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Dataset results
548 results for “data classification”
mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. </p> <p>Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. </p> <p>In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download:</p> <p>• Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO)</p> <p>• RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM)</p> <p>• Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM)</p> <p>• Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD)</p> <p>• German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)</p>
mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)</p>
Supplementary data to The vegetation of Chile and the EcoVeg approach in the context of the International Vegetation Classification project
<p>The rar file contains a map of Macrogroups of Chile in ESRI shapefile format. Macrogroups are hierarchically included in the categories of division and formation of IVC classification. These categories can also be displayed using the table associated with the shapefile. Likewise, Chilean zonal vegetation units of Luebert & Pliscoff (2017) are included, so the crosswalk for generating the map of Macrogroups based on the Chilean zonal vegetation units is fully documented.</p>
Crop Classification Data-set
<p>The dataset consists of crop type training and testing data of more than 10 classes, collected using Ground Truth Surveys, in Harichand region of Khyber Pakhtoonkhwa, Pakistan. </p> <p>The dataset also contains 2 tiff files having Planet-Scope and Sentinel-2 raster data. </p> <p>https://drive.google.com/drive/folders/1SweabTezj78btq9wd3PRZYWrR_4gUuC4</p>
ARIPO patent data for IVD-related patent classifications
<p>ARIPO Dataset_v2.ods dataset was scraped from the ARIPO E-Service database using Octoparse using the IPC classifications below. The exported task is provided as ARIPO Database_Copy.otd:</p> <table> <tbody> <tr> <td> <p><strong>IPC Code</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>B01L 3/00</p> </td> <td> <p>Containers or dishes for laboratory use, e.g. laboratory glassware (bottles B65D; apparatus for enzymology or microbiology C12M 1/00); Droppers (receptacles for volumetric purposes G01F) [2006.01]</p> </td> </tr> <tr> <td> <p>G01N 33/50</p> </td> <td> <p>Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing (measuring or testing processes other than immunological involving enzymes or microorganisms, compositions or test papers therefor, processes of forming such compositions, condition responsive control in microbiological or enzymological processes C12Q) [2006.01]</p> </td> </tr> <tr> <td> <p>C12N 9/00</p> </td> <td> <p>Enzymes, e.g. ligases (6.); Proenzymes; Compositions thereof (preparations containing enzymes for cleaning teeth A61K 8/66, A61Q 11/00; medicinal preparations containing enzymes or proenzymes A61K 38/43; enzyme containing detergent compositions C11D); Processes for preparing, activating, inhibiting, separating, or purifying enzymes [2006.01]</p> </td> </tr> <tr> <td> <p>C12N 11/00</p> </td> <td> <p>Carrier-bound or immobilised enzymes; Carrier-bound or immobilised microbial cells; Preparation thereof [2006.01]</p> </td> </tr> <tr> <td> <p>C12N 15/00<br> </p> </td> <td> <p>Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor (mutants or genetically engineered microorganisms C12N 1/00, C12N 5/00, C12N 7/00; new plants A01H; plant reproduction by tissue culture techniques A01H 4/00; new animals A01K 67/00; use of medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseases, gene therapy A61K 48/00; peptides in general C07K) [2006.01]</p> </td> </tr> <tr> <td> <p>C12Q</p> </td> <td> <p>Measuring or testing processes involving enzymes, nucleic acids or microorganisms (immunoassay G01N 33/53); compositions or test papers therefor; processes of preparing such compositions; condition-responsive control in microbiological or enzymological processes [3]</p> </td> </tr> </tbody> </table> <p><strong>Octoparse Method</strong></p> <ol> <li> <p>Go to ARIPO Advanced Search website</p> </li> </ol> <ol> <li> <p>Uncheck Utility Model, Industrial Design, Trademark checkboxes</p> </li> <li> <p>Enter date range 01011960~04222022</p> </li> <li> <p>Enter IPC categories as per Appendix 1 [only one per search]</p> </li> <li> <p>Click Search</p> </li> <li> <p>Adjust items per page from 10 to 40</p> </li> <li> <p>Extract data, looping through each record on the page</p> </li> <li> <p>Click next page button and repeat</p> </li> <li> <p>Click next button to bring up next five pages and repeat until end.</p> </li> </ol> <p> </p> <p><strong>ARIPO_Journal_Dataset_v2.ods</strong></p> <p>This dataset was downloaded from the ARIPO journal publication server using a browser-based scraping software.</p> <p> </p> <p> </p>
Data Cleaning, Translation & Split of the Dataset for the Automatic Classification of Documents for the Classification System for the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft
<ul> <li>Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer.</li> <li>Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset.</li> <li>ger_train.csv – The German training set as CSV file.</li> <li>ger_validation.csv – The German validation set as CSV file.</li> <li>en_test.csv – The English test set as CSV file.</li> <li>en_train.csv – The English training set as CSV file.</li> <li>en_validation.csv – The English validation set as CSV file.</li> <li>splitting.py – The python code for splitting a dataset into train, test and validation set.</li> <li>DataSetTrans_de.csv – The final German dataset as a CSV file.</li> <li>DataSetTrans_en.csv – The final English dataset as a CSV file.</li> <li>translation.py – The python code for translating the cleaned dataset.</li> </ul>
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
Data from: Crowdsourcing training material for automated bird sound classification – a pilot study
