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7,523 results for “Annotation”
Syzygium syzygioides genome assembly and annotation
<p><em>De novo</em> genome assembly and annotation of <em>Syzygium syzygioides</em>.</p> <p>The following files are available:</p> <ul> <li>ssyz.fa.gz: reference genome sequence in fasta format</li> <li>ssyz.gff3.gz: gene annotation in GFF3 format</li> <li>ssyz.gtf.gz: gene annotation in GTF format</li> <li>ssyz.tx.fa.gz: transcript sequences in fasta format</li> <li>ssyz.cds.fa.gz: coding sequences in fasta format</li> <li>ssyz.prot.fa.gz: protein sequences in fasta format</li> <li>ssyz.tsv.gz: gene functional annotation in TSV format</li> <li>ssyz.chr_to_id.tsv.gz: mapping of sequence names to ids in TSV format</li> </ul>
Nicotiana sylvestris genome assembly and annotation
<p><em>De novo</em> genome assembly and annotation of <em>Nicotiana sylvestris</em>.</p> <p>The following files are available:</p> <ul> <li>nsyl.fa.gz: reference genome sequence in fasta format</li> <li>nsyl.gff3.gz: gene annotation in GFF3 format</li> <li>nsyl.gtf.gz: gene annotation in GTF format</li> <li>nsyl.tx.fa.gz: transcript sequences in fasta format</li> <li>nsyl.cds.fa.gz: coding sequences in fasta format</li> <li>nsyl.prot.fa.gz: protein sequences in fasta format</li> <li>nsyl.tsv.gz: gene functional annotation in TSV format</li> <li>nsyl.rt.fa.gz: retrotransposon sequences in fasta format</li> <li>nsyl.rt.gff3.gz: retrotransposon annotation on GFF3 format</li> <li>nsyl.rt.tsv.gz: retrotransposon annotation in TSV format</li> <li>nsyl.chr_to_id.tsv.gz: mapping of sequence names to ids in TSV format</li> </ul>
Clove (Syzygium aromaticum) genome assembly and annotation
<p><em>De novo</em> genome assembly and annotation of clove (<em>Syzygium aromaticum</em>).</p> <p>The following files are available:</p> <ul> <li>saro.fa.gz: reference genome sequence in fasta format</li> <li>saro.gff3.gz: gene annotation in GFF3 format</li> <li>saro.gtf.gz: gene annotation in GTF format</li> <li>saro.tx.fa.gz: transcript sequences in fasta format</li> <li>saro.cds.fa.gz: coding sequences in fasta format</li> <li>saro.prot.fa.gz: protein sequences in fasta format</li> <li>saro.tsv.gz: gene functional annotation in TSV format</li> <li>saro.chr_to_id.tsv.gz: mapping of sequence names to ids in TSV format</li> </ul>
Annotated Corpora of Historical Catalan (HisCat) - Llibre dels Fets
<p>This repository is part of the Annotated Corpora of Historical Catalan (HisCat). It contains the first POS-tagged text that is partially manually corrected and used to train Old Catalan POS taggers, described in the following paper:</p> <p>Meelen, Marieke & Pujol i Campeny, Afra, (2021) 'Old Catalan Morphosyntax: developing an annotated corpus' in <em>Journal of Open Humanities Data</em>.</p> <p>This POS-tagged text is the 13th century <em>Llibre dels Fets</em>, a historical chronicle. The version of the text used for this project is</p> <p>Bruguera, J. (1991). <em>El Llibre dels Fets del Rei en Jaume</em>. Barcelona: Barcino.</p> <p>as prepared for the <em>Corpus Informatitzat del Català Antic</em></p> <p>Torruella, J., Pérez Saldanya, M., & Martines, J. (2009). <em>Corpus Informatitzat del Català Antic</em>. URL: <a href="http://cica.cat/">http://cica.cat/</a>.</p> <p>The subcorpus counts with 164,096 POS-annotated tokens (165,538 tokens including punctuation and folio markers), of which 60,000 have been manually corrected. This subcorpus contains a total of and 4,506 main clauses. POS tagging of this text was done with the Memory-Based Tagger by TiMBL (<a href="https://languagemachines.github.io/mbt/">https://languagemachines.github.io/mbt/</a>). The code accompanying the paper can be found on GitHub: <a href="https://github.com/lothelanor/catalancorpora">https://github.com/lothelanor/catalancorpora</a>). In addition to memory-based tagging, have tried neural-based tagging with TARGER (<a href="https://github.com/achernodub/targer">https://github.com/achernodub/targer</a>) for which we created word embeddings that can be found on <a href="https://doi.org/10.5281/zenodo.5615556">Zenodo</a>. Results for memory-based tagging were better, however, which is why this version is uploaded here.</p> <pre> </pre>
Datasets for CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning
<p>This repository contains datasets used in the project CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning. This project aims at learning cell annotations for missing cell labels via NMF and recursive k-Means clustering.</p>
Improved genome annotation of Rhynchosporium commune isolate UK7 using Illumina short reads of in vitro and in plantae conditions
<p>Improved genome annotation of the <em>Rhynchosporium commune</em> isolate UK7 using Illumina short reads of <em>in vitro</em> and <em>in plantae</em> conditions. The short reads used for the annotation are available at <a href="https://doi.org/10.5281/zenodo.5729968">https://doi.org/10.5281/zenodo.5729968</a> and <a href="https://doi.org/10.5281/zenodo.5729863">https://doi.org/10.5281/zenodo.5729863</a>.To create the gene models, we used tophat v. 2.0.14 to align short reads to the UK7 reference genome (Trapnell et al., 2009). The Intron splice site hints were generated using bam2hints, included in the AUGUSTUS v. 3.2.1 software (Stanke et al., 2006). Due to the very high RNA-sequencing depth available, intron splice hints were filtered for a minimum coverage of 20 reads to avoid an impact of spurious splice signals on gene prediction. To produce <em>ab initio </em>gene models, the BRAKER v. 1.0 pipeline (Hoff et al., 2016) combining GeneMark-ET <em>ab initio </em>gene model predictions and AUGUSTUS v. 3.2.1. GeneMark-ET was trained using the RNA-seq-based splice information as hints. AUGUSTUS was automatically trained using <em>ab initio </em>gene models that were fully supported by splice information. Finally, AUGUSTUS was used to predict gene models using both RNA-seq splice information and coding sequence hints based on exonerate protein alignments as extrinsic evidence.</p>
