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”
Structural and socioeconomic features of cities predict migratory bird species richness
<p>Cities are aggregates of human activities where our decisions shape the environment creating heterogeneity across urban centers that can have significant ecological effects on wildlife. Many bird species are found in cities during the breeding season, which implies they find sufficient resources in cities to support them during this energetically costly time. As populations of many migratory bird species are declining, knowledge of how they are affected by urbanization is needed. Yet, we know little about how the species richness of migratory birds varies across different types of cities. Here we ask if cities' structural and socioeconomic features can predict the species richness of migratory birds that generally select different breeding habitats during the breeding season. We used eBird data from census-designated urban areas in the United States to model the relationship between features of cities (housing density, median income, city age, and commuting time), environmental disturbance (measured by the human footprint index) and species richness by fitting generalized linear models to data. We show that commuting time was the most important factor determining species richness across cities and the rest of the city features were weakly associated with species richness. Overall species were responding to city variation in similar ways. While we expected that cities with more disturbance would have lower species richness, our results indicate that some species are able to tolerate even highly disturbed cities and that cities in certain regions may act as a refuge to birds. This knowledge is important for our general understanding of cities as habitat for birds and how migratory birds respond to across-city variation during the breeding season.</p>
Job Prediction based on Skills and Years of experience
<h2><strong>Purpose:</strong></h2><p>This dataset was meticulously collected by me during my academic journey. It comprises information on various attributes related to individuals' academic backgrounds, programming skills, work experiences, and roles in the field of technology. The dataset is specifically designed for job prediction purposes, helping to analyze and forecast potential job roles based on individuals' qualifications.</p>
ABodyBuilder2 predicted structures of paired antibody sequences from Observed Antibody Space.
<p>We used ABodyBuilder2 (https://doi.org/10.1038/s42003-023-04927-7) to model ~1.5M paired antibody structures from paired antibody sequences in Observed Antibody Space (https://opig.stats.ox.ac.uk/webapps/oas/oas_paired/). We have save the structures in folders and sub folders that correspond to the OAS files they came from. Parent folders are named according to study. Within each parent folder are sub folders names according to the files (named by SRA ID) containing sequences. Each structure is then named with the parent file followed by the row number from this file.</p>
baseline_predict
Open the record for dataset details and reuse information.
Partial Atomic Model of the Tailed Lactococcal Phage TP901-1 as Predicted by AlphaFold2
Open the record for dataset details and reuse information.
Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
<p>Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be</p>
Global prediction of extreme floods in ungauged watersheds
<p>This dataset accompanies the following article:</p> <p><a href="https://doi.org/10.1038/s41586-024-07145-1">Nearing, Grey, et al. "Global prediction of extreme floods in ungauged watersheds." Nature (2024).</a></p> <div> </div> <p>The code repository associated with this data is repository here: https://github.com/google-research-datasets/global_streamflow_model_paper/. It is highly recommended to use the associated code repository to process this data.</p> <p>The `model_data.tgz` repository includes reforecasts from the Google model and reanalyses from the GloFAS model. Google model outputs are in units [mm/day] and GloFAS outputs are in units [m3/s]. Model outputs are daily and timestamps are right-labeled, meaning that model outputs labeled, .e.g., 01/01/2020 correspond to streamflow predictions for the day of 12/31/2019.</p>
Resource competition predicts assembly of in vitro gut bacterial communities
<p>Microbiota dynamics arise from a plethora of interspecies interactions, including resource competition, cross-feeding, and pH modulation. The individual contributions of these mechanisms are challenging to untangle, especially in natural or complex laboratory environments where the landscape of resource competition is unclear. Here, we developed a framework to estimate the extent of multi-species niche overlaps by combining metabolomics data of individual species, growth measurements in pairwise spent media, and mathematical models. When applied to an in vitro model system of human gut commensals in complex media, our framework revealed that a simple model of resource competition described most pairwise interactions. By grouping metabolomic features depleted by the same set of species, we constructed a coarse-grained consumer-resource model that predicted assembly compositions to reasonable accuracy. Moreover, deviations from model predictions enabled us to identify and incorporate into the model additional interactions, including pH-mediated effects and cross-feeding, which improved model performance. In sum, our work provides an experimental and theoretical framework to dissect microbial interactions in complex in vitro environments.</p>
