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668 results for “embeddings”
Hierarchically embedded scales of movement shape the social networks of vampire bats
<p>Social structure can emerge from <em>hierarchically embedded scales of movement</em>, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (<em>Desmodus rotundus</em>), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks.</p>
Time-frequency maps of gravitational waves embeded in noise
<p>This data set constains the time-frequency maps of gravitational waves embeded in noise. </p>
On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection
<p>Additional resources for the paper:</p> <h3><strong><a href="https://ieeexplore.ieee.org/abstract/document/10826088">On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection.</a></strong></h3> <p>Lewis, N., Cavalcante, C. C., Boukouvalas, Z., & Corizzo, R.</p> <p><em>2024 IEEE International Conference on Big Data (BigData)</em> (pp. 3277-3281). IEEE.</p> <pre> </pre> <p> </p> <p>MMHS150K [1] is a manually labeled multimodal dataset that contains $150000$ tweets with two modalities: text, and corresponding image. Tweets are collected from September 2018 until February 2019 and are labeled according to different types of hate speech: no attacks to any community, racist, sexist, homophobic, religion-based attacks, or attacks to other communities. </p> <p>We extract vector embeddings leveraging different text (BERT, OpenAI) and image (ResNet, PVT, ViT) modele backbones and assess their effectiveness in the hate speech detection task.</p> <p> </p> <h2>Citation:</h2> <pre>@inproceedings{lewis2024effectiveness, title={On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection}, author={Lewis, Nora and Cavalcante, Charles C and Boukouvalas, Zois and Corizzo, Roberto}, booktitle={2024 IEEE International Conference on Big Data (BigData)}, pages={3277--3281}, year={2024}, organization={IEEE} }</pre>
Metadata of "Polydopamine/Ethylenediamine Nanoparticles Embedding a Photosynthetic Bacterial Reaction Center for Efficient Photocurrent Generation"
<p>Metadata of "Polydopamine/Ethylenediamine Nanoparticles Embedding a Photosynthetic Bacterial Reaction Center for Efficient Photocurrent Generation"</p>
Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for orbital and ionization energies) results discussed in the paper titled "Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations" by Richard Asamoah Opoku, Céline Toubin, and André Severo Pereira Gomes.</p>
Symmetric embeddings between standard contexts of lattices of integer partitions
<p>We consider lattices of integer partitions ordered by dominance, and their standard contexts as studied in formal concept analysis. The present dataset contains all 29 symmetric context embeddings of the standard context of the lattice of partitions of the integer 8 into the standard context of the lattice of 10. Further information and a detailed description of the files in the dataset can be obtained from the file context_embeddings.pdf (the code source for this file is given in context_embeddings.tex).</p>
Comparative analysis of Printed Circuit Boards with Surface and Embedded Components under Natural and Forced Convection
<p>Figures of heat distribution on PCB depending on the installation method (surface and embedded) and the speed of forced airflow.</p>
Protein language model embeddings and predictions for the fly proteome (FlyBase)
<p>Residue and sequence embeddings of the fly (drosophila melanogaster) proteome (FlyBase for organism drosophila melanogaster, downloaded on 2022.03.01) computed using bio_embeddings (bioembeddings.com) using the ProtT5 embedder at full precision (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3). To open the embeddings file, please see <a href="https://github.com/sacdallago/bio_embeddings/blob/develop/notebooks/open_embedding_file.ipynb">this notebook</a>. The embeddings will be indexed by numbers according to the mapping file (mapping_file.csv) in this dataset. All following results will share the same mapping (for instance, to access the variation prediction results, by accessing index "0", you will query results for the sequence "FBpp0304622").</p> <p>Additionally:</p> <p>- Sequence-level predictions of subcellular localization in 10 classes using LA (https://www.biorxiv.org/content/10.1101/2021.04.25.441334v1)</p> <p>- Residue-level three state secondary structure prediction (alpha, sheet or other) using models reported in the ProtTrans paper (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3)</p> <p>- Residue-level prediction of conservation (in 9 states) and of variation effect (from 0 [no-effect] to 1 [effect]) using VESPAl (https://doi.org/10.1007/s00439-021-02411-y)</p> <p> </p> <p>Files included:</p> <p>- dmel-all-translation-r6.44.fasta --> FASTA-formatted sequences of drosophila melanogaster from FlyBase</p> <p>- mapping_file.csv --> A CSV file mapping the identifiers used in the following files (from 0 to 30737) to the identifiers in the FlyBase fasta file (dmel-all-translation-r6.44.fasta).