Podcast annotation dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts "
<p>Dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts". Please refer to the paper for details. Compared to the dataset used in the paper, 20 out of the 417 episodes have been removed due to copyright issues. </p> <p>The data file contains the following fields:</p> <p>- "episode_intro_start": the time stamp for episode introduction start (in milliseconds)</p> <p>- "episode_intro_end": the time stamp for episode introduction end (in milliseconds)</p> <p>- "program_intro_start": the time stamp for program introduction start (in milliseconds)</p> <p>- "program_intro_end": the time stamp for program introduction end (in milliseconds)</p> <p>- "program_name": name of the podcast program</p> <p>- "episode_name": name of the podcast episode</p> <p>- "transcription": JSON string containing the transcription, including the timestamps.</p> <p>- "annotator": anonymized annotator ID.</p>
Annotation dataset for the article titled "On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind"
<p>This is the resulting dataset from the text annotation exercise in the article titled "<strong>On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind</strong>" that will appear in the journal Open Archeology in a special issue titled Archaeological Practice on Shifting Grounds (edited by Åsa Berggren and Antonia Davidovic-Walther). The article is accepted for publication and the annotations are final. CIDOC CRM is used for text annotations.</p>
Machine Learning Dataset for Poultry Diseases Diagnostics - PCR annotated
<p>The dataset of poultry disease diagnostics was annotated using Polymerase Chain Reaction (PCR). Polymerase Chain Reaction (PCR) is a molecular biology technique for rapid diagnostics. We gathered both the fecal images and fecal samples from layers, cross and indigenous breeds of chicken from poultry farms in Arusha and Kilimanjaro regions in Tanzania between September 2020 and February 2021. Each fecal sample collected was coded to its corresponding image during data collection. PCR method is used for detection and identification of pathogens through amplification of DNA sequences unique to the pathogen. We used existing primers from literature to amplify the target DNA/RNA on the poultry fecal samples for PCR. The targets were Coccidiosis, Newcastle disease and Salmonella. We used the primers for PCR diagnostics at the molecular laboratory of the Nelson Mandela African Institution of Science and Technology (NM-AIST). The fecal samples were stored at -80 degrees celsius. The PCR diagnostics were conducted using reagents and kits from Zymo Research and the protocol is summarized in these five stages: 1. DNA sample loading 2. DNA extraction 3. Amplification; 4. Quantification and 5. Detection.</p> <p>All the PCR annotated fecal images are in the <strong><strong>.zip files</strong></strong>; “pcrcocci.zip” has 373 images, “pcrhealthy.zip” has 347 images, “pcrsalmo.zip” has 349 images, "pcrncd.zip" has 186 images. A total of 1,255 image files are labeled.</p> <p>The research project is funded by the Organization for Women in Science for the Developing World (OWSD) with Grant Award Number: 4500406715.</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - METC Subset
<p><strong>Overview:</strong> This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209 </a>- data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Note #1:</strong> we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.</p> <p><strong>Application: </strong>The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).</li> <li>Resolution: 640x480</li> <li>Number of videos: 212</li> <li>Number of annotation files: 212</li> </ul> <p>Movement codes (in JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Note #2: </strong>The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.</p> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset
<p><strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209</a> - data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: 30 FPS</li> <li>Resolution: 320x240 and 640x480</li> <li>Number of videos: 2427</li> <li>Number of annotation files: 4818</li> </ul> <p>Movement codes (both in CSV and JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>7: Turning off the faucet with a paper towel</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Figs 367–373. Liroetis violaceipennis Zhang, Li in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 367–373. Liroetis violaceipennis Zhang, Li & Yang, 2008. 367–371 – Male, possible holotype (8.9 mm). 367 – dorsal view; 368 – ventral view; 369 – lateral view; 370 – head and pronotum; 371 – labels. 372 – Male (10.5 mm), dorsal view. 373 – Female (14.5 mm), dorsal view.