Model Output accompanying 'A predicted pause in the rapid warming of the Northwest Atlantic Shelf in the coming decade'
<p>Postprocessed model output and observations necessary for reproducing all figures from the main body of the artcile 'A predicted pause in the rapid warming of the1 Northwest Atlantic Shelf in the coming decade'. Please see the README.txt file </p>
Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity
<p>This repository contains personal immune profiles of 323 healthy individuals (300BCG) subjected to Bacillus Calmette-Guérin (BCG) with blood samples collected immediately before (day 0), and 14 and 90 days after the vaccination. The personal immune profiles comprise:</p> <ul> <li>immune cell concentrations measured with flow cytometry and a hematology analyzer</li> <li>plasma concentrations of 73 circulating inflammatory markers</li> <li>30 measurements of cytokine and lactate production capacity of peripheral blood mononuclear cells (PBMCs) in response to four microbial stimuli (Candida albicans, Escherichia coli lipopolysaccharide [LPS], Staphylococcus aureus, Mycobacterium tuberculosis).</li> </ul> <p>Visit <a href="http://300BCG.bocklab.org/">http://300BCG.bocklab.org/</a> to learn more.</p>
Efficient Drug-Target Interactions Prediction Framework via Transferable Knowledge Fusing
<p><span>Drug-target integrations (DTI) prediction is a niche in drug discovery, streamlining the search for potential drugs. Computer-aided drug discovery (CADD) has gained traction for its precise predictions, efficiency, and adaptability across various situations. Yet, the computational demands of current top CADD models hinder their practical use due to heavy resource needs.</span></p> <p><span>In this research, we introduce TransFusE DTI, an effective framework for predicting DTIs that leverages pre-trained knowledge to construct models that optimize predictive accuracy while minimizing computational demands. The encoder uses a pre-extracted embedding vector from ProtBERT to reduce computational load and adapts a smaller ProtBERT model. It also includes target-related functional text to boost predictive accuracy. We evaluate the performance of TransFusE DTI using three widely-recognized benchmark datasets: BIOSNAP, DAVIS, and BindingDB, and compare its results to prior studies.</span></p> <p><span>Our results demonstrate that TransFusE DTI exhibits superior predictive performance on the BIOSNAP and BindingDB datasets. Notably, the model's parameter count is only 60% of that of the previous top-performing model by </span><span><a href="https://www.mdpi.com/1999-4923/14/8/1710"><span>Kang et al. (2022)</span></a></span><span>, and it operates efficiently with a learning rate of 26%. Furthermore, the model's video memory requirement is 11.2 GB, rendering it suitable for use on general-purpose Graphics Processing Units (GPUs). </span></p>
Data from: Does the number of functional olfactory receptor genes predict olfactory sensitivity and discrimination performance in mammals?
<p>The number of functional genes coding for olfactory receptors differs markedly between species and has repeatedly been suggested to be predictive of a species' olfactory capabilities. To test this assumption, we compiled a database of all published olfactory detection threshold values in mammals and used three sets of data on olfactory discrimination performance that employed the same structurally related monomolecular odor pairs with different mammal species. We extracted the number of functional olfactory receptor genes of the 20 mammal species for which we found data on olfactory sensitivity and/or olfactory discrimination performance from the Chordata Olfactory Receptor Database. We found that the overall olfactory detection thresholds significantly correlates with the number of functional olfactory receptor genes. Similarly, the overall proportion of successfully discriminated monomolecular odor pairs significantly correlates with the number of functional olfactory receptor genes. These results provide the first statistically robust evidence for the relation between olfactory capabilities and their genomics correlates. However, when analysed individually, of the 44 monomolecular odorants for which data on olfactory sensitivity from at least five mammal species are available, only five yielded a significant correlation between olfactory detection thresholds and the number of functional olfactory receptors genes. Also, for the olfactory discrimination performance, no significant correlation was found for any of the 74 relationships between the proportion of successfully discriminated monomolecular odor pairs and the number of functional olfactory receptor genes. While only a rather limited amount of data on olfactory detection thresholds and olfactory discrimination scores in a rather limited number of mammal species is available so far, we conclude that the number of functional olfactory receptor genes may be a predictor of olfactory sensitivity and discrimination performance in mammals.</p>