</p> <p>- DSSP3_fly_ProtT5Sec.fasta --> Secondary structure predictions in three states for each residue of each protein in dmel-all-translation-r6.44.fasta. "H" stands for Helix; "E" stands for Sheet; "C" stands for Other.</p> <p>- subcell_fly_LA_ProtT5.csv --> Subcellular location (10 states) and memrane-boundness (2 states) for each protein in dmel-all-translation-r6.44.fasta</p> <p>- embeddings_file.h5 --> per-residue embeddings of sequences in dmel-all-translation-r6.44.fasta. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length Lx1024, with L being the length of the protein sequence. Datasets are indexed using integers. The original sequence identifier (from the FASTA header) can be accessed through the "original_id" attribute. See https://docs.bioembeddings.com/v0.2.0/notebooks/open_embedding_file.html for information on how to open the file.</p> <p>- reduced_embeddings_file.h5 --> per-sequence embeddings of sequences in dmel-all-translation-r6.44.fasta (obtained by mean-pooling the residue-embeddings along the length dimension of the protein sequence). Each dataset in the .h5 file represents a protein sequence and contains a vector of size 1024 (meaning, each sequence has the same dimension).</p> <p>- conspred_probs.h5 --> per-sequence conservation probability (softmax) prediction of sequences in dmel-all-translation-r6.44.fasta in 9 classes. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length 9xL, with L being the length of the protein sequence, and 9 being the predicted conservation class (index 0 = very variable; index 8 = very conserved)</p> <p>- vespal_SAVeffect_fly.zip --> zipped .h5 file of per-sequence variation predictions of sequences in dmel-all-translation-r6.44.fasta on a scale from 0 (neutral) to 1 (effect). -1 indicates WT substitution. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length 20xL, with L being the length of the protein sequence, and 20 being the predicted variation score for each residue substitution (AAs in the following order: "<strong>ALGVSREDTIPKFQNYMHWC</strong>" . Meaning that index 0 = substitution of the residue to "A", index = 1 substitution to residue "L", aso.)</p>
Kiez Benchmarking Knowledge Graph Embeddings
<p>This upload contains pre-calculated Knowledge Graph Embeddings produced by our study "<a href="https://dbs.uni-leipzig.de/file/KIEZ_KEOD_2021_Obraczka_Rahm.pdf">An Evaluation of Hubness Reduction Methods for Entity Alignment with Knowledge Graph Embeddings</a>"</p>
KGlove+Glove embedding for MAKG citation network
<p>Entity embedding by using KGlove+Glove on MAKG citation network (citation network from <a href="https://zenodo.org/record/4617285/files/08.PaperReferences.nt.bz2?download=1">https://zenodo.org/record/4617285/files/08.PaperReferences.nt.bz2?download=1</a>)</p>
French CBOW FastText embeddings with min count 250
<p>FastText embeddings built from Common Crawl French dataset</p> <table> <caption>Parameters</caption> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">Value(s)</th> </tr> </thead> <tbody> <tr> <td>Dimensions</td> <td>512</td> </tr> <tr> <td>Context window</td> <td>5</td> </tr> <tr> <td>Negative sampled</td> <td>10</td> </tr> <tr> <td>Epochs</td> <td>1</td> </tr> <tr> <td>Number of buckets</td> <td>262144</td> </tr> <tr> <td>Min n</td> <td>3</td> </tr> <tr> <td>Max n</td> <td>6</td> </tr> </tbody> </table>
Embeddings models for Buddhist Sanskrit: Evaluation Datasets
<p>Evaluation Dataset used for the study published as Embeddings models for Buddhist Sanskrit, <em>LREC 2022 proceedings</em>. It contains a semantic similarity dataset and an analogy dataset, as well as the published study and a ReadMe file containing the guidelines used for scoring semantic similarity and some notes about the manual scoring task.</p> <p> </p> <p>The evaluation datasets have been prepared by Ligeia Lugli, Bruno Galasek-Hul, Luis Quiñones and Jai Paranjape</p> <p> </p>
Trained Models from "General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings"
<p>Trained models from the paper:</p> <p>Lukas Galke, Isabell Cuber, Christoph Meyer, Henrik Ferdinand Noelscher, Angelina Sonderecker, and Ansgar Scherp: <strong>General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings</strong>, in: <em>International Joint Conference on Neural Networks (IJCNN), </em>2022.</p> <ul> <li>File seq2mat_hybrid_bidirectional_sbertlike-100p-bsz512 holds the model from pretraining</li> <li>File ws2020_transformer_final_models holds the fine-tuned models for each task of the GLUE benchmark</li> </ul>
