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59 results for “Fine-grained”
Evolution of software code at the level of fine-grained elements: data files
<p>The data files available here (68GB uncompressed) have been used for studying the evolution of code at the level of fine-grained elements. The data are associated with the processing of the 89 open source software repositories hosted on GitHub. Details regarding each individual GitHub project are stored in the repos folder under directories matching the owner and project name used on GitHub. For example, the files under repos/KDE/kdevelop correspond to the project hosted on https://github.com/KDE/kdevelop. Data associated with the statistical analysis of the processed repositories are stored in the statistical-analysis folder. The file project_details.txt contains the data used for selecting the processed projects.</p>
The dataset of lamina structure in fine-grained rocks
<p>Fine-grained sedimentary rocks account for two-thirds of all sedimentary rocks, and contain multi-scale lamina structure. Lamina structure contain important information about paleoenvironmental reconstruction, climate change, depositional processes, hydrocarbon accumulation and even global carbon cycle. Fine-grained sedimentary rocks can be divided into massive, layered and laminated according to core-scale lamina observation. Thin section observation reveals that the mineral composition of individual lamina includes carbonate, silt, clay mineral, tuffaceous lamina and organic matter lamina in the micrometer-scales. However, for intervals without core data control, geophysical well logs are needed to predict the multi-scale lamina structure in fine-grained sedimentary rocks. High resolution image logs provide 5 mm vertical resolution images, however, they don’t highlight the lamina characteristics, therefore image logs are used to generate the slab image and button conductivity curves to facilitate the recognition of lamina structure up to 2.5 vertical resolution. This novel and multidisciplinary approach provides a powerful method for continuously identifying lamina structure with a 2.5 mm vertical resolution using well logs.</p>
FG-OVD: Fine-grained Open-Vocabulary Object Detection Benchmark Suite
<p>A collection of annotations for PACO images containing free-form fine-grained textual captions of objects, their parts, and their attributes. It also comprises several sets of negative captions that can be used to test and evaluate the fine-grained recognition ability of open-vocabulary models.</p>
WikiChurches – A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
<p>WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level. In addition, we provide 631 bounding box annotations of characteristic visual features for 139 churches from four major categories. These annotations can, for example, be useful for research on fine-grained classification, where additional expert knowledge about distinctive object parts is often available.</p> <p>Please refer to the README.md file for information about the different files contained in this dataset.</p>
Data for: Microstructure evolution and texture development during production of homogeneous fine-grained aluminum wire by friction extrusion
<p>This dataset contains the data for paper “Microstructure evolution and texture development during production of homogeneous fine-grained aluminum wire by friction extrusion” published in Materials Characterization.</p> <p>Abstract: This study aims to understand the microstructure evolution and texture development during friction extrusion of aluminium alloys, focusing on AA7075 as exemplary alloy system. Electron backscatter diffraction technique has been employed to obtain crystallographic data from various regions in front of the die and in the wire. It can be deduced that the combination of continuous dynamic recrystallization and geometric dynamic recrystallization mainly govern the formation of a fine-grained structure, however discontinuous dynamic recrystallization may also play a role at high temperature. The global shear deformation during the process was characterized as a simple shear deformation with dominant <span class="math-tex">\(B/\overline{B}\)</span> simple shear texture components. The material flow is mainly driven by the in-plane shear strain and the extrusion-induced shear strain that are determined by die rotational speed and extrusion force, respectively. The in-plane shear strain strongly affects the formation of a homogeneous fine-grained microstructure in the aluminum wire. In this regard, a novel material flow model for friction extrusion has been proposed.</p>
UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network
<p>The UBGG dataset provides easily access and leverage to researchers and analysts, which is stored in the following Zenodo repository (<a href="https://doi.org/10.5281/zenodo.8053333">https://doi.org/10.5281/zenodo.8352777</a>). The UBGG dataset consists of two main components:</p> <ul> <li><strong>UBGG-3m: the fine-grained UBGG map product of 36 metropolises in China.</strong> The UBGG-3m dataset captures the intricate urban landscape features with remarkable precision, providing a detailed representation at an impressive 3-meter resolution. Fig. 1 in User Guides shows the classification results for 36 Chinese metropolises. Researchers can delve into the nuances of the UBGG continuum, gaining invaluable insights into the interplay between the blue, green, and gray elements of urban environments in each metropolis.</li> </ul> <ul> <li><strong>UBGGset:</strong> <strong>the large-volume sample dataset to support the UBGG deep learning research.</strong> Complementing the UBGG-3m dataset, UBGGset serves as a large-volume sample dataset specifically tailored to support and foster UBGG research endeavors (Fig. 2). The UBGGset consists of 14,627 sample images (without data augmentation), with dimensions of 256 pixels in length and width, covering an urban area of approximately 2,272 km<sup>2</sup>. The UBGGset was constructed with co-registered pairs of 3 m Planet images and fine-annotated urban landscapes labeled on 1 m Google Earth image. This dataset encompasses 15 typical cities, offering researchers a rich and diverse resource to drive exploration, analysis, and innovation in the field of urban landscape studies.</li> </ul> <p> </p> <p><strong>Citation format for paper and dataset:</strong></p> <p>[1] Zhiyu Xu, Shuqing Zhao. Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network. <em>Sci Data</em> 11, 266 (2024). https://doi.org/10.1038/s41597-023-02844-2</p> <p>[2] Zhiyu Xu, Shuqing Zhao, Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments,<br>Science Bulletin, (2024). https://doi.org/10.1016/j.scib.2024.03.060</p> <p>[3] Zhiyu Xu, Shuqing Zhao. UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network (v1.0) [Data set]. (2023). Zenodo. https://doi.org/10.5281/zenodo.8352777</p>
Phenotypic plasticity in response to fine-grained environmental variation in predation.
1. In nature, organisms experience environmental variability at coarse-grained (inter-generational) and fine-grained (intra-generational) scales and a common response to environmental variation is phenotypic plasticity. The emphasis of most empirical work on plasticity has been on examining coarse-grained variation with the goal of understanding the costs and benefits of plastic responses in response to a particular environment. 2. In this study, we investigated the effects of fine-grained variation in predation on the inducible defences of larval wood frogs (Rana sylvatica) by widely altering the density and feeding schedule of caged predators (Dytiscusspp.) while holding average predation constant. 3. We found that predator cues induced change in tadpole behaviour, morphology, and mass. Surprisingly, however, temporal variation in predation did not cause the tadpoles to alter their activity (compared to a constant predation treatment) or mass. Temporal variation in predation did alter tadpole tail depth, but only when experiencing our most extreme variation treatment in which the predators were fed once every 8 days. Under these conditions, the predator-induced tadpole tail was less extreme compared to environments containing constant predation. 4. While a number of previous studies have examined behavioural responses of prey to temporal variation in predation risk without holding average predation constant, this appears to be the first test of temporal variation per se. As in previous studies of organism responses to temporal variation in resources, our results suggest that fine-grained environmental variability can affect the expression of phenotypically plastic traits, but our tadpoles appear to be generally unresponsive to this finegrained variation for many of their traits.
