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2,139 results for “recognition”
Appendix A - The Implications of Handwritten Text Recognition for Accessing the Past at Scale
<p>List of works identified through a Grounded Theory Method (GTM) of the current and near future implications of Handwritten Text Recognition (HTR) on the historical method and wider information environment. The findings of this data collection are provided in 'The Implications of Handwritten Text Recognition for Accessing the Past at Scale'</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>
The proteolytic cleavage of TLR8 Z-loop by furin protease - molecular recognition, reaction mechanism and role of water molecules DATASET_v2
<p>The dataset comprises:<br>i) AlphaFold-Multimer predictions for TLR8LRR-furin complex<br>ii) The optimised structures of QM cluster models for reactant (RE), intermediate1-3 (INT1-INT3), and product (PROD)<br>iii) The optimised structures of QM/MM model for RE, INT1-INT3, PROD<br>iv) Input structures used in MD simulations and parameterization files for non-standard residues for RE, INT1-INT3, PROD<br>v) PyMOL sessions from AQUA-DUCT calculations for RE, INT1-INT3, PROD</p>
AI4PROFHEALTH - Automatic Silver Gazetteer for Named Entity Recognition and Normalization
<p>This dataset comprises a professions gazetteer generated with automatically extracted terminology from the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. </p> <p>A silver gazetteer for mention classification and normalization is created combining the predictions of automatic Named Entity Recognition models and normalization using Entity Linking to three controlled vocabularies SNOMED CT, NCBI and ESCO. The sources are 265,025 different documents, where 249,538 correspond to <a href="https://zenodo.org/records/4707104">MESINESP2 Corpora</a> and 15,487 to clinical cases from open clinical journals. From them, 5,682,000 mentions are extracted and 4,909,966 (86.42%) are normalized to any of the ontologies: SNOMED CT (4,909,966) for diseases, symptoms, drugs, locations, occupations, procedures and species; ESCO (215,140) for occupations; and NCBI (1,469,256) for species.</p> <p>The repository contains a .tsv file with the following columns:</p> <ul> <li><strong><code>filenameid</code></strong>: A unique identifier combining the file name and mention span within the text. This ensures each extracted mention is uniquely traceable. Example: biblio-1000005#239#256 refers to a mention spanning characters 239–256 in the file with the name biblio-1000005.</li> <li> <p><strong><code>span</code></strong>: The specific text span (mention) extracted from the document, representing a term or phrase identified in the dataset. Example: centro oncológico.</p> </li> <li> <p><strong><code>source</code></strong>: The origin of the document, indicating the corpus from which the mention was extracted. Possible values: mesinesp2, clinical_cases.</p> </li> <li> <p><strong><code>filename</code></strong>: The name of the file from which the mention was extracted. Example: biblio-1000005.</p> </li> <li> <p><strong><code>mention_class</code></strong>: Categories or semantic tags assigned to the mention, describing its type or context in the text. Example: ['ENFERMEDAD', 'SINTOMA'].</p> </li> <li> <p><strong><code>codes_esco</code></strong>: The normalized ontology codes from the European Skills, Competences, Qualifications, and Occupations (ESCO) vocabulary for the identified mention (if applicable). This field may be empty if no ESCO mapping exists. Example: 30629002.</p> </li> <li> <p><strong><code>terms_esco</code></strong>: The human-readable terms from the ESCO ontology corresponding to the <code>codes_esco</code>. Example: ['responsable de recursos', 'director de recursos', 'directora de recursos'].</p> </li> <li> <p><strong><code>codes_ncbi</code></strong>: The normalized ontology codes from the NCBI Taxonomy vocabulary for species (if applicable). This field may be empty if no NCBI mapping exists.</p> </li> <li> <p><strong><code>terms_ncbi</code></strong>: The human-readable terms from the NCBI Taxonomy vocabulary corresponding to the <code>codes_ncbi</code>. Example: ['Lacandoniaceae', 'Pandanaceae R.Br., 1810', 'Pandanaceae', 'Familia'].</p> </li> <li> <p><strong><code>codes_sct</code></strong>: The normalized ontology codes from SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) vocabulary for diseases, symptoms, drugs, locations, occupations, procedures, and species (if applicable). Example: 22232009.</p> </li> <li> <p><strong><code>terms_sct</code></strong>: The human-readable terms from the SNOMED CT ontology corresponding to the <code>codes_sct</code>. Example: ['adjudicador de regulaciones del seguro nacional'].</p> </li> <li> <p><strong><code>sct_sem_tag</code></strong>: The semantic category tag assigned by SNOMED CT to describe the general classification of the mention. Example: environment.