Buddhist Chinese Word Embeddings
<p>Buddhist Chinese word embeddings trained with FastText on the Buddhist texts present in the Kanseki repository.</p> <p>There are four models present here (and the full binary output for one), each differing in how the Kanseki repository was segmented into tokens. The "chinese_model" was segmented into 1-grams (individual characters). The "chinese_model_word" was segmented into words using a dictionary of buddhist terms and phrases not found were segmented with the classical Chinese word segmenter distributed with the stanza python library (https://stanfordnlp.github.io/stanza/available_models.html). The "chinese_model_hybrid_char_term" was segmented with a glossary of Buddhist terms and phrases not found were divided into 1-grams. "chinese_model_hybrid_char_term_2" uses a more extensive glossary of Buddhist terms and missing sections are divided into 1-grams.</p> <p>All models are default 100 dimensional FastText models created for this pilot study: Felbur, Rafal, Marieke Meelen & Paul Vierthaler (2022), 'Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan' in <em>Journal of Open Humanities Data</em>.</p> <p>This research was done with generous funding from the Open Philology project. This project (running 2018–2022) is funded by the European Research Council (ERC) under the Horizon 2020 program (Advanced Grant agreement No 741884). It is based at the Leiden University Institute for Area Studies.</p>
Strand-switching mechanism of Pif1 helicase induced by its collision with a G-quadruplex embedded in dsDNA_data_NAR
<p>This document includes data corresponding to the paper entitled :"Strand-switching mechanism of Pif1 helicase induced by its collision with a G-quadruplex embedded in dsDNA" published in Nucleic Acid Research, 2022. The link to the zenodo depository of the homemade software to open the data is given in the readme.txt file as well as the way the data files are organized</p>
Jodel Emoji Embedding Dataset (JEED1488)
<p>Jodel Emoji Embedding Dataset (JEED1488).</p> <p>Please refer to the README for more information.</p>
Insight into the Mechanical Coupling Behavior of Loose Sediment and Embedded Fiber-optic Cable using Discrete Element Method
<p>The dataset contains the simulation codes and generated data in the manuscript titled "Insight into the mechanical coupling behavior of loose sediment and embedded fiber-optic cable using discrete element method". The codes (M files) were written in MatDEM, version 3.0 (free access at <strong>www.matdem.com</strong>), and the data is stored in MAT files.</p> <ul> <li>Test2D_2L1.m - codes for initial compacted elements</li> <li>Test2D_2L1.mat - generated data for initial compacted elements</li> <li>Test2D_2L2.m - codes for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L2.mat - generated data for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L3.m – codes for confining pressure setting</li> <li>Test2D_2L-0MPa3.mat ~ Test2D_2L-1.0MPa3.mat - generated data for confining pressure setting</li> <li>Test2D_2L4.m – codes for fiber-optic cable pullout tests under various confining pressures</li> <li>Test2D_2L-05-26-20mm-0MPa-un-No1-4.mat ~ Test2D_2L-07-21-20mm-1MPa-un-No1-4.mat - generated data for fiber-optic cable pullout tests under various confining pressures</li> </ul>
Datasets from "Electrostatic Embedding of Machine Learning Potentials"
<p>Data required to reproduce results in "Electrostatic Embedding of Machine Learning Potentials" <a href="https://doi.org/10.26434/chemrxiv-2022-rknwt">article</a>. See <a href="https://github.com/emedio/embedding">https://github.com/emedio/embedding</a> for details.</p> <ul> <li>QM7_B3LYP_cc-pVTZ.tgz - outputs of single point B3LYP/cc-pVTZ calculations of structures in <a href="http://quantum-machine.org/data/qm7.mat">QM7 dataset</a> with ORCA 5. Include molecular dipolar polarizabilities.</li> <li>QM7_B3LYP_cc-pVTZ_horton.tgz - MBIS partitioning of the B3LYP/cc-pVTZ densities with <a href="https://github.com/theochem/horton">Horton 2.1.0</a>.</li> <li>mpro_xyz.tgz - coordinates of the ligand and surrounding point charges from 100 snapshots of SARS-CoV-2 Mpro complex with PF-00835231.</li> <li>mpro_*.tgz - DFT and semiempirical single point calculations with ORCA 5 for the coordinates from mpro_xyz.tgz, <em>in vacuo </em>and in presence of point charges.</li> <li>mlmm.mat - learned parameters and SOAP feature vectors of reference atomic environments</li> </ul>
Social Sciences Word Embeddings in FastText