CESNET-TLS22: A large dataset for fine-grained classification of TLS services
<p><strong>Please refer to the original article for further data description:</strong> Jan Luxemburk et al. Fine-grained TLS services classification with reject option, Computer Networks, 2023, 109467, ISSN 1389-1286, <a href="https://doi.org/10.1016/j.comnet.2022.109467">https://doi.org/10.1016/j.comnet.2022.109467</a></p> <p><strong>We recommend using the</strong> <strong>CESNET DataZoo python library, which facilitates the work with large network traffic datasets. </strong>More information about the DataZoo project can be found in the GitHub repository <a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a>.</p> <p>The recent success and proliferation of machine learning and deep learning have provided powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat detection. These methods, neural networks in particular, are often complex and require a huge corpus of training data. Moreover, because most of the network traffic is being encrypted, the traditional deep-packet-inspecting (DPI) solutions are becoming obsolete, and there is an urgent need for modern classification methods capable of analyzing encrypted traffic. These methods have to forgo the packet's opaque payload and focus on flow statistics and packet metadata sequences like packet sizes, directions, and inter-arrival times. The classification can be further extended with the task of "rejecting" unknown traffic, i.e., the traffic not seen during the training phase. This makes the problem more challenging, and neural networks offer superior performance for tackling this problem.<strong> When the factors of (1) the hardness of classification of encrypted traffic with unknown traffic detection and (2) the neural networks' inherent need for large datasets are combined, the requirement for a rich, large, and up-to-date dataset is even stronger.</strong></p> <p>Therefore, we created a large dataset spanning two weeks, consisting of 141 million network flows, and having 191 fine-grained service labels. The dataset is intended as a benchmark for the task of identification of services in encrypted traffic with the detection of unknown services.</p> <p><strong>Data capture</strong> The data was captured in the flow monitoring infrastructure of the <a href="https://www.cesnet.cz">CESNET2</a> network. The capturing was done for two weeks between 4.10.2021 and 17.10.2021. The following table provides per-week flow count, capture period, and uncompressed size:</p> <ul> <li><strong>W-2021-40</strong> <ul> <li>Uncompressed Size: 22 GB</li> <li>Capture Period: 4.10.2021 - 10.10.2021</li> <li>Flows: 73.2M</li> </ul> </li> <li><strong>W-2021-41</strong> <ul> <li>Uncompressed Size: 20 GB</li> <li>Capture Period: 11.10.2021 - 17.10.2021</li> <li>Flows: 68.5M</li> </ul> </li> <li><strong>CESNET-TLS22</strong> <ul> <li>Uncompressed Size: 42 GB</li> <li>Capture Period: 4.10.2021 - 17.10.2021</li> <li>Flows: 141.7M</li> </ul> </li> </ul> <p><strong>Dataset structure</strong> The dataset flows are delivered in compressed CSV files, which contain one flow per row. For each flow data file, there is a JSON file with the number of saved flows per service. There is also the <em>stats-week.json</em> file aggregating flow counts of a whole week and the <em>stats-dataset.json</em> file aggregating flow counts for the entire dataset. The mapping between services and service providers is provided in the <em>servicemap.csv</em> file, which also includes SNI domains used for ground truth labeling. The following table describes flow data fields in CSV files:</p> <ul> <li><strong>ID:</strong> Unique identifier</li> <li><strong>BYTES:</strong> Number of transmitted bytes from client to server</li> <li><strong>BYTES_REV:</strong> Number of transmitted bytes from server to client</li> <li><strong>PACKETS:</strong> Number of packets transmitted from client to server</li> <li><strong>PACKETS_REV:</strong> Number of packets transmitted from server to client</li> <li><strong>DURATION:</strong> Duration of the flow in seconds</li> <li><strong>PPI:</strong> Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]]</li> <li><strong>PPI_LEN:</strong> Number of packets in the PPI sequence</li> <li><strong>PPI_DURATION:</strong> Duration of the PPI sequence in seconds</li> <li><strong>PPI_ROUNDTRIPS:</strong> Number of roundtrips in the PPI sequence</li> <li><strong>APP:</strong> Web service label</li> <li><strong>CATEGORY:</strong> Service category</li> <li><strong>TCP_FLAGS:</strong> TCP flags sent from client to server</li> <li><strong>TCP_FLAGS_REV:</strong> TCP flags sent from server to