.<br> Exp_CTRL_TEMP_2007-2018_monmean.nc is the averaged ocean temperature from 2007-2018 for Exp_CTRL. <br> Exp_CTRL_UEL_2014-2018_mean.nc is the averaged oceanic velocity (u-component) from 2014-2018 for Exp_CTRL. <br> Exp_CTRL_VEL_2014-2018_mean.nc is the averaged oceanic velocity (v-component) from 2014-2018 for Exp_CTRL. </p> <p>fesom.initial.mesh.diag.nc contains the area and volume of the CORE-II mesh.<br> mesh_core2.tar.bz contains the detailed CORE-II mesh information.<br> </p>
Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms - Raw Data
<p>Raw data from investigation published as 'Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms.'</p>
ScienceDex guides
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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.