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.
156
datasets available to search
ShareScore release 0.9.0
Dataset results
156 results for “explorative modeling”
The best of both worlds: highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration
<p><span>Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry is used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.</span></p>
Data Sets for Article: Exploration on Learning Molecular Docking with Deep Learning Models
<p>The MOL2 and CSV file of the clustered compounds from ChemDiv are available in <strong>Additional file 2</strong></p> <p>Docking scores of training set1 and the following traing set2 for each target were saved as csv files and provided in <strong>Additional file 3.</strong></p> <p>The SMILES, MOL2, SDF of DUD-E compounds and PDB of receptors used for validation are provided in <strong>Additional file 4</strong>.</p> <p>The SMILES of 500,000 compounds randomly selected from the ChEMBL database are provided in <strong>Additional file 5</strong></p> <p>The SMILES of compounds with activities from the ChEMBL database for each target are provided in <strong>Additional file 6</strong></p>
Dataset for Exploring Western North Pacific Tropical Cyclone Activity in the High-Resolution Community Atmosphere Model
<p>This dataset contains results from high-resolution, tropical cyclone-permitting experiments using Community Atmosphere Model version 5.</p>
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
<p>Training and test datasets and scripts for training models.</p>
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data
Open the record for dataset details and reuse information.
Exploring The Potential of GPT-3-based Large Language Model For Melody Generation
<p>Here, we provide the dataset used, all generated melodies and melodies used to conduct subjective listening test.</p>
Exploring doxorubicin transport in 2D and 3D models of MDA-MB-231 sublines: impact of hypoxia and cellular heterogeneity on doxorubicin accumulation in cells
<p><span>This data set includes raw data supporting the paper "</span><strong><span>Exploring doxorubicin transport in 2D and 3D models of MDA-MB-231 sublines: impact of hypoxia and cellular heterogeneity on doxorubicin accumulation in cells</span></strong><span>".</span></p> <p><strong><span>The files include the following data</span></strong><span>:</span></p> <p><span>1. MTT assay results used for calculation of <strong>cell viability</strong> / <em><strong><span>EC</span></strong></em></span><strong><sub><span>50</span></sub></strong><strong><span> values</span></strong><span> for doxorubicin in MDA-MB-231 cell line and its sublines;</span></p> <p><span>2. The raw data used for calculating <strong><span>doxorubicin </span></strong></span><strong><span>transport</span></strong><strong><span> </span></strong><span>in <strong><span>2D</span></strong> cells under <strong>normoxia</strong>;</span></p> <p><span>3. The raw data used for calculating <strong><span>doxorubicin </span></strong></span><strong><span>transport</span></strong><strong><span> </span></strong><span>in <strong><span>2D</span></strong> cells under <strong>hypoxia</strong>;</span></p> <p><span>4. The raw data used for calculating <strong><span>doxorubicin </span></strong></span><strong><span>transport</span></strong><strong><span> </span></strong><span>in cancer spheroids (<strong><span>3D</span></strong> cultures).</span></p>
Exploring Stellar Evolution Models of sdB Stars using MESA
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2015ApJ...806..178S">Exploring Stellar Evolution Models of sdB Stars using MESA</a></p>
Model 6 in Ready Species One: Exploring the Use of Augmented Reality to Enhance Systematic Biology with a Revision of Fijian Strumigenys (Hymenoptera: Formicidae)
Model 6. Strumigenys parzival (CASENT0186960, holotype) presented as a computer-generated 3D mesh model optimized for augmented reality. Volumetric surfaces rendered from micro-ct data and texture mapped from standard specimen photographs. An interactive version of this model is available in the HTML version of this article online and at https://sketchfab. com/3d-models/2d608f095a1d4daea3ba34e0cae83255
Model 4 in Ready Species One: Exploring the Use of Augmented Reality to Enhance Systematic Biology with a Revision of Fijian Strumigenys (Hymenoptera: Formicidae)
Model 4. Strumigenys gunter (CASENT0184984, holotype) presented as a computer-generated 3D mesh model optimized for augmented reality. Volumetric surfaces rendered from micro-ct data and texture mapped from standard specimen photographs. An interactive version of this model is available in the HTML version of this article online and at https://sketchfab. com/3d-models/2e5ace8bb36843b19a41b0c9f7f2b842
Model 1 in Ready Species One: Exploring the Use of Augmented Reality to Enhance Systematic Biology with a Revision of Fijian Strumigenys (Hymenoptera: Formicidae)
Model 1. Strumigenys anorak (CASENT0186900, holotype) presented as a computer generated 3D mesh model optimized for augmented reality. Volumetric surfaces rendered from micro-ct data and texture mapped from standard specimen photographs. An interactive version of this model is available in the HTML version of this article online and at https://sketchfab. com/3d-models/9bdd4f1426a44a27996289172a2bbe33
Model 3 in Ready Species One: Exploring the Use of Augmented Reality to Enhance Systematic Biology with a Revision of Fijian Strumigenys (Hymenoptera: Formicidae)
Model 3. Strumigenys avatar (CASENT0185902, holotype) presented as a computer-generated 3D mesh model optimized for augmented reality. Volumetric surfaces rendered from micro-ct data and texture mapped from standard specimen photographs. An interactive version of this model is available in the HTML version of this article online and at https://sketchfab.com/3d-models/b258462cc47c44d48e5b9edf8b 50e632
Figure 8 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 8. Divergence ages (median and 95% HPD) for all dating models, shown for the main groups of Folivora of the present classification. Time scale in million years ago.
Figure 5 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 5. Estimated rate multipliers for anatomical partitions in each model. Partition colours as in Figure 1.
Figure 2 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 2. Diversity through time for sloth genera sampled and its association with geological epochs. Time scale in million years ago.
Figure 1 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 1. Anatomical partitions and partitioning schemes. Coloured anatomical regions in the skeleton of Paramylodon harlani (modified from Stock, 1925) correspond to the maximally partitioned data subsets, as used in model A7, whereas their combinations into composite partitions used in schemes A1 to A6 are indicated by other colours in the table. UN, unpartitioned model.
Figure 4 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 4. Selected trees, with node supports (Poisson boostrap and posterior probabilities), depicting the overall variation in topologies obtained. A, parsimony IW100. B, parsimony IW5. C, Bayesian UN_p. D, Bayesian IW100_e. All topologies and branch lengths for Bayesian trees are available in the Supporting Information (File S9).
Figure 3. A in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 3. A, marginal likelihoods of Bayesian models. B, normalized Robinson–Foulds (nRF) distances among topologies (with IW100_e used as reference). C, distribution of node supports, with posterior probabilities for Bayesian inferences and bootstrap values for maximum parsimony.
Figure 7 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 7. Stratigraphic fit of maximum parsimony and Bayesian topologies evaluated with two metrics, considering fossil age intervals as known ranges or as stratigraphic uncertainty. A, stratigraphic consistency index (SCI). B, gap excess ratio (GER).
Figure 10 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 10. Relative rates (median and 95% HPD) of speciation, extinction and fossilization obtained with a skyline fossilized birth-death process for seven consecutive time bins.
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.