client</li> <li><strong>FLAG_CWR:</strong> Presence of the CWR flag</li> <li><strong>FLAG_CWR_REV:</strong> Presence of the CWR flag in the reverse direction</li> <li><strong>FLAG_ECE:</strong> Presence of the ECE flag</li> <li><strong>FLAG_ECE_REV:</strong> Presence of the ECE flag in the reverse direction</li> <li><strong>FLAG_URG:</strong> Presence of the URG flag</li> <li><strong>FLAG_URG_REV:</strong> Presence of the URG flag in the reverse direction</li> <li><strong>FLAG_ACK:</strong> Presence of the ACK flag</li> <li><strong>FLAG_ACK_REV:</strong> Presence of the ACK flag in the reverse direction</li> <li><strong>FLAG_PSH:</strong> Presence of the PSH flag</li> <li><strong>FLAG_PSH_REV:</strong> Presence of the PSH flag in the reverse direction</li> <li><strong>FLAG_RST:</strong> Presence of the RST flag</li> <li><strong>FLAG_RST_REV:</strong> Presence of the RST flag in the reverse direction</li> <li><strong>FLAG_SYN:</strong> Presence of the SYN flag</li> <li><strong>FLAG_SYN_REV:</strong> Presence of the SYN flag in the reverse direction</li> <li><strong>FLAG_FIN:</strong> Presence of the FIN flag</li> <li><strong>FLAG_FIN_REV:</strong> Presence of the FIN flag in the reverse direction</li> </ul> <p><strong>Link to other CESNET datasets</strong></p> <ul> <li><a href="https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/">https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/</a></li> <li><a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a></li> </ul> <p><strong>Please cite the original article:</strong></p> <blockquote> <p>@article{luxemburk_fine-grained-tls_2023, author = {Jan Luxemburk and Tomáš Čejka}, title = {Fine-grained TLS services classification with reject option}, journal = {Computer Networks}, volume = {220}, pages = {109467}, year = {2023}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2022.109467}, url = {https://www.sciencedirect.com/science/article/pii/S1389128622005011} }</p> </blockquote>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Oxford-IIIT Pet)
<p>Preprocessed dataset for Oxford-IIIT Pet in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes (derived from segmentation mask).</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Oxford-IIIT Pet)
<p>Oxford-IIIT Pet Dataset with ground truth labels for breeds (from https://public.roboflow.com/object-detection/oxford-pets).</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (All Dev and Test Images, Single Folder)
<p>Kashtanka Pets images, with all Dev and Test images (total 66639 images). In a single folder, with filenames indicating path of file in original dataset distribution.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Tsinghua Dogs)
<p>Preprocessed dataset for Tsinghua Dogs in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes</p>
Large-scale and fine-grained phenological stage annotation of herbarium specimens datasets
<p>This upload is constituted of four datasets of specimens from American herbaria covering different levels of information precision and different floras - from temperate to equatorial.</p> <p>Three of these datasets consist of selected specimens from herbaria located in different geographic and environmental regions. Each specimen of these three datasets was annotated with the following fields: family, genus, species name, fertile / non-fertile, presence / absence of flower(s), presence / absence of fruit(s). The resulting dataset was composed of 163,233 herbarium specimens belonging to 7,782 species, 1,906 genera, and 236 families. Specimens were annotated as “fertile” if any reproductive structures were present, such as sporangia (ferns), cones (gymnosperms), flowers, or fruits (angiosperms). Non-fertile specimens were those that lacked any reproductive structures.</p> <p>The fourth dataset consists of 20,371 herbarium specimens from 11 genera in the sunflower family (<em>Asteraceae</em>). The main difference in this dataset is that it is annotated with fine-grained phenophase scores rather than presence/absence attributes (see description below).</p> <p>Each of these datasets is described below:</p> <ul> <li> <p>NEVP: this dataset of New England vascular plant (NEVP) specimens was produced by members of the Consortium of Northeastern Herbaria. The dataset comprises 42,658 digitized specimens that belong to 1,375 species and come from several North American institutions. Most of the specimens in this dataset are from the north-temperate region of the northeastern United States.