<p>Data from the manuscript "Crowdsourcing training material for automated bird sound classification – a pilot study" by Petteri Lehikoinen, Meeri Rannisto, Ulisses Camargo, Aki Aintila, Patrik Lauha, Esko Piirainen, Panu Somervuo & Otso Ovaskainen</p>
OpenAlex Topic Classification v1 Model Artifacts and Training Data
<p>This is all data used to train the topic classification model and also the model artifacts to deploy the model. Please see the github repo for more information:</p> <p>https://github.com/ourresearch/openalex-topic-classification</p>
Data for "Superphot+: Realtime Fitting and Classification of Supernova Light Curves"
<p>This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:</p> <ul> <li>superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plus -- This version corresponds to commit: 956b5d555f58800c01a74b3977e0a3b5476ea9cd and tag v0.0.8.</li> <li>dataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.</li> <li>dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.</li> <li>model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.</li> <li>model_0.yaml: Configuration file associated with model_0.pt.</li> <li>model_z_0.pt: Same as model_0.pt, but trained using redshift information.</li> <li>model_z_0.yaml: Configuration file associated with model_z_0.pt.</li> <li>early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.</li> <li>early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.</li> <li>probs_concat.csv: Spectroscopic set's classification results without using redshift information.</li> <li>probs_z_concat.csv: Spectroscopic set's classification results using redshift information.</li> <li>probs_photometric_v2.mrt: Superphot+'s probabilities for the photometric set without using redshift information. Updated to correct for missing IAU names.</li> </ul>
Data from: Genomic data provide insights into the classification of extant termites
<p>The higher classification of termites requires substantial revision as the Neoisoptera, the most diverse termite lineage, comprise many paraphyletic and polyphyletic higher taxa. Here, we produced an updated termite classification using genomic-scale analyses. We reconstructed phylogenies under diverse substitution models with ultraconserved elements analyzed as concatenated matrices or within the multi-species coalescence framework. Our classification is further supported by analyses controlling for rogue loci and taxa, and topological tests. We show that the Neoisoptera are composed of seven family-level monophyletic lineages, including the Heterotermitidae Froggatt, Psammotermitidae Holmgren, and Termitogetonidae Holmgren, raised from subfamilial rank. The species-rich Termitidae are composed of 18 subfamily-level monophyletic lineages, including the new subfamilies Crepititermitinae, Cylindrotermitinae, Forficulitermitinae, Neocapritermitinae, Protohamitermitinae, and Promirotermitinae, and the revived Amitermitinae Kemner, Microcerotermitinae Holmgren, and Mirocapritermitinae Kemner. Building an updated taxonomic classification on the foundation of unambiguously supported monophyletic lineages makes it highly resilient to potential destabilization caused by the future availability of novel phylogenetic markers and methods. The taxonomic stability is further guaranteed by the modularity of the new termite classification, designed to accommodate as-yet undescribed species with uncertain affinities to the herein delimited monophyletic lineages in the form of new families or subfamilies.</p>
Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
<p>The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.</p>
Fig. 14 Erythraeus regalis, larva. a in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 14 Erythraeus regalis, larva. a Gnathosoma and idiosoma, dorsal view. b Dorsal opisthosomal seta. c Gnathosoma and idiosoma, ventral view
Fig. 11 Erythraeus cinereus, larva. a Leg I. b Leg II. c Leg III. d Tarsus I. e Tarsus II in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 11 Erythraeus cinereus, larva. a Leg I. b Leg II. c Leg III. d Tarsus I. e Tarsus II (d, e, only specialized setae shown)
Fig. 9 Erythraeus cinereus, larva. a Chelicera. b in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 9 Erythraeus cinereus, larva. a Chelicera. b Gnathosoma (and scutum), dorsal view. c Gnathosoma, ventral view. d Palp tibia. e Palp tarsus
Fig. 8 Erythraeus cinereus, adult. a Palp, medial view. b Crista metopica and eyes. c Dorsal opisthosomal setae. d in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 8 Erythraeus cinereus, adult. a Palp, medial view. b Crista metopica and eyes. c Dorsal opisthosomal setae. d Serratala on genu I. e Serratala on genu IV. f Diversity of serratalae and setae of non-serratalae type on telofemora, genua, and tibiae of legs I–IV
Fig. 6 Erythraeus phalangoides, larva. a in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 6 Erythraeus phalangoides, larva. a Gnathosoma and idiosoma, dorsal view. b Dorsal opisthosomal setae. c Gnathosoma and idiosoma, ventral view. d Seta ps
Fig. 10 Erythraeus cinereus, larva. a in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 10 Erythraeus cinereus, larva. a Gnathosoma and idiosoma, dorsal view. b Dorsal opisthosomal setae. c Gnathosoma and idiosoma, ventral view. d Seta ps
Fig. 12 Erythraeus regalis, adult. a Palp, medial view. b Crista metopica and eyes. c Dorsal opisthosomal setae. d in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 12 Erythraeus regalis, adult. a Palp, medial view. b Crista metopica and eyes. c Dorsal opisthosomal setae. d Serratala on genu I. e Serratala on genu IV. f Diversity of serratalae and setae of non-serratalae type on telofemora, genua, and tibiae of legs I–IV
Fig. 16 Erythraeus regalis, larva. a Leg I. b Leg II. c Leg III. d Genu-tarsus I. e Genu-tarsus II in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 16 Erythraeus regalis, larva. a Leg I. b Leg II. c Leg III. d Genu-tarsus I. e Genu-tarsus II. Tibia-tarsus III (d–f, only specialized setae shown)
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.