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.
345
datasets available to search
ShareScore release 0.9.0
Dataset results
345 results for “classification analysis”
FIGURE 25 in Cladistic analysis reveals polyphyly of Tomarus (Coleoptera: Scarabaeidae: Dynastinae): new classification and taxonomic revision
FIGURE 25. Habitus of the species of Ligyrus. Subgenus Anagrylius: A) Ligyrus cuniculus, B) L. moroni. Subgenus Ligyrodes: C) L. peruvianus, D) L. relictus, E) L. sallaei. Subgenus Ligyrus: F) L. allonasutus, G) L. bidentulus, H) L. burmeisteri. Scale bar: 5 mm.
FIGURE 29 in Cladistic analysis reveals polyphyly of Tomarus (Coleoptera: Scarabaeidae: Dynastinae): new classification and taxonomic revision
FIGURE 29. Strict consensus topology of the most parsimonious trees (L = 336, CI = 0.601, RI = 0.907) showing in colors the new generic classification. Values of bootstrap greater than 50% (above) and Bremer support (below) are reported for each node. Outgroups and species belonging to Oxyligyrus and Euetheola are in bold font.
FIGURE 28 in Cladistic analysis reveals polyphyly of Tomarus (Coleoptera: Scarabaeidae: Dynastinae): new classification and taxonomic revision
FIGURE 28. Habitus of the species of Tomarus: A) Tomarus maracaiboensis, B) T. maternus, C) T. pilcopataensis, D) T. pullus, E) T. pumilus, F) T. roigjunenti, G) T. rostratus, H) T. selanderi, I) T. subtropicus. Scale bar: 5 mm.
FIGURE 24. Habitus. A in Cladistic analysis reveals polyphyly of Tomarus (Coleoptera: Scarabaeidae: Dynastinae): new classification and taxonomic revision
FIGURE 24. Habitus. A) Euligyrus ebenus, B) E. similis, C) Proculigyrus cicatricosus. Scale bar: 5 mm.
FIGURE 4. Maxilla. A in Cladistic analysis reveals polyphyly of Tomarus (Coleoptera: Scarabaeidae: Dynastinae): new classification and taxonomic revision
FIGURE 4. Maxilla. A) Euligyrus similis, B) Proculigyrus cicatricosus, C) Ligyrus (Ligyrodes) relictus, D) L. (Ligyrus) burmeisteri, E) L. (Ligyrus) villosus, F) Tomarus gyas, G) Tomarus maternus, H) T. selanderi, I) T. subtropicus.
FIGURE 30 in Cladistic analysis reveals polyphyly of Tomarus (Coleoptera: Scarabaeidae: Dynastinae): new classification and taxonomic revision
FIGURE 30. Character mapped onto the strict consensus topology showing unambiguous synapomorphies. Black circles: exclusive synapomorphies. Empty circles: non-exclusive synapomorphies.
A Machine Learning based approach to osteoporosis classification: correlational and comparative analysis between Osseus and DXA exams
<p>The osseus dataset is composed of data from 505 individuals who underwent the osseus triage and DXA exam at the University Hospital Onofre Lopes of Federal University of Rio Grande do Norte, Brazil. This dataset provides elementary data to analyze the prediction of changes in bone mineral density by Osseus using supervised classification algorithms. Supplementary file presents the dictionary used during the data analysis.</p>
Infant movement classification through pressure distribution analysis
<p>This repository contains data set and code for the paper:</p> <p><span>Kulvicius, T., </span><span>Zhang, D.</span><span>, Nielsen-Saines, K., Bölte, S., Kraft, M., </span><span>Einspieler, C., </span><span>Poustka, L., Wörgötter, F., and </span><span>Marschik, P. B.</span><span> (2023). Infant movement classification through pressure distribution analysis. </span><span>Communications Medicine, 3(112). </span><span>DOI:10.1038/s43856-023-00342-5</span>.</p> <p>For more details please see README.md file.</p>
nCNV-seq: nanopore-based CNV analysis tool for brain tumor classification & grading
<p>An available glioma test-dataset designed for nCNV-seq analysis and its corresponding database</p>
Analysis of Caesarean Section Rate According to the Robson Classification System
ClinicalTrials.gov study NCT03794063. IPD Sharing: YES. Countries: 1. Publications: 7.