</p> </li> <li> <p>FSU: this dataset was produced by the Florida State University's Robert K. Godfrey Herbarium (FSU), a collection that focuses on northern Florida and the U.S. Southeast Coastal Plain, one of North America's biodiversity hotspots. This dataset contains 54,263 digitized herbarium specimen records that belong to 3,870 species, making it the taxonomically richest dataset in this study. Most species in this dataset grow under subtropical or warm temperate conditions in the southeastern region of the United States.</p> </li> <li> <p>CAY: this dataset comes from the IRD’s Herbarium of French Guiana (CAY). CAY is dedicated to the Guayana Shield flora, with a strong focus on tropical tree species. This dataset is composed of 66,312 herbarium specimens that belong to 3,024 species. All digitized specimens of this herbarium are accessible online. Most specimens were collected in the tropical rainforests of French Guiana, with the remaining specimens coming mostly from Suriname and Guyana.</p> </li> <li> <p>PHENO: this dataset includes 20,371 herbarium specimens of 139 species in the <em>Asteraceae</em> produced in a study of phenological trends in the U.S. Southeast Coastal Plain. The dataset is composed of specimen records from 57 herbaria. Each recorded specimen was annotated for quartile percentages (0, 25, 50, 75, or 100%) of (i) closed buds, (ii) buds transformed into flowers, and (iii) fruits. According to the distribution of these three categories for each specimen, a phenophase code was computed.</p> </li> </ul> <p> </p> <p><strong>Datasets format</strong></p> <p>These datasets are grouped in 3 tasks:</p> <ol> <li>fertility detection</li> <li>flowers and/or fruit detection</li> <li>phenophase classification</li> </ol> <p>The first 2 tasks are carried on the first 3 previous datasets and thus are based on the same set of images, unlike the third task which has its own disjoint set of images. This is why the dataset is presented into two separated files, one for each set of images.</p> <p><em>Fertility detection & flower/fruit detection</em></p> <p>These tasks are contained into the <em>herbarium_fertility_annotations.zip</em> archive. It consists of 3 files:</p> <ul> <li><em>metadata.csv</em>: general information about all the herbarium specimens for these tasks <ul> <li><em>id</em>: specimen identifier</li> <li><em>collection</em>: which of NEVP, FSU or CAY does the specimen come from</li> <li><em>herbarium</em>: institution of origin of the specimen, especially for NEVP collection</li> <li><em>clade</em>,<em> family</em>,<em> genus</em>,<em> species</em>: classification of the specimen</li> <li><em>URL</em>: URL of the scan</li> </ul> </li> <li><em>fertility_task.csv</em>: specific information regarding the fertility detection task <ul> <li><em>id</em>: specimen identifier</li> <li><em>is_fertile</em>: <em>True</em> if the specimen has an expression of fertility, <em>False</em> otherwise</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em>, <em>random_test</em>, <em>species_test</em> and <em>herbarium_test</em></li> </ul> </li> <li><em>flower_fruit_task.csv</em>: specific information regarding the flower/fruit detection task <ul> <li><em>id</em>: specimen identifier, note that in this case not all the specimen described in <em>metadata.csv</em> are included in this task</li> <li><em>has_flower</em>: <em>True</em> if the specimen has at least one flower, <em>False</em> otherwise</li> <li><em>has_fruit</em>: <em>True</em> if the specimen has at least one fruit, <em>False</em> otherwise</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em>, <em>random_test</em>, <em>species_test</em> and <em>herbarium_test</em></li> </ul> </li> </ul> <p><em>Phenophase classification</em></p> <p>These tasks are contained into the <em>herbarium_asteraceae_phenophase_annotations.zip</em> archive. It consists of a single file:</p> <ul> <li><em>annotations.csv</em>: <ul> <li><em>id</em>: specimen identifier</li> <li><em>URL</em>: URL of the scan</li> <li><em>genus</em>: genus of the specimen</li> <li><em>phenophase</em>: integer from 1 to 9 describing the phenophase of the specimen</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em> and <em>test</em></li> </ul> </li> </ul> <p> </p> <p><strong>Additional ressources</strong></p> <p>More information can be found in the related paper:<br> <em>Lorieul, T., K. D. Pearson, E. R. Ellwood, H. Goëau, J.-F. Molino, P. W. Sweeney, J. M. Yost, J. Sachs, E. Mata-Montero, G. Nelson, P. S. Soltis, P. Bonnet, and A. Joly. 2019. Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences 7(3): e1233.</em></p> <p>For an example of usage of these datasets as well as a baseline, see: <a href="http://doi.org/10.5281/zenodo.2549996">http://doi.org/10.5281/zenodo.2549996</a></p> <p> </p>
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs.
SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles
<p>SciHyp is a dataset that supports researchers in understanding and identifying hypotheses in scientific literature, serving as a valuable resource across various scientific disciplines. SciHyp provides invaluable insights into the formulation and structure of hypotheses in scientific literature, making it a crucial resource for researchers in various scientific disciplines.</p> <p>This repository contains the ontology and datasets described in our paper, SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles.</p> <p>🚨 <strong>Latest Version Update</strong>: Please note that all information, data files, and SPARQL queries outlined here are based on the latest version of our paper, "SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles." This includes updates to the ontology and RDF data files available on <a href="https://gitlab.ifi.uzh.ch/DDIS-Public/scihyp/-/tree/main/data?ref_type=heads">GitLab</a>. We encourage users to refer to this most recent version to ensure compatibility and relevance in their research and analysis.</p> <p><strong>crowd.ttl</strong>: The data in this file has been curated utilizing the SciHyp pipeline, which employs a Hybrid-LLM-Crowd methodology described in the paper.</p> <p><strong>expert.ttl</strong>: Contrasting the crowd-sourced data, this file is composed of data curated through expert annotation. It reflects a more specialized and precise perspective, offering insights grounded in expert knowledge and analysis.</p> <p><strong>scihyp_VoiD.ttl</strong>: Serving as a metadata file containing a VoiD/DCAT description (Vocabulary of Interlinked Datasets/Data Catalog Vocabulary).</p> <p><strong>CrowdAlytics_7.5.owl</strong>: This file is the backbone of the dataset, outlining the underlying ontology that defines the structure and relationships within the SciHyp data.</p> <p>You can find a detailed description of the data and other resources <a href="https://gitlab.ifi.uzh.ch/DDIS-Public/scihyp/-/tree/main/data?ref_type=heads" target="_blank" rel="noopener">here</a>.</p> <p><strong>SPARQL Endpoint</strong></p> <p>For querying the current SciHyp dataset, you can use our SPARQL endpoint. This endpoint allows you to execute SPARQL queries to explore and extract data from the SciHyp dataset interactively.</p> <p>SPARQL Endpoint URL: <a href="https://crowdalytics.ifi.uzh.ch/sparql/dataset.html"><code>https://crowdalytics.ifi.uzh.ch/sparql/dataset.html</code></a></p> <p><br>Below is an example query to retrieve some annotations. </p> <p> <br><code><em> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#></em></code><br><em><code> PREFIX ca: <http://ddis.ifi.uzh.ch/ontologies/2021/crowdalytics#></code></em><br><em><code> PREFIX disk: <http://disk-project.org/ontology/disk#></code></em><br><em><code> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#></code></em><br><em><code> PREFIX sqo: <https://w3id.org/sqo#></code></em><br><br></p> <div> <div><code>SELECT DISTINCT ?hypothesis ?relation_Operator ?leftGroup ?rightGroup</code></div> <div> </div> <div><code>WHERE {</code></div> <div> </div> <div> <code>?TriggeredLineOFInquiry disk:hasLineOfInquiry ?LineOfInquiryURI .</code></div> <div><code> ?LineOfInquiryURI disk:hasHypothesisQuery ?hypothesis .</code></div> <div><code> ?LineOfInquiryURI ca:hasGroup ?groupPair .</code></div> <div><code> ?groupPair ca:hasLeftGroup ?left .</code></div> <div><code> ?left ca:hasGroupName ?leftGroup .</code></div> <div><code> ?groupPair ca:hasRightGroup ?right .</code></div> <div><code> ?right ca:hasGroupName ?rightGroup .</code></div> <div><code> ?LineOfInquiryURI ca:hasOperator ?Operator .</code></div> <div><code> ?Operator rdfs:label ?relation_Operator .</code></div> <br> <div><code> FILTER(CONTAINS(LCASE(?leftGroup), "game") || CONTAINS(LCASE(?rightGroup), "treatment"))</code></div> <div><code> FILTER(CONTAINS(LCASE(?relation_Operator), "similar") || CONTAINS(LCASE(?relation_Operator), "same"))</code></div> <div><code>}</code></div> <div><code>GROUP BY ?hypothesis ?relation_Operator ?leftGroup ?rightGroup</code></div> </div> <p> </p> <p><strong>Note:</strong> You can find more example queries <a href="https://gitlab.ifi.uzh.ch/DDIS-Public/scihyp/-/blob/main/data/example_queries.md" target="_blank" rel="noopener"><em>here</em></a>.<br> </p>
Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones. in A New Oligocene Flora From Ludvíkovice Near Děčín (České Středohoří Mts., The Czech Republic)
Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones.
