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38 results for “semantic model”
Semantic Triples from "A Collaborative, Realism-Based, Electronic Healthcare Graph: Public Data, Common Data Models, and Practical Instantiation"
<p>These RDF triples (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_graph_exportable.nq.zip?versionId=7aff7c4a-bb0c-46ae-b006-7a80bcec0925">synthea_graph_exportable.nq.zip</a>) are the result of modeling electronic health records (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_csv_output_turbo_cannonical.zip">synthea_csv_output_turbo_cannonical.zip), </a>that were synthesized with the Synthea software (https://github.com/synthetichealth/synthea). Anyone who loads them into a triplestore database is encouraged to provide feedback at https://github.com/PennTURBO/EhrGraphCollab/issues. The following abstract comes from a paper, describing the semantic instantiation process, and presented to the ICBO 2019 conference (https://drive.google.com/file/d/1eYXTBl75Wx3XPMmCIOZba-8Cv0DIhlRq/view).</p> <p>ABSTRACT: There is ample literature on the semantic modeling of biomedical data in general, but less has been published on realism-based, semantic instantiation of electronic health records (EHR). Reasons include difficult design decisions and issues of data governance. A collaborative approach can address design and technology utilization issues, but is especially constrained by limited access to the data at hand: protected health information.</p> <p>Effective collaboration can be facilitated by public EHR-like data sets, which would ideally include a large variety of datatypes mirroring actual EHRs and enough records to drive a performance assessment. An investment into reading public EHR-like data from a popular common data model (CDM) is preferable over reading each public data set’s native format.</p> <p>In addition to identifying suitable public EHR-like data sets and CDMs, this paper addresses instantiation via relational-to-RDF mapping. The completed instantiation is available for download, and a competency question demonstrates fidelity across all discussed formats.</p>
Langmark: annotations for scenes with semantic inconsistencies connecting distributional semantic models to vision science – data and code
<p>Data (including object annotations) and code from the following manuscript:</p> <p><em>Langmark: annotations for scenes with semantic inconsistencies connecting distributional semantic models to vision science</em>.</p>
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</p>
Mapping data files to semantic data models using the CaosDB crawler
<p>Data from data acquisition can lead to a high variety of data files on file systems. The figure illustrates that these files can be mapped to semantic data models in the research data management system CaosDB using a customizable crawler.</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>
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>
Data for "Improving semantic video retrieval models by training with a relevance-aware online mining strategy"
<p>This repository contains all the data available for the publication:</p> <p><a href="https://doi.org/10.1016/j.cviu.2024.104035">Alex Falcon, Giuseppe Serra, and Oswald Lanz. <em>Improving semantic video retrieval models by training with a relevance-aware online mining strategy</em>. <strong>Computer Vision and Image Understanding</strong>. 2024.</a></p> <p>Code is available at: <a href="https://github.com/aranciokov/ranp/">https://github.com/aranciokov/ranp/</a></p> <p>The data includes:</p> <ul> <li>pre-extracted features (ordered_feature_*.zip files)</li> <li>annotations, such as pre-extracted semantic graphs, glove checkpoints, class annotations, etc (annotations_*.zip files)</li> <li>train/val/test, when available, split information (public_split_*.zip) files</li> <li>pretrained models for HGR and EAO (details in the github repo)</li> </ul>
Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers: Validation Model Suite
<p>Dataset of the Scientific Students’ Association Report titled Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers.</p> <p>These files contain the validation model test suite and the generated traces. The models and traces are in the format of the Gamma modeling tool.</p> <p><em>validation-model-suite/model/package<Letter>/model<Number> </em>contains the files for a given model:<br> - stm.gcd is the statemachine,<br> - default.ggen (and in Package F also abstraction.ggen) is the Gamma script executing trace generation and the generated traces can be found in the default (and abstraction) directories.<br> The report of Theta on possible coverage violation is in the traces directory (report.txt).</p> <p> </p> <p>The prototype implementation of trace generation can be found at: https://github.com/AdamZsofi/gamma/tree/dev-tracegen</p>
The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging
ClinicalTrials.gov study NCT06864702. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
Data from: A semantic model for species description applied to the ensign wasps (Hymenoptera: Evaniidae) of New Caledonia
Open the record for dataset details and reuse information.