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction
<p><strong>Data</strong></p> <ul> <li><strong>22-12-05-data</strong>: original molecular XANES data created from <a href="https://doi.org/10.1103/PhysRevResearch.5.013180">Ghose <em>et al.</em></a></li> <li><strong>23-04-26-ml-data</strong>: machine learning-ready data which is prepared in the format required by <a href="https://github.com/matthewcarbone/Crescendo">Crescendo</a>.</li> <li><strong>23-05-03-hp</strong>: hyper-parameter tuning results from 23-04-26-ml-data.</li> <li><strong>23-05-05-ensembles</strong>: ensemble results from 23-04-26-ml-data.</li> <li><strong>23-05-11-ml-data-CUTOFF8</strong>: a special machine learning-ready dataset constructed by a unique partitioning: only molecules with less than or equal to 8 atoms/molecule are used for training/validation, the rest are used for testing.</li> <li><strong>23-12-06_torch_models</strong>: torch only models which can be easily used independently of our ML helper repository, Crescendo. Instead, it can be used with a few lines of code found in multimodal_molecules/core.py, in our <a href="https://github.com/AI-multimodal/multimodal-molecules">GitHub respository</a>.</li> </ul> <p><strong>Funding</strong></p> <p>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704.</p>
Predictive mapping of tree species assemblages in an African montane rainforest
<p>Conservation of mountain ecosystems can benefit from knowledge of habitats and their distribution patterns. This benefit is particularly true for diverse ecosystems with high conservation values such as the "Afromontane" rainforests. We mapped the vegetation of one such forest: the rugged Bwindi Impenetrable Forest, Uganda—a World Heritage Site known for its many restricted-range plants and animal taxa including several iconic species. Given variation in elevation, terrain and human impacts across Bwindi, we hypothesised that these factors influence the composition and distribution of tree species. To test this, detailed surveys were carried out using stratified random sampling. We established 289 georeferenced sample sites (each with 15 trees ≥20 cm dbh) ranging from 1,320 to 2,467 m a.s.l. and measured 4,335 trees comprising 89 species that occurred in four or more sample sites. These data were analysed against twenty-one digitally mapped biophysical variables using various analytical techniques including non-metric multidimensional scaling (NMDS) and random forests. We identified six tree species assemblages with distinct compositions. Among the biophysical variables, elevation had the strongest correlation with the ordination (r<sup>2</sup>=0.5; <em>p</em><0.001). The "out-of-bag" (OOB) estimate of the error rate for the best final model was 50.7% meaning that nearly half of the variation was accounted for using a limited set of variables. We demonstrate that it is possible to predict the spatial pattern of such a forest based on sampling across a highly complex landscape. Such methods offer accurate mapping of composition that can guide conservation.</p>
Predicting the pre-service teachers' teaching intention from educator-created (dis)empowering climates: A self-determination theory-based longitudinal approach
<p><span>Guided by self-determination theory, this two-wave longitudinal research aims to examine the associations between pre-service teachers’ perceptions of educator-created (dis-)empowering climates and their teaching intention, considering the bright and dark motivational pathways. A total of 1,258 secondary pre-service teachers (55.5% women, <em>M<sub>age =</sub></em> 26.17, <em>SD </em>= 5.66) have participated. The results from path analysis have shown positive associations between educator-created empowering climates and need satisfaction, autonomous motivation, and teaching intention in pre-service teachers, while educator-created disempowering climates have been positively related to pre-service teachers’ need frustration, controlled motivation and amotivation. The conclusions have suggested that pre-service teachers’ perceptions of educator-created (dis-)empowering climates during their initial teacher education programme play a determining role in their teaching intention. </span></p>
ESCOTT mutational effect predictions for ProteinGym Substitutions Dataset with Colabfold MSAs