<p>These social science word embeddings in FastText have been created from 37,604 open access social science research papers from the social science access repository (https://www.gesis.org/ssoar/home). They are available in German and English.</p> <p>(skipgram model, n-grams with n≥3 and n≤6, different dimensions (100, 150, 200, 300, 500), five epochs, learning rate 0.05, five negative examples)</p> <p>Please cite:</p> <p>Schiffers, Ricardo, Dagmar Kern, and Daniel Hienert. 2022. "Evaluation of Word Embeddings for the Social Sciences." In <em>Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature</em>, edited by Stefania Degaetano, Anna Kazantseva, Nils Reiter, and Stan Szpakowicz, 1-6. Gyeongju: Association for Computational Linguistics. <a href="https://aclanthology.org/2022.latechclfl-1.1">https://aclanthology.org/2022.latechclfl-1.1</a>.</p>
Tigrinya Analogy Test for evaluating Word Embeddings
<p><strong>Tigrinya Analogy Test for evaluating Word Embeddings</strong></p> <p>This is a Tigrinya version of the Google Analogy Test set, which is used to evaluate English word-embedding models. The analogy test is a well-established strategy to empirically evaluate the quality of word-embedding models. More information about the English task can be found at the <a href="https://aclweb.org/aclwiki/Google_analogy_test_set_(State_of_the_art)">ACL Wiki</a>.</p> <p>This data is was first machine translated then manually verified by a native speaker to reduce errors.</p> <p>Some aspects of the original analogy test is focused on English and may not transfer well to other languages, such as those related to grammar or morphology. Therefore, we have discarded examples that became irrelevant in Tigrinya when adapting the task. Finally, there are a total of <strong>18465</strong> entries in the Tigrinya Analogy Test set, while the source English data has <strong>19544</strong> entries.</p> <p>An entry is dropped if the translations led to one of the following conditions:</p> <ol> <li>If the source word pair map to one Tigrinya word, for example, lucky & luckiest both correspond to ዕድለኛ.</li> <li>If the source word results in a multi-word expression. For example, grandson (ወዲ ጓል / ወዲ ወዲ), granddaughter (ጓል ጓል / ጓል ወዲ). This because the typical word-embedding approaches such as <em>word2vec</em> are not designed to predict multi-word phrases.</li> </ol> <p> </p> <p><strong>Test Sections</strong></p> <p>The test includes a series of semantic and syntactic analogies divided up into subsections including world capitals, currencies, family, tense, and plurality. The test contains the following sections:</p> <ol> <li>capital-world</li> <li>currency</li> <li>city-in-state</li> <li>family</li> <li>gram1-adjective-to-adverb</li> <li>gram2-opposite</li> <li>gram3-comparative</li> <li>gram4-superlative</li> <li>gram5-present-participle</li> <li>gram6-nationality-adjective</li> <li>gram7-past-tense</li> <li>gram8-plural</li> <li>gram9-plural-verbs</li> </ol> <p> </p> <p><strong>Examples:</strong></p> <ul> <li>Semantic section of World Capitals: “ኣስመራ: ኤርትራ as ፓሪስ: ?” and if the model responds correctly it will return: “ፈረንሳ”.</li> <li>Semantic section of Family section: “ሰብኣይ: ሰበይቲ as ወዲ: ጓል”.</li> <li>Syntax section with tense, a sample analogy might be “Walk: Walked as Run: Ran”.<br> </li> </ul> <p><strong>Evaluation</strong></p> <p>The final accuracy of a model is the proportion of the questions that the model answers correctly.<br> Generally, a better-quality model would answer more questions correctly than a model of lower quality.<br> However, note that a model with low performance on this analogy test, might still contain useful information, but may not be robust or good enough for more complex tasks.</p> <p> </p> <p><strong>Limitations</strong></p> <ul> <li>The analogy test could be a good indicator of the quality of word-embeddings, but it should be used with caution when comparing models trained on varying domains of data. It shall not be expected to generalize equally to all domains.</li> <li>The final score can be affected by the size, vocabulary, and domain of the text with which the models are trained on. For example, this may not be a good benchmark to compare models trained on news text <em>vs</em> posts on social media.</li> <li>Even though a manual sanity check was performed, we note that the semi-automatic construction of the Tigrinya test set might contains errors. If you discover any, you are welcome to contribute back by either opening an <em>Issue</em> at the GitHub repo, <a href="https://github.com/fgaim/tigrinya-analogy-test">https://github.com/fgaim/tigrinya-analogy-test</a>.</li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>If you use this resource in your research, please cite it accordingly.</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.