chen-echo/FGNER-corpus: Geological fine-grained corpus
<ul> <li>A corpus for the identification of CHINESE geologically named entities based on three-part geological reports.</li> <li>Contains twenty-one labels, the corresponding entities for the labels are listed in the below.</li> <li>ROC.SEDI 沉积岩 ROC.META 变质岩 ROC.IG 岩浆岩</li> <li>SMG.ROC 岩性地层 SMG.chrono 年代地层</li> <li>MIN.native 自然元素矿物 MIN.sulASIM 硫化物及其类似化合物 MIN.halide 卤化物矿物 MIN.oxihydro 氧化物及其氢氧化物矿物 MIN.oxis 含氧盐矿物 MIN.ROC 岩石类非金属矿物</li> <li>GCH.AR 太古宙 GCH.PT 元古宙 CGH.PH 显生宙</li> <li>GST.fold 褶皱 GST.fault 断裂 GST.joint 节理 GST.contact 接触关系</li> <li>GAC.ENDO 内力地质作用 GAC.EXO 外力地质作用</li> <li>OT 其他</li> </ul>
Fine-grain predictions are key to accurately represent continental-scale biodiversity patterns
Open the record for dataset details and reuse information.
Fine-Grained Activities of Daily Living Data with Structural Vibration and Electrical Load Sensing
<p>Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. We utilize structural vibration sensing and electrical load sensing to acquire multiple fine-grained kitchen activities under a lab structure setting.</p> <p>Each file contains the following values:<br> -RawData: time series of data for each channel (vibration on the table, vibration on the floor, load)<br> -Label: manually fine-grained labels of events<br> -Table: detected events start/stop index for vibration sensor on the table<br> -Floor: detected events start/stop index for vibration sensor on the floor<br> -Load: detected events start/stop index for load sensor<br> <br> Label notation:<br> 1 -- operating the kettle<br> 2 -- kettle on<br> 3 -- operating the microwave<br> 4 -- microwave on<br> 5 -- put things on the stove<br> 6 -- operating with stove<br> 7 -- stove on<br> 8 -- operating vacuum<br> 9 -- sweep floor<br> 10 -- walking/step<br> 11 -- miscellaneous<br> 12 -- synchronization signal (knock on the floor)<br> 13 -- vacant<br> 14 -- microwave door open</p>
Experimental data for the paper "Scalable Fine-Grained Proofs for Formula Processing"
<p>We provide here the binary, options and experimental data for our CADE paper and the companion report.</p> <p><strong>Setup</strong></p> <p>The tarball containing the source code of veriT used in our experiments is available here. The command line parameters of veriT used in each of the configurations described in the paper are:</p> <ul> <li>Basic: "--old-processing --disable-sym --disable-simp --disable-unit-simp --disable-unit-subst-simp --disable-ackermann --disable-bclause"</li> <li>Extended: "--old-processing --disable-sym --disable-unit-simp --disable-unit-subst-simp --disable-ackermann --disable-bclause"</li> <li>Complete: "--old-processing"</li> <li>with proofs: "--proof=/dev/null --proof-with-sharing"</li> <li>with new code: remove parameter "--old-processing"</li> </ul> <p>The benchmarks are from the SMT-LIB categories QF_ALIA, QF_AUFLIA, QF_IDL, QF_LIA, QF_LRA, QF_RDL, QF_UF, QF_UFIDL, QF_UFLIA, QF_UFLRA, AUFLIA, AUFLIRA, UF, UFIDL, UFLIA, and UFLRA.</p> <p>Our experiments were conducted on servers equipped with two Intel Xeon E5-2630 v3 processors, with eight cores per processor, and 126 GB of memory. The time limit was set to 30 s.</p>
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