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575
datasets available to search
ShareScore release 0.9.0
Dataset results
575 results for “Modeling Codes”
Data and code from: Wide-Angle Lung Experiment Segmentation (WALES): A novel methodology for quantitative assessment of lung pathology in model systems
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Modeling data and R code for Chrysodeixis chalcites ecological niche
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Computer code for a model describing the emergence of a long transient regular spatial pattern from interaction of competing aquatic macrophytes and a biocontrol agent
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Data and code for: Leveraging long-term data to improve biodiversity monitoring with species distribution models
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Spectral data and R modeling code from: Polarized light sensitivity in Pieris rapae is dependent on both color and intensity
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All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
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Models of evolutionary rescue with plasticity and nonlinear environmental change: code and data
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Code for: A metapopulation model of social group dynamics and disease applied to Yellowstone wolves
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Data and model code from: Tracing growth patterns in cod (Gadus morhua L.) using bioenergetic modelling
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Supplementary data and modeling code file for: Soft deployable airless wheel for lunar lava tube in-tact exploration
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Raw data and R code for statistical analyses from: Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue
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Data and codes for: A link model approach to identify congestion hotspots
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Measuring the aggregated impact of research: Establishing criteria for coding Translational Science Benefits Model data
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Data and R code from: Modelling the evolution of cognitive styles
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Model code for reconstructing biomass burning emissions from ice core records of acetylene, ethane, and methane
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Giant clam nitrogen isotopic data and code for N isotope model
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Open source physiological data and physiological-based kinetic model code for the chicken (Gallus gallus domesticus)
<p>This excel file and mode code (DOI:10.5281/zenodo.3603114) provides:</p> <p>1. Physiological parameters and associated inter-individual variability (sample size, mean, coefficient of variation,) for chicken (<em>Gallus gallus domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020).</p> <p>2. An R code for the generic chicken physiologically based model as well as the “soboljansen” code to carry out sensitivity analysis using sobol plots. The code for the generic model allows to run:</p> <p>a. A deterministic PBK model which represents only a single animal.</p> <p>b. A probabilistic PBK model to simulate individual differences in physiological parameters within a population. Sensitivity analyses can be performed to identify which parameters have the most impact on the model’s outputs. Predictions can be compared with experimental data. The model can be used to assess the influence of physiological parameters on the kinetics of chemicals. For PBK modelling purposes, species and chemical specific kinetics (e.g clearance, absorption rate, etc…) should be provided by the user.</p> <p>The full data collection and implementation of the models using case studies are described in (Lautz et al., 2020).</p> <p><strong>The dataset providing the physiological parameters is available in Excel.<br> The R code is presented as meta data to be implemented in R.</strong></p>
Datasets for: Semantic Robustness of Models of Source Code
<p>Datasets for Semantic Robustness of Models of Source Code.</p> <p>Includes the c2s/java-small, csn/java, csn/python, and sri/py150 in the following representations:</p> <ol> <li>Raw [in raw.tar.gz]</li> <li>Normalized [in normalized.tar.gz]</li> <li>Pre-processed (<em>ast-paths and tokens</em>) [in preprocessed.tar.gz]</li> <li>Transformed [in transformed.tar.gz] <ol> <li>Normalized <ol> <li>transforms.All</li> <li>transforms.ShuffleLocalVariables</li> <li>transforms.ShuffleParameters</li> <li>transforms.RenameLocalVariables</li> <li>transforms.RenameFields</li> <li>transforms.RenameParameters</li> <li>transforms.ReplaceTrueFalse</li> <li>transforms.InsertPrintStatements</li> <li>transforms.Identity</li> </ol> </li> <li>Pre-processed (<em>ast-paths and tokens</em>) <ol> <li>transforms.Identity</li> <li>transforms.InsertPrintStatements</li> <li>transforms.ReplaceTrueFalse</li> <li>transforms.RenameParameters</li> <li>transforms.RenameFields</li> <li>transforms.RenameLocalVariables</li> <li>transforms.ShuffleParameters</li> <li>transforms.ShuffleLocalVariables</li> <li>transforms.All</li> </ol> </li> </ol> </li> </ol>
Model code and output of CSIBv4 pan-Arctic sea ice-ocean DMS simulation
<p>here i deposit the model source code and output of the manuscript "Spatio-temporal variability in modelled bottom-ice and sea-surface dimethylsulfide concentrations and fluxes in the Arctic during 1979-2015" by Hayashida et al. (2020).</p> <p><strong>See "readme.txt" for information.</strong></p> <p>For question, contact Hakase Hayashida.</p>
Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles (Dataset, Models & Code)
<p>Many digital libraries recommend literature to their users considering the similarity between a query document and their repository. However, they often fail to distinguish what is the relationship that makes two documents alike. In this paper, we model the problem of finding the relationship between two documents as a pairwise document classification task. To find the semantic relation between documents, we apply a series of techniques, such as GloVe, Paragraph-Vectors, BERT, and XLNet under different configurations (e.g., sequence length, vector concatenation scheme), including a Siamese architecture for the Transformer-based systems. We perform our experiments on a newly proposed dataset of 32,168 Wikipedia article pairs and Wikidata properties that define the semantic document relations. Our results show vanilla BERT as the best performing system with an F1-score of 0.93,<br> which we manually examine to better understand its applicability to other domains. Our findings suggest that classifying semantic relations between documents is a solvable task and motivates the development of recommender systems based on the evaluated techniques. The discussions in this paper serve as first steps in the exploration of documents through SPARQL-like queries such that one could find documents that are similar in one aspect but dissimilar in another.</p> <p>Additional information can be found on <a href="https://github.com/malteos/semantic-document-relations/">GitHub</a>.</p> <p>The following data is supplemental to the experiments described in our research paper. The data consists of:</p> <ul> <li>Datasets (articles, class labels, cross-validation splits)</li> <li>Pretrained models (Transformers, GloVe, Doc2vec)</li> <li>Model output (prediction) for the best performing models</li> </ul> <p><strong>Dataset</strong></p> <p>The Wikipedia article corpus is available in <code>enwiki-20191101-pages-articles.weighted.10k.jsonl.bz2</code>. The original data have been downloaded as <a href="https://dumps.wikimedia.org/enwiki/">XML dump</a>, and the corresponding articles were extracted as plain-text with <a href="https://radimrehurek.com/gensim/scripts/segment_wiki.html">gensim.scripts.segment_wiki</a>. The archive contains only articles that are available in training or test data.</p> <p>The actual dataset is provided as used in the stratified k-fold with <code>k=4</code> in <code>train_testdata__4folds.tar.gz</code>.</p> <pre><code>├── 1 │ ├── test.csv │ └── train.csv ├── 2 │ ├── test.csv │ └── train.csv ├── 3 │ ├── test.csv │ └── train.csv └── 4 ├── test.csv └── train.csv 4 directories, 8 files </code></pre> <p>Pretrained models</p> <p>PyTorch: vanilla and Siamese BERT + XLNet</p> <p>Pretrained model for each fold is available in the corresponding model archives:</p> <pre><code># Vanilla model_wiki.bert_base__joint__seq512.tar.gz model_wiki.xlnet_base__joint__seq512.tar.gz # Siamese model_wiki.bert_base__siamese__seq512__4d.tar.gz model_wiki.xlnet_base__siamese__seq512__4d.tar.gz </code></pre>
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.