</p> </li> </ul> <p> </p> <p><strong>Suggestion</strong>: If you load the dataset using python, it is recommended to read the columns containing lists as follows</p> <div> <blockquote> <div>import ast</div> <div>df["mention_class"] = df["mention_class"].apply(lambda x: ast.literal_eval(x) if isinstance(x, str) else x)</div> </blockquote> </div> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong>. This means you are free to:</p> <ul> <li>Share: Copy and redistribute the material in any medium or format.</li> <li>Adapt: Remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p><strong>Attribution Requirement</strong>: Please credit the dataset creators appropriately, provide a link to the license, and indicate if changes were made.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested, you might want to check out these corpora and resources:</p> <ul> <li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> <li><a href="10.5281/zenodo.5070540" target="_blank" rel="noopener">MEDDOPROF corpus </a></li> <li> <p><a href="https://zenodo.org/record/4722741">Codes Reference List</a> (for MEDDOPROF-NORM)</p> </li> <li> <p><a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4524658">Occupations Gazetteer</a></p> </li> </ul>
FEater dataset: A molecular fragment dataset to benchmark the robustness of 3D flexible object recognition
<p>This dataset is associated with the work: Benchmarking the robustness of the correct identification of flexible 3D objects using common machine learning models</p> <pre><code># Original FEater-Single and FEater_Dual dataset. FEater_Single ├── TestSet_coord.h5 ├── TrainingSet_coord.h5 └── ValidationSet_coord.h5 FEater_Dual ├── TestSet_coord.h5 ├── TrainingSet_coord.h5 └── ValidationSet_coord.h5 # Non-redundant baseline dataset FEater_Baseline ├── TestSet_Dual.h5 ├── TestSet_Single.h5 ├── TrainingSet_Dual.h5 └── TrainingSet_Single.h5 # FEater-Single and FEater_Dual in different sample size FEater_Mini200 ├── Mini200_Dual.h5 └── Mini200_Single.h5 FEater_Mini400 ├── Mini400_Dual.h5 └── Mini400_Single.h5 FEater_Mini800 ├── Mini800_Dual.h5 └── Mini800_Single.h5</code></pre> <p>For further details of the usage, please visit the original GitHub repository: <a title="FEater_repo" href="https://github.com/miemiemmmm/FEater" target="_blank" rel="noopener">https://github.com/miemiemmmm/FEater</a></p>
Image recognition based on deep learning in Haemonchus contortus motility assays
<p>The repository contains the data associated with the paper `Image recognition based on deep learning in Haemonchus contortus motility assays`. The following folders form part of the repository:</p> <p>- Annotation Data - annotated microscope images used for the training of the Mask R-CNN model. The data are divided into `train` and `val`<br> - Mask R-CNN - contains the trained weights for the Mask R-CNN model<br> - Motility Output - output of the 3 compared algorithms (Wiggle Index, WF-NTP and Mask R-CNN).<br> - Motility Videos - input videos used for the motility detection. The naming convetno is `XXXzYYY.avi`, where `XXX` denotes the motility group and `YYY` the sequence number for the video within a given group</p>
Evaluation Dataset: SemOI2 – Building Adaptive And Cost-Effective Recognition Applications With Semantic Augmentation
<p>Evaluation dataset for the paper "SemOI2 – Building Adaptive And Cost-Effective Recognition Applications With Semantic Augmentation", presented at the AAAI-Make conference 2022, to be published in: <em>A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F. van Harmelen (Eds.), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21–23, 2022.</em></p>
Dataset used in the article: Evaluation of goal recognition systems on unreliable data and uninspectable agents
<p>Goal or intent recognition, where one agent recognizes the goals or intentions of another, can be a powerful tool for effective teamwork and improving interaction between agents. Such reasoning can be challenging to perform, however, because observations of an agent can be unreliable and, often, an agent does not have access to the reasoning processes and mental models of the other agent. Despite this difficulty, recent work has made great strides in addressing these challenges.</p> <p>In particular, two Artificial Intelligence (AI)-based approaches to goal recognition have recently been shown to perform well: goal recognition as planning, which reduces a goal recognition problem to the problem of plan generation; and Combinatory Categorical Grammars (CCGs), which treat goal recognition as a parsing problem. Additionally, new advances in cognitive science with respect to Theory of Mind reasoning have yielded an approach to goal recognition that leverages analogy in its decision making.</p> <p>However, there is still much unknown about the potential and limitations of these approaches, especially with respect to one another. Here, we explore this space and compare three state-of-the-art approaches to goal recognition along two different axes: reliability of observations and inspectability of the other agent's mental model. Overall, we show that no approach dominates across all cases and discuss the relative strengths and weaknesses of these approaches. Scientists interested in goal recognition problems can use this knowledge as a guide to select the correct starting point for their specific domains and tasks.</p> <p> Research Topic: Advances in Goal, Plan and Activity Recognition</p>