Figs 352–358 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 352–358. Spermatheca (Figs 352–354) and sternite VII (Figs 355–358) of Liroetis. 352, 355 – L. apicicornis Jacoby, 1896; 353, 356 – L. pallidulus (Jiang, 1990); 354, 358 – L. violaceipennis Zhang, Li & Yang, 2008; 357 – L. reitteri (Pic, 1934). Scales 0.25 mm for Figs 352–354, 0.5 mm for Figs 355–358.
Figs 359–366 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 359–366. Liroetis reitteri (Pic, 1934). 359–363 – Male, syntype (11.2 mm). 359 – dorsal view; 360 – lateral view; 361 – abdomen; 362 – head and pronotum; 363 – labels. 364–365 – Female, syntype (11.3 mm): 364 – dorsal view; 365 – labels. 366 – Pseudoliroetis trifasciata Jiang, 1992, drawing of elytra (reproduced from JංൺඇǤ 1992).
Figs 339–346 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 339–346. Liroetis pallidulus (Jiang, 1990). 339–343 – Male, paratype (6.9 mm). 339 – dorsal view; 340 – lateral view; 341 – abdomen; 342 – head and pronotum; 343 – labels. 344–346 – Female, paratype (7.7 mm): 344 – dorsal view; 345 – ventral view; 346 – labels.
Figs 333–338. Liroetis apicicornis Jacoby, 1896. 333–335 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 333–338. Liroetis apicicornis Jacoby, 1896. 333–335 – Male, syntype (9.7 mm). 333 – dorsal view; 334 – ventral view; 335 – labels. 336–337 – Male (9.5 mm): 336 – dorsal view; 337 – head and pronotum. 338 – Female (10.3 mm).
Figs 326–332. Liroetis yulongnis Jiang, 1988. 326–329 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 326–332. Liroetis yulongnis Jiang, 1988. 326–329 – Male, paratype (9.2 mm). 326 – dorsal view; 327 – ventral view; 328 – lateral view; 329 – labels. 330–331 – Male (10.1 mm): 330 – dorsal view; 331 – head and pronotum. 332 – Female (12.0 mm).
Figs 324–325 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 324–325. Liroetis suwai (Takizawa, 1988). 324 – Male (9.6 mm), dorsal view. 323 – Female (10.3 mm), dorsal view.
Figs 299–310 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 299–310. Spermatheca (Figs 299–304) and sternite VII (Figs 305–310) of Liroetis. 299, 305 – L. alticola Jiang, 1988; 300, 306 – L. apicalis Gressitt & Kimoto, 1963; 301, 307 – L. octopunctatus (Weise, 1889); 302, 308 – L. paragrandis Jiang, 1988; 303, 309 – L. suwai (Takizawa, 1988); 304, 310 – L. yulongnis Jiang, 1988. Scales 0.25 mm for Figs 299–304, 0.5 mm for Figs 305–310.
Figs 291–298 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 291–298. Aedeagus of Liroetis, dorsal and lateral views, apex of dorsal process in frontal view. 291 – L. alticola Jiang, 1988; 292 – L. apicalis Gressitt & Kimoto, 1963; 293 – L. nigropictus (Fairmaire, 1889); 294 – L. obliquevirgatus Lopatin, 2013; 295 – L. octopunctatus (Weise, 1889); 296 – L. paragrandis Jiang, 1988; 297 – L. suwai (Takizawa, 1988); 298 – L. yulongnis Jiang, 1988. Scale 0.5 mm.
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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.