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14,447 results for “Identification”
Tigrinya Dialect Identification (TDI)
<p><strong>The Tigrinya Dialect Identification (TDI)</strong> dataset contains text on three Tigrinya dialects or varieties namely: Z, D, and L. The purpose of this dataset is to study dialect identification for Tigrinya using machine learning.</p> <p>For the Z variety, we used snippets from the book ኽልተ ዛንታት (Kilte Zantatat). For the L variety, we used book chapters from ፋቶ (Fato) and ዕርቂ እንደርታ (Erqi Enderta). For the D variant, we could not find a book. Instead, we collected data from two Facebook users, Akeza Awalom and Guraya Asadi Raya that consistently write in that variety. Sentences collected for each dialect were translated to the other dialect with expert native speakers in the target dialect.</p> <p><br> <strong>Source by Dialect</strong></p> <table align="left"> <tbody> <tr> <td> <p><strong>Dialect</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>No. sentences</strong></p> </td> </tr> <tr> <td> <p>Z</p> </td> <td> <p>Kilte Zantatat</p> </td> <td> <p>1041</p> </td> </tr> <tr> <td> <p>L</p> </td> <td> <p>Fato </p> </td> <td> <p>764</p> </td> </tr> <tr> <td> </td> <td>Erqi nderta</td> <td>405</td> </tr> <tr> <td> <p>D</p> </td> <td> <p><a href="https://www.facebook.com/akeza.awealom">Akeza Awalom</a></p> </td> <td> <p>224</p> </td> </tr> <tr> <td> </td> <td><a href="https://www.facebook.com/guraya.asadiraya">GualRaya</a></td> <td>530</td> </tr> </tbody> </table> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p> </p> <p>Special thanks to Meles Solomon, who provided us with his book, ዕርቂ እንደርታ (Erqi Enderta). He also helped with translations to the L dialect. Thanks also goes to Tesfay Gebreegziabher and Gidey Gebrekidan for allowing us to use their books Fato and Kilte Zantatat respectively. Many thanks to Teklay Berhane, Abeba Asemu, Haftu Abadi, Tsegay Kinfe, Moges Bekru, Kahsay Berhe Adhana, Kibrom Mulugeta, Tsegazeab Kidanu, Tsgab Weldemariam, Abu W Debay, Solomon Shibabaw, Hagos Hiete for their valuable contributions as translators.</p>
RattanID - a molecular identification toolkit for rattan palms
<p>This repository contains laboratory protocols, reference datasets, auxiliary files (target file and sequencing adapters) and example data for the RattanID molecular identification toolkit (https://github.com/BenKuhnhaeuser/RattanID). It also contains a dataset detailing rattan occurrence records at species level, rattan uses and extinction risk predictions, as well as distribution maps built based on the rattan occurrence records dataset.</p>
Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study VCF files
<p>VCF files used and cited in the article: Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study</p>
Data set and classification method for low quality web traffic identification in video marketing campaigns
<p>Final outcomes of the InPreVi (AI4Media) project developed in 2022. </p> <p>1. Data set describing the statistics of the video ad marketing campaigns</p> <p>2. Script for web traffic classification</p>
A software for automatic identification of oyster species
<p>The files includes all data and final analysis of the work done in CS8 - oysters, task 8.2, CSTP8.2.2_A new software for automatic identification of oyster species. This includes the data management descriptor document (DataSheet_oyster_image_classification.docx), images used (oyster_classification_images.zip), the code developed (oyster_classification_code_package.zip) and different models evaluated (oyster_classification_models.zip), the genetics data produced (oyster_classification_biometrics and PCR.xlsx) and the project report (C639_ostronklassificering.pdf). The content of the files is described briefely below. The data is used in deliverables D1.2, D1.4, D1.5 and D1.6 in the AquaVitae project.</p> <p>oyster_classification_images.zip</p> <p>The data set contains the images used for training the classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas. The images are sorted in folders named “train” (training data) and “validation” (validation data) with both folders containing sub-folders called “mg” (images of Magallana gigas) and “oe” (images of Ostrea edulis).</p> <p>oyster_classification_code_package.zip</p> <p>The data set contains the code for training a neural network for classifying oyster species based on images. The code also includes localization of oyster within an image and inference of the classification along with the trained models.</p> <p>oyster_classification_models.zip</p> <p>The data set contains the trained classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas.</p> <p>oyster_classification_biometrics and PCR.xlsx</p> <p>The data set contains biometric information for a subset of 240 Ostrea edulis, 240 Magallana gigas and 204 oysters of unsure species denotation sampled as a start pool for the image analysis project and for genetic evaluation of species belonging.</p>
Core-loss EELS dataset and neural networks for element identification
<p>We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification. </p> <p>The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra.</p> <p>Code on how to <br> - read the simulated data<br> - transform HDF5 format to TFRecord format<br> - train and evaluate neural networks using the simulated data<br> - use the trained networks for automated element identification<br> is available on GitHub at arnoannys/EELS_ID</p> <p>A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at: Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. <em>Sci Rep</em> 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7</p> <p>[1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585<br> [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. __APL Mater__ 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)</p>