Appendix A - Understanding the application of Handwritten Text Recognition technology in heritage contexts
<p>This appendix lists all the categorised works mentioning the HTR software Transkribus, used in 'Understanding the application of Handwritten Text Recognition technology in heritage contexts: a systematic review of Transkribus in published research'</p>
Alternative reproductive tactics in male freshwater fish influence the accuracy of species recognition
<p>Sexual conflict can result in coercive mating. Because males bear low costs of heterospecific mating, coercive males may engage in misdirected mating attempts toward heterospecific females. In contrast, sexual selection through consensual mate choice can cause mate recognition cues among species to diverge, leading to more accurate species recognition. Some species show both coercive mating and mate choice–associated courtship behaviors as male alternative reproductive tactics. We hypothesized that<br> if the selection pressures on each tactic differ, then the accuracy of species recognition would also change depending on the mating tactic adopted. We tested this hypothesis in the guppy (<em>Poecilia reticulata</em>) and mosquitofish (<em>Gambusia affinis</em>) by a series of choice experiments. <em>Poecilia reticulata</em> and <em>G. affinis</em> males both showed imperfect species recognition and directed all components of mating behavior towards heterospecific females. They tended to direct courtship displays more frequently towards conspecific than heterospecific females. With male <em>P. reticulata</em>, however, accurate species recognition disappeared when they attempted coercive copulation: they directed coercions more frequently towards heterospecific females. We also found that heterospecific sexual interaction had little effect on the fecundity of gravid females, which suggests that pre-pregnancy interactions likely underpin the exclusion of <em>G. affinis</em> by <em>P. reticulata</em> in our region.</p>
Cell recognition in clear cell renal cell carcinoma tissue
<p>This dataset contains a set of 426 PNG images extracted from clear cell renal cell carcinoma samplers of the Cancer Genome Atlas. A JSON file included contains the bounding boxes and classes of each cell found in the PNG images. The label 2 is utilized for lymphocytes and the label 1 for cells other than lymphocytes.</p> <p> </p> <p>The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.</p>
FiNER-139: A Financial Numeric Entity Recognition Dataset
<p>FiNER-139 is published with the article: FiNER: Financial Numeric Entity Recognition for XBRL Tagging</p> <p>In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022.</p> <p>We release FiNER-139, a dataset of 1.1M sentences with gold XBRL tags. Unlike typical entity extraction datasets, FiNER-139 uses a much larger label set of 139 entity types. Most annotated tokens are numeric, with the correct tag per token depending mostly on context, rather than the token itself.</p>
Dvoice : An open source dataset for Automatic Speech Recognition on African Languages and Dialects
<p>DVoice is a community initiative that aims to provide African languages and dialects with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each language. Two different approaches are currently used: the DVoice platform, which is based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling the recordings. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke. The Swahili-labeled data present in this version was obtained after automatic labeling via the learning transfer of the Voxlingua107 dataset. For a first time, we also advocate for the increase of data given their small size that we currently have. Thus this version of the dataset contains easily identifiable augmented data.</p>
Substrate recognition and cryo-EM structure of the ribosome-bound TAC toxin of Mycobacterium tuberculosis
