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.
179
datasets available to search
ShareScore release 0.9.0
Dataset results
179 results for “Classification systems”
TB-CARE: A Novel Convolutional Autoencoder-based Tuberculosis Classification System with Enhanced EfficientNet
<p><span>The novel framework for TB classification using Convolutional AutoencodeR with EfficientNet (TB-CARE) involves the utilization of a convolutional autoencoder for feature extraction, an affinity propagation clustering method for selecting templates, and an enhanced EfficientNet (EEffNet) for classification. Extensive tests are performed on datasets that are freely accessible. The results of our methodology surpassed those of previous approaches, demonstrating its practicality for real-world applications. By leveraging deep learning models within the ensemble method, TB classification achieves a notable area under the receiver operating characteristic of up to 0.99, outperforming other tested classifiers and setting a new benchmark. EEffNet exhibits outstanding performance with an accuracy of 99.8%, sensitivity of 99.8%, and specificity of 99.7% on the NIH chest X-ray dataset and accuracy of 99.6%, sensitivity of 99.9%, and specificity of 99.4% on TBX11 k Dataset. These results indicate that employing features extracted from various image sources can significantly enhance the detection rate.</span></p>
A Construction Classification System Database for Understanding Resource Use in Building Construction
<p>Welcome to the Construction Classification System Database for Understanding Resource Use in Buildings.</p> <p>This database provides a novel dataset and a building material data structure to facilitate study of resource use in building design and construction. The ontology developed for this database uses UniFormat (CSI and CSC, 2010) in conjunction with MasterFormat (CSI and CSC, 2016) for organizing and storing the building material data.</p> <p>The dataset was developed by collecting design or construction drawings for the studied buildings and performing material take-offs based on the drawings. The ontology is based on Uniformat and MasterFormat to facilitate interoperability with existing construction management practices, and to suggest a standardized structure for future MI studies. The structure of the database and these guidelines builds on the structure presented by (Heeren & Fishman, 2019).</p> <p>The initial database version is created by the research team supervised by Prof. Shoshanna Saxe at the University of Toronto and published in the journal Scientific Data (Guven et al. 2022) in February 2022 to describe the dataset and the associated methods and details.</p> <p>Thank you for considering contributing to the database. Data contributors must follow the steps detailed below and must ensure that their inputs do not infringe any intellectual property or copyright agreements.</p> <p>References</p> <p>i. CSI and CSC. (2010). UniFormat - A Uniform Classification of Construction Systems and Assemblies. Constructions Specification Institute (CIS) and Construction Specifications Canada (CSC).</p> <p><br> ii. CSI and CSC. (2016). MasterFormat Numbers & Titles (pp. 1–186). pp. 1–186. Constructions Specification Institute (CIS) and Construction Specifications Canada (CSC).</p> <p><br> iii. Guven, G., Arceo, A., Bennett, A., Tham, M., Olanrewaju, B., McGrail, M., Isin, K., Olson, A. W., and Saxe, S. (2022). “A construction classification system database for understanding resource use in building construction.” Scientific Data, Springer US, 9(1), 42.</p> <p><br> iv. Heeren, N., & Fishman, T. (2019). A database seed for a community-driven material intensity research platform. Scientific Data, 1–10. https://doi.org/10.1038/s41597-019-0021-x</p>
Texas Statewide Landcover, Ecological Systems, and Percentage Canopy Classifications (10-meter Resolution) (2021)
<p>Statewide landcover, ecosystem, and percentage canopy cover classifications for Texas were mapped at a spatial resolution of 10-meters. Classifications were run for 16 zones across the state corresponding to available cloud-free multitemporal Sentinel-2 satellite imagery for each zone. For each zone, RandomForest classifications were run using data stacks comprised of spectral bands from three dates (winter, early growing season, late growing season/leaf-off) of imagery, as well as multiple vegetation indices (NDVI, EVI2, MSAVI2). Over 50,000 training points were selected from ground trips and high-resolution aerial image surveys to run the entire pixel-based classification. The overlapping zones were merged using a feathering algorithm to produce a single statewide land-cover classification map. The landcover mapping results were further refined using multiple spatial masks (e.g., urban, water, crop) along with logical rulesets and ancillary data. To map ecological systems, the land-cover classification was then intersected with an enduring features dataset derived primarily from soil map-unit polygons (gSSURGO) and other geophysical variables. Additionally, we produced a statewide percentage canopy cover map at a 10-meter spatial resolution using multiple techniques. For the western 2/3 of the Texas, a nested machine learning approach was used (<a href="https://doi.org/10.1016/j.rse.2020.111748">Sunde et al., 2020</a>), and for the eastern 1/3 of the state, a combination of LiDAR derived training data and machine learning was used.</p> <p>This dataset includes four items:</p> <ol> <li><strong>"TX_10m_landcover_2021.zip" - Statewide landcover classification for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_10m_ecoclass_map_2021.zip" - Statewide ecological systems classification for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_10m_canopy_cover_2021.zip" - Statewide percentage canopy cover map for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_lu_ecoclass_key.xlsx" - Table containing keys for the mapped landcover, canopy, and ecological systems classes</strong></li> </ol> <p>(To facilitate display of the datasets within ESRI software, .lyr files are included in the respective archive folders)</p> <p><em>This work was funded by the Texas A&M Forest Service.</em></p>
Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa
<p>This is a set of datasets used for the publication of the article titled: Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa</p>
Prospective Study Comparing Two Classification Systems for Second-degree Vaginal Tears During Spontaneous Childbirth to Assess Their Ability to Predict Postpartum Complications.