Data from: Survival analysis and classification methods for forest fire size
Open the record for dataset details and reuse information.
Data from: The fossil Osmundales (Royal Ferns)—a phylogenetic network analysis, revised taxonomy, and evolutionary classification of anatomically preserved trunks and rhizomes
The Osmundales (Royal Fern order) originated in the late Paleozoic and is the most ancient surviving lineage of leptosporangiate ferns. In contrast to its low diversity today (less than 20 species in six genera), it has the richest fossil record of any extant group of ferns. The structurally preserved trunks and rhizomes alone are referable to more than 100 fossil species that are classified in up to 20 genera, four subfamilies, and two families. This diverse fossil record constitutes an exceptional source of information on the evolutionary history of the group from the Permian to the present. However, inconsistent terminology, varying formats of description, and the general lack of a uniform taxonomic concept renders this wealth of information poorly accessible. To this end, we provide a comprehensive review of the diversity of structural features of osmundalean axes under a standardized, descriptive terminology. A novel morphological character matrix with 45 anatomical characters scored for 15 extant species and for 114 fossil operational units (species or specimens) is analysed using networks in order to establish systematic relationships among fossil and extant Osmundales rooted in axis anatomy. The results lead us to propose an evolutionary classification for fossil Osmundales and a revised, standardized taxonomy for all taxa down to the rank of (sub)genus. We introduce several nomenclatural novelties: (1) a new subfamily Itopsidemoideae (Guaireaceae) is established to contain Itopsidema, Donwelliacaulis, and Tiania; (2) the thamnopteroid genera Zalesskya, Iegosigopteris, and Petcheropteris are all considered synonymous with Thamnopteris; (3) 12 species of Millerocaulis and Ashicaulis are assigned to modern genera (tribe Osmundeae); (4) the hitherto enigmatic Aurealcaulis is identified as an extinct subgenus of Plenasium; and (5) the poorly known Osmundites tuhajkulensis is assigned to Millerocaulis. In addition, we consider Millerocaulis stipabonettiorum a possible member of Palaeosmunda and Millerocaulis estipularis as probably constituting the earliest representative of the (Todea-)Leptopteris lineage (subtribe Todeinae) of modern Osmundoideae.
Data from: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme
With the rapid increase in social networks and blogs, the social media services are increasingly being used by online communities to share their views and experiences about a particular product, policy and event. Due to economic importance of these reviews, there is growing trend of writing user reviews to promote a product. Nowadays, users prefer online blogs and review sites to purchase products. Therefore, user reviews are considered as an important source of information in Sentiment Analysis (SA) applications for decision making. In this work, we exploit the wealth of user reviews, available through the online forums, to analyze the semantic orientation of words by categorizing them into +ive and -ive classes to identify and classify emoticons, modifiers, general-purpose and domain-specific words expressed in the public's feedback about the products. However, the un-supervised learning approach employed in previous studies is becoming less efficient due to data sparseness, low accuracy due to non-consideration of emoticons, modifiers, and presence of domain specific words, as they may result in inaccurate classification of users' reviews. Lexicon-enhanced sentiment analysis based on Rule-based classification scheme is an alternative approach for improving sentiment classification of users' reviews in online communities. In addition to the sentiment terms used in general purpose sentiment analysis, we integrate emoticons, modifiers and domain specific terms to analyze the reviews posted in online communities. To test the effectiveness of the proposed method, we considered users reviews in three domains. The results obtained from different experiments demonstrate that the proposed method overcomes limitations of previous methods and the performance of the sentiment analysis is improved after considering emoticons, modifiers, negations, and domain specific terms when compared to baseline methods.
Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
Open the record for dataset details and reuse information.
Supplementary material 2 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Table S2
Supplementary material 4 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Supplementary tree file
Supplementary material 3 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Figure S1
Supplementary material 1 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Table S1
Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods
<p>The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.</p> <p>Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. </li> <li><em>DeepClassifier: </em>Trained deep learning time series classifier.</li> <li><em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.</li> </ul>
FIGURE 73 in A phylogenetic analysis of the tribe Zopherini with a review of the species and generic classification (Coleoptera: Zopheridae)
FIGURE 73. Strict consensus tree of 4 shortest trees found using PAUP.
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.