Dataset for Automatic Refactoring Candidate Identification Leveraging Effective Code Representation
<p>The dataset consists of positive and negative case methods for Extract Method refactoring for selected GitHub repositories.<br> <br> <em>Each sample format - </em></p> <pre><code class="language-json">{ "repo_name": "...", "repo_url": "...", "positive_case_methods": ["...", "...", ...], "negative_case_methods": ["...", "...", ...] }</code></pre> <p> </p>
Variant calls for 'Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration'
<p>A VCF of SNP calls used for input to GWAS in https://elifesciences.org/articles/26255</p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-16, and the abundance values range from log10(VMR) = -3.5 to -1.5 in steps of 0.5 and 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input H2O value (abundance case), the retrieved H2O value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for H2O. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input H2O': str})</p>
InsectSet47 & InsectSet66: Expanded datasets for automatic acoustic identification of insects (Orthoptera and Cicadidae)
<p><strong>Updated full version with training, validation and test sets.</strong></p> <p>Two newly compiled datasets for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was <a href="https://doi.org/10.1371/journal.pcbi.1011541">published in PLOS</a> Computational Biology and the machine learning implementations were published on <a href="https://github.com/mariusfaiss/InsectSet47-InsectSet66-Adaptive-Representations-of-Sound-for-Automatic-Insect-Recognition">Github</a>.</p> <p>These datasets expand on the previously published <a href="https://doi.org/10.5281/zenodo.7072196">InsectSet32</a> by including recently published collections of insect recordings by citizen scientists from around the world. Recordings from <a href="https://bio.acousti.ca/">BioAcoustica</a>, <a href="http://xeno-canto.org/">xeno-canto</a> and <a href="http://inaturalist.org/">iNaturalist</a>, as well as private collections by <a href="https://orcid.org/0000-0002-8929-2737">Baudewijn Odé</a> were downloaded and manually inspected. Files with strong noise interference or intense filtering, as well as files containing sounds of multiple species were removed to compile these datasets. The files were standardised to 44.1 kHz mono WAV files ranging in length from less than one second to several minutes. Files containing long periods without insect sounds were edited into multiple smaller files with silent periods no longer than 5 seconds. These files are marked as edits in the annotation file and should be assigned together into train/validation/test sets to prevent data leakage. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).</p> <p>InsectSet47 expands on <a href="https://doi.org/10.5281/zenodo.7072196">InsectSet32</a> with recordings from <a href="http://xeno-canto.org/">xeno-canto</a> and contains 1006 original recordings from 47 species, with at least ten files per species. The total length of InsectSet47 is 22 hours. InsectSet66 further expands on InsectSet47 by adding research-grade audio observations from <a href="http://inaturalist.org/">iNaturalist</a>, with a total of 1554 recordings from 66 species, a total length of over 24 hours and a minimum of ten files per species.</p> <p>The datasets were split into the training, validation and test sets while ensuring a roughly equal distribution of audio files and audio material for every species in all three subsets. This resulted in a 60/20/20 split (train/validation/test) by file number and a 64/19.5/16.5 split by file length.</p>
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
Identification of factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium
<p>The model allows to identify factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium and to verify the importance of ion adsorption and protein adsorption in this process. </p> <p>Model confirms the significant effect of protein adsorption on the hydrodynamic diameter of metal oxide particles in the biological medium, and does not confirm the significant effect of ion adsorption in this process. It’s an example of modeling the properties of nanoparticles, where apart from the descriptors describing the structure of nanoparticles, there are also parameters characterizing the medium.</p>
Identification of a lineage-specific protein network at the trypanosome nuclear envelope
<p>The nuclear envelope (NE) separates translation and transcription and is the location of multiple functions, including chromatin organization, nucleocytoplasmic transport, ribosomal maturation and mRNA processing/quality control. The molecular basis for many of these functions have diverged between different eukaryotic lineages. <em>Trypanosoma brucei</em>, a member of the early branching eukaryotic lineage Discoba, highlight many of these, including a distinct lamina and kinetochore composition. Here we describe a cohort of proteins interacting with both the lamina and NPC, which we term lamina-associated proteins (LAPs). LAPs represent a diverse group of proteins, including two candidate NPC-anchoring pore membrane proteins (POMs) with architecture conserved with <em>S. cerevisiae </em>Pom152 and <em>H. sapiens </em>Nup210, and additional peripheral components of the NPC. While many of the LAPs are specific to Trypanosomatids, we also identified broadly conserved proteins, indicating an amalgam of divergence and conservation within the NE proteome of trypanosomes, highlighting the diversity of nuclear biology across the eukaryotes and increasing our understanding of eukaryotic and NPC evolution.</p>