<p>Datasets for the Figures 2 and S2 of the manuscript "Substrate recognition and cryo-EM structure of the ribosome-bound TAC toxin of Mycobacterium tuberculosis".</p> <p> </p> <p>The HTML files describe the analysis and the raw counts after nEMOTE-conv treatment.</p> <p>There are 5 files for each MMEMOTExx dataset:</p> <p>EmoteBarcodesReport.csv = summary<br> UnambNegTable.csv = counts of unique cuts on the reverse strand<br> UnambPosTable.csv = counts of unique cuts on the forward strand<br> AmbPosTable.csv = counts of all cuts on the forward strand<br> AmbNegTable.csv = counts of all cuts on the reverse strand</p> <p> </p>
Face mask detection and masked facial recognition dataset (MDMFR Dataset)
<p>The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0170">MDMFR, 2022</a>) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, <a href="https://www.sciencedirect.com/topics/computer-science/illumination-condition">illumination conditions</a>, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0215">Plastiliq, 2022</a>).</p>
Multilingual named entity recognition for medieval charters. Datasets and models
<p>Annotated dataset for training named entities recognition models for medieval charters in Latin, French and Spanish.</p> <p> </p> <p>The original raw texts for all charters were collected from four charters collections</p> <p>- HOME-ALCAR corpus : <a href="https://zenodo.org/record/5600884">https://zenodo.org/record/5600884</a></p> <p>- CBMA : <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwisvNOa3qP3AhULyIUKHenpBoAQFnoECA0QAQ&url=http%3A%2F%2Fwww.cbma-project.eu%2F&usg=AOvVaw0blsASXzOKSNz_EkixwfJT">http://www.cbma-project.eu</a></p> <p>- Diplomata Belgica : <a href="https://www.diplomata-belgica.be">https://www.diplomata-belgica.be</a></p> <p>- CODEA corpus :<a href="http://https://corpuscodea.es/"> https://corpuscodea.es/</a></p> <p> </p> <p>We include (i) the annotated training datasets, (ii) the contextual and static embeddings trained on medieval multilingual texts and (iii) the named entity recognition models trained using two architectures: Bi-LSTM-CRF + stacked embeddings and fine-tuning on Bert-based models (mBert and RoBERTa)</p> <p>Codes, datasets and notebooks used to train models can be consulted in our gitlab repository: <a href="https://gitlab.com/magistermilitum/ner_medieval_multilingual">https://gitlab.com/magistermilitum/ner_medieval_multilingual</a></p> <p>Our best RoBERTa model is also available in the HuggingFace library: <a href="https://huggingface.co/magistermilitum/roberta-multilingual-medieval-ner">https://huggingface.co/magistermilitum/roberta-multilingual-medieval-ner</a></p>
Multi-task self-supervised learning for wearables - human activity recognition
<p>Datasets used to train and evaluated the self-supervised-learning model</p>
BiodivNERE: Gold Standard Corpora for Named Entity Recognition and Relation Extraction in Biodiversity Domain
<p>BiodivNER+RE are two gold-standard manually annotated corpora that are meant to be for Named Entity Recognition (NER) and Relation Extraction (RE) tasks based on abstracts and metadata files from the biodiversity domain</p> <p>Such corpora are designed for machine learning techniques, for example, NER as TokenClassification technique, and RE as SequenceClassification technique.</p>
Data from: Species recognition limits mating between hybridizing ant species
<p><span>Identifying mechanisms limiting hybridization is a central goal of speciation research. Here, we studied pre-mating and post-mating barriers to hybridization between two ant species, <em>Formica selysi</em> and <em>Formica cinerea</em>. These species hybridize in the Rhône valley in Switzerland, where they form a mosaic hybrid zone, with limited introgression from <em>F. selysi</em> into <em>F</em>. <em>cinerea</em>. There was no sign of temporal isolation between the two species in the production of queens and males. With choice experiments, we showed that queens and males strongly prefer to mate with conspecifics. Yet we did not detect post-mating barriers caused by genetic incompatibilities. Specifically, hybrids of all sexes and castes were found in the field and F1 hybrid workers did not show reduced viability compared to non-hybrid workers. To gain insights into the cues involved in species recognition, we analyzed the cuticular hydrocarbons of queens, males and workers and staged dyadic encounters between workers. Cuticular hydrocarbon profiles differed markedly between species, but were similar in <em>F. cinerea</em> and hybrids. Accordingly, workers also discriminated species, but they did not discriminate <em>F. cinerea</em> and hybrids. We discuss how the CHC-based recognition system of ants may facilitate the establishment of pre-mating barriers to hybridization, independent of hybridization costs.</span></p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Tsinghua Dogs)
<p>Tsinghua Dogs Dataset with ground truth labels for breeds in YOLOv5 format.</p>
ScienceDex guides
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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.