ClinicalTrials.gov study NCT07124676. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Validation of a Comprehensive Classification Automation System for Kidney Allograft Biopsies
ClinicalTrials.gov study NCT05306795. IPD Sharing: UNDECIDED. Countries: 1. Publications: 17.
The Category-Modifier system: a hierarchical classification scheme for vertebrate tooth marks - supplementary tables
Open the record for dataset details and reuse information.
Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification
Open the record for dataset details and reuse information.
Spike-timing based coding in neuromimetic tactile system enables dynamic object classification
Open the record for dataset details and reuse information.
Supplementary material 3 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
The area and share of main ecosystem types and sub ecosystem types (ETs, see Tab. 1)) in the German land cover model (LBM-DE) for the time periods 2012, 2015 and 2018. Linear elements such as small scale structures and infrastructures from the topographic-cartographic Information system (ATKIS) were added to the land cover model.
Supplementary material 4 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
Tab. D: Detailed matrix of pre- and post-use of settlement and transportation areas in Germany in the period 2013-2018 in hectare per day (ha/d). (Data source: IOER)
Supplementary material 1 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
Proposal of a classification system for ecosystem types (ETs) in Germany, assignment to the European ecosystem types according to EUNIS and to the CLC types of the database LBM-DE
Supplementary material 2 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
Supplementation of ecosystem types (ETs) by more differentiated spatially and non-spatially explicit data (system of assignment of biotope and habitat types relevant for nature conservation to ETs
Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence
<p>This is the replication package for the paper: "Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence". It contains the dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following.</p> <p><strong>1. dataset folder</strong></p> <ul> <li>dataset_user_reviews.xlsx contains 1278 labelled non-requirement user reviews.</li> <li>readme.txt describes the meaning of the data in dataset_user_reviews.xlsx in detail.</li> </ul>
Supplementary material 1 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049
Table S1. Exotic species located in Quadrant 1 (see Figure 3) and impacts on biodiversity, agriculture and cattle raisng
DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification
<p><strong>The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potentialfor advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology. </strong></p>
Data-driven versus Köppen–Geiger systems of climate classification
<p>Results of data-driven climate zone classification study</p>
Determination of the Damage Severity of Wood-Boring Beetles According to the Bevan Damage Classification System
<p>The aim of the study was to determine damage severity of wood-destroying insects on logs stored in forest depots. The Bevan damage classification (BDC) system, developed in 1987, was utilized to determine damage severity in log depots in 21 locations throughout seven provinces in Turkey. Pheromone traps were placed in those locations at the beginning of April in 2015 and 2016. Furthermore some stored wood within the log depots were checked and split into small pieces to collect insects that damage wood. The BDC system was used for the first time to measure the severity of insect damage in log depots. Twenty-eight families, 104 genera and 123 species were identified in this study. Based on the BDC system, the highest damage was found from the Cerambycidae and Buprestidae families. <em>Arhopalus rusticus</em> was determined as the insect responsible for the highest amount of damage with 8.8% severity rating in the pheromone-trapped insects group. When the stored wood material was considered, <em>Hylotrupes bajulus</em> was found to be the cause of the highest damage. The lowest damage values were among the predator insects (Cleridae, Trogossitidae, Cantharidae) and those feeding on fungi colonized on the wood (Mordellidae, Cerylonidae, Nitidulidae). Some other predator insects of the Tenebrionidae family (<em>Uloma cypraea, Uloma culinaris, Menephilus cylindricus</em>) and Elateridae family (<em>Lacon punctatus</em>, <em>Ampedus</em> sp.) exhibited relatively higher damage severity values since they had built tunnels and made holes in the stored wood material. When the environmental factors were considered, the Buprestidae family exhibited a very strong positive relationship (<em>p</em> <0.005) with insect frequency distribution (r = 0.922), number of species (r = 0.879) and insect density (r = 0.942). Both families showed the highest number and frequency during July and August, highlighting the importance of insect control and management during these months.</p>
A developmental classification system for the comparison of Puya raimondii giant Andean rosettes
<p>Two datasets containing measurements of Puya raimondii plants, used as the basis of a developmental classification system for these giant Andean rosettes: "All data" contains data for all measured plants, while "Equal N data" contain a subset of the whole dataset used in statistical comparisons. <br>"Microhabitat data" contain estimates of variables used to characterize different sampling locations.</p>
GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020
<p><span>A G</span>lobal <span>C</span>oast<span>L</span>ine <span>D</span>ataset (GCL_FCS30) with a detailed classification system, including categories for (0) artificial, (1) biogenic, (2) sandy, (3) muddy, (4) rocky, and (5) estuary coastlines for 2010, 2015, and 2020. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index (MNDWI), an adaptive threshold segmentation method based on the Maximum Between-Class Variance <span>Method </span>(OTSU), and the Canny edge detector. The coastline classification was performed using a hybrid transect classifier that integrates a random forest algorithm with globally stable training samples derived from multi-source geophysical data.</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.