DROP: Molecular voucher database for identification of Drosophila parasitoids
<p>A curated open-access molecular reference database for <em>Drosophila</em> parasitoids (DROP). Identifying <em>Drosophila</em> parasitoids is challenging and poses major impediment to realize the full potential of this model system in studies ranging from molecular mechanisms to food webs, and in biological control of <em>Drosophila suzukii</em>. In DROP, genetic data are linked to voucher specimens and, where possible, the voucher specimens are identified by taxonomists and vetted through direct comparison with primary type material. An updated taxonomic catalogue for the research community is also part of DROP. DROP offers accurate molecular identification and improves cross-referencing between individual studies that we hope will catalyze research on this diverse and fascinating model system. Our effort should also serve as an example for researchers facing similar molecular identification problems in other groups of organisms.</p>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Dataset
<p>This dataset provides output of RAMEFI for the wind feature climatology presented in Eisenstein et al. (2023; 10.5194/wcd-2023-10) for the winter months October to March 2000-2019 using COSMO-REA6 (https://reanalysis.meteo.uni-bonn.de/?COSMO-REA6).</p> <p><strong>rf_crea_<yyyymm>.nc</strong> include the unfiltered probabilities for 'no feature' (p0), warm jet (p1), cold-frontal convection (p2), cold jet (p3) and cold-sector winds (p5) for each month.</p> <p>To filter for cyclone tracks, use <strong>cyclone_tracks.csv</strong>. The<strong> </strong>file includes interpolated ERA5 cyclone tracks for the investigated area and time period (see Section 2.4 of the paper).</p> <p><strong>mask.nc</strong> includes a land sea mask, height of surface level and a mask to exclude certain grid points as discussed in the manuscript (e.g., grid points with an altitude over 800m and the Balkans) for further filtering.</p>
Entomological identification of the cadaver fauna of wolf and lynx carcasses in Germany between 2014-2021
The frequency of illegal killings of animals that are under special protection in Germany is continuously increasing. Especially the carnivores wolf and lynx are strongly threatened by humans. To counteract these developments, it is necessary to investigate and prosecute such offenses. An important clue for police investigations is the time of death of the deceased animal. Forensic entomology makes use of the knowledge of temperature-dependent development rates as well as succession patterns of necrophagous flies and beetles and to determine an approximate time of death. While the carcass fauna of other species as well as humans is well studied, the composition of insects on wolves and lynx worldwide has not yet been deciphered. The aim of this work is to investigate the carcass fauna of wolf and lynx. For this purpose, the species composition of insects from 70 wolf carcasses and 8 lynx carcasses was analysed with respect to different habitat types, decomposition stages, and seasons. This data set provides extensive knowledge of the arthropod fauna on wolf and lynx carcasses. The frequency of illegal killings of animals that are under special protection in Germany is continuously increasing. Especially the carnivores wolf and lynx are strongly threatened by humans. To counteract these developments, it is necessary to investigate and prosecute such offenses. An important clue for police investigations is the time of death of the deceased animal. Forensic entomology makes use of the knowledge of temperature-dependent development rates as well as succession patterns of necrophagous flies and beetles and to determine an approximate time of death. While the carcass fauna of other species as well as humans is well studied, the composition of insects on wolves and lynx worldwide has not yet been deciphered. The aim of this work is to investigate the carcass fauna of wolf and lynx. For this purpose, the species composition of insects from 70 wolf carcasses and 8 lynx carca
Blair et al. 2020: Machine learning identification of ground beetles (repackaging of occurrences published by the NEON Biorepository Data Portal)
Blair, J.; Weiser, M. D.; Kaspari, M.; Miller, M.; Siler, C.; Marshall, K. E. 2020. Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale. Ecology and Evolution 10 (23): 13143-13153. https://doi.org/10.1002/ece3.6905 Additional NEON samples (not yet archived at the Biorepository) were used in this research: full list of occurrences used.
Identification of taxa, counts, biomass and carbon biomass calculations for phytoplankton and ciliates from artic lakes near Toolik Lake LTER in summer 1998.
Identification of taxa, counts, biomass and carbon biomass calculations for phytoplankton and ciliates from artic lakes near Toolik Lake LTER in summer 1998.
Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences IX - Plant Identifications 2015
This data set was collected as a part of Brian Houseman's MS Thesis, Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences (December 2017). Data include plant voucher collections that were collected on study plots in 2015. Data were collected on study plots established across two burn scars (2004 Boundary Fire and 1971 Wickersham Dome Fire) within the Yukon-Tanana Uplands ecoregion of interior Alaska.
InterTVA. A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification.
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