<p>This dataset includes all escott calculations for ProteinGym dataset and the calculations were performed with v1.6.0 of PRESCOTT package. </p> <p>ProteinGym dataset contains 72 proteins and there are 87 experiments performed on these 72 proteins.</p> <p>This dataset is organized into three folders:<br>1-predictions<br>2-analyses<br>3-figures-and-csv-files</p> <p>'predictions' folder has 72 subdirectories, one for each protein.<br>Let's use BLAT as an example to show the most essential files in each protein folder inside 'predictions'.<br>You can find the following files in BLAT_ECOLX_full_11 directory:<br> *aliBLAT.fasta (input file): This is the MSA file used for the calculations and it was obtained with Colabfold.<br> *ranked_0.pdb (input file): This is the pdb model used to deduce structural parameters by escott algorithm.<br> *BLAT_ECOLX_Stiffler_2015.mut (input file): This file is a list of mutations in simple text format. <br> *BLAT_jet.res (data file): This file contains trace(tjet), cv and pc parameters for each amino acid in a protein. This data is produced and used by escott.<br> *BLAT_ECOLX_Stiffler_2015_normPred_evolCombi.txt (output file): This file contains raw (unprocessed) escott predictions. This file is the most important output of escott algorithm.<br> *BLAT_ECOLX_Stiffler_2015_singleline.csv (experimental data file): We compare our escott predictions to the experimental measurements given in this file.<br> <br> <br>'analyses' folder contains 3 types of analyses that we used in our study:<br>1-secondary-structure-analysis<br>2-escott-averages-analysis<br>3-amino-acid-type-analysis</p> <p>'figures-and-csv-files' folder contains png files and their data in csv format.</p>
Generative AI in the Advancement of Viral Therapeutics for Predicting and Targeting Immune-Evasive SARS-CoV-2 Mutations
<p>This dataset <strong>encompasses</strong> and describes the following features:</p> <ul> <li>Mutations in viruses like SARS-CoV-2 can make them escape vaccines and treatments.</li> <li>Accurately predicting these mutations is crucial for developing effective countermeasures.</li> <li>The study uses a type of AI called a Generative Adversarial Network (GAN) to analyze the virus's spike protein, which plays a key role in infection.</li> <li>The GAN generates protein sequences similar to natural ones, but which are also likely to evade immune responses.</li> <li>By analyzing these generated sequences, the researchers improve their AI model's ability to predict real-world escape mutations.</li> <li>This improved prediction could help design better vaccines and treatments, and prepare for future viral threats.</li> </ul>
Predictability of abrupt shifts in dryland ecosystem functioning
<p>Data and code accompanying the paper:</p> <p>Bernardino, P., De Keersmaecker, W., Horion, S., Oehmcke, S., Gieseke, F., Fensholt, R., Van De Kerchove, R., Lhermitte, S., Abel, C., Van Meerbeek, K., Verbesselt, J., and Somers, B. 2024. Predictability of abrupt shifts in dryland ecosystem functioning. Nature Climate Change, doi.org/10.1038/s41558-024-02201-0.</p> <p> </p>
Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding - Nature Communications
<p>The full analysis pipeline used in this study is available at the following link: </p> <p>https://doi.org/10.5281/zenodo.11072410</p> <p>Additional in-house-built code and functions used in this study are part of the LBPD repository which is available at the following link: </p> <p>https://doi.org/10.5281/zenodo.10701724</p> <p> </p> <p>Abstract</p> <p>Our brain is constantly extracting, predicting, and recognising key spatiotemporal features of the physical world in order to survive. While neural processing of visuospatial patterns has been extensively studied, the hierarchical brain mechanisms underlying conscious recognition of auditory sequences and the associated prediction errors remain elusive. Using magnetoencephalography (MEG), we describe the brain functioning of 83 participants during recognition of previously memorised musical sequences and systematic variations. The results show feedforward connections originating from auditory cortices, and extending to the hippocampus, anterior cingulate gyrus, and medial cingulate gyrus. Simultaneously, we observe backward connections operating in the opposite direction. Throughout the sequences, the hippocampus and cingulate gyrus maintain the same hierarchical level, except for the final tone, where the cingulate gyrus assumes the top position within the hierarchy. The evoked responses of memorised sequences and variations engage the same hierarchical brain network but systematically differ in terms of temporal dynamics, strength, and polarity. Furthermore, induced-response analysis shows that alpha and beta power is stronger for the variations, while gamma power is enhanced for the memorised sequences. This study expands on the predictive coding theory by providing quantitative evidence of hierarchical brain mechanisms during conscious memory and predictive processing of auditory sequences.</p> <p> </p> <p>The data is provided after pre-processing (Maxfilter, ICA for removing eye blink and heart beat, co-registration with the individual MRI T1) and epoching.</p> <p>Source data files are provided for Supplementary Figures and Tables (referred to as Supplementary Data in the paper).</p> <p>Supplementary Figures are provided in PDF format in high resolution.</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.