Semantic Interoperability Model LinkedDL Dataset
<p>This dataset contain configuration files and tools used for setting the experiment environment of the PhD thesis "A Semantic Interoperability Model based on Linked Data for Building Digital Libraries".</p>
Investigating Distributional Robustness: Semantic Perturbations Using Generative Models (ImageNet Examples)
<p>This dataset contains examples of semantically-perturbed images, for NeurIPS 2020 submission #4915.</p> <p>There are four top-level folders, each containing results for semantic perturbations restricted to adjust the activation values at only certain layers of the BigGAN generative network: the first six layers, the middle six layers, the last six layers, and all layers.</p> <p>Within each top-level folder, there are a further four folders, each corresponding to a classifier neural network whose evaluation is being evaluated. These are EfficientNet-B4 with NoisyStudent training [1], the standard ResNet50 [2], a pixel-perturbation-robust ResNet50 trained by Engstrom et al. [3] and another trained by Wong et al. [4], using their "Fast is better than free" technique.</p> <p>Within each of these, there are many folders, named 'version_$N'. Each one of these contains three images: the unperturbed generated image, named unpert_generated_x_grid_0.png; the semantically-perturbed generated image, named generated_x_grid_0.png; and an image named semantic_pert_diffs_grid_0.png showing the pixel-space effect of the semantic perturbation, that is, the diff between the perturbed and unperturbed images. Note that if the perturbed and unperturbed images are identical, and the classifier misclassifies the unperturbed images, and so we skip this example.</p> <p>Along with the 'version_$N' folders containing the images, there exists a file for each classifier named results.json. Each top-level item in this JSON file corresponds to one 'version_$N' example. There are 5 attributes: 'label', indicating the target label of the unperturbed image; 'magnitude', which gives the magnitude of the semantic perturbation found; 'skipped_cla', which is 1 if the example is skipped because the classifier did not correctly classify the unperturbed image; 'skipped_judge', which is 1 if the human judged that the unperturbed image did not match its label, so this example is skipped; and 'pert_judgement', which is 1 if the semantically-perturbed image is judged by the human to be of the same class as the unperturbed image. These judgements on these images were used to construct the main graphs in the paper.</p> <p> </p> <p>[1] Qizhe Xie, Eduard H. Hovy, Minh-Thang Luong, and Quoc V. Le. Self-training with Noisy Student improves ImageNet classification. CoRR, abs/1911.04252, 2019. URL http://arxiv.org/abs/1911.04252.</p> <p>[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 770–778. IEEE Computer Society, 2016. doi: 10.1109/CVPR.2016.90. URL https://doi.org/10.1109/CVPR.2016.90.</p> <p>[3] Logan Engstrom, Andrew Ilyas, Shibani Santurkar, and Dimitris Tsipras. Robustness (Python library), 2019. URL ttps://github.com/MadryLab/robustness.</p> <p>[4] Eric Wong, Leslie Rice, and J. Zico Kolter. Fast is better than free: Revisiting adversarial training. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL<br> https://openreview.net/forum?id=BJx040EFvH.</p>
Replication Package for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>Replication package for our analysis of Semantic Versioning in Ansible Galaxy role repositories.</p> <p>This replication package consists of three parts:</p> <ul> <li> <p>Classification Model: Contains Jupyter notebooks used to train and evaluate a Random Forest classification model based on structural features. Training and evaluation data is included.</p> </li> <li> <p>Quantitative Notebooks: Contains Jupyter notebooks used to perform quantitative analyses of versions and changes.</p> </li> <li> <p>data: CSV files of the data used in the Quantitative Notebooks, and the source data for the classification model. Should be downloaded separately fromthe classification model. Should be downloaded separately from <a href="https://doi.org/10.5281/zenodo.4991955">https://doi.org/10.5281/zenodo.4991955</a>.</p> </li> </ul> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench
<p>This is the full dataset of GPTCloneBench (version 2)</p>
EIAH data model: semantic interoperability between distributed digital repositories
<p>The authors described their information architecture project aimed at improving access to the Encyclopaedia of Iranian architectural history (EIAH) by signalling relationships between concepts and between concepts and documents. The outcome will be presented in a semantic portal or might be used for complex search queries by end users.</p>
Modeling Factual Claims with Semantic Frames
<p>We introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. Modeling is a robust tool that can be leveraged in many different tasks such as matching claims to existing fact-checks and translating claims to structured queries. Our work introduces 11 new manually crafted frames along with 9 existing FrameNet frames, all of which have been selected with fact-checking in mind. Along with these frames, we are also providing 2,540 fully annotated sentences, which can be used to understand how these frames are intended to work and to train machine learning models. </p>
Dataset and models for Adversarial Semantic Collisions (EMNLP20)
<p>Dataset and models for Adversarial Semantic Collisions (EMNLP20),</p>
Evaluation Data for "Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation"
<p><strong>Contents</strong><br> <br> The four CSV files are the data used for the evaluation in:</p> <ul> <li>Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035.</li> <li>DOI: <a href="http://doi.org/10.1007/978-3-030-45439-5_15">10.1007/978-3-030-45439-5_15</a></li> <li>Code: <a href="https://github.com/IllDepence/ecir2020">github.com/IllDepence/ecir2020</a></li> </ul> <p>The evaluation was conducted in a citation re-prediction setting.</p> <p><strong>CSV Format</strong></p> <ul> <li>7 columns divided by \u241E <ol> <li>cited document ID <ul> <li>for *_nomarker.csv: citation marker position ambiguous</li> <li> for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context</li> </ul> </li> <li>adjacent cited document IDs <ul> <li>only given in citrec_unarxive_*.csv</li> <li>divided by \u241F</li> <li>order matches 'CIT' markers in citation context</li> </ul> </li> <li>citing document ID</li> <li>citation context</li> <li>MAG field of study IDs <ul> <li>divided by \u241F</li> </ul> </li> <li>predicate:argument tuples generated based on PredPatt <ul> <li>JSON</li> </ul> </li> <li>noun phrases <ul> <li>for *_nomarker.csv: divided by \u241F</li> <li>for *_withmarker.csv: <ul> <li>divided by \u241D into</li> <li>noun phrases</li> <li>noun phrase directly preceding citation marker</li> </ul> </li> </ul> </li> </ol> </li> </ul> <p><strong>Data Sources</strong></p> <ol> <li>citrec_unarxive_cs_withmarker.csv <ul> <li>data set <ul> <li>unarXive</li> <li>Paper DOI: <a href="http://doi.org/10.1007/s11192-020-03382-z">10.1007/s11192-020-03382-z</a></li> <li>Data DOI: <a href="http://doi.org/10.5281/zenodo.2553522">10.5281/zenodo.2553522</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science</li> <li>cited doc is cited at least 5 times</li> </ul> </li> </ul> </li> <li>citrec_mag_cs_en.csv <ul> <li>data set <ul> <li>Microsoft Academic Graph (MAG)</li> <li>Paper DOI: <a href="http://doi.org/10.1145/2740908.2742839">10.1145/2740908.2742839</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science and in English</li> <li>citing doc abstract in MAG given</li> <li>cited doc is cited at least 50 times</li> </ul> </li> </ul> </li> <li>citrec_refseer.csv <ul> <li>data set <ul> <li>RefSeer</li> <li>Paper URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">ojs.aaai.org/index.php/AAAI/article/view/9528</a></li> <li>Data URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">psu.app.box.com/v/refseer</a></li> </ul> </li> <li>filter <ul> <li>for citing and cited docs title, venue, venuetype, abstract, and year not NULL</li> </ul> </li> </ul> </li> <li>citrec_acl-arc_withmarker.csv <ul> <li>data set <ul> <li>ACL ARC</li> <li>Paper URL: <a href="http://aclanthology.org/L08-1005">aclanthology.org/L08-1005</a></li> <li>Data URL: <a href="http://acl-arc.comp.nus.edu.sg/">acl-arc.comp.nus.edu.sg/</a></li> </ul> </li> <li>filter <ul> <li>cited doc has a DBLP ID</li> </ul> </li> </ul> </li> </ol> <p> </p> <p><strong>Paper Citation</strong></p> <pre